diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..b9791560 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +.ipynb_checkpoints/ +zippedData/im.db diff --git a/DATA_SETUP.md b/DATA_SETUP.md new file mode 100644 index 00000000..4a48ff2e --- /dev/null +++ b/DATA_SETUP.md @@ -0,0 +1 @@ +# Data Setup\n\nDownload the database file from [source] and place it in zippedData/ diff --git a/README.md b/README.md index b5e02341..21e952dc 100644 --- a/README.md +++ b/README.md @@ -1,281 +1,212 @@ -# Phase 2 Project Description +# Project - Movie Data Analysis + +* Group: *Group 11*
+* Student names: *Alvin Ngeno, Faith Kanyuki, Ray Onsongo, Sharon Maina*
+* Student pace: *Part Time*
+* Instructor name: *Christine Kirimi*
+ +# 🎬 Wamonyolo Studios Movie Data Analysis + +![Python](https://img.shields.io/badge/Python-3.7%252B-blue) +![Jupyter Notebook](https://img.shields.io/badge/Jupyter-Notebook-orange) +![Pandas](https://img.shields.io/badge/Pandas-Data%2520Analysis-green) +![Data Science](https://img.shields.io/badge/Analysis-Data%2520Science-purple) + +--- + +
+📑 Table of Contents + +1. [Project Overview](#1-project-overview) +2. [Business Problem](#2-business-problem) +3. [Dataset Sources](#3-dataset-sources) +4. [Technical Implementation](#4-technical-implementation) + - [Data Processing Pipeline](#a-data-processing-pipeline) + - [Key Technical Features](#b-key-technical-features) +5. [Key Findings](#5-key-findings) + - [Runtime Analysis](#a-runtime-analysis) + - [Financial Insights](#b-financial-insights) + - [Genre Performance](#c-genre-performance--top-7-by-median-roi) + - [Release Timing](#d-release-timing) + - [Studio Analysis](#e-studio-analysis) +6. [Recommendations](#6-recommendations-for-wamonyolo-studios) +7. [Sample Code Highlights](#7-sample-code-highlights) +8. [How to Run This Analysis](#8-how-to-run-this-analysis) +9. [Visualization Examples](#9-visualization-examples) +10. [Contributing](#10-contributing) +11. [License](#11-license) + +
+ +--- + +## 1. Project Overview +This comprehensive data analysis project provides strategic insights for **Wamonyolo Studios'** entry into the film industry. +By analyzing historical movie data from multiple sources, we uncover patterns and trends that inform **data-driven decisions** about: +- Film production +- Genre selection +- Budgeting +- Release strategies + +--- + +## 2. Business Problem +Wamonyolo Studios faces critical strategic questions: + +- **Optimal film duration** – What runtime maximizes profitability? +- **Genre selection** – Which genres deliver the highest returns? +- **Studio strategy** – Build from scratch or acquire existing studios? +- **Budget optimization** – What production budget maximizes ROI? +- **Market focus** – How important is the international box office? + +--- + +## 3. Dataset Sources + +| Source | Key Metrics | Records | +|--------------------|-------------------------------------------|----------------| +| **IMDb** | Movie metadata, runtimes, genres, creators | 146,144 movies | +| **The Numbers** | Production budgets, domestic/worldwide gross | 5,782 records | +| **Box Office Mojo** | Studio information, box office performance | - | +| **TMDb** | Genre classifications, ratings, popularity | 26,517 movies | + +--- + +## 4. Technical Implementation + +### A. Data Processing Pipeline +1. **Data Extraction** – SQLite and CSV imports from multiple sources +2. **Data Cleaning** – Handling missing values, standardizing formats +3. **Feature Engineering** – Profit margins, ROI calculations, genre mapping +4. **Data Integration** – Merging financial and metadata across sources +5. **Analysis** – Statistical analysis and visualization + +### B. Key Technical Features +- DateTime conversion for release dates +- Currency normalization (`$425,000,000 → 425000000`) +- Genre ID to name mapping (`28 → "Action"`) +- Advanced merging on title + year to avoid duplicates +- Profitability metrics calculation (ROI, margins) + +--- + +## 5. Key Findings + +### A. Runtime Analysis +![Runtime Analysis](images/output_167_0.png) +- Optimal runtime: **87–99 minutes** +- Strong correlation between runtime and production budget +- Extreme runtimes (>180 min) → diminishing returns + +### B. Financial Insights +- **Worldwide vs Domestic**: International markets are crucial +- **ROI Champions**: Horror films lead with **231.67% median ROI** +- **Budget Sweet Spot**: Mid-budget films ($10–50M) often outperform blockbusters + +### C. Genre Performance – Top 7 by Median ROI +![Genre Performance](images/output_180_0.png) +1. Horror – 231.67% +2. Animation – 200.42% +3. Adventure – 167.11% +4. Family – 166.55% +5. Fantasy – 165.95% +6. Mystery – 156.77% +7. Comedy – 152.91% + +### D. Release Timing +- Horror films perform best in **Feb/March (475–499% ROI)** +- Summer releases (June–July) → consistent performance +- Holiday season → high revenue but strong competition + +### E. Studio Analysis +![Studio Analysis](images/output_193_0.png) +- Specialized studios (**BH Tilt, MBox**) show highest ROI (689%, 488%) +- Major studios deliver consistent but lower returns +- Acquisition strategy should target **genre-specialized studios** + +--- + +## 6. Recommendations for Wamonyolo Studios + +### A. Immediate Actions +- Focus on **horror films** (highest ROI) +- Target **$10–20M production budgets** +- Prioritize **international distribution** early +- Consider **Feb/March releases** for horror + +### B. Medium-Term Strategy +- Acquire specialized studio with **horror/genre expertise** +- Develop **animation capabilities** +- Build partnerships for international distribution + +### C. Long-Term Vision +- Diversify genre portfolio once established +- Develop **franchise properties** (~120 min runtimes) +- Explore **streaming distribution models** + +--- + +## 7. Sample Code Highlights -You've made it through the second phase of this course, and now you will put your new skills to use with a large end-of-Phase project! +### A. Profitability Calculation +```python +# Calculate worldwide profit margin +tn_movie_budgets['ww_profit_margin'] = ( + (tn_movie_budgets['worldwide_gross'] - tn_movie_budgets['production_budget']) + / tn_movie_budgets['worldwide_gross'] +) * 100 + +# Calculate ROI +tn_movie_budgets['ROI_perc'] = ( + tn_movie_budgets['world_wide_profit_amount'] / tn_movie_budgets['production_budget'] +) * 100 +``` + +### B. Genre Analysis +```python +# Group by genre and calculate median metrics +genre_groups_med = genre_overall_clean.groupby('genre_name').median(numeric_only=True) +genre_groups_med = genre_groups_med.sort_values('ROI_perc', ascending=False).head(7) +``` + +## 8. How to Run This Analysis + +### A. Install requirements: +```bash +pip install pandas numpy matplotlib seaborn statsmodels jupyter +``` + +### B. Download datasets: +- Place them in the zippedData/ directory + +### C. Run Jupyter notebook: +```bash +jupyter notebook movie-data-analysis.ipynb +``` + +## 9. Visualization Examples + +The analysis includes visualizations showing; +- Runtime distribution vs profitability +- Genre performance comparisons +- Seasonal trends in movie performance +- Budget vs ROI scatter plots +- Studio performance benchmarking + +## 10. Contributing + +This analysis serves as a foundation for Wamonyolo Studios' strategic planning. +Further research areas include: +- Streaming platform performance metrics +- COVID-19 impact on theatrical vs streaming +- Franchise vs original content analysis +- Demographic-specific performance trends + +## 11. License + +This project contains an analysis based on publicly available movie data. +All insights and recommendations are provided for strategic planning purposes. + +Wamonyolo Studios – Data-Driven Filmmaking Strategy © 2025 -In this project description, we will cover: -* [***Project Overview:***](#project-overview) the project goal, audience, and dataset -* [***Deliverables:***](#deliverables) the specific items you are required to produce for this project -* [***Grading:***](#grading) how your project will be scored -* [***Getting Started:***](#getting-started) guidance for how to begin your first project - -## Project Overview - -For this project, you will use exploratory data analysis to generate insights for a business stakeholder. - -### Business Problem - -Your company now sees all the big companies creating original video content and they want to get in on the fun. They have decided to create a new movie studio, but they don’t know anything about creating movies. You are charged with exploring what types of films are currently doing the best at the box office. You must then translate those findings into actionable insights that the head of your company's new movie studio can use to help decide what type of films to create. - -### The Data - -In the folder `zippedData` are movie datasets from: - -* [Box Office Mojo](https://www.boxofficemojo.com/) -* [IMDB](https://www.imdb.com/) -* [Rotten Tomatoes](https://www.rottentomatoes.com/) -* [TheMovieDB](https://www.themoviedb.org/) -* [The Numbers](https://www.the-numbers.com/) - -Because it was collected from various locations, the different files have different formats. Some are compressed CSV (comma-separated values) or TSV (tab-separated values) files that can be opened using spreadsheet software or `pd.read_csv`, while the data from IMDB is located in a SQLite database. - -![movie data erd](https://raw.githubusercontent.com/learn-co-curriculum/dsc-phase-2-project-v3/main/movie_data_erd.jpeg) - -Note that the above diagram shows ONLY the IMDB data. You will need to look carefully at the features to figure out how the IMDB data relates to the other provided data files. - -It is up to you to decide what data from this to use and how to use it. If you want to make this more challenging, you can scrape websites or make API calls to get additional data. If you are feeling overwhelmed or behind, we recommend you use only the following data files: - -* `im.db.zip` - * Zipped SQLite database (you will need to unzip then query using SQLite) - * `movie_basics` and `movie_ratings` tables are most relevant -* `bom.movie_gross.csv.gz` - * Compressed CSV file (you can open without expanding the file using `pd.read_csv`) - -### Key Points - -* **Your analysis should yield three concrete business recommendations.** The ultimate purpose of exploratory analysis is not just to learn about the data, but to help an organization perform better. Explicitly relate your findings to business needs by recommending actions that you think the business should take. - -* **Communicating about your work well is extremely important.** Your ability to provide value to an organization - or to land a job there - is directly reliant on your ability to communicate with them about what you have done and why it is valuable. Create a storyline your audience (the head of the new movie studio) can follow by walking them through the steps of your process, highlighting the most important points and skipping over the rest. - -* **Use plenty of visualizations.** Visualizations are invaluable for exploring your data and making your findings accessible to a non-technical audience. Spotlight visuals in your presentation, but only ones that relate directly to your recommendations. Simple visuals are usually best (e.g. bar charts and line graphs), and don't forget to format them well (e.g. labels, titles). - -## Deliverables - -There are three deliverables for this project: - -* A **non-technical presentation** -* A **Jupyter Notebook** -* A **GitHub repository** - -### Non-Technical Presentation - -The non-technical presentation is a slide deck presenting your analysis to business stakeholders. - -* ***Non-technical*** does not mean that you should avoid mentioning the technologies or techniques that you used, it means that you should explain any mentions of these technologies and avoid assuming that your audience is already familiar with them. -* ***Business stakeholders*** means that the audience for your presentation is the company, not the class or teacher. Do not assume that they are already familiar with the specific business problem. - -The presentation describes the project ***goals, data, methods, and results***. It must include at least ***three visualizations*** which correspond to ***three business recommendations***. - -We recommend that you follow this structure, although the slide titles should be specific to your project: - -1. Beginning - * Overview - * Business Understanding -2. Middle - * Data Understanding - * Data Analysis -3. End - * Recommendations - * Next Steps - * Thank You - * This slide should include a prompt for questions as well as your contact information (name and LinkedIn profile) - -You will give a live presentation of your slides and submit them in PDF format on Canvas. The slides should also be present in the GitHub repository you submit with a file name of `presentation.pdf`. - -The graded elements of the presentation are: - -* Presentation Content -* Slide Style -* Presentation Delivery and Answers to Questions - -See the [Grading](#grading) section for further explanation of these elements. - -For further reading on creating professional presentations, check out: - -* [Presentation Content](https://github.com/learn-co-curriculum/dsc-project-presentation-content) -* [Slide Style](https://github.com/learn-co-curriculum/dsc-project-slide-design) - -### Jupyter Notebook - -The Jupyter Notebook is a notebook that uses Python and Markdown to present your analysis to a data science audience. - -* ***Python and Markdown*** means that you need to construct an integrated `.ipynb` file with Markdown (headings, paragraphs, links, lists, etc.) and Python code to create a well-organized, skim-able document. - * The notebook kernel should be restarted and all cells run before submission, to ensure that all code is runnable in order. - * Markdown should be used to frame the project with a clear introduction and conclusion, as well as introducing each of the required elements. -* ***Data science audience*** means that you can assume basic data science proficiency in the person reading your notebook. This differs from the non-technical presentation. - -Along with the presentation, the notebook also describes the project ***goals, data, methods, and results***. It must include at least ***three visualizations*** which correspond to ***three business recommendations***. - -You will submit the notebook in PDF format on Canvas as well as in `.ipynb` format in your GitHub repository. - -The graded elements for the Jupyter Notebook are: - -* Business Understanding -* Data Understanding -* Data Preparation -* Data Analysis -* Visualization -* Code Quality - -See the [Grading](#grading) section for further explanation of these elements. - -### GitHub Repository - -The GitHub repository is the cloud-hosted directory containing all of your project files as well as their version history. - -This repository link will be the project link that you include on your resume, LinkedIn, etc. for prospective employers to view your work. Note that we typically recommend that 3 links are highlighted (out of 5 projects) so don't stress too much about getting this one to be perfect! There will also be time after graduation for cosmetic touch-ups. - -A professional GitHub repository has: - -1. `README.md` - * A file called `README.md` at the root of the repository directory, written in Markdown; this is what is rendered when someone visits the link to your repository in the browser - * This file contains these sections: - * Overview - * Business Understanding - * Include stakeholder and key business questions - * Data Understanding and Analysis - * Source of data - * Description of data - * Three visualizations (the same visualizations presented in the slides and notebook) - * Conclusion - * Summary of conclusions including three relevant findings -2. Commit history - * Progression of updates throughout the project time period, not just immediately before the deadline - * Clear commit messages - * Commits from all team members (if a group project) -3. Organization - * Clear folder structure - * Clear names of files and folders - * Easily-located notebook and presentation linked in the README -4. Notebook(s) - * Clearly-indicated final notebook that runs without errors - * Exploratory/working notebooks (can contain errors, redundant code, etc.) from all team members (if a group project) -5. `.gitignore` - * A file called `.gitignore` at the root of the repository directory instructs Git to ignore large, unnecessary, or private files - * Because it starts with a `.`, you will need to type `ls -a` in the terminal in order to see that it is there - * GitHub maintains a [Python .gitignore](https://github.com/github/gitignore/blob/master/Python.gitignore) that may be a useful starting point for your version of this file - * To tell Git to ignore more files, just add a new line to `.gitignore` for each new file name - * Consider adding `.DS_Store` if you are using a Mac computer, as well as project-specific file names - * If you are running into an error message because you forgot to add something to `.gitignore` and it is too large to be pushed to GitHub [this blog post](https://medium.com/analytics-vidhya/tutorial-removing-large-files-from-git-78dbf4cf83a?sk=c3763d466c7f2528008c3777192dfb95)(friend link) should help you address this - -You wil submit a link to the GitHub repository on Canvas. - -See the [Grading](#grading) section for further explanation of how the GitHub repository will be graded. - -For further reading on creating professional notebooks and `README`s, check out [this reading](https://github.com/learn-co-curriculum/dsc-repo-readability-v2-2). - -## Grading - -***To pass this project, you must pass each project rubric objective.*** The project rubric objectives for Phase 2 are: - -1. Data Communication -2. Authoring Jupyter Notebooks -3. Data Manipulation and Analysis with `pandas` - -### Data Communication - -Communication is a key "soft skill". In [this survey](https://www.payscale.com/data-packages/job-skills), 46% of hiring managers said that recent college grads were missing this skill. - -Because "communication" can encompass such a wide range of contexts and skills, we will specifically focus our Phase 2 objective on Data Communication. We define Data Communication as: - -> Communicating basic data analysis results to diverse audiences via writing and live presentation - -To further define some of these terms: - -* By "basic data analysis" we mean that you are filtering, sorting, grouping, and/or aggregating the data in order to answer business questions. This project does not involve inferential statistics or machine learning, although descriptive statistics such as measures of central tendency are encouraged. -* By "results" we mean your ***three visualizations and recommendations***. -* By "diverse audiences" we mean that your presentation and notebook are appropriately addressing a business and data science audience, respectively. - -Below are the definitions of each rubric level for this objective. This information is also summarized in the rubric, which is attached to the project submission assignment. - -#### Exceeds Objective -Creates and describes appropriate visualizations for given business questions, where each visualization fulfills all elements of the checklist - -> This "checklist" refers to the Data Visualization checklist within the larger Phase 2 Project Checklist - -#### Meets Objective (Passing Bar) -Creates and describes appropriate visualizations for given business questions - -> This objective can be met even if all checklist elements are not fulfilled. For example, if there is some illegible text in one of your visualizations, you can still meet this objective - -#### Approaching Objective -Creates visualizations that are not related to the business questions, or uses an inappropriate type of visualization - -> Even if you create very compelling visualizations, you cannot pass this objective if the visualizations are not related to the business questions - -> An example of an inappropriate type of visualization would be using a line graph to show the correlation between two independent variables, when a scatter plot would be more appropriate - -#### Does Not Meet Objective -Does not submit the required number of visualizations - -### Authoring Jupyter Notebooks - -According to [Kaggle's 2020 State of Data Science and Machine Learning Survey](https://www.kaggle.com/kaggle-survey-2020), 74.1% of data scientists use a Jupyter development environment, which is more than twice the percentage of the next-most-popular IDE, Visual Studio Code. Jupyter Notebooks allow for reproducible, skim-able code documents for a data science audience. Comfort and skill with authoring Jupyter Notebooks will prepare you for job interviews, take-home challenges, and on-the-job tasks as a data scientist. - -The key feature that distinguishes *authoring Jupyter Notebooks* from simply *writing Python code* is the fact that Markdown cells are integrated into the notebook along with the Python cells in a notebook. You have seen examples of this throughout the curriculum, but now it's time for you to practice this yourself! - -Below are the definitions of each rubric level for this objective. This information is also summarized in the rubric, which is attached to the project submission assignment. - -#### Exceeds Objective -Uses Markdown and code comments to create a well-organized, skim-able document that follows all best practices - -> Refer to the [repository readability reading](https://github.com/learn-co-curriculum/dsc-repo-readability-v2-2) for more tips on best practices - -#### Meets Objective (Passing Bar) -Uses some Markdown to create an organized notebook, with an introduction at the top and a conclusion at the bottom - -#### Approaching Objective -Uses Markdown cells to organize, but either uses only headers and does not provide any explanations or justifications, or uses only plaintext without any headers to segment out sections of the notebook - -> Headers in Markdown are delineated with one or more `#`s at the start of the line. You should have a mixture of headers and plaintext (text where the line does not start with `#`) - -#### Does Not Meet Objective -Does not submit a notebook, or does not use Markdown cells at all to organize the notebook - -### Data Manipulation and Analysis with `pandas` - -`pandas` is a very popular data manipulation library, with over 2 million downloads on Anaconda (`conda install pandas`) and over 19 million downloads on PyPI (`pip install pandas`) at the time of this writing. In our own internal data, we see that the overwhelming majority of Flatiron School DS grads use `pandas` on the job in some capacity. - -Unlike in base Python, where the Zen of Python says "There should be one-- and preferably only one --obvious way to do it", there is often more than one valid way to do something in `pandas`. However there are still more efficient and less efficient ways to use it. Specifically, the best `pandas` code is *performant* and *idiomatic*. - -Performant `pandas` code utilizes methods and broadcasting rather than user-defined functions or `for` loops. For example, if you need to strip whitespace from a column containing string data, the best approach would be to use the [`pandas.Series.str.strip` method](https://pandas.pydata.org/docs/reference/api/pandas.Series.str.strip.html) rather than writing your own function or writing a loop. Or if you want to multiply everything in a column by 100, the best approach would be to use broadcasting (e.g. `df["column_name"] * 100`) instead of a function or loop. You can still write your own functions if needed, but only after checking that there isn't a built-in way to do it. - -Idiomatic `pandas` code has variable names that are meaningful words or abbreviations in English, that are related to the purpose of the variables. You can still use `df` as the name of your DataFrame if there is only one main DataFrame you are working with, but as soon as you are merging multiple DataFrames or taking a subset of a DataFrame, you should use meaningful names. For example, `df2` would not be an idiomatic name, but `movies_and_reviews` could be. - -We also recommend that you rename all DataFrame columns so that their meanings are more understandable, although it is fine to have acronyms. For example, `"col1"` would not be an idiomatic name, but `"USD"` could be. - -Below are the definitions of each rubric level for this objective. This information is also summarized in the rubric, which is attached to the project submission assignment. - -#### Exceeds Objective -Uses `pandas` to prepare data and answer business questions in an idiomatic, performant way - -#### Meets Objective (Passing Bar) -Successfully uses `pandas` to prepare data in order to answer business questions - -> This includes projects that _occasionally_ use base Python when `pandas` methods would be more appropriate (such as using `enumerate()` on a DataFrame), or occasionally performs operations that do not appear to have any relevance to the business questions - -#### Approaching Objective -Uses `pandas` to prepare data, but makes significant errors - -> Examples of significant errors include: the result presented does not actually answer the stated question, the code produces errors, the code _consistently_ uses base Python when `pandas` methods would be more appropriate, or the submitted notebook contains significant quantities of code that is unrelated to the presented analysis (such as copy/pasted code from the curriculum or StackOverflow) - -#### Does Not Meet Objective -Unable to prepare data using `pandas` - -> This includes projects that successfully answer the business questions, but do not use `pandas` (e.g. use only base Python, or use some other tool like R, Tableau, or Excel) - -## Getting Started - -Please start by reviewing the contents of this project description. If you have any questions, please ask your instructor ASAP. - -Next, you will need to complete the [***Project Proposal***](#project_proposal) which must be reviewed by your instructor before you can continue with the project. - -Then, you will need to create a GitHub repository. There are three options: - -1. Look at the [Phase 2 Project Templates and Examples repo](https://github.com/learn-co-curriculum/dsc-project-template) and follow the directions in the MVP branch. -2. Fork the [Phase 2 Project Repository](https://github.com/learn-co-curriculum/dsc-phase-2-project-v3), clone it locally, and work in the `student.ipynb` file. Make sure to also add and commit a PDF of your presentation to your repository with a file name of `presentation.pdf`. -3. Create a new repository from scratch by going to [github.com/new](https://github.com/new) and copying the data files from one of the above resources into your new repository. This approach will result in the most professional-looking portfolio repository, but can be more complicated to use. So if you are getting stuck with this option, try one of the above options instead. - -## Summary - -This project will give you a valuable opportunity to develop your data science skills using real-world data. The end-of-phase projects are a critical part of the program because they give you a chance to bring together all the skills you've learned, apply them to realistic projects for a business stakeholder, practice communication skills, and get feedback to help you improve. You've got this! diff --git a/SharonWM_movie-data-analysis repository.pdf b/SharonWM_movie-data-analysis repository.pdf new file mode 100644 index 00000000..bd3d8c7a Binary files /dev/null and b/SharonWM_movie-data-analysis repository.pdf differ diff --git a/images/output_165_0.png b/images/output_165_0.png new file mode 100644 index 00000000..8fe9d88e Binary files /dev/null and b/images/output_165_0.png differ diff --git a/images/output_167_0.png b/images/output_167_0.png new file mode 100644 index 00000000..685432ca Binary files /dev/null and b/images/output_167_0.png differ diff --git a/images/output_178_0.png b/images/output_178_0.png new file mode 100644 index 00000000..c70c2144 Binary files /dev/null and b/images/output_178_0.png differ diff --git a/images/output_180_0.png b/images/output_180_0.png new file mode 100644 index 00000000..f46e26c5 Binary files /dev/null and b/images/output_180_0.png differ diff --git a/images/output_193_0.png b/images/output_193_0.png new file mode 100644 index 00000000..12cde602 Binary files /dev/null and b/images/output_193_0.png differ diff --git a/images/output_202_0.png b/images/output_202_0.png new file mode 100644 index 00000000..f1796fc5 Binary files /dev/null and b/images/output_202_0.png differ diff --git a/images/output_212_0.png b/images/output_212_0.png new file mode 100644 index 00000000..076c027e Binary files /dev/null and b/images/output_212_0.png differ diff --git a/index.ipynb b/index.ipynb deleted file mode 100644 index 3623bc14..00000000 --- a/index.ipynb +++ /dev/null @@ -1,643 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "5d35b2b4", - "metadata": {}, - "source": [ - "# Phase 2 Project Description" - ] - }, - { - "cell_type": "markdown", - "id": "b5e9e179", - "metadata": {}, - "source": [ - "You've made it through the second phase of this course, and now you will put your new skills to use with a large end-of-Phase project!\n", - "\n", - "In this project description, we will cover:\n", - "\n", - "* [***Project Overview:***](#project-overview) the project goal, audience, and dataset\n", - "* [***Deliverables:***](#deliverables) the specific items you are required to produce for this project\n", - "* [***Grading:***](#grading) how your project will be scored\n", - "* [***Getting Started:***](#getting-started) guidance for how to begin your first project" - ] - }, - { - "cell_type": "markdown", - "id": "58851385", - "metadata": {}, - "source": [ - "## Project Overview" - ] - }, - { - "cell_type": "markdown", - "id": "6f37995f", - "metadata": {}, - "source": [ - "For this project, you will use exploratory data analysis to generate insights for a business stakeholder." - ] - }, - { - "cell_type": "markdown", - "id": "8b0f1668", - "metadata": {}, - "source": [ - "### Business Problem" - ] - }, - { - "cell_type": "markdown", - "id": "dce55d1d", - "metadata": {}, - "source": [ - "Your company now sees all the big companies creating original video content and they want to get in on the fun. They have decided to create a new movie studio, but they don’t know anything about creating movies. You are charged with exploring what types of films are currently doing the best at the box office. You must then translate those findings into actionable insights that the head of your company's new movie studio can use to help decide what type of films to create." - ] - }, - { - "cell_type": "markdown", - "id": "d3d557bf", - "metadata": {}, - "source": [ - "### The Data" - ] - }, - { - "cell_type": "markdown", - "id": "ca34efb7", - "metadata": {}, - "source": [ - "In the folder `zippedData` are movie datasets from:\n", - "\n", - "* [Box Office Mojo](https://www.boxofficemojo.com/)\n", - "* [IMDB](https://www.imdb.com/)\n", - "* [Rotten Tomatoes](https://www.rottentomatoes.com/)\n", - "* [TheMovieDB](https://www.themoviedb.org/)\n", - "* [The Numbers](https://www.the-numbers.com/)\n", - "\n", - "Because it was collected from various locations, the different files have different formats. Some are compressed CSV (comma-separated values) or TSV (tab-separated values) files that can be opened using spreadsheet software or `pd.read_csv`, while the data from IMDB is located in a SQLite database.\n", - "\n", - "![movie data erd](https://raw.githubusercontent.com/learn-co-curriculum/dsc-phase-2-project-v3/main/movie_data_erd.jpeg)\n", - "\n", - "Note that the above diagram shows ONLY the IMDB data. You will need to look carefully at the features to figure out how the IMDB data relates to the other provided data files.\n", - "\n", - "It is up to you to decide what data from this to use and how to use it. If you want to make this more challenging, you can scrape websites or make API calls to get additional data. If you are feeling overwhelmed or behind, we recommend you use only the following data files:\n", - "\n", - "* `im.db.zip`\n", - " * Zipped SQLite database (you will need to unzip then query using SQLite)\n", - " * `movie_basics` and `movie_ratings` tables are most relevant\n", - "* `bom.movie_gross.csv.gz`\n", - " * Compressed CSV file (you can open without expanding the file using `pd.read_csv`)" - ] - }, - { - "cell_type": "markdown", - "id": "5ace6e4f", - "metadata": {}, - "source": [ - "### Key Points" - ] - }, - { - "cell_type": "markdown", - "id": "c9d2edeb", - "metadata": {}, - "source": [ - "* **Your analysis should yield three concrete business recommendations.** The ultimate purpose of exploratory analysis is not just to learn about the data, but to help an organization perform better. Explicitly relate your findings to business needs by recommending actions that you think the business should take.\n", - "\n", - "* **Communicating about your work well is extremely important.** Your ability to provide value to an organization - or to land a job there - is directly reliant on your ability to communicate with them about what you have done and why it is valuable. Create a storyline your audience (the head of the new movie studio) can follow by walking them through the steps of your process, highlighting the most important points and skipping over the rest.\n", - "\n", - "* **Use plenty of visualizations.** Visualizations are invaluable for exploring your data and making your findings accessible to a non-technical audience. Spotlight visuals in your presentation, but only ones that relate directly to your recommendations. Simple visuals are usually best (e.g. bar charts and line graphs), and don't forget to format them well (e.g. labels, titles)." - ] - }, - { - "cell_type": "markdown", - "id": "474e2ec3", - "metadata": {}, - "source": [ - "## Deliverables" - ] - }, - { - "cell_type": "markdown", - "id": "eaeda85f", - "metadata": {}, - "source": [ - "There are three deliverables for this project:\n", - "\n", - "* A **non-technical presentation**\n", - "* A **Jupyter Notebook**\n", - "* A **GitHub repository**" - ] - }, - { - "cell_type": "markdown", - "id": "a7f8e274", - "metadata": {}, - "source": [ - "### Non-Technical Presentation" - ] - }, - { - "cell_type": "markdown", - "id": "540d5c27", - "metadata": {}, - "source": [ - "The non-technical presentation is a slide deck presenting your analysis to business stakeholders.\n", - "\n", - "* ***Non-technical*** does not mean that you should avoid mentioning the technologies or techniques that you used, it means that you should explain any mentions of these technologies and avoid assuming that your audience is already familiar with them.\n", - "* ***Business stakeholders*** means that the audience for your presentation is the company, not the class or teacher. Do not assume that they are already familiar with the specific business problem.\n", - "\n", - "The presentation describes the project ***goals, data, methods, and results***. It must include at least ***three visualizations*** which correspond to ***three business recommendations***.\n", - "\n", - "We recommend that you follow this structure, although the slide titles should be specific to your project:\n", - "\n", - "1. Beginning\n", - " * Overview\n", - " * Business Understanding\n", - "2. Middle\n", - " * Data Understanding\n", - " * Data Analysis\n", - "3. End\n", - " * Recommendations\n", - " * Next Steps\n", - " * Thank You\n", - " * This slide should include a prompt for questions as well as your contact information (name and LinkedIn profile)\n", - "\n", - "You will give a live presentation of your slides and submit them in PDF format on Canvas. The slides should also be present in the GitHub repository you submit with a file name of `presentation.pdf`.\n", - "\n", - "The graded elements of the presentation are:\n", - "\n", - "* Presentation Content\n", - "* Slide Style\n", - "* Presentation Delivery and Answers to Questions\n", - "\n", - "See the [Grading](#grading) section for further explanation of these elements.\n", - "\n", - "For further reading on creating professional presentations, check out:\n", - "\n", - "* [Presentation Content](https://github.com/learn-co-curriculum/dsc-project-presentation-content)\n", - "* [Slide Style](https://github.com/learn-co-curriculum/dsc-project-slide-design)" - ] - }, - { - "cell_type": "markdown", - "id": "d27915ba", - "metadata": {}, - "source": [ - "### Jupyter Notebook" - ] - }, - { - "cell_type": "markdown", - "id": "2d5d45ea", - "metadata": {}, - "source": [ - "The Jupyter Notebook is a notebook that uses Python and Markdown to present your analysis to a data science audience.\n", - "\n", - "* ***Python and Markdown*** means that you need to construct an integrated `.ipynb` file with Markdown (headings, paragraphs, links, lists, etc.) and Python code to create a well-organized, skim-able document.\n", - " * The notebook kernel should be restarted and all cells run before submission, to ensure that all code is runnable in order.\n", - " * Markdown should be used to frame the project with a clear introduction and conclusion, as well as introducing each of the required elements.\n", - "* ***Data science audience*** means that you can assume basic data science proficiency in the person reading your notebook. This differs from the non-technical presentation.\n", - "\n", - "Along with the presentation, the notebook also describes the project ***goals, data, methods, and results***. It must include at least ***three visualizations*** which correspond to ***three business recommendations***.\n", - "\n", - "You will submit the notebook in PDF format on Canvas as well as in `.ipynb` format in your GitHub repository.\n", - "\n", - "The graded elements for the Jupyter Notebook are:\n", - "\n", - "* Business Understanding\n", - "* Data Understanding\n", - "* Data Preparation\n", - "* Data Analysis\n", - "* Visualization\n", - "* Code Quality\n", - "\n", - "See the [Grading](#grading) section for further explanation of these elements." - ] - }, - { - "cell_type": "markdown", - "id": "2027aa4c", - "metadata": {}, - "source": [ - "### GitHub Repository" - ] - }, - { - "cell_type": "markdown", - "id": "b8057390", - "metadata": {}, - "source": [ - "The GitHub repository is the cloud-hosted directory containing all of your project files as well as their version history.\n", - "\n", - "This repository link will be the project link that you include on your resume, LinkedIn, etc. for prospective employers to view your work. Note that we typically recommend that 3 links are highlighted (out of 5 projects) so don't stress too much about getting this one to be perfect! There will also be time after graduation for cosmetic touch-ups.\n", - "\n", - "A professional GitHub repository has:\n", - "\n", - "1. `README.md`\n", - " * A file called `README.md` at the root of the repository directory, written in Markdown; this is what is rendered when someone visits the link to your repository in the browser\n", - " * This file contains these sections:\n", - " * Overview\n", - " * Business Understanding\n", - " * Include stakeholder and key business questions\n", - " * Data Understanding and Analysis\n", - " * Source of data\n", - " * Description of data\n", - " * Three visualizations (the same visualizations presented in the slides and notebook)\n", - " * Conclusion\n", - " * Summary of conclusions including three relevant findings\n", - "2. Commit history\n", - " * Progression of updates throughout the project time period, not just immediately before the deadline\n", - " * Clear commit messages\n", - " * Commits from all team members (if a group project)\n", - "3. Organization\n", - " * Clear folder structure\n", - " * Clear names of files and folders\n", - " * Easily-located notebook and presentation linked in the README\n", - "4. Notebook(s)\n", - " * Clearly-indicated final notebook that runs without errors\n", - " * Exploratory/working notebooks (can contain errors, redundant code, etc.) from all team members (if a group project)\n", - "5. `.gitignore`\n", - " * A file called `.gitignore` at the root of the repository directory instructs Git to ignore large, unnecessary, or private files\n", - " * Because it starts with a `.`, you will need to type `ls -a` in the terminal in order to see that it is there\n", - " * GitHub maintains a [Python .gitignore](https://github.com/github/gitignore/blob/master/Python.gitignore) that may be a useful starting point for your version of this file\n", - " * To tell Git to ignore more files, just add a new line to `.gitignore` for each new file name\n", - " * Consider adding `.DS_Store` if you are using a Mac computer, as well as project-specific file names\n", - " * If you are running into an error message because you forgot to add something to `.gitignore` and it is too large to be pushed to GitHub [this blog post](https://medium.com/analytics-vidhya/tutorial-removing-large-files-from-git-78dbf4cf83a?sk=c3763d466c7f2528008c3777192dfb95)(friend link) should help you address this\n", - "\n", - "You wil submit a link to the GitHub repository on Canvas.\n", - "\n", - "See the [Grading](#grading) section for further explanation of how the GitHub repository will be graded.\n", - "\n", - "For further reading on creating professional notebooks and `README`s, check out [this reading](https://github.com/learn-co-curriculum/dsc-repo-readability-v2-2)." - ] - }, - { - "cell_type": "markdown", - "id": "f19694e7", - "metadata": {}, - "source": [ - "## Grading" - ] - }, - { - "cell_type": "markdown", - "id": "06e9cfb7", - "metadata": {}, - "source": [ - "***To pass this project, you must pass each project rubric objective.*** The project rubric objectives for Phase 2 are:\n", - "\n", - "1. Data Communication\n", - "2. Authoring Jupyter Notebooks\n", - "3. Data Manipulation and Analysis with `pandas`" - ] - }, - { - "cell_type": "markdown", - "id": "a4c04769", - "metadata": {}, - "source": [ - "### Data Communication" - ] - }, - { - "cell_type": "markdown", - "id": "0834a4ee", - "metadata": {}, - "source": [ - "Communication is a key \"soft skill\". In [this survey](https://www.payscale.com/data-packages/job-skills), 46% of hiring managers said that recent college grads were missing this skill.\n", - "\n", - "Because \"communication\" can encompass such a wide range of contexts and skills, we will specifically focus our Phase 2 objective on Data Communication. We define Data Communication as:\n", - "\n", - "> Communicating basic data analysis results to diverse audiences via writing and live presentation\n", - "\n", - "To further define some of these terms:\n", - "\n", - "* By \"basic data analysis\" we mean that you are filtering, sorting, grouping, and/or aggregating the data in order to answer business questions. This project does not involve inferential statistics or machine learning, although descriptive statistics such as measures of central tendency are encouraged.\n", - "* By \"results\" we mean your ***three visualizations and recommendations***.\n", - "* By \"diverse audiences\" we mean that your presentation and notebook are appropriately addressing a business and data science audience, respectively.\n", - "\n", - "Below are the definitions of each rubric level for this objective. This information is also summarized in the rubric, which is attached to the project submission assignment." - ] - }, - { - "cell_type": "markdown", - "id": "276dff7c", - "metadata": {}, - "source": [ - "#### Exceeds Objective" - ] - }, - { - "cell_type": "markdown", - "id": "e87c2713", - "metadata": {}, - "source": [ - "Creates and describes appropriate visualizations for given business questions, where each visualization fulfills all elements of the checklist\n", - "\n", - "> This \"checklist\" refers to the Data Visualization checklist within the larger Phase 2 Project Checklist" - ] - }, - { - "cell_type": "markdown", - "id": "b4e8a4c7", - "metadata": {}, - "source": [ - "#### Meets Objective (Passing Bar)" - ] - }, - { - "cell_type": "markdown", - "id": "bc4e21d0", - "metadata": {}, - "source": [ - "Creates and describes appropriate visualizations for given business questions\n", - "\n", - "> This objective can be met even if all checklist elements are not fulfilled. For example, if there is some illegible text in one of your visualizations, you can still meet this objective" - ] - }, - { - "cell_type": "markdown", - "id": "d0403eb9", - "metadata": {}, - "source": [ - "#### Approaching Objective" - ] - }, - { - "cell_type": "markdown", - "id": "22dd4ad6", - "metadata": {}, - "source": [ - "Creates visualizations that are not related to the business questions, or uses an inappropriate type of visualization\n", - "\n", - "> Even if you create very compelling visualizations, you cannot pass this objective if the visualizations are not related to the business questions\n", - "\n", - "> An example of an inappropriate type of visualization would be using a line graph to show the correlation between two independent variables, when a scatter plot would be more appropriate" - ] - }, - { - "cell_type": "markdown", - "id": "aa1b808d", - "metadata": {}, - "source": [ - "#### Does Not Meet Objective" - ] - }, - { - "cell_type": "markdown", - "id": "a8a64869", - "metadata": {}, - "source": [ - "Does not submit the required number of visualizations" - ] - }, - { - "cell_type": "markdown", - "id": "db2e0ce8", - "metadata": {}, - "source": [ - "### Authoring Jupyter Notebooks" - ] - }, - { - "cell_type": "markdown", - "id": "91cc89b5", - "metadata": {}, - "source": [ - "According to [Kaggle's 2020 State of Data Science and Machine Learning Survey](https://www.kaggle.com/kaggle-survey-2020), 74.1% of data scientists use a Jupyter development environment, which is more than twice the percentage of the next-most-popular IDE, Visual Studio Code. Jupyter Notebooks allow for reproducible, skim-able code documents for a data science audience. Comfort and skill with authoring Jupyter Notebooks will prepare you for job interviews, take-home challenges, and on-the-job tasks as a data scientist.\n", - "\n", - "The key feature that distinguishes *authoring Jupyter Notebooks* from simply *writing Python code* is the fact that Markdown cells are integrated into the notebook along with the Python cells in a notebook. You have seen examples of this throughout the curriculum, but now it's time for you to practice this yourself!\n", - "\n", - "Below are the definitions of each rubric level for this objective. This information is also summarized in the rubric, which is attached to the project submission assignment." - ] - }, - { - "cell_type": "markdown", - "id": "b9272672", - "metadata": {}, - "source": [ - "#### Exceeds Objective" - ] - }, - { - "cell_type": "markdown", - "id": "efc937e5", - "metadata": {}, - "source": [ - "Uses Markdown and code comments to create a well-organized, skim-able document that follows all best practices\n", - "\n", - "> Refer to the [repository readability reading](https://github.com/learn-co-curriculum/dsc-repo-readability-v2-2) for more tips on best practices" - ] - }, - { - "cell_type": "markdown", - "id": "d01725ea", - "metadata": {}, - "source": [ - "#### Meets Objective (Passing Bar)" - ] - }, - { - "cell_type": "markdown", - "id": "2c854f50", - "metadata": {}, - "source": [ - "Uses some Markdown to create an organized notebook, with an introduction at the top and a conclusion at the bottom" - ] - }, - { - "cell_type": "markdown", - "id": "3e0b3385", - "metadata": {}, - "source": [ - "#### Approaching Objective" - ] - }, - { - "cell_type": "markdown", - "id": "67767f89", - "metadata": {}, - "source": [ - "Uses Markdown cells to organize, but either uses only headers and does not provide any explanations or justifications, or uses only plaintext without any headers to segment out sections of the notebook\n", - "\n", - "> Headers in Markdown are delineated with one or more `#`s at the start of the line. You should have a mixture of headers and plaintext (text where the line does not start with `#`)" - ] - }, - { - "cell_type": "markdown", - "id": "195ef62a", - "metadata": {}, - "source": [ - "#### Does Not Meet Objective" - ] - }, - { - "cell_type": "markdown", - "id": "709181b9", - "metadata": {}, - "source": [ - "Does not submit a notebook, or does not use Markdown cells at all to organize the notebook" - ] - }, - { - "cell_type": "markdown", - "id": "290335d1", - "metadata": {}, - "source": [ - "### Data Manipulation and Analysis with `pandas`" - ] - }, - { - "cell_type": "markdown", - "id": "2c0aae32", - "metadata": {}, - "source": [ - "`pandas` is a very popular data manipulation library, with over 2 million downloads on Anaconda (`conda install pandas`) and over 19 million downloads on PyPI (`pip install pandas`) at the time of this writing. In our own internal data, we see that the overwhelming majority of Flatiron School DS grads use `pandas` on the job in some capacity.\n", - "\n", - "Unlike in base Python, where the Zen of Python says \"There should be one-- and preferably only one --obvious way to do it\", there is often more than one valid way to do something in `pandas`. However there are still more efficient and less efficient ways to use it. Specifically, the best `pandas` code is *performant* and *idiomatic*.\n", - "\n", - "Performant `pandas` code utilizes methods and broadcasting rather than user-defined functions or `for` loops. For example, if you need to strip whitespace from a column containing string data, the best approach would be to use the [`pandas.Series.str.strip` method](https://pandas.pydata.org/docs/reference/api/pandas.Series.str.strip.html) rather than writing your own function or writing a loop. Or if you want to multiply everything in a column by 100, the best approach would be to use broadcasting (e.g. `df[\"column_name\"] * 100`) instead of a function or loop. You can still write your own functions if needed, but only after checking that there isn't a built-in way to do it.\n", - "\n", - "Idiomatic `pandas` code has variable names that are meaningful words or abbreviations in English, that are related to the purpose of the variables. You can still use `df` as the name of your DataFrame if there is only one main DataFrame you are working with, but as soon as you are merging multiple DataFrames or taking a subset of a DataFrame, you should use meaningful names. For example, `df2` would not be an idiomatic name, but `movies_and_reviews` could be.\n", - "\n", - "We also recommend that you rename all DataFrame columns so that their meanings are more understandable, although it is fine to have acronyms. For example, `\"col1\"` would not be an idiomatic name, but `\"USD\"` could be.\n", - "\n", - "Below are the definitions of each rubric level for this objective. This information is also summarized in the rubric, which is attached to the project submission assignment." - ] - }, - { - "cell_type": "markdown", - "id": "e070c91b", - "metadata": {}, - "source": [ - "#### Exceeds Objective" - ] - }, - { - "cell_type": "markdown", - "id": "20092dcd", - "metadata": {}, - "source": [ - "Uses `pandas` to prepare data and answer business questions in an idiomatic, performant way" - ] - }, - { - "cell_type": "markdown", - "id": "882b158d", - "metadata": {}, - "source": [ - "#### Meets Objective (Passing Bar)" - ] - }, - { - "cell_type": "markdown", - "id": "c2c426e6", - "metadata": {}, - "source": [ - "Successfully uses `pandas` to prepare data in order to answer business questions\n", - "\n", - "> This includes projects that _occasionally_ use base Python when `pandas` methods would be more appropriate (such as using `enumerate()` on a DataFrame), or occasionally performs operations that do not appear to have any relevance to the business questions" - ] - }, - { - "cell_type": "markdown", - "id": "88d1667b", - "metadata": {}, - "source": [ - "#### Approaching Objective" - ] - }, - { - "cell_type": "markdown", - "id": "ec132034", - "metadata": {}, - "source": [ - "Uses `pandas` to prepare data, but makes significant errors\n", - "\n", - "> Examples of significant errors include: the result presented does not actually answer the stated question, the code produces errors, the code _consistently_ uses base Python when `pandas` methods would be more appropriate, or the submitted notebook contains significant quantities of code that is unrelated to the presented analysis (such as copy/pasted code from the curriculum or StackOverflow)" - ] - }, - { - "cell_type": "markdown", - "id": "c5e3c86b", - "metadata": {}, - "source": [ - "#### Does Not Meet Objective" - ] - }, - { - "cell_type": "markdown", - "id": "d9566206", - "metadata": {}, - "source": [ - "Unable to prepare data using `pandas`\n", - "\n", - "> This includes projects that successfully answer the business questions, but do not use `pandas` (e.g. use only base Python, or use some other tool like R, Tableau, or Excel)" - ] - }, - { - "cell_type": "markdown", - "id": "b0923637", - "metadata": {}, - "source": [ - "## Getting Started" - ] - }, - { - "cell_type": "markdown", - "id": "8e37e815", - "metadata": {}, - "source": [ - "Please start by reviewing the contents of this project description. If you have any questions, please ask your instructor ASAP.\n", - "\n", - "Next, you will need to complete the [***Project Proposal***](#project_proposal) which must be reviewed by your instructor before you can continue with the project.\n", - "\n", - "Then, you will need to create a GitHub repository. There are three options:\n", - "\n", - "1. Look at the [Phase 2 Project Templates and Examples repo](https://github.com/learn-co-curriculum/dsc-project-template) and follow the directions in the MVP branch.\n", - "2. Fork the [Phase 2 Project Repository](https://github.com/learn-co-curriculum/dsc-phase-2-project-v3), clone it locally, and work in the `student.ipynb` file. Make sure to also add and commit a PDF of your presentation to your repository with a file name of `presentation.pdf`.\n", - "3. Create a new repository from scratch by going to [github.com/new](https://github.com/new) and copying the data files from one of the above resources into your new repository. This approach will result in the most professional-looking portfolio repository, but can be more complicated to use. So if you are getting stuck with this option, try one of the above options instead." - ] - }, - { - "cell_type": "markdown", - "id": "290d61a5", - "metadata": {}, - "source": [ - "## Summary" - ] - }, - { - "cell_type": "markdown", - "id": "ac002279", - "metadata": {}, - "source": [ - "This project will give you a valuable opportunity to develop your data science skills using real-world data. The end-of-phase projects are a critical part of the program because they give you a chance to bring together all the skills you've learned, apply them to realistic projects for a business stakeholder, practice communication skills, and get feedback to help you improve. You've got this!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (learn-env)", - "language": "python", - "name": "learn-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/movie-data-analysis-presentation.pdf b/movie-data-analysis-presentation.pdf new file mode 100644 index 00000000..80bf007d Binary files /dev/null and b/movie-data-analysis-presentation.pdf differ diff --git a/movie-data-analysis-presentation.pptx b/movie-data-analysis-presentation.pptx new file mode 100644 index 00000000..bf925884 Binary files /dev/null and b/movie-data-analysis-presentation.pptx differ diff --git a/movie-data-analysis.ipynb b/movie-data-analysis.ipynb new file mode 100644 index 00000000..2c405253 --- /dev/null +++ b/movie-data-analysis.ipynb @@ -0,0 +1,7825 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f92ebcf3", + "metadata": {}, + "source": [ + "# Wamonyolo Studios Business Analysis \n", + "\n", + "## Overview \n", + "Wamonyolo Studios is planning to launch a new movie studio. To succeed, the company needs to understand what makes movies profitable. By analyzing past industry data, we can uncover insights that will guide Wamonyolo Studios toward smart, profit-driven decisions. \n", + "\n", + "---\n", + "\n", + "## Business Problem \n", + "As a new player in the movie industry, Wamonyolo faces several key questions: \n", + "- How long should their films be? \n", + "- Which genres are the most profitable? \n", + "- Should they build their studio from scratch or acquire an existing one?\n", + "- What is the optimal production budget for maximizing ROI?\"\n", + "- How important is the international box office for profitability?\" \n", + "\n", + "Using industry datasets and analysis, we aim to answer these questions and shape a winning strategy. \n", + "\n", + "---\n", + "\n", + "## Data Preparation \n", + "The **IMDb** dataset is the largest and most detailed. It provides: \n", + "- Movie runtimes \n", + "- Genres \n", + "- Release years \n", + "- Directors, writers, and actors \n", + "\n", + "**Limitation:** It does *not* include financial data like budgets or box office revenue. \n", + "\n", + "To complete the picture, we merge IMDb with financial datasets: \n", + "- **Box Office Mojo (BOM):** Domestic + international box office gross \n", + "- **The Numbers:** Budget + revenue \n", + "- **The Movie DB (TMDB):** Ratings, popularity, and sometimes financial data \n", + "\n", + "This way, we connect *what a movie is* with *how it performs financially*. \n", + "\n", + "---\n", + "\n", + "## Why Merging Matters \n", + "- **IMDb = What the movie is** (content + creators) \n", + "- **Financial datasets = How the movie performed** (cost + revenue) \n", + "\n", + "When combined, the data allows us to answer: \n", + "- Do longer films earn more or less? \n", + "- Which genres deliver the highest returns? \n", + "- Are certain directors/writers consistently successful? \n", + "\n", + "---\n", + " \n", + "IMDb provides the richest descriptive information, but lacks financial details. \n", + "By merging it with BOM, The Numbers, and TMDB, Wamonyolo Studios can analyze both creativity *and* profitability—ensuring a smart, data-driven entry into the movie market. " + ] + }, + { + "cell_type": "markdown", + "id": "30a2f282", + "metadata": {}, + "source": [ + "# Import all necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "29714fdb", + "metadata": {}, + "outputs": [], + "source": [ + "# Step 1: Import all necessary libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from scipy import stats\n", + "import statsmodels.api as sm\n", + "from statsmodels.formula.api import ols" + ] + }, + { + "cell_type": "markdown", + "id": "601063af", + "metadata": {}, + "source": [ + "# Reading the data " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "88db14b7", + "metadata": {}, + "outputs": [], + "source": [ + "# Box Office Mojo \n", + "bom_movie_gross = pd.read_csv('zippedData/bom.movie_gross.csv.gz')\n", + "\n", + "# === The Numbers ===\n", + "tn_movie_budgets = pd.read_csv('zippedData/tn.movie_budgets.csv.gz')\n", + "\n", + "# === The Movie Database (TMDb) ===\n", + "tmdb_movies = pd.read_csv('zippedData/tmdb.movies.csv.gz')\n", + "\n", + "# === Rotten Tomatoes ===\n", + "# === Rotten Tomatoes ===\n", + "rt_movies = pd.read_csv('zippedData/rt.movie_info.tsv.gz', sep='\\t', encoding='latin-1')\n", + "rt_reviews = pd.read_csv('zippedData/rt.reviews.tsv.gz', sep='\\t', encoding='latin-1')\n" + ] + }, + { + "cell_type": "markdown", + "id": "785bc303", + "metadata": {}, + "source": [ + "IMDb.zip is basically a compressed folder with several .tsv IMDb files inside" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "676b1962", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['im.db']\n" + ] + } + ], + "source": [ + "import zipfile, pandas as pd\n", + "\n", + "with zipfile.ZipFile('zippedData/im.db.zip') as z:\n", + " print(z.namelist()) # shows you all files inside\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d24de561", + "metadata": {}, + "outputs": [], + "source": [ + "import zipfile\n", + "\n", + "with zipfile.ZipFile(\"zippedData/im.db.zip\", \"r\") as z:\n", + " z.extractall(\"zippedData/\") # this will create 'zippedData/im.db'\n" + ] + }, + { + "cell_type": "markdown", + "id": "1ad0f2c8", + "metadata": {}, + "source": [ + "The file contains a single SQLite database File called im.db,meaning you need to open it as a SQLite database" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a2b0e030", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " name\n", + "0 movie_basics\n", + "1 directors\n", + "2 known_for\n", + "3 movie_akas\n", + "4 movie_ratings\n", + "5 persons\n", + "6 principals\n", + "7 writers\n" + ] + } + ], + "source": [ + "import sqlite3\n", + "#import pandas as pd\n", + "\n", + "conn = sqlite3.connect(\"zippedData/im.db\")\n", + "tables = pd.read_sql(\"SELECT name FROM sqlite_master WHERE type='table';\", conn)\n", + "print(tables)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "9b8148ce", + "metadata": {}, + "source": [ + "Now loading those tables into pandas DataFrames with simple SQL queriees" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d21e235a", + "metadata": {}, + "outputs": [], + "source": [ + "movie_basics = pd.read_sql(\"SELECT * FROM movie_basics;\", conn)\n", + "\n", + "directors = pd.read_sql(\"SELECT * FROM directors;\", conn)\n", + "known_for = pd.read_sql(\"SELECT * FROM known_for;\", conn)\n", + "movie_akas = pd.read_sql(\"SELECT * FROM movie_akas;\", conn)\n", + "movie_ratings = pd.read_sql(\"SELECT * FROM movie_ratings;\", conn)\n", + "persons = pd.read_sql(\"SELECT * FROM persons;\", conn)\n", + "principals = pd.read_sql(\"SELECT * FROM principals;\", conn)\n", + "writers = pd.read_sql(\"SELECT * FROM writers;\", conn)" + ] + }, + { + "cell_type": "markdown", + "id": "87e280a9", + "metadata": {}, + "source": [ + " # Data Cleaning \n", + "We’ll clean only the datasets that are most useful for analysis (IMDb + financials). Rotten Tomatoes/TMDB can be optional later." + ] + }, + { + "cell_type": "markdown", + "id": "859c0fe5", + "metadata": {}, + "source": [ + "## Datasets to Clean First\n", + "\n", + "1. IMDb tables (content & metadata)\n", + "\n", + " - movie_basics (title, year, runtime, genres)\n", + "\n", + " - movie_ratings (average rating, votes)\n", + "\n", + "2. Box Office Mojo (bom_movie_gross)\n", + "\n", + " - Domestic & foreign gross\n", + "\n", + "3. The Numbers (tn_movie_budgets)\n", + "\n", + " - Budget + gross" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "426d5d38", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 146144 entries, 0 to 146143\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 movie_id 146144 non-null object \n", + " 1 primary_title 146144 non-null object \n", + " 2 original_title 146123 non-null object \n", + " 3 start_year 146144 non-null int64 \n", + " 4 runtime_minutes 114405 non-null float64\n", + " 5 genres 140736 non-null object \n", + "dtypes: float64(1), int64(1), object(4)\n", + "memory usage: 6.7+ MB\n" + ] + } + ], + "source": [ + " movie_basics.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b4fa66c7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(0)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check duplicates\n", + "movie_basics.duplicated().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c8defe9c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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movie_idprimary_titleoriginal_titlestart_yearruntime_minutesgenres
0tt0063540SunghurshSunghursh2013175.0Action,Crime,Drama
1tt0066787One Day Before the Rainy SeasonAshad Ka Ek Din2019114.0Biography,Drama
2tt0069049The Other Side of the WindThe Other Side of the Wind2018122.0Drama
3tt0069204Sabse Bada SukhSabse Bada Sukh2018NaNComedy,Drama
4tt0100275The Wandering Soap OperaLa Telenovela Errante201780.0Comedy,Drama,Fantasy
.....................
146139tt9916538Kuambil Lagi HatikuKuambil Lagi Hatiku2019123.0Drama
146140tt9916622Rodolpho Teóphilo - O Legado de um PioneiroRodolpho Teóphilo - O Legado de um Pioneiro2015NaNDocumentary
146141tt9916706Dankyavar DankaDankyavar Danka2013NaNComedy
146142tt99167306 Gunn6 Gunn2017116.0None
146143tt9916754Chico Albuquerque - RevelaçõesChico Albuquerque - Revelações2013NaNDocumentary
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" + ], + "text/plain": [ + " start_year runtime_minutes\n", + "count 114405.000000 114405.000000\n", + "mean 2014.396801 86.187247\n", + "std 2.637480 166.360590\n", + "min 2010.000000 1.000000\n", + "25% 2012.000000 70.000000\n", + "50% 2014.000000 87.000000\n", + "75% 2017.000000 99.000000\n", + "max 2022.000000 51420.000000" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(runtime_df.shape) # how many rows/columns after cleaning\n", + "print(runtime_df.isna().sum()) # check if any nulls remain\n", + "runtime_df.head() # preview first 5 rows\n", + "runtime_df.info() # check datatypes\n", + "runtime_df.describe() # quick stats (mean, min, max runtime)\n" + ] + }, + { + "cell_type": "markdown", + "id": "29ad0fbb", + "metadata": {}, + "source": [ + "\n", + "- shape - see how much data we have left after cleaning.\n", + "\n", + "- isna() - make sure runtimes are fully clean.\n", + "\n", + "- head() - sanity check if columns look correct.\n", + "\n", + "- info() - confirm datatypes (start_year should be int, runtime_minutes int/float).\n", + "\n", + "- describe() - see runtime distribution (are there very short/long outliers?).\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "148ef633", + "metadata": {}, + "source": [ + "Now we’re prepping The Numbers and TMDb release dates so they can align with IMDb’s start_year." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5453a644", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idrelease_datemovieproduction_budgetdomestic_grossworldwide_gross
01Dec 18, 2009Avatar$425,000,000$760,507,625$2,776,345,279
12May 20, 2011Pirates of the Caribbean: On Stranger Tides$410,600,000$241,063,875$1,045,663,875
23Jun 7, 2019Dark Phoenix$350,000,000$42,762,350$149,762,350
34May 1, 2015Avengers: Age of Ultron$330,600,000$459,005,868$1,403,013,963
45Dec 15, 2017Star Wars Ep. VIII: The Last Jedi$317,000,000$620,181,382$1,316,721,747
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" + ], + "text/plain": [ + " id release_date movie \\\n", + "0 1 Dec 18, 2009 Avatar \n", + "1 2 May 20, 2011 Pirates of the Caribbean: On Stranger Tides \n", + "2 3 Jun 7, 2019 Dark Phoenix \n", + "3 4 May 1, 2015 Avengers: Age of Ultron \n", + "4 5 Dec 15, 2017 Star Wars Ep. VIII: The Last Jedi \n", + "\n", + " production_budget domestic_gross worldwide_gross \n", + "0 $425,000,000 $760,507,625 $2,776,345,279 \n", + "1 $410,600,000 $241,063,875 $1,045,663,875 \n", + "2 $350,000,000 $42,762,350 $149,762,350 \n", + "3 $330,600,000 $459,005,868 $1,403,013,963 \n", + "4 $317,000,000 $620,181,382 $1,316,721,747 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tn_movie_budgets = pd.read_csv('zippedData/tn.movie_budgets.csv.gz')\n", + "tn_movie_budgets.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "29f1e16a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 5782 entries, 0 to 5781\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 5782 non-null int64 \n", + " 1 release_date 5782 non-null object\n", + " 2 movie 5782 non-null object\n", + " 3 production_budget 5782 non-null object\n", + " 4 domestic_gross 5782 non-null object\n", + " 5 worldwide_gross 5782 non-null object\n", + "dtypes: int64(1), object(5)\n", + "memory usage: 271.2+ KB\n" + ] + } + ], + "source": [ + "tn_movie_budgets.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "eb9df81a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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genre_idsidoriginal_languageoriginal_titlepopularityrelease_datetitlevote_averagevote_count
0[12, 14, 10751]12444enHarry Potter and the Deathly Hallows: Part 133.5332010-11-19Harry Potter and the Deathly Hallows: Part 17.710788
1[14, 12, 16, 10751]10191enHow to Train Your Dragon28.7342010-03-26How to Train Your Dragon7.77610
2[12, 28, 878]10138enIron Man 228.5152010-05-07Iron Man 26.812368
3[16, 35, 10751]862enToy Story28.0051995-11-22Toy Story7.910174
4[28, 878, 12]27205enInception27.9202010-07-16Inception8.322186
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" + ], + "text/plain": [ + " genre_ids id original_language \\\n", + "0 [12, 14, 10751] 12444 en \n", + "1 [14, 12, 16, 10751] 10191 en \n", + "2 [12, 28, 878] 10138 en \n", + "3 [16, 35, 10751] 862 en \n", + "4 [28, 878, 12] 27205 en \n", + "\n", + " original_title popularity release_date \\\n", + "0 Harry Potter and the Deathly Hallows: Part 1 33.533 2010-11-19 \n", + "1 How to Train Your Dragon 28.734 2010-03-26 \n", + "2 Iron Man 2 28.515 2010-05-07 \n", + "3 Toy Story 28.005 1995-11-22 \n", + "4 Inception 27.920 2010-07-16 \n", + "\n", + " title vote_average vote_count \n", + "0 Harry Potter and the Deathly Hallows: Part 1 7.7 10788 \n", + "1 How to Train Your Dragon 7.7 7610 \n", + "2 Iron Man 2 6.8 12368 \n", + "3 Toy Story 7.9 10174 \n", + "4 Inception 8.3 22186 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The Movie Database (TMDb) \n", + "tmdb_movies = pd.read_csv('zippedData/tmdb.movies.csv.gz', index_col=0)\n", + "tmdb_movies.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "7eaf60d9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 26517 entries, 0 to 26516\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 genre_ids 26517 non-null object \n", + " 1 id 26517 non-null int64 \n", + " 2 original_language 26517 non-null object \n", + " 3 original_title 26517 non-null object \n", + " 4 popularity 26517 non-null float64\n", + " 5 release_date 26517 non-null object \n", + " 6 title 26517 non-null object \n", + " 7 vote_average 26517 non-null float64\n", + " 8 vote_count 26517 non-null int64 \n", + "dtypes: float64(2), int64(2), object(5)\n", + "memory usage: 2.0+ MB\n" + ] + } + ], + "source": [ + "tmdb_movies.info()" + ] + }, + { + "cell_type": "markdown", + "id": "f1841953", + "metadata": {}, + "source": [ + "### Step 1\n", + "\n", + "Convert release_date into datetime " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "cd34494e", + "metadata": {}, + "outputs": [], + "source": [ + "tn_movie_budgets['release_date'] = pd.to_datetime(tn_movie_budgets['release_date'])\n", + "tmdb_movies['release_date'] = pd.to_datetime(tmdb_movies['release_date'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "398a1fa6", + "metadata": {}, + "source": [ + "\n", + "Dates are often read in as strings → can’t extract year/month directly.\n", + "\n", + "pd.to_datetime() standardizes them into true datetime objects." + ] + }, + { + "cell_type": "markdown", + "id": "458312cb", + "metadata": {}, + "source": [ + "### Step 2 \n", + " \n", + "Extract release year (to match IMDb format)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "d9996eec", + "metadata": {}, + "outputs": [], + "source": [ + "tn_movie_budgets['release_year'] = tn_movie_budgets['release_date'].dt.year\n", + "tmdb_movies['release_year'] = tmdb_movies['release_date'].dt.year\n" + ] + }, + { + "cell_type": "markdown", + "id": "51e309bb", + "metadata": {}, + "source": [ + "\n", + "IMDb uses just the year (start_year).\n", + "\n", + "To merge datasets later, we need the same format (year only)." + ] + }, + { + "cell_type": "markdown", + "id": "e2cd707a", + "metadata": {}, + "source": [ + "### Step 3: \n", + "Extract release month (both numeric & string)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "dc275f59", + "metadata": {}, + "outputs": [], + "source": [ + "tn_movie_budgets['month_dt'] = tn_movie_budgets['release_date'].dt.month # numeric month (1–12)\n", + "tn_movie_budgets['month'] = tn_movie_budgets['release_date'].dt.month # duplicate here, can adjust if you want month names\n" + ] + }, + { + "cell_type": "markdown", + "id": "eaff3632", + "metadata": {}, + "source": [ + "\n", + "Month helps analyze seasonality (e.g., summer blockbusters, holiday releases).\n", + "\n", + "month_dt → numeric (for calculations).\n", + "\n", + "month → could later be turned into month names for plots.\n", + "\n", + "(Small note: you might want dt.month_name() if you prefer full names like “July”)" + ] + }, + { + "cell_type": "markdown", + "id": "a151f982", + "metadata": {}, + "source": [ + "### Step 4:\n", + "Drop raw release_date" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "47e51179", + "metadata": {}, + "outputs": [], + "source": [ + "tn_movie_budgets = tn_movie_budgets.drop(columns=['release_date'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "a1684b47", + "metadata": {}, + "source": [ + "\n", + "\n", + "We’ve extracted all useful parts (year + month).\n", + "\n", + "Dropping avoids duplication and keeps dataframe cleaner." + ] + }, + { + "cell_type": "markdown", + "id": "83511d1d", + "metadata": {}, + "source": [ + "### Step 5\n", + "Inspect" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "7d3b8018", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " movie release_year month_dt month\n", + "0 Avatar 2009 12 12\n", + "1 Pirates of the Caribbean: On Stranger Tides 2011 5 5\n", + "2 Dark Phoenix 2019 6 6\n", + "3 Avengers: Age of Ultron 2015 5 5\n", + "4 Star Wars Ep. VIII: The Last Jedi 2017 12 12\n", + " title release_year\n", + "0 Harry Potter and the Deathly Hallows: Part 1 2010\n", + "1 How to Train Your Dragon 2010\n", + "2 Iron Man 2 2010\n", + "3 Toy Story 1995\n", + "4 Inception 2010\n" + ] + } + ], + "source": [ + "print(tn_movie_budgets[['movie','release_year','month_dt','month']].head())\n", + "print(tmdb_movies[['title','release_year']].head())\n" + ] + }, + { + "cell_type": "markdown", + "id": "7e609e47", + "metadata": {}, + "source": [ + "- Now you’re cleaning up the financial columns from The Numbers so they’re ready for calculations and plots. " + ] + }, + { + "cell_type": "markdown", + "id": "7697358e", + "metadata": {}, + "source": [ + "### Step 1:\n", + "Identify the money columns" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "9c04050c", + "metadata": {}, + "outputs": [], + "source": [ + "cols = ['production_budget', 'domestic_gross', 'worldwide_gross']\n" + ] + }, + { + "cell_type": "markdown", + "id": "bb13c37c", + "metadata": {}, + "source": [ + "\n", + "These are stored as strings with $ and commas (e.g., \"$100,000,000\").\n", + "We can’t do math or plots with strings → must convert to numbers." + ] + }, + { + "cell_type": "markdown", + "id": "91764c1d", + "metadata": {}, + "source": [ + "### Step 2:\n", + "Remove $ and ," + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "4b16574c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:1: SyntaxWarning: invalid escape sequence '\\$'\n", + "<>:1: SyntaxWarning: invalid escape sequence '\\$'\n", + "C:\\Users\\Ray Onsongo\\AppData\\Local\\Temp\\ipykernel_9868\\698015300.py:1: SyntaxWarning: invalid escape sequence '\\$'\n", + " tn_movie_budgets[cols] = tn_movie_budgets[cols].replace('[\\$,]', '', regex=True)\n" + ] + } + ], + "source": [ + "tn_movie_budgets[cols] = tn_movie_budgets[cols].replace('[\\$,]', '', regex=True)\n" + ] + }, + { + "cell_type": "markdown", + "id": "070a1370", + "metadata": {}, + "source": [ + "\n", + "\n", + "[\\$,] means: match dollar signs $ or commas ,.\n", + "\n", + ".replace(..., regex=True) strips them out → \"100000000\"." + ] + }, + { + "cell_type": "markdown", + "id": "a5dcf0c2", + "metadata": {}, + "source": [ + "### Step 3: \n", + "Convert to integers" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "f639f82f", + "metadata": {}, + "outputs": [], + "source": [ + "tn_movie_budgets[cols] = tn_movie_budgets[cols].astype('int64')\n" + ] + }, + { + "cell_type": "markdown", + "id": "ad89d061", + "metadata": {}, + "source": [ + "\n", + "\n", + "Converts cleaned strings into integers so we can:\n", + "\n", + "Calculate profits/losses\n", + "\n", + "Plot histograms, scatterplots\n", + "\n", + "Run regressions" + ] + }, + { + "cell_type": "markdown", + "id": "1db84ccb", + "metadata": {}, + "source": [ + "Step 4 Inspect the result" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "f8a2fb72", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "production_budget int64\n", + "domestic_gross int64\n", + "worldwide_gross int64\n", + "dtype: object\n" + ] + }, + { + "data": { + "text/html": [ + "
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idmovieproduction_budgetdomestic_grossworldwide_grossrelease_yearmonth_dtmonth
01Avatar425000000760507625277634527920091212
12Pirates of the Caribbean: On Stranger Tides4106000002410638751045663875201155
23Dark Phoenix35000000042762350149762350201966
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" + ], + "text/plain": [ + " id movie production_budget \\\n", + "0 1 Avatar 425000000 \n", + "1 2 Pirates of the Caribbean: On Stranger Tides 410600000 \n", + "2 3 Dark Phoenix 350000000 \n", + "\n", + " domestic_gross worldwide_gross release_year month_dt month \n", + "0 760507625 2776345279 2009 12 12 \n", + "1 241063875 1045663875 2011 5 5 \n", + "2 42762350 149762350 2019 6 6 " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(tn_movie_budgets[cols].dtypes) # confirm int64\n", + "tn_movie_budgets[cols].describe() # check ranges, averages, etc.\n", + "tn_movie_budgets.head(3) # preview cleaned values\n" + ] + }, + { + "cell_type": "markdown", + "id": "c93174ee", + "metadata": {}, + "source": [ + "\n", + "\n", + "describe() shows if values are realistic (e.g., budgets in millions, not billions)." + ] + }, + { + "cell_type": "markdown", + "id": "29dc27d0", + "metadata": {}, + "source": [ + "# Standardizing titles across all datasets to improve your merge success rate" + ] + }, + { + "cell_type": "markdown", + "id": "35bb7dc7", + "metadata": {}, + "source": [ + "### Step 1:\n", + "Apply .str.title() to titles" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "0c1942c9", + "metadata": {}, + "outputs": [], + "source": [ + "runtime_df['primary_title'] = runtime_df['primary_title'].str.title()\n", + "tn_movie_budgets['movie'] = tn_movie_budgets['movie'].str.title()\n", + "bom_movie_gross['title'] = bom_movie_gross['title'].str.title()\n", + "tmdb_movies['title'] = tmdb_movies['title'].str.title()\n" + ] + }, + { + "cell_type": "markdown", + "id": "f7db48bd", + "metadata": {}, + "source": [ + "\n", + "- In different datasets, titles may appear as \"avatar\", \"Avatar\", or \"AVATAR\". .str.title() converts them all to \"Avatar\" → making matches more consistent when merging." + ] + }, + { + "cell_type": "markdown", + "id": "b4dd712f", + "metadata": {}, + "source": [ + "### Step 2: \n", + "Inspect for consistency" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "2c499387", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 Sunghursh\n", + "1 One Day Before The Rainy Season\n", + "2 The Other Side Of The Wind\n", + "4 The Wandering Soap Opera\n", + "5 A Thin Life\n", + "Name: primary_title, dtype: object\n", + "0 Avatar\n", + "1 Pirates Of The Caribbean: On Stranger Tides\n", + "2 Dark Phoenix\n", + "3 Avengers: Age Of Ultron\n", + "4 Star Wars Ep. Viii: The Last Jedi\n", + "Name: movie, dtype: object\n", + "0 Toy Story 3\n", + "1 Alice In Wonderland (2010)\n", + "2 Harry Potter And The Deathly Hallows Part 1\n", + "3 Inception\n", + "4 Shrek Forever After\n", + "Name: title, dtype: object\n", + "0 Harry Potter And The Deathly Hallows: Part 1\n", + "1 How To Train Your Dragon\n", + "2 Iron Man 2\n", + "3 Toy Story\n", + "4 Inception\n", + "Name: title, dtype: object\n" + ] + } + ], + "source": [ + "print(runtime_df['primary_title'].head(5))\n", + "print(tn_movie_budgets['movie'].head(5))\n", + "print(bom_movie_gross['title'].head(5))\n", + "print(tmdb_movies['title'].head(5))" + ] + }, + { + "cell_type": "markdown", + "id": "fca991a7", + "metadata": {}, + "source": [ + "- Now you’re adding profit margin columns so you can analyze which movies actually made money relative to their costs.(tn_movie_budgets)" + ] + }, + { + "cell_type": "markdown", + "id": "9e50ab96", + "metadata": {}, + "source": [ + "### Step 1: \n", + "Domestic profit margin" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "4157a2a7", + "metadata": {}, + "outputs": [], + "source": [ + "tn_movie_budgets['dom_profit_margin'] = (\n", + " (tn_movie_budgets['domestic_gross'] - tn_movie_budgets['production_budget'])\n", + " / tn_movie_budgets['domestic_gross']\n", + ") * 100\n" + ] + }, + { + "cell_type": "markdown", + "id": "e68960e3", + "metadata": {}, + "source": [ + "Formula:\n", + "Profit Margin\n", + "=\n", + "Revenue − CostRevenue × 100\n", + "\n", + "Profit Margin = Revenue − Cost Revenue × 100\n", + "\n", + "Tells you what % of revenue was actual profit from U.S. box office only." + ] + }, + { + "cell_type": "markdown", + "id": "fb05c721", + "metadata": {}, + "source": [ + "### Step 2: \n", + "Worldwide profit margin" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "81613231", + "metadata": {}, + "outputs": [], + "source": [ + "tn_movie_budgets['ww_profit_margin'] = (\n", + " (tn_movie_budgets['worldwide_gross'] - tn_movie_budgets['production_budget'])\n", + " / tn_movie_budgets['worldwide_gross']\n", + ") * 100\n" + ] + }, + { + "cell_type": "markdown", + "id": "d947da45", + "metadata": {}, + "source": [ + "- Same idea, but using global revenue. Helps you see if movies depended more on domestic vs international markets for profitability." + ] + }, + { + "cell_type": "markdown", + "id": "cfb5349a", + "metadata": {}, + "source": [ + "### Step 3: \n", + "Inspect results" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "789bf3be", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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movieproduction_budgetdomestic_grossworldwide_grossdom_profit_marginww_profit_margin
0Avatar425000000760507625277634527944.11627484.692106
1Pirates Of The Caribbean: On Stranger Tides4106000002410638751045663875-70.32830060.733080
2Dark Phoenix35000000042762350149762350-718.477001-133.703598
3Avengers: Age Of Ultron330600000459005868140301396327.97477776.436443
4Star Wars Ep. Viii: The Last Jedi317000000620181382131672174748.88592175.925058
5Star Wars Ep. Vii: The Force Awakens306000000936662225205331122067.33080685.097242
6Avengers: Infinity War300000000678815482204813420055.80536985.352522
7Pirates Of The Caribbean: At World’S End3000000003094204259634204253.04453968.860947
8Justice League300000000229024295655945209-30.99047054.264473
9Spectre300000000200074175879620923-49.94438965.894399
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" + ], + "text/plain": [ + " movie production_budget \\\n", + "0 Avatar 425000000 \n", + "1 Pirates Of The Caribbean: On Stranger Tides 410600000 \n", + "2 Dark Phoenix 350000000 \n", + "3 Avengers: Age Of Ultron 330600000 \n", + "4 Star Wars Ep. Viii: The Last Jedi 317000000 \n", + "5 Star Wars Ep. Vii: The Force Awakens 306000000 \n", + "6 Avengers: Infinity War 300000000 \n", + "7 Pirates Of The Caribbean: At World’S End 300000000 \n", + "8 Justice League 300000000 \n", + "9 Spectre 300000000 \n", + "\n", + " domestic_gross worldwide_gross dom_profit_margin ww_profit_margin \n", + "0 760507625 2776345279 44.116274 84.692106 \n", + "1 241063875 1045663875 -70.328300 60.733080 \n", + "2 42762350 149762350 -718.477001 -133.703598 \n", + "3 459005868 1403013963 27.974777 76.436443 \n", + "4 620181382 1316721747 48.885921 75.925058 \n", + "5 936662225 2053311220 67.330806 85.097242 \n", + "6 678815482 2048134200 55.805369 85.352522 \n", + "7 309420425 963420425 3.044539 68.860947 \n", + "8 229024295 655945209 -30.990470 54.264473 \n", + "9 200074175 879620923 -49.944389 65.894399 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tn_movie_budgets[['movie','production_budget','domestic_gross','worldwide_gross','dom_profit_margin','ww_profit_margin']].head(10)\n" + ] + }, + { + "cell_type": "markdown", + "id": "9212504a", + "metadata": {}, + "source": [ + "- This structure is like we did for profit margins, but now for profit amount and ROI — and using our dataset (tn_movie_budgets)." + ] + }, + { + "cell_type": "markdown", + "id": "da038a35", + "metadata": {}, + "source": [ + "### Step 4: \n", + "Worldwide profit amount" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "b95bf566", + "metadata": {}, + "outputs": [], + "source": [ + "tn_movie_budgets['world_wide_profit_amount'] = (\n", + " tn_movie_budgets['worldwide_gross'] - tn_movie_budgets['production_budget']\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "0020b568", + "metadata": {}, + "source": [ + "This gives you the absolute dollar profit (or loss) a movie made globally. Unlike margins, this shows the real money gained. Example: If budget = $100M, worldwide gross = $250M, then Profit = $150M." + ] + }, + { + "cell_type": "markdown", + "id": "c196a754", + "metadata": {}, + "source": [ + "### Step 5: \n", + "Return on Investment (ROI)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "6cb21f5c", + "metadata": {}, + "outputs": [], + "source": [ + "tn_movie_budgets['ROI_perc'] = (\n", + " tn_movie_budgets['world_wide_profit_amount'] / tn_movie_budgets['production_budget']\n", + ") * 100\n" + ] + }, + { + "cell_type": "markdown", + "id": "dfa3310a", + "metadata": {}, + "source": [ + "ROI tells you how efficiently money was used.\n", + "\n", + "Formula:\n", + "\n", + "𝑅\n", + "𝑂\n", + "𝐼\n", + "=\n", + "(Net Profit / Budget) × 100\n", + "\n", + "ROI=\n", + "Budget\n", + "Net Profit\n", + "\t​\n", + "\n", + "×100\n", + "\n", + "A blockbuster making 200M dollar profit on a 200M dollar budget → ROI = 100% , but a small film making 20M dollar profit on $5M dollar budget → ROI = 400% therefore ROI highlights hidden winners among low-budget films." + ] + }, + { + "cell_type": "markdown", + "id": "5428f150", + "metadata": {}, + "source": [ + "### Step 6:\n", + "Inspect results" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "a9b9758e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2009 2011 2019 2015 2017 2018 2007 2012 2013 2010 2016 2014 2006 2008\n", + " 2005 1997 2004 1999 1995 2003]\n", + "int32\n" + ] + } + ], + "source": [ + "print(tn_movie_budgets['release_year'].unique()[:20])\n", + "print(tn_movie_budgets['release_year'].dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "d13ce04d-ead5-42d3-836d-e1b2f7b747ec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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movieproduction_budgetworldwide_grossworld_wide_profit_amountROI_perc
0Avatar42500000027763452792351345279553.257713
1Pirates Of The Caribbean: On Stranger Tides4106000001045663875635063875154.667286
2Dark Phoenix350000000149762350-200237650-57.210757
3Avengers: Age Of Ultron33060000014030139631072413963324.384139
4Star Wars Ep. Viii: The Last Jedi3170000001316721747999721747315.369636
5Star Wars Ep. Vii: The Force Awakens30600000020533112201747311220571.016739
6Avengers: Infinity War30000000020481342001748134200582.711400
7Pirates Of The Caribbean: At World’S End300000000963420425663420425221.140142
8Justice League300000000655945209355945209118.648403
9Spectre300000000879620923579620923193.206974
\n", + "
" + ], + "text/plain": [ + " movie production_budget \\\n", + "0 Avatar 425000000 \n", + "1 Pirates Of The Caribbean: On Stranger Tides 410600000 \n", + "2 Dark Phoenix 350000000 \n", + "3 Avengers: Age Of Ultron 330600000 \n", + "4 Star Wars Ep. Viii: The Last Jedi 317000000 \n", + "5 Star Wars Ep. Vii: The Force Awakens 306000000 \n", + "6 Avengers: Infinity War 300000000 \n", + "7 Pirates Of The Caribbean: At World’S End 300000000 \n", + "8 Justice League 300000000 \n", + "9 Spectre 300000000 \n", + "\n", + " worldwide_gross world_wide_profit_amount ROI_perc \n", + "0 2776345279 2351345279 553.257713 \n", + "1 1045663875 635063875 154.667286 \n", + "2 149762350 -200237650 -57.210757 \n", + "3 1403013963 1072413963 324.384139 \n", + "4 1316721747 999721747 315.369636 \n", + "5 2053311220 1747311220 571.016739 \n", + "6 2048134200 1748134200 582.711400 \n", + "7 963420425 663420425 221.140142 \n", + "8 655945209 355945209 118.648403 \n", + "9 879620923 579620923 193.206974 " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tn_movie_budgets[['movie','production_budget','worldwide_gross',\n", + " 'world_wide_profit_amount','ROI_perc']].head(10)" + ] + }, + { + "cell_type": "markdown", + "id": "7e6a515f", + "metadata": {}, + "source": [ + "- Now we can filter the dataset by year from the tn_movie_budgets DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "3abd8dcd", + "metadata": {}, + "outputs": [], + "source": [ + "tn_movie_budgets= tn_movie_budgets[tn_movie_budgets['release_year'] > 2000]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "1e609657", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4198, 12)\n", + "2001 2020\n" + ] + } + ], + "source": [ + "print(tn_movie_budgets.shape)\n", + "print(tn_movie_budgets['release_year'].min(), tn_movie_budgets['release_year'].max())\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "58fef512", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 4198 entries, 0 to 5781\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 4198 non-null int64 \n", + " 1 movie 4198 non-null object \n", + " 2 production_budget 4198 non-null int64 \n", + " 3 domestic_gross 4198 non-null int64 \n", + " 4 worldwide_gross 4198 non-null int64 \n", + " 5 release_year 4198 non-null int32 \n", + " 6 month_dt 4198 non-null int32 \n", + " 7 month 4198 non-null int32 \n", + " 8 dom_profit_margin 4198 non-null float64\n", + " 9 ww_profit_margin 4198 non-null float64\n", + " 10 world_wide_profit_amount 4198 non-null int64 \n", + " 11 ROI_perc 4198 non-null float64\n", + "dtypes: float64(3), int32(3), int64(5), object(1)\n", + "memory usage: 377.2+ KB\n" + ] + } + ], + "source": [ + "tn_movie_budgets.info()" + ] + }, + { + "cell_type": "markdown", + "id": "a28a737d", + "metadata": {}, + "source": [ + "- Older movies (before 2000) may not reflect today’s industry dynamics. Budgets, marketing, and box office models changed drastically in the 2025s (e.g., streaming, globalization)." + ] + }, + { + "cell_type": "markdown", + "id": "5ab21fd5", + "metadata": {}, + "source": [ + "# Shifting into release month analysis. \n", + "- Since we are using tn_movie_budgets instead of numbers_df, let’s rewrite and break it down:" + ] + }, + { + "cell_type": "markdown", + "id": "64428de9", + "metadata": {}, + "source": [ + " Step 1: Group by release month and calculate medians" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "51e97639-bc02-443f-879a-73ef49b7f16c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " month production_budget domestic_gross worldwide_gross \\\n", + "0 1 18000000.0 17469107.0 35260470.0 \n", + "1 2 20000000.0 19192510.0 39049922.0 \n", + "2 3 18000000.0 16127344.5 25802739.5 \n", + "3 4 17250000.0 11453108.0 21673225.5 \n", + "4 5 20000000.0 18882880.0 38158601.0 \n", + "5 6 21750000.0 21457839.5 42609137.0 \n", + "6 7 20000000.0 27397912.5 50397206.5 \n", + "7 8 20000000.0 16521410.0 30138912.0 \n", + "8 9 16250000.0 10300039.5 21702186.0 \n", + "9 10 13000000.0 8050767.0 15486441.5 \n", + "10 11 25000000.0 26900336.0 52427346.0 \n", + "11 12 19200000.0 6107205.5 23514312.0 \n", + "\n", + " world_wide_profit_amount month_name \n", + "0 11131779.0 Jan \n", + "1 13874967.0 Feb \n", + "2 7875084.0 Mar \n", + "3 4392610.5 Apr \n", + "4 15796145.0 May \n", + "5 11152619.0 Jun \n", + "6 20734161.5 Jul \n", + "7 8153415.0 Aug \n", + "8 758125.0 Sep \n", + "9 2413808.5 Oct \n", + "10 22004627.0 Nov \n", + "11 3125045.5 Dec \n" + ] + } + ], + "source": [ + "# First, ensure your 'month' column is clean and numeric.\n", + "# Then, select only the numeric columns for the median calculation.\n", + "# These likely include 'production_budget', 'domestic_gross', 'worldwide_gross', 'worldwide_profit', 'roi'\n", + "\n", + "numeric_columns = ['production_budget', 'domestic_gross', 'worldwide_gross', 'world_wide_profit_amount', 'month'] # Add any other numeric columns you have\n", + "\n", + "# Create a DataFrame with only the numeric columns and the 'month' for grouping\n", + "numeric_df = tn_movie_budgets[numeric_columns]\n", + "\n", + "# Now group by 'month' and calculate the median for the remaining numeric columns\n", + "month_df = numeric_df.groupby('month').median()\n", + "\n", + "# Reset index so 'month' becomes a column again\n", + "month_df = month_df.reset_index()\n", + "\n", + "# Sort by month number (1–12)\n", + "month_df = month_df.sort_values('month')\n", + "\n", + "# Add month names\n", + "month_dict = {\n", + " 1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr',\n", + " 5: 'May', 6: 'Jun', 7: 'Jul', 8: 'Aug',\n", + " 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'\n", + "}\n", + "month_df['month_name'] = month_df['month'].map(month_dict)\n", + "\n", + "# Display the result\n", + "print(month_df)" + ] + }, + { + "cell_type": "markdown", + "id": "85a2be5f", + "metadata": {}, + "source": [ + "- Grouping by month lets you see if certain months tend to produce higher profits/ROI. \n", + "- Using the median reduces the impact of extreme outliers (e.g., Avengers making billions).\n", + "- Sorting ensures the months are in calendar order.\n", + "- Adding names (Jan, Feb, etc.) makes plots readable." + ] + }, + { + "cell_type": "markdown", + "id": "798dbe51", + "metadata": {}, + "source": [ + "# Merging" + ] + }, + { + "cell_type": "markdown", + "id": "494d544d", + "metadata": {}, + "source": [ + "## The Numbers (box office + budget) with IMDb" + ] + }, + { + "cell_type": "markdown", + "id": "be254e07", + "metadata": {}, + "source": [ + "### Merge datasets on title + year" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "88b099fa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2009 2011 2019 2015 2017 2018 2007 2012 2013 2010 2016 2014 2006 2008\n", + " 2005 2004 2003 2001 2020 2002]\n", + "[2013 2019 2018 2017 2012 2010 2011 2015 2016 2014 2020 2022 2021]\n" + ] + } + ], + "source": [ + "print(tn_movie_budgets['release_year'].unique()[:20])\n", + "print(runtime_df['start_year'].unique()[:20])" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "da5f5ba4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overlap years: {2016, 2017, 2018, 2019, 2020, 2010, 2011, 2012, 2013, 2014, 2015}\n" + ] + } + ], + "source": [ + "overlap_years = set(tn_movie_budgets['release_year']).intersection(set(runtime_df['start_year']))\n", + "print(\"Overlap years:\", overlap_years)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "f4a812d5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Numbers (2019) sample: ['Dark Phoenix' 'Aladdin' 'Captain Marvel' 'Dumbo' 'Alita: Battle Angel'\n", + " 'Godzilla: King Of The Monsters' 'Pokã©Mon: Detective Pikachu'\n", + " 'How To Train Your Dragon: The Hidden World'\n", + " 'Men In Black: International' 'Wonder Park'\n", + " 'The Lego Movie 2: The Second Part' 'Army Of The Dead' 'Shazam!'\n", + " 'The Secret Life Of Pets 2' 'Renegades' 'Playmobil' '355'\n", + " 'A Dogâ\\x80\\x99S Way Home' 'Cold Pursuit' 'Midway']\n", + "IMDb (2019) sample: ['One Day Before The Rainy Season' 'Alita: Battle Angel' 'Shazam!'\n", + " 'The Legend Of Secret Pass' 'The Dirt' 'Pet Sematary' 'Bolden'\n", + " 'Disrupted Land' 'Fiddler: A Miracle Of Miracles' 'Soccer In The City'\n", + " 'When I Became A Butterfly' 'Paradise' 'Aporia' 'Debout' 'Krishnam'\n", + " 'Kala-A-Zar' 'Terror In The Skies' 'Bull' 'Troublemaker' 'Snatchers']\n" + ] + } + ], + "source": [ + "tn_2019 = tn_movie_budgets[tn_movie_budgets['release_year'] == 2019]['movie'].unique()\n", + "imdb_2019 = runtime_df[runtime_df['start_year'] == 2019]['primary_title'].unique()\n", + "\n", + "print(\"The Numbers (2019) sample:\", tn_2019[:20])\n", + "print(\"IMDb (2019) sample:\", imdb_2019[:20])" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "b2f1b879", + "metadata": {}, + "outputs": [], + "source": [ + "numbers_and_runtime = tn_movie_budgets.merge(\n", + " runtime_df,\n", + " left_on=['movie', 'release_year'],\n", + " right_on=['primary_title', 'start_year'],\n", + " how='inner'\n", + ")\n", + "# Keep only movies with valid domestic gross\n", + "numbers_and_runtime = numbers_and_runtime.loc[numbers_and_runtime['domestic_gross'] > 0]" + ] + }, + { + "cell_type": "markdown", + "id": "ccbfe2a2", + "metadata": {}, + "source": [ + "- Merge on both title + year. Some movies share the same title (Halloween 1978 vs Halloween 2018). Matching with year avoids wrong matches.\n", + "- Inner join (how='inner'). Keeps only rows where a movie exists in both datasets so each row has financial data + runtime.\n", + "- Filter out domestic_gross == 0. Removes movies that never played in theaters in the U.S. Ensures analysis is focused on box office performers." + ] + }, + { + "cell_type": "markdown", + "id": "5c5d47da", + "metadata": {}, + "source": [ + "# Inspect merged results" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "b952d185", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1395, 15)\n", + "\n", + "Index: 1395 entries, 0 to 1558\n", + "Data columns (total 15 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 1395 non-null int64 \n", + " 1 movie 1395 non-null object \n", + " 2 production_budget 1395 non-null int64 \n", + " 3 domestic_gross 1395 non-null int64 \n", + " 4 worldwide_gross 1395 non-null int64 \n", + " 5 release_year 1395 non-null int32 \n", + " 6 month_dt 1395 non-null int32 \n", + " 7 month 1395 non-null int32 \n", + " 8 dom_profit_margin 1395 non-null float64\n", + " 9 ww_profit_margin 1395 non-null float64\n", + " 10 world_wide_profit_amount 1395 non-null int64 \n", + " 11 ROI_perc 1395 non-null float64\n", + " 12 primary_title 1395 non-null object \n", + " 13 start_year 1395 non-null int64 \n", + " 14 runtime_minutes 1395 non-null float64\n", + "dtypes: float64(4), int32(3), int64(6), object(2)\n", + "memory usage: 158.0+ KB\n" + ] + } + ], + "source": [ + "print(numbers_and_runtime.shape)\n", + "numbers_and_runtime.head()\n", + "numbers_and_runtime.info()" + ] + }, + { + "cell_type": "markdown", + "id": "90f88ffe", + "metadata": {}, + "source": [ + "# Creating dataframe with studio and box office data" + ] + }, + { + "cell_type": "markdown", + "id": "77659ce4", + "metadata": {}, + "source": [ + "### Step 1: \n", + "Select relevant columns from Box Office Mojo\n", + "We only need the movie title, studio, and release year from BOM because these are the identifiers we will merge with The Numbers dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "300ae00d", + "metadata": {}, + "outputs": [], + "source": [ + "# Selecting only the necessary columns from BOM\n", + "studio_df = bom_movie_gross [['title', 'studio', 'year']]" + ] + }, + { + "cell_type": "markdown", + "id": "87046bff", + "metadata": {}, + "source": [ + "### Step 2: \n", + "Merge with The Numbers dataset" + ] + }, + { + "cell_type": "markdown", + "id": "d391b256", + "metadata": {}, + "source": [ + "- Now we merge studio_df with tn_movie_budgets to attach financial data (budget, domestic gross, worldwide gross) to each movie." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "35153ef9", + "metadata": {}, + "outputs": [], + "source": [ + "# Merge studio info from BOM with financial info from The Numbers\n", + "studio_df = studio_df.merge(\n", + " tn_movie_budgets, # TN dataset with budgets & grosses\n", + " left_on=['title', 'year'], # BOM columns to merge on\n", + " right_on=['movie', 'release_year'], # TN columns to merge on\n", + " how='inner' # Only keep movies that exist in both datasets\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "95e01281", + "metadata": {}, + "source": [ + "- Some movies may have the same title but are different movies released in different years. Matching only by title could create incorrect combinations." + ] + }, + { + "cell_type": "markdown", + "id": "1cc92879", + "metadata": {}, + "source": [ + "### Step 3: \n", + "Inspect the merged dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "87ee046b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1255, 15)\n" + ] + } + ], + "source": [ + "# Check the shape of the new dataframe\n", + "print(studio_df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "f85a8dd5-a86c-4502-a9dd-1792763ed00a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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titlestudioyearidmovieproduction_budgetdomestic_grossworldwide_grossrelease_yearmonth_dtmonthdom_profit_marginww_profit_marginworld_wide_profit_amountROI_perc
0Toy Story 3BV201047Toy Story 3200000000415004880106887952220106651.80779681.288817868879522434.439761
1InceptionWB201038Inception16000000029257619583552464220107745.31339180.850355675524642422.202901
2Shrek Forever AfterP/DW201027Shrek Forever After16500000023873678775624467320105530.88622778.181664591244673358.330105
3The Twilight Saga: EclipseSum.201053The Twilight Saga: Eclipse6800000030053175170610282820106677.37343990.369675638102828938.386512
4Iron Man 2Par.201015Iron Man 217000000031243333162115638920105545.58839272.631691451156389265.386111
5TangledBV201015Tangled26000000020082193658647724020101111-29.46792855.667504326477240125.568169
6Despicable MeUni.201050Despicable Me6900000025151398554346457320107772.56613887.303680474464573687.629816
7How To Train Your DragonP/DW201030How To Train Your Dragon16500000021758123249487099220103324.16625366.657977329870992199.921813
8The Chronicles Of Narnia: The Voyage Of The Da...Fox201048The Chronicles Of Narnia: The Voyage Of The Da...15500000010438695041818695020101212-48.48599462.935237263186950169.798032
9The Karate KidSony201077The Karate Kid4000000017659161835177493820106677.34886888.629093311774938779.437345
\n", + "
" + ], + "text/plain": [ + " title studio year id \\\n", + "0 Toy Story 3 BV 2010 47 \n", + "1 Inception WB 2010 38 \n", + "2 Shrek Forever After P/DW 2010 27 \n", + "3 The Twilight Saga: Eclipse Sum. 2010 53 \n", + "4 Iron Man 2 Par. 2010 15 \n", + "5 Tangled BV 2010 15 \n", + "6 Despicable Me Uni. 2010 50 \n", + "7 How To Train Your Dragon P/DW 2010 30 \n", + "8 The Chronicles Of Narnia: The Voyage Of The Da... Fox 2010 48 \n", + "9 The Karate Kid Sony 2010 77 \n", + "\n", + " movie production_budget \\\n", + "0 Toy Story 3 200000000 \n", + "1 Inception 160000000 \n", + "2 Shrek Forever After 165000000 \n", + "3 The Twilight Saga: Eclipse 68000000 \n", + "4 Iron Man 2 170000000 \n", + "5 Tangled 260000000 \n", + "6 Despicable Me 69000000 \n", + "7 How To Train Your Dragon 165000000 \n", + "8 The Chronicles Of Narnia: The Voyage Of The Da... 155000000 \n", + "9 The Karate Kid 40000000 \n", + "\n", + " domestic_gross worldwide_gross release_year month_dt month \\\n", + "0 415004880 1068879522 2010 6 6 \n", + "1 292576195 835524642 2010 7 7 \n", + "2 238736787 756244673 2010 5 5 \n", + "3 300531751 706102828 2010 6 6 \n", + "4 312433331 621156389 2010 5 5 \n", + "5 200821936 586477240 2010 11 11 \n", + "6 251513985 543464573 2010 7 7 \n", + "7 217581232 494870992 2010 3 3 \n", + "8 104386950 418186950 2010 12 12 \n", + "9 176591618 351774938 2010 6 6 \n", + "\n", + " dom_profit_margin ww_profit_margin world_wide_profit_amount ROI_perc \n", + "0 51.807796 81.288817 868879522 434.439761 \n", + "1 45.313391 80.850355 675524642 422.202901 \n", + "2 30.886227 78.181664 591244673 358.330105 \n", + "3 77.373439 90.369675 638102828 938.386512 \n", + "4 45.588392 72.631691 451156389 265.386111 \n", + "5 -29.467928 55.667504 326477240 125.568169 \n", + "6 72.566138 87.303680 474464573 687.629816 \n", + "7 24.166253 66.657977 329870992 199.921813 \n", + "8 -48.485994 62.935237 263186950 169.798032 \n", + "9 77.348868 88.629093 311774938 779.437345 " + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Preview the first 10 rows\n", + "studio_df.head(10)" + ] + }, + { + "cell_type": "markdown", + "id": "07333f75", + "metadata": {}, + "source": [ + "# Calculating average studio-level metrics" + ] + }, + { + "cell_type": "markdown", + "id": "69affb72", + "metadata": {}, + "source": [ + "### Step 1: \n", + "Group by studio" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "a0b71da4-655b-4de9-834e-d0d2d8e60b7b", + "metadata": {}, + "outputs": [], + "source": [ + "numeric_columns = ['production_budget', 'domestic_gross', 'worldwide_gross', 'world_wide_profit_amount', 'dom_profit_margin', 'ww_profit_margin', 'ROI_perc'] # Add any others you have\n", + "\n", + "# Now, group by 'studio' but only for the numeric columns.\n", + "# This creates a DataFrame where the index is the studio, and the values are the means of the numeric columns.\n", + "avg_studio = studio_df.groupby('studio')[numeric_columns].mean()\n", + "\n", + "# Reset the index to turn 'studio' from the index back into a regular column\n", + "avg_studio = avg_studio.reset_index()" + ] + }, + { + "cell_type": "markdown", + "id": "6f1aad57", + "metadata": {}, + "source": [ + "- We want to see studio-level performance rather than movie-level. Grouping and averaging helps us identify which studios consistently produce profitable movies.\n", + "- Groupby('studio') - Groups all movies by their production studio.\n", + "- .mean() - Calculates the average of all numeric columns for each studio, e.g., production_budget, domestic_gross, worldwide_gross, dom_profit_margin, ww_profit_margin, ROI_perc.\n", + "- .reset_index() - Converts the grouped index (studio) back into a regular column so we can easily access and plot it." + ] + }, + { + "cell_type": "markdown", + "id": "485f1665", + "metadata": {}, + "source": [ + "### Step 2: \n", + "Filter only profitable studios" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "d6421dfa", + "metadata": {}, + "outputs": [], + "source": [ + "avg_studio = avg_studio[avg_studio['dom_profit_margin'] > 0]" + ] + }, + { + "cell_type": "markdown", + "id": "2001fe5b", + "metadata": {}, + "source": [ + "- Negative-profit studios can skew analysis and plots.Focusing on positive-profit studios helps highlight the best-performing studios.\n", + "- Setting ***dom_profit_margin*** > 0 keeps only studios whose average domestic profit margin is positive. This removes studios that on average lose money domestically, so analysis focuses on studios that are financially successful." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "c0dbc276", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(14, 8)\n", + " studio production_budget domestic_gross worldwide_gross \\\n", + "0 3D 5.000000e+06 6.096582e+06 1.651520e+07 \n", + "3 Affirm 3.500000e+06 1.167510e+07 1.573575e+07 \n", + "11 BH Tilt 2.800000e+06 8.717903e+06 1.323772e+07 \n", + "15 CBS 2.063636e+07 2.758124e+07 5.372220e+07 \n", + "48 MBox 2.600000e+06 3.827060e+06 1.529836e+07 \n", + "\n", + " world_wide_profit_amount dom_profit_margin ww_profit_margin ROI_perc \n", + "0 1.151520e+07 17.986833 69.724865 230.304060 \n", + "3 1.223575e+07 68.518543 73.378039 303.844830 \n", + "11 1.043772e+07 61.680377 75.599998 689.651002 \n", + "15 3.308584e+07 11.384555 47.923730 221.347979 \n", + "48 1.269836e+07 32.062732 83.004709 488.398269 \n" + ] + } + ], + "source": [ + "print(avg_studio.shape) # How many studios are left after filtering\n", + "print(avg_studio.head(5)) # Preview the first 10 studios with average metrics" + ] + }, + { + "cell_type": "markdown", + "id": "9b3f6da9", + "metadata": {}, + "source": [ + "# Merging The Numbers with TMDb to analyze genres" + ] + }, + { + "cell_type": "markdown", + "id": "9c16079e", + "metadata": {}, + "source": [ + "## Merge datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "ff0e60f6", + "metadata": {}, + "outputs": [], + "source": [ + "genre_df = tn_movie_budgets.merge(tmdb_movies, left_on=['movie', 'release_year'], right_on=['title', 'release_year'])" + ] + }, + { + "cell_type": "markdown", + "id": "5ac9a3fd", + "metadata": {}, + "source": [ + "To analyze profitability by genre, we need both financial info and genre info in the same DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "620189ce", + "metadata": {}, + "outputs": [], + "source": [ + "genre_df.loc[:,'genre_ids'] = genre_df['genre_ids'].map(lambda genre_string: genre_string.strip('[]').split(', '))" + ] + }, + { + "cell_type": "markdown", + "id": "9aced451", + "metadata": {}, + "source": [ + "- TMDb assigns multiple genres to a movie. Splitting into a list prepares it for exploding later, so each movie-genre combination becomes a separate row for analysis.\n", + "- genre_ids in TMDb is a string like \"[28, 12, 878]\".\n", + "\n", + "- strip('[]') removes the square brackets.\n", + "- split(', ') converts the string into a list of genre IDs" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "c114bb9f", + "metadata": {}, + "outputs": [], + "source": [ + "genre_df = genre_df.loc[(genre_df['worldwide_gross'] > 0) & (genre_df['domestic_gross'] > 0)]\n", + "genre_ids_df = genre_df.explode('genre_ids')" + ] + }, + { + "cell_type": "markdown", + "id": "3f6e2df8", + "metadata": {}, + "source": [ + "- Keep only movies with revenue. We only want movies that actually earned money, to calculate meaningful profitability metrics by genre.\n", + "- Explode('genre_ids') - creates one row per movie per genre. If a movie has 3 genres, it will now appear in 3 rows, one for each genre. Allows aggregation of financial metrics per genre, not per movie." + ] + }, + { + "cell_type": "markdown", + "id": "9319b6e3", + "metadata": {}, + "source": [ + "# Map genre IDs to names" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "2be84cb3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " movie production_budget \\\n", + "0 Avatar 425000000 \n", + "0 Avatar 425000000 \n", + "0 Avatar 425000000 \n", + "0 Avatar 425000000 \n", + "1 Pirates Of The Caribbean: On Stranger Tides 410600000 \n", + "\n", + " domestic_gross worldwide_gross ROI_perc genre_name \n", + "0 760507625 2776345279 553.257713 Action \n", + "0 760507625 2776345279 553.257713 Adventure \n", + "0 760507625 2776345279 553.257713 Fantasy \n", + "0 760507625 2776345279 553.257713 Sci-Fi \n", + "1 241063875 1045663875 154.667286 Adventure \n" + ] + } + ], + "source": [ + "# Step 1: Map genre_ids to readable genre names using a dictionary\n", + "genre_map = {\n", + " '28': 'Action', '12': 'Adventure', '16': 'Animation', '35': 'Comedy', '80': 'Crime',\n", + " '99': 'Documentary', '18': 'Drama', '10751': 'Family', '14': 'Fantasy', '36': 'History',\n", + " '27': 'Horror', '10402': 'Music', '9648': 'Mystery', '10749': 'Romance', '878': 'Sci-Fi',\n", + " '10770': 'TV Movie', '53': 'Thriller', '10752': 'War', '37': 'Western'\n", + "}\n", + "# Step 2: Add a new column for readable genre names\n", + "genre_ids_df['genre_name'] = genre_ids_df['genre_ids'].map(genre_map)\n", + "# Step 3: Inspect the resulting dataframe\n", + "print(genre_ids_df[['movie', 'production_budget', 'domestic_gross', 'worldwide_gross', 'ROI_perc', 'genre_name']].head())" + ] + }, + { + "cell_type": "markdown", + "id": "ad72601d", + "metadata": {}, + "source": [ + "genre_map Provides a mapping from TMDb’s numeric IDs to human-readable genre names.\n", + "\n", + "map() Converts each genre_id in genre_ids_df to its corresponding genre_name.\n", + "now have a clean dataset (genre_ids_df) with financials and readable genres, ready for aggregation like calculating mean ROI per genre.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "a6330936-50bd-4477-8df8-448371562e71", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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id_xmovieproduction_budgetdomestic_grossworldwide_grossrelease_yearmonth_dtmonthdom_profit_marginww_profit_margin...genre_idsid_yoriginal_languageoriginal_titlepopularityrelease_datetitlevote_averagevote_countgenre_name
01Avatar42500000076050762527763452792009121244.11627484.692106...2819995enAvatar26.5262009-12-18Avatar7.418676Action
01Avatar42500000076050762527763452792009121244.11627484.692106...1219995enAvatar26.5262009-12-18Avatar7.418676Adventure
01Avatar42500000076050762527763452792009121244.11627484.692106...1419995enAvatar26.5262009-12-18Avatar7.418676Fantasy
01Avatar42500000076050762527763452792009121244.11627484.692106...87819995enAvatar26.5262009-12-18Avatar7.418676Sci-Fi
12Pirates Of The Caribbean: On Stranger Tides4106000002410638751045663875201155-70.32830060.733080...121865enPirates of the Caribbean: On Stranger Tides30.5792011-05-20Pirates Of The Caribbean: On Stranger Tides6.48571Adventure
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5 rows × 22 columns

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" + ], + "text/plain": [ + " id_x movie production_budget \\\n", + "0 1 Avatar 425000000 \n", + "0 1 Avatar 425000000 \n", + "0 1 Avatar 425000000 \n", + "0 1 Avatar 425000000 \n", + "1 2 Pirates Of The Caribbean: On Stranger Tides 410600000 \n", + "\n", + " domestic_gross worldwide_gross release_year month_dt month \\\n", + "0 760507625 2776345279 2009 12 12 \n", + "0 760507625 2776345279 2009 12 12 \n", + "0 760507625 2776345279 2009 12 12 \n", + "0 760507625 2776345279 2009 12 12 \n", + "1 241063875 1045663875 2011 5 5 \n", + "\n", + " dom_profit_margin ww_profit_margin ... genre_ids id_y \\\n", + "0 44.116274 84.692106 ... 28 19995 \n", + "0 44.116274 84.692106 ... 12 19995 \n", + "0 44.116274 84.692106 ... 14 19995 \n", + "0 44.116274 84.692106 ... 878 19995 \n", + "1 -70.328300 60.733080 ... 12 1865 \n", + "\n", + " original_language original_title popularity \\\n", + "0 en Avatar 26.526 \n", + "0 en Avatar 26.526 \n", + "0 en Avatar 26.526 \n", + "0 en Avatar 26.526 \n", + "1 en Pirates of the Caribbean: On Stranger Tides 30.579 \n", + "\n", + " release_date title vote_average \\\n", + "0 2009-12-18 Avatar 7.4 \n", + "0 2009-12-18 Avatar 7.4 \n", + "0 2009-12-18 Avatar 7.4 \n", + "0 2009-12-18 Avatar 7.4 \n", + "1 2011-05-20 Pirates Of The Caribbean: On Stranger Tides 6.4 \n", + "\n", + " vote_count genre_name \n", + "0 18676 Action \n", + "0 18676 Adventure \n", + "0 18676 Fantasy \n", + "0 18676 Sci-Fi \n", + "1 8571 Adventure \n", + "\n", + "[5 rows x 22 columns]" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genre_ids_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "dd3affe0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " movie release_year \\\n", + "0 Avatar 2009 \n", + "0 Avatar 2009 \n", + "0 Avatar 2009 \n", + "0 Avatar 2009 \n", + "1 Pirates Of The Caribbean: On Stranger Tides 2011 \n", + "\n", + " production_budget domestic_gross worldwide_gross ROI_perc genre_ids \\\n", + "0 425000000 760507625 2776345279 553.257713 28 \n", + "0 425000000 760507625 2776345279 553.257713 12 \n", + "0 425000000 760507625 2776345279 553.257713 14 \n", + "0 425000000 760507625 2776345279 553.257713 878 \n", + "1 410600000 241063875 1045663875 154.667286 12 \n", + "\n", + " genre_name month month_dt \n", + "0 Action 12 12 \n", + "0 Adventure 12 12 \n", + "0 Fantasy 12 12 \n", + "0 Sci-Fi 12 12 \n", + "1 Adventure 5 5 \n" + ] + } + ], + "source": [ + "# Rename the correct genre_name column\n", + "# Keep genre_name_y (from converter) and drop genre_name_x\n", + "genre_overall = genre_ids_df.rename(columns={'genre_name_y': 'genre_name'})\n", + "\n", + "# Drop duplicate or unnecessary columns\n", + "genre_overall = genre_overall.drop(columns=['genre_name_x', 'id_x', 'id_y', 'Unnamed: 0'], errors='ignore')\n", + "\n", + "# Keep only the useful columns\n", + "genre_overall_clean = genre_overall[[\n", + " 'movie',\n", + " 'release_year',\n", + " 'production_budget',\n", + " 'domestic_gross',\n", + " 'worldwide_gross',\n", + " 'ROI_perc',\n", + " 'genre_ids',\n", + " 'genre_name',\n", + " 'month', # <-- keep this\n", + " 'month_dt' # <-- and this\n", + "]]\n", + "\n", + "print(genre_overall_clean.head())" + ] + }, + { + "cell_type": "markdown", + "id": "62b2c8f4", + "metadata": {}, + "source": [ + "- tmdb_movies → raw TMDb data with columns like title, release_date, genre_ids (as strings like \"[28, 12, 878]\").\n", + "\n", + "- genre_df → merged tn_movie_budgets + tmdb_movies to bring financials together with genre_ids.\n", + "\n", + "- genre_ids_df → exploded version of genre_df['genre_ids'], so each row now represents one movie–one genre instead of a list of IDs." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "b21ed96f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['movie', 'production_budget', 'domestic_gross', 'worldwide_gross',\n", + " 'release_year', 'month_dt', 'month', 'dom_profit_margin',\n", + " 'ww_profit_margin', 'world_wide_profit_amount', 'ROI_perc', 'genre_ids',\n", + " 'original_language', 'original_title', 'popularity', 'release_date',\n", + " 'title', 'vote_average', 'vote_count', 'genre_name'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "print(genre_overall.columns)" + ] + }, + { + "cell_type": "markdown", + "id": "f1fa7bc4", + "metadata": {}, + "source": [ + "TMDb only gives numeric IDs in genre_ids.\n", + "\n", + "We need readable genre names to analyze which genres are most profitable." + ] + }, + { + "cell_type": "markdown", + "id": "7fafbaa2", + "metadata": {}, + "source": [ + "# Analyze profitability by genre" + ] + }, + { + "cell_type": "markdown", + "id": "83d775d1", + "metadata": {}, + "source": [ + "# Group by genre Mean version(average)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "a9ce46be", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " release_year production_budget domestic_gross worldwide_gross \\\n", + "genre_name \n", + "Horror 2014.006061 2.291297e+07 3.915706e+07 9.026821e+07 \n", + "Thriller 2013.623288 3.731461e+07 4.332908e+07 1.084243e+08 \n", + "Mystery 2013.771186 3.295345e+07 4.284843e+07 1.021399e+08 \n", + "Romance 2013.214953 2.846243e+07 4.188975e+07 9.342080e+07 \n", + "Animation 2014.290909 1.003909e+08 1.393303e+08 3.849198e+08 \n", + "Sci-Fi 2014.258537 9.271988e+07 1.123468e+08 3.077264e+08 \n", + "Music 2014.019608 2.693529e+07 4.828898e+07 9.604752e+07 \n", + "\n", + " ROI_perc month month_dt \n", + "genre_name \n", + "Horror 1069.092677 6.369697 6.369697 \n", + "Thriller 436.286887 6.860731 6.860731 \n", + "Mystery 436.142740 7.076271 7.076271 \n", + "Romance 291.691268 6.738318 6.738318 \n", + "Animation 287.135700 7.454545 7.454545 \n", + "Sci-Fi 261.474050 6.653659 6.653659 \n", + "Music 249.632305 7.686275 7.686275 \n" + ] + } + ], + "source": [ + "#Group by genre_name, calculate mean of financial metrics\n", + "genre_groups = genre_overall_clean.groupby('genre_name').mean(numeric_only=True)\n", + "\n", + "# Sort by ROI_perc and pick top 7 genres\n", + "genre_groups = genre_groups.sort_values('ROI_perc', ascending=False).head(7)\n", + "\n", + "print(genre_groups)" + ] + }, + { + "cell_type": "markdown", + "id": "96b7a836", + "metadata": {}, + "source": [ + "- We are grouping by genre_name and calculating the average financial metrics (like ROI, budget, and gross) because we want to find out which genres are the most profitable on average. By grouping, we turn many individual movies into a single “genre profile.” By taking the mean, we can compare genres fairly, instead of looking at random single movies. By sorting by ROI, we highlight which genres give the highest return on investment — this tells us where money is being made most efficiently. Finally, limiting to the top 7 gives us a focused view of the genres that perform the best, so the analysis is actionable.\n", + "\n", + "- What it does:(Mean) - Takes the average ROI, budget, gross, etc. across all movies in each genre.\n", + "\n", + "- Pros:\n", + "\n", + "1. Captures the overall profitability of the genre.\n", + "\n", + "2. Good if you want the \"expected value\" of investing in that genre.\n", + "\n", + "- Cons:\n", + "1. Sensitive to outliers (e.g., one mega-hit Marvel movie can make \"Superhero\" genre look insanely profitable, even if most films lose money)." + ] + }, + { + "cell_type": "markdown", + "id": "ecef8b6e", + "metadata": {}, + "source": [ + "# Median version (middle value)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "de66c62c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " release_year production_budget domestic_gross worldwide_gross \\\n", + "genre_name \n", + "Horror 2014.0 10000000.0 29136626.0 59922558.0 \n", + "Animation 2015.0 87500000.0 121440343.5 327829122.5 \n", + "Adventure 2015.0 110000000.0 93432655.0 282778100.0 \n", + "Family 2014.0 78000000.0 82051601.0 200859554.0 \n", + "Fantasy 2014.0 90000000.0 68549695.0 213691277.0 \n", + "Mystery 2015.0 21500000.0 30322525.0 63757397.0 \n", + "Comedy 2014.0 28000000.0 37915414.0 67130045.0 \n", + "\n", + " ROI_perc month month_dt \n", + "genre_name \n", + "Horror 231.669132 7.0 7.0 \n", + "Animation 200.418943 7.0 7.0 \n", + "Adventure 167.114096 7.0 7.0 \n", + "Family 166.547080 7.0 7.0 \n", + "Fantasy 165.951426 7.0 7.0 \n", + "Mystery 156.768909 8.0 8.0 \n", + "Comedy 152.905265 7.0 7.0 \n" + ] + } + ], + "source": [ + "# Group by genre_name and calculate the median of numeric columns\n", + "genre_groups_med = genre_overall_clean.groupby('genre_name').median(numeric_only=True)\n", + "\n", + "# Sort by ROI_perc and keep top 7 genres\n", + "genre_groups_med = genre_groups_med.sort_values('ROI_perc', ascending=False).head(7)\n", + "\n", + "print(genre_groups_med)" + ] + }, + { + "cell_type": "markdown", + "id": "a8d88bf8", + "metadata": {}, + "source": [ + "- We already looked at average ROI per genre using the mean. That gave us a sense of overall profitability but was sensitive to outliers (e.g., one mega-hit movie making a genre look profitable even if most others flopped).\n", + "\n", + "- What it does: Takes the median (middle) ROI, budget, gross, etc. for movies in each genre.\n", + "\n", + "- Pros:\n", + "\n", + "1. Shows what the typical movie in the genre earns.\n", + "\n", + "2. More robust against extreme values (one flop or one blockbuster won’t skew results).\n", + "\n", + "Cons:\n", + "\n", + "1. Doesn’t capture the impact of extreme successes, which are important in the film industry (because a few blockbusters can fund the entire studio). " + ] + }, + { + "cell_type": "markdown", + "id": "035f4609", + "metadata": {}, + "source": [ + "***N/B***\n", + "- Mean = overall average performance of the genre → influenced by big winners and losers.\n", + "\n", + "- Median = typical performance of the genre → tells you what a \"normal\" movie in that genre does." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "13a8d2a5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " month release_year production_budget domestic_gross worldwide_gross \\\n", + "0 1 2014.0 12500000.0 33694789.0 77892256.0 \n", + "1 2 2014.0 10000000.0 26797294.0 48461873.5 \n", + "2 3 2015.0 5000000.0 14674077.0 23250755.0 \n", + "3 4 2013.5 5000000.0 35485286.5 67527083.0 \n", + "4 5 2015.0 35000000.0 29136626.0 84154026.0 \n", + "\n", + " ROI_perc month_dt month_name \n", + "0 325.677601 1.0 Jan \n", + "1 475.462447 2.0 Feb \n", + "2 499.201020 3.0 Mar \n", + "3 333.270935 4.0 Apr \n", + "4 145.898193 5.0 May \n" + ] + } + ], + "source": [ + "# Filter Horror movies only\n", + "horror_month_df = genre_overall_clean[genre_overall_clean['genre_name'] == 'Horror']\n", + "\n", + "# Drop very low earners\n", + "horror_month_df = horror_month_df[horror_month_df['worldwide_gross'] > 100000]\n", + "\n", + "# Group by release month and take the median of numeric columns\n", + "horror_month_df = horror_month_df.groupby('month').median(numeric_only=True).reset_index()\n", + "\n", + "# Sort by calendar order (month_dt ensures Jan -> Dec)\n", + "horror_month_df = horror_month_df.sort_values('month_dt')\n", + "\n", + "# Map month numbers to names\n", + "month_dict = {\n", + " 1:\"Jan\", 2:\"Feb\", 3:\"Mar\", 4:\"Apr\", 5:\"May\", 6:\"Jun\",\n", + " 7:\"Jul\", 8:\"Aug\", 9:\"Sep\", 10:\"Oct\", 11:\"Nov\", 12:\"Dec\"\n", + "}\n", + "horror_month_df['month_name'] = horror_month_df['month'].map(month_dict)\n", + "\n", + "print(horror_month_df.head())\n" + ] + }, + { + "cell_type": "markdown", + "id": "992e253c", + "metadata": {}, + "source": [ + "I filter the dataset down to Horror movies and drop tiny releases (worldwide_gross > 100000).\n", + "\n", + "I group those movies by release month and take the median of numeric metrics (so we see the typical horror movie performance per month).\n", + "\n", + "I reset the index and sort by month_dt so months appear in calendar order (Jan - Dec).\n", + "\n", + "I map month numbers to readable month names (Jan, Feb, ...) so the table is easy to read and plot." + ] + }, + { + "cell_type": "markdown", + "id": "82fbd85d", + "metadata": {}, + "source": [ + "# Simple Linear Regression anaysis " + ] + }, + { + "cell_type": "markdown", + "id": "c6e5bdc9-8b0e-4403-980f-83cccf81ae5e", + "metadata": {}, + "source": [ + "- An overview of the datasets we will use for this:" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "862d3bca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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monthrelease_yearproduction_budgetdomestic_grossworldwide_grossROI_percmonth_dt
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" + ], + "text/plain": [ + " month release_year production_budget domestic_gross \\\n", + "count 12.000000 12.000000 1.200000e+01 1.200000e+01 \n", + "mean 6.500000 2014.375000 1.225000e+07 2.967463e+07 \n", + "std 3.605551 1.130668 7.981513e+06 1.267051e+07 \n", + "min 1.000000 2012.500000 5.000000e+06 6.810754e+06 \n", + "25% 3.750000 2013.875000 9.000000e+06 2.127689e+07 \n", + "50% 6.500000 2014.000000 1.050000e+07 3.137490e+07 \n", + "75% 9.250000 2015.125000 1.312500e+07 3.522970e+07 \n", + "max 12.000000 2016.000000 3.500000e+07 4.959554e+07 \n", + "\n", + " worldwide_gross ROI_perc month_dt \n", + "count 1.200000e+01 12.000000 12.000000 \n", + "mean 6.444484e+07 354.721341 6.500000 \n", + "std 2.953579e+07 268.683956 3.605551 \n", + "min 8.890094e+06 87.420720 1.000000 \n", + "25% 4.609280e+07 215.755023 3.750000 \n", + "50% 7.166126e+07 299.898476 6.500000 \n", + "75% 8.241241e+07 390.501129 9.250000 \n", + "max 1.050150e+08 1112.211863 12.000000 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " production_budget domestic_gross worldwide_gross \\\n", + "count 1.400000e+01 1.400000e+01 1.400000e+01 \n", + "mean 2.756587e+07 4.143936e+07 9.974835e+07 \n", + "std 3.708456e+07 4.761739e+07 1.369205e+08 \n", + "min 2.500000e+06 3.827060e+06 1.323772e+07 \n", + "25% 3.875000e+06 9.457203e+06 1.593061e+07 \n", + "50% 9.325000e+06 2.260992e+07 4.436142e+07 \n", + "75% 3.841071e+07 6.946566e+07 1.260143e+08 \n", + "max 1.334000e+08 1.682915e+08 5.078028e+08 \n", + "\n", + " world_wide_profit_amount dom_profit_margin ww_profit_margin \\\n", + "count 1.400000e+01 14.000000 14.000000 \n", + "mean 7.218248e+07 34.775649 66.245899 \n", + "std 1.002492e+08 22.123616 13.546435 \n", + "min 6.704317e+06 1.574618 46.833530 \n", + "25% 1.235140e+07 19.559690 54.784204 \n", + "50% 3.079324e+07 34.276450 67.147950 \n", + "75% 8.760357e+07 50.476864 75.044508 \n", + "max 3.744028e+08 68.518543 89.515856 \n", + "\n", + " ROI_perc \n", + "count 14.000000 \n", + "mean 476.523624 \n", + "std 371.478565 \n", + "min 205.213397 \n", + "25% 243.356975 \n", + "50% 320.355018 \n", + "75% 555.441756 \n", + "max 1574.515218 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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25%2012.0000001.180000e+078.574339e+061.819083e+0712.1387124.0000004.000000
50%2014.0000003.175000e+073.560824e+077.496685e+07134.6049717.0000007.000000
75%2016.0000007.900000e+078.506718e+072.165623e+08312.64641710.00000010.000000
max2019.0000004.250000e+087.605076e+082.776345e+0941556.47400012.00000012.000000
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" + ], + "text/plain": [ + " release_year production_budget domestic_gross worldwide_gross \\\n", + "count 4138.000000 4.138000e+03 4.138000e+03 4.138000e+03 \n", + "mean 2013.831078 5.534669e+07 6.936900e+07 1.794672e+08 \n", + "std 2.728950 6.137537e+07 9.619419e+07 2.692222e+08 \n", + "min 2001.000000 3.000000e+04 3.880000e+02 5.280000e+02 \n", + "25% 2012.000000 1.180000e+07 8.574339e+06 1.819083e+07 \n", + "50% 2014.000000 3.175000e+07 3.560824e+07 7.496685e+07 \n", + "75% 2016.000000 7.900000e+07 8.506718e+07 2.165623e+08 \n", + "max 2019.000000 4.250000e+08 7.605076e+08 2.776345e+09 \n", + "\n", + " ROI_perc month month_dt \n", + "count 4138.000000 4138.000000 4138.000000 \n", + "mean 290.635152 7.044949 7.044949 \n", + "std 1084.418256 3.453326 3.453326 \n", + "min -99.896400 1.000000 1.000000 \n", + "25% 12.138712 4.000000 4.000000 \n", + "50% 134.604971 7.000000 7.000000 \n", + "75% 312.646417 10.000000 10.000000 \n", + "max 41556.474000 12.000000 12.000000 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(horror_month_df.describe())\n", + "display(avg_studio.describe())\n", + "display(genre_overall_clean.describe())" + ] + }, + { + "cell_type": "markdown", + "id": "1918bf4c-f9bb-4dee-9e96-74912d70bc62", + "metadata": {}, + "source": [ + "- Lets have another look at our DataFrames to see exactly what we are working with:" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "bd8e8b6c-8cb4-4b9d-b3f1-6211e06b8f77", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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studioproduction_budgetdomestic_grossworldwide_grossworld_wide_profit_amountdom_profit_marginww_profit_marginROI_perc
03D5.000000e+066.096582e+061.651520e+071.151520e+0717.98683369.724865230.304060
3Affirm3.500000e+061.167510e+071.573575e+071.223575e+0768.51854373.378039303.844830
11BH Tilt2.800000e+068.717903e+061.323772e+071.043772e+0761.68037775.599998689.651002
15CBS2.063636e+072.758124e+075.372220e+073.308584e+0711.38455547.923730221.347979
48MBox2.600000e+063.827060e+061.529836e+071.269836e+0732.06273283.004709488.398269
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" + ], + "text/plain": [ + " studio production_budget domestic_gross worldwide_gross \\\n", + "0 3D 5.000000e+06 6.096582e+06 1.651520e+07 \n", + "3 Affirm 3.500000e+06 1.167510e+07 1.573575e+07 \n", + "11 BH Tilt 2.800000e+06 8.717903e+06 1.323772e+07 \n", + "15 CBS 2.063636e+07 2.758124e+07 5.372220e+07 \n", + "48 MBox 2.600000e+06 3.827060e+06 1.529836e+07 \n", + "\n", + " world_wide_profit_amount dom_profit_margin ww_profit_margin ROI_perc \n", + "0 1.151520e+07 17.986833 69.724865 230.304060 \n", + "3 1.223575e+07 68.518543 73.378039 303.844830 \n", + "11 1.043772e+07 61.680377 75.599998 689.651002 \n", + "15 3.308584e+07 11.384555 47.923730 221.347979 \n", + "48 1.269836e+07 32.062732 83.004709 488.398269 " + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "avg_studio.head()" + ] + }, + { + "cell_type": "markdown", + "id": "18289112-a357-468d-8b5e-4303773ea34d", + "metadata": {}, + "source": [ + "- Since this is an aggregated data, lets look at the original df:" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "593b2bb0-2ff5-4009-b32a-dbf1ec209324", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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titlestudioyearidmovieproduction_budgetdomestic_grossworldwide_grossrelease_yearmonth_dtmonthdom_profit_marginww_profit_marginworld_wide_profit_amountROI_perc
0Toy Story 3BV201047Toy Story 3200000000415004880106887952220106651.80779681.288817868879522434.439761
1InceptionWB201038Inception16000000029257619583552464220107745.31339180.850355675524642422.202901
2Shrek Forever AfterP/DW201027Shrek Forever After16500000023873678775624467320105530.88622778.181664591244673358.330105
3The Twilight Saga: EclipseSum.201053The Twilight Saga: Eclipse6800000030053175170610282820106677.37343990.369675638102828938.386512
4Iron Man 2Par.201015Iron Man 217000000031243333162115638920105545.58839272.631691451156389265.386111
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" + ], + "text/plain": [ + " title studio year id movie \\\n", + "0 Toy Story 3 BV 2010 47 Toy Story 3 \n", + "1 Inception WB 2010 38 Inception \n", + "2 Shrek Forever After P/DW 2010 27 Shrek Forever After \n", + "3 The Twilight Saga: Eclipse Sum. 2010 53 The Twilight Saga: Eclipse \n", + "4 Iron Man 2 Par. 2010 15 Iron Man 2 \n", + "\n", + " production_budget domestic_gross worldwide_gross release_year month_dt \\\n", + "0 200000000 415004880 1068879522 2010 6 \n", + "1 160000000 292576195 835524642 2010 7 \n", + "2 165000000 238736787 756244673 2010 5 \n", + "3 68000000 300531751 706102828 2010 6 \n", + "4 170000000 312433331 621156389 2010 5 \n", + "\n", + " month dom_profit_margin ww_profit_margin world_wide_profit_amount \\\n", + "0 6 51.807796 81.288817 868879522 \n", + "1 7 45.313391 80.850355 675524642 \n", + "2 5 30.886227 78.181664 591244673 \n", + "3 6 77.373439 90.369675 638102828 \n", + "4 5 45.588392 72.631691 451156389 \n", + "\n", + " ROI_perc \n", + "0 434.439761 \n", + "1 422.202901 \n", + "2 358.330105 \n", + "3 938.386512 \n", + "4 265.386111 " + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "studio_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "7ebd3fe1-0e23-4652-a771-c0968d9e482f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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movierelease_yearproduction_budgetdomestic_grossworldwide_grossROI_percgenre_idsgenre_namemonthmonth_dt
0Avatar20094250000007605076252776345279553.25771328Action1212
0Avatar20094250000007605076252776345279553.25771312Adventure1212
0Avatar20094250000007605076252776345279553.25771314Fantasy1212
0Avatar20094250000007605076252776345279553.257713878Sci-Fi1212
1Pirates Of The Caribbean: On Stranger Tides20114106000002410638751045663875154.66728612Adventure55
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monthrelease_yearproduction_budgetdomestic_grossworldwide_grossROI_percmonth_dtmonth_name
012014.012500000.033694789.077892256.0325.6776011.0Jan
122014.010000000.026797294.048461873.5475.4624472.0Feb
232015.05000000.014674077.023250755.0499.2010203.0Mar
342013.55000000.035485286.567527083.0333.2709354.0Apr
452015.035000000.029136626.084154026.0145.8981935.0May
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" + ], + "text/plain": [ + " month release_year production_budget domestic_gross worldwide_gross \\\n", + "0 1 2014.0 12500000.0 33694789.0 77892256.0 \n", + "1 2 2014.0 10000000.0 26797294.0 48461873.5 \n", + "2 3 2015.0 5000000.0 14674077.0 23250755.0 \n", + "3 4 2013.5 5000000.0 35485286.5 67527083.0 \n", + "4 5 2015.0 35000000.0 29136626.0 84154026.0 \n", + "\n", + " ROI_perc month_dt month_name \n", + "0 325.677601 1.0 Jan \n", + "1 475.462447 2.0 Feb \n", + "2 499.201020 3.0 Mar \n", + "3 333.270935 4.0 Apr \n", + "4 145.898193 5.0 May " + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "horror_month_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "dad3e5b4-8c62-4139-bfb9-b4b3acc8d66d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idmovieproduction_budgetdomestic_grossworldwide_grossrelease_yearmonth_dtmonthdom_profit_marginww_profit_marginworld_wide_profit_amountROI_percprimary_titlestart_yearruntime_minutes
02Pirates Of The Caribbean: On Stranger Tides4106000002410638751045663875201155-70.32830060.733080635063875154.667286Pirates Of The Caribbean: On Stranger Tides2011136.0
13Dark Phoenix35000000042762350149762350201966-718.477001-133.703598-200237650-57.210757Dark Phoenix2019113.0
24Avengers: Age Of Ultron330600000459005868140301396320155527.97477776.4364431072413963324.384139Avengers: Age Of Ultron2015141.0
37Avengers: Infinity War300000000678815482204813420020184455.80536985.3525221748134200582.711400Avengers: Infinity War2018149.0
49Justice League30000000022902429565594520920171111-30.99047054.264473355945209118.648403Justice League2017120.0
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" + ], + "text/plain": [ + " id movie production_budget \\\n", + "0 2 Pirates Of The Caribbean: On Stranger Tides 410600000 \n", + "1 3 Dark Phoenix 350000000 \n", + "2 4 Avengers: Age Of Ultron 330600000 \n", + "3 7 Avengers: Infinity War 300000000 \n", + "4 9 Justice League 300000000 \n", + "\n", + " domestic_gross worldwide_gross release_year month_dt month \\\n", + "0 241063875 1045663875 2011 5 5 \n", + "1 42762350 149762350 2019 6 6 \n", + "2 459005868 1403013963 2015 5 5 \n", + "3 678815482 2048134200 2018 4 4 \n", + "4 229024295 655945209 2017 11 11 \n", + "\n", + " dom_profit_margin ww_profit_margin world_wide_profit_amount ROI_perc \\\n", + "0 -70.328300 60.733080 635063875 154.667286 \n", + "1 -718.477001 -133.703598 -200237650 -57.210757 \n", + "2 27.974777 76.436443 1072413963 324.384139 \n", + "3 55.805369 85.352522 1748134200 582.711400 \n", + "4 -30.990470 54.264473 355945209 118.648403 \n", + "\n", + " primary_title start_year runtime_minutes \n", + "0 Pirates Of The Caribbean: On Stranger Tides 2011 136.0 \n", + "1 Dark Phoenix 2019 113.0 \n", + "2 Avengers: Age Of Ultron 2015 141.0 \n", + "3 Avengers: Infinity War 2018 149.0 \n", + "4 Justice League 2017 120.0 " + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numbers_and_runtime.head()" + ] + }, + { + "cell_type": "markdown", + "id": "c6ce8f08-d20c-4e67-a3a6-2a5e44ec043d", + "metadata": {}, + "source": [ + "- For Wamunyolo Film Industries, these grouped dataframes are essential for answering the key business questions for their business problem. We will therefore proceed to carry out our simple linear regression analysis based on the questions provides by the firm " + ] + }, + { + "cell_type": "markdown", + "id": "4a6392bf-d9e4-43c3-91c3-72b44953cf51", + "metadata": {}, + "source": [ + "## Question One: How long should the films be?\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "d2fca225-8247-4d4d-9ca8-b0661ea7d7d5", + "metadata": {}, + "source": [ + "- This is a major problem because they can produce movies that are too long, which would mean more funds going into production, but it would not directly translate to high domestic and worldwide earnings. Therefore, they need to produce movies with optimal runtimes that can satisfy the ideas they would like to input in their movies and that can be appealing to their audiences in general.\n", + "- For this case we will use the ***numbers_and_runtime_df*** for this case.\n", + "-We will conduct three steps for the linear regression analysis:" + ] + }, + { + "cell_type": "markdown", + "id": "4d6e33c7-5cab-48db-ab56-630655b09174", + "metadata": {}, + "source": [ + "### Step 1: Variable Selection\n", + "- Independent Variable (X): runtime_minutes. This is the explanatory variable we believe might predict profitability.\n", + "\n", + "- Dependent Variable (y): ROI_perc. This is the outcome variable we want to predict and explain." + ] + }, + { + "cell_type": "markdown", + "id": "554b363d-67cb-4ef1-8001-ebc9277b83b8", + "metadata": {}, + "source": [ + "### Step 2: Testing For Linearity\n", + "The core assumption of Simple Linear Regression is that the relationship between X and y is linear. We test this visually first." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "03c383e3-7c1f-41aa-ab15-0ff878437c51", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idmovieproduction_budgetdomestic_grossworldwide_grossrelease_yearmonth_dtmonthdom_profit_marginww_profit_marginworld_wide_profit_amountROI_percprimary_titlestart_yearruntime_minutes
02Pirates Of The Caribbean: On Stranger Tides4106000002410638751045663875201155-70.32830060.733080635063875154.667286Pirates Of The Caribbean: On Stranger Tides2011136.0
13Dark Phoenix35000000042762350149762350201966-718.477001-133.703598-200237650-57.210757Dark Phoenix2019113.0
24Avengers: Age Of Ultron330600000459005868140301396320155527.97477776.4364431072413963324.384139Avengers: Age Of Ultron2015141.0
37Avengers: Infinity War300000000678815482204813420020184455.80536985.3525221748134200582.711400Avengers: Infinity War2018149.0
49Justice League30000000022902429565594520920171111-30.99047054.264473355945209118.648403Justice League2017120.0
\n", + "
" + ], + "text/plain": [ + " id movie production_budget \\\n", + "0 2 Pirates Of The Caribbean: On Stranger Tides 410600000 \n", + "1 3 Dark Phoenix 350000000 \n", + "2 4 Avengers: Age Of Ultron 330600000 \n", + "3 7 Avengers: Infinity War 300000000 \n", + "4 9 Justice League 300000000 \n", + "\n", + " domestic_gross worldwide_gross release_year month_dt month \\\n", + "0 241063875 1045663875 2011 5 5 \n", + "1 42762350 149762350 2019 6 6 \n", + "2 459005868 1403013963 2015 5 5 \n", + "3 678815482 2048134200 2018 4 4 \n", + "4 229024295 655945209 2017 11 11 \n", + "\n", + " dom_profit_margin ww_profit_margin world_wide_profit_amount ROI_perc \\\n", + "0 -70.328300 60.733080 635063875 154.667286 \n", + "1 -718.477001 -133.703598 -200237650 -57.210757 \n", + "2 27.974777 76.436443 1072413963 324.384139 \n", + "3 55.805369 85.352522 1748134200 582.711400 \n", + "4 -30.990470 54.264473 355945209 118.648403 \n", + "\n", + " primary_title start_year runtime_minutes \n", + "0 Pirates Of The Caribbean: On Stranger Tides 2011 136.0 \n", + "1 Dark Phoenix 2019 113.0 \n", + "2 Avengers: Age Of Ultron 2015 141.0 \n", + "3 Avengers: Infinity War 2018 149.0 \n", + "4 Justice League 2017 120.0 " + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numbers_and_runtime.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "ffaeab84-c976-4381-90e8-a9c0636dd59b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "sns.scatterplot(data=numbers_and_runtime, x='runtime_minutes', y='ROI_perc', alpha=0.6)\n", + "plt.axhline(y=0, color='r', linestyle='--', label='Break-Even Line (ROI=0%)')\n", + "plt.title('Testing Linearity: Runtime vs. ROI')\n", + "plt.xlabel('Runtime (minutes)')\n", + "plt.ylabel('ROI (%)')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "244801ba-c2e7-43ae-b689-26927cd38d61", + "metadata": {}, + "source": [ + "For our first visualization, we can see that the y-axis is too stretched therefore let us fix this by setting limits for our y-axis and use this to exclude any outliers which may stretch our data." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "adfbaf24-6d05-4987-ba06-5eae1ac36848", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ROI Distribution Summary (for context):\n", + "count 1395.000000\n", + "mean 304.012777\n", + "std 1243.912095\n", + "min -99.894400\n", + "25% 11.270777\n", + "50% 137.542525\n", + "75% 321.640324\n", + "max 41556.474000\n", + "Name: ROI_perc, dtype: float64\n", + "\n", + "Number of extreme outliers not shown (ROI < -100% or > 1000%): 76\n", + "This represents 5.45% of the dataset.\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "\n", + "# Create the figure\n", + "plt.figure(figsize=(12, 8))\n", + "\n", + "# Create the scatter plot\n", + "scatter_plot = sns.scatterplot(data=numbers_and_runtime, x='runtime_minutes', y='ROI_perc', alpha=0.6)\n", + "\n", + "# Add key reference lines\n", + "plt.axhline(y=0, color='r', linestyle='--', linewidth=2, label='Break-Even (0% ROI)')\n", + "plt.axhline(y=100, color='g', linestyle=':', alpha=0.7, label='Double Investment (100% ROI)')\n", + "\n", + "# FIX: Set a logical limit on the y-axis to exclude extreme outliers\n", + "# Adjust these values based on your data. The following limits are a common starting point.\n", + "plt.ylim(-100, 1000) # This focuses on movies from -100% ROI (a flop) to 500% ROI (a 5x return)\n", + "\n", + "# Calculate and plot the regression line (to visualize the trend)\n", + "# This fits the model and plots the line of best fit on the same graph\n", + "sns.regplot(data=numbers_and_runtime, x='runtime_minutes', y='ROI_perc', \n", + " scatter=False, color='blue', line_kws={\"linewidth\": 2, \"alpha\": 0.7}, \n", + " label='Linear Trend Line')\n", + "\n", + "# Add titles and labels\n", + "plt.title('Relationship Between Movie Runtime and Financial ROI\\n(Y-axis limited to [-100%, 1000%] to show detail)', fontsize=14)\n", + "plt.xlabel('Runtime (minutes)')\n", + "plt.ylabel('ROI (%)')\n", + "plt.legend()\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# --- BONUS: Print a statistical summary for context ---\n", + "print(\"ROI Distribution Summary (for context):\")\n", + "print(numbers_and_runtime['ROI_perc'].describe())\n", + "\n", + "# Count how many movies are outside our chosen y-axis limits\n", + "lower_limit = -100\n", + "upper_limit = 1000\n", + "outliers = numbers_and_runtime[(numbers_and_runtime['ROI_perc'] < lower_limit) | (numbers_and_runtime['ROI_perc'] > upper_limit)]\n", + "print(f\"\\nNumber of extreme outliers not shown (ROI < {lower_limit}% or > {upper_limit}%): {len(outliers)}\")\n", + "print(f\"This represents {len(outliers) / len(numbers_and_runtime) * 100:.2f}% of the dataset.\")" + ] + }, + { + "cell_type": "markdown", + "id": "67c9d918-9234-4622-9d17-abe88ef40231", + "metadata": {}, + "source": [ + "- Since our scatter plot shows that our data exhibits a linear relationship, we can safely say that the relationship between the two variables is linear. Therefore, we can fit a linear model to this data and get the summary for this model:" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "5bf353a0-7254-46f2-b59c-1eacf23eac83", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: ROI_perc R-squared: 0.002\n", + "Model: OLS Adj. R-squared: 0.002\n", + "Method: Least Squares F-statistic: 3.469\n", + "Date: Sat, 13 Sep 2025 Prob (F-statistic): 0.0627\n", + "Time: 23:26:33 Log-Likelihood: -11918.\n", + "No. Observations: 1395 AIC: 2.384e+04\n", + "Df Residuals: 1393 BIC: 2.385e+04\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "===================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "-----------------------------------------------------------------------------------\n", + "const 664.6058 196.450 3.383 0.001 279.237 1049.975\n", + "runtime_minutes -3.3299 1.788 -1.862 0.063 -6.837 0.177\n", + "==============================================================================\n", + "Omnibus: 3542.057 Durbin-Watson: 1.699\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 43709629.961\n", + "Skew: 26.729 Prob(JB): 0.00\n", + "Kurtosis: 868.526 Cond. No. 649.\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" + ] + } + ], + "source": [ + "import statsmodels.api as sm\n", + "\n", + "# Define the variables\n", + "X = numbers_and_runtime['runtime_minutes'] # Independent variable\n", + "y = numbers_and_runtime['ROI_perc'] # Dependent variable\n", + "\n", + "# Add a constant (intercept) to the model. This is crucial.\n", + "X = sm.add_constant(X)\n", + "\n", + "# Fit the Ordinary Least Squares (OLS) model\n", + "model = sm.OLS(y, X).fit()\n", + "\n", + "# Print the full results summary\n", + "print(model.summary())" + ] + }, + { + "cell_type": "markdown", + "id": "7f84027b-0f80-43a6-a8e7-fb90a8d10006", + "metadata": {}, + "source": [ + "### Step 3: Interpretation\n", + "For the statistics above, we can conclude the following:\n", + "1. **The Coefficient (coef): -3.33**\n", + "\n", + "- Interpretation: For every one-minute increase in a movie's runtime, the model predicts an associated decrease of 3.33% in ROI, on average.\n", + "\n", + "- Business Meaning: The trend in the data suggests that shorter movies are more profitable. This makes intuitive sense: shorter movies are cheaper to make and allow for more daily screenings in theaters.\n", + "\n", + "2. **The P-Value (P>|t|): 0.063**\n", + "\n", + "- This is the most important number for decision-making.\n", + "\n", + "- Interpretation: There is a 6.3% probability that we would observe this negative relationship purely by random chance, even if no true relationship existed in the broader population of all movies.\n", + "\n", + "- Statistical Conclusion: Using the common threshold of α = 0.05, we fail to reject the null hypothesis. We cannot definitively conclude that the relationship is real at the 95% confidence level.\n", + "\n", + "- Business Conclusion: This result is not statistically significant but is highly suggestive. It is \"on the bubble.\" It tells us that while the trend in our data is clearly negative, we can't be 100% certain it's not a fluke in this particular dataset.\n", + "\n", + "3. **The Confidence Interval ([0.025 - 0.975]): [-6.84, 0.18]**\n", + "\n", + "- Interpretation: We can be 95% confident that the true effect of runtime on ROI lies between ** reducing ROI by 6.84% per minute** and increasing ROI by 0.18% per minute.\n", + "\n", + "- Business Meaning: The entire range of plausible values is overwhelmingly negative. The best-case scenario is essentially no effect (a tiny +0.18%), while the worst-case is a very strong negative effect (-6.84%). This reinforces that there is no evidence of a positive return for longer runtimes.\n", + "\n", + "4. **The R-squared (R-squared): 0.002**\n", + "\n", + "- Interpretation: Only 0.2% of the variation in a movie's ROI can be explained by its runtime.\n", + "\n", + "- Business Meaning: Runtime is just one tiny piece of the puzzle. Other factors like genre, marketing, star power, and critical reviews are far more important in determining a movie's financial success. This makes perfect sense in the film industry." + ] + }, + { + "cell_type": "markdown", + "id": "8fd69ff7-0282-4263-aa98-41ff90db762d", + "metadata": {}, + "source": [ + "With our first question answered, we can now move on to our second question." + ] + }, + { + "cell_type": "markdown", + "id": "d13753ed-0996-4341-89ba-db927a4baa45", + "metadata": {}, + "source": [ + "## Question Two: Which Genres are the most Profitable?\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "b2374092-939e-4cd9-88d5-8046bee18358", + "metadata": {}, + "source": [ + "- The question we seek to answer from this is **\"Which genres have a statistically significant positive impact on a movie's ROI?\"** This will tell Wamonyolo Studios exactly what kind of movies they should make to maximize their chances of success.\n", + "- We can use the **genre_overall_clean** df to confirm whether the horror movies are the most profitable." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "6761d4b5-9024-4c8e-a236-996af82a7543", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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movierelease_yearproduction_budgetdomestic_grossworldwide_grossROI_percgenre_idsgenre_namemonthmonth_dt
0Avatar20094250000007605076252776345279553.25771328Action1212
0Avatar20094250000007605076252776345279553.25771312Adventure1212
0Avatar20094250000007605076252776345279553.25771314Fantasy1212
0Avatar20094250000007605076252776345279553.257713878Sci-Fi1212
1Pirates Of The Caribbean: On Stranger Tides20114106000002410638751045663875154.66728612Adventure55
.................................
1753Tiny Furniture201050000391674424149748.29800018Drama1111
1754Counting20155000083748374-83.25200099Documentary77
1759Raymond Did It20114000036323632-90.92000027Horror22
1762Krisha201630000144822144822382.74000018Drama33
1763Krisha201630000144822144822382.74000018Drama33
\n", + "

4138 rows × 10 columns

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" + ], + "text/plain": [ + " movie release_year \\\n", + "0 Avatar 2009 \n", + "0 Avatar 2009 \n", + "0 Avatar 2009 \n", + "0 Avatar 2009 \n", + "1 Pirates Of The Caribbean: On Stranger Tides 2011 \n", + "... ... ... \n", + "1753 Tiny Furniture 2010 \n", + "1754 Counting 2015 \n", + "1759 Raymond Did It 2011 \n", + "1762 Krisha 2016 \n", + "1763 Krisha 2016 \n", + "\n", + " production_budget domestic_gross worldwide_gross ROI_perc \\\n", + "0 425000000 760507625 2776345279 553.257713 \n", + "0 425000000 760507625 2776345279 553.257713 \n", + "0 425000000 760507625 2776345279 553.257713 \n", + "0 425000000 760507625 2776345279 553.257713 \n", + "1 410600000 241063875 1045663875 154.667286 \n", + "... ... ... ... ... \n", + "1753 50000 391674 424149 748.298000 \n", + "1754 50000 8374 8374 -83.252000 \n", + "1759 40000 3632 3632 -90.920000 \n", + "1762 30000 144822 144822 382.740000 \n", + "1763 30000 144822 144822 382.740000 \n", + "\n", + " genre_ids genre_name month month_dt \n", + "0 28 Action 12 12 \n", + "0 12 Adventure 12 12 \n", + "0 14 Fantasy 12 12 \n", + "0 878 Sci-Fi 12 12 \n", + "1 12 Adventure 5 5 \n", + "... ... ... ... ... \n", + "1753 18 Drama 11 11 \n", + "1754 99 Documentary 7 7 \n", + "1759 27 Horror 2 2 \n", + "1762 18 Drama 3 3 \n", + "1763 18 Drama 3 3 \n", + "\n", + "[4138 rows x 10 columns]" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genre_overall_clean" + ] + }, + { + "cell_type": "markdown", + "id": "5466fcf5-890f-4fac-a390-05655de8aeaf", + "metadata": {}, + "source": [ + "We can now follow our steps for this:" + ] + }, + { + "cell_type": "markdown", + "id": "527fc345-0ca2-40c6-84f5-5031a8122d39", + "metadata": {}, + "source": [ + "### Step 1: Variable Selection\n", + "- Independent Variable (X): genre_name (Categorical). This is a predictor we believe influences profitability.\n", + "\n", + "- Dependent Variable (y): ROI_perc (Continuous). The outcome we want to explain." + ] + }, + { + "cell_type": "markdown", + "id": "6b31432c-1606-47dc-bc36-5758ce94e291", + "metadata": {}, + "source": [ + "### Step 2: Testing for Linearity\n", + "- Since our independent variable is categorical (genres), we cannot use a scatterplot to test linearity in the same way. Instead, we test the assumption that the residuals of our model will be normally distributed. We will do this after fitting the model.\n", + "\n", + "- First, we visualize the raw relationship. The appropriate plot is a boxplot or violin plot to see the distribution of ROI for each genre." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "17413079-f5ff-40b7-8d14-2eae82f76cd4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(14, 8))\n", + "\n", + "# SOLUTION: Reset the index to avoid duplicate index labels\n", + "plot_data = genre_overall_clean.reset_index()\n", + "\n", + "# Now use this new DataFrame for plotting\n", + "genre_order = plot_data.groupby('genre_name')['ROI_perc'].median().sort_values(ascending=False).index\n", + "\n", + "sns.boxplot(data=plot_data, x='genre_name', y='ROI_perc', order=genre_order)\n", + "plt.axhline(0, color='red', linestyle='--', label='Break-Even (ROI=0%)')\n", + "plt.title('Distribution of ROI by Movie Genre')\n", + "plt.xlabel('Genre')\n", + "plt.ylabel('ROI (%)')\n", + "plt.xticks(rotation=45, ha='right')\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "24a4a04a-6048-4991-958c-0fae6ac2cbe3", + "metadata": {}, + "source": [ + "As before, we can see that there are some outliers which are causing our y-axis to be stretched, therefore we can set limits to the y-axis." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "5e610a97-5f5a-4ce8-9b5e-b7a9480f344c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ROI Distribution Summary (for context):\n", + "count 4138.000000\n", + "mean 290.635152\n", + "std 1084.418256\n", + "min -99.896400\n", + "25% 12.138712\n", + "50% 134.604971\n", + "75% 312.646417\n", + "max 41556.474000\n", + "Name: ROI_perc, dtype: float64\n", + "\n", + "Number of extreme outliers not shown (ROI < -100% or > 2000%): 71\n", + "This represents 1.72% of the dataset.\n" + ] + } + ], + "source": [ + "plt.figure(figsize=(14, 8))\n", + "\n", + "# SOLUTION 1: Reset the index to avoid duplicate index labels\n", + "plot_data = genre_overall_clean.reset_index()\n", + "\n", + "# Calculate the genre order based on median ROI (this is good practice)\n", + "genre_order = plot_data.groupby('genre_name')['ROI_perc'].median().sort_values(ascending=False).index\n", + "\n", + "# Create the boxplot\n", + "sns.boxplot(data=plot_data, x='genre_name', y='ROI_perc', order=genre_order)\n", + "\n", + "# FIX: Set logical limits on the y-axis to exclude extreme outliers\n", + "plt.ylim(-100, 2000) # This focuses on movies from -100% ROI (a flop) to 2000% ROI (a 5x return)\n", + "\n", + "# Add key reference lines\n", + "plt.axhline(0, color='red', linestyle='--', linewidth=1.5, label='Break-Even (0% ROI)')\n", + "plt.axhline(100, color='green', linestyle=':', alpha=0.7, label='Double Investment (100% ROI)')\n", + "\n", + "# Add titles and labels\n", + "plt.title('Distribution of ROI by Movie Genre (Y-axis limited to -100% to 1000%)', fontsize=14)\n", + "plt.xlabel('Genre')\n", + "plt.ylabel('ROI (%)')\n", + "plt.xticks(rotation=45, ha='right')\n", + "plt.legend()\n", + "plt.grid(True, alpha=0.3) # Adds a light grid for easier reading\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# --- BONUS: Print context about the limits ---\n", + "print(\"ROI Distribution Summary (for context):\")\n", + "print(plot_data['ROI_perc'].describe())\n", + "\n", + "# Count how many movies are outside our chosen y-axis limits\n", + "lower_limit = -100\n", + "upper_limit = 2000\n", + "outliers = plot_data[(plot_data['ROI_perc'] < lower_limit) | (plot_data['ROI_perc'] > upper_limit)]\n", + "print(f\"\\nNumber of extreme outliers not shown (ROI < {lower_limit}% or > {upper_limit}%): {len(outliers)}\")\n", + "print(f\"This represents {len(outliers) / len(plot_data) * 100:.2f}% of the dataset.\")" + ] + }, + { + "cell_type": "markdown", + "id": "c8a5a359-68f0-491e-b8e2-0f8bcd521f9b", + "metadata": {}, + "source": [ + "- We can see above that the boxplot for Horror movies shows a higher median value, tight spreads and has few outliers hence it confirms that the horror genre is the most profitable.\n", + "- We can now proceed to fitting our linear model. Because genre_name is categorical, we must first convert it into dummy variables (one-hot encoding) before we can use it in a regression model." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "a98e0282-14f1-4caf-8b81-30273619dd2b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: y R-squared: 0.026\n", + "Model: OLS Adj. R-squared: 0.022\n", + "Method: Least Squares F-statistic: 6.589\n", + "Date: Sat, 13 Sep 2025 Prob (F-statistic): 8.84e-16\n", + "Time: 23:26:35 Log-Likelihood: -34694.\n", + "No. Observations: 4133 AIC: 6.942e+04\n", + "Df Residuals: 4115 BIC: 6.954e+04\n", + "Df Model: 17 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "const 201.0213 52.024 3.864 0.000 99.027 303.016\n", + "x1 21.9238 80.332 0.273 0.785 -135.570 179.418\n", + "x2 86.1144 114.731 0.751 0.453 -138.821 311.050\n", + "x3 38.7538 69.461 0.558 0.577 -97.427 174.934\n", + "x4 -34.0839 88.682 -0.384 0.701 -207.949 139.781\n", + "x5 10.9768 202.603 0.054 0.957 -386.235 408.189\n", + "x6 33.6829 64.418 0.523 0.601 -92.611 159.977\n", + "x7 39.1141 94.467 0.414 0.679 -146.093 224.321\n", + "x8 38.7645 96.723 0.401 0.689 -150.865 228.394\n", + "x9 -54.2139 131.402 -0.413 0.680 -311.834 203.406\n", + "x10 868.0713 98.375 8.824 0.000 675.203 1060.940\n", + "x11 48.6110 158.935 0.306 0.760 -262.988 360.210\n", + "x12 235.1214 111.599 2.107 0.035 16.327 453.916\n", + "x13 90.6699 89.897 1.009 0.313 -85.577 266.917\n", + "x14 60.4527 91.200 0.663 0.507 -118.349 239.254\n", + "x15 235.2655 73.025 3.222 0.001 92.098 378.434\n", + "x16 -58.4358 161.805 -0.361 0.718 -375.662 258.790\n", + "x17 -112.9096 239.750 -0.471 0.638 -582.950 357.131\n", + "==============================================================================\n", + "Omnibus: 10485.105 Durbin-Watson: 0.805\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 182150473.291\n", + "Skew: 27.871 Prob(JB): 0.00\n", + "Kurtosis: 1029.950 Cond. No. 16.4\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" + ] + } + ], + "source": [ + "# 1. CREATE A CLEANED DATAFRAME (MORE ROBUST)\n", + "regression_df = genre_overall_clean.copy()\n", + "\n", + "# Ensure ROI_perc is numeric and drop missing\n", + "regression_df['ROI_perc'] = pd.to_numeric(regression_df['ROI_perc'], errors='coerce')\n", + "regression_df = regression_df.dropna(subset=['ROI_perc'])\n", + "\n", + "# 2. CRITICAL STEP: Clean the 'genre_name' column\n", + "# Convert all genre names to strings and handle missing values\n", + "regression_df['genre_name'] = regression_df['genre_name'].astype(str) # Force to string\n", + "# Optional: Replace any 'nan' strings if they exist\n", + "regression_df['genre_name'] = regression_df['genre_name'].replace('nan', pd.NA)\n", + "regression_df = regression_df.dropna(subset=['genre_name']) # Drop rows where genre is NA\n", + "\n", + "# 3. CREATE DUMMY VARIABLES\n", + "genre_dummies = pd.get_dummies(regression_df['genre_name'], prefix='genre', drop_first=True)\n", + "\n", + "# 4. DEFINE VARIABLES\n", + "y = regression_df['ROI_perc']\n", + "X = genre_dummies\n", + "\n", + "# 5. ADD CONSTANT\n", + "X = sm.add_constant(X)\n", + "\n", + "# 6. FINAL CHECK: Convert everything to numeric arrays explicitly\n", + "# This bypasses any pandas dtype issues\n", + "y_final = np.asarray(y, dtype=float)\n", + "X_final = np.asarray(X, dtype=float)\n", + "\n", + "# 7. Fit the model using the numeric arrays\n", + "model = sm.OLS(y_final, X_final).fit()\n", + "\n", + "# 8. PRINT RESULTS\n", + "print(model.summary())" + ] + }, + { + "cell_type": "markdown", + "id": "794b01f0-ed71-4125-aebc-4354824fa041", + "metadata": {}, + "source": [ + "For the statistical tests, we can define our null and alternative hypothesis as follows:\n", + "\n", + "- Null Hypothesis (H₀): There is no difference in ROI between this genre and the baseline genre.\n", + "\n", + "In mathematical terms: β₁ = 0 (The coefficient for genre_Horror is zero).\n", + "\n", + "- Alternative Hypothesis (H₁): There is a difference in ROI between this genre and the baseline genre.\n", + "\n", + "In mathematical terms: β₁ ≠ 0." + ] + }, + { + "cell_type": "markdown", + "id": "73fa2f48-3c7d-4d79-8e49-08e394d725d2", + "metadata": {}, + "source": [ + "### Step 3: Interpretation\n", + "First let us decode the variables(x1, x2,..., x17)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "783d0ff6-46f0-4b35-a083-ce6046179eb8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Genre Dummy Variable Order:\n", + "x1 = genre_Adventure\n", + "x2 = genre_Animation\n", + "x3 = genre_Comedy\n", + "x4 = genre_Crime\n", + "x5 = genre_Documentary\n", + "x6 = genre_Drama\n", + "x7 = genre_Family\n", + "x8 = genre_Fantasy\n", + "x9 = genre_History\n", + "x10 = genre_Horror\n", + "x11 = genre_Music\n", + "x12 = genre_Mystery\n", + "x13 = genre_Romance\n", + "x14 = genre_Sci-Fi\n", + "x15 = genre_Thriller\n", + "x16 = genre_War\n", + "x17 = genre_Western\n" + ] + } + ], + "source": [ + "# This shows the order of the dummy variables, which matches x1, x2, x3...\n", + "print(\"Genre Dummy Variable Order:\")\n", + "for i, col in enumerate(X.columns[1:], start=1): # Skip the 'const' column\n", + " print(f\"x{i} = {col}\")" + ] + }, + { + "cell_type": "markdown", + "id": "173fb3c0-aa44-4a90-8f31-0b5312eee030", + "metadata": {}, + "source": [ + "**Interpretation of Key Results**\n", + "1. Overall Model Fit:\n", + "\n", + "- R-squared: 0.026 - Only 2.6% of the variation in ROI can be explained by genre alone. This is expected - genre is important, but many other factors (marketing, stars, timing) affect profitability.\n", + "\n", + "- Prob (F-statistic): 8.84e-16 - This is essentially 0.000. This means that genre, as a whole, is a statistically significant predictor of ROI. We reject the null hypothesis that all genres perform the same.\n", + "\n", + "2. The Baseline:\n", + "\n", + "- const: 201.02% ROI - This is the average ROI for whatever genre was used as the baseline (likely the first genre alphabetically, like \"Action\" or \"Adventure\"). This means the typical movie in the baseline genre returns about 3x its budget.\n", + "\n", + "3. Identifying the Most Profitable Genres:\n", + "\n", + "- Look for coefficients that are:\n", + "\n", + "- Large and Positive (high ROI above baseline)\n", + "\n", + "- Statistically Significant (P>|t| < 0.05)\n", + "\n", + "- Based on our output, two genres stand out:\n", + "\n", + " x10: coef = 868.07, P>|t| = 0.000\n", + "\n", + "- Interpretation: This genre has an ROI that is 868 percentage points higher than the baseline genre.\n", + "\n", + "- Business Meaning: Movies in this genre are EXTREMELY profitable. Their total ROI would be 201% + 868% = 1069% (a 10x return on investment).\n", + "\n", + "- This is almost certainly Horror (it's famously profitable due to low budgets and high returns).\n", + "\n", + " x15: coef = 235.27, P>|t| = 0.001\n", + "\n", + "- Interpretation: This genre has an ROI that is 235 percentage points higher than the baseline.\n", + "\n", + "- Total ROI: 201% + 235% = 436% (a 4.3x return).\n", + "\n", + "- This could be Thriller, Mystery, or Crime.\n", + "\n", + " x12: coef = 235.12, P>||t|| = 0.035\n", + "\n", + "- Also significant at the 5% level. Another highly profitable genre.\n", + "\n", + "4. Identifying Genres to Avoid:\n", + "- Look for large negative coefficients. While not significantly negative in your output, x17 has a large negative coefficient (-112.91), though it's not statistically significant (p=0.638)." + ] + }, + { + "cell_type": "markdown", + "id": "882a5b45-9006-4fe2-b310-27ebd3d44ec7", + "metadata": {}, + "source": [ + "## Question Three: Should they build their studio from scratch or acquire an existing one?\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "1488ae52-5f89-4fae-a61b-f46cc48c3b68", + "metadata": {}, + "source": [ + "For this, we will use studio df." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "4b1705b2-3004-4aab-8a03-a277371801bd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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titlestudioyearidmovieproduction_budgetdomestic_grossworldwide_grossrelease_yearmonth_dtmonthdom_profit_marginww_profit_marginworld_wide_profit_amountROI_perc
0Toy Story 3BV201047Toy Story 3200000000415004880106887952220106651.80779681.288817868879522434.439761
1InceptionWB201038Inception16000000029257619583552464220107745.31339180.850355675524642422.202901
2Shrek Forever AfterP/DW201027Shrek Forever After16500000023873678775624467320105530.88622778.181664591244673358.330105
3The Twilight Saga: EclipseSum.201053The Twilight Saga: Eclipse6800000030053175170610282820106677.37343990.369675638102828938.386512
4Iron Man 2Par.201015Iron Man 217000000031243333162115638920105545.58839272.631691451156389265.386111
................................................
1250GottiVE201864Gotti1000000042863676089100201866-133.297802-64.227883-3910900-39.109000
1251Ben Is BackRAtt.201895Ben Is Back130000003703182963311120181212-251.049449-34.951212-3366889-25.899146
1252Bilal: A New Breed Of HeroVE2018100Bilal: A New Breed Of Hero30000000490973648599201822-6010.315639-4525.354032-29351401-97.838003
1253MandyRLJ201871Mandy600000012145251427656201899-394.020296-320.269309-4572344-76.205733
1254Lean On PeteA24201813Lean On Pete800000011630562455027201844-587.843062-225.861997-5544973-69.312162
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1255 rows × 15 columns

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" + ], + "text/plain": [ + " title studio year id \\\n", + "0 Toy Story 3 BV 2010 47 \n", + "1 Inception WB 2010 38 \n", + "2 Shrek Forever After P/DW 2010 27 \n", + "3 The Twilight Saga: Eclipse Sum. 2010 53 \n", + "4 Iron Man 2 Par. 2010 15 \n", + "... ... ... ... ... \n", + "1250 Gotti VE 2018 64 \n", + "1251 Ben Is Back RAtt. 2018 95 \n", + "1252 Bilal: A New Breed Of Hero VE 2018 100 \n", + "1253 Mandy RLJ 2018 71 \n", + "1254 Lean On Pete A24 2018 13 \n", + "\n", + " movie production_budget domestic_gross \\\n", + "0 Toy Story 3 200000000 415004880 \n", + "1 Inception 160000000 292576195 \n", + "2 Shrek Forever After 165000000 238736787 \n", + "3 The Twilight Saga: Eclipse 68000000 300531751 \n", + "4 Iron Man 2 170000000 312433331 \n", + "... ... ... ... \n", + "1250 Gotti 10000000 4286367 \n", + "1251 Ben Is Back 13000000 3703182 \n", + "1252 Bilal: A New Breed Of Hero 30000000 490973 \n", + "1253 Mandy 6000000 1214525 \n", + "1254 Lean On Pete 8000000 1163056 \n", + "\n", + " worldwide_gross release_year month_dt month dom_profit_margin \\\n", + "0 1068879522 2010 6 6 51.807796 \n", + "1 835524642 2010 7 7 45.313391 \n", + "2 756244673 2010 5 5 30.886227 \n", + "3 706102828 2010 6 6 77.373439 \n", + "4 621156389 2010 5 5 45.588392 \n", + "... ... ... ... ... ... \n", + "1250 6089100 2018 6 6 -133.297802 \n", + "1251 9633111 2018 12 12 -251.049449 \n", + "1252 648599 2018 2 2 -6010.315639 \n", + "1253 1427656 2018 9 9 -394.020296 \n", + "1254 2455027 2018 4 4 -587.843062 \n", + "\n", + " ww_profit_margin world_wide_profit_amount ROI_perc \n", + "0 81.288817 868879522 434.439761 \n", + "1 80.850355 675524642 422.202901 \n", + "2 78.181664 591244673 358.330105 \n", + "3 90.369675 638102828 938.386512 \n", + "4 72.631691 451156389 265.386111 \n", + "... ... ... ... \n", + "1250 -64.227883 -3910900 -39.109000 \n", + "1251 -34.951212 -3366889 -25.899146 \n", + "1252 -4525.354032 -29351401 -97.838003 \n", + "1253 -320.269309 -4572344 -76.205733 \n", + "1254 -225.861997 -5544973 -69.312162 \n", + "\n", + "[1255 rows x 15 columns]" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "studio_df" + ] + }, + { + "cell_type": "markdown", + "id": "24dde14d-f703-434e-b96a-d5c24d4e2605", + "metadata": {}, + "source": [ + "- The goal is to use linear regression to determine if the choice of studio has a statistically significant impact on a movie's profitability (ROI_perc). This will allow us to rank studios by their average contribution to ROI and identify potential acquisition targets." + ] + }, + { + "cell_type": "markdown", + "id": "3c57be15-6e5b-466a-8a6c-85655e439d6b", + "metadata": {}, + "source": [ + "### Step 1: Variable Selection\n", + "- Variable (X): studio (Categorical). The studio that produced the film.\n", + "\n", + "- Dependent Variable (y): ROI_perc (Continuous). The financial performance metric we want to explain." + ] + }, + { + "cell_type": "markdown", + "id": "d47f4b7f-2603-4eb2-aa32-c27a710b4ee4", + "metadata": {}, + "source": [ + "### Step 2: Testing For Linearity\n", + "- Since our independent variable is categorical, we visualize the relationship with a boxplot to see the distribution of ROI for each studio." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "9b24030c-c381-414e-bcff-ea6bcbf36fd7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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GDhyY7zsW1uN8Y6E6yvWcc845LXOix0cskr/pTW/KDZmj0XSUWipu3L2l53biiSemI444Ik2aNCkv0kaJoWiEHIu2H/rQhza47601fvz4vOD9nve8J/32t7/NmQvxXKK8UDzXre1hsTERNHjlK1+ZM20mTpyYMwh23XXX/Jjxvr7qVa/Kr2c0ho7F/mhMHRkVUbLqda973VY/bkfexxDnE2WeIpgUC+XFDby31Ac/+MFOf/0jiBEBj+iHEc3XC703NmXGjBn5ucf8eL6RvRGvwYIFC3LZtwhcbEx8b8ZjvfnNb06HHHJIDrRE4OkrX/lKDiRecsklLXMPOuigvI0m55FxFa9fvJbRk+Ptb397Pu8IyMVnN4J+7fXyiCBQfA7ifKPMVQQy4+siWyZ67mzKtGnT8vnG1zY0NOQ+JPHaxnlGTxAAAMpUMwAAlKFrr702mju03Pr06dM8ePDg5je+8Y3NV111VfPTTz+9wddMnTo1zy2YN29e80knndS855575q+P7bve9a7mhx9+uNXXfe9732t+xSte0dy7d+/89fHY4bDDDmseNWpUu+cXx+JW8LOf/Sx/7de//vXmiy66qHmPPfZo3n777ZuPP/745iVLlmzw9Z/5zGeaX/ziFzf37du3ua6urvnee+/d4D43dW5nnnlm87Bhw1rN/ec//9l8wQUX5Oe53XbbNb/sZS9r/tSnPtXc1NTUal7cz8SJEzc4p7i/uN/NWbFiRfN73/ve5t122y2/rvvvv3/LebW9v3j+HbGpuV/96ldbPfeCxYsXN59zzjnNQ4cOzc83zufNb35z81133bXBfRTen5tuummT57Gl72P49a9/nb/m6KOPbu6owmf1b3/72ybntfdedfT1D/He77XXXvl+pk2btsHxeA3be20fffTR5jPOOCN/z8VrG5/VE044ofnb3/72Js/3sccea7744oubDznkkPzaxed29913z6/fT3/601Zz161b13zeeefl4z169Gj1vRuvyymnnNLcv3//5l122aX5/e9/f/ODDz7Y7rl+5zvfad5vv/3y91J8r9x8883tfn/E18brXux3v/td8zHHHNO844475sc64ogjmu++++5NPkcAAEqrR/yn1IEVAACAShWZC1EGKUpWRUN6AACg6+ipAQAAsA2i30eUWTr55JNLfSoAAFD19NQAAADYCtGAOxpSz549O5177rktja8BAICuo/wUAADAVogm3dFAO5pcX3/99Z3eJB0AACiz8lOXXXZZes1rXpN/+d9jjz3SW97ylvTQQw+1mrNmzZo0ceLEtOuuu+aU7lNOOSX/4VBs6dKl6fjjj0/9+/fP9/PRj340rVu3rtWcn//85+lVr3pV6tu3b9pnn33SV7/61W55jgAAQHX685//nJ577rl0yy23CGgAAEAtBDXuvPPOHLC455570h133JFeeOGFdPTRR6dnn322Zc4FF1yQ07pvuummPP+JJ55oVat2/fr1OaDx/PPPp7vvvjt97WtfywGLiy++uGXO4sWL85wjjjgi3X///elDH/pQet/73pd+/OMfd/tzBgAAAAAAqqD81N/+9recaRHBi/r6+rRq1aq0++67pxtvvDG97W1vy3P+93//N+23335pwYIF6ZBDDkk//OEP0wknnJCDHYMGDcpzrrnmmvTxj38831+fPn3y///gBz9IDz74YMtjnXrqqWnlypXpRz/6UcmeLwAAAAAAUKGNwiOIEQYOHJi3v/3tb3P2xlFHHdUy5+Uvf3kaOnRoS1Ajtvvvv39LQCNETdsJEyakRYsWpTFjxuQ5xfdRmBMZG+1Zu3ZtvhU0NTWlJ598MpfA6tGjR6c/bwAAAAAAqFXNzc3pn//8Z9pzzz1Tz549KyOoEYGDCDLU1dWlV77ylXls+fLlOdNi5513bjU3AhhxrDCnOKBROF44tqk5Tz/9dK6Bu/3222/Q66OxsbELniUAAAAAANCexx9/PL3kJS9JFRHUiN4aUR7qF7/4RalPJV100UXpwgsvbJVBEtkh8YLutNNOJT23cvLII4+k888/P33uc5/LzdcBAAAAAGBLRQLCXnvtlV70ohdtdm5ZBDXOPffcdNttt6X58+e3isIMHjw4NwCP3hfF2RorVqzIxwpzfv3rX7e6vzheOFbYFsaK50SAom2WRujbt2++tRXzBTX+Y8cdd0y9e/fOW68LAAAAAADboiPtHzZdnKob6mRFQOO73/1u+ulPf5qGDx/e6vhBBx2UtttuuzRv3ryWsYceeigtXbo0jR07Nu/H9ve//33661//2jLnjjvuyIvsr3jFK1rmFN9HYU7hPgAAAAAAgPLXu9Qlp2688cb0ve99L6eVFHpgDBgwIGdQxPbss8/OpaCieXgEKs4777wcjIgm4eHoo4/OwYvTTz89XX755fk+Jk+enO+7kG3xgQ98IH3+859PH/vYx9JZZ52VAyjf+ta30g9+8INSPn0AAAAAAGALlDRTY9asWblfxeGHH56GDBnScvvmN7/ZMueKK65IJ5xwQjrllFNSfX19LiV18803txzv1atXLl0V2wh2nHbaaemMM85Il156acucyACJAEZkZxxwwAHpM5/5TPrSl76UjjnmmG5/zgAAAAAAwNbp0Rw1oNhsk5LIGokAjN4R//Hwww+n8ePHp9mzZ6eRI0eW+nSgqqxfvz4tXLgwPfnkkzlTbfTo0Tl4CwAAAJXyd+0LL7xQ6tMAykifPn1Sz549t3kNviwahQPwH/Pnz08zZ85sKckXIkutoaEhZ6wBAABAuYrrp+Pv2ZUrV5b6VIAyEwGNqKoUwY1tIagBUGYBjalTp+ZyelOmTMk/6BcvXpzmzJmTxxsbGwU2AAAAKFuFgMYee+yR+vfvn3r06FHqUwLKQFNTU3riiSfSsmXL0tChQ7fpZ4OgBkAZpeZGhkYENKZNm9aSjjdq1Ki8P3ny5NyLqK6uTikqAAAAyvLv2kJAY9dddy316QBlZvfdd8+BjXXr1qXtttuuMhuFA/Af0UMjrmgZN27cBvUFYz/GI5od8wAAAKDcFHpoRIYGQFuFslMRAN0WghoAZSKagocoOdWewnhhHgAAAJQjJaeArvzZIKgBUCYGDhyYt9FDoz2F8cI8AAAAAKg1ghoAZWL06NFp8ODBuSl4NE8qFvsxPmTIkDwPAAAAqA6XXHJJOvDAA1M1eeihh/Iaxz//+c9Ubf7f//t/6bzzziv1adQ0QQ2AMhHNvxsaGtKCBQtyU/BFixal1atX523sx/iECRM0CQcAAIBO9J73vCeXxSncosn5m970prLuafnVr3611TkXbv369Uvl4KKLLsoL/y960YtaxuL1PPTQQ/M57rXXXunyyy9v9TV33HFHGjlyZNppp53S6aefnp5//vmWY6tWrcrHlixZstnHLn494r5e85rXpO9973sbzHvuuefS1KlT8/327ds37bbbbuntb397XofZVNDpIx/5SPra176WHnvssS1+XegcghpQI6IBz3333ZfmzZuXt9vakIeuUV9fnxobG/M/jBMnTkzHHXdc3kbpqRiP4wAAAEDniiDGsmXL8i3WTnr37p1OOOGEDjVGL5VYsC+cc+HWkUX/rrZ06dJ022235WBRwdNPP52OPvroNGzYsPTb3/42fepTn8rBgtmzZ7dUqHj3u9+dPvCBD+SLOu+9996WY4XsiDgWX98R1157bX494n7q6urS2972tvT73/++5fjatWvTUUcdlb7yla+kadOmpYcffjjNnTs3rVu3Lh188MHpnnvu2eh9R/DjmGOOSbNmzdrKV4htJagBNWD+/Plp3Lhx6YILLkif+MQn8jb2Y5zyE4GLKDV1xRVXpClTpuTtDTfcIKABAAAAXSSu1I9ySXGLq/JjEf3xxx9Pf/vb3/LxP//5z/nK/29+85vpsMMOy9kG8bd7+NKXvpT222+/PPbyl788zZw5s9V9f/zjH8/ZAP37908jRozIf+tvKiDy6KOP5nnnnntuam5u3ui8OJ/CORdugwYNysciILDnnntuUN76pJNOSmeddVbLfmQwvOpVr8rnHo8ZF1TGwn7xY8Tze+tb35rP/2Uve1n6/ve/v8nX8lvf+lY64IAD0otf/OKWsXitIvMiggijRo1Kp556ajr//PPTZz/72Xz873//e75FBYs4/uY3vzn98Y9/zMfuvvvu9Jvf/CZ98IMfTB21884759cjXvdYC4vn9LOf/azl+JVXXpmDJxF8ecc73pGDJa997WvTd77znfxenn322Zt87U888cT0jW98o8PnQ+cS1IAqF4GLSKWLf5hmzJiRo86xjf0YF9goT1FiasyYMenII4/MWyWnAAAAqFjPPrvx25o1HZ/73HMdm7uNnnnmmXxx4T777JNLURWLYEcsrseCe1ytH4v1F198cZo+fXoe++QnP5mDFlGeqCBKMEW5qD/84Q/pqquuSl/84hfzBYztiRJNr3/963PWwuc///kcVNgaUUbpH//4R6uF/CeffDL96Ec/yhe6hrvuuiudccYZ+fnEuX3hC1/I5xnPpVgEOmLhP84tKkrE18d9bUzc76tf/epWYxFAiIs1+/Tp0zIWr1/03njqqafS7rvvnvuI3n777bkUd9xH9BSN4E+U4o5z25q1kQhmfPnLX87/X/zYN954Y3rjG9+Ygy/FevbsmS8GjtfjgQce2Oj9RgDkL3/5Sw520f0ENaCKRYmpuDpg7NixOZUuIt0RVY9t7Md4pMopRQUAAAB0mR133PjtlFNaz91jj43PPfbY1nNf+tL2522FuGJ/xx13zLcIQkQ2QmRlxCJ3sQ996EPp5JNPTsOHD8+L8HHB6Gc+85mWsdjGongswhdEn8zXve516aUvfWm+wj96MkQ2Q1uRkXD44Yfn47FusznRZ6JwzoXbsf9+jXbZZZf8/7F4X/Dtb387l0464ogjWoIVEaQ588wz88WvscgfWQ3F5x6ijNS73vWuHOSJoE0EfX79619v9LyiBFZkiRRbvnx5SxZJQWE/jkXwJl6TePxYt4oLPCOj5L//+7/z+UYmSZSR2nfffXOwZ3PifOP1iAyceD/itY/ATEGUm4qMjPYUxmPOxhSeXzmU+6pFvUt9AkDXiQh6/MMQVwi0/Uc49iOyHv0aYl78YwEAAABQi2LhvNAjITIH4iLRCArE4n1xH4fiDIRnn302l4qKUkXnnHNOq+yAAQMGtOxHcORzn/tcnhsBgTge/TDa9qGIoEJkSUTgpFgszhecdtpp6Zprrsn/H8GX3/3ud63mbr/99i3/H+s+cV7xXGJxP7JKouxTYY0oMhF++ctftsrMiAtf16xZk7Ml4sLYEBkTBTvssEM+97/+9a8bfS2jAffWNCyPDJUoM1UQQYXrrrsu94aNLI/IKIn35JWvfGXeLz6vtiITJnpmRM/SCGrE6z9w4MBWczZVXmpzCq9zvE50P0ENqGKFVMC4UqA9hfFNpQwCAAAAbJNnntn4sbYlhTaxWJ7aXLCZOrH0TyzWRyZCQfSRiMBElIoqzpqIeQURoAgxJ5pLFyuUSoqySxFciKyIKLcU9xm9GCK7o1iUX4qr/7/+9a/nDIXioMf999/f8v/F4xGcKD7ntiIrJBbuf/CDH6TXvOY1uaRTcdmrOP84r8guaas4KLHddtu1OhZZFW17dRSLbJAIDBWL/hYrVqxoNVbYj2Ptef/7359fp3isCGxESa0ItERPkzvvvHOTQY24z3ht4hZNw6NsVpSU2iMygVLKvTYKPTvaKozHnI0prKXF+0b3E9SAKlaIQC9evDin7rUV48XzAAAAADpdUSCgZHO3UCzcR9Agsg42JsonRSAisgEKfSraKykVmR6TJk1qGWuvZFFc+R8lsGLxPYIf0VsiMjHCpgIXmxKBiQhYRIbGI488kks3RVPwgvj/6Gmxtfe/MVENJAIIxaIEerwG0SOjECS544478jlFqay2og9GrFdFw/BCgKTQXD22W1JKPfpfHHTQQTkjJXqahMhYifOJbJXivhoRQInAzyte8YoN+m0Ue/DBB/PzaG+9ja6npwZUsYhYR2Q6/vFqG0GP/RiP+o+bimwDAAAAVLu1a9fmEt5xiyv1zzvvvJzJENkOmxKZDpdddlkubxTlkn7/+9/nzIDPfvaz+fjLXvayXFoqsjOi/FTM++53v9vufUUWSGRV9O7dO5dZKmSCbExkYRTOufhWvAYUwZa4z6985SsbBF6iwXmUd4rnsGjRovy84zyjB8i2iKBMZKgUBx6i8Xk06o5SXfFYUZIrAgwXXnjhBl8fpa0iO+bqq6/O+xH0iD4XV155Zb7fefPm5f4aWyJKekWvkP/7v//L+1GSKoId8f7edNNN+T2K0lennHJKfh0iqLKpJu2R9XLooYe2KvdF9xHUgCoWqY4NDQ35B378gxT/aEStv9jGfoxPmDChJSUSAAAAoBb96Ec/yhd+xi1KScUCdyx2R+PuTXnf+96XS1VFIGP//ffPpZG++tWvtpT8jkyDWEA/99xz04EHHpgzN6L36cZE/4wf/vCHOWBx/PHH574dG/P000+3nHPxrbjfxRve8Iac8RAZGRFYaBt8iOyQyAqJ8lSHHHJIzlIo7iGyNSIgE4GZn/zkJy1jUXYrHieqhkTWxIc//OEcVBk/fvwGXx+9M+J4cbPxeE0j4HLCCSekj370o/l8t8Sb3vSm/J4U+odEFstPf/rTdMYZZ6T/+q//ytkqMSfWyO655578WmxKnEtxHxW6V4/mbemIUiPiB0R8461atWqDJj61LKLP8YNn9uzZm6wxR+nNnz8/N4WKaH1B/CMXAY1orAQAAACwraLBdCxax+Lx1jSKpnrMmDEjff/7308//vGPU7WJoFMEXRYuXJiDN3TOz4gtWYP3qkMNiMBFpOXFD9toZBQR+ig5JUMDAAAAgM4WTb5XrlyZ/vnPf7b0BqkWkT0TmTkCGqXjlYcaEQGMaNQEAAAAAF0pFvyLm6NXk7e97W2lPoWap6cGAAAAAABQEWRqQI1Yv3698lMAAAAAQEUT1IAabRQ+ePDg1NDQoFE4AAAAAFAxlJ+CGghoTJ06NY0YMSLNmDEjzZ07N29jP8bjOAAAAABAJRDUgCovORUZGmPHjk3Tpk1Lo0aNSv3798/b2I/xWbNm5XkAAAAAAOVOUAOqWPTQiJJT48aNSz17tv52j/0YX7ZsWZ4HAAAAAFDuBDWgikVT8DB8+PB2jxfGC/MAAAAAAMqZoAZUsYEDB+bt4sWL2z1eGC/Mo3xESbD77rsvzZs3L2+VCAMAAIDqcPjhh6cPfehDm5zz0pe+NF155ZXddk5s3umnn54++clPlvo0ytY111yTTjzxxG55LEENqGKjR49OgwcPTnPmzElNTU2tjsV+jA8ZMiTPo3xE8/YoDXbBBRekT3ziE3kb+5q6dz3BJAAAgNrznve8J/Xo0SPftttuuzRo0KD0xje+MX3lK1/ZYD2lksTzueWWW1K5uOSSS9KBBx6YysXPf/7z/BqtXLlys3MfeOCBNHfu3HT++ee3jN18883p6KOPTrvuumu+n/vvv3+Dr1uzZk2aOHFinrPjjjumU045Ja1YsaLVnKVLl6bjjz8+98HdY4890kc/+tG0bt26luOxPjFmzJj89RE0eLKo4krMO+igg9Kvf/3rzT6HCJQVPufxWPvvv3/60pe+tMG8WAu54oor8vF+/fqlXXbZJR177LHpl7/8Zat5X/3qV9POO+/csn/WWWel3/3ud+muu+5KXU1QA6pYr169UkNDQ1qwYEGaPHlyWrRoUVq9enXexn6MT5gwIc+jPETgYurUqWnEiBFpxowZ+R/M2MZ+jAtsdB3BJAAAgNr1pje9Kfcd/fOf/5x++MMfpiOOOCJ98IMfTCeccEKrBWZq09VXX53e/va358BCwbPPPpte//rXp//5n//Z6NfF2sKtt96abrrppnTnnXemJ554Ip188smtAggR0Hj++efT3Xffnb72ta/lYMHFF1/cMud973tfesMb3pADBqtWrWqVLfKZz3wm1dXVpde+9rUdeh6XXnpp/pw/+OCD6bTTTkvnnHNO/rwXNDc3p1NPPTXPi8//H//4xxz82WuvvXKG0aaCZH369Envfve70+c+97nU5ZrZrFWrVjXHSxVb/uOhhx5qPuyww/KW8nbnnXc2v/Od78zvV+F26qmn5nHKx7p16/L7dNFFFzWvX7++1bHYj/F432IenSu+Fw4//PD8Gj/44IPNzz77bN7Gfoz7XgEAANi85557rvkPf/hD3laSM888s/mkk07aYHzevHl5TfCLX/xiy9iSJUua3/zmNzfvsMMOzS960Yua3/72tzcvX758k/f1wQ9+MK/FFMT/T5w4Md922mmn5l133bV58uTJzU1NTS1zhg0b1nzFFVe07D/11FPNZ599dvNuu+2WH/eII45ovv/++zf5vOLcv/vd7+b/X7x4cd7/zne+k//O3X777ZtHjx7dfPfdd+fjse7Zr1+/5rlz57a6j5tvvrl5xx13zH8nh6VLl+bnPGDAgOZddtklvxZx3wU/+9nPml/zmtc09+/fP8953ete1/znP/+5+dprr82PX3yLscJ5XnPNNc3HH398Pq+Xv/zl+bz+9Kc/5dcq7mvs2LHNjzzySKtzu+WWW5rHjBnT3Ldv3+bhw4c3X3LJJc0vvPBCq+cf791b3vKWfL/77LNP8/e+971Wr0fxLd679sQ6TDyX2267rd3jhfu67777Wo2vXLmyebvttmu+6aabWsb++Mc/5rkLFizI+/F69+zZs9VnaNasWflzsXbt2rwf5x5fF2bOnNl83HHH5f9/9NFHm1/2spc1P/30080d0fYzFQYOHNh8wQUXtOx/4xvfyOf3/e9/f4OvP/nkk/Nn9Zlnnsn78f7F61Is1k/69OnTvHr16i3+GbEla/AyNaAG1NfX51JTkTo2ZcqUvL3hhhvyOOVj4cKFafny5Tk7oGfP1j+eYz/GI5oe8+g8cVXEzJkz09ixY9O0adPSqFGjchpmbGM/xmfNmqUUFQAAwFZas25Nvv1rnflf1jWty2MvrH+hw3OfX/98h+Z2lrg6/oADDshlhkKUojrppJNy+Z+46v6OO+5Ijz32WHrnO9+5xfcdV+T37t07lw266qqr0mc/+9l2SwEVRJbAX//613xV/W9/+9v0qle9Kh155JGtShF1xKRJk9JHPvKRXCpp5MiR6V3velfORNlpp51yVsqNN97Yan6sJ73lLW/Jfye/8MIL6ZhjjkkvetGLcomhKEcUmQuR5RKZBnE/Mfewww7LaxdRIWT8+PG53FG8Rh/+8Ifz39qxthG34tctKiacccYZ+bxe/vKX5yv+3//+96eLLroo3Xvvvfk9Pvfcc1vmx+PH/Mgm+MMf/pC+8IUv5AyH6dOntzr/xsbG9I53vCOfz3HHHZfXVuI1i8yD73znO3nOQw89lM8n3of2xNdGhsSrX/3qLXqt432K1+yoo45qGYvnNnTo0PzahNhGmacoeVYQr/HTTz+dK62E+AzGZy1e3yiXPfrfZeQ/8IEPpMsvvzy/H1sqPsvx/J966qmcYVEQ7398LtrrjRHv3z/+8Y98LhsTr1Gc569+9avUlQQ1oEZEiamovxf/4MVWyanyU/hFZPjw4e0eL4xv6S8sbJpgEgAAQNd6+01vz7en1z7dMnbzH2/OY9fce02ruafdfFoe/9vqv7WM/eDhH+Sxz/2qdVmbs79/dh5//OnHW8bmPTavU889FqGjJFW+73nz0u9///u88Bt9DA4++OB03XXX5QDHb37zmy2631hUj4tO99133/x353nnnZf32/OLX/wiBz+ihFEsGr/sZS9Ln/70p3M/g29/+9tb9LgR0IhyR7FwHQv+S5YsSY888kg+FucR5YWidHmIhfUf/OAHeTx885vfzIvhEXyJhfj99tsvXXvttbknRJQoivmx+B/Bkb333jsfP/PMM/Mi/vbbb58DIBHIif6vcYuxgve+9705+BDn9fGPfzy/5vG4scAf9xPBi3iMgjj3//f//l++/yjZHT1QIjASwY22/VIicLPPPvvksk3PPPNMfi1jXWzgwIF5TvSxiPMZMGBAu69ZvEYxP+ZtiVhriIBBcd+JEAGMOFaYUxzQKBwvHAvxesf7HK9p3N9FF12Urr/++hxoes1rXpNfo3h+UWp+c+K1jfehb9++6W1ve1vulxHlrQoefvjh/Hq3pzAeczYmzilex3jNupKgBkCZKPxjunjx4naPF8YL8+gcgkkAAABsTGQIRKZBiP4CEYyIW8ErXvGKvGgdx7bEIYcc0nK/IaoE/OlPf2q3SkA0qY7F+EKz6cIt1gkeffTRLXrcwlX+YciQIXkbGSAhMhmiUfr3v//9vB9X8kcGRyHTIM4jAiCRGVA4h1ijiGbYcR7x/xFEiEX2uNI/Mh/iIsEtPa/Con4ETorH4nEicFI4l+j7UPx6RH+IeLxCUKbt/e6www75+RSeb0c999xzOQhQ/H51p8huicBZBAoioPbCCy/kvquf//znczDsda97XX49IqMo+ndsSjQhj2yYn/70pzkoF4G0CIgUK8582hoRrCp+D7pC7y69dwA6LP6hjSsDIrUzyh4VZw3ElRAxHr9wFP+DTOcGk+IXhVIFk+IX18gGieBJPFa8zzKqAACAanDT22/K2769+raMnbzfyenN+7459erR+u+eG06+YYO5x488Ph2zzzGpZ4/W12d/+c1f3mDukSOO7NRzj2DFxi6Ca0/8Ld92UTgWobdFBDRiPaA4U6GgbRbA5kTQoqCwSB9rDiGyAOLq/Vg4j2bRsY0SUZFdUTiPyFCJ9Ym2dt9997yNzI3zzz8//ehHP8qZHZE9EOWKIoizpee1qXONc4lsjeKm2wX9+vVr934L91O4j47abbfd8iJ9lNgqLtW0ObHGE1+zcuXKVu/TihUr8rHCnMgcKRbHC8fac+GFF6YPfehD6SUveUn+TMQaUgRsIgMn9tsrHVX8XCKIEbfI/InAUWT/RHAuRKbMxgJ0hfGYsymxrlH4PHQVmRoAZSIWsBsaGnI9xfhHP2onxj+asY39GJ8wYYKF7i4MJrX9xaa7gknz58/PabUXXHBBTpeNbezHOAAAQKXr17tfvhVf6d67Z+88tl2v7To8t0+vPh2a21niavYoN3XKKae0lN95/PHH860g+jnEonVhUTgWc9tmJ8SV8W217Tlwzz335LJS7f3NH/0zohRRBBcKC9KFWyxSd6b4WzQCErEWEc+/UHqqcB6RTRJlmNqeR3Hppih7HiWS7r777vTKV76ypU9HBAQ6q19lnEv0wmh7HnFrW1p6YwoBis2d04EHHtjyXm+JCABFUCXKlhXEOUe5rsjMCbGNz1hx9kgEgSKjpPCZKhb39cc//rGlv0iceyFoFtsteX0j4yiCVvFeFUQwK97j9jI+PvOZz+RsoSj1tTGRsRMZNfEZ6EqCGgBlJJq3x5UG0Whs4sSJOfUztpEtEOOau1dfMCkCF5E2GjVAZ8yYkebOnZu3sR/jAhsAAABdb+3atTlw8H//93/pd7/7Xe6/EE3Boz9ENKQOUYYprmyPhf6YE1fYx7FojF1oIh3NxaOxdfTaiMXh+LvuwQcf3ODxYmE7rriPRe6vf/3r6eqrr859I9oTjxuL39GE+/bbb8/9JiJgEE2/47E6U6w7xIV/8RwjQyVKFBXEWARR4nWJRt2xVhGZAZGZ8Ze//CXvxwJ5/B0dpZLiXOM1KPRieOlLX5rnRJDn73//e37Nt9bFF1+cX+NYK4m/32Oh/xvf+EaH+koUDBs2LAfEbrvttvS3v/0tZ3+0JwJVEUSJ3iZtMxLiuRSCHfFexn6hF0YEes4+++z8Pv/sZz/LjcOjd0i8l4XMlaOPPjoHL04//fRcQurHP/5xfg6xFhQlr4pFsCCCGbNnz24J3NTV1eU1hPjaKBcW+1siPnMRwCh8jiKo8da3vjX3Kvnyl7+cP2tRVSKatkdZsujvEVkhGxOfi1jPiP4fXUlQA6DMxC8QkR0QdQ2nTJmStzfccIOARhUGk+IKipkzZ+ZfaCJdNMpfRVOt2MZ+jM+aNavTrmQBAACgfZGdEFn6sfD+pje9KS9Cf+5zn0vf+973Wi5yiwXw2I/myvF3YgQbYgE3yiwVRD+J+Fv+Yx/7WG7i/M9//rMlKFIsxqJXw2tf+9r892csLo8fP77dc4vHjQvg4jFjUTzK/8TicwQO2jaZ3lbxWNFYOxbJi7M0Qvy9GhfeRePvKPsUwYpYtI/F9sgsiOP/+7//mzNb4hzj+cRziwXxEOPx2h5xxBE5UBDBnK0Vr3MEIyJwEq9zBAli/SQCFR314he/uKXheLyOheyH9kQz7bZlt2KRPzISouxTiPck9q+55pqWOXFOERiL514IGEXvi4L4bMXziG2sAZx22mn5sxH9QtqKc43HOvDfmSMhPqMRSIn7jrJThayijoqASgRWIkhUeP+/9a1vpf/6r/9qaWR/6KGH5s9aBLAisLYp8Z5Gb5Ou1qN5Wzt/1IBoQBORtVWrVuVvUFJLp/v44RTRwc3VUgOoBN3d1+K+++7Lpabiqor2+nnE1SbxC2D8ItHVqZsAAADbKha34+KwuMK/uK8BVLoIQMUCfwSwCqWj2HANIzKVYs24uBxZR39GbMkavEbhAPBvEcDozuBBBE/CxprOFcYL8wAAAIDut/322+dyV1E2i/ZFL5l4jTYW0OhMghoAUCKRDRLiKoX2MjVivHgeAAAAUBqHH354qU+hrB111FHd9lh6agBAiUR5q6inGXU5m5qaWh2L/RiPmq4xDwAAAABBDQAoabmrhoaGtGDBgjR58uRcf3L16tV5G/sxPmHChC7t6wEAAABQSZSfAoASqq+vT42NjWnmzJm5KXhBZGjEeBwHAAAA4F8ENQCgxCJwUVdXlxYuXJibgkcPjSg5JUMDAACoRG3L6wKE5ubm1BkENQCgDEQAY8yYMaU+DQAAgK3Wp0+f1LNnz/TEE0+k3XffPe/36NGj1KcFlElA429/+1v+mbDddttt030JagAAAAAA2ywCGsOHD0/Lli3LgQ2AYhHQeMlLXrLNlSkENQAAAACAThHZGUOHDk3r1q1L69evL/XpAGUkMjQ6o9S2oAYAAAAA0GkK5WW2tcQMQHt6tjsKAAAAAABQZgQ1AAAAAACAiiCoAQAAAAAAVARBDQAAAAAAoCIIagAAAAAAABVBUAMAAAAAAKgIghoAAAAAAEBFENQAAAAAAAAqgqAGAAAAAABQEQQ1AAAAAACAiiCoAQAAAAAAVARBDQAAAAAAoCIIagAAAAAAABVBUAMAAAAAAKgIghoAAAAAAEBFKGlQY/78+enEE09Me+65Z+rRo0e65ZZbWh2PsfZun/rUp1rmvPSlL93g+H//93+3up+FCxemQw89NPXr1y/ttdde6fLLL++25wgAAAAAAFRBUOPZZ59NBxxwQJoxY0a7x5ctW9bq9pWvfCUHLU455ZRW8y699NJW884777yWY08//XQ6+uij07Bhw9Jvf/vbHBC55JJL0uzZs7v8+QEAAAAAAJ2ndyqhY489Nt82ZvDgwa32v/e976UjjjgijRgxotX4i170og3mFsyZMyc9//zzOSDSp0+fNGrUqHT//fenz372s2n8+PGd9EwAAAAAAICuVjE9NVasWJF+8IMfpLPPPnuDY1Fuatddd01jxozJmRjr1q1rObZgwYJUX1+fAxoFxxxzTHrooYfSU0891W3nDwAAAAAAVHCmxpb42te+ljMyTj755Fbj559/fnrVq16VBg4cmO6+++500UUX5RJUkYkRli9fnoYPH97qawYNGtRybJdddtngsdauXZtvxSWsAAAAAACA0qqYoEaUjxo3blxu9l3swgsvbPn/0aNH54yM97///emyyy5Lffv23arHiq9tbGzc5nMGAAAAAABqrPzUXXfdlctFve9979vs3IMPPjiXn/rzn/+c96PXRpSuKlbY31gfjsj2WLVqVcvt8ccf75TnAQAAAAAAVHlQ48tf/nI66KCD0gEHHLDZudEEvGfPnmmPPfbI+2PHjk3z589PL7zwQsucO+64I+27777tlp4KkeGx0047tboBAAAAAAA1HNR45plnchAibmHx4sX5/5cuXdqqn8VNN93UbpZGNAG/8sor0wMPPJAee+yxNGfOnHTBBRek0047rSVg8e53vzuXpIoG44sWLUrf/OY301VXXdWqbBUAAAAAAFD+StpT4957701HHHFEy34h0HDmmWemr371q/n/v/GNb6Tm5ub0rne9q92Mijh+ySWX5Mbe0RA8ghrFAYsBAwak22+/PU2cODFne+y2227p4osvTuPHj++W5wgAAAAAAHSOHs0RMWCTIlskgiPRX0Mpqv94+OGHc3Bo9uzZaeTIkaU+HYBttn79+rRw4cL05JNPpoEDB6bRo0enXr16lfq0AAAAAKra01uwBl/STA0AKBfRf2nmzJlp+fLlLWODBw9ODQ0Nqb6+vqTnBgAAAEAFNQoHgK4OaEydOjWNGDEizZgxI82dOzdvYz/G4zgAAAAApSeoAUCq9ZJTkaExduzYNG3atDRq1KjUv3//vI39GJ81a1aeBwAAAEBpKT9Fh6xYsSLXMyu2ZMmSVttiUf9s0KBB3XZ+AFsremhEyakpU6aknj1bx/pjf9y4cWnixIl53pgxY0p2ngAAAAAIatDBgMZpp5+RXnh+bbvHp0+fvsHYdn36phuuv05gAyh70RQ8DB8+vN3jhfHCPAAAAABKR1CDzYoMjQhoPDfisNTUb8Bm5/dcsyqlx+7MXyeoAZS7gQMH5u3ixYtzyam2Yrx4HgAAAAClo6cGHRYBjaYddtv8rQOBD4ByMXr06DR48OA0Z86c1NTU1OpY7Mf4kCFD8jwAAAAASktQA4Ca1qtXr9TQ0JAWLFiQJk+enBYtWpRWr16dt7Ef4xMmTMjzAAAAACgt5acAqHn19fWpsbExzZw5MzcFL4gMjRiP4wAAAACUnqAGAPw7sFFXV5cWLlyYm4JHD40oOSVDAwAAAKB8CGoAwL9FAGPMmDGlPg0AAAAANkJPDQAAAAAAoCIIagAAAAAAABVBUAMAAAAAAKgIghoAAAAAAEBFENQAAAAAAAAqQu9SnwBsqfXr16eFCxemJ598Mg0cODCNHj069erVq9SnBQAAAABAFxPUoKLMnz8/zZw5My1fvrxlbPDgwamhoSHV19eX9NwAAAAAAOhayk9RUQGNqVOnphEjRqQZM2akuXPn5m3sx3gcBwAAAACgeglqUDElpyJDY+zYsWnatGlp1KhRqX///nkb+zE+a9asPA8AAAAAgOokqEFFiB4aUXJq3LhxqWfP1h/b2I/xZcuW5XkAAAAAAFQnQQ0qQjQFD8OHD2/3eGG8MA8AAAAAgOojqEFFGDhwYN4uXry43eOF8cI8AAAAAACqj6AGFWH06NFp8ODBac6cOampqanVsdiP8SFDhuR5AAAAAABUJ0ENKkKvXr1SQ0NDWrBgQZo8eXJatGhRWr16dd7GfoxPmDAhzwMAAAAAoDr1LvUJQEfV19enxsbGNHPmzDRx4sSW8cjQiPE4DgAAAABA9RLUoKJE4KKuri4tXLgwNwWPHhpRckqGBgAAAABA9RPUoOJEAGPMmDGlPg0AAAAAALqZnhoAAAAAAEBFENQAAAAAAAAqgqAGAAAAAABQEQQ1AAAAAACAiiCoAQAAAAAAVARBDQAAAAAAoCIIagAAAAAAABVBUAMAAAAAAKgIghoAAAAAAEBFENQAAAAAAAAqgqAGAAAAAABQEQQ1AAAAAACAiiCoAQAAAAAAVITepT4BADa0fv36tHDhwvTkk0+mgQMHptGjR6devXqV+rQAAAAAoKQENQDKzPz589PMmTPT8uXLW8YGDx6cGhoaUn19fUnPDQAAAABKSfkpgDILaEydOjWNGDEizZgxI82dOzdvYz/G4zgAAAAA1CpBDYAyKjkVGRpjx45N06ZNS6NGjUr9+/fP29iP8VmzZuV5AAAAAFCLBDUAykT00IiSU+PGjUs9e7b+8Rz7Mb5s2bI8DwAAAABqkaAGQJmIpuBh+PDh7R4vjBfmAQAAAECtEdQAKBMDBw7M28WLF7d7vDBemEd1ibJi9913X5o3b17eKjMGAAAAsKHe7YwBUAKjR49OgwcPTnPmzMk9NIpLUDU1NeXxIUOG5HlUl2gAH/1UovxYQXwWGhoaUn19fUnPDQAAAKCcyNQAKBO9evXKi9gLFixIkydPTosWLUqrV6/O29iP8QkTJuR5VFdAY+rUqWnEiBFpxowZae7cuXkb+zEexwEAAAD4F5kaAGUkrspvbGzMV+1PnDixZTwyNGLcVfvVJUpMxXs9duzYVtk5o0aNyvsRzJo1a1aqq6sTzAIAAAAQ1AAoPxG4iEXshQsX5qbg0UMjSk5Z1K4+8R5HyakpU6a0KjcWYn/cuHE5uBXzxowZU7LzBAAAACgXghoAZSgCGBaxq18ErcLw4cPbPV4YL8wDAAAAqHV6agBAiUQWTli8eHG7xwvjhXkAAAAAtU5QAwBKJMqKDR48OM2ZMyc1NTW1Ohb7MR79VGIeAAAAAMpP1aQ1a9akpUuXbvT40KFDU79+/br1nABqtcxYQ0NDmjp1am4KHj00ouRUZGhEQGPBggW5Qbx+KgAAAAD/IqhRgyKgMX78+I0enz17dho5cmS3nhNAZ1i/fn3FNViPxvARuJg5c2ZuCl4QGRoxHscBAAAA+BdBjRoUmRgRuAhLlixJ06dPT5MmTUrDhg1rOQ5QaebPn58DA8uXL28Zi9JOkQlR7oGBOL+6urqKC8gAAAAAdDdBjRoUpaXaZmJEQEN2BlDJAY0o4TR27Ng0ZcqUViWcYrwSMh4igDFmzJhSnwYAAABAWdMoHICKLzkVGRoR0Jg2bVoaNWpU6t+/f97GfozPmjUrzwMAAACgsglqAFDRomRTlJyKJts9e7b+Zy32Y3zZsmV5HgAAAACVTVADgIoWPShClJxqT2G8MA8AAACAyiWoAUBFi6baIXpotKcwXpgHAAAAQOUS1ACgoo0ePToNHjw4NwVvampqdSz2Y3zIkCF5HgAAAACVTVADgIrWq1ev1NDQkBYsWJAmT56cFi1alFavXp23sR/jEyZMyPMAAAAAqGwlDWrMnz8/nXjiiWnPPfdMPXr0SLfcckur4+95z3vyePHtTW96U6s5USM9msDutNNOaeedd05nn312euaZZ1rNieawhx56aOrXr1/aa6+90uWXX94tzw+A7lFfX58aGxvTY489liZOnJiOO+64vI3SUzEexwEAAACofL1L+eDPPvtsOuCAA9JZZ52VTj755HbnRBDj2muvbdnv27dvq+MR0Fi2bFm644470gsvvJDe+973pvHjx6cbb7wxH3/66afT0UcfnY466qh0zTXXpN///vf58SIAEvMAqA4RuKirq8uB7Ah4Rw+NKDklQwMAAACgepQ0qHHsscfm26ZEECNqpbfnj3/8Y/rRj36UfvOb36RXv/rVeezqq6/OV+h++tOfzhkgUUv9+eefT1/5yldSnz590qhRo9L999+fPvvZzwpqUFPWr19vsZeqF5/pMWPGlPo0AAAAAKjGoEZH/PznP0977LFH2mWXXdIb3vCGNG3atLTrrrvmY1EnPTIuCgGNEBkZPXv2TL/61a/SW9/61jwnrt6NgEbBMccck/7nf/4nPfXUU/l+21q7dm2+FUS2B1SyKPU2c+bMtHz58paxCBZGHwJleQAAAACASlHWjcKj9NR1112X5s2bl4MQd955Z87siCvOQyzQRsCjWO/evfNV6IXF29gOGjSo1ZzCfvECb7HLLrssDRgwoOUWfTigkgMaU6dOTSNGjEgzZsxIc+fOzdvYj/E4DgAAAABQCco6qHHqqaemN7/5zWn//fdPb3nLW9Jtt92WS01F9kZXuuiii9KqVatabo8//niXPh50lQgARobG2LFjc5ZTlF/r379/3sZ+jM+aNaslUAgAAAAAUM7KOqjRVlxZvttuu6VHHnmkpXzOX//611Zz1q1bl3sGFPpwxHbFihWt5hT2N9arI/p47LTTTq1uUImih0ZkJI0bNy6XZSsW+zG+bNmyPA8AAAAAoNxVVFDjL3/5S/rHP/6RhgwZkvfjKvOVK1em3/72ty1zfvrTn6ampqZ08MEHt8yJ8jovvPBCy5w77rgj7bvvvu3204BqEgG+MHz48HaPF8YL8wAAAAAAyllJgxrPPPNMuv/++/MtLF68OP//0qVL87GPfvSj6Z577kl//vOfc1+Nk046Ke2zzz650XfYb7/9ct+Nc845J/36179Ov/zlL9O5556by1btueeeec673/3u3CT87LPPTosWLUrf/OY301VXXZUuvPDCUj516BbRX6bwvdWewnhhHgAAAABAOStpUOPee+9NY8aMybcQgYb4/4svvjj16tUrl8SJnhojR47MQYmDDjoo3XXXXbk8VMGcOXPSy1/+8nTkkUem4447Lr3+9a9Ps2fPbjkejb5vv/32vHgbX//hD3843//48eNL8pyhO40ePTqXWYvvk8hgKhb7MR6ZTzEPAAAAAKDc9S7lgx9++OGpubl5o8d//OMfb/Y+4grzG2+8cZNzYsE2giFQayI42NDQkKZOnZomT56ce2hEyakI8kVAY8GCBamxsTHPAwAAAAAodyUNagBdr76+PgcuZs6cmSZOnNgyHhkaMR7HAQAAAAAqgaAG1IAIXNTV1eWSbtEUPDKcIoNJhgYAAAAAUEkENaBGRACj0L+G8rVmzZq0dOnSTc4ZOnRo6tevX7edEwAAAACUC0ENgDISAY3x48dvcs7s2bPTyJEju+2cAAAAAKBcCGoAlJHIwoigRViyZEmaPn16mjRpUho2bFirOQAAAABQiwQ1AMpIlJVqm4URAQ2ZGQAAAACQUs9SnwAAAAAAAEBHCGoAAAAAAAAVQVADAAAAAACoCIIaAAAAAABARRDUAAAAAAAAKoKgBgAAAAAAUBF6l/oEAAC2xvr169PChQvTk08+mQYOHJhGjx6devXqVerTAgAAALqQoAYAlAEL9Ftm/vz5aebMmWn58uUtY4MHD04NDQ2pvr6+pOcGAAAAdB1BDQAoMQv0W/56TZ06NY0dOzZNmTIlDR8+PC1evDjNmTMnjzc2NnrdAAAAoErpqQEAZbBAP2LEiDRjxow0d+7cvI39GI/jtM5oiQBQBDSmTZuWRo0alfr375+3sR/js2bNyvMAAACA6iOoAQAlYoF+y0WJrshoGTduXOrZs/WvMbEf48uWLcvzAAAAgOojqAEAJWKBfstFz5EQJafaUxgvzAMAAACqi6AGAJSIBfotF03UQ/TQaE9hvDAPAAAAqC6CGgBQIhbot9zo0aNzE/VoCt7U1NTqWOzH+JAhQ/I8AAAAoPoIagBAiVig33K9evVKDQ0NacGCBWny5Mlp0aJFafXq1Xkb+zE+YcKEPA8AAACoPoIaAFAiFui3Tn19fWpsbEyPPfZYmjhxYjruuOPyNjJbYjyOAwAAANWpd6lPAABqWWGBfubMmXlhviAyNCzQb1y8LnV1dbmJevQciRJdkdEiAAQAAADVTVADAErMAv3WiddnzJgxpT4NAAAAoBsJagBAGbBADwAAALB5ghoAAAB0uzVr1qSlS5ducs7QoUNTv379uu2cAAAof4IaAAAAdLsIaIwfP36Tc2bPnp1GjhzZbecEAED5E9QAAACg20UWRgQtCpYsWZKmT5+eJk2alIYNG9YyBwAAiglqAAAA0O2irFR7WRgR0JCdAQDAxghqAAAA/Nv69evTwoUL05NPPpkGDhyYRo8enXr16lXq0wIAAP5NUAMAACClNH/+/DRz5sy0fPnylrHBgwenhoaGVF9fX9JzAwAA/qXnv7cAAAA1HdCYOnVqGjFiRJoxY0aaO3du3sZ+jMdxAACg9AQ1AACAVOslpyJDY+zYsWnatGlp1KhRqX///nkb+zE+a9asPA8AACgtQQ0AAKCmRQ+NKDk1bty41LNn6z+RYj/Gly1blucBAAClJagBAADUtGgKHoYPH97u8cJ4YR4AAFA6ghoAAEBNGzhwYN4uXry43eOF8cI8AACgdAQ1AACAmjZ69Og0ePDgNGfOnNTU1NTqWOzH+JAhQ/I8AACgtAQ1AACAmtarV6/U0NCQFixYkCZPnpwWLVqUVq9enbexH+MTJkzI8wAAgNLqXeLHBwAAKLn6+vrU2NiYZs6cmSZOnNgyHhkaMR7HAQCA0hPUAAAA+Hdgo66uLi1cuDA3BY8eGlFySoYGAHTM+vXr/TsKdDlBDQAAgH+LhZcxY8aU+jQAoOLMnz8/ZzwuX768ZSx6VkWJRxmPQGfSUwMAAAAA2KaAxtSpU9OIESPSjBkz0ty5c/M29mM8jgN0FkENAAAAAGCrS05FhsbYsWPTtGnT0qhRo1L//v3zNvZjfNasWXkeQGcQ1AAAAAAAtkr00IiSU+PGjUs9e7Zeaoz9GF+2bFmeB9AZBDUAAAAAgK0STcHD8OHD2z1eGC/MA9hWghoAAAAAwFYZOHBg3i5evLjd44XxwjyAbSWoAQAAAABsldGjR6fBgwenOXPmpKamplbHYj/GhwwZkucBdAZBDQAAAABgq/Tq1Ss1NDSkBQsWpMmTJ6dFixal1atX523sx/iECRPyPIDO0LtT7gUAAAAAqEn19fWpsbExzZw5M02cOLFlPDI0YjyOA3QWQQ0AAAAAYJtE4KKuri4tXLgwNwWPHhpRckqGBtDZBDUAAAAAgG0WAYwxY8aU+jSAKqenBgAAAAAAUBEENQAAAAAAgIogqAEAAAAAAFQEQQ0AAAAAAKAiaBQOQNVYv359WrhwYXryySfTwIED0+jRo3OjOgAAAACqg6AGAFVh/vz5aebMmWn58uUtY4MHD04NDQ2pvr6+pOcGAAAAQOdQfgqAqghoTJ06NY0YMSLNmDEjzZ07N29jP8bjOAAAAACVT1ADgIovORUZGmPHjk3Tpk1Lo0aNSv3798/b2I/xWbNm5XkAAAAAVDZBDQAqWvTQiJJT48aNSz17tv5nLfZjfNmyZXkeAAAAAJVNUAOAihZNwcPw4cPbPV4YL8wDAAAAoHIJagBQ0QYOHJi3ixcvbvd4YbwwDwAAAIDKJagBQEUbPXp0Gjx4cJozZ05qampqdSz2Y3zIkCF5HgAAAACVTVADgIrWq1ev1NDQkBYsWJAmT56cFi1alFavXp23sR/jEyZMyPMAAAAAqGwlDWrMnz8/nXjiiWnPPfdMPXr0SLfcckvLsRdeeCF9/OMfT/vvv3/aYYcd8pwzzjgjPfHEE63u46UvfWn+2uLbf//3f7eaE81hDz300NSvX7+01157pcsvv7zbniMAXa++vj41Njamxx57LE2cODEdd9xxeRulp2I8jgMAAABQ+XqX8sGfffbZdMABB6SzzjornXzyya2OxVW2v/vd79KUKVPynKeeeip98IMfTG9+85vTvffe22rupZdems4555yW/Re96EUt///000+no48+Oh111FHpmmuuSb///e/z4+28885p/Pjx3fAsAegOEbioq6vLgexoCh49NKLklAwNIKxfv97PBwAAgCpQ0qDGsccem2/tGTBgQLrjjjtajX3+859Pr33ta9PSpUvT0KFDWwUxop56e6KW+vPPP5++8pWvpD59+qRRo0al+++/P332s58V1ACoMrFAOWbMmFKfBlBmIjt45syZafny5S1j8btjlK6TyQUAAFBZKqqnxqpVq3J5qciyKBblpnbddde8kPWpT30qrVu3ruVY1FKPP1YjoFFwzDHHpIceeihnfwAAUN0BjalTp6YRI0akGTNmpLlz5+Zt7Md4HAcAAKByVExQY82aNbnHxrve9a600047tYyff/756Rvf+Eb62c9+lt7//venT37yk+ljH/tYy/G4Im/QoEGt7quwX3y1XrG1a9fmslXFNwAAKq/kVGRojB07NvfXiezduOAltrEf47NmzcrzAAAAqAwlLT/VUdE0/B3veEdqbm7Of3gWu/DCC1v+P2ojR0ZGBDcuu+yy1Ldv3616vPja+EMXAIDKFT004iKWE088MZ1++ukblJ+K8bvvvjvPU7oOAACgMvSslIDGkiVLco+N4iyN9hx88MG5/NSf//znlj9YV6xY0WpOYX9jfTguuuiiXOqqcHv88cc77fkAANA9oil4+NKXvtRu+akYL54HAABA+etZCQGNP/3pT+knP/lJ7puxOdEEvGfPnmmPPfbI+1FWIGolx30VRHBk3333Tbvssku79xEZHhE8Kb4BAFBZCn3YXvnKV6Zp06alUaNGpf79++dt7Md48TwAAADKX0nLTz3zzDPpkUceadlfvHhxDkoMHDgwDRkyJL3tbW9Lv/vd79Jtt92Wax0XSgbE8SgzFTWRf/WrX6UjjjgivehFL8r7F1xwQTrttNNaAhbvfve7cymps88+O/fkePDBB9NVV12VrrjiipI9bwAAAAAAoMKCGvfee28OSLTtj3HmmWemSy65JH3/+9/P+wceeGCrr4um4IcffnjOqIgm4TE3mnsPHz48BzWK+2wMGDAg3X777WnixInpoIMOSrvttlu6+OKL0/jx47vteQIA0P1WrlyZt7///e/T5MmT07hx4/Lvi3EhzZw5c/J48TwAAADKX0mDGhGYiObfG7OpY+FVr3pVuueeezb7ONFA/K677tqqcwQAoDJFdm8455xz0q233povcimIrOD3ve99ua9GYR4AAADlr6RBDQAA6CpxYcvgwYPTokWL0vXXX5/LkEZT8AhiRD+NqVOn5uBGzAMAAKAylHWjcAAA2Fq9evVKDQ0Nue9aBDCiJ9vYsWPzNvZjfMKECXkeAAAAlUGmBgAAVau+vj41NjammTNnblB+KsbjOAAAAJVDUAMAgKoWgYu6urq0cOHClvJTUXJKhgYAAEDlEdQAAKDqRQBjzJgxpT4NAAAAtpGeGgAAAAAAQEWQqQEANW79+vXK8lD1fM4BAACqg6AGANSw+fPn5wbKy5cvbxkbPHhwamho0ECZquFzDgAAUD2UnwKAGl7onTp1ahoxYkSaMWNGmjt3bt7GfozHcah0PucAAADVRVADAGq0FE9cuT527Ng0bdq0NGrUqNS/f/+8jf0YnzVrVp4HlcrnHAAAoPoIatSQFStWpIcffrjVbcmSJflYbNsei/kAVKfoLRCleMaNG5d69mz960Dsx/iyZcvyPKhUPucAAADVR0+NGhEBitNOPyO98Pzado9Pnz59g7Ht+vRNN1x/XTecHQDdLZolh+HDh7d7vDBemAeVyOccAACg+ghq1IhVq1blgMZzIw5LTf0GbHZ+zzWrUnrszvx1AFSfgQMH5u3ixYtzKZ62Yrx4HlQin3MAAIDqo/xUjYmARtMOu23+1oHABwCVa/To0Wnw4MFpzpw5qampqdWx2I/xIUOG5HlQqXzOAQAAqo+gBgDUoF69eqWGhoa0YMGCNHny5LRo0aK0evXqvI39GJ8wYUKeV66iufN9992X5s2bl7eaPVONn3MAAABaU34KAGpUfX19amxsTDNnzkwTJ05sGY8r12M8jper+fPn5/OOJtAFcUV+LGCX83nT/Sr5cw4AAMCGBDUAoIbFgm5dXV1auHBhbpYcvQWiFE85X7keAY2pU6emsWPHpilTpuRmz9EbIUoJxbiFaqrhcw4AAED7BDUAoMbFwu6YMWNSJYgSU3HFfQQ0pk2blnr2/FclzWgCHftRUmjWrFl5AduCNZX6OQcAAGDj9NQAACpGXGkfJafGjRvXEtAoiP0YX7ZsWZ4HAAAAVB9BDQCgYkTpoBAlp9pTGC/MAwAAAKqLoAYAlElZpfvuuy/Nmzcvb2OfDUUvhBA9NNpTGC/MAwAAAKqLnhoAUAaNr6NPRJRVKhg8eHBqaGjQ8LqNaO4cr000BS/uqRGampry+JAhQ/I8AAAAoPrI1ACAEgc0pk6dmkaMGJFmzJiR5s6dm7exH+NxnNbNniPYs2DBgtwUfNGiRWn16tV5G/sxPmHCBE3CAQAAoErJ1ACAEokSU5GhMXbs2FZZB6NGjcr7sUg/a9asVFdXZ5G+SGSvNDY25tdu4sSJLeORoRHjslsAAACgeglqAECJLFy4MJecmjJlSqsySiH2x40blxftY96YMWNKdp7lKAIXEeyJ1yaagkcPjSg5JfgDAAAA1U1QAwBKJBbjw/Dhw9s9XhgvzKO1CGAI9gAAAEBt0VMDAEoksgvC4sWL2z1eGC/MAwAAAKh1ghoAUCJRLmnw4MFpzpw5qampqdWx2I/x6BMR8wAAAAAQ1ACAkpZPamhoSAsWLMhNwRctWpRWr16dt7Ef4xMmTNAnAgAAAODf9NQAgBI3vG5sbEwzZ87MTcELIkMjxuM4AAAAAP8iqAEAJRaBi7q6urRw4cLcFDx6aETJKRkaAAAAAK0JagBAGYgAxpgxY0p9GnTQ+vXrBaEAAACgBAQ1AAC2wPz583O5sOXLl7eMRcP36I+iXBgAAAB0LUENAIAtCGhMnTo1HXLIIemd73xn6tu3b1q7dm369a9/ncf1QQEAAICuJagBANDBklORoTFy5Mi0ePHitGDBglaZGjE+a9as3B9FKSoAAADoGj276H4BAKpK9NCIklMPP/xwGjFiRJoxY0aaO3du3sZ+jC9btizPAwAAALqGoAYAQAf8/e9/z9vXvva1adq0aWnUqFGpf//+eRv7MV48DwAAAOh8ghoAAB2wcuXKvD300ENTz56tf4WK/de//vWt5gEAAACdT08NAIAO2HnnnfP2rrvuSscdd1yrwEZTU1P6xS9+0Woe5WHNmjVp6dKlGz0+dOjQ1K9fv249JwAAALaeoAYAQAfstttuefurX/0qTZ48OY0bNy4NHz48Nw2fM2dOHi+eR3mIgMb48eM3enz27Nm5yTsAAACVQVADAKADRo8enQYPHpwGDBiQHn300TRx4sSWYzG+7777pqeffjrPo3xEJkYELsKSJUvS9OnT06RJk9KwYcNajpez9evX5+bzTz75ZBo4cGD+fPXq1avUpwUAAFAyghoAAB0QC8kNDQ1p6tSp6ZBDDkmnnnpq6tu3b1q7dm369a9/ne65557U2NhowbnMRGmptpkYEdCohOyM+fPnp5kzZ6bly5e3CqDF57C+vr6k5wYAAFAqghoA8G+uiGZzYiE5Ahex0LxgwYKW8SFDhuRxC810ZkAjAmhjx45NU6ZMaVXqLMZ93gAAgFolqAEArohmC8Tnoa6uTgCMLg2wxs+jCGhMmzatpSn9qFGj8n70dJk1a1b+HPrcAQAAteZffyEBQA0rXBE9YsSINGPGjDR37ty8jf0Yj+NQLBaSx4wZk4488si8tbBMZ4qAWQRYoxl9IaBREPsxvmzZsjwPAACg1ghqAFDT2l4RHVdC9+/fv+WK6BiPK6JjHkB3iAygECWn2lMYL8wDAACoJYIaANQ0V0RDx0Vw77777kvz5s3LW8G+rhElzUL00GhPYbwwDwAAoJboqQFATXNFNHSMvjPdJ3q0xGsbTcGLe2qEpqamPB7N6WMeAABArZGpAUBNc0U0bJ6+M90rerREsGjBggW5KfiiRYvS6tWr8zb2Y3zChAl6uQAAADVJUAOAmlZ8RXRcAV3MFdGg70ypRPZLY2Njeuyxx9LEiRPTcccdl7cRaI1x2TEAAECtUn4KgJpWuCI6rjaPK6Cjh0aUnIqFwwhoxBXRsYDoimhqve/MlClTNtp3JhbbY96YMWNKdp7VKAIXdXV1+bWNEniRMRYBVj+PAACAWiaoAUDNK1wRHVejx+JsQWRouCKaWqfvTGlFAEOwCAAA4D8ENQAouTVr1qSlS5ducs7QoUNTv379uuwcXBENm+87EyWn2tJ3BgAAgO4kqAFAyUVAY/z48ZucM3v27DRy5MguPQ9XRMOm+85ED43iElT6zgAAANDdBDUA6FTRLHhLsx0iCyOCFmHJkiVp+vTpadKkSWnYsGGt5gDdT98ZAAAAyomgBgCdZv78+bkvRTQVLogrvGNBdFN9KaKsVNssjAhodHVmBtAx+s4AAABQLgQ1AOi0gEZcyT127Ng0ZcqUVldyx7iFT6hs+s4AAFCttqbiAFA6ghoAdMovgHEFdwQ0imvuR1Ph2I+SNbNmzcoLon4xhMql7wwAANVmaysOAKXzn06PALCV4oqW+AUwau0XNxEOsR/jy5Yty/MAAACgnCoOjBgxIs2YMSPNnTs3b2M/xuM4UH4ENQDYZpGiG6LkVHsK44V5AAAAUE4VB6LSQP/+/VsqDsR4VByIeUAFl59qampKd955Z7rrrrvSkiVL0urVq9Puu++eyxAcddRRaa+99uq6MwWgbEXN0RA9NOIXwLZivHgeAAAAlEPFgegJubGKAxMnTszzlGCFCszUeO6553KEMoIWxx13XPrhD3+YVq5cmesqP/LIIzkdK67CjWP33HNP1581AGUlmqhFzdFoCh4B8GKxH+NDhgzJ8/iPNWvWpIcffniTt5gDAABA51JxAKo8U2PkyJE55eqLX/xieuMb35i22267DeZE5saNN96YTj311DRp0qR0zjnndMX5AlCGIsgdTdQiyB1NweOKlvgFMDI0IqCxYMGC1NjYqEl4G0uXLk3jx4/f5JzZs2fnf4cBAADoPCoOQJUHNW6//fa03377bXLOsGHD0kUXXZQ+8pGP5EUaAGpLfX19DlxETdJI0S2IDI0Yj+O0NnTo0By0KL5AYPr06fnigPh3tTAHAACArqs4EBVqiktQqTgAVRDU2FxAo1hkcey9997bck4AVKgIXNTV1eWao5GiG1e0xC+AMjTa169fv3azMCKgITsDAACg66g4AFXeU6M969atSzNmzEhvf/vb08knn5w+85nPbHHd7/nz56cTTzwx7bnnnqlHjx7plltuaXW8ubk5XXzxxTkquv322+dm5H/6059azYlFs/ihs9NOO6Wdd945nX322emZZ55pNScW1w499NC8eBR9QS6//PKtfdoAbEb8whdN1I488si89QsgAAAA5Vxx4LHHHssVB6JfcGwjsKHiAFR4pkZ7zj///NzANAIaL7zwQrruuuvSvffem77+9a93+D6effbZdMABB6Szzjor309bEXz43Oc+l772ta/lSOmUKVPSMccck/7whz/kAEWIgMayZcvSHXfckc/jve99b65PHv09wtNPP52OPvroHBC55ppr0u9///v8eBEA2VwdcwAAAACgeqk4AFUc1Pjud7+b3vrWt7bqs/HQQw+1fINHsOGQQw7Zogc/9thj8609kaVx5ZVX5vSvk046KY9F4GTQoEE5oyMakv/xj39MP/rRj9JvfvOb9OpXvzrPufrqq3NU9dOf/nTOAIl0seeffz595StfSX369MmNf+6///702c9+VlADAAAAAGpcoeIAUGXlpyIo8Ja3vCU98cQTef9Vr3pV+sAHPpCDCrfeemv62Mc+ll7zmtd02olFmtfy5ctzhkXBgAED0sEHH5xr2oXYRsZFIaARYn409vnVr37VMicirhHQKIgATARknnrqqXYfe+3atTnDo/gGAAAAAABUSFAjAhfvete70uGHH56zIWbPnp37WEyaNCmXhYpeFYWST50hAhohMjOKxX7hWGz32GOPVsd79+6d08SK57R3H8WP0dZll12WAyiFWzw3AAAAAACgghqFv/Od70y//vWvc1+KyHY47bTT0m9/+9tczimahu++++6pGlx00UVp1apVLbfHH3+81KcEAAAAAAA1b4sbhUe5p8jSmD9/fjrjjDPSm970pvSJT3yipXF3Zxk8eHDerlixIg0ZMqRlPPYPPPDAljl//etfW33dunXrclOfwtfHNr6mWGG/MKetvn375hsAUL7Wr1+vmR8AAADUmA5naixdujS94x3vSPvvv38aN25cetnLXpazNPr3758OOOCA9MMf/rBTT2z48OE56DBv3ryWsehtEb0yxo4dm/dju3LlynweBT/96U9TU1NT7r1RmBMBmBdeeKFlzh133JH23XfftMsuu3TqOQMA3SP+bY/fRy644IJ8cUVsYz/GAQAAgOrV4aBGZGVEA+5PfepTuY/F+9///tx8u7GxMd1yyy25D0UEPbbEM888k0tXxa3QHDz+PwIoPXr0SB/60IfStGnT0ve///1c8irOYc8998wNy8N+++2XM0XOOeecXBbrl7/8ZTr33HPTqaeemueFd7/73fk8zz777LRo0aL0zW9+M1111VXpwgsv3LJXCoAOXz1/33335aB0bGO/UlTyudeSCFxMnTo1jRgxIpe/nDt3bt7GfowLbAAAAED16nD5qXvvvTc98MADae+99879NCKToiCCC7GAEGWptkTc5xFHHNGyXwg0nHnmmemrX/1q+tjHPpaeffbZNH78+JyR8frXvz796Ec/alXqas6cOTmQceSRR+agyymnnJI+97nPtRyPRt+33357mjhxYjrooIPSbrvtli6++OJ8nwB0rvi3YObMmWn58uUtY5F119DQkOrr61M5q+RzryURaIr3KTIx48KKBx98MC1YsCCXn4r9CGrMmjUr1dXVKUUFAAAAtRzUiIBABAMi4PCTn/wkl6Fqa0sDBYcffnhqbm7e6PHI1rj00kvzbWNiEePGG2/c5ONEje277rpri84NgK27ej4Wm6dMmZKD35GBF8HnGI8F53INDlTyudea6KERgacTTzwxnX766RsEoU444YR0991353ljxowp6bkCAAAAJSw/dd1116W1a9fmmtX/93//l77whS90wekAUOlXz0fZwFGjRuWeS7GN/RiPq+fLsZxTJZ97LYqm4OGLX/xiu+WnvvSlL7WaBwAAANRoUGPYsGHp29/+du5LEVeuFnpWAEDh6vlo1BylAIvFfowvW7Yszys3lXzutWjnnXfO28gYbS8IVcgkLcwDAAAAajCoEX0ttsSWzgegshWuii/ut1SsMF6OV89X8rkDAAAA1JoOBTX22Wef9N///d/5StWNid4Yd9xxRzr22GNbNeoGoPpFf6MQfSjaUxgvzCsnlXzutWjlypV5Gw3CJ0+enDNIV69enbexH+PF8wAAAIAabBT+85//PP3Xf/1XuuSSS9IBBxyQXv3qV+fyU/369UtPPfVU+sMf/pAWLFiQevfunS666KL0/ve/v+vPHICyMXr06NykOcoTRgmg4jJOTU1NeXzIkCF5Xrmp5HOvRYXg0vve97506623pokTJ7Yci/cpxqPfhiAUAAAA1HBQY999903f+c530tKlS9NNN92U7rrrrnT33Xen5557Lu22225pzJgxeQEhsjR69erV9WcNQFmJn/0NDQ1p6tSp+Wr56EMRZZsiyyGCAhH4bmxsLMt/Iyr53GtRIQgVmRnXX399zsyI0mARxHjlK1+Z30dBKAAAAKjxoEbB0KFD04c//OF8AyrL+vXrc6PjwuJfLPhZpKUz1dfX58X/mTNnbnD1fIzH8XJVyedea4qDUHGLINTYsWNzECr2BaEAAACgum1RUAOoTPPnz8+LtcuXL28ZiyudY2HQYi2dKT5PdXV1FRlAq+RzrzWCUNtGkBsAAIBKJqgBNRDQiKuX40rmKVOmtCqrE+MWAOlssTgaZQkrUSWfe60RhNo6gtwAAABUuv90QwWq8mrcWLyKgEY0QB41alTq379/3sZ+jM+aNSvPA6jUINSRRx6ZtwIaHQtyjxgxIs2YMSPNnTs3b2M/xuM4AAAAlDtBDahicQVzXI0bNed79mz97R77Mb5s2bI8D4DqJcgNAABAtRDUgCoWJVlClJxqT2G8MA8qXSzI3nfffWnevHl5a4G2unm/O06QGwAAgJrrqdHRP3KjnjVQHqLGfIgeGnE1blsxXjwPKpleAbXF+71lBLkBAACouaDGgQcemHr06JGam5s3OFYYj62rJKF8RJAxFvmiKXiUFym+OrepqSmPDxkyRDCSqukVECV0pkyZkhdoI2gXn/EYb2xstNBdRbzfW06QGwAAgJorPxV/7D722GN52/ZWGI8tUD6iaW5ctbxgwYI0efLktGjRorR69eq8jf0YnzBhgua6VDS9AmqL93vbg9wR1C4myA0AAEBVBjWGDRvWoRtQXuJq5bhqOYKOEydOTMcdd1zeRiDS1cxUA70Caov3e+sIcgMAAFBz5acKfvOb36Svf/3r6eGHH877I0eOTO9+97vTq1/96q44P6ATROCirq4uL/JFvfQoLxJX41q8ohroFVBbvN/bHuSOTJcIbhdEhoYgNwAAAFUZ1PjYxz6WPv3pT6cdd9wxjRgxIo/deeed6aqrrkof+chH0v/8z/901XkC2ygCGGPGjCn1aUCn0yugtni/t40gNwAAADVTfuprX/tauvrqq9PnPve59I9//CPdf//9+RZ/EF9xxRV5/LrrruvaswWANvQKqC3e784Lch955JF5K6ABAABAVQY1ZsyYkT75yU+mc889N2233XYt4/H/559/fpo+fXr6/Oc/31XnCQDt0iugtni/AQAAoLZ1uPxULBacdNJJGz3+lre8JU2ZMqWzzgsAOkyvgNri/QYAAIDa1eGgRlzx+Pzzz2/0+AsvvOCqSABKRq+A2uL9BgAAgNrU4aDGq171qlyn+hOf+ES7x6+//vo8BwBK3SuA2uD9BgAAgNrT4aDGRz7ykVxiau3atenDH/5wGjRoUB5fvnx5+sxnPpOuvPLK9N3vfrcrzxUAAAAAAKhhHQ5qnHDCCemKK67IwY0IYgwYMCCPr1q1KvXu3Tt9+tOfznMAAAAAAABKGtQI5513XnrrW9+abrrppvSnP/0pj40cOTKdcsopaa+99uqSEwQAAAAAANjioEZ4yUteki644IJ2jz333HNp++2398oCAAAAAACdrmdn3En02YiSVMOHD++MuwMAAAAAANj6oEYELi666KL06le/Or3uda9Lt9xySx6/9tprczAjGoVvLIMDAAAAAACg28pPXXzxxekLX/hCOuqoo9Ldd9+d3v72t6f3vve96Z577kmf/exn836vXr22+YQAAAAAAAC2KagRzcGvu+669OY3vzk9+OCDafTo0WndunXpgQceSD169Ojo3QAAAAAAlI3169enhQsXpieffDINHDgwr3u6eBuqIKjxl7/8JR100EH5/1/5ylemvn375nJTAhoAAAAAQCWaP39+mjlzZlq+fHnL2ODBg1NDQ0Oqr68v6bkB29hTIyKWffr0adnv3bt32nHHHTv65QAAAAAAZRXQmDp1ahoxYkSaMWNGmjt3bt7GfozHcaCCMzWam5vTe97znpyhEdasWZM+8IEPpB122KHVvJtvvrnzzxIAAAAAoJPEBdyRoTF27Ng0bdq01LPnv679HjVqVN6fPHlymjVrVqqrq1OKCio1qHHmmWe22j/ttNO64nwAAIAyoLY0AFDN4vecKDk1ZcqUloBGQeyPGzcuTZw4Mc8bM2ZMyc4T2IagxrXXXtvRqQAAQAVTWxoAqHZx4UYYPnx4u8cL44V5QAX21AAAgFJmDdx3331p3rx5eRv7dA21pQGAWhCZqGHx4sXtHi+MF+YBFZipwb888sgj+Yfa6tWr06OPPrrReXvvvXfq379/jurus88+3XqOwLaJnkFLly7d5JyhQ4emfv36dds5AdQyWQPdR21pAKBWRGnN+J1yzpw5rX7vCU1NTXl8yJAheR5QXgQ1ttDVV1+dHnjggQ7PP+CAA9JVV13VpecEdK4IaIwfP36Tc2bPnp1GjhzZbecEUOtZA7HIHvWO44KRuMAk/siM8cbGRoGNTqS2NABQK+ICjbhIJn6njAs34vec4t81FyxYkH/XdCEHlB9BjS103nnnbXGmBlBZIgsjghZhyZIlafr06WnSpElp2LBhreYA0LVkDXQ/taUBgFoSF8dE4CJ+54wLNwoiQ8PFM1C+BDW2UJSSUk4KqluUlWqbhREBDZkZAN1L1kBpa0tH8KgttaUBgGoTgYu4SCZ+p4wLN+L3nCg55aIZKF+CGgBUjeeffz5973vfS0888UTac88900knnZT69OlT6tMCtpKsge6ntjQAUIsigOEiGagcghrUjM01f9b4GSrbNddck2666aZcrqZ47O1vf3v6wAc+UNJzA7aOrIHup7Y0AABQ7gQ1qBmba/6s8TNUrghefOMb30i77LJLOvvss3P9/Vh4+/KXv5zHg8AGVB5ZA6WhtjQAAFDOBDWoGZtr/qzxM1RuyanI0IiARmx79/7XP20nnHBCetOb3pQzNWL8rLPOUooKKoysgdJRWxoAAChXghrUDM2foXtE+afuXASLHhrxmJGhUQhoFMR+BDM+85nP5HkR4KB6dPdnjdKQNVA6tVpb2s8WAAAob4IaAHSa+fPn54XH5cuXt4xF6Zi40rqrFh6jKXiIklPtKYwX5m2KhazKUYrPGqUja4Du4mcLAACUP0ENADptIShKxEQQYcqUKa1KxMR4V11Rveeee+ZtlKGJklNtxXjxvGpcyKq1YEypPmu1/JqXg1rNGqC2frYAAACbJ6gBQKcs8EZAIBaCipv5jho1Ku9HLfxZs2blK607e+H3pJNOyo3Coyl49NAoLkG1bt269JWvfCU/ZsyrxoWsSg7GVNpnrVZfc6gF5fCzBQAA6Jh//bYOANsgrliPBd5o4ltYCCqI/RhftmxZntfZovl39Mp46qmn8vbWW29Nf//73/O2eHxjTcLbLmTFAlb//v1bFrJiPBayYl65KQRjRowYkWbMmJHmzp2bt7Ef43G82pTys1arrznUglL/bAEAADpOUAOAbRYleEJkOLSnMF6Y19k+8IEPpFNPPTU9/fTTuSn42972tryN/RiP49W2kFXJwZhK/azV6msOtaDU/44BAAAdJ6gBwDaLngIhSja1pzBemNcVInDxwx/+ME2cODG99a1vzdvY31RAo5IXsio1GFPJn7Vafc2hFpTDv2MAAEDHCGoAsM2iSXL0FIgeFE1NTa2OxX6MDxkyJM/rSoVSVB/84Ac3WXKqGhayKjUYU8mftVp9zaEWlMu/YwAAwOYJagCwzaJpajRJXrBgQW6mumjRorR69eq8jf0YnzBhQlk2V63UhaxKDcZU8metVl9zqAWV/O8YAADUmt6lPgEAqkN9fX1qbGzMPQei9FNBBARiPI6X80JWNHmOhasoIRRX3McCdQQ0YiErzr/cFrKKgzHRz6G4HFI5B2Mq+bNWLq959OyIEleRERIBlHi8cvt8QiWq1H/HAACg1ghqANBpYsHnkEMOSd/73vfSE088kfbcc8900kkndagMVClV4kJWpQZjOku8J3V1dd26uF8Or/n8+fPz5zR6exREoCXOqxw/p1BpSvGzBQAA2DKCGgB06YLrd77znYpYcK3EhaxKDMZ0pnhvxowZUzOveXx/RUBl7NixacqUKa0CKjFeC+85VOvPFgAAoOMENQDoFNWw4FqJC1mVGIypdKV4zaPkVARS4vuruPTVqFGj8n5kjsyaNSufl/ceAACAaqZROACdvuAaC639+/dvWXCN8VhwjXl0XTDmyCOPzFuL2tX3mkcAJTKgouRVcS+PEPsxvmzZsjwPAAAAqplMjW70yCOP5KuWw+rVq9Ojjz660bl77713XhCMK5332WefbjxLgK1fcI0MjY0tuEapnphXaZkQUA4iIyTE7wXtKYwX5gEAAEC1EtToRldffXV64IEHtuhrDjjggHTVVVd12TkBdAYLrtC1osRViIsjIgOqrcJFE4V5AAAAUK0ENbrReeedt1WZGgDlzoJraUVZr0rtqVHJ596d4nUZPHhw7lFT3FMjNDU15fFoVh7zqpXPCgAAAEFQoxtFGSmlpIBqZMG1tA3ao59JlP8qiPeioaGh7BuzV/K5d7dYvI/XZerUqbkpeJR0iwsfImAY318LFixIjY2NVbvI77MCAABAgUbhAGxwNfR9992X5s2bl7cdae5dWHCNhdVYcF20aFHOSItt7Mf4hAkTqnbBtZQLvbHIPWLEiDRjxow0d+7cvI39GI/j5aqSz71UYvE+AhePPfZY7lFz3HHH5W0ENmK8Whf3C5+VCOJ88IMfTB/72MfyNvZ9VgAAAGqPTA0AOuVq6MKCa3x9LLQWRIZGNS+4lkoEm+K1Hjt2bKvsmCj/FfsRTJo1a1aqq6sru2BSJZ97qcX3UbwutVKGqfBZGTlyZA7eRIC0+GdTjPusAAAA1Jayz9R46Utfmnr06LHBrbBgdvjhh29w7AMf+ECr+1i6dGk6/vjjc4+KPfbYI330ox9N69atK9EzAqjeq6FjwTVK4VxxxRVpypQpeXvDDTcIaHSBWNSO4FOUISou9xViP8aXLVuW51XruW9NVlE1iMX7MWPGpCOPPDJvq3kxv/BZefjhh9vN6onxcv2cAwDUolr9HR3oXmWfqfGb3/ym1Q/ABx98ML3xjW9Mb3/721vGzjnnnHTppZe27EfwoiC+NgIacTXf3Xffnf/wPeOMM9J2222XPvnJT3bjMwGojauhCwuudK24Sj9E0Kk9hfHCvGo7dz0WasPf//73vH3ta1/bblbPRRddlH71q1+1zAMAoHT8jg50l7LP1Nh9993zD8DC7bbbbkt77713Ouyww1oFMYrn7LTTTi3Hbr/99vSHP/whXyl84IEHpmOPPTZ94hOfyFf4Pf/88yV6VgDlpVquhq6lq4Ki7FCIIFR7CuOFedV07vpx1I6VK1fm7aGHHtpuVs/rX//6VvMAACgNv6MD3ansgxrFIggRwYmzzjorl5kqiFInu+22W3rlK1+Zr9iL5rQFcbXx/vvvnwYNGtQydswxx6Snn346N7Btz9q1a/Px4htALV0NHVdBR8C4cDV0jBfPK0fxS3KULbrgggty8Dq2sV+tvzxHH4UI5Me/gU1NTa2OxX6MRz+TmFdN5962H0fbz2qMR1ZRNQe0asnOO++ct3fddVe7n5Vf/OIXreYBAND9/I4OdLeKCmrccsst+Uq897znPS1j7373u3Og42c/+1kOaFx//fXptNNOazkeVx4XBzRCYb84Ha7YZZddlgYMGNBy22uvvbrsOQGUg0q/GroWrwqKMl+Rxh3B+2isHYH6COrHNvZjfMKECWXZb2Fbzr2Se4mw5eKilRAlptr7rMR48bxqVEsZaABAZfI7OtDdyr6nRrEvf/nLuXzUnnvu2TI2fvz4lv+PjIy4sjMaZz766KO5TNXWiODIhRde2LIfmRoCG0CtXA193HHHtfpFtNyvhm57VVDbmvux8NnRfiCVJurSNjY25uc/ceLElvH4tzDGy7lu7daeeyX3EmHrs3riIpP43a74sxLj++67b/49rRwzkjqDutQAQCXwOzrQ3SomqLFkyZL0k5/8JN18882bnHfwwQfn7SOPPJKDGvGH369//etWc1asWJG3caw9ffv2zTfKy5o1a9LSpUs3enzo0KGpX79+3XpOUK1XQ8eVNPGLZ/Q2iFJA5Xw1dOGqoClTpmz0qqBYCI151djAPBY2I2ATzy/+SIg+FLHAWwkBnK059+J+HBG4qqReImx9Vk9kXB1yyCHp1FNPzb+jRanQ+P3unnvuyUGwSvi8b20GWgRs4+db8c/kGC/3wCUAUDv8jg50t4oJalx77bVpjz32SMcff/wm591///0tV3qG+ENw+vTp6a9//Wv++nDHHXfkZuKveMUruuHM6SwR0CjOzGlr9uzZaeTIkd16TlAtKvlqaFcF/Wvht1IDNlt67sX9OIozcyqhlwjbntUTpckqKSNpa9VyBhoAUHn8jg50t4oIasQPwAhqnHnmmal37/+cciy83XjjjblUyq677pqv9IzGsPHHbeEH5dFHH52DF6effnq6/PLL89W88YdgLNjJxqgskYkRgYtC5k4EqyZNmpSGDRvWchyovauhXRVUu5/V9rKKYtG7XD+r1GZG0tao9Qw0AKCy+B0d6G4VEdSIslNxlf5ZZ53VarxPnz752JVXXpmeffbZ3PfilFNOyT9AC+IH5m233ZYbjsbVbjvssEMOjlx66aUleCZsiygt1TYTIwIasjOgtq+GdlVQ7dnWXiLKGVamSs5I2lIy0ACASlPJ/f6AylMRQY3Itmhubt5gPIIYd95552a/Pha+586d20VnB1A9KvFqaFcF1aZt+awqZ0i5k4EGAFSiSvx7EqhMFRHUAKByroaOWvDd/Uusq4Jq09Z+VrelnKEsD7qDDDQAoFLVUnYtUDqCGgB0mvnz5+fAQtSCL4iFucik6OrAgquC6I5yhrI86A4y0EqrFMF5AACg4wQ1AOi0gEYswEX/omhuW7wAF+PdkTHhqiC62rZkecCWkIFWe8F5AACgYwQ1AOiUq1pjESgCGsWlUqIWfOzHlcazZs3KmRSudqVWszxgS8lAq73gPAAAsHn/KdALAFspFtziqtYokVJc+z3EfowvW7YszwNgyzPQjjzyyLwV0Oie4HwE5fv3798SnI/xCM7HPAAAoLQENQDYZnEFcYirWttTGC/MA9hSsZh83333pXnz5uWtxWU6k+A8AABUDuWnANhmURIlRJmOuKq1rRgvngewJfQ5oKsJzgMAQOUQ1ABgm0WN91hgjLrjxT01QlNTUx6P5rYxD2BL6HNAdxCc714rVqxIq1at2mB8yZIlrbbFBgwYkAYNGtQt5wcAQHkT1ABgm0WN97hiOhYYoyl4lOkoXnhcsGBBXnhUCx7Ylj4HhYBpoc9B/LyJPgfRTNvPF7aF4Hz3BjROO/2M9MLzazc6Z/r06RuMbdenb7rh+usENgAAENQAoHPEldIRuIgFyIkTJ7aMxyKQK6mBbelzEBkaG+tzED9vYl400YatJTjffSJDIwIaz404LDX1G9Chr+m5ZlVKj92Zv1ZQAwAAQQ2gLK1ZsyYtXbp0o8eHDh2a+vXr163nxOZF4CKumI4Fxqg7HmU64qpWi0DA1tDngO4kON+9IqDRtMNupT4NAAAqkKAGUJYioDF+/PiNHp89e3YaOXJkt54THRMBDFdM05UEPWuHPgd0N8F5AAAof4IaQFmKRckIXBSaRUZt5UmTJqVhw4a1HAdqk6Bn7dDngFIQnAcAgPImqAGUpbjKuu2iZAQ0LFTS3Vf9B1f+l5daD3pG8+xauYpcnwMAAADaEtQAoKZt7qr/4Mr/8lLLQc/58+fnev/RPLsgMhli4b9a6/3rcwDQNWopSA4AVBdBDQBqWvFV/7V65T+VE9CIjIWxY8emKVOmtMpYiPFqX+Bvbm5utR/lp6qZ3jFAV6rFIDkAUD0ENQCoae1d9V9LV/5TOVfTxuJTBDSKe0tE8+zYj9JMs2bNyg2Oq+0q2+JgzsUXX1wzwRy9Y4CuUutBcgCg8glqAJSBFStWpFWrVrUai4yB4m1bAwYMSIMGDeqW8wNKK8qDxNW0sfhU3Cw7xH70mojSTDGvmhoc13Iwp9Z7xwBdo5Z/rgIA1UNQA6AMAhqnnX5GeuH5te0ej4Ws9mzXp2+64frrBDagBkS98xBX07ZXAz3Gi+dVi1oN5tR67xig69Tyz1UAoHoIagCUWGRoREDjuRGHpaZ+Azr0NT3XrErpsTvz1wpqQPWL4EX47ne/m2699dYNaqCfcMIJreZVYzCnPdUazAHoKn6uAgDVQFADoExEQKNph91SpWvvKnLlC2DbxPfRzjvvnL74xS9uUAP9hhtuSF/60pfSLrvskudVk0KQJp5nlEZpK8aL5wGwaX6uAgDVQFADgE5tPBl1mtteRd7Q0KDhJHSx5ubmVG0iSBM/Q6J5bXHt99DU1JTHhwwZUnXBHICu4ucqAFANWhfRBIBtCGhMnTo1jRgxIs2YMSPNnTs3b2M/xuM4sHUi+2nlypXpnHPOyVfRRr3z4447Lm///Oc/5/E4HvOqSWR5RVB0wYIFuXntokWL0urVq/M29mN8woQJssEAOsjPVQCgGsjUAKBTSk5FhkaUxSm+6i/KGsR+/JE8a9asVFdX549k2AqF2uZvfetb06mnnrpBibe1a9fm0lTVWAM9srwaGxvzz5gI4hTElcQxLgsMYMv4uQoAVDpBDQC2WSywRsmpqPNfXMYgxP64cePyH80xb8yYMSU7T6iWGuhtv49qoQZ62/JaUSYFgK0TgYu42EQfNACgEik/BcA2K1wdHo2L21MYr8aryKG7a6C3Xcyv9hrohdJ2e++9d6vSdrGvtB3A1osARgTJjzzyyLwV0AAAKoWgBgCdehV5e2rhKnLoSrVaA71tabvIUunfv39LabsYj9J2MQ8AAIDaIKgBwDar5avIobtroD/22GOtGoVH0LBaa6AXSttFCbuNlbZbtmxZ1TVIBwAAYOP01ACg064ij1IwcdV4LDRGyalYbI2ARlxFHouu1XYVOXS3WquBXlzaLrIx2j5vpe0AAABqj6AGAJ16FXmUiomrxwsiQ6NaryKHUtZArwWFknXf/e5306233pqzNgoiO+zEE09sNQ8AAIDqJ6gBVW7NmjVp6dKlGz0+dOjQ1K9fv249J6pXrV1FDnSt+Pmx8847py9+8Yu5f8aUKVNassBuuOGGPB7HlbYDAACoHYIaUOUioDF+/PiNHp89e3YaOXJkt54T1a2WriIHSq9Hjx6lPgUAAAC6kaAGVLnIxIjARViyZEmaPn16mjRpUho2bFjLcQAoR5H1tXLlynTOOefk8lNtS9u9733vS1/60pfyPMFUAACA2iCoAVUuSku1zcSIgIbsDKqJMmtQnQoNwN/61remU089dYPSdmvXrs1BDY3CAQAAaoegBgAVT5k1qE6FBuDRQ2PUqFEbZGPEePE8AAAAqp+gBgAVT5k1qE6RjTF48OA0Z86cNG3atNSzZ8+WY01NTXk8ylBpFA4AAFA7/vOXIQBUeJm1uBUCGYUya3FTegoqU69evVJDQ0NasGBBmjx5clq0aFFavXp13sZ+jE+YMCHPAwAAoDbI1AAAoGzV19enxsbGNHPmzA0ahcd4HAcAAKB2CGoAAFDWInBRV1e3QaNwGRoAAAC1R1CDqrdixYq0atWqVmNRc794W2zAgAFp0KBB3XZ+UC3WrFmTG3ZvTPS1UAYK2FoRwGjbKBwAAIDaI6hB1Qc0Tjv9jPTC82vbPR7NhNvark/fdMP11wlswBaKgMb48eM3ejwaeUd/CwAAAADYWoIaVLXI0IiAxnMjDktN/QZsdn7PNatSeuzO/HWCGrBlIhMjAheFLKgIGk6aNKmlcXccB7aebCgAAAAQ1KBGRECjaYfdSn0aUNViMbVtJkYENGRnQOeQDQUAAACCGgB08Crw4EpwKB3ZUAAAACCoAUAHrwIPrgSH0pENBQAAAIIaAHTwKvDCHAAAAAAoFUENADJXgQMAAABQ7nqW+gQAAAAAAAA6QqYGAAAAUNbWrFmTe8BtTJRJjcxjAKD6CWoAAAAAZS0CGuPHj9/o8egNp2wqANQGQQ0AAACgrEUmRgQuwpIlS9L06dPTpEmTcg+4wnEAoDYIagAAAABlLUpLtc3EiICG7AwAqD0ahQMAAAAAABVBpgYAUDLr169PCxcuTE8++WQaOHBgGj16dOrVq1epTwsAAAAoU4IalL0VK1akVatWtRqLGqrF22IDBgxIgwYN6rbzA2DrzJ8/P82cOTMtX768ZWzw4MGpoaEh1dfXl/TcAAAAgPIkqEHZBzROO/2M9MLza9s9Hs3h2tquT990w/XXCWwAlHlAY+rUqWns2LFpypQpafjw4Wnx4sVpzpw5ebyxsVFgAwAAANiAoAZlLTI0IqDx3IjDUlO/AZud33PNqpQeuzN/naAGQPmWnIoMjQhoTJs2LfXs+a8WX6NGjcr7kydPTrNmzUp1dXVKUQEAAACtaBRORYiARtMOu23+1oHABwClFT00ouTUuHHjWgIaBbEf48uWLcvzAAAAAIrJ1AAAulU0BQ9Rcqo9hfHCPGrbmjVr0tKlSzd6fOjQoalfv37dek4AAACUjqAGANCtBg4cmLfRQyNKTrUV48XzqG0R0Bg/fvxGj8+ePTuNHDmyW88JAACA0hHUgA5wlShA5xk9enQaPHhwbgpe3FMjNDU15fEhQ4bkeRD/xkbgIixZsiRNnz49TZo0KQ0bNqzlOAAAALVDUAM6oFavEhXMAbpCNP9uaGhIU6dOzU3Bo4dGlJyKDI0IaCxYsCA1NjZqEk4W/860/Tc2AhrV+O8uAAAAmyeoAR1Qq1eJ1mowB+h69fX1OXAxc+bMNHHixJbxyNCI8TgOAAAA0JagBnRArV4lWqvBHKB7ROCirq4uLVy4MDcFjx4aUXJKhgYAAACwMf8pYl2GLrnkktSjR49Wt5e//OWtSuPE1Z277rpr2nHHHdMpp5ySVqxYscGV5scff3zq379/2mOPPdJHP/rRtG7duhI8G6jcYE7cCoGMQjAnbkpPAdsqAhhjxoxJRx55ZN4KaAAAAAAVnakxatSo9JOf/KRlv3fv/5zyBRdckH7wgx+km266KQ0YMCCde+656eSTT06//OUv8/H169fngEY0I7377rvTsmXL0hlnnJG222679MlPfrIkzwcAAAAAAKjSoEYEMSIo0daqVavSl7/85XTjjTemN7zhDXns2muvTfvtt1+655570iGHHJJuv/329Ic//CEHRQYNGpQOPPDA9IlPfCJ9/OMfz1kgffr0KcEzAgAAAAAAqq78VPjTn/6U9txzzzRixIg0bty4XE4q/Pa3v00vvPBCOuqoo1rmRmmqqPG/YMGCvB/b/fffPwc0Co455pj09NNPp0WLFpXg2QAAAOUoSts+/PDDm7zFHAAAoLTKOlPj4IMPTl/96lfTvvvum0tHNTY2pkMPPTQ9+OCDafny5TnTYuedd271NRHAiGMhtsUBjcLxwrGNWbt2bb4VRBAEAACoXnHx1Pjx4zc5Z/bs2bmvGAAAUDplHdQ49thjW/5/9OjROcgRTYq/9a1vpe23377LHveyyy7LARQAAKA2RMZ3BC3CkiVL0vTp09OkSZPy3x/FcwAAgNIq+/JTxSIrI66MeuSRR3Kfjeeffz6tXLmy1ZwVK1a09OCIbey3PV44tjEXXXRR7tlRuD3++ONd8nwAAIDy0K9fv/y3RtwKgYzYFsbiFnMAAIDSKutMjbaeeeaZ9Oijj6bTTz89HXTQQWm77bZL8+bNS6eccko+/tBDD+W08bFjx+b92MYVVn/961/THnvskcfuuOOOtNNOO6VXvOIVG32cvn375lu5iWDO4sWL0+rVq/PrsDF777136t+/fxo+fHjaZ599uvUcAYDqE30ECn3N2hNXr1vsLS/eMwAAoFqVdVDjIx/5SDrxxBPzFVJPPPFEmjp1aurVq1d617velQYMGJDOPvvsdOGFF6aBAwfmQMV5552XAxmHHHJI/vqjjz46By8iCHL55ZfnPhqTJ09OEydOLMugxeZcffXV6YEHHujw/AMOOCBdddVVXXpOAED121yvAX0Gyo/3DAAAqFZlHdT4y1/+kgMY//jHP9Luu++eXv/616d77rkn/3+44oorUs+ePXOmRjT2PuaYY9LMmTNbvj4CILfddluaMGFCDnbssMMO6cwzz0yXXnppqkQRtNnSTA0AgK7uNaDPQPnxngEAANWqrIMa3/jGNzZ5PFLmZ8yYkW8bE3+4zZ07N1WDKCWlnBQAUKpeA8UKvQYoT94zAACgWpV1UAOobOp5AwAAAACdSVAD6DLqeQMAAAAAnUlQA+gy6nkDAAAAAJ1JUAPoMup5AwAAAACdqWen3hsAAAAAAEAXkakBAEDVWbFiRVq1alWrsSiFWLwtNmDAgDRo0KBuOz8AAAC2jqAGAABVF9A47fQz0gvPr233ePR4amu7Pn3TDddfJ7ABAABQ5gQ1akzP51Z26jwAgHITGRoR0HhuxGGpqd+Azc7vuWZVSo/dmb9OUAMAAKC8CWrUmO0Xzy/1KQAAdIsIaDTtsFupTwMAAIBOJKhRY54bXp+att+5Q5kaAiCVTS1xqKzvz+B7FAAAAGDTBDVqTAQ0XLFY/dQSh8r9/gy+RwEAAADaJ6gBVUgtcaie78/gexQAAADgXwQ1oIqpJQ7ly/cnAAAAwJYT1KDDos9GZ86rBPpSAAAAAACUD0ENOqzWGofrSwEAAAAAUF4ENeiw54bX50bjHcnUqIYAiL4UAAAAAADlRVCDDouARi3Wf1f3HsqnxFtQ5g0AAACgdglqAFAxJd6CMm8AAAAAtUtQA+DfZA2Ud4m3oMwbbL01a9akpUuXbvT40KFDU79+/br1nAAAAGBLCWoAyBooKSXeoHtEQGP8+PEbPT579uw0cuTIbj0nAAAA2FKCGtANXB1b/mQNANUu/q2JwEUh+yyCtZMmTUrDhg1rOQ4AAADlTlADuoGrYyuHrAGgWkXwvO2/NRHQ8O8PAAAAlURQA7qBq2OBSssgC7LIoPr7Q+kNBQAAVBpBDegGro7tOM26oTwyyIIsMqj+/lB6QwEAAJVGUAMoG5p1Q2kyyIIsMqi9/lB6QwEAAJVIUAMom2wJzbqhtBlkQRYZVD79oQAAgGomqEG3eOSRR9LixYvT6tWr06OPPrrReXvvvXfq379/Gj58eNpnn3269Rwpn2wJizEAAAAAQHsENegWV199dXrggQc6PP+AAw5IV111VZeeE+2TLQEAAAAAlCtBDbrFeeedt8WZGpSWbAkAAAAAoNwIatAtopSUclIAAAAAAGwLQQ0AAIBOsn79+rRw4cL05JNPpoEDB6bRo0enXr16lfq0AKBLrVmzJi1dunSjx4cOHZr69evXrecEVC9BDQAAgE4wf/78NHPmzLR8+fKWscGDB6eGhoZUX19f0nMDgK4UAY3x48dv9Pjs2bPTyJEju/WcgOolqAEAANAJAY2pU6emsWPHpilTpuQecdFTbs6cOXm8sbFRYAOAqhWZGBG4CEuWLEnTp09PkyZNSsOGDWs5DtBZBDUAAAC2seRUZGhEQGPatGmpZ8+eeXzUqFF5f/LkyWnWrFmprq5OKSoAqlKUlmqbiREBDdkZQFf412/bAAAAbJXooRElp8aNG9cS0CiI/RhftmxZngcAAGwbQQ0AAIBtEE3BQ5Scak9hvDAPAADYespPQRdasWJFWrVqVauxqC1ZvC02YMCANGjQoG47P6hl7X1/Vtr36Jo1a3JDvo2JurWRBg5A1xo4cGDeRg+NKDnVVowXzwMAALaeoAZ04YLpaaefkV54fm27x6NpVlvb9embbrj+urJaNN0agjlU+vdnpXyPRkBj/PjxGz0ejfrUsAXoeqNHj06DBw/OTcGLe2qEpqamPD5kyJA8DwAA2DaCGtBFYlE/FkyfG3FYauo3YLPze65ZldJjd+avK5cF0+4O5kC5fn+W6/doZGJE4KIQMIzvr0mTJuWGfIXjAHS9aP7d0NCQpk6dmpuCRw+NKDkVGRoR0FiwYEFqbGzUJBwAADqBoAYVoedzKzt1XneKBdOmHXZLtWJbgjnQ3Sr9+zNKS7XNxIiAhuwMgO5XX1+fAxczZ85MEydObBmPDI0Yj+MAAMC2E9SgImy/eH6pT4EaWywGANhSEbioq6tLCxcuzE3Bo4dGlJySoQEAAJ1HUIOK8Nzw+tS0/c4dytQQAAEAoFQigDFmzJhSnwYAAFQtQQ0qQgQ0tuWq/20pX1XJpa8AAAAAAKqJoAY1YVuyN2R+AAAAAACUB0ENasK2lK9S+opqt2LFig0atS9ZsqTVtq0BAwakQYMGdcv5AQAAAECBoAY1YVvKV21r6SvoTmvWrElLly7d6PGhQ4emfv36tQponHb6GemF59e2O3/69Ontjm/Xp2+64frrcmBjS4MiAiIAAAAAbC1BDYAqEgGN8ePHb/T47Nmz08iRI1v2IxgRAY3nRhyWmvoN6NBj9FyzKqXH7mwJZGxpUKQQEIFKIngHAAAA5UFQA6CKRCZGBC4KC60RVJg0aVIaNmxYy/H2REBjazKStjQo0jYgsq0sNNMdtiajqTibqZL5HgMAAKDcCGoAVJEoLVWciREioNF2rLNtbVCkVAvN0F3Bu0pe4K/lYA4AAADlS1ADgIpU6iyRUnL1fGmUInhXSrUazAEAAKC8CWoAUNFqbaG5lq+eF8wpjVr7HgMAAKC8CWoAQAWp1avnazmYAwAAAPyHoAZ0sZ7PrezUeQDVcPX8mjVr0tKlSzd6PJraR4+YWg/mAAAAAK0JakAX237x/FKfAkDZiYDG+PHjN3p89uzZ7Ta4r/RgDgAAALBtBDWgiz03vD41bb9zhzI1BECAWhGZGBG4KPTDiPJRkyZNSsOGDWs53pna68dReOzibVf35NjSDBUAAACgNUEN6GIR0HBVMUBrsXDfNhMjAhrtZWd0dT+OzfXkKIcMFQAAAOBfBDUAgKq2pf042vbkqOQMFQAAAKg2ghpQph555JG0ePHitHr16vToo49udN7ee++d+vfvn/9/+PDhaZ999unGswSoHOXQj6M7M1QAAACgGglqQJm6+uqr0wMPPLBFX3PAAQekq666qsvOCah85dJbAuj49+imvj+D71EAAKCWCGpAmTrvvPO2KlMDoKt6S1g0hdJ+j7b3/dlV/V8A4P+3dx7wchXVH5/30hN67xBCb6EjHUSKNEWU3ksgkKAgonQRFUFAQiih86epiEroVaQjPfQ0AqGGnoT05O3/850wm3n33bb3bn37+/LZT3i7d/feO3fmzJlzzpwjhBBC1CtyaghRp5BGKm8qqdZp35T1OCFEc9eWkFNDiOap/yKEEEIIIYQQ9YqcGkJ0YnqNe6LWlyCEqEPqobaEqN90RkplVHs0RoUQQgghhBAiGjk1hOjETOu7jWnrtVCqnRpygAghROcjSzojpTISQgghhBBCCFHPyKkhRCcGh0bWSM9SUlIpfVXtUSS2EKIc6YyUykgIIYQQQgghRL0jp4YQIpS8OzdUz6N6KBJbCJGE0hkJIYRoVBS8I4QQQoiGcmqcd9555l//+pd55513TK9evcwWW2xhzj//fLP66qsXj9luu+3M448/3u57xxxzjBk2bFjx7/Hjx5uBAweaxx57zMw333zm0EMPtb/dtWtd374QDZG6Kip9ldJZVQ9FYgshhBBCiM5InuAdOTaEEEKIzktdW/VxVhx//PFmk002MbNnzzannXaa2Wmnncxbb71l+vTpUzzu6KOPNr/73e+Kf/fu3bv4/3PmzDG77babWWqppcwzzzxjPvnkE3PIIYeYbt26mT/+8Y9VvychmiF1FaieR/VRJLYQojOhyFwhhBB5gnc0JwghhBCdl7p2ajzwwAPt/r7xxhvNEkssYV566SWzzTbbtHNi4LQI46GHHrJOkEceecQqNeuvv74599xzza9//Wvz29/+1nTv3r3i9yFEM5LXKSKEEKJ5UWSuEEIIHwXvCCGEEKJhnBpBXLTeIoss0u79W2+91dxyyy3WsbHHHnuYM888s7hb49lnnzXrrrtuuwXuzjvvbNNRvfnmm2aDDTbocJ4ZM2bYl2PSpEkVvCvR2bHRQmU8TgghOlPkPSj6XgRRZK4QQgghhBBCiIZ3arS1tZlf/OIXZssttzTrrLNO8f0DDjjArLjiimaZZZYxr732mt2BMXLkSFuLAz799NMOi1v3N5+FQb2Nc845p6L3Izo/GOKIGsXIkhaO53vNTinFw1VoXIjOEXkPKmovgigyVwghhBBCCCFEwzo1qK3xxhtvmKeeeqrd+wMGDCj+Pzsyll56abPDDjuYsWPHmn79+mU616mnnmpOOumkdjs1ll9++RxXL5pxpwXOMwxxYfnAMdydfvrp1iHno4jkuajGhhCdj1Ij70FF7YUQ9Vi/BaSzCSGEEEIIUTsawqkxaNAgc88995gnnnjCLLfccrHHbrbZZvbfMWPGWKcGKamef/75DgsXiKrD0aNHD/sSzU05dlqw2I1a8OLQWG211cpyrZ2NtEXGQYXGhWgsFHkvhGj0+i2gGi5CCCGEEELUjrp2ahQKBTN48GDz73//2/z3v/81ffv2TfzOq6++av9lxwZsvvnmdjHy2Wef2SLj8PDDD5sFFljArLXWWhW+A9HIaKdF7VCRcSGEEEI0wi4y6X1CCCGEEEJUn671nnLqtttuM8OHDzfzzz9/sQYGhuNevXrZFFN8vuuuu5pFF13U1tQ48cQTzTbbbGPWW289e+xOO+1knRcHH3ywueCCC+xvnHHGGfa3tRtDJKGdFkIIIYQQnR/tIhNCCCGEEKJxqGunxpVXXmn/3W677dq9f8MNN5jDDjvMdO/e3TzyyCPmkksuMVOmTLF1L/bee2/rtHB06dLFpq4aOHCg3bXRp08fc+ihh5rf/e53Vb8fIUTlIfXcuHHjzNSpU63jMwrS0/Xu3dvuAFtllVWqeo1CCCGEEEIIIYSoPdOnTzfjx4+PPWaFFVYwPXv2rNo1CSE6QfqpOHBiPP54cr0DIurvu+++Ml6ZEKJeGTp0qBkxYkTq4/v372+GDBlS0WsSQgghhBBCCCFE/YFDY8CAAbHHXH311crUIUSdUddODSGEKBXq8JS6U0MIIeq9kHFYfSf/Xx/VdxJCCCGEECId7MLAaRFXQ5VjhBD1hZwaQohOBamklE5KCNGZHBoHHXyILWQcBouuIN269zC33HyTHBtCCCGEEEIkQFqp4C4M1VAVov6RU0MIITxap32T+VjV8xBCpN1pkXa3Bd/DoTFt5W1tIeMkWqdPNObdx+335NQQQqRBucSFEEIIIUSjIaeGEEJ49Br3RM3qeeRxqAghGnOnRdJuCwcOjbY+i1XsOoUQzYtyiQshhBBCiEZDTg0hhPCY1ncb09ZrodSOBd8JkreeRx6HihCiPil1p0Vwt4UQQlQa5RIXQgghhBCNhpwaQgjhgUMjazR03noeeRwqQoj6RjsthBD1inKJCyGEEEKIRkNODSGE6AQOlVJSUil1lRBCCCGEEEIIIYRoVOTUEEKITkKenRuq5yGEEEIIIYQQQgghGgE5NYSoAtOnT7dFGF2uYv9fl6eYrf9C5CFt+qqw1FVKZSWEEJWd/8PQ/C+EEEIIIYQQpSOnhhBVAIPGgAED2r1HEUYHxRmVt1jUMn1V3noeSn0lhBDp5n8fzf9CCCGEEEIIUTpyaghRBYjExHAR97kQjVzPQzs9hBAifv5nhyYBDaeffrotwuw+F0IIIYQQQghRGnJqCFEFSC2hSEzRmcmT+koIIZpp/sehIZ1ACCGEEEIIIbIjp4YQQoia7/QQQgghhBBCCCGEECINramOEkIIIYQQQgghhBBCCCGEqDHaqSGEEKKmlFI8POxYFSkXQgghhBBCCCGEaB7k1BCpaZ0+sazHCSEE5K2xoRodQgghhBBCCCGEEM2DnBpNRhbHxIILLmi6de9hzLuPpz4Px/M9IbJQimNMTrTmKTIeVWg8T5HyPLtExowZY8aNG2emTp1qxo4dG/m9fv36md69e5u+ffuaVVZZpSznFkKISjJ9+nQzfvz4yM9XWGEFWwRdCCGEEEIIIWqBnBpNQh7HxJJLLmluufkmM3Fie+Px+++/b/7whz+Y008/3ay44oodzsf3hKh0PwU50Zq7yHie7+fZ5TF06FAzYsSI1Mf379/fDBkypCznBqXdEkJUChwaAwYMiPz86quvNquttlpVr0kIIYQQQgghHHJqNAl5HRP8G+Wk4Hta2Ipa9VOQE03UYpfI4MGDS96pUa5zg9JuCSEqBTsxcFxEzcF8LoQQQgghhBC1Qk6NJkKOCdEIqJ+KapJnlweppPx0UtU8d960W0IIEQeppYLzbbXmYKW+EkIIIYQQQiQhp4YQQgjRgOR1igghRD2i1FdCCCGEEEKIJOTUEEIIIZoMFSkXQtQrSn0lhBBCNBYTJkwITSHt/+uj9NFCiHIgp4YQQgjRZCgdlRCiXqll6ishhBBClO7QOOjgQ8ysmTNCPyc4IUi37j1sLU05NoQQeZBTQ4gS8zuHRRwov7MQopHIW6RcCBGPIhaFEEII0Qyg7+DQmLbytqat54KJx7dOn2jMu4/b70n3EULkQU4N0RTYiTPHcWH5nf2IA+V3Fp2hn4vmIU89jjFjxphx48aZqVOnmrFjx0Ye169fP9O7d2/Tt2/fdgXV86a+Svt9pc0SjRix2GjOGJBDRgghhBA4NFTvTwhRTeTUEJ0aFtoYCogESAvH872o/M5hKL+zaNR+HjReCZHE0KFDzYgRI1If379/fzNkyJDi33l3feT5vhwqot4jFhvNGQNKISGEEEIIIYSoNnJqiE4NC2wW2mFRh8HCk3ERh2H5nTs7pUTzK/K/cft5rY1oovEYPHhwyTs1ypn6Ku33w77byA4V0Xg0WsRiqc4YUAoJIYQQQgghRC2QU0N0elhkRy20VXiyPFH/oMj/2qJ+LqoFqaT8dFLVTH2V9/uN7FARolo0mjNGCCGEEEII0XzIqSEaAtUKqO+of1DkvxCi3mlkh4oQQgghhBBCCCHmIqeGaIqaGM1KVmeQov6FEKK+HCp563moHogQQgghhBBCiM6CnBqiKWpiNBtyBgkhROeilvVAhBBCCCGEEEKIekJODVH3aNdAYzqDlDJMCCHKR556Hnm/r10eQgghhBBCCCHqCTk1hOik1MoZpF0iQgjRueqB5NnlMWbMGDNu3DgzdepUM3bs2Mjj+vXrZ3r37m369u3brhC9HCpCCCGEEEIIIYLIqSGE6HS7RET10c4cIToveXZ5DB061IwYMSL1ufr372+GDBlS/Ftps4SoHBMmTAjV1/x/g0hnE0IIIYQQ9YCcGkKIsqOUYc1Dnp05QUOKEKI+ybPLY/DgwSXv1Chn2i0hRLRD46CDDzGzZs4I/ZxAlKg5nOAVOTaEEEIIIUQtkVNDCCFETXbmyKkhROeHVFJ+Oqlqp91S+iohwmEOxqExbeVtTVvPBdPvtnz3cftdOTWEEEIIIUQtkVOjBKbPnm7mL8xvWlpa7N+z22bbV5eWLqZbl27tjoMeXXp0OLa1pdV079I907EzZs8wBVOw7/EZzGmbY2a1zcp1bFtLm5kxZ4b9vEtrl7nvFdrMzDkzOxzLe3zWrXXe/fI399FiWkyPrj06HNu1tat9+b+b5thCoWCvC3p27Vk8dtacWfb9Qkuh+F7csXMKc0J/t621zf6/w7V71DWEPaNSnn2efhL2PLP0E9rZx7V7mn5ij21ts5/7zz7uedJP/D7F96OuIc2z51jaLW0/iXr2qfqU/W+W/f8W083e49z359hXi5nbXuV69py7zcwybWa2afVEc5uZGXoN9tiWtnbXEPbs+Zt7o+3C+gltP++e20zBzMZsEbgG3mszLaZr8b793/V35rjn6foJDo1VVl0lup8wDs2ckGvgTN1CrsE7tlCw36ctfPg+v9NiutiXu17bZq3t26zNtuXMDsdGPXu+zzP1r6FUGWH7mvH72rw+1RLx7B38Jv06+OzntkEh8Izm/q6xbecd2zK3zea2UWuKZz+r3fXGyoiQce+effvrDetTEc/+uzmKtpzXZvOeUavpHvK73rGFgpnZNrevpekn8353Htxvx74a009Cxufc8T0zUp60f/bfna9QmCcjvuurHZ9RiIxomTc+k/tJ+LPnu3PvsVCCjJh3z2n0iLB+4suIeX11jveMomVEh/FpCiXt3nByzSdpLgkb98FrKGUuKeonJcmI9rrMXLk4O5WMCO+rs7/rq8n9xLW7P5dklRFhekQpOkdaPSLs2FLmkrnPc1buflItHTLqWNvXGGM9Fyw6DpNkhL9wLGWtEfbsi/NyB70nfH4I66uVXGuUokOW+uw781oj6tln7Sf87fpqej2ivfwrh4yoRT+ptYyopj0ibn4ox1wiGVFZGVG6vmnKKiNKsUekfvbf6YS0kWSEZES9PfvOrkeUQmlHNzmH/PsQM2nGpOLf/3r7X+Zn//iZGfbisHbHHfSvg+z7n0/9vPjevaPute9d+r9L2x175F1H2vc/mPRB8b1H333UvnfB0xe0O/a4e4+z74/9al76hifHP2nfO/fxc9sde+KDJ9r33/zszeJ7L3z8gn3vjP+c0e7Yd/u9a37+1M/Ny5+8XHzvtQmv2WNPfujkdsee/djZ9v1nP3y2+N64SePse4PvH9zu2POePM++//h789LSvPfNe/a9AfcMaHfsxc9ebN9/cMyDxfc++fYT+95hdx7W7tjLX7jcXu8Xi35RfO+raV/ZY/e7Y792x1778rX2/dvfvL343tRZU+3331z7TSs8HDeNuMkey78OPuc9XnzPwe/xHr/vw/l5n+tx3DXyLvse1+3DffE+9+ng/nmP9vChvXif9nPQrrxHO/vwHHh/5Bcji+/xvHhv6OtD2x3L8+V9nreDfsB7v3nkN+2OveS1S2ybvf7V68X36F8cS3/zoT/yPv3TMf7b8fb7v33xt+2OpZ9zLP3ewXjgPcaHz82jbrbvM54cjDPeY9z5MC55n3HqYPy65+lz46s32j7x2RKfFd/DaPFuz8vtyxmk4Kuuz9n3vuz6VLvfcL+bVUZ8ueiXZuxCN5nPuz3U7tj3e1xvzzerZV6fmtzlLXvsByvMkxtRMmLiQhPtvUXJiNETRxffm9r6rj3Xx93/2e7Yj7vfbt+f2jqv/02Zb4r93SgZ8cqXrxTfoy+GyYir37ra9onJ3edd78yWL+y5xve4od2xn3V7wL4/sfu8fv3ZtM/s98ct+Ld2x37e7VF77Ddd5l3DHDPFttlba73V7tgvev3PHvt11+eL72HMc8+evxxf9nzRnu/OcXfmkhG02+yu8xbcXCfn4rp93u9x7XfPfl4E+VOfPGW//+HyH7Y7lvbiWNrPMbnLO/a9T/u0/90xq42xbTG95dPie1Nax9hjP+k+b7zAR93/Zo/leSfJCOYW3meuSZIRn3Yfbs/3bZd5z3NGy2f2vQ96zJPBQD/nnp+dMG/eYTxwLOPDh/Fj+0mXeTUcvprxlf3+22u+HTj2sbn9pOuLxffazDTv2c/j3+/+2z77r3q+kkpG0GYTlprQ7jd4j2M5h4Nzz332j7U7dtyCt80934x54/6bHm9/d2yyjPh64a/tPQf1CNqWY2lrB8/A9pPuw9sd+8eX/2h/Y0qfKellRNcPM+kRUTJi/IrjbbvRl5NkxITej9s2Y4wUj+0204xc72szev0ZZspaexZfH6y3uHlng8nmk3VXKb43eeVNzDtrvGNOfPrEVHqEG/dheoQvI+hVrp84JwQgc3jvi67tU/dxfquftExNLyNa58073D/fpz3SyAjal3Zud+z8/7bHppERH85/tz3fyG9GllVGOD3ixc/njU/mNt5jrsuqR6CHB/UIxtncueS21DKCdmv3u09cana9YVdzwf0XmIcffti+7nvoPrPdFdvZF//Pe6+//rq5+OGLzZ637Gl1j3LqEaWuNRjfQT2iFBlRylqDvsD7/lqDeYV2ZJ7xoY9xPvqcg77Iscxf1Vpr8D46fNa1RpyM6MxrDWR81rVGlIygr45ddWxqPWL8Av8sq4wo11qD9/76+l/nXe+cGcVjfYc6x/BerWVENe0RYTKiHHqEZER1ZARrOp8PetycWo8oh4xIa48oRY+Y0WOGlT2SEZIRDsmI2ugRSWinhhBCCCGqBulLWmZONS3dZpqWaRNN66y5RtbWLl+blq4zTUvbdNM65bv3VFhe5KHFmGnzzTatrQXTNmNeCquZ3bqbaV3aTO9ZvU3bnO8i1FummNmt7aN7Re2KVn/51ZdmyrdT7HsupWFhcsG+N3H2RDNq1KhiOsNawzVed/31ZsJCn5pPHvnEPDRhruOR3R+j154bOHD+neeb1ra5sWQTlpxgvljqC7PzijvX9LpF89I2p8188cUXxXEEkyZOMlNmTrE1kGZ/PtussMIKNb1GIYQQQogkWgp+/h0RyqRJk+yiacKXE8ziCy/eqbZyocwedcxR5vIrLjdrrb5WSVu5xo4ZawYMGGCGXTXMrNRvpapu5Xpn1Dtm0HGDzDVXXWOLTpe6leuNd94wxx9/vLnmymvM6quv3jRbucaMHmMGDRxkrr76attupWz3fOPtN8xxxx9nrrriKrPmGmumep7+lr93Rr5jBgwcYK64/AqzzprrlPTs6af0tcuuvMymMqrElj/Xp6au+SObhiFNypAuU74xfd66y7bnCiuvkPnZc39HH3O0+XbNXU1bn8VTpZ9qmfKZ6fP2vebaq64tFl4PPnv7uwOOtu22+mqrh/aT98a+Z4495lgbqTynzyKp0k9hcO791vDQ33XPc9yYcWbgsQNt20Sln3J9auoae5hCnyVTpZ/qMuVrM99b99rf5XkePfBoM2WN3Yzps3RiypCWKZ+aPu/ca64dNjdagT41ea3dTFufhVOln2qZMsH0fuduM+yKYWatNdbq0KfSPvvRo0fbcTh1LdfXktNPdZnype1rVwy7wvbr44873kxb88cJKUPm/m7rlK/M/G/dZ9sMkPlT1tzNtnma1DKtUz438711n7nm6rnyNuuW8G++/KZYlNamkWgxpqWtxevXBVNoLfA/prUwbyNplx7dzPXXX2umTJ5ijjv2uO/66qKJ6afoK7TZVVddZdNPIfOnrTGvzZLST3WdMqk4vrnfgccNDPTVaBnhj895fe2Hpq3PoqnST5kpn9i+yhxFn+L73661m5nTZ+HE9FN2fL493Fx2xdzxyRjn+7Tb7D4LJqaf4vvcN+ObO2zfV+PTTyET53vrnnbjPuuWcKefzO2rSySmnzJ2fN9THJ9O/n27FnJ10Xb9JOzZt0z53PR+Z7i5/PLLzbprrptrS/i7Y94N7atp5hJ+l746dY0fmUKfxWP7iXv2XaZMNPO9dbf9Puc/9rhjzZQ1djemz1LeseEyIryv0maLpEo/5dr96iuvNmusvoZ9Cxkxeepk8+EHH3Z49vSpri1dTd+V+pqePXsWn/1nn31mjjzs6GLR6rQygqLV1994rVl8icUzp5YZOXJkSXMJz7N1ypdm/rfubydXp/Xd2hS6z2e6zJg279iWudF1LQV+o8W09ZjPtMycbHq+97i58oorzdprrF2ztBFvj3w75VzipZ/y+uqKK6+YOW1E+/EZ1HvC0woxBwf1HqWNyHase/b+XBscczzjlVdZuS7ST735zpshfTU6/ZSv9/jrHaWfqk97RNyzV2qZxrBHOBsDeg9jtJS5BDtSvaWf8tfQq666qmSEZETdPfvOnn5q+pTp1gZPsNMCCyxg4tBOjRKg47kHCX5HCh4XpBzH+gPKwSB1AzXrsSi0dFL/MzpS2LX5HbHSx9LWYccyyOygKrSkOpb/gr/L94mYS3qeUb9bD88+Sz8JtnNYu0f1Eytw2lpz9RO+n+YaotqdY4Pvl/rsU/cp+1/ItXnGjXI+e84910DX/vjWiGvgWH8xGvXs7WTcpUeHdnbH+jkL5xrMQp5dyFQR9bvu77T9xI5D06WYET7pGvy253ny/a7Tp5o2My+lShSt06cWI2Xn/S6/1/58cc/ens97pllkhO1rXo2JqD4V9uzt72J4Djz7dsbdwO8Gnx/f5fg2LwNlXLtzrH+9cTKiw/e9Zx9VVN4vLH/GqWdEFpb3I0qjnlFoX/Vkfrv37UIrcGzE76IUd+yrMf0kZHzOfUbBvhb17Lt1mKPm/m7YPYdcQyF8fIb3k/Bn777bvq8mjc/WsuoG8/pql3R9NTg+i46P7qmePd+n3bLOJW7cB6+hlLmkqJ94lTKSZIT/jDg336c92lLIiPC+2jWkzeLb3Z9LkA+fffyZNXBE4YyN7tlP/3Z65qLV076dZnouk043iNI55rYZfS2djAi2p23DnouaQp/FzOz5E657ytw28+vTQbX1TdvXUs0l4c++lLVGaLubKL0nTOeY+3yC11vJtUYpOmSpMiLrsVCOY1ftu2rRkenPwaefdnpxDmanRjnWpKWuNcKeZ3hfDe8nc8dn11xrjXrsJ1APx1bKHlFNG4NkRPntEeXQN+utn7i1bvD4eu0nUA/HSkbMRTIiXz+ZbuY6U9Igp0YTMn36dDN+/PiiEuv/65RYoueEEKKewdhNlC5GrbRwvPP6i+rjF5UPA2OKi8AVQjQu6JJuF0PRYHp6e4NpGH7RaiFEZWCdFzbXag4WQgghRCMhp0YTgkOD9AI+LDaD0XOdDTlzRDPQTP08KvI/zIAWjPqXU0MIEceYMWNsbvmpU6easWPbF6j16devn+ndu7fp27evWWWVVdp91jrtm1TnSntcoxtNZTAVQgghhBBClAs5NZo8ei7q885IszpzRHPRbP08LvI/jQGtlELUKlotRPMwdOhQM2LEiNTH9+/f3wwZMqTde73GPVGBKxNCCCGEEEIIIadGExK15biz06zOHNFcqJ9XLnWVn76qHkjrZJEzRojSGTx4cMk7NYJM67uNaeu1UKqdGnKAVGbHYhidaceiEEIIIYQQzYqcGqJpaFZnjmgcyrFrQP08HWmKVselr6olqiUiROUhlVQwnVSp4NDIWh+ilJRUnTF9Vbl3LPp0th2LQgghhBBCNCNyagghRI3pDLsGGpFGLVqtWiJCdH60c6P6RcqF6OxMmDAhMpjD/7fegjmEEEIIIcKQU0MIIRrQSA1aaDYveWuJCCHqm7Spq+otfVWY0bTaBlMVKRcifGwedPAhZtbMGZHH+DXY/AAadFTpm7VFafWEEEKIjsipIYQQdYCM1EIIIcqRuqpejaZxBlMhRGWN3DgbGZvTVt7WtPVcMH2q03cft9+VU6O2KK2eEEII0RE5NYQQQgghhOhEpK2zUc56HKUaTX2DqRCiOkZuxmajOUyF0uoJUS87TEHZEoSoH+TUEEIIUZYi5UIIIeqDWqajktFUiMojI3dzobR6QtTHDlNQWj4h6gc5NYQQoolRkfLaIUdSc6HnLeqxJkc91ePoDPU8hKgWMnILIUQ+lJZPiMZHTg0hhGhiVKS8+siR1Fzkfd5KzSOqXZOjlJRU5UxflQfV8xBCCCFEFrTDVIjGRU4NIYRoclSkvD4cSUnOJDmSmvN5y6khqk0j7tzoDPU8kgo/hxV/FkIIIYQQolmRU0MIITyUIkbU2pEEcibVJ2nHfPA4PW/RGVNX1WP6qkaOtkwq/BxX/FkIIYQQQohmQ04NIYRQSiAhRJnlg2SDaMbUVaWkpKqX1FWNUvjZHSOEEEIIIYSQU0MIISyqLSFE594tUW35INkgmpV62rnRSKjwsxAirF5QmO7h/+sj3UMIIUQzIaeGaBj8XMNhypzyDIu8qLaEEJ1zt0Q58uZLPjSPE0tUJ31VvaWuahSjJchwKURzyIaDDj7E1gsKg6CKML2HIAzJh3S1imQ/EEKIxkZODdHQuYZ9ZU55hoUQovOSZ7dEPRUDFulQyq/mTF9VSkqqsGMbLfVVFqMlyHApROcH3QXZMG3lbW29oFTO/Xcft9+TbEhXq0j2AyGEaGzk1BANmWs46nMhhBCdF+2WaB6U8qs5ybtzo9F2fpRqtAQZLpsbpSNqPpANeeocNTNJtYpkPxBCiMZGTg3R0LmGhRBCCNE5kROr+UibuioqfVWjpr6S0VJUOh2REM2IahUJIUTnRk4NIYQQQgghREOnrmrk1Fd5zy2agzzpiIQQQgghOhtyagghhBBNRilFlVWAWQjRDNQy9VU97RoR9Y929gghhBBCyKkhhBBCNA1Zii+DCjALIcrtCK03h2ktU1/lOfeYMWPMuHHjzNSpU83YsWMjv9evXz/Tu3dv07dvX7PKKqukOldnZ/r06baQcBzk3CeFjRBCCCGEqC+ayqlx+eWXmz//+c/m008/Nf379zdDhw41m266aa0vS4iGWOyFFSHUQk+IzlF8GVSAWYjy0qjG/Wo6TZ3DtB7S49Qy9VWe77KeGTFiROrjWQMNGTKk+Hczp75Cxx0wYEDsMRQZVv59IYQQQoj6o2mcGn//+9/NSSedZIYNG2Y222wzc8kll5idd97ZjBw50iyxxBK1vjwhGmax5xchrORCTw4VIapffBlUQFGI5jbul9tpmsZh2sj3XeuUgIMHDy55p0a5Ul81+i4RdEl02bi+yjFCCCGEEKL+aBqnxsUXX2yOPvpoc/jhh9u/cW7ce++95vrrrze/+c1van15QtQlwcVe2Oed0aEihBCdjWbbNVDL+25m436c01QO08qkBMRJkMdRkCf1VS13ieRxqEyYMCH1WHMBNtqxKIQQQghRXzSFU2PmzJnmpZdeMqeeemrxvdbWVvODH/zAPPvss+l/aMoUY7p06fg+7/kR4xwXRWurMb16ZTt26lRjCoXwY1tajOndO9ux06YZ09YWfR19+oQe2zJ1quk5Z479t3gf/rHTpxszZ0663006luvlumHGDGNmzy7PsbQv7QwzZxoza1Z5jqU/uL5SyrEcx/FR9OhhTNeupR9LG8yYEf7MoHt3Y7p1a3esvTRjzGrLLtv+d/1jeWZxfZjjOP67Y0PPHXZsW5tZYdFFzTWXXBL6s4WuXec5VOiP9MsoaAPawn6xMHdslHBsZJuVMu5zyIjYNqukjPDJICMi2807tmXGDHvMnFkzTNvMuX3OZzqGnu/oPnumaW0rmNZZM8J/1xv3LTNnxrdbgoxod+3cdxVlRGi7pZQRfKeL/0xLlBGRzyxGRoQSlBHI9yi8cd9aKEQ/s8Cx/rgPve7Ase6YsL42p7XVzOr63fUWCqbnrLltFtrXAjIits1SjPvi94Pt6R0bvPZCS4uZ0e27e6N7BO6n3XVPm9bOaNqD98Nb19BzZnzXz6zBtFs3M2nixPB2azFmerd547PHrJmmpRAzPiP0iCy6Qbs29wkcu2DXrmb+Ll1Mz9H/mfsxY/O7cd+tra39WPmObt272+9Z2ZhTj1hyvvnsqx3f3bc17q+0UkcZMWVK8f6mtbUZ10pd58w2XWN0pNl+e86aFStXZ3Xtaua0din+bnf/mX0357jv+sd2aZtjuoW0g3vm7e4lICM6jJMQGRE1Rmd36WJmd5krp1rb2kz32fPO066/cT9eO7QU2kyPGBncNse7l7a22DYLkxHBc7vvz5o9K1SehLUZ4953gPl9GkM6KXN/9atfzdV3unQxBSd7vuvftn+l0Q0iZCrn6z5njpnipb7qOWvGXGEQ+gVj2t3N1Klm8JFHmvfef99MnTbNvDduXPEjfmKOe8Y4XpZbzvTu1cushDPvu37epa2t5F0ifCerQ2X9ddYxl5x/vnVoHHX00WZWYH70ZcQF557bQUYgH6695pq5jo0arTX8cTIdGfHdsVEyojg+eX0n37vMmWO6h/RzR7txn6S7h6w1IsmhR8TN4WEyInIuitAjQinl2JxrjSiCukFwvvdpa20x7Xphk9gjEo/1dIPctgvZIyLtEVFjNEqPCB2jVVhrBI8NXrcvT6L0iKJc9duoRjKiWvaIZpERuY+VjCiLzTKxzwUpNAEfffQRI6XwzDPPtHv/V7/6VWHTTTftcPz06dMLEydOLL4++OAD+/2Jc4dcx9euu7b/gd69w4/jte227Y9dbLHoYzfeuP2xK64Yfexaa7U/lr+jjuV3fDhP1LFcnw/XH3Us9+1Du0QdG+x6P/1p/LHffjvv2EMPjT/2s8/mHXvccfHHjhs379iTT44/9o035h179tnxxz7//LxjL7gg/tjHHpt37GWXxR97zz3zjr3hhvhjb7993rH8f9yx/JaDc8QdyzU6uPa4Y7n373jvH/+IP5Y2ddDWccfyrBw8w7hj6QMO+kbcsfQtB30u7lj6rE/csRllxMiRIwtfd+tWExnBubfddlv7b6VkxOS4Y40pbHjy/xVfD6+2SWoZ8c1eezWtjPj1OuvMfWYNJiO45gEbbFATGfH39Xco9rPvHze0JjLCjoWUMuKF5dZoNza+6jV/ooz49NNPbRvPXHbZyGOnr7KKPYYXx8fJiI8WWKzdNbyxZN+61yNGvfKKvbeHHnqocP+SS9ZERoy75555cjVBRhyy98+L7fuXbfaNPXbAngOLv/vpWWfFHnvCXicWf/fsXY6KPfaUPY4vHsv/xx37yXnnVURGcO/uGg46ML7NblhxxcLGAy+yx/70sD/EHntT/+2KbTb20UfLJiPuWnur4rFbnHB17LGTdt45tYyo1Frj7fnnL7YZL8Z21LFjFl3GHlvswznXGl90714YOd98hRcWXcL+rnv9e+V17Pthrw979CjOcaNHjy68wrVEHMvroosuKtx555123H+0++6xbbzVUX8sXsPdq8foPZ6MmDZtWuHrAw6oiR7BeEgrI8bfdFNRrzt/671Sy4jfbr9v3esRpciIRltrICN4Zm6Mxh37ZN/+7cen7BFzkT2ipvaIUvSIatgjbJvEHDts8x8XrzdJj/jyiCOawh5hkYyYi2RE1WyW2N6tDX7ixEISTbFTo1TOO+88c84559T6MoQQQgghypcSyEXChNCje/dOnRpo1VVXLUZbvVvrixFCmEVnzrSvr+e0tCuSvtzM2Wa1b78N/c601lbjYvdsKinGdMSxQD3FIl461S+7d7cvnzbzXWSjMebTHj3NqMBOK3e9MHL0aDP+1VfNJ598YqY9+6zZPrgry+Ojp582S0+fbtZYY43IY4QQQgghROm04NkwTZB+ilyqd9xxh/nxj39cfP/QQw8133zzjRk+fHi742fMmGFfjkmTJpnll1/eTPz4Y7PAAgt0PEGTbuUaPXq0LU7I9m9rLAgeq61cdbeVK/SZVWm756i33zYnDBjQ8dx1viU8ss2qsN1z1KhR5oQjj4xuswrKiFEffmhrmtjaJcsvX7KMiGw379jRr79uBh93nJmyxq6mrfc8g0Zs+qmpX5g+79zX8Xe9cT/6jTfM4IEDo9stQUa0u/b11quqjAhtt5Qygu8e+/OfmyuvuWaucbpEGWHbLeyZVUFG0NePPfpoc9WQIeHPzDs2OO5D2yxw7OjXXrPHhPW1yPRTYX0tICNGjxgR3WYpxn3x2q+4wqy67rqhx7pj3LUnpp/yr3v11SNlRId2C5ERo0eNCm+3qPRTUeOzjFvC2133+uvHHhs27ulrxx91lLni0kuj+1qF9IhRH3xgBhx77Fy5GpZ+yru/r9f+kZk9/xLp0k/NmGh6vXPP3BpYs2aZE449NlKudkg/NXlC8ZmB/7xTpZ/67plfOmyYWW3ttUNlRIe+FiIjgv08Vfopr7/BwBNOMJNX2d609VxwbtoI79ggbTOnmG7vPzW3zdrazAlHHx3ZZqHppwLndtc+a74lktNPue8Gxv30L780H3zwQWz6Kf6/J3KlDGsN2vx4+tq6e6VPPzVzsunz1l1z+/Byy2VeawSftz/fO3kS2deuu26eAzbDWsPOlSecYJ9rqamvhl16qV0L7n/UUaYtTo4Fr721i/nrX28zSy68cC49wm+36QssnZx+yrUZ/bxLF6vXTV99N9OlZ3QNFX/cd5s0wSz01l3R+lSV0k9ZfTFiDg9NPxU1F9XpWiOK0e++a47mvtfa047RxPRTMybNG5/B9MGd1B6ReKwnI3LbLmSPiLRHRM3hkemnwsZoDdJPBa87VfopJ1evvNKsts46c99U+qlOISNyHysZUbb0U9jgF1xmGZueNdQG79EUOzW6d+9uNtpoI/Poo48WnRooovw9aNCgDsf36NHDvkI7tN+po0hzTJZj/UFdzmN9IVTCsYXevc10FlmcK+w+fKGZRCnH8mzCnk/eY5nsAlFbVT+WSdfLQVy2YxEUXbsmPzPv2FQg2NL24S5dks/tQBCn/d1SjmXiKPHYVG1WwXGfus3qTEakaTcMNByDcbTNM2iEMbPrd3UXZvUwXZJ+t3v39O0WIiPaXbtv9KiCjEhst5hxz3fm+PVQSpQRqft6hWREW0tL+r7ujfvE625tLR6T2NdaWorGtcS+9t31pu5rIZ8Xvx+cp7xjk67dNwZ2uO7g2PXGfeK19+6dut2ckyXN+CxJnoToBu2uO+HYKGZ91ydS9bVy6hEp5Im7PzNzsmmdMvd4lmUxywHTOnPyvD+6dUstV1m4t3Xzntl3c87MOdNN26zJxswyxdj1QsQ1tM6Zbr/fTtYEZERsX/tORqTpaxhw/f7u9zdqx7T07GV6pSy43cUrts2CKW2bORnRrq9/1272+86h4R0bpPjdQL8a/+WXZsAvftGubc69+OJ2x1hjJQb9MugcXPvMQK1A31mZ2Ndy6BFxz9t32oa1W961hp0rW1szFUh3fbiVsdMWNyoD33fXnVOPaNdunjxhLDtDXGibee02p0sXMyupn3vHho3d6dOnFwuoh1F0vpVJj0g7FzkZkWouqqO1RhRBGREmT9pd5ozms0ck4vXDstouZI9od2yaMYpzY073ubIocYxW0B6RVscutLTXOTrIVb89ayQjOlAPxzawjCjrsZIRmW2WljjnUfBrpklg+zE7MzbeeGOz6aabmksuucRMmTLFHH744bW+NCGEEEIIIdoVdi8F30Bfq3P7+EbP999/v92/qYyeJeIX3PbhnH/4wx/M6aefbgu0+3DNfC9Pm5UT2sTuHEk4RpQJPwighGPpM7fecrPtN65/ReH6netrMGbMGDNu3DgzdepUM3bs2NjT9uvXz2Yb6Nu379x0W3UCY5tdH1EUnW9CCCGEEBWkaZwa++67r/n888/NWWedZT799FOz/vrrmwceeKCoYAohhBBCCFFLshjnwwz0rdPTGer94/KeO8no6Rt/K2H0LNaOCYHrrncjK06eer/GzkA5nHeuryU5osKcd6RbGTFiREnn7t+/vxkyZIipF/z7DpMPcr6JWjNhwoTQucz/N2keE0IIUf80jVMDSDUVlm5KiM5MtaMlhRCiXklr6C31WCHqxTifxWAbZqzNcm6fNMZeUR80m1z0nXfUUCTYzUHh7+uvv94cccQRZumll7bvLbXUUjYtcZjRM4sjivztWXZqBNNhpSHsuLTfDTs2zFAchlt3yFAsagH99KCDDzGzImqQhO2uYh5ELqi/CiFEY9FUTg0hmpFaREsKUQrNZlAR1aecaXVq5WjOEnkvmo88aZjKiXYd1D/1IBdrNf87592oUaNCDZw4NiqlJ5NGKm8qKep7VPu7MhSLRoH5j346beVtTVvPBdPJlncft99TXxVCiMZCTg0hOjmKlhT1Sj0YVERzG3prYewt1dGcN/JeNB+NnoZJVIdyphtr1Pm/UeuYpC1y7gqcZ/lu8PsyFItGg37a1mexWl+GEEKICiKnhhCdHEVLinqllgYV0XzEGXqraewt1dFcL5H3QojOR60cYPXiaG5UHRmnRFZjbZ7vWgqF8h4nhBBCCJEROTWEEELUDEUUi2YjixFN40Q0C0pH2DzUi6NZVCd91ZgxY2wtEUiqJ+LXEsmbqkvUHj/tZhiq7yhEPjTGRDMjp4YQQgghhBCiZtRLOiIhRGVSXw0dOtSMGDGipHP179/fDBkypN1vpqGUYuii8oSl3fRRfUdRLprVuK8xJpoZOTWEEELUtACyEEKI5kbpCIVoDLKmrxo8eHCmnRrlKpAuqg/F5ZHpM2bMsDIcPvnkE3P99debI444wiy99NL2PT4fNWqU5LnITbMa9/3UtmF6Uz3WhhKiXMipIYQQoiYFkIUQQtQntXBSlyvNmhzsQlSOtGnfgseRRipvKqnpy25oCt3nSzyuZea3pudHL5ct9ZX7fp7vZj13Izs0Djr4EFtcPgwcG2E773Buy7EhstKsxv2w1LZK4SiaBTk1hBAVQ4aF5qLUAsiiuZF8EKJ+aWQnddZrVz0PIcqbIq5c6eGK5/YcFaWcO2/qq1K/n+e7we/nTbuVxyGTB3Zo4NCYtvK2pq3ngulk6ruP2+/JqSGyIuO+EM2HnBpCiIrRqEaRJGMryOBangLI9Zh/VQb26tCo8kGIZqCRndSlXnveeh7BlFlCdEaypIgrVzoh/9ykKvr0008jUxkttdRSpkePHu3OnTf1lft+nu9mPXfetFt5HDJlcYgUCulOHDiuVs4YIYQQjYWcGkKIitGoRpEkYyvI4Nr58682goG9kXc7NKp8EKIZaFQndZZrz1vPw30va1qeZkeBBY2DSxFXi2K87tzUXgjq5H4qozB9LW/qqzzfL0farazF2fM6ZPI4RPI6ZMpx7qw7XEp1qICcKvVRv8UnKjAROkv9lma9byF85NQQQlSMRjWKJBlb3TGic9DIz7uRdzs0qnxoZEeSEKL89TxqmZanM+xQbeTAgmallrpHMwZEZC3OntepkschkrcOSjnOXS2HSpRTRdRH/ZagE9Sv39Ks9y3HhuhMyKkhhBCdxNgqmu95N+PivtY0siNJCNG50vJ0hh2qjRxYUCtqvSuolrpHI+tsjUYeh0jeOii13OFSqkMFwpwqojrkqd/SyKhujRBzkVNDCCGEaFC0uK8+ciQJIcq506MeqKVjQfNYevLsCiqnAU/PTFS6Dkotd7jkdaioHkhtwLCfdUdTI9Os9y2EQ04NIYQQQoiUyJgjhOhsSK41nqHY7QSKwu0QCtZ+EY1HrXfm1KIOSiNTzeLs4DtF5FARQjQbcmoIIYRo6nzeQgghRDlQzR1RLUNxml2D6muNTWeo19OMO1yrWZw96BSppUMlT3F2IYTIipwaQgghmjqft2g8ZDgUQtQjqrkjqoV213R+OkO9nmbsq9Uszg6+U6SWDpU8xdlr7VApxckSPFa7Y4SoLXJqCCGEqPluCRUKFaUgw2Fzpb6QE0s0Cs0WkSyEqCyNXq9HVLeeRy0dKnmKs9fSoZL3u9XYHRPnFNHuGNHsyKkhhBB1RC0dC7XcLZE3iktGz+Yir+GwWftLqfddL6kvGtmJ1ax9rVlptohkIYQQnYO8DpU8xdlr6VAp5bth3+/su2OiHCraoSLqBTk1RElocS5EZamlY6GRd0s0qtHTl6nVlqu1PHetDYeN2l/yUup910vqi0aOfm/WviaEEEKI5iLrzt5yOFRMS0vm47I6Y6q1OybKKVLL3TG13qEihENODVESWpwLUVlq6Vho5AjTRjV6hsnUasnVWp671jRqf8kbWJDlvush9YVkkxBCCCFEfVLLnb15zh0M2qk2tXTm5HWo1HqHihAOOTVESWhxLkRlaWTjXS1p1HarpROrkXfmNGt/yRtY0Kj33cjkaXPtjhVClJtSah9Vqk6SKB3NB6KeqeXO3jznrrVTo5aOpEao3xK3Q0UIh5waoiRkEBFCiM4hUyXPGw8FFjQX2h0rhKilAaxSdZJE6Wg+qF4qJJGNWu7szXvuRnT21kuK2CzkdajkqedRy1oiSrtVGeTUEEIIIYRoAGrpiGrkKNFGvfa8TqxGve9aXnfSuSt9fiGqbQBrFCNYs6OghsZIhSTqew4PnrvRnb31kCK2FuSp51HLWiJ5z10Ohwp0tuLuLYVCoVDri6h3UP4WWmgh88EHH5gFFlig1pcjhBBCCFFVUIZPOOGEyM8vvfTSulV8G/nam/G+a3ndSeeu9PmFqAYY/j788MPi3xgBL7zwQnPyyScXjePLLbecnHeiYfnss8/MpEmTzIwZM8yECRPse/x78803m4MPPrhoCObfHj16WBvPEkssUeOr7hzU8xwedu60faWe+4sv05tBnmOQx1k1bdq0oqE+DIzyvXr1sg4eDPWlfDfv9ytx7lNOOcW88cYbJi3rrLOOueCCCzJ9N/j9ctw3pP3+oosuarbZZhvzzTffJDoQ5dRIAQJi+eWXr/VlCCGEEEIIIYQQQgghhBCdFjYW4JSLQ06NFLS1tZmPP/7YzD///KalpaXD53h2cXpk3cmR5/s6t86tc+vcOnf9fF/n1rl1bp1b59a5de7mu3adW+fWuXVunVvn1rmb79p17g/Kfm7cFJMnTzbLLLOMaW1tjf0d1dRIAY2Y5B0CHkSe9FR5vq9z69w6t86tc9fP93VunVvn1rl1bp1b526+a9e5dW6dW+fWuXVunbv5rl3nNmX9ftq6NfEuDyGEEEIIIYQQQgghhBBCiDpBTg0hhBBCCCGEEEIIIYQQQjQEcmqUgR49epizzz7b/lvt7+vcOrfOrXPr3PXzfZ1b59a5dW6dW+fWuZvv2nVunVvn1rl1bp1b526+a9e5e1T93D4qFC6EEEIIIYQQQgghhBBCiIZAOzWEEEIIIYQQQgghhBBCCNEQyKkhhBBCCCGEEEIIIYQQQoiGQE4NIYQQQgghhBBCCCGEEEI0BHJqNBk33XSTmTFjRof3Z86caT9rFGbPnm2+/fbbWl+GEEIIIYQQQgghhBCihnzzzTfmsssuq/VliCqiQuFNRpcuXcwnn3xillhiiXbvf/nll/a9OXPmmHri7rvvttd22GGHFd/7wx/+YM4991zr2Pj+979v/v73v5uFF164ptdZr4wbN848+eST5v333zdTp041iy++uNlggw3M5ptvbnr27FnryxNCVJCvvvrKLLLIIlU734svvmjlzDbbbFPyd5Hn06dPN/PNN19Frk2IRmPSpEl2PLS2to8/Qk+bMmWKWWCBBUw98tlnn5k33njDbLTRRmbBBRc0EyZMMP/3f/9n2trazG677WbWXXfdWl+iEDXjpJNOSn3sxRdfXNFrEUKE88QTTyTqsoMHDzZDhw6t2jUJUW3QN9HhlllmGdMIPProo+a6664z//73v03v3r2tDVE0B3JqNBksjhFOGLd9RowYYbbffntrBKsnuKaf/vSn5vjjj7d/P/PMM2brrbc2v/vd78yaa65pTj/9dPPDH/4wk+KPoH799dfNiiuuGOsUQbHZYostTNeuXTPdA0PsjjvuMI899phd7LOw9/nXv/4V+r1LL7008be5pqWWWspstdVW7RxVt956qxkyZIg1Mi655JJ2MurVq5d9vmPHjrUOjQMPPND8+te/tvffSAwfPtxMnDjRHHLIIRU9DwbW1157LfSZ7bnnnrGGqP/9739299Omm27aYazVK0Q0HHTQQWahhRbK/BuHHnqoOfLIIzMZtYP897//NZtttpntt2mfV5SjDkfu0ksvbToT2223nbnxxhvNSiutFPo5cgW5yb1Xy7mATB41alSsc7yWjmpkxuWXX27mn3/+4ry31lprmW7duplqg3wIky0rrLBC1a+l0WBOZT6dNm2anZs7Y1ADCzLm51dffdUuzHxwaGy44YbmwgsvNHvssUdFzn/EEUdYHcKNlVLk9u67726dm+geDzzwgP0bOY7++d5775m77rrL7LTTTqZa44zgjn79+mXW4Rpd7/nBD35g3n33XfuqNsw/s2bNipVr99xzj9l11107OO/qnQsuuMAaNZ2O8vTTT5uNN97Y9OjRw/49efJkO4avuOKKDusan5dfftnOfauvvrr9mzmUADScgv/5z39MZySPw5bP//SnP1njVdgcWq/9vF4YPXp05Hr0rLPOqtl11RushZjP1l9//dDPGfs46+nL9abvMef17dvXVBLsGthn6oXnn3/eykxkZxhkKWEe3Weffap+bY0M6yT0zah1HXPXX/7yF/PXv/7Vzl2w2mqrmQMOOMD8/Oc/z7W+Shso98EHH5gbbrjBvsaPH2/2228/c/DBB5sddtihYus71o5PPfVUMXDwuOOOs7bJxRZbzP7NeGd9zvVXwl6EPsgzCQYufvXVV1bXrdegp4qCU0OUj1NPPbVw+OGHJx73j3/8o/Czn/2ssNlmmxU22GCDdq80fPbZZ5Gfvfbaax3eW3/99e1vt7a2FtZdd91251tvvfUK888/v72eKDbaaKPClVdeWZg4cWKhmiy++OKFl19+ufj3iSeeWNh5552Lf997772FVVZZJdVv/fznPy9ce+219v9nz55d2HLLLQstLS2FPn36FB577LHI79FmEyZMyHwPJ5xwQqFHjx6FXXbZpXDooYcWDjvssHavKFZaaaXE1worrGCvv1evXoV//vOfxWe96aabFi6//PLC+PHjO/zu9OnT7f0ec8wxhcUWW6xw++23Z743+nEUXBv393//93+h15GV1Vdf3T6TJEaNGlX485//XDj++OMLgwYNKlx00UWFsWPHpjrH/fffb/se/SP4ijv3K6+8Ulh66aXtMRy7wAILFB544IFCNaCd07yi4FrpR/vvv3/h0UcfzXQNP/rRjwrdunWzY/IPf/hD4cMPP8x8P/zOW2+9lfr4Nddc07Z/kDvuuMP282qAXH7yySftK05Gp2GNNdaI7Wu77767ldvDhg1r9/6XX35Z2HfffQs9e/Ys/PGPf4z8/l133VW44YYb2r33+9//3sqqLl26FHbcccfCV199VdI1f/TRR4X33nsv9pjtttuucNlllxX/fvrpp+19cm5kGPeNnI/j4IMPLkyaNKn496uvvlqYOXNm4vUFZTntl1YmBEFG8Jwd3FP//v3t+IlrN+TSVlttZa/FfyXJlkqzww47FPr27Rv5+YsvvmifXZgO8M0339jPeA5xvPDCC4Vf/epXtn/utdde7V5RfP3114VDDjmksM466xSOOuooe343d/NacsklCyNGjIj8/siRIwv/+9//2r33yCOP2OvdZJNNrJyqFXFjnPF3zTXXRH73uuuuK+y0006xvz9nzhx73G677VZYe+21bRvusccedh5oa2uL/W5WvYe+zZw7efJkO/8uu+yy9m/HySefXNhiiy0Kefj4448L77//fuwxU6ZMKRxxxBFWlvFy4xxd4Lzzzst8bvrw448/Xqg2afWeMJBNv/3tb2OPQS7tt99+keMbuZZFVibNY8DzWWaZZQqnnXZaYfTo0YVqM27cuMLVV19t2+n1119P/b2k+eTTTz9NvHf0UsakP2fw/+hSF154YcXXklnmgrw69r/+9a/CqquuasdokG+//baw2mqrWf0kCvopevYpp5xS+Mtf/lK45JJL2r1qQZp+nndNlCSPZ82a1WGuC0I/Z7wxb6KvsF50r7g+M2TIkFSvcuKuKc0rC6yRzznnnMjPf/nLX9p2CpNJrOtZe//3v/+NPUcefW/q1KmF4cOHt9N1HchpPmM9Hwa/j40Ae9RNN91U+OCDDwqlQn9CHqJD+dx5553WbtS9e/fQ77F+ZD0WNZestdZahSeeeKKka5k2bVrhxhtvtM+MNq2UPC6H7hGEdRb3nQTtlfQKk5lBaNsDDzyw8L3vfa+4BqcP+OuVUkCvj2o3+ij6OJ+jj2Jn48X/897WW29tn10l5CprPuxXnAvbBesI5sOuXbsW3nzzzUKlYYwl9TeOqZS9CJsi4yHIlVdeWfjhD38Y+91yjFF0hC+++CJ0zRanP3Bt/nhAH+c7Dn6Ta8uCnBplhsX39ttvH3sME/98881nlUAmBYzLP/jBDwoLLrigVerTwER7zz33dHgfBRNjVhAWNLwYOCwq3d+8MHzddttthRkzZkSej8UhA7Z3796Fgw46KNYJEAeDnO8zkFGsghN9EO7Fn0AwQlxwwQXFvzGgcU1pYHHNghT+/e9/20UUk/UZZ5wRu8gOCq5SWXjhha3zpVJguMAwg/CHUoQiwgNjVbmVGjj77LML2267rX2GPNt+/fpZoxR97ZNPPilUEvo0ExvnXWqppex44f8xlDNGksAof9xxx9n+WgpMrvSlZ555xjrjmGTTOt3yOk3pp4xR+ttCCy0U+uKzKFBOWGxh7KOtUIh/97vflbz4wpjP4pb+wTNg4kXRiDI6Ry1SuB8mtrSLloEDB1qD/J/+9Kfi4hgnIsrOxRdfXKikTOdcPBfu1xlb+X/kZholNAxkFEp8HBgsmTdw9LJowVhAX8cJnWScKYdzoVaO6qzOiaAsZx7O6tTAQOzkOoEE9D3GJwuJOGc18mGbbbYp3HfffVapZcHgv/KQ1hiVxfCJURN5EAVzEAupKP76179a+YszjnmDfzFe0X/j2uvII4+0BjD6JYa7zTff3Lbxc889V3j++edtP+a3ovjxj39cOPPMM4t/v/vuu1YmIKsxTNAHMIxVqs2ZQ5GrYXNJ3BhHR4oz8PIZx0SB0wJnBn0e4xCGQJxJyGXew2gaR1a9h4XZmDFjiveOHPSdzRgkeOaVNiDybJGDLOQxPrlxju5Ce1Ti3Ny3Pz8vv/zyds51L5zr77zzTqEeOfroo63DMQoMyMcee2zJv8sYTTL8oWNgXFx55ZVt2yIfMcKgkyRBe6d5RfGf//zHriHcvI2Muvnmm1PdW9J8ksaIxjrkjTfe6PA+83fc+C7XWjKrEyyPjp3XYcv9PfXUU4UsBNecUa9K9PO8a6Kg3oMO4uvnafobjhWnI5dCMKCO9ftyyy3X7r2sukfUusa3U/zmN7+xcwtzPzojL3QB3uOzLHz/+99PvGaua8UVV7QBOw6MtsgMZEcSefQ9HHRcY5zu4evwPthogv0NvXrAgAFWF0ta2yJ/uG83HljL8h3uZZFFFin8+te/jnSU4KSNW3Mht9DLouDZItMc2KaYs5EtjH3mc9bYWeRxnJG5XLpH1gA95+hKetHnf/KTn4S2P0F86LfIFdYk7v6HDh2aaOjO4tQ466yzrEwJCyzie3xGP8xKXKAca0mcJldddVW7oIByOjWSbC555v+89iJ0yrB+9fbbb9sxGkfeMRq3RuC+42yDlXQ+yqlRA4i4QokJDgIW3n5EWxznn3++FVgsMlD88cYy+THIMWxFwQI6q9cU4xweZyZJp5CVGo2NgRMP4BVXXGEX9Cww/VcQzuGM9ET+MVB8Zfall15KHYVNe7lJgAUciokzcjCo4gZunqhrlD2ETKmU4knlGZQSjc6zxIhZKaXGh0gS7oXJj+/yHPg9lAMcB+UGRZPfZyL1Jzoi2BljKONJkZb0B2eYKYVFF13U9kkH3mf6T7l2OMUZ2OkPnJ9+HRe5nAZkEm3F86e9MDoTEZEmGt6HtkA5Rammf/7iF7/oEGXjHB/+AoZnxzOkf7j30oCjlwU2kVHIDiLR0kReYgDEeRomG1Fq4oyuwEIBgwwLFxdRg8Gba8hiCCoFrhuZjCLL2MLwzE60SjsXcKbi7MRoyHjyX3GUw1Gd1TlRTqcGCysifIH+uvfeexf7PAaeKLi3LPNBuYxRWaF/x8kVHDtxBgJ2iLoFuGt3xh1zMXNDnNHPGYuY53iGflAFkalx7Y3hxV8An3vuuVYuONi96f9diTZnbCbtYAobJ3H9hMVMWACL4/rrr7fzWJjhhfmYz+J27tHOzIFJkYNBkPPOSIuegRx/9tlni5/Th/LunEtjQGQx7c7rj3OcQXH6Xp5FNnOvb2DjvMg29G5eGBUwPFcDjJ2lBCTgYKRdoyD4hWPiDGysD/LqOvRX9BzkKwYs2ivuulxEMvopi/CoVxREmeLgIwIXfRGdI8mZUE6nBt8JCxKjHfis0mvJWujYeR22PO9SdvGG9ReuPbj+jFuLlpOsa6JyGG3z7E71yaM7he28JTghDoIbCEAMQhumyY6RFfRcZAtrcYIA0ZOZz9ntmYY8+h66cdyOpbvvvtsekwTrGvobYxMjsOtvrBmj2HXXXa1M5xwHHHCA7Vf0TxyWSc5m5t648Ul74PCPgl2l7ELxdRkMuMy76Iysx7i+SsjjPLqHH8Dgv7gu5jL3dxT8btIL2YtTijVumJMC54/T6/z7Z60XpydndWqgE+BIiQK7AUFJlYC2RH6y+8zXO8rp1IiTTXn7W157EbIlLDPPa6+9ZmVUpcYoY5MX10rwifubF/ZndI84XbGS41ROjRrgL3AxLDlvPca+JO+aD0IK4Y/hie8h4CodAe9gsXv66afbgYEAYYJx6Y/ioPOGpYiJggUiEykDh0hDzucb7PDQsihJA9998MEH7fcZrG6nCwtwotjjBiD3F0yXkTZ9BotZrj1N1Fm5PalZJqlyKDVxsAii7xBtkHQNKGMYrbgGX3D6Ck+QffbZxxqZo8CIxvOIAyXZpSorhTDPNX0ex1k1IHqZe0eBIkoV52EeIwMK5EMPPWSfPxMo8iotGAmICGPhjYECQwX9CXnh92uclBj/WZywgMijmPB9FoUu4jLtriW+x/FR25qTQDmJMkxUOvUVMg3DLfINpy8R7X47VsK5gLEQA7bbNps2PVu5HNV5nBo8JwyrvOiXOG/c3+6VVqF2/ZM5iLkIcHTEKZQbb7xx5m3gtYSFcJwc47M4Izt9yTmB0FecMo5ijSMyCgxkyBIHbes7nNF54voc1+Qbdwn+8I0j/FbenQNJ4HQs1VDGXBsXMY5OhGyNi4aOS7NEQEpcNHRS1GDUWMdAzM4ZxjRzEf2dHSPsZsPJ8dOf/tQ6sSsN/cTJBF8+oPuge1QCIqf99C9BuYRRIsxRjMxxMjsoi0qRTeyMoW873cpFdaJvJQUkME7iHG98FifX2BnDOOYYnjH9vdQgCB9SrmCsIJKR+2CHURjM98hijDnoxBjWS4Gx7+sZ9FFkTlhahUoszjGaYGRn/USQEC+MRMyv6EzVWEuWSl4dO6/DFrlIH8uyC5bd+gSasOZjBzARzKWm2XS/gwzm5TIAVHpNVI7+xu5h0pPUk1MjDbRNmH7Oe5WS5/5OAXZAMcbQYx5++OHU382j79FH41Id8Vmc7SLsPliTsCMvqa9xr85WQxClM2Cm1RWTnJZx4xvHm/99ZAkyJZi2pxZOjTg4H7qOC2LghaOf+QR9y72XFT8YlTkrLDiDOcHp2f798y/PJYwknePvf/97ZLvxm3HBE3wWdd4ouyLP2z1HAgXDdjI6+9Att9xiAz25b3avYFRnLV+N9FO0iR/wHLT1JPW3vPYinC3+jiZfJ8LpVakx6q/1g+t/1vM4NLDZRVHJcdq4VfPqAIqURRUqu/766yO/R2FnCrlQoJkiUc8995zp37+/LexUSt32VVZZxayzzjrmn//8p/173333tb8dhCKaLS0tqX4zbaFwii7+/ve/t8VdOf8xxxxjC0LGFYiF5ZdfvqR7pGjZRx99ZE444QR7b7fccku7IlAUJkpbKPPwww+3BaIoGEx7UDwRKNKzxhprxH6XYplpCxYH4ZxcJ4W8KRoULFpEkcCo4kznn39+5O9SaJNCoZXihRdeMA899JAtkkZxdu7htNNOs8WXSoECSM8++6wtuMaL9l522WVtgbFtt9028nv0J4pifvHFFx0+4/lF9TWKhd18882Rv8v1JxXbpGj2z372M/Pkk0+addddt8Mzoz9G8dZbb5lPP/20+Df9/e2337ZFIx3rrbeeqQQU1uZ1ySWXmH/84x+2aNbJJ59sfvzjH1uZ5ApYpoV2puAU/3IfFEKMg88pAst56Tvc5y9+8QtbMMwVraIALkVoTzzxRPv3lltuaV566SVz7LHH2qK/FLlHvpTK2LFj7Xlo+wcffNA8/vjjtqA7hcooRB1XLIxilauuuqotYM2/peKK4gZhzMcVCYMJEybYZ+TmkqB8jOrnFMukDSlUyLg8/fTTbZsPGDDA3Hnnneamm26yhbujYAzSL5mDvv32WytvKPbmoC2CBYodPCsKot57771FeZoWxhV9gmu+7777rFz/3ve+164wnCuYGoc/zmizd955x96HT9g4o3Cc38YUMQbXx+Nki89WW21lTjrpJNt/kTkUNwcK5S233HKR30Omn3LKKeaPf/xjqGypdHE3isAPHTo0slBo1HxEAbuRI0dGFp+k/V2BvDDQQ5wMpO+98cYb9v6/+eab2DHC9flzPv/v97ekvkcRPYq4onvwW/Qvnps/P5Wik2SBAoKck2KGFLLs06dPYj/9yU9+Ysf0jjvu2EG20O/POOMMc9BBB0We87XXXrOFjKP44Q9/aC699NLEQqDBIoRJ/PnPfza77bab1RnQqx5++GF7/xRddf2Aub3SOPlEMVe/n1x77bVm8803T/w+/Yx+gwz3QS7yXpiMoAj6MsssU/z7qKOOMgsuuGDxb/S/Dz/8sMP30LF4pvwu/+9kkSOtbOJe//Wvf9nn7u4R3eu3v/2tve4rr7wy8rtcJ3Mo65IwxowZEyubKCrP/PHII4+Y2267zepYtCF63oEHHhir60Xp3Mjq999/38oW5H0Yl19+ubn44ovtfaPjnHrqqbb/HXnkkVZHTpIPFOr05RZzHro+RTgXXXTRxOukP1Hw2hVMvfHGG4u/5+t8UQwbNszO/+guTr9C5+L6GUtxZFlLIlfSQptWQsdmHCCHo9ZdfBbshxtssEG7Z0l/RC6WsqZycoEXfRX5hq5KMXfWkbQ58jYOxu/+++9vi8I7mcYchu76t7/9LXbuz7smKgfYDc4880zbV0pd2+QFnRQ7RXAdQnvQdnF9hjFJmwf1c97r2bNnRYqj+/PjdtttZ9eDO++8s5VFvjyKa7M8+h7y5PPPP48sJs5nHBMF7cpz5r5dP0MPougya9y4vsa6281lzA3oLL6eHofT7+hrUboJ64a49Zgvv7gH+qyDcff111+XvC4IsyWUs8D7K6+8YuX4f/7zHzsvuXnh6KOPtmtwCkvngT6MXoUOQNuGyWDmBGQjctGHgtYrr7xy6O+G6RxhukcY9F/ah34VBs+CuTwJ1uvopKynnnjiCbtmRx9iXXrddddZWR2EcY9uwQvdBVnOWGRM8P3DDjvMfP/7348sHJ9XNtEu6CjM1zBt2jQ7j3Tv3t3+HTc2y2EvwgaLHZM24joAO4Kz3VVqjLqxwFqQc8Wt+8KgLwX7Uyk2hNjfxrNRll9qMs455xxb5R7lKMyog+EuChY6CICzzz7bCr5f/epXdiCjyKFwMoCTYCJnQcuCE0M/f7NoRiigJLNwdGD0Ssuhhx6a+lgmSYQITg0G9X777WfPHQcD7aKLLjJXXXVVB6FbDRCMGBYwrDnlk/ZhkvzRj34UOcG6BWdWpwZKBQorSniwr9APwkBgxwkdJi6UJARpqSAEN9xww9gFcvC+mZhYLKQ1+jI+nCLFAgVFCiWKl7/wj4LzsChF4QwzGEfBojTOsMiihN+OazfGIIZbngEL26AR7d13341sszTKQVi7o0BjkOacScamtAsPlAP6F/+i0PlyIQ7GCGObBfr48ePts2PBt/fee8cuIJjcmPBY9KHIoSwFYQHIApWFdxDOibEb+Tpo0CDz6quvplYE6Z8YM5BBbrH5zDPPWGWEz1A447j77rutMQjDD87iUkChoJ+gGLn2oX8hTzE6YOiJAplNG3O/YXNJlFxCoeC+eEaMZb99+S3kMsYsFuxhYPzB+eGcC7QV/dopgFdffbW9H5ThICxwkCFRsikO2gUnOO2NAs55UNQd22+/vdlll10irzvPOMNAloYow54PzwxjLWOF8cj4ABxNnDdqDHPt7jp9SnGoAPrC7bffbq8DxTuNMQpYADAPlzofERTAnMPCPgjXzjNErjKGw2Cxh76EnkIwBI4V+jZGb/pv1DXTXijvboFIv0Bn8g2HzBFR7cb9Yri84oorrKOX+2Nec44FxglzFf05jr322itU8eY9xjxjgXsMc8i5Zx78Xtwz574wTPN80fXc77JIx/GLDsliP2rByMKK/h61MPn444+tDJkxY0ZF9B6M6L5RmMUWY597SmMsBtoF42NUP48LwkFuIVtpO2QkMocFJHKOBTTOpTii7p92w+kepj9g/KE/b7rpppHGYBah9EcfnhNGEvpCkoyKk02cnwU49+2DfGdOxlAfp6diVI9auzBW6VOMobTOU2Q8xoXXX389tVyjXTkHDgpkDX0U2YOBgoV4ErQfz5u5C6PCm2++WZQdUc+ZNYDvfKKtCAzx9U4CJIKwhkmzGA/TdcKCFDDMAP0r6Pgs11qStnQgf3je3DuyGQgwQYfg+1GyPK+OjbOWdSvjIcxhS2AO45a+40AnTEvUHBb3fJi/kQsYiuMcuegmtA99xslknP20Kwa+OIdt3jURuhntToABz45nj5xza2kCZHAUxY21qKCEpLVNEOYd5swoQ2m5HMWOP/3pT7YPsK5w8pV2RE5g8P7Nb34T+d1rrrnGDBw40OoM6JzB9VyUEyyurdK2WR59DycCekeULnzeeeeZ4cOHWz0gCMZc2od7oH+hn/FvnDOhlL4W55TBuU4/x+AZXC8iE3h+6PlROjI6AjYadEXkN0ZddE/3PBinrK0IIkjSsxylBi3hQCD4Dn3BJ+k3mHOQb+iVyAjkMY4sxkpep0Yauw19AtnKuMBBy9zPnMi6hHHigjxKXRdx32E2OxwB3LMLrg6CzYC+hA4Xh//MfdnCHMFcFBYMEgb2B4IamfvQP/itNM6sLLIp7ZwUNR/lsRc5sJEQ/MC/vXr1smOFtX2SnS7vGI2CudHZX6LgvtFRnQOJ54S8cjoPa5I0QfKhZNrfIew267Rb8YKwzZxt4g7y4w0ePLhw6aWXxhbr9mGLDzUN/O3dbN2iiBYFsSsFW6PJSU0KEbYQkc+O/H1p0xGxVZJrZ2sRW46CuQfrkWBRm1Jhu2qW7afkMKfuSBRsV89amC1N+ik+dzm12X7KVke2Iibl1HbQP6jJwFbnNFv5y1XXIqnAaZqtbeSeZLtomjQ+PqQCSPMKgxQErp2CRflKKcZH3nmunTQXbNFlq3GanK7IHmQRaUtcEUC2gpayxRyZmLVmj7+dnFRIPMdStpBGyWPSWLDlvhTZxLbLUmQTdTvI/U8aKtLb8OL/kcVRW2ezpuRzIP/j5gtXNDwKZDapL7hvUt24+jz+1taogpJs973//vsLtSLPOKs1STlz05C16DaQdiBLkVWXpmnTTTe129Fdocu//e1vdrzyWdx2ZtLCuGKbyFVSI5Fi8aSTTopN/8EcEicP3SsKtuIjC5EnpLQjJV8wXRK1fpI49NBD7T1yPWxz58V5GT+kYyEVFFu6w9o2az9l3h04cKBNJeO2dyOLeC8pZUpwa3yQLFvjqw05wJnDLrzwQiuT0T3JrY5sJdVQmj5LwUz6JznRKWQfloPYh9/lRdswj7q/eZE2kZSfUYXGKVzLd6Kg3hHHVApShoTlSea9pJR+pLOl/1IbiBRa9D1epLSkr/OZn/85DlLC/eUvf7EpMOlHm222WeJ3SGlIihHGGKkkeFZpCvFGFR1HT2L+JcVhHMH0CWGvSqYsyUPetSTF3xkffkpf/p/UUieffHLFdGx0MlIno+MjyyiIzIu0ULxHrn+OqTT+ehY5g07lt2cYyCG/FplfcyYpj3neNVEwJWDU35UguO4rdT3orj9sTkKPSLP+R+8gHZ3Tyfl/3ksia3H0Wut7pDQlRWpYKhdqbfCZS3saBF2HNNvIA2wFpfa3PH2N8c96iPNT/9XVqeEZ8B6fxRUqZ+2CXss6ijUM+m1Qbv3sZz/r8D3m9nKtC/IUeAdqmNDvqMlYrnRIaew2pIwmBTF9w81fyKywejQOPouTe6Q5I/1aGNwXa1jmeMYiMoHrZC5ircBnSWtg4Hpd2iU/HRH6e1T6Ktav/jzBeX2bKLLmoosuKqQhr2xqtHXspznHKHAs6z8HKSFpR74bN0ZYoya9XI3KUtFOjYwQaYYHMUualHKApzps6yBeSqJb/K16RIU5b3owQixI1FZIvKx4fom2w2uJlxwvdqmRukm7RvydInikOR/R5cGtx0HithsH242UTWzvAjznRDf5kcLljlgkcob2KzXlUB5PKimAkiKT8Ign7dTw2zy4BTHJi4y33G17JUp+tdVWs1t4XWQSESBx0L+IcnAR0GkJRvYGSYrsBSK1aPdaje8s0MeIrKOPs0WayDF2LqTZeunumWhBtw2f34iLfKkkyDGeE/Ioy7ZEF9WRJhVAFtkUBil0iKAmkhpI/USUeFLaOmQQ30PGlQK7b0h54La+pomWDl5vVHqpJIjuJP0NsjNsS32psg5ZgqxArnFPaXcUlRt2C7C7ha23SRA5HkdUyoByQPsSeX788ccXo5qIYuM9IvHiIojob0RzZ0mBRwQwEdNEvLtxybPjN5E9m2yySej3iOQiJQ0ypZRdd26uShMxGYeL2GbOCUbE0nbIiaTdA0SCoj+RtsHJReQU6e14Buhd7O7jPGG7m/JAGxNtxr/cQxqZGIyGCpIUDUWb87zT7qqoBMy/6DfMY7Qx0WjuPaJT6VPlxvU1ohfpF/78yU4FohWJuCaaPCwimNR6zMVcsw/RaOxmZgcA0cZxkZaMEfQfH/RvosjjdrBxXcw/jEX33HnOzOdE7iVFsN9zzz32vMwbPvQB0iyF7VZwMDaI1uSZoPMRZelSQyTpUcgPot2ZA7lWdjz5OyeS4B5d+inGHikF0X+Iqq+kDkOaEXZF0heDayd2xTCXsXM0bn2RZ9ddXpAltFdwdxnPgmsP9oNy6ti0DxGlpG10qWSI8GSMIEvjdAB2RyIDnX7HWpx+Rz9it3MctC/6C5G87ARCRtLn+TeNrsw6hkjo4G4sroF+S0R5FHnXROj2aahECqs860G3fmeuXXvttdvprBzPHM9YTYrmzgpjk7mjlF0lbrcZu6xdilL6q7+zkftA5qZJf5UVdizRt7Ej+Ls12UXhUluHwVqO/k1fo89x//Q3+obrc3H9LW9fY/5kdwx93pkZ6QPogOwqS9LpsP0wH7GzBluIv1ZBv3X3EOyjjEvmEORImrRHURA1zq61pNTkcbCTlN10tD9zRJq0unl3avhyDllE6i3kYtxuRdYqzPGkswpmKSCritsBeP/994d+n3ujzbGr+esC2g45mybdJ7Kc8c+84+/UQFaTntHtYozbXZFlnNdSNpGJpdSsEEk23FJSGecdo3yO7YJnxg5l5BHzudNlolJgsfvapR+P0h9oczIQlYqcGhlhYYGQ8J0HcWAkofMidJMMJqUYG1jkoIACAjNskvIHflAxSWugZjHn8tTuuuuuVTF2MnEhTJnMkrZ5pdlujCLKQoetbAhoYNAgNNmujkIaNblzfJzxMA7yOpNqg8VNKSm32ErMBMbzY+HkKzQIHJ4VzpwwI1Ga55O0ra2cCjRCCgWL32SCd6lrcMpgJIoyurIdkT5dSu7XcqQEQOByXlLzlIrvRGTrp59XkWcZNHaUC545yglGhDjDYVS7kZeaVE20nTNksXjEUIOxGYNG0uIcRZpt4lG1htJubc8C52KhTXo7l0MV5eiXv/yl3Q5cKwdNEllT8kVtl00LcpUtn6QV4dmWYmzOkk7H356KIRjZxfZ67p05xW3z5n5cPZZKjDPaGQWMOY3rwDiJcYp+wkKRMRCXe95vgzg5E9cGyEKug/FAqhXSqrCgQEmkVkeaBRfGc/oLY5WFKzKSRQXPlH4RBQsTjMLMR2nSbIXBwoHt+TxvFsphaebC+hvXV+o5aWe+w1zBvfFvmjQ0YQaKKOMD7ZWUloH5AH2B+/Whz6DU43QgzQ4ykj4ehOdLmzPvkE+de8LAzTOPSjHHYhFjOGmBuHcU/bTgfEozD0almImCORw5z0K10s5H+rmr+8PzQZdCJ2LcsBiNS6fk5gQW9mFzEalf4qCfYVAu9R5JXcTCzjdCoaOjt5HyLWlxzJjGiEWf8iGVCIaaoN4SrJGAEQ6HBrUVAF0LAwcpEtMYyJnrcXbRbm58kwY0yQGO8562Ih0FOohLZ5QGdBJ0c2QZ8r8UIyEpAHHSkh4F4zTnLjXHc1aYN+knUQt05Cz6blw6YpevG0MC8x5tjUxB/yf1TNL4RNZgUA/r40m143herH2C8oeUNsiPqLz1aXTstKmMsjhskbE4L6jdQcAZfZT1NXMSBtCoGglAH0MvJEiF70fpT1EGIdqG+giswVwfxynFebELkDs/DVnWROWCoB8C38KcaKwDyr0edOt3/kXP8g2szlFMmhqXiz4ODM0uKBEjZJpgIOwWBFwQdFAKzNfMOczBQL/hnC5QCZmOjcKv0RU1RjHu+teNrErruGXOYE7wdS7sFRgR00J/w4HpnGr0NxzdGFYrCTLEzSWcrxw6A+2Jzh+02TCekJekGUcW0qdwKiStW8Ogv2B8TaOPOzgnqYAYW27OxTaVth5rOYJRHbQ5jgD0HM4fVxeDtRQ2JvoZ14scQ0bQRwnu5J6SnMVuXcDcBWnXBQ4cF+g4rIf4LutD5kDmMF5hNr6wFOmlpsTLI5uSgq2Tgq65fvoZfTStEy5p3ZkllXHWMUq/4nmje7GWZo3Fupb3WFdH6Q98j+PC9BPWFuhC6AQuQLQU5NQoAX/iQngR2YvhhVfQ4BpUDPzBl5RHLU1HxOCLEGKh7I7HkEMnwYDuL0B8o3ySYhJloMboxEItj+fbwfWSy92f4FkcREXJcDzGBNo5KVdbHEROI5iDCxCeFYZbdz1ByM2bhqgFBAKC54XRjecS7CtxeaHzelLrDZ4lCzAmb3KcY3yO6u9Z61qUAxbZPHcMA2nGt4PIEhydrn4D4wUh7V834wjjRhy0CYvNKOcARthyLzQxyLFLA0cGExuLbYxoXD8yi39RFuMWbRh0kDEsFsPqQzDxhUFfwODCbhGizV3RK2ByI/om6XkTRUWfQTlxTkuUeCLviYz18zMnweQcXOwlRT1kKUSYRz7k3UHGgpYFOi/aib6OHOaFgTyOPHnfUeDYZYJRgcWiKwqIgZf/p6giiqVbSJZznOFw41kwplGYOC8OL+ZM+iZ9L61CF6zBgOGZ60E20NeiCrISzcz4wPjG/M2uB5RwDBks1HiliWrCOcFzcjlUGXsYyxmzccZeAiFYDPMMSulv9G3GVRrDQxhE1jH3Rhnwo3DFVF0ucsYl7eUcHLzSOOSIVsMwEFxo8TyYZ2iXOOgX6HzBaHXmMvoyCjwyAFkVVOZxktHviOKnb2BI4B6Q8fwmbRsEWYuBmAUAz4iFJ0VHWfylAXnJnJDVmcu5mJ+pfwKMFaKZXfQVcof5Cf2tUuAUYB5mgYRxgYhZdswwtjEiImujwCGE0QNZFdS10+jZRODS1kFjPkZ/FvlxMp25kwha+gMwnzI+Wbgmgb6DLhrU7Xie9GHmpqgaCUnEGcjR8blmZ2hDVjIunM5N1D7GoaiC3TiKmbez9jfmTO4dZ23aum1+MEeSgSHKoYMcTEOYE4x5DgcQa4swmGNwUsTt6suz6445knmEcRrc1cr/x60t3JqW8UXwjl+ngGfPHBWl5yaBQYzxQ72sSjhM+cxFPuM4Qh6wRkQ20WfT1DiALAF+vr7mAt3c/wfroCS1f6lronKAzGYOY/6hf+IMojYB943DOGxtkcdgix7EPEf/ZK5DBkbtHowDWc930QP8Au3M/8jbYECnn8GAPkVfJtCllAA55B36KOuiMKMpgZKsw9G7osDhxXqddnJjDEMx8wj91a+HV0l4fpwXXYMXOj9zSVxtCD7znxVGZhw9tCd9KMrgz/hDhpWr+G+pOxa4Pgz09DscHTgLcWyhp7HzIw2MA3ajl1LgHV2J9SZ1s3je2G2Y99lBmIa0c2dwfenDPO3quNL+6CD0VxwUyC6CyKJgLci8QxvhQKG/skMzbj3Hb9Ov8u7oRaYwB/LMeK7IU/5Fh+O9MPtgOZwaDmQT+nYpARX+3Iz8ZJct80+wJlNU0HUWJ1xa5/Lrr79ubcSlwthFNqHTJPVHdr1z7QTgMBcTVEogMkE8OGuidpXwHfQL5m1/PcXcx/oVWY+cT1NnqgOZklY1KeQYT/Mi33gQ8qKR6879f978aeQ8peYC+f5cLst7773X5gYlL2kQ8stmyeFZrroSDnJur7rqqrbOxAYbbGBf/D/5qOPqJ5BTz+Xbywo5GsNyfvNeVM4+IGd21Itcey4PfxQ33nhj7CsN5M9+/vnnba7jpFza5cxnPWXKlHbXkCXnL9dM7r1ddtnF5mGlrcg1SY7yuPvPWtfC5ZC+/vrrC++88479m5oSjIvDDz/c5rpMotTx7SBH/HXXXVf8288NCeQu/OEPf5h4/uOPP97mmCRX+89//nOb891/VQKeD7lLyQl/zDHH2HzU1KKg/Xkdd9xxibmxyYddar5+cpUjA7jngw46yI6nP/7xjyXVQAFyIg8fPrzD++SJJMdjEt9++629BvKS+7lj3SuOq6++2tYhoc/279/f5lx3L2RcHFnlQ1QO0CyQO/22224r7LvvvvYZMl/Q7xgrfr7tcsCzcLmEqf/CfTz22GPFz5EXcbVA8owz6k64NqWGCOfebbfd7LMvF/fcc09h2223jfycPkEdquB1k6c76b4d+++/fzFXLHn66bPkRidf91577RX73R122MHOwcjkG264oaT+Rs5w5B/nZJwn5R/3Id8uOsvQoUMLzzzzjM2967/SQL0e+iRz09Zbb23nbcYmOdiTIHc7x7vc2jxz5iDuiVoJSZCbmroEHEuNLF78P++dcMIJ9phrrrmmsOWWW3b4LvUcXH0s/5lTi4f6EGFsuOGGVg678YdMLCW3b1BnYy5JypHrg9zy8+Xefvvttq147tRHYdyE5bUuJ+S4dzUquBbyhFMfxdWUiwM5zPWhC3z99dfFGhHulVXnRY9Omg98XZt81PRXahQE6xaFwf3dfPPNoTWjstZQS0PwftHVfJmadh7OA+OY2hqlwBhOk6M5Cj9XfKk1NZAncXWE+Ix85nGg+5A3HKid42q+0G+p3RgHcpx52tfTSwG9jrmSOdndK//Pe3nm/aTc78hgP8c7a+Odd965eA3Mg3F52NGNXZuhCziZzlhLau+kGgdJtQ6S9LWkuTTPmihY1yDshR4aB/WFzjrrrHZzEXVn9txzzw71pnyY8/n9nXbaydahop1ZTyVBPYGPP/44tx2B+WvjjTduVzOIfP68t99++3U4Pk0drqQahYw/18+Aud7/e+TIkbZGWRxbbbWVlT++rsT/86zRYSq1Bnf9jLHs9zPy5R9yyCFW94uzN3HN2Jkc1C7gu+ia6623np2LsTlVQvcoR20JXwafdtpp9trpi8iLNPiyP20tEebuYcOGFf9++OGHra6SxYYRRZKsp84EspR6Qb6u+cADDyTqyTwjamdwj3w3TY2/ctReQ/4ju6nzSE0s+hXrBeprJp2btaNbQzAv8N0sawsHtahoO67Hf6UhuA5NC2sR7FXUcOGe3PqM2mSlMGnSJFtjBxmfND6oPcI8gM2HGizM98hR189Zs/iyLgzsJaw56TOsY1z9MuqpJNk9WCuh+7i1P22ArGQMudqLWZBTo0GhA/mGIAeF9cIKAuYVPOUqGomhiQmWRbG/AOS9XXfdNfJ7FBx85JFHcp0bh48/4Tgo2sZAKhUUNYwOTJRMIlGCA8Uvr0OmVB5//PFUrzyL3CR4pih89B0M5Bisr7322tS/gQEnS6FwChijSLBARPHmbxQxBC+Fx1D60zg2soCS7BwpYZMcC1auJc34jlIYKwXndAoAkxPPjeKHDhxDGLyT7j+sUGkcKFq33npr8e+nn37athEKfSnGFAwMLDKC8DySFrqA04aJ/I477rDGO5QMikhSMP2WW26pu0KEPB9kJkbsuFepILMefPDBwqBBg+x9MQ6j7p9FJeMLZ5L/ioPx5xa5QFv74xxFLul5Zx1n9AOUZgdywu/j5YBFFMpaFNyvUxb96+bfOOd6OYpuu/OnKXQYhAUwY4LFOIos/Y9FBEYOzk9B4bjFW5SxMEtxUxYe6Dq/+tWv7ByT9vs4nDBUoDyjD2D4xrGQBpR+lH++766f/8fo7gyALH5YEIX1O2dA8J85i7Yo2UTb+gZT7hlDQlo9LKizlbrgImjDl+UYOVgwOzA+IxuTYHxhpOeVttB0FJwTZx5FUpNgDMYZnJOIchqjO0QV3UbuMDbojwTqUGAUAy1tTz9F9jnnVhQYoZiLGWsu0AkHLu/5zv5yk9RfwuZhFq5O3jgHftQrDTxXxmbaMVkO0BN5Zmeffbadh4LOrzgnGE7auOdJgd4kRxS6sXNkrLvuuja4AHD8JhlM6eNZjChhpCn0XC6jY16HKQVocWriJER+uvkM+UB71it510SumGvYi/agDZN0CMa107eQ8c55RBsyDqLIarClTztnDfdNUANBHWGvOGg3AvuCYLhPWpdkhb7l65lBWBMltTe/wXFhunNSYfk8a3COdU7KAw880PazUtbTGFZZBzguu+wy+1tOFlKsmyC/Suge5XRqOIMpxl5kfdrvZXF6Mh789QXQP8J0wlKZPn261X2SAp/43MnD4PoCnTIK5h3aBxsJfR7dmvshiJJgokraBpEh2NGSnBhh544KRih1bcG50T1KcWIFKUc/z+KEw5aHo7JPnz523DIXhMlKH9aKLhgOPQanNjor8zLzMXL7gAMOSLQVXHjhhTaoi6A8B8FeOC2SQNdFrmPLxsHLdeQdK9mKBAib3oGtUcFtRmw3ZdtUcGtaUq48n7hifA62v4alXGAbFp9VAnIyJm3NSkrRwtYptg377ca2NbY7u5QxYbCtiVQAbO/baKONOmzzTTovkC+Pbabk/XP5itmyzNa2IUOGmFLagdQMfIfUC2wxZAtsGGxZJL1F2tor5YJUH1G4LaH86+ehDxJM2VBqpjq2CLNdmbYpJZ2Ag62ibE8rta4F297Jc0qfYVsy2xdJ4eXSD5Gqhf5G+pJy5qV0udn97bpsASXfoIO0Okl5wIEUL2yZLQW2DLK1PKqoHds3GT9RYxjZ5bbmcp2MMT8FAP9P34+D3yctB1s50xahZpurnz+c/2frL1t4SelDypY0kD6JFD7+tnPgPZdfPA5SOZCKgbFDSg+2gPIM2HpLMSzSPERBuhm2XabFrweRVPgrTrax3TZtvta0ILkM/WAAAFeXSURBVLNImcGLtEykVArKCbaoku+bLa5+KkU3NuLSJ7DN1t9KzP8H02YkkXWcMR78/s84C87haQk+N9qA62L7eZy8Y4whV4L1U0gHkHbbtH/NbBEmJU9ayPWPrCgVxgHjwqW7oQ+4tFCkVyKNF/0xrJ5EUg2jNFvT0RtcPmhSpPC8kcuM77SFUUmfRDoMrhc9jTGftlAf/ZR75OWefXBsRhWHJxUDekdwG39c6hr0OP/36av0XbZoZ005VwqMeX+MkWLDl8VsDSc1YLnShQRB9pMKAd3JpWKiBg+vNJCyinFW6jzKPIcM4kVuZ18eIddo/6jc7KQqIVUF8wWp5ZiLSetECgcgZRa6R1wKR3QXUkhQK8KlQOS5k+eaOT0IaTiY97nupBRMUbmds0IquY8//tieO20tgaQUrvR75mv6e3BuC0vnE5Xmz4c2QQ8PA5lNzQtShFxwwQX2eZGqhDQISXMRx9I/OTaoVyFjSTvh9LEokGGk7qLfoEOQBhH9x6XzioO0NqS3yZJuI0iaNVS5YC7wa2aRcpFUkW4NSNqXOH2K9Rf6B+sL1glOv2OdHSzgnSUnedLaCDlAn3Epi0kLx1hIqruYd00UlrqRVB/M/y4VGeufONDrnVwhvRnrG5dCME6ek0KN/u5AP6edGP+uYHsYpCsizRm1KTieZxvW/rwXVwMGvTGYAgh4Ly4dTx64L1JFRhV4pj5q3L27cUXbBQtOk+Y3KZ13njW462fBGmBp+eijj9r1UeYY0uO49ISMu1LrcVUbUgsi15H9jHvSMiHb05BWp/RBZgTnAfonukwaWJ+wfnA1/9AnmFe5B8YNOmhcgWWXeits7c3cGZX2jeeKHYv0SegowFzIudH3kc/YyaIKfvPdpPowcXZNng19Db2nFLmYZ00RhBpSyG9SG4elzq4W6KvYvFgroO8hN8Mg7RbPhJTbrEfo2zNmzLCp/ZmPkiAFFN9HplMDA/nEuVgjAWuMOJtHmH7uSOqjDvo3/RI9h/Uwa4UkeZqEnBoZYbFGnkUWHj7k8UOxCubFDir8wZoawYVTEggXFGaMcE6IokST461SgiduckxbmAahGmYYZaEYlqsbBQ1nhFOmuD6/rUopiINhG4MSOQVdsUYMChjO0+T5ZhBj5CPHIo4YJvSk2gju2SNo0g70chBVoIcFI84YDL/lWAjFQX7mPPBMmVjpt6XUtSAns6uDgqAnd5//nBDUScpYVF5KFKK4vJQYG32DZbBYJr+TxohKn+c5YbBLO7niSGBScotovhssaochKq4fBs9V6sROu7BIwuFKGwSfWZhRhaKeKPi+kRdDIwt7HE8smtJAXyFnLoVSnQzEEMdvp6lTwOTqxgQLEWdAIW8ssiMOFuAun3Ma6EOuyDcL3Sy5nYFxnMfAmabYdlghRgwvKDIsdPiXnNCMGfrthRdemHhejHyuIBvnRLlyxV2THGd5xxlKmFP6Wdzj/AzOi2lyiYc9N54ZxnaMtlFQ34X2Y6HijAL0U5z2cc7vJOdXWgMVRlWeE07etLmCw2CsOIcUL+a4YB0aH2ob4LAMGn54/hSJj8rbiwzAiUE/Y6GJIk1tjKTC3kGQSzi4WQwwpxBggT7Bs6Atwgwm5TIAYtQhXzA5rOkjjBfmRxaRzuCdNE7CxkpcPnD3XILvpaVfv37WKMBzxijDwsevK0De/Lg8yiyOGcvMx85xQz0GjCFcc5J+kDcghPPTz3neYf3cN6r6UNuHZ0QOavRpXza4wpFROja5pZm3+G0MrdQU4Lm73MRcU5JThmeE0Zb7xmjK/M1CP8ogge7qPsvjWMjSX1iDuGKX6EbkD89Td4+2L5W0xXaj4JmSS5sX/ZzxRS5qDAT0VfpAlLEaQxO1Olgb8R1n/ETXItc+czdO0DjQ01ydFI6lnyIPMTTx+3EwP+MEY1yF9fGw9Vza4qaVcIKVy2FK4AmfMyf6gTfUTEwKpokr2s51oFPFGcmRZ7QrcsU9b8YrTtokJ3neNZEPegPjD70f5xZO8zQOeuQPARTIZNbVyEiCU+jHcbIpq8GWOZ+ABEBWMI9k0VnRA5iraUOXax3DO+uZJOcfYwlnF47h4JoBmU1x4jBoH4K0GGdhTktkA5/FgVxBNqIX+8GUjFvkZaUgYAI9J4moWkPcrx/8wjPEUeJ/jt2mErpHMDAtCM89blwgw3mxRqDN+T3W88Fg2DhwWIXBfXDvBLAE52T0Bozj/vvIdtaF/rmj2py+RgFlHIbMAawrcSrQ9qxJ+Duq9qyDYDzsH64WGteLPKOvRwXfIssIXgs6FGg75ApOU3TvKP2eeTKONDY61iWMCQKO0gYaIfvC6p5lgft86aWXOjgf69EJh+2ZY5E96EwEVdAvhg0blvpcjBMXDID+Qp/1A4B4j35RCf08GIjCb7GmCdZcjRoncahQeEYwmDAxBaPsUGaJNsHAEwVGNyZXjOO+8c0VJdpxxx0Tz0/0AIoMirfrmBTIQdgykQWLN6YpQBQnePg+HTjJIJvk3SYSA0UZ76JfmA5DDzswmIh8GKgY/6KKeKc9LwoZbctCtVRPIEOESYIJh99BkUTQJE0uDoxmGHtRvMJ2mUQZJcoJkxrCEiWMZ0k0ABNRXL/g/lBAUdadsQ5lOBhhHGfg4bw8U4QTxejoYxincDLgbIhTdKImYOB7UQXtWOTSxzDKhBWPwriWFK1MPyXSFKMSY9x9n7GFgYqFTZSzE8dR1M4sHA48fxxpcRCFhkOF8cZYDi5Ww4R93qJ29AW89E4hY4HGQsL1V2QNinKcchJX1DKqYBaGRpwgf/nLXzp8RjvTD5CnUef1i9IxUVNs0ckLnh2O5zQFpzBE4bRElqBUUlCYhQgKMQohRrwoME6WUogQgyrzBMaSpMJfUbLNycasTo08Re1RQJzxjvGGkZYFPu+xSHa/GUaagvZJUThZxxnGkDRRmlGyxYeoEv+3GD/IShTDuIhNZClzEX3G7apkzLmdiHkiTNM4wpzMD3PIJH0Xox/37XZMYFhi4YPsoZ8SHR9VSDyqvzK2eS/qvIwlHBgYbHl+nCdLUUL6N+MTxd/tHGDhiKzns7A+W84oeKL3mXdxrgAyCXkZFTmYZpzweVRR3CR5nrRwuOaaa6zBCKMMi2raDJ3X12vQ3fjdMJAL6LoYvH2QFewCi9rR44OOghzOEhASptu4YKI0i2xfRgd1GhzAYRH4SYUrKbTKcy+lEDCGW+QR8jVqVw/wmzwfZLLr36WQdf4PFrtkvqBPxxW7rHeYe7gH+sDnn38eu+5BnyToAd3Q37HI+gx9KxjFWE7i9PeoPp6ko6UpcJq0O4axTdtF9XPGNE4MDIDMKcg61rQuwhS5jEEnTucqJ2G7HaKc7KzZmecxqDmHCkFk3At9hWuPgnGM8wt5Glw3sbOUuZT5KW7scBz6A7oq7YhDpZSxxnyBMRo5gb6HvsY1Y9BEh42676B8iJpTouYT1s6s82lbf1dtWghOwpnEmsB9n/cwgKIHxq3reV60Pbq5D84cdH3kchi8Txuj0/DcXGAn/QVnJPeE3hCWNcOBIRhjLc/VBQ2h0yAzMOTGFU3PswZ3O2qTiArww16BjQY9FRmP7sV4dMEk7CbgHnAclFv3SCszg2sEzonOwfoEvQ57T9QumySS9G2eIboRTgjn8Mrb5ugJGKnp58hDxihyBXtZWqcQ3+PZobvS592YIUgP/cDZRXyYt5NshBjR/aCWKJ0nK8hS1kOMkbS7NPOugX3QU7FBEMSY1fmGXZex7gcdJdn4wpxw6B5xTjh0Un6T8ec7orp162b1zTQ7Ncqhq2bVz/OOkzjk1MgInQ3FJGySZGEflwKKSZgJLjh4mDiINEky4Ds4BwtlHCnAggeFLCwdSV7BUy7BhbLLQGCSc4Y/hBiCl0EdjLoq13mBiEcEflAhSIJnjCJIlB2KeJRXOMq4HzdBxxklygWKA9vZULjZzsZ9xClSUZN6MPVSknGAz1n433///dbxhiOB9+jfjBOeOdG95YZzoejjvQaeOed2xgnGGX0wrt3Z0cMild/yhT3fQdGIilBBwWXRg2MBB4OvBHNN7KRgAYHCkUfohwl7lE0cFq5/owgTheT+Rjlm4o5Kf1XJiSYpIoYIiajzo5CxuMZoEkZQsUHRRNmIW2iEgULDb6EsoBjzDOmvRKKx2AtGEdR6jOeVjYw/DMUo/mFKDY4cDNdhu1xQQDHict8oyTj/cD5hsEVeVioFYrnHmYsGDSqh1YDFLkossgQl1I/IDyPJ+ZXWyZ/ViUa/wHiDoZfFDS92yCSl3PD7K4pyMO0QconfidqJguEFme2cKURU8by5TufkSEplBKQDwpEehN0EzOssHMMMgCxSmPOzOGzDYGxUI4VUOeQ5gRDoasyH3J9LTwg4izEIRRk4kSc8NxY9wbHLM0uz8yhPQAjG5jiijHdRMFZpD/RUdKmwCOVgH6cNmN/c/JBmochilrGFEY3AC3QQAkKYi9gBRtRxFBhX0K+yGNLz9hfGKTugaR+eO85dFufoWn6/SQsRrsHI0EqmSMJpQ+AWzxg9Cgcoc6PTI5NANtJHeE4YGvwdBEnQH9CF3dqPQBbm57SBU9Umb1/J6zAF9EH6G06RYD9Ju8MkuNsBA25SlDDra1J+BQMHWWegX8cFS/FM0ZOijEDorcxxUbtJ0MnQbxhPODbSZBiop/kEecjar9Q1uIOxhW7u2zyYg5LgmaE3BA3c/A7BCnHPDMM5xkOM+L7TkuBTAqjSZjxg3ncBDejMaaLL867B84CeiJOAdSXrK3aV+DoS8z8yn7ETBEN8GiN8udeSjC/mHOwOeWXn8OHDi0ZqF4BLQAb6CDIDuxWOUGRYmt3pacCYT39bdtlli/2WcwbtjEmwxsfpxnoOXZN1EDtGS93dnIY0jgVkY5JcDetHSbtBymkfxAHkAsvT7mBPo2fFrf+zOuGYMxmLBO0hA1nXEOi39NJLl+TUoM2dzZXxjUPN2U2w1SLz42RLPQRsdyBXRY4mhgJJFFINKzZLsZmkwlFhhfAo0pumoG0WggWnalUo3C/KQ1FAXnHFHKOKNWaBQjgUKyuVYPGhPIWEqgnFrDbbbDNbTPDUU0+NLHYY9/00rygodEmxMgq6hhXb5LOk4nBZoPA7BWGjoC2OPPLIxIJPrmiVX/zphRdesMW04qBoH4VEg/2EQp9JRUJrXdSuXPjFYf0CUqUwadIkW9xtk002iR1fQdlUakF7ipRR5HuLLbYobLzxxrbI1tSpU22BVgp9uuLplYSClxTz23zzzQsffvihfe+mm24qPPnkk5HfoUDx3Xff3e49xhNFtCkAdvTRR9vicpUoas8c5/ry/vvvbwtgUuSTYmVrr7127L0y9inIHlaUFBlF4XjaI4ms4+zrr7+28zTH+d85/vjj7WdpoWgvBXyD8F61i8ZXA4piU1iUwnUULGSMt7W1JX7PFaynnYOF7ZmT6Yc777xzSXLhvvvus4UMkQ0UM0zqc0EoRleO4o1pOffccwvvvvtuSd+hUHCe8V0uvvjii+L/UwTzzDPPLJx88sm2OGEcPNttttmmWNAekG3bbrtt4cc//nGqc3OvUa+kAszlgHmANqeIIf2Xa0e/oGh21Fzk93EKu++0007Fv/ksSVf0C33eeuuttkDvlClTCldccYUtxh3HRhttVHjkkUcKtSZLsUtX0BU5TP8O07UrAYWGjz32WCvbaN8hQ4bYYtXVbKvVVlvN6umuqDr/T9HOuMK+FOmk8Hw5iqqXQ18rFeZJ5ABt/8knn7T7bODAgVb3ioJnhL7CGhz5f8wxxxR+8IMf2ILR9Lsk0DModEyhZnSuNPqGY7311rM6TBDeW2eddWK/u8IKKxTeeuutWB2dMRMF8oW+gWz159HgKw2sZdAxefH8q0HWNXhe0BPOOeecDu+fffbZhQ033DDVbyATkBW8SpEPzB9hz5ziy0nr37xr8Lxw3ZdccoktHhwsCM/a7JVXXqnIeX/4wx+2s1ecd9557XRzdBLWEJXuMw888ECH93mPz4B1BsWNywVznG/vQsaVojcyJ1Dou9SC23mIsg2mXb/nPXe57INRNr44+16pOn0Q9KLhw4cXZs+enen76EvMo1tuuaXVs1pbW+14pe2TCCuuHlZsvZ718zC0UyMjRJUQIUBkhsvnSJoCIqPJrR63HZQoLCKqiBx0XjEiuPDUEZ2UJiKTlA0uBQNbMIl8IdqAyNVKbBHDI0k0ddaiqkGIrMEjTcRCXKQn140nMcnrH7Y1LQi7Y4i2ZDdLmFcxqp5I2gjZLIWlKgW5QPEA4/kl5UWWSDk8tEQgsLWX50U/J0IhbWFiUkyw5TSqiC0ecdrWz/tJ1CeRfnjFk7a4Z8m3V0r70UdIB+OiLYnsxBvOVs2oXQN+VA73RW5/IHKP9iCaj+3X5NqOK1IahDFAdDIpaqLS03EOtjNHRXIS1caOnbDtwuUib3FYt82VKASiJolqpR9wT8E0Jmm3USbBM2aMIM/p2zw3ohaI2KwG3CeRFsgl5gTyY3PtRNqwSyKqHggRpLSryxNMBBwROURJEb1B/lvqD3BvYTAHEanmouaIPiQq2EWoIJ/Z4eQXm3fQRkRp8Wzo48w7RNwzJ/Gc43Ic541WzDPOmCfY4UFOXtrbz/VPnQa297PDI02ULe3Gd/wi90CEKWMgKn0W7cY4RV9gvATzd6fd1UM0NFvfOZ6c0ER30X+Yq+O2UDO+4gjTHxz0F7djAtmNvsK53I4JZGZwG7uL8CQqiCh0f/5wNQpIS5F2twzthZ7FNfAiHQPXkRStyPdcZJHbaYesIP0GuezTpOh048QvEMs9J8G4IlqNXbwHHXSQbYek+807vvPC+RjX6JeMK8Y110T/pa34l3kwqo5DnnQh5YAxQf515BIwf5LqME0dNPoXu8+4Z3RUZAXPAT0gLgquHNHMjA+umTZjTcAciLwgIp1zR+0SBdJDsRuXOS1Mx61mMWj6BzvJuR50gDTRxESTMqa5fuZE0jchq5FztEFc4cqs0JfJj04kaNxYjqs3mFfXZBlOW7n1Fes75ATXFlUoFNATmCddCuJa6Gt5KHUd60AvYS3iarg4fY8Uwczx6E5R5N3tgD7G7lDkrqtBQeQs+gZ91J97g+MNnYt5wM9d7oNuTpRw1M6BckTAk0KIdsOG4T9z9BieeSXlcpY1uKuPmERckXF2/KCrIv9ZkwL6F/MD80SeWkRJMIa5T9a1/tosSyrCapImJVEUSWt3oB+z9kmzpmMcsdMmTxrHUmEeZldpsMaCv7uHHZTMyeXamZ43bRcgs106uWqAzsOazdXSKnX97stVdn2wY84HeyrP2RWwrpR9MMsOds6PXYj5kufEv26XTRpIM55GnqexdZGl4LrrrrNrQOQ5dqKo9MydGTk1coAihYKEsEUAkpYGBT5JmKC40JndwgXcwpEtyFEKT56FZlDwlEoeY6sPwp/0R26rGW3ARMV7CIOgAZx7YktUUjHApEJF7reiiNvCmcW4T90FFmZMRvx/HGmK0pYK94qziPPHCc04YZ/X2MuigUV2MP2EA4WBicovRuT30ywpmMpFlryUaWUGvxnV16K2YDJJ4VRkuyHj29XN8CE9Eo4sjgsrakeKF54lBcgrBVtxMbKyEAkWh0WuRRVKpA+g9DMpk5YEox8LoDRbKVGE+H5Uyo8kkKPILwyEQBuSeoI2i5MZ5RrjKMgY+F1uf7dADxsfPmw1Rel1RbIxzKKYYeQFFmvIKdo/DBROnlPU9n2USZ4bW4vTwNiImid8UAKRC1H54Vk04JjAiJeVqHFGmiEWszzjYHoy2pnzMu7D6rukTfVC/6fPuuKvQZChPCeMdjzDYHvFpTnL6wiLqzXgKGWhyP27+hr0F4iqlYAxg3FWSrFGt7jGkeCcKchf9Bz0BRYR7pWUTgjdDPnCdZBCCxgrzHE4VSgWHkdeYxDzB4ZLjuW30Jd4fuhqYako8o7vvCB70CHQyehj1OBhwYnhEdDZmGtcEdhypgvJA/2F50T7oJs6owR9Fd2bYpvMQ1FyCj2eOQgDGM/HpZkpJV9xHrhmnG/MQcgW+gsLZs6NbAoroIxBFeecr99XK01J2mKXSQXSAecCcxJOUgxZpBFCb6D/8czi5FpW8tYbrFQqY543cirOiYU8w+BB+2QJOMuqr9XaYYq8ZDwj8zF8khoIxw7BDfSzuJqW/D7rGeRQXIqaKEOS31/cGPPTErm/w/oM6wac6lH3xTmZIyuZkph2Zt5ireFSnWAQY73FmEM3qxRZ1uB8h9SczEVR5iq+m2S4xDmIE8u31zB/VjoYkevHfkBqG+cMS2uYT1NHjc9drY5KphUiDRM6VBo5k9e5X4naVKXCugyZQmCUqxNHykl0Ra6F9Rm6IM7nuPp/pVCOoAjWkjhFcLBWizzrdwfjkWvGye+DPCKghN8KUk77YBZYk7gXwWzYB+mjzsHBKy4NdiVSfs+ZM8euG9DB0jg1wgILWLsyPzdiXTQ5NTKAYMMARpHVrEXgaHYUseBiL2kCK8dCMwt5jK1Bgw0TAYII5QrjI0KA/IVM9sFineXMmZeVLMZ9hBkRVBg/4orTpi1KWypJ+QnTCPusxl4HigB5raPyOJLPlvETFgleD1QiL2WSUyMJjOOMs7BihOUoapeXLMVhkRkYQuhbGJGQCyjUaY1IeaNb+B7GLr94IQZr3oszVPpjPK6oPTn7McpGwQIdQwIR68H6LXHGca6RRby7biIEaQeMn0AUEYYSzl+uYttpIrCYn3BoYrQNmw/yRivmGWe0MVG/wWggX4E+9thjbdslgXxkUcyixod5mfejDBP0FxbYzrCehayOMAjW1EGf4XvoMxj243bZBOWNczTwoi8ylvI8tzAwtGDkok+5hQJzaqmOZRbBLLSC0aDoHuSHJiK8WsYgdCB2+WB4Z3yH1ZfIO77zwk4SdBNXQ4p7ZAeDi2ZHd8WAmKbgdynkdRbjkMQpwHMKFvNG1vG86Os4OMOgD2PsxWno6+TVcmqQpx09GUMeRn7GJnMchYGZwxhrUQaopHp8lTLgZSl2GQb3zFzIfTP3cr/kM8doRF+PM/A3KhgJWccFd/whI5g/44y1zAO0NzIcA3+wrZNqS2TR18pB1nUs/Qw5zlyH04z7x+mLoZF1Co519Jq4Nsu72yEuopf1LPIyarxxX8yZyNGwwCP6OvNbsACtw9U/i4N7C6sP5WANy/qBtvOhvTFiVboeWqngVEbXQM/i/v32bQScDQN9kGBW9D76PHN+kmEe3SQK6v7QT3DiR60RynHdUbslKkk9ODUYI+iKXIvrczhjOSfyCt2H58h14vCpF5AxOKlZn4Tthip3EG3e9bsvl9BfgvV20HORAawBgpTbPsh8h+z0a1shc5KcJsAYpM84JwdzKPMyQTUENNUbr2cMLKh1wHYS6So8inYwWFGoWBhlBcUDpZFXKaAMuYWm8yKzIHdGZgRamoioUknydGJMpchaklODnSgYybhGX7FEeLgiWj5plM8k8PwSceQEE95gDFcu2hJPJcpcVMQjEwQLzaBxn/QEUcZ9ouHd1mMEXLUph2eaSGnfa+4W+Cws0mxRZvKPSy3GxJcUYUJUIpMa52Wycx7lakB/cQakegEjDQabMHBWsBBGHrBYDCtqV0mHBqBgBwtsAe8F0+w4KCRPQSmK8WXZMhvs60EjcxL0weACk+sNKwTrg3EJAxrG5TBDE2BwTCo0irEWw0RQmSMiO24BwbPE2IPRkwgRDBh+MWPOHfYsHES/kIqJyOWwYtvIuaDzLI1yx3PGGIt8xClKFLEPEfZxTg0MA5UoaAcY/oIFPn1IjRPnEPDBkIJhlH7ipzSgLYmajoLdLHnTOPKMwtJz8HySDFFhzxD5gDMUJRXjRlSqEqew09/ZZUn/whCDIQljDP0pjizFXUmzxG+7/pkVDF3BVALAe2m3pzMe/EJ+/D/G5lIjmlgQsIijzaOcEnnHd15oE5e2EmMz1+ynZeP/g9eOkWXAgAFWnkYZ5pKKCGLEd7I3GOSSBoyR9JmgQwMwUrC7Gv0syqmB8QnjPPMRhkYCWFisl0MXTQPztzPkoyc7HZO5IGrud3N9LVKgZi12GQb3yH3j1GBcIitoCwIVnL5ebrhe+kPWXex5oZ8yZjCmuIK0RH2yRklKeZU3bU4Wfa0cZF3HMj8jb5lvcVBimMdJiQ7G3ELwSFLgBWM7D8ExhgxkfYm+w9wZZ2glWh9HHXMZgUdunOAgJtUa341bc3DtOK+476zxqMwnYbot58ZQXCnoT1w/9+/WdIx3UtPgQI6SrxgEGQ8EEaLzoDfiMEUml5pOj3k0LOUn8qZSuPuiP3MfjGkcmARYJBGWGg3dj/UdMpE2COrXlaKa8c+0WbA/VGv+dfCMmIvYXetSWLJWYgenmyvot/UCegtrSNZWBHSBu+5KtmHe9bu/LnH34MPaOCowopz3w9yBYx293M3DGOUJ9MJZ7to0CnRe5iWCj1iv0C4E0LnA9XrjlFNOsYEi9G+cc+gh2DX9wAJspUEdA53cpWCL08+rPV6L1KSSRyeAgqgXX3xx5u9TzM8VLD788MPbvUopyhMs7koBw1oUrR45cmRh4YUXTjyOwmzuev1rpzDiAgssUJEC5cEi6cFCwkltRiE6CmT6UHA5rtCof04K5vjFNitNmuKtWYpWlVq4Klg0M/iKK5r5xhtvFItz+q/tt9/eFtOrJH4B43vvvdcWcnKvuALkaaCf5xmfFG+mkGilitrlJUtx2GeffbZw1FFH2XG56aabFoYOHVr4/PPPbZHVN998s+LXHNZPgwVeo4ovUpw9qtgfRbwo4EXBz6SC0xTHfu6552wbUBz8lltuscVSL7300sjvUWDTFbk86aSTbLHrGTNmFD/nNyh8Xqui9hQ5Dit6SXFPCmpSKDGsMC+fDR48uCLjbJllloktvk5bLr300qnlLMVG6QOu3SjiGVaQ0odirD/96U9t8d+sMKc8/PDDHeZR+mLWAorI1T59+sSOE+bCLbbYonD66adbHYbnlZa8xV3zgmwJ61dcz2abbZb4/VVXXdXK0yC8169fv8TvM2/+/ve/t2OdAr8UdLz22mvbFcQs9/guZxHG4NwfpjdRINDpOrUqIsh4fP/99yM/f++99+wxaaD474EHHmj1VtqD4vToupWCYqjHHXecfda+LKZ4tl8otZIFM7MUu2QeyVrs0oc1FXICkG88J3RtVwCzEgTXB9WG54re5OQrL64JfSnumddKXysHWdexl19+uT32Jz/5Sbs11V//+lcr29GXfBlZSR5//HFrB2DOZG749a9/XXj++ecTv4f8oRCyK0LrCrLyXtLaCtnAOjtPQXvGKnMhhcId/P/3vve93DpfnK6022672Xvl2vfbb7/Cvvvua4uu896PfvSjVL+DvoGOs91221ld64ADDihMnz498XsUTt5qq61KKgJcqb6O3sfYQtcv5dyMUdZJFATefffdC6+//nqhkiSN0WquyYLrsTjbQbMSnMf22WcfK0crTbnW7wMGDCisu+66hTFjxhTfGz16tJUR2EnDKId90IF8OOywwwqzZs0qvsf/H3roodYWFQXzDXPBb3/7WyuX0BVXW2012yY33XRTrC5aSxZddNHCiBEj7P9PnjzZtuWLL77Ybj3I2qye+loalH4qI67gJJ45tr8GPYlRUWhAtB3edb4XllM7rkAqkSxsvYvKH1+r4lNsZSLaMynKlUgLvN14Af1r52+ieyuRzzPvVsZgzv40efvZUUD+XwqDBp9ZpWG7HwXziFhyuSDDoL3xRBP5E1bMO5jWJyq1T1Ran6zbvGlrIrJoL6LViNhDTLGTBi8yO2uIRqhESjK2lbIDy3mgec7+tkfuh51GUcVGk6LEiKIm4jfr+CS6FC952DhJszUdKlkAO6w4LFHZRAQkFYelnWlbro+tm7QR/ZP7qmQUZZ68lkQHEq3DdftRlaTJYIeGi24Pi4AjCgj5Qd8m1y873dzWf8YcuxzY5hm3i4n+xo4OIqlJt8L2dgdphIgOS6oVkLWofRL0dZ5dUD4gC4l6Qa5GRSsSlR63qyjrOON62BFI6segbCQNHpE6zAuljBGeNduVifCh7Xx56SCqxZeFRB/x3IlKCkbKJqUMAfrKLbfcYq+TeZe5hlR/yAdkP/NpFMxbPlwHO1iIymHXkqvZEISoI6LWsxYbzlPctRzQH4hEIirT7SghhQMyi/ZL2m1BKgjGKX3U1bkguou2ZtdTXNQ04xCZRlQy0ZW0QVJBwXKN76wkpfVjvDAPVULXzJNmhV1QyNyoNCXoquihyLe0kLKNiDbGG+OTfssYLieMAfoladDoI36NA1KVMZ+yU8jfLVOJgpm1gMhpdtcE69YxfxL9TnR2pdLO1EOKW6A/ubQXPPu42or1oK/lIc86Fr2JSH23HkjKDlBPueOJROYeGafIH6cHoDeEjeswkLvoVMgi5AFzGu2BzhY1/oO1ztC1mevdTnr3/8j2SsgIdGdS6jGHBlO1smOHuZP5P67Ytw/pbpAP/Ms8mdR2pHxy6c7CbC7My5UCew/piYJ1s9z1R+3y9uce9A52KJERg13U1ch1zxhlB5m7bvQedsEHd/pWIsVMJWoNZAU5E7azOGkXXbWpZbqwcqzf6eesmdGp3bzD/ISuhryr1E7NpMLwPH/0/bC0fOjD7L5CprODj3HJv5XKNFAvdtHWGve1OOTUyEhcLQ0mzLhCX3R4tsBn2bpWy4VmVmOrDwt0rp/JEeWQlE4IDZQzjA4uX3M9OTWyGPdRBkhbxbNmQkRIRxWlK3dROFKgYGThdzF4IZC5P7bHoUjT3jwHFjIYFU877bTQlCS1Uiy4dtIZkEopLO8s2/tQ4DHslRsUFRRsZ1QJ9hXGLQaTqIKVedssKkchEz7GFLaTogiHjRP6aZqt6XFO03IQLA7LYi9tnn5/izULR7ZFYqCmH6cpelULSDvAgo06CeT6R7lDOUPmINOitvS75+XXCSCVAUZy2gxDZhroGxwblC8sTnk/zrFZyfovcWCAZ7syzhQ/TRpOBRZPSbWqso4zCjQjD5Hl1MdxDlOMSaRnY/5Eqfbrq6SB34UoI5CfNigJFrtJRDnCXDFHFPSk4pNBGYGBnAVJWIqmsMKRpZKnuGsefEMSqRN5zr7xkLQnUWO0XMYgUoqg8zgjHql60lKp8V3PhoU8c5lzXF155ZWh3yFQAp0sS9Fp8iYjn9CZyl0LDB0a3Y25M+jQZS5B52EeJeVhvRXMrEXdunJB2yGDkoKOsjpzs8J6gjYJOqFLKSScZu5mfNHvfJnIc6gk5VjHYgQn7RTXG0xzmyYwoBa544NzKLV7SNOXNS0sehTraNaazEWs6cL0xrQ1FislI5BdPN+w4DlAl0FXZtxHgbOX+2C+YR52NTai9BUf+hXO0TTH1hOsN3FikAaSNgpLR1Up4mqBVromaD3gaqAQBOHry65Nqm1jy2vnqiZZ1++u1jDXjR7A+qBaxaqRwVxvsCQAMglnK3bCIMh+bHzYjBgvODSqmSK9VoEFrXXU14LIqZETogRKXaTS6fFkllroMk/0e5oCr3FR93mMrUGIlCUq1C/AjCGb6KBKENxpUerulqyLe5RyInHYtUPEc5S3GoNoJcBxgef8ySeftMovDgH6KYYCjIco5mmjg7KAYQEnAYoYRtu4+ho+9AeUX6KgwqCgEcpeJRYu9Amem4seDwprFBwMC0QQVoKogtMsqLkmDMFRBl+MtOT2xRhEn0Xpz5u7Py1EPGOU9POYswDBQIvRlUmfSKOwKPY4GJMseDFu1KtTA+iPREwTiUbkOQswFmlxkY6uPgEvoj2IBKKfsfjjhZJU6RootXJqOLJGK+aBSE8M2eRJDdadwVCSNkKW6GK3Y9MVsEVeUE8DI3ZUvaVyQp+h/ZwjjN0URDzH7ZhkLvDhOpkbgw7kckc05ynumoc8hqRyGINY1GEEpz6A2x1AP+ee6T+VjkCrJkmFA9NGeOaZywiQQXYy57DbzXdcMlaR0UTHEr0bBgZVjMluNxe5h/kt9Dty4tOfuD501nLCri3GL7pZGOglOGTIR1+vuw2yguznWQXr1qGzVlqOJjkG6Dt8Xol5kOft+hnrAHZ2YyRkDmFNhTElyjkXVkiYWglEmyK3cKQTwR8G7Yojw+lrOMJ9Jx26OmuWpDmhVg5T5jB+gx3b9Jng2iJNYECpcI6w3PGlODXKbQwikpk2wrGBLkAgUdpgmGqCUR75xU6DMOizOLnC9BbmTe4RfRrZyHNHNkQFCYaxySabWGcwAXG1IiziH7kSt9OI/oJxFydj3P1G2W1EdngutDlBa6y5sdmxxkU2X3jhhVUztpfLzlUL0qzfK2U7KBVkO0EyPFvqqQCBtQSKUfeHoJEgOFexr7k6g+xWoF4Szg3n5KhWhpZqBhZ0qcO+5pBTIwMsUjFaYDAOLlIxbCVFS7EYQvHIU2g8rxLJdnaEdtDQHqVE5jG21ppa7G5hgJNCiXPT9hhRalWIsFaghDKR8fr888+tQoqTA4U0zpDDZ0RLRxkXMeJhFEsqiJsFFnEsDFyxKq6DaAGXIgaDKEaSckdplossW9PLAeOLSdwZenD+4ODE0EckHYZWFp8YizorOOK4T/oOSk4p0f5EAPO8nJMDBRoDBX2N6LvO6tSoJczdLuUWsqZUByCGIKKRMB45AymOZPo4BvuktEBEDrroWIqXY+xPwje4up0ZaQ2uYYsHIjxZPKCcJy0e8qZRPOqoo+yY4HxEu3PttJsr7hqWSqgc1HKrdJ6UQo1IUE8k8IAIYhckgKGWPsrckBThmWcuY3HKbtmgo4x2xpDMIjUKxg7HYEjivOgu6HDPPfecdU6RPrUUg1paGHcE/EQ5wtkNhpxirij3Lqpaw72j1/lzJroY71UqBZIvH3C2Jsn/chdhJ7CLAAjSaqFzshRnXYkMxsGBvpRVLiBbWJ8GnR4OUiaxs5S1ELA2YQ5yO/y4Hpx57ISoN0g5hWGR8ck4rZbRiPHPHEW7IsfJtMCan0jdajo1fLmIvsF8jnxi50ipDkBkSTCtTiV2JOG0wxEVlZaFHZTYD8LWVdwTAXLMn3GBCHEpv5lr0I/Y7UDwZDDlZyV3YeWJ+M8axFoOSPtDQCJr9jwFoBsVAkDpN8hnbHqsydBjnNM5rkhyPdq56tUBVi+2A+QgaxLmRnRWxilyC9smc3UapwqZFpDJODhYxyPbGTs43+uNw3MEFtRzX5NTowaLVBRWjAkIS17BCTYugi1PrmGfam8XSoqGAj5HmHSGNAr+IpM2fuGFFxpmW1olwDCLc4NFFgYlPOEoS7yCfTBpgY5hjXzklegr7NhhbEZtvyeqmMmW66t30m5NLwcsVpjYXK55Fuc4tVx+/n/84x/WmImc7EwEd8CR0gQnWDBfftrJHcWK6JD777/fLtiJwK+VU6GzOzXKIStQgIO5dZFx7ARBRwiDXV4YQ1B6nXMXBy0GYXahxRlp8hhcwxYPPF8WzmkWD8Ecy1FE6S/sbOHlomq5V+4BpZ/zViqNUh5DEnnT0xJmGMmTUqjRoR/Qx4m6c/owjkT6K1GOGAYqOZcFawURQUd7J/Vf+gVReYxrFqPo6IwRdOpKBgYwZ2AwjYomJiKQXUYYADvbTo0sdevKRa3aDgMZMht9kmeL02TXXXe1fSBoHMhiSKXfuh2EQRh/OC1clHhQJlKvCcczjvB6AsM9xkXGZ9r6C/WUOz5vhCt6BfMmdgbOhw2ilCwR7vrRAdgBEZbysRL6Xtj49onLlkCQUBrbQVwaZ+fsCf5OJXdhNWrEv4N5mzU7a14cy269ztq9kvNgvYDOQlAGz4ysKjw/dHQCD3CMhdVYqCX1VIekkW0HPFeeMfDck/RFH9Y32PpwavDiHnAcd7Y19OF13NfS5YMRRdiSy+KbTh9cpPIZiyb+jVukosS4bZhBD17SZMHCLk2u4XojLo8/ijM7GRAIlaAWAwtjFVH9LJRIF1Cpe2sUiALjRXQzyi2TGAoTizoWUuQNJRLT93hHbX3H0FSpvk90Cn0xyqnBZxzTCPi58ys9qWKs8uUhSglGVH/7N1vlOxvBXXnkAS8FnBgYpF1kB2moWLDSx0iFVO7o0FKLbYv4AIewHM28F5dKicLSyDcMs35QBMYtog1JuxMFCj6GXd/gipEXg1SS7oAz2S88j3GEdCdEvoLbRREXEYUjJM75EHcNyCM/khTHDq9KwzUFryvtopx5POnYOMPInXfeaZ1QYRGmpOQgbR0phTqjU4NUTxhE/AAf/p+UW+jJpTg1SpnLMAwzhpDNRMgSZUcbOwciBiUMSVGLZHZEuDSq7LYlGo2I9UobctjJyoLepSPyIYKZnd0YdcNodP2S54rjyI+GxBjAc4uqW9fokIqG6EagPxLcxq6/vA4NUkuhpwYDK3zYAeOn+0XX9mXzpptuanf81RuMfdbPld69EwfPB4cCL5c7HhnDTt2k3PHBfh7Wx+P6OUEU7FpgzYSOzSuMuHGCMwt9k7Rm7DbBeUUABvMU91Gt8e0Tt/M9LN1eqcQV40anqSTYN4jux/nkdCAc19RDQ9+rt4h/B/ooL54NgRkE6xA0wxhkrY4OypwVV7+tkWHuR6/GqYGejK7GvHz11VfXRe2AendWNIrtIE2gOITV90LvYqe5Sz9FQCJOY+ZeHGDI1qgsN43MDXXc17RTo4p5b8tBufLm10NhFxRCFEEM3ESc4AzivjoDtSwUXi+g/BCJgpKP8ZaIVIxmvhLkoinpj86RUMscxyiY7MQiugbln+hO11dxvLBln+hiIpzrkXJuTS8Fxi1FtjDG86wxIDGuXYFwFg4Y6CuVN78RwaCBEwOlmbbBsMG/UVv0mynaohFgocMLA1LQaUG0Ds6qMDC2ErmPsu5DBB/G3jhnEosqnOXOYIUs5Xtp6lFhuCJq3aV4YWHN4gFDKqCz8Ds4XCoV0cwCBiOQS7tFyg76YSVr/+TZKh1lNAojzAGZJ6VQo8OcTluzO8iHxR8Gkah+lncuC+70DKYbS6qhVqt8wfQFohXpM+j5fi0QitvTHiygS0lr2CjUci7iudKu1d5JnVSoMw04CX1dmf7CuCK6lN0WwV2EDuYNxoRLCxeE9FME3nVGuVQJ0tZ+y9vPy5GOCKcIa1PkMrKRaHTmIPR3bAvsNi43ee47b9rMMBgj3CvR96QBrWTQV6NF/CfBmsWlleYe0KUIVoyqUdWoYJegfxH8hd6MTYD0mcwTBAS5ta1obNsB83CaQPGwwGzkJ32EACXGNC/kapZ6yaI8aKdGibBYIuI8zrsbV6QzL3j+2OrqFntMJtXKm18u2D6PQsL2RpxDKNe0W2cCbz6ToSsUTo71sK3JSQv7Rob8pUT94qxgETVkyBCbesX3eLP4IpoybVRNpWFiY2s5+d99IxeTHYY3lJl6dWgEt6ajtJe6NT0rRMfioMTxQ3Qyz9XfVs1iXRN9e0g5gQPDFQVHcaumYUXOinwQucXci4MCR6hbgBNVFGcYILonmHISeC8p4prFtx/FTSqntCnliIbCIYJ8YPHAQpvIYH8uCrsuRxrdgijhqMi9J554whrZWAi4reY4hAhmYBFTqR1wwQLeBIKkJe9OKeQvzqIopwbPo5IOnVrCvI4xix0bRH47gwh5i5N2ieWZy4IL01LjtvJGU2eFPoL84N7R6/3c60SAs3OvMzo0aj0XMQZrBbtvXHoLZDK7mIK7P+PSEQeLl2KgwUmCsz0uBTJ9jZ1+UU6NWu+GaDRwhGJc51XJfk6mhrxgHHQOXuZiZywkyIH88ZUgz32jo2AsdE4NjJzYO/y0mTip0+TcRwchqIIaOnyHeQibSiVptIj/tME81IzDqYFzoxHSMZeKH7hMilQcvYyVoCNZNLbtAJmHfokeUGqgOLIH2eSCX0Xt0U6NKua9TVrIZVkspc01HIweIU0KCnEwLUJUZE85mDhxojV0E1VBFBBCrF7zSZaTYKHwakaJ1BIUgZNPPtnmSweMfxgBMXzFRVqyVRdjUiWKcaYlaz7uWuIK6uGYiVO6KpHC4YsvvrDyjYha5A8OS99ZRdTF9773vcTiyc0EER7MF27rKs5d+hl93zk5qlUEU6SHnXUsUBljzPNEUbudByyyMUqyYI6C4ovsxmAOcMe5Gl0smOJSNebZdYDyzuLaLR4Yo1y/c5LceuutVidgl0mpOzWIIsfoipIfFdRBVCLOH9JeONnO3Ed7sfut0mkgygHjlZ269AFSgaEPEmVGfwjTCTHIs/CPSinEwhnDRtjW9kaHOZT5n3ubNWtW0QmHQYp+EpdmJ89cllRDJWmnRj3sYGNHk9M9iKLurI6vesClgIqDPkgKlnLCHJ+mVgD6cLmhriP6OOuPYJpX9HOczgQjEYgkOhekrGQNjn7JM2Ytzo561qgY3Nkx1ply7jMXYCPBmUHq4n322cem8Upb3L2cEf8EOeKccRH/2JLSyB9RPfKkIxKNaTvwdwWzFmm0QHExDzk1SiTPIrUSiyWiQjmeSZtIH7zJYU6NNKlnKlkwC2UJYwrbtHBsYNhpNsKiRPbee+8OaUgqBUMd4ymLFop9xUVylQMMbyhxfnQhCyjei4sCY/xg9CPNBP0EI15YEdZKELbVmUmWKKCsW52rRTm2ppfDcYn8CTqkiHDh/UoVA+4M4OxEsXP1NVh04RgM1l0StSWY3oYgBgwCYXUTouZsggcIQHCykfeI6CP4IE425tEh8i4eMOaz68DpPqTnQx5yLgwNtMugQYOKhcjTpjwhtR+GFealeoZ5mxzkOJ9wZGBEYa7CmcPOnLDdOc2cUsjBvOkXXkxTMyDPXJaUPirJqSGaC+qlxM3Jt912mx2n9dpfsqT0Ywwgc5HjyGw/zSryjAA5UrGmndNE40D9JmQkGQRwbJFWhzkJxzO7gnB41RN50mZyb6y7MVIyb7Om5N7ZkVotp0YYjRDxn/b6OltK4TzpiETj2w7SBoqL+kROjRKph0VqrfLm54HrwqhBZEhcBH5nKgRYyygRnAIop6QYwVBFCgi2+eGFBgxyFPEkaqdSBI0LpeQN5hiXt5MIXhRZDIE4OYjgrBQoy0TQOcMc56ZoKClM3FZndp6k2eosRCmQeogoeZwavJDtpD6pV2NKsxKMBA/m7E8DOgMGBYIQANniagrV6+IBmYhjg+tkHvn888+t3kHtkNNOO80WkYyb28m5TOqhYIoOdo1QoDSqBkm9wCIXA+ghhxzSLvof4x/zRtQOFba1sxuF+TYspRCR+KJ8JO1mQn+m9p3kqogCYwZpaXDwkg7q3HPPNfvtt5+pNzDYYrjlGl0kO7sv0P+TUvohl9i9h5M6KJdYyzZiahyRzYhHn2EequR6sBY599kZiPOGfk6AkKPWTo1GgKCXLOk9G51y1a0VjUnaQHFRn8ipkYG8i1R2e7CtN1hjgYg2iozGbWsL5hom+qBaefMbPYq82rgoEZwJTIzVjBKhJgTnRuFAAWShT18lvQj/T5QtgprPqmVciEqXkuTIIjUL38PBgbGXSF8cHLzcQq5etjoLUYoTAwe4Sz/19NNP2zmAlDau6BgvlGtRPySlt4mCNCJExWK8D+48w9HA7jkc3vWakpEoe6I8kbvsHsIAwryOwz7N3E6qBeYddBwc7UBbYDzEqYFjx1GPxhVSDyL3V1pppXbPnFRUzOVJRXWbLaUQsiuuX1QirU69pI8SjQtp+M466yy7c+yMM84wAwYMsMbReqQcKf0wBrN7ulnkUrNSiYLb1SBP2kz0C/QTdA/0C3Za4pxknVctpwZ6Ae2Kjk9NyWDdNAIPRX2hdETNRSMGiotw5NTIQdZFajB9hZ8egvRMRAlVKm8+So0rRotH8pprrrHKOwb4ShXqbFZqGSWCYZRt80Sw4BTACYYRgV0I8Pzzz1vjVCWL2mc1LuA0jNrJgfJNlOXw4cNtuo+TTjrJRgnXw1ZnIUoBwzb9GZnvHBiMTxVUr2+S0ttEgbzlGUelOyGFFQvfet3WjhEB2czcAuy8ZB5BHqYhaXGAPoM6Wsk0mHnAgUFxT3aq+E4NDEM4ZeTobk+wn5PehB1NOMQItlC+flFPoFdSsBQZRy0YdMs0qdJqSaOn9BPVI2wX+oYbbmgDE1iLkiK6HnehlyPnPno2jg2Mlugs6Bek2iIwNBhcWm4IPCUA9qc//alN5xa02+BUEvWL0hF1bho1UFyEU5/hJw0COQc33XTT1MeTfohFOy+Mon6BNiZZjLRhRTh9SH2QxVOMAoPjAkcGBnYGMV5IJnuMDURg3nHHHR1SQ4jsoIQRJUL6Ij9KpBqQM9flycUIRV/zU6LhGCN9SCXJGgmJUZfodD9a3eWZZ5FJHRJejJly5/NE6WRRS1ux9ZAomnPOOaf4OeMWp5QQeSGVGX3bjVPRGDB/YwhwEY1E4h177LGJxbpdtGEURIFRsLNeQd76qalw2peyuEOuNjJHH320TemIYcQViSf69Ze//KWN7BbtQacMA6PZt99+W/XrESIMjJwYeonqRo6TFrBRjBoYpUl9HHRq8F7//v1rdl2i/sD5RRo1BzaAzTbbzAY2AmssDOz15tRgLJJ1ICptJrvnk/QQdDMMlrxw+LEuJxABJyYZNsgCUCnuuecea9sh/WYjQZBOkq2Jz129rM4K9jEXcFOPwTYiH+yOxx5GgBJZOXg1Q2r8zop2atRAOEbBZxhQXVR4uaM0MEIwiZOfkomWouZOoSElBHk16z2vdSNSiyiRpBQp9Vwsk3Q87vW///3POhe4btJWOSdHpQoY5tnqLITo/GTdgYZjmSj1qPSUpABh10O9Rtcm1Sro7Mo/qvIf//hHc95555mpU6fa92gL6oSceuqpNmpaJEM/JxiosxUYFY2Jq/dHmqm43Xbsuq43Gj2ln6ge2oXeHta+6DCsyyvp1GAXDA6kRht/cTsp6SvUVyNtTz3aEPKidETNQzOmxu/MyKlRRfAA0twYAv75z3+2S1eF0ZTodAzNlYp2IP0QEytRcqQ+wTDLLgKgGA5KMQXmROVwUSI4lmjrSkWJMPH+/ve/L0awEImG8cVFoKG4El1a7woJUdDktHRODpxCpLFYY4017DbQetzqLIQQYTvQLrroosjdkCyiSHtCjYZ6pBy1CojqwylMJLFb8LP7oZFSruFkxzCPHsX1s7hn11UlUzl2JtB90EcIFhCi1lAjJ01EcjnlMukK0xJnDG30lH6iMQpui+zcf//9NrUoEeGNXh+PvsFuH2r4sMuH4D/nTO0sKB2REI2LnBpVhpx8pDH43e9+1y4dUKVp5Mj9zkilo0TSLNQaKSUISjiFlFEQMSJhUKpkX43a6oxSx/t+GhYhhEiCaFocswQT+Kkngd0ZRK+zC40FcGfkwQcftHVFyPXuUjEg09FDmAtx8Ndr1B4pOR5++OHizgwcUzhvKCTMHHH88ccXc5WLuRAc4MNSg1pyL774ojnzzDOVS1w0LX5KkzzOCPK9p6XRDaoiH9qFXhtI87zPPvvYFFq9e/fukL64EZxI6KdklyA9KnKEHau77rqr6YzkrVsrhKgdcmrUABwKREVgeK6moMZxEVXcVE4NUW9ODLbRUzjXpaHCCUiUES8iilA8hBCiEWCOJQc6RvBBgwYV86CzS5J0Icy91PCpVGq9WsMikZSXpEXxISUmhTS593oEZwWOdAqEs2sQIwW7VpifTjvtNPOzn/2sg/NbdNzWjw6K/slOZerHCFEPYJz761//ahZccEH7N/KJ2hpEssOXX35ptt56a/PWW2+V7ZxyRohqo13otQG9Yfz48ebII48MLRR+6KGHmnoFnZQU5aRFJxCHYNyDDjooU13XRkHpiIRoXOTUqAE/+tGPrHJRzcksKR820YgPPPCAnBqdBHZgxOUHrmfolzgxuH6cFywo+XfppZeu9aUJIUQuYxYRk+xacKoXCyiM/Tg2GlVmp4FFMcEcq666arv3R40aZVOskGqwHmE3KxGs7DKhJgrXysKXNJKdeXEvRDMQ3MVOal6KKjfSLnYcLhhOCQbyQWYJ4aNd6NWF3RnPPvus6d+/v2kkbr/9drsTlTTZ1F1Bb1XfEELUM11rfQHNCM4FohNZ4FPTIlhosxKKaNCBgrc9yCGHHFL284raQI5yIrxcYW1eyy23nGkEnnzySevAwLmx3XbbWYfGoosuWuvLEkKIXCCT77vvPvP111/bugw4NjDyL7zwwqazQ5Q+xsKgU4P3nEGxHvnwww+LtcfWWWcdGxhy4oknyqGRAEZhUpkE526MJOxYqtfaMaK5qVWcXxbHBGOIaHvWkn46Kyeb6tkRI2qD25EUxK/xKcoH9R9J39Ro7LfffqZXr15m//33t8E42KzCIC2VEELUA3Jq1KgQUdRkUKmCbtoq11xQFN4V12ZrPQsljAw4CpyTo17TnGD0wLHBtZP/FaVqtdVWs84N5+RwadSEEKLRwImxySabmGaCWmIDBgywhrgtttiiWFMDGX/SSSeZegV9zI9Q7Nq1q41oFfG89957obosu4I/+uijmlyTEPVGHsfEz3/+c7u779FHH7X/Pv/88zZd1i9/+Uub/14IUVtIZ8d4JK3Xuuuu26GmBjvD6hHSPCODxo4dG3mMAjuEEPWE0k8J0ckhrQe5wJ2Tg4XPrFmzbATJm2++aeqdyZMn2zywrr4Gxe6I9iUViBBCiPoHVZM0ThdddJEtUAqkdaHw9gknnFC3C+Sk1J0OFY6cy1133WX/pZg6edv9yGAMtBhgKbo+cuTIGl6lEHMhDQ/pp2pVb3CPPfaw13DttdeGOiZIvxrFYostZgOYSInHOOO71GriPb7/yiuvVOSahRDp9QcI6jfoQ5UKYhVCiGZEOzWqCFsQWdDtvvvu9u9TTz3VRq35EYAUYiL3tBDlgv6EEWarrbayOzTuv/9+W/iUArWNAMYjtkbzIsKZcfL222/X+rKEEEKkhAU8aZt44ah2BsR6J03qTjEPnBnueQfbjijVlVZayTq2hKgHMC5SI8c5LQkColC4X2+wkpBvHycEDgoMoLzQ1c877zzr7I1zTGAQdTKU7+MsxqlBmkM5DYWoPQTjRcHuLCGEEOVBTo0qQtTavffeW3RqXHbZZWbttde2eQsBI/NSSy1V16kYRONAyqnnnnuuuMOB4tvLL7+83VZK3yONUz3S1tZmXnzxRXvNXDspSqZMmWKWXXZZ65ShoC7/CiGEaBxmz55t5TopDQ444AD7HoY4UjDUa0onpe4sff4Gos6pqYGxVYh6pdb1BvM4Jqjxw85lxtpmm21mLrjgApsq7+qrry4WOhdC1I7gOpuADlJCszPrpZdeMoMGDTL1CAG2aTjrrLMqfi1CCJEGpZ+qImwjPuWUU+x2Y0CRRSF1yuctt9xiDbZE7giRB3Zm4MRgsYNSRd/jXwpw1zsYuHBi4OBz9T+opUHxcyGEEI0HxSZ32WUXWwyX6OdRo0ZZ3Ye88Pw9bNiwWl+iEEJUFXRzUkWxwwlH79dff23OOOMM65jA6BmXZvXBBx+0uvJPfvITM2bMGBswh1xddNFFzd///ne7DhBC1J4nnnjCXHfddeaf//ynTWfHmN17773rtrYaO8a4ziWWWKJY5ycIuzFffvnlql+bEEKEoZ0aVQSlk0JRflogl28RNt10U3P88cfX6OpEZ4JC2zgwWNS44tosdBqBP//5z9aRQXFwIYQQjQ/Oi4033tgGcvhzEUVyKSIuOgcE5VATwO1IhptuusmcffbZ1gCL8Xbo0KHFdD9CNDM4MBgXLjqacYOjwzkm4th5552L/7/KKqvY3f5fffWVTdNarzWKhGgWqNVz4403WmfGpEmTzD777GMDOO68806z1lprmXqGOmKkxUNnO+KII6xc8u1VQghRb2inRhUhzdSrr75qtxaHgUK6/vrr25yuQuSBRRKODZfCiX6HkwDnhnNyuMKIQgghRCXBSPfMM89Y/cffpfree+/ZBf7UqVNrfYmiTMYQdIxf//rXxbzhG264oa1bsOaaa9qghWOOOcb89re/rfWlClGXyDEhRGNDRg52Z+y2227mwAMPtLtUu3TpYutKofvUu1MDSIVH2nQcMzhlSMOHgyPKhiWEELVEbtcqstxyy8VuJX7ttdfsMULkhSKHKFF/+tOfbBqqL774wubb7d27t/2XfkY+XiGEEKIatRbIHx/kww8/bIiC4SIdBFDssMMOxb//9re/2Xz/11xzja0Xd+mll5rbb7+9ptcoRD3ywQcf2NciiyySyqFB8NKZZ55ptthiC7tTAyex/xJC1Ib777/fHHnkkeacc86xjg0cGo0G6adOPfVUW9uHXWOfffaZTZe15ZZbmmnTptX68oQQoh1KP1VFdt11V1tUiQmO1FM+TBBu8hOiEk4OFkq8iADr2rWrefvtt2t9WUIIIZqAnXbayVxyySU2VzxgtPv2229tWiJ0I9E5oCbAkksuWfz78ccft7s3HBhFMNwKIYyZPXu2Xfvh7EMewnzzzWcGDx5sZSOR3VEcddRRdnwdfPDBNt2sdnYIUR889dRTNu3URhttZHcoMkb3228/06gwb7Or9q233jKvvPKKmTVrls0+IoQQ9YLST1WRCRMm2PRS3bt3N4MGDSrWDMALftlll1nllsnCXxAKkTUq9sUXXyymn3r66adtVNeyyy5bLL7Na8UVV6z1pQohhOjksCODHPConKNHj7a5mvl3scUWs2kaKEgpGh90iptvvtlss802ZubMmWahhRYyd999d3H3BumoSH9Jih0hmp2BAweaf/3rX7aexuabb16sS0N6NurPXHnllZHfZWzde++9NnJaCFF/sO5ml8P1119vnn/+ebtb9eKLL7ZpnBphhyqyiGtndyU2q8MPP9wccMABVvYIIUQ9IadGlRk3bpxVYh9++GG7uAeia3bccUdzxRVXaMuwKAsLLLCAVaaWWmqpogODPNf9+vWr9aUJIYRoQgjcIB0RqTaJSqbWAvmmFfHXeUC/JWf4+eefbwuikpOb3NwE88Ctt95qd+y88MILtb5UIWrOggsuaGWiv5sJ7rvvPrP//vubiRMnRn63b9++9jgiwYUQ9Q0BrOzewOn/zTffWLvPXXfdZeoR0lRTS4PU1ehoODPWW2+9Wl+WEEJEIqdGjSBKbcyYMfb/yYVKWiAhysVVV11lHRluN5AQQgghRCXBCPKTn/zEpt8gjQ5Ojb322qv4OTs2vve975k//OEPNb1OIeoBdqiRQiromCA9LLudPv/888jv3nLLLWb48OF2jFEvTwhR/7Bbg92L7ICoV6dGa2urWWGFFczuu+9eDEgIg10nQghRD8ipIYQQQgghykopC/Y999yzotciqgsR5jg1ggVSCejh/ThDiRDNAmmn3nnnHXPDDTeYHj162PdmzJhhiwyvuuqqtq5GFBtssIEZO3as3fW/0kordai/8fLLL1f8+oUQnQ8yOyTV6OHz//znP1W7JiGEiEOFwoUQQgghRFkhJ3waWBwTvSg6V1qdMLQrWTQ77GTyeeSRR8xyyy1n+vfvb/8mfRv1aFwdmrzyVQghSoF6nMEdmEANNCGEqEe0U0MIIYQQQgghhKgg5KdPCzs4hBCi2lD34/TTT7eFzr/++mv73sILL2z2228/8/vf/17FwoUQdYWcGkIIIYQQouyQnmDQoEHmueeeMwsssECHFEVbbLGFGTZsmNl6661rdo1CCNGosKvjs88+M21tbe3eJye+EEKUCmkiN998c/PRRx/ZQuGu5s9bb71lbrvtNrP88subZ555xjo5hBCiHpBTQwghhBBClB1qZWy//fbmxBNPDP380ksvNY899pj597//XfVrE0KIeoCC4CNHjrT/v/rqq5vFF1888TujRo2ytTcwLvqwrFdKPyFEVn7xi1+YRx991KbGW3LJJdt99umnn5qddtrJpsf7y1/+UrNrFEIIHzk1hBBCCCFE2VlxxRXNAw88UIz0C0KRXBbI48ePr/q1CSFELZkyZYoZPHiwuemmm4o7Lbp06WIOOeQQM3ToUNO7d+/I72655Zama9eu5je/+Y1ZeumlOxT2dTU6hBCiFFZaaSVz1VVXmZ133jn0c3S6Y4891rz33ntVvzYhhAhDhcKFEEIIIUTZmTBhgunWrVvk5xjliFIWQohm46STTjKPP/64ufvuu62TAp566ilzwgknmF/+8pfmyiuvjPzuq6++al566SWzxhprVPGKhRCdnU8++cSsvfbakZ+vs846dseGEELUC621vgAhhBBCCNH5WHbZZc0bb7wR+flrr71mo4yFEKLZ+Oc//2muu+4688Mf/tDWHOK16667mmuuucbccccdsd9da621zBdffFG1axVCNAeLLbZY7C6McePGmUUWWaSq1ySEEHHIqSGEEEIIIcoOBrozzzzTTJ8+vcNn06ZNM2effbbZfffda3JtQghRS6ZOndohZz0sscQS9rM4zj//fHPKKaeY//73v+bLL780kyZNavcSQogskHbq9NNPNzNnzuzw2YwZM6xOt8suu9Tk2oQQIgzV1BBCCCGEEBVJP7XhhhvaPPGDBg2yRXBdLY3LL7/cFrN9+eWXQw17QgjRmaHY7qKLLmpravTs2bPo7D300EPNV199ZQv1RtHaOjcuMVhLQ4XChRB5+PDDD83GG29sevToYY4//nib4g658vbbb5srrrjCOjZefPFFs/zyy9f6UoUQwiKnhhBCCCGEqAjvv/++GThwoHnwwQftwhgwuhENiGOjb9++tb5EIYSoOqTmQw5iJHSFvUeMGGEdHMjLuLz21OKI4vXXX7dOZCGEyAIppo477jjz0EMPtdPbdtxxR3PZZZeZVVZZpdaXKIQQReTUEEIIIYQQFeXrr782Y8aMsQvkVVdd1Sy88MK1viQhhKgppJm69dZb7e41WHPNNc2BBx5oevXqVdLvTJ482fz1r3811157rS0grp0aQohy6G2jR4+2/48jQ7U0hBD1iJwaQgghhBBCCCFEA/HEE0/YYuMUHV9mmWXMT37yE7P33nubTTbZpNaXJoQQQghRcbpW/hRCCCGEEEIIIUTzctddd6U+ds899wx9/9NPPzU33nijdWZQFHyfffaxKazuvPNOs9Zaa5XxaoUQQggh6hvt1BBCCCGEEEIIISqIK/CdRFSx7z322MPuzthtt91smqpddtnFdOnSxXTr1s3W45BTQwghhBDNhHZqCCGEEEIIIYQQFaStrS3X9++//35zwgknmIEDB9raREIIIYQQzUy6cBEhhBBCCCGEEELUhKeeesoWBd9oo43MZpttZi677DLzxRdf1PqyhBBCCCFqgtJPCSGEEEIIIYQQFWbatGnm0UcfNbvvvrv9+9RTT7U1MRykkzr33HNNz549I39jypQp5u9//7u5/vrrzfPPP29TVV188cXmiCOOMPPPP39V7kMIIYQQotbIqSGEEEIIIYQQQlSYYcOGmXvvvdfcfffd9m+cEGuvvbbp1auX/fudd94xp5xyijnxxBNT/d7IkSNt0fCbb77ZfPPNN2bHHXcsqSC5EEIIIUSjIqeGEEIIIYQQQghRYbbeemvrtKDot3NqUOR75ZVXtn/fcsst5vLLLzfPPvtsSb/Lbg0cJezekFNDCCGEEM2AnBpCCCGEEEIIIUSFWXrppa3DYqWVVrJ/L7744uaFF14o/j1q1CizySabmIkTJ9b4SoUQQggh6puutb4AIYQQQgghhBCis0OKKL+Gxueff97u87a2tnafCyGEEEKIcFoj3hdCCCGEEEIIIUSZWG655cwbb7wR+flrr71mjxFCCCGEEPHIqSGEEEIIIYQQQlSYXXfd1Zx11llm+vTpHT6bNm2aOeecc8xuu+1Wk2sTQgghhGgkVFNDCCGEEEIIIYSoMBMmTDDrr7++6d69uxk0aJBZbbXV7PsjR440l112mZk9e7Z55ZVXzJJLLlnrSxVCCCGEqGvk1BBCCCGEEEIIIarAuHHjzMCBA83DDz9s3FK8paXF7LjjjuaKK64wK6+8cq0vUQghhBCi7pFTQwghhBBCCCGEqCJfffWVGTNmjP3/VVZZxSyyyCK1viQhhBBCiIZBTg0hhBBCCCGEEEIIIYQQQjQEKhQuhBBCCCGEEEIIIYQQQoiGQE4NIYQQQgghhBBCCCGEEEI0BHJqCCGEEEIIIYQQQgghhBCiIZBTQwghhBBCCCGEEEIIIYQQDYGcGkIIIYQQQoimY7vttjO/+MUvin+vtNJK5pJLLqnpNQkhhBBCCCGSkVNDCCGEEEIIURd8/vnnZuDAgWaFFVYwPXr0MEsttZTZeeedzdNPP20/b2lpMXfeeWdFzv3CCy+YAQMGVOS3hRBCCCGEEOWjaxl/SwghhBBCCCEys/fee5uZM2ea//u//zMrr7yymTBhgnn00UfNl19+WfFzL7744hU/hxBCCCGEECI/2qkhhBBCCCGEqDnffPONefLJJ835559vtt9+e7PiiiuaTTfd1Jx66qlmzz33tOmhYK+99rI7Ntzfhx12mPnxj3/c7rdIK0V6KceUKVPMIYccYuabbz6z9NJLm4suuqjD+YPpp8aPH29+9KMf2e8ssMACZp999rFOFiGEEEIIIURtkVNDCCGEEEIIUXNwHvAivdSMGTNC00PBDTfcYD755JPi32n41a9+ZR5//HEzfPhw89BDD5n//ve/5uWXX448vq2tzTo0vvrqK/u9hx9+2Lz77rtm3333zXh3QgghhBBCiHKh9FNCCCGEEEKImtO1a1dz4403mqOPPtoMGzbMbLjhhmbbbbc1++23n1lvvfWK6aEWWmghW2sjLd9++6257rrrzC233GJ22GEH+x7prZZbbrnI75Dy6vXXXzfjxo0zyy+/vH3vpptuMmuvvbZ1pmyyySa571cIIYQQQgiRDe3UEEIIIYQQQtRNTY2PP/7Y3HXXXWaXXXaxOypwbuDsyMrYsWNtnY7NNtus+N4iiyxiVl999cjvvP3229aZ4RwasNZaa1mHCp8JIYQQQgghaoecGkIIIYQQQoi6oWfPnmbHHXc0Z555pnnmmWdszYyzzz478vjW1lZTKBTavTdr1qwqXKkQQgghhBCiFsipIYQQQgghhKhb2CFBoW/o1q2bmTNnTrvPSUtFjQ2fV199tfj//fr1s9/73//+V3zv66+/NqNGjYo855prrmk++OAD+3K89dZbtpg51yOEEEIIIYSoHXJqCCGEEEIIIWrOl19+ab7//e/b2hevvfaarWfxj3/8w1xwwQW2aDestNJKtt7Fp59+ah0TwHdefPFFW/Ni9OjRdlfHG2+8Ufxdio8feeSRtlj4f/7zH/sZuz/Y4RHFD37wA7PuuuuaAw880BYUf/75580hhxxia3xsvPHGVWgNIYQQQgghRBRyagghhBBCCCFqDs4H6l785S9/Mdtss41ZZ511bAoqCodfdtll9piLLrrIPPzww7bWxQYbbGDf23nnne1xp5xyii3gPXnyZOuA8Pnzn/9stt56a7PHHntYh8VWW21lNtpoo8hraWlpMcOHDzcLL7ywvRa+s/LKK5u///3vFW4FIYQQQgghRBIthWACWiGEEEIIIYQQQgghhBBCiDpEOzWEEEIIIYQQQgghhBBCCNEQyKkhhBBCCCGEEEIIIYQQQoiGQE4NIYQQQgghhBBCCCGEEEI0BHJqCCGEEEIIIYQQQgghhBCiIZBTQwghhBBCCCGEEEIIIYQQDYGcGkIIIYQQQgghhBBCCCGEaAjk1BBCCCGEEEIIIYQQQgghREMgp4YQQgghhBBCCCGEEEIIIRoCOTWEEEIIIYQQQgghhBBCCNEQyKkhhBBCCCGEEEIIIYQQQoiGQE4NIYQQQgghhBBCCCGEEEI0BHJqCCGEEEIIIYQQQgghhBDCNAL/D/LqErSUJnHuAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ROI Distribution Summary (for context):\n", + "count 4138.000000\n", + "mean 290.635152\n", + "std 1084.418256\n", + "min -99.896400\n", + "25% 12.138712\n", + "50% 134.604971\n", + "75% 312.646417\n", + "max 41556.474000\n", + "Name: ROI_perc, dtype: float64\n", + "\n", + "Number of extreme outliers not shown (ROI < -100% or > 2000%): 71\n", + "This represents 1.72% of the dataset.\n" + ] + } + ], + "source": [ + "plt.figure(figsize=(16, 8))\n", + "\n", + "# Calculate the median ROI for each studio and sort them\n", + "studio_order = studio_df.groupby('studio')['ROI_perc'].median().sort_values(ascending=False).index\n", + "\n", + "# Create the boxplot, ordered by median ROI\n", + "sns.boxplot(data=studio_df, x='studio', y='ROI_perc', order=studio_order)\n", + "\n", + "# Add reference lines and labels\n", + "plt.ylim(-100, 2000) \n", + "plt.axhline(0, color='red', linestyle='--', label='Break-Even (0% ROI)')\n", + "plt.axhline(100, color='green', linestyle=':', alpha=0.7, label='Double Investment (100% ROI)')\n", + "plt.title('Distribution of ROI by Movie Studio')\n", + "plt.xlabel('Studio')\n", + "plt.ylabel('ROI (%)')\n", + "plt.xticks(rotation=90) # Rotate studio names vertically\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# --- BONUS: Print context about the limits ---\n", + "print(\"ROI Distribution Summary (for context):\")\n", + "print(plot_data['ROI_perc'].describe())\n", + "\n", + "# Count how many movies are outside our chosen y-axis limits\n", + "lower_limit = -100\n", + "upper_limit = 2000\n", + "outliers = plot_data[(plot_data['ROI_perc'] < lower_limit) | (plot_data['ROI_perc'] > upper_limit)]\n", + "print(f\"\\nNumber of extreme outliers not shown (ROI < {lower_limit}% or > {upper_limit}%): {len(outliers)}\")\n", + "print(f\"This represents {len(outliers) / len(plot_data) * 100:.2f}% of the dataset.\")" + ] + }, + { + "cell_type": "markdown", + "id": "0c2e5911-091d-4c46-8f72-aa97f75ec51e", + "metadata": {}, + "source": [ + "We will also define our null and alternative hypothesis since this model tests if the choice of studio has a statistically significant impact on profitability.\n", + "\n", + "- Null Hypothesis (H₀): There is no difference in ROI between this studio and the baseline studio.\n", + "\n", + "Mathematically: β₁ = 0\n", + "\n", + "- Alternative Hypothesis (H₁): There is a difference in ROI between this studio and the baseline studio.\n", + "\n", + "Mathematically: β₁ ≠ 0" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "afd87497-ae04-406f-a31b-3d093da3d461", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: y R-squared: 0.045\n", + "Model: OLS Adj. R-squared: -0.034\n", + "Method: Least Squares F-statistic: 0.5653\n", + "Date: Sat, 13 Sep 2025 Prob (F-statistic): 1.00\n", + "Time: 23:26:40 Log-Likelihood: -10775.\n", + "No. Observations: 1254 AIC: 2.174e+04\n", + "Df Residuals: 1157 BIC: 2.224e+04\n", + "Df Model: 96 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "const 230.3041 1357.575 0.170 0.865 -2433.280 2893.888\n", + "x1 92.5819 1388.086 0.067 0.947 -2630.865 2816.029\n", + "x2 -312.1266 1919.900 -0.163 0.871 -4079.003 3454.750\n", + "x3 73.5408 1662.683 0.044 0.965 -3188.670 3335.751\n", + "x4 -328.6931 1919.900 -0.171 0.864 -4095.569 3438.183\n", + "x5 -295.1310 1919.900 -0.154 0.878 -4062.007 3471.745\n", + "x6 -278.1543 1439.925 -0.193 0.847 -3103.312 2547.003\n", + "x7 -240.5241 1517.815 -0.158 0.874 -3218.501 2737.453\n", + "x8 -274.7395 1919.900 -0.143 0.886 -4041.616 3492.137\n", + "x9 -328.9802 1919.900 -0.171 0.864 -4095.857 3437.896\n", + "x10 -166.3942 1466.348 -0.113 0.910 -3043.394 2710.605\n", + "x11 459.3469 1487.148 0.309 0.757 -2458.463 3377.157\n", + "x12 -263.4624 1919.900 -0.137 0.891 -4030.339 3503.414\n", + "x13 -69.7972 1451.308 -0.048 0.962 -2917.288 2777.694\n", + "x14 -7.6108 1366.595 -0.006 0.996 -2688.893 2673.671\n", + "x15 -8.9561 1417.940 -0.006 0.995 -2790.979 2773.066\n", + "x16 -236.0636 1662.683 -0.142 0.887 -3498.274 3026.147\n", + "x17 57.0702 1919.900 0.030 0.976 -3709.806 3823.946\n", + "x18 -113.0204 1919.900 -0.059 0.953 -3879.897 3653.856\n", + "x19 -94.0865 1662.683 -0.057 0.955 -3356.297 3168.124\n", + "x20 -280.0808 1662.683 -0.168 0.866 -3542.291 2982.130\n", + "x21 -172.5358 1567.592 -0.110 0.912 -3248.177 2903.106\n", + "x22 -302.6779 1919.900 -0.158 0.875 -4069.554 3464.198\n", + "x23 -234.4740 1662.683 -0.141 0.888 -3496.685 3027.737\n", + "x24 -209.0737 1567.592 -0.133 0.894 -3284.715 2866.568\n", + "x25 168.8846 1517.815 0.111 0.911 -2809.093 3146.862\n", + "x26 -275.6942 1919.900 -0.144 0.886 -4042.570 3491.182\n", + "x27 801.2105 1423.836 0.563 0.574 -1992.380 3594.801\n", + "x28 -329.3538 1919.900 -0.172 0.864 -4096.230 3437.523\n", + "x29 16.8733 1375.321 0.012 0.990 -2681.530 2715.277\n", + "x30 31.1672 1363.622 0.023 0.982 -2644.281 2706.616\n", + "x31 115.6359 1372.252 0.084 0.933 -2576.744 2808.016\n", + "x32 -300.4944 1567.592 -0.192 0.848 -3376.136 2775.147\n", + "x33 -285.5191 1919.900 -0.149 0.882 -4052.395 3481.357\n", + "x34 -245.2454 1919.900 -0.128 0.898 -4012.122 3521.631\n", + "x35 -168.0416 1439.925 -0.117 0.907 -2993.199 2657.116\n", + "x36 548.0390 1919.900 0.285 0.775 -3218.837 4314.915\n", + "x37 -49.0394 1384.460 -0.035 0.972 -2765.373 2667.294\n", + "x38 -224.5434 1919.900 -0.117 0.907 -3991.420 3542.333\n", + "x39 -325.1718 1919.900 -0.169 0.866 -4092.048 3441.704\n", + "x40 -298.8531 1919.900 -0.156 0.876 -4065.729 3468.023\n", + "x41 -85.2096 1919.900 -0.044 0.965 -3852.086 3681.667\n", + "x42 -329.7765 1919.900 -0.172 0.864 -4096.653 3437.100\n", + "x43 -58.1945 1919.900 -0.030 0.976 -3825.071 3708.682\n", + "x44 -262.5580 1662.683 -0.158 0.875 -3524.769 2999.653\n", + "x45 75.7512 1379.297 0.055 0.956 -2630.453 2781.955\n", + "x46 108.3275 1368.140 0.079 0.937 -2575.985 2792.640\n", + "x47 -13.8783 1919.900 -0.007 0.994 -3780.755 3752.998\n", + "x48 258.0942 1919.900 0.134 0.893 -3508.782 4024.970\n", + "x49 -25.0907 1662.683 -0.015 0.988 -3287.301 3237.120\n", + "x50 -249.9903 1487.148 -0.168 0.867 -3167.800 2667.820\n", + "x51 -155.9914 1391.100 -0.112 0.911 -2885.353 2573.370\n", + "x52 -198.4027 1662.683 -0.119 0.905 -3460.613 3063.808\n", + "x53 -324.6503 1919.900 -0.169 0.866 -4091.527 3442.226\n", + "x54 -296.2223 1919.900 -0.154 0.877 -4063.099 3470.654\n", + "x55 -274.6008 1919.900 -0.143 0.886 -4041.477 3492.276\n", + "x56 158.7633 1919.900 0.083 0.934 -3608.113 3925.640\n", + "x57 -299.8354 1919.900 -0.156 0.876 -4066.712 3467.041\n", + "x58 -2.9790 1382.486 -0.002 0.998 -2715.440 2709.482\n", + "x59 623.5173 1919.900 0.325 0.745 -3143.359 4390.394\n", + "x60 -208.9725 1662.683 -0.126 0.900 -3471.183 3053.238\n", + "x61 -158.3803 1517.815 -0.104 0.917 -3136.358 2819.597\n", + "x62 5.3800 1423.836 0.004 0.997 -2788.210 2798.970\n", + "x63 -305.8409 1919.900 -0.159 0.873 -4072.717 3461.035\n", + "x64 36.0716 1662.683 0.022 0.983 -3226.139 3298.282\n", + "x65 -279.8435 1919.900 -0.146 0.884 -4046.720 3487.033\n", + "x66 106.5611 1662.683 0.064 0.949 -3155.649 3368.772\n", + "x67 347.4855 1366.841 0.254 0.799 -2334.280 3029.251\n", + "x68 218.0329 1567.592 0.139 0.889 -2857.609 3293.675\n", + "x69 -48.4474 1385.569 -0.035 0.972 -2766.956 2670.061\n", + "x70 -306.5098 1919.900 -0.160 0.873 -4073.386 3460.366\n", + "x71 -20.6807 1431.009 -0.014 0.988 -2828.344 2786.983\n", + "x72 -50.2077 1380.783 -0.036 0.971 -2759.326 2658.911\n", + "x73 -143.8346 1919.900 -0.075 0.940 -3910.711 3623.042\n", + "x74 40.2514 1383.436 0.029 0.977 -2674.072 2754.575\n", + "x75 -280.7142 1919.900 -0.146 0.884 -4047.590 3486.162\n", + "x76 -47.8736 1378.624 -0.035 0.972 -2752.756 2657.009\n", + "x77 2.7373 1402.097 0.002 0.998 -2748.200 2753.675\n", + "x78 -278.9442 1919.900 -0.145 0.885 -4045.821 3487.932\n", + "x79 -313.9993 1919.900 -0.164 0.870 -4080.876 3452.877\n", + "x80 17.8282 1366.717 0.013 0.990 -2663.692 2699.349\n", + "x81 -189.3860 1662.683 -0.114 0.909 -3451.597 3072.825\n", + "x82 -37.6510 1413.008 -0.027 0.979 -2809.997 2734.695\n", + "x83 -323.0192 1919.900 -0.168 0.866 -4089.896 3443.857\n", + "x84 261.4217 1394.775 0.187 0.851 -2475.150 2997.994\n", + "x85 -214.6732 1919.900 -0.112 0.911 -3981.549 3552.203\n", + "x86 886.8945 1567.592 0.566 0.572 -2188.747 3962.536\n", + "x87 337.6152 1363.219 0.248 0.804 -2337.044 3012.274\n", + "x88 -300.1066 1567.592 -0.191 0.848 -3375.748 2775.535\n", + "x89 -222.3890 1919.900 -0.116 0.908 -3989.265 3544.487\n", + "x90 68.1487 1451.308 0.047 0.963 -2779.342 2915.640\n", + "x91 -4.0191 1363.845 -0.003 0.998 -2679.906 2671.868\n", + "x92 1344.2112 1374.869 0.978 0.328 -1353.305 4041.727\n", + "x93 -324.8334 1919.900 -0.169 0.866 -4091.710 3442.043\n", + "x94 128.8564 1374.440 0.094 0.925 -2567.817 2825.529\n", + "x95 -311.7626 1919.900 -0.162 0.871 -4078.639 3455.114\n", + "x96 -71.8015 1567.592 -0.046 0.963 -3147.443 3003.840\n", + "==============================================================================\n", + "Omnibus: 3036.850 Durbin-Watson: 2.025\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 26110487.530\n", + "Skew: 23.639 Prob(JB): 0.00\n", + "Kurtosis: 708.328 Cond. No. 358.\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" + ] + } + ], + "source": [ + "# 1. CREATE A CLEANED DATAFRAME (MORE ROBUST)\n", + "regression_df2 = studio_df.copy()\n", + "\n", + "# Ensure ROI_perc is numeric and drop missing\n", + "regression_df2['ROI_perc'] = pd.to_numeric(regression_df2['ROI_perc'], errors='coerce')\n", + "regression_df2 = regression_df2.dropna(subset=['ROI_perc'])\n", + "\n", + "# 2. CRITICAL STEP: Clean the 'genre_name' column\n", + "# Convert all genre names to strings and handle missing values\n", + "regression_df2['studio'] = regression_df2['studio'].astype(str) # Force to string\n", + "# Optional: Replace any 'nan' strings if they exist\n", + "regression_df2['studio'] = regression_df2['studio'].replace('nan', pd.NA)\n", + "regression_df2 = regression_df2.dropna(subset=['studio']) # Drop rows where genre is NA\n", + "\n", + "# 3. CREATE DUMMY VARIABLES\n", + "studio_dummies = pd.get_dummies(regression_df2['studio'], prefix='studio', drop_first=True)\n", + "\n", + "# 4. DEFINE VARIABLES\n", + "y = regression_df2['ROI_perc']\n", + "X = studio_dummies\n", + "\n", + "# 5. ADD CONSTANT\n", + "X = sm.add_constant(X)\n", + "\n", + "# 6. FINAL CHECK: Convert everything to numeric arrays explicitly\n", + "# This bypasses any pandas dtype issues\n", + "y_final = np.asarray(y, dtype=float)\n", + "X_final = np.asarray(X, dtype=float)\n", + "\n", + "# 7. Fit the model using the numeric arrays\n", + "model = sm.OLS(y_final, X_final).fit()\n", + "\n", + "# 8. PRINT RESULTS\n", + "print(model.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "ac758de7-ee3b-4b9d-85cc-15e6771e77ac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Genre Dummy Variable Order:\n", + "x1 = studio_A24\n", + "x2 = studio_ATO\n", + "x3 = studio_Affirm\n", + "x4 = studio_Alc\n", + "x5 = studio_Amazon\n", + "x6 = studio_Anch.\n", + "x7 = studio_Annapurna\n", + "x8 = studio_App.\n", + "x9 = studio_BBC\n", + "x10 = studio_BG\n", + "x11 = studio_BH Tilt\n", + "x12 = studio_BSC\n", + "x13 = studio_BST\n", + "x14 = studio_BV\n", + "x15 = studio_CBS\n", + "x16 = studio_CE\n", + "x17 = studio_CJ\n", + "x18 = studio_Cleopatra\n", + "x19 = studio_Cohen\n", + "x20 = studio_Drft.\n", + "x21 = studio_EC\n", + "x22 = studio_ELS\n", + "x23 = studio_ENTMP\n", + "x24 = studio_EOne\n", + "x25 = studio_Eros\n", + "x26 = studio_FCW\n", + "x27 = studio_FD\n", + "x28 = studio_First\n", + "x29 = studio_Focus\n", + "x30 = studio_Fox\n", + "x31 = studio_FoxS\n", + "x32 = studio_Free\n", + "x33 = studio_GK\n", + "x34 = studio_Global Road\n", + "x35 = studio_Gold.\n", + "x36 = studio_GrtIndia\n", + "x37 = studio_IFC\n", + "x38 = studio_IM\n", + "x39 = studio_IVP\n", + "x40 = studio_IW\n", + "x41 = studio_Jan.\n", + "x42 = studio_KE\n", + "x43 = studio_Kino\n", + "x44 = studio_LD\n", + "x45 = studio_LG/S\n", + "x46 = studio_LGF\n", + "x47 = studio_LGP\n", + "x48 = studio_MBox\n", + "x49 = studio_MGM\n", + "x50 = studio_MNE\n", + "x51 = studio_Magn.\n", + "x52 = studio_Mira.\n", + "x53 = studio_Mont.\n", + "x54 = studio_NFC\n", + "x55 = studio_NM\n", + "x56 = studio_Neon\n", + "x57 = studio_OMNI/FSR\n", + "x58 = studio_ORF\n", + "x59 = studio_Orch.\n", + "x60 = studio_Osci.\n", + "x61 = studio_Over.\n", + "x62 = studio_P/DW\n", + "x63 = studio_P4\n", + "x64 = studio_PFR\n", + "x65 = studio_PH\n", + "x66 = studio_PNT\n", + "x67 = studio_Par.\n", + "x68 = studio_ParV\n", + "x69 = studio_RAtt.\n", + "x70 = studio_RLJ\n", + "x71 = studio_RTWC\n", + "x72 = studio_Rela.\n", + "x73 = studio_Relbig.\n", + "x74 = studio_SGem\n", + "x75 = studio_SMod\n", + "x76 = studio_SPC\n", + "x77 = studio_STX\n", + "x78 = studio_Saban\n", + "x79 = studio_Scre.\n", + "x80 = studio_Sony\n", + "x81 = studio_Studio 8\n", + "x82 = studio_Sum.\n", + "x83 = studio_TFA\n", + "x84 = studio_TriS\n", + "x85 = studio_Trib.\n", + "x86 = studio_UTV\n", + "x87 = studio_Uni.\n", + "x88 = studio_VE\n", + "x89 = studio_Viv.\n", + "x90 = studio_W/Dim.\n", + "x91 = studio_WB\n", + "x92 = studio_WB (NL)\n", + "x93 = studio_WHE\n", + "x94 = studio_Wein.\n", + "x95 = studio_XL\n", + "x96 = studio_Yash\n" + ] + } + ], + "source": [ + "# This shows the order of the dummy variables, which matches x1, x2, x3...\n", + "print(\"Genre Dummy Variable Order:\")\n", + "for i, col in enumerate(X.columns[1:], start=1): # Skip the 'const' column\n", + " print(f\"x{i} = {col}\")" + ] + }, + { + "cell_type": "markdown", + "id": "4bdc92a7-b02d-448d-9802-85de6dd698cf", + "metadata": {}, + "source": [ + "### Step 3: Interpretation\n", + "The model itself tells a critical story:\n", + "\n", + "- R-squared: 0.045 - Only 4.5% of the variation in ROI can be explained by which studio made the film. This is very low.\n", + "\n", + "- Prob (F-statistic): 1.00 - This is the most important number. A value of 1.0 means there is absolutely no statistical evidence that any studio's performance is different from any other. We fail to reject the null hypothesis that all studios have the same average ROI.\n", + "\n", + "- Adjusted R-squared: -0.034 - This negative value indicates that the model (using studio alone) is worse than useless—it's actively a poorer predictor than just using the simple average ROI of all movies.\n", + "\n", + "Interpretation: The choice of studio, by itself, is not a meaningful predictor of a movie's financial success. The enormous p-value (1.0) means the apparent differences in studio performance visible in a boxplot are almost certainly due to random chance in this dataset.\n", + "\n", + "Now, let's look for the \"acquisition targets\" by examining individual coefficients. We are looking for studios with:\n", + "\n", + "- A high, positive coefficient.\n", + "\n", + "- A low p-value (P>|t| < 0.05).\n", + "\n", + "The Results Are Clear: There are none.\n", + "\n", + "- No Statistical Significance: Not a single studio has a p-value less than 0.05. The smallest p-value is 0.328 for x92 = studio_WB (NL), which is far from significant.\n", + "\n", + "- No Meaningful Signal: The coefficients are all over the place, but with enormous standard errors. For example:\n", + "\n", + " x92 = studio_WB (NL): coef = 1344.21, p-value = 0.328\n", + " x27 = studio_FD: coef = 801.21, p-value = 0.574\n", + " x86 = studio_UTV: coef = 886.89, p-value = 0.572\n", + "\n", + "- These large coefficients are statistical noise, not real signals. Their high p-values mean we cannot be confident these results aren't just random fluctuations." + ] + }, + { + "cell_type": "markdown", + "id": "6efb3f03-dd95-4038-bf15-1cf70b4e1eed", + "metadata": {}, + "source": [ + "## Question Four: What is the optimal production budget for maximizing ROI?\n", + "- Why it matters: This tells them how much to spend on a film. Is it better to make ten 10M movies or one 100M movie?\n", + "- For this we will use the **tn_movie_budgets** dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "5c9a11b0-d273-45c4-bc85-c648e398a7ed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idmovieproduction_budgetdomestic_grossworldwide_grossrelease_yearmonth_dtmonthdom_profit_marginww_profit_marginworld_wide_profit_amountROI_perc
01Avatar42500000076050762527763452792009121244.11627484.6921062351345279553.257713
12Pirates Of The Caribbean: On Stranger Tides4106000002410638751045663875201155-70.32830060.733080635063875154.667286
23Dark Phoenix35000000042762350149762350201966-718.477001-133.703598-200237650-57.210757
34Avengers: Age Of Ultron330600000459005868140301396320155527.97477776.4364431072413963324.384139
45Star Wars Ep. Viii: The Last Jedi31700000062018138213167217472017121248.88592175.925058999721747315.369636
.......................................
577677The Mongol King700090090020041212-677.777778-677.777778-6100-87.142857
577778Red 1170000020181212-inf-inf-7000-100.000000
577980Return To The Land Of Wonders500013381338200577-273.692078-273.692078-3662-73.240000
578081A Plague So Pleasant140000201599-inf-inf-1400-100.000000
578182My Date With Drew110018104118104120058899.39240399.39240317994116358.272727
\n", + "

4198 rows × 12 columns

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" + ], + "text/plain": [ + " id movie production_budget \\\n", + "0 1 Avatar 425000000 \n", + "1 2 Pirates Of The Caribbean: On Stranger Tides 410600000 \n", + "2 3 Dark Phoenix 350000000 \n", + "3 4 Avengers: Age Of Ultron 330600000 \n", + "4 5 Star Wars Ep. Viii: The Last Jedi 317000000 \n", + "... .. ... ... \n", + "5776 77 The Mongol King 7000 \n", + "5777 78 Red 11 7000 \n", + "5779 80 Return To The Land Of Wonders 5000 \n", + "5780 81 A Plague So Pleasant 1400 \n", + "5781 82 My Date With Drew 1100 \n", + "\n", + " domestic_gross worldwide_gross release_year month_dt month \\\n", + "0 760507625 2776345279 2009 12 12 \n", + "1 241063875 1045663875 2011 5 5 \n", + "2 42762350 149762350 2019 6 6 \n", + "3 459005868 1403013963 2015 5 5 \n", + "4 620181382 1316721747 2017 12 12 \n", + "... ... ... ... ... ... \n", + "5776 900 900 2004 12 12 \n", + "5777 0 0 2018 12 12 \n", + "5779 1338 1338 2005 7 7 \n", + "5780 0 0 2015 9 9 \n", + "5781 181041 181041 2005 8 8 \n", + "\n", + " dom_profit_margin ww_profit_margin world_wide_profit_amount \\\n", + "0 44.116274 84.692106 2351345279 \n", + "1 -70.328300 60.733080 635063875 \n", + "2 -718.477001 -133.703598 -200237650 \n", + "3 27.974777 76.436443 1072413963 \n", + "4 48.885921 75.925058 999721747 \n", + "... ... ... ... \n", + "5776 -677.777778 -677.777778 -6100 \n", + "5777 -inf -inf -7000 \n", + "5779 -273.692078 -273.692078 -3662 \n", + "5780 -inf -inf -1400 \n", + "5781 99.392403 99.392403 179941 \n", + "\n", + " ROI_perc \n", + "0 553.257713 \n", + "1 154.667286 \n", + "2 -57.210757 \n", + "3 324.384139 \n", + "4 315.369636 \n", + "... ... \n", + "5776 -87.142857 \n", + "5777 -100.000000 \n", + "5779 -73.240000 \n", + "5780 -100.000000 \n", + "5781 16358.272727 \n", + "\n", + "[4198 rows x 12 columns]" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tn_movie_budgets" + ] + }, + { + "cell_type": "markdown", + "id": "953f2a0f-0856-464d-9280-bb7da753a083", + "metadata": {}, + "source": [ + "### Step 1: Variable Selection\n", + "- Independent Variable (X): production_budget (Continuous). We want to see how changes in budget predict changes in ROI.\n", + "\n", + "- Dependent Variable (y): ROI_perc (Continuous). This is our measure of efficiency and profitability." + ] + }, + { + "cell_type": "markdown", + "id": "71b8f0f8-e0c3-40d6-a2a8-aa1aaf826c31", + "metadata": {}, + "source": [ + "### Step 2: Testing For Linearity\n", + "We use a scatter plot to visualize the fundamental relationship between our chosen variables." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "5eb4252f-b0a2-426d-ac92-3e543aaaddf6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ROI Distribution Summary (for context):\n", + "count 4198.000000\n", + "mean 239.561107\n", + "std 1289.586087\n", + "min -100.000000\n", + "25% -62.568668\n", + "50% 54.580653\n", + "75% 240.290439\n", + "max 43051.785333\n", + "Name: ROI_perc, dtype: float64\n", + "\n", + "Number of extreme outliers not shown (ROI < -100% or > 2000%): 1\n", + "This represents 0.02% of the dataset.\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "\n", + "# Create the figure\n", + "plt.figure(figsize=(12, 8))\n", + "\n", + "# Create the scatter plot\n", + "scatter_plot = sns.scatterplot(data=tn_movie_budgets, x='production_budget', y='ROI_perc', alpha=0.6)\n", + "\n", + "# Add key reference lines\n", + "plt.axhline(y=0, color='r', linestyle='--', linewidth=2, label='Break-Even (0% ROI)')\n", + "plt.axhline(y=100, color='g', linestyle=':', alpha=0.7, label='Double Investment (100% ROI)')\n", + "\n", + "# FIX: Set a logical limit on the y-axis to exclude extreme outliers\n", + "# Adjust these values based on your data. The following limits are a common starting point.\n", + "plt.ylim(-100, 2000) # This focuses on movies from -100% ROI (a flop) to 500% ROI (a 5x return)\n", + "\n", + "# Calculate and plot the regression line (to visualize the trend)\n", + "# This fits the model and plots the line of best fit on the same graph\n", + "sns.regplot(data=tn_movie_budgets, x='production_budget', y='ROI_perc', \n", + " scatter=False, color='blue', line_kws={\"linewidth\": 2, \"alpha\": 0.7}, \n", + " label='Linear Trend Line')\n", + "# Add titles and labels\n", + "plt.title('Relationship Between Production Budget and Financial ROI\\n(Y-axis limited to [-100%, 2000%] to show detail)', fontsize=14)\n", + "plt.xlabel('Budget (dollars)')\n", + "plt.ylabel('ROI (%)')\n", + "plt.legend()\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# --- BONUS: Print a statistical summary for context ---\n", + "print(\"ROI Distribution Summary (for context):\")\n", + "print(tn_movie_budgets['ROI_perc'].describe())\n", + "\n", + "# Count how many movies are outside our chosen y-axis limits\n", + "lower_limit = -100\n", + "upper_limit = 2000\n", + "outliers = [(tn_movie_budgets['ROI_perc'] < lower_limit) | (tn_movie_budgets['ROI_perc'] > upper_limit)]\n", + "print(f\"\\nNumber of extreme outliers not shown (ROI < {lower_limit}% or > {upper_limit}%): {len(outliers)}\")\n", + "print(f\"This represents {len(outliers) / len(tn_movie_budgets) * 100:.2f}% of the dataset.\")" + ] + }, + { + "cell_type": "markdown", + "id": "89e70eda-a369-420e-b0f7-1e7859f9be5e", + "metadata": {}, + "source": [ + "From this we can define our null and alternative hypothesis:\n", + "- Null Hypothesis (H₀): There is no linear relationship between production budget and ROI.\n", + "\n", + " Mathematically: β₁ = 0 (The slope is zero).\n", + "\n", + "- Alternative Hypothesis (H₁): There is a linear relationship between production budget and ROI.\n", + "\n", + " Mathematically: β₁ ≠ 0.\n", + "We can now then fit our model to the data" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "b71b5b05-860a-4994-8ffa-80ee04e590b0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: ROI_perc R-squared: 0.001\n", + "Model: OLS Adj. R-squared: 0.001\n", + "Method: Least Squares F-statistic: 4.525\n", + "Date: Sat, 13 Sep 2025 Prob (F-statistic): 0.0335\n", + "Time: 23:26:42 Log-Likelihood: -36020.\n", + "No. Observations: 4198 AIC: 7.204e+04\n", + "Df Residuals: 4196 BIC: 7.206e+04\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "=====================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "-------------------------------------------------------------------------------------\n", + "const 271.4087 24.899 10.901 0.000 222.594 320.223\n", + "production_budget -9.189e-07 4.32e-07 -2.127 0.033 -1.77e-06 -7.2e-08\n", + "==============================================================================\n", + "Omnibus: 9797.560 Durbin-Watson: 1.647\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 78561733.898\n", + "Skew: 22.873 Prob(JB): 0.00\n", + "Kurtosis: 671.615 Cond. No. 7.21e+07\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "[2] The condition number is large, 7.21e+07. This might indicate that there are\n", + "strong multicollinearity or other numerical problems.\n" + ] + } + ], + "source": [ + "import statsmodels.api as sm\n", + "\n", + "# Define the variables\n", + "X = tn_movie_budgets['production_budget'] # Independent variable\n", + "y = tn_movie_budgets['ROI_perc'] # Dependent variable\n", + "\n", + "# Add a constant (intercept) to the model.\n", + "X = sm.add_constant(X)\n", + "\n", + "# Fit the Ordinary Least Squares (OLS) model\n", + "model = sm.OLS(y, X).fit()\n", + "\n", + "# Print the comprehensive results summary\n", + "print(model.summary())" + ] + }, + { + "cell_type": "markdown", + "id": "2ce46d54-96bb-46b9-bbc0-8c48ca3184de", + "metadata": {}, + "source": [ + "### Step 3: Interpretation\n", + "1. The Relationship (The Coefficient)\n", + "Coefficient for production_budget: -9.189e-07\n", + "\n", + "Interpretation: For every additional dollar spent on the production budget, the model predicts an associated decrease of 0.0000009189% in ROI.\n", + "\n", + "More Practical Interpretation: For every $1 million increase in budget, the model predicts a 0.9189% decrease in ROI.\n", + "\n", + "Business Meaning: The relationship is negative. Higher budgets are associated with lower returns on investment. This makes sense: a movie that costs $200 million needs to earn $500 million to get a 150% ROI. A movie that costs $5 million only needs to earn $12.5 million to achieve the same ROI, which is a much easier task.\n", + "\n", + "2. Statistical Significance (The P-Value)\n", + "P-value (P>|t|) for production_budget: 0.0335\n", + "\n", + "Interpretation: There is a 3.35% probability that we would observe this negative relationship purely by random chance, even if no true relationship existed in reality.\n", + "\n", + "Statistical Conclusion: Using the standard significance level of α = 0.05, we reject the null hypothesis. The relationship between budget and ROI is statistically significant.\n", + "\n", + "Business Meaning: We can be confident that the negative trend we see in the data is real and not a fluke. Budget is a genuine factor influencing profitability.\n", + "\n", + "3. Model Fit and Practical Importance (R-squared & Notes)\n", + "R-squared: 0.001\n", + "\n", + "Interpretation: Only 0.1% of the variation in a movie's ROI can be explained by its production budget alone.\n", + "\n", + "Business Meaning: This is the most important part of the output for strategy. It means that while the relationship is statistically real, budget is a negligible factor in determining success. Other elements—like genre, marketing, critical reception, and star power—are vastly more important. A high budget doesn't guarantee failure, and a low budget doesn't guarantee success; it just slightly nudges the odds.\n", + "\n", + "Note [2]: The condition number is large, 7.21e+07.\n", + "\n", + "Interpretation: This is a technical warning that there might be numerical issues, but in this context, it's almost certainly caused by the huge scale difference between the const (which is on the scale of hundreds) and the production_budget coefficient (which is on the scale of millionths). It does not invalidate the finding." + ] + }, + { + "cell_type": "markdown", + "id": "2e5cec18-904c-4b6d-b9b6-f3fd4cd37c48", + "metadata": {}, + "source": [ + "## Question Five: How important is the international box office for profitability?\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "9c53277a-170c-4ae2-947a-d763d3c14981", + "metadata": {}, + "source": [ + "- Why it matters: This informs marketing and distribution strategy. Should they focus on stories with global appeal?\n", + "- For this we will use the **tn_movie_budgets** dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "ff03da8c-b482-4695-aaa4-b38ec7546997", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idmovieproduction_budgetdomestic_grossworldwide_grossrelease_yearmonth_dtmonthdom_profit_marginww_profit_marginworld_wide_profit_amountROI_perc
01Avatar42500000076050762527763452792009121244.11627484.6921062351345279553.257713
12Pirates Of The Caribbean: On Stranger Tides4106000002410638751045663875201155-70.32830060.733080635063875154.667286
23Dark Phoenix35000000042762350149762350201966-718.477001-133.703598-200237650-57.210757
34Avengers: Age Of Ultron330600000459005868140301396320155527.97477776.4364431072413963324.384139
45Star Wars Ep. Viii: The Last Jedi31700000062018138213167217472017121248.88592175.925058999721747315.369636
.......................................
577677The Mongol King700090090020041212-677.777778-677.777778-6100-87.142857
577778Red 1170000020181212-inf-inf-7000-100.000000
577980Return To The Land Of Wonders500013381338200577-273.692078-273.692078-3662-73.240000
578081A Plague So Pleasant140000201599-inf-inf-1400-100.000000
578182My Date With Drew110018104118104120058899.39240399.39240317994116358.272727
\n", + "

4198 rows × 12 columns

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" + ], + "text/plain": [ + " id movie production_budget \\\n", + "0 1 Avatar 425000000 \n", + "1 2 Pirates Of The Caribbean: On Stranger Tides 410600000 \n", + "2 3 Dark Phoenix 350000000 \n", + "3 4 Avengers: Age Of Ultron 330600000 \n", + "4 5 Star Wars Ep. Viii: The Last Jedi 317000000 \n", + "... .. ... ... \n", + "5776 77 The Mongol King 7000 \n", + "5777 78 Red 11 7000 \n", + "5779 80 Return To The Land Of Wonders 5000 \n", + "5780 81 A Plague So Pleasant 1400 \n", + "5781 82 My Date With Drew 1100 \n", + "\n", + " domestic_gross worldwide_gross release_year month_dt month \\\n", + "0 760507625 2776345279 2009 12 12 \n", + "1 241063875 1045663875 2011 5 5 \n", + "2 42762350 149762350 2019 6 6 \n", + "3 459005868 1403013963 2015 5 5 \n", + "4 620181382 1316721747 2017 12 12 \n", + "... ... ... ... ... ... \n", + "5776 900 900 2004 12 12 \n", + "5777 0 0 2018 12 12 \n", + "5779 1338 1338 2005 7 7 \n", + "5780 0 0 2015 9 9 \n", + "5781 181041 181041 2005 8 8 \n", + "\n", + " dom_profit_margin ww_profit_margin world_wide_profit_amount \\\n", + "0 44.116274 84.692106 2351345279 \n", + "1 -70.328300 60.733080 635063875 \n", + "2 -718.477001 -133.703598 -200237650 \n", + "3 27.974777 76.436443 1072413963 \n", + "4 48.885921 75.925058 999721747 \n", + "... ... ... ... \n", + "5776 -677.777778 -677.777778 -6100 \n", + "5777 -inf -inf -7000 \n", + "5779 -273.692078 -273.692078 -3662 \n", + "5780 -inf -inf -1400 \n", + "5781 99.392403 99.392403 179941 \n", + "\n", + " ROI_perc \n", + "0 553.257713 \n", + "1 154.667286 \n", + "2 -57.210757 \n", + "3 324.384139 \n", + "4 315.369636 \n", + "... ... \n", + "5776 -87.142857 \n", + "5777 -100.000000 \n", + "5779 -73.240000 \n", + "5780 -100.000000 \n", + "5781 16358.272727 \n", + "\n", + "[4198 rows x 12 columns]" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tn_movie_budgets" + ] + }, + { + "cell_type": "markdown", + "id": "3c5fa735-8a91-4aa5-b341-1d811d72377f", + "metadata": {}, + "source": [ + "- From this, we can create the key derived variable:\n", + "\n", + " intl_gross_pct = (worldwide_gross - domestic_gross) / worldwide_gross * 100\n", + "\n", + "- This represents the percentage of a film's total box office that comes from international markets." + ] + }, + { + "cell_type": "markdown", + "id": "bded82d3-49fe-4411-9b99-6b0cadfce2fd", + "metadata": {}, + "source": [ + "### Step 1: Variable Selection\n", + "- Independent Variable (X): intl_gross_pct (Continuous). This is our measure of reliance on the international market.\n", + "\n", + "- Dependent Variable (y): ROI_perc (Continuous). This is our measure of profitability." + ] + }, + { + "cell_type": "markdown", + "id": "23c43555-8302-4fdc-8ba4-e49ec5ad148e", + "metadata": {}, + "source": [ + "### Step 2: Testing for Linearity\n", + "We use a scatter plot to visualize the fundamental relationship." + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "932ebb1e-214f-4453-9730-3d081e6f6188", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ROI Distribution Summary (for context):\n", + "count 4198.000000\n", + "mean 239.561107\n", + "std 1289.586087\n", + "min -100.000000\n", + "25% -62.568668\n", + "50% 54.580653\n", + "75% 240.290439\n", + "max 43051.785333\n", + "Name: ROI_perc, dtype: float64\n", + "\n", + "Number of extreme outliers not shown (ROI < -100% or > 2000%): 1\n", + "This represents 0.02% of the dataset.\n" + ] + } + ], + "source": [ + "# Calculate International Gross %\n", + "tn_movie_budgets['intl_gross_pct'] = ((tn_movie_budgets['worldwide_gross'] - tn_movie_budgets['domestic_gross']) / tn_movie_budgets['worldwide_gross']) * 100\n", + "\n", + "# Create the figure\n", + "plt.figure(figsize=(12, 8))\n", + "\n", + "# Create the scatter plot\n", + "scatter_plot = sns.scatterplot(data=tn_movie_budgets, x='intl_gross_pct', y='ROI_perc', alpha=0.6)\n", + "\n", + "# Add key reference lines\n", + "plt.axhline(y=0, color='r', linestyle='--', linewidth=2, label='Break-Even (0% ROI)')\n", + "plt.axhline(y=100, color='g', linestyle=':', alpha=0.7, label='Double Investment (100% ROI)')\n", + "\n", + "# FIX: Set a logical limit on the y-axis to exclude extreme outliers\n", + "# Adjust these values based on your data. The following limits are a common starting point.\n", + "plt.ylim(-100, 2000) # This focuses on movies from -100% ROI (a flop) to 500% ROI (a 5x return)\n", + "\n", + "# Calculate and plot the regression line (to visualize the trend)\n", + "# This fits the model and plots the line of best fit on the same graph\n", + "sns.regplot(data=tn_movie_budgets, x='intl_gross_pct', y='ROI_perc', \n", + " scatter=False, color='blue', line_kws={\"linewidth\": 2, \"alpha\": 0.7}, \n", + " label='Linear Trend Line')\n", + "# Add titles and labels\n", + "plt.title('Relationship Between International Gross Returns and Financial ROI\\n(Y-axis limited to [-100%, 2000%] to show detail)', fontsize=14)\n", + "plt.xlabel('Budget (dollars)')\n", + "plt.ylabel('ROI (%)')\n", + "plt.legend()\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# --- BONUS: Print a statistical summary for context ---\n", + "print(\"ROI Distribution Summary (for context):\")\n", + "print(tn_movie_budgets['ROI_perc'].describe())\n", + "\n", + "# Count how many movies are outside our chosen y-axis limits\n", + "lower_limit = -100\n", + "upper_limit = 2000\n", + "outliers = [(tn_movie_budgets['ROI_perc'] < lower_limit) | (tn_movie_budgets['ROI_perc'] > upper_limit)]\n", + "print(f\"\\nNumber of extreme outliers not shown (ROI < {lower_limit}% or > {upper_limit}%): {len(outliers)}\")\n", + "print(f\"This represents {len(outliers) / len(tn_movie_budgets) * 100:.2f}% of the dataset.\")" + ] + }, + { + "cell_type": "markdown", + "id": "9a14f2a5-c3cf-4781-bc93-2794868571db", + "metadata": {}, + "source": [ + "We can then define our null and alternative hypothesis as follows:\n", + "- Null Hypothesis (H₀): There is no linear relationship between international reliance and ROI.\n", + "\n", + " Mathematically: β₁ = 0\n", + "\n", + "- Alternative Hypothesis (H₁): There is a linear relationship between international reliance and ROI.\n", + "\n", + " Mathematically: β₁ ≠ 0\n", + "\n", + "Proceeding to our model:" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "fd8fdf02-c1c3-409a-9809-8ec9d3917db8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: ROI_perc R-squared: 0.000\n", + "Model: OLS Adj. R-squared: 0.000\n", + "Method: Least Squares F-statistic: 1.580\n", + "Date: Sat, 13 Sep 2025 Prob (F-statistic): 0.209\n", + "Time: 23:26:43 Log-Likelihood: -33239.\n", + "No. Observations: 3856 AIC: 6.648e+04\n", + "Df Residuals: 3854 BIC: 6.649e+04\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "----------------------------------------------------------------------------------\n", + "const 229.6245 38.497 5.965 0.000 154.148 305.101\n", + "intl_gross_pct 0.8815 0.701 1.257 0.209 -0.493 2.256\n", + "==============================================================================\n", + "Omnibus: 8875.351 Durbin-Watson: 1.649\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 62249715.644\n", + "Skew: 22.109 Prob(JB): 0.00\n", + "Kurtosis: 623.879 Cond. No. 97.9\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" + ] + } + ], + "source": [ + "import statsmodels.api as sm\n", + "\n", + "# Prepare the data - drop rows where worldwide_gross is 0 to avoid division by zero errors\n", + "analysis_df = tn_movie_budgets[tn_movie_budgets['worldwide_gross'] > 0].copy()\n", + "analysis_df['intl_gross_pct'] = ((analysis_df['worldwide_gross'] - analysis_df['domestic_gross']) / analysis_df['worldwide_gross']) * 100\n", + "\n", + "# Define the variables\n", + "X = analysis_df['intl_gross_pct'] # Independent variable\n", + "y = analysis_df['ROI_perc'] # Dependent variable\n", + "\n", + "# Add a constant (intercept) to the model.\n", + "X = sm.add_constant(X)\n", + "\n", + "# Fit the Ordinary Least Squares (OLS) model\n", + "model = sm.OLS(y, X).fit()\n", + "\n", + "# Print the comprehensive results summary\n", + "print(model.summary())" + ] + }, + { + "cell_type": "markdown", + "id": "6afccc5d-3559-41ae-834e-d69cacb38689", + "metadata": {}, + "source": [ + "### Step 3: Interpretation\n", + "1. The Relationship (The Coefficient)\n", + "Coefficient for intl_gross_pct: 0.8815\n", + "\n", + "Interpretation: The model suggests that for every additional percentage point of a film's total gross that comes from international markets, its ROI increases by 0.88%.\n", + "\n", + "Business Meaning: The direction of the relationship is positive, which aligns with the hypothesis that international revenue is good for profitability. However, the size of the effect is relatively small. A film that gets 70% of its revenue internationally would only have a (70 * 0.88%) = ~61.6% higher ROI than a film that gets 0% internationally, according to this model.\n", + "\n", + "2. Statistical Significance (The P-Value)\n", + "P-value (P>|t|) for intl_gross_pct: 0.209\n", + "\n", + "Interpretation: There is a 20.9% probability that we would observe this positive relationship purely by random chance, even if no true relationship existed in reality.\n", + "\n", + "Statistical Conclusion: Using the standard significance level of α = 0.05, we fail to reject the null hypothesis. We do not have sufficient evidence to conclude that the relationship between international reliance and ROI is statistically significant.\n", + "\n", + "Business Meaning: This is the most important part of the output. The positive trend we see is so weak that it could easily be noise. We cannot be confident that a greater share of international revenue actually causes an increase in ROI across all movies.\n", + "\n", + "3. Model Fit and Practical Importance (R-squared)\n", + "R-squared: 0.000\n", + "\n", + "Interpretation: 0.0% of the variation in a movie's ROI can be explained by the percentage of its revenue that comes from international markets.\n", + "\n", + "Business Meaning: This confirms the story from the p-value. The international revenue share is practically irrelevant in predicting whether a movie will be profitable. It is not a key driver of financial success. The success or failure of a movie is determined by other factors." + ] + }, + { + "cell_type": "markdown", + "id": "9ba2de62-8332-457f-917b-4a066b90ab53", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "8fca8e96-a15e-4e0c-a400-5441f857b7f0", + "metadata": {}, + "source": [ + "Now that we have done our linear regression analysis, we can make conclusions and make recommendations for Wamunyolo Studios." + ] + }, + { + "cell_type": "markdown", + "id": "0682823f-fb6c-451e-9418-9d87be3afb7e", + "metadata": {}, + "source": [ + "# Conclusions\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "2f6bf588-8a9c-40d0-bcc9-637deb957892", + "metadata": {}, + "source": [ + "- **Question One**:\n", + "\n", + "Our analysis finds a suggestive but not statistically definitive trend that longer runtimes are associated with lower profitability. The data strongly indicates that there is no financial advantage to making longer films. Therefore, the conservative and data-driven strategy for Wamonyolo Studios is to focus on producing films with runtimes below 120 minutes, as this is the range where the vast majority of profitable movies are found.\"\n", + "\n", + "- **Question Two**:\n", + "\n", + "Our regression analysis reveals that genre is a statistically significant predictor of movie profitability (p < 0.001). While genre alone explains a modest portion of ROI, we identified clear winners:\n", + "\n", + "The top-performing genre (x10) delivers an astounding 868% higher ROI than the baseline genre. This result is highly statistically significant (p < 0.001).\n", + "\n", + "Two other genres (x15 and x12) also show significantly elevated ROI, approximately 235% above baseline.\n", + "\n", + "- **Question Three**:\n", + "\n", + "Our regression analysis reveals a crucial insight: the film studio behind a project is not, on its own, a statistically significant predictor of its financial ROI (p=1.0). This means that the perceived 'brand value' or track record of a studio does not provide a reliable guarantee of profitability for future projects. The success of a film is driven by other factors—such as genre, budget, talent, and marketing—rather than the studio's name alone.\n", + "\n", + "Therefore, from a purely financial perspective, there is no statistical evidence to support the high cost of acquiring an existing studio. The data suggests that a well-managed new studio, making smart decisions about genre and production, has an equal chance of achieving profitability.\n", + "\n", + "- **Question Four**:\n", + "\n", + "Our linear regression model confirms a statistically significant negative relationship between production budget and ROI (p = 0.033). This means that, on average, more expensive movies generate a lower return on investment.\n", + "\n", + "However, the model's R-squared value is exceedingly low (0.001), indicating that a film's budget explains almost none of its financial performance. This tells us that a low budget is not a magic bullet for success, nor is a high budget a guaranteed path to failure.\n", + "\n", + "- **Question Five**:\n", + "\n", + "Our regression analysis yields a surprising but critical insight: the proportion of revenue a film earns internationally is not a statistically significant predictor of its profitability (ROI). The positive relationship we observed is weak and could be due to random chance (p = 0.209). In fact, this factor explains 0% of the variation in ROI.\n", + "\n", + "This does not mean the international box office is unimportant. It means that both profitable and unprofitable movies can have either a high or a low share of international revenue. The key is the total absolute revenue (worldwide_gross) relative to the budget, not where that revenue comes from.\n", + "\n", + "A more useful way to frame this is: The international market is not a driver of profitability; it is a prerequisite for it for most major films. A movie can be 100% reliant on international revenue and still be a flop if its total gross is low. Conversely, a movie can be hugely profitable with a primarily domestic audience if its total gross is high relative to its budget." + ] + }, + { + "cell_type": "markdown", + "id": "2f8817a8-d682-43c5-88bd-0d63c800029f", + "metadata": {}, + "source": [ + "# Recommendations\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "2ad1dab5-0a07-4ffa-b3a5-b2275e6b50b0", + "metadata": {}, + "source": [ + "Based on our findings, these are our recommendations for Wamunyolo Studios:" + ] + }, + { + "cell_type": "markdown", + "id": "a25fbdf7-6181-4aa4-a88b-93f5972e634c", + "metadata": {}, + "source": [ + "1. Wamonyolo Studios should not aim for long runtimes. The optimal strategy is to let the story dictate the length but prioritize efficiency. The data shows that shorter runtimes are not a hindrance to profitability and are likely beneficial. The focus should be on other, more impactful factors like genre and production budget, where your other analyses have already shown a clearer path to profit (e.g., Horror films).\n", + "\n", + "2. The data provides overwhelming evidence to focus initial production efforts on the genre represented by x10 (Horror). This genre offers the highest probability of delivering exceptional financial returns, with a typical project returning approximately 10x its production budget.\n", + "\n", + "3. The capital required for a major acquisition would be better invested in production and marketing. We recommend building a new studio from scratch and focusing its strategy on the proven drivers of success identified in our other analyses: producing low-to-mid-budget Horror films with efficient runtimes.\n", + "\n", + "4. Given the goal to maximize ROI, the data supports a focus on lower-to-mid-budget productions. This strategy minimizes initial financial risk while preserving the opportunity for outsized returns. The budget should be appropriate for the chosen genre (e.g., a horror film can be made for 10M dollars, while an action film may require 40M dollars to be credible). The key is to prioritize the other factors that truly drive success, which our analysis has shown to be genre and release timing.\n", + "\n", + "5. The strategy should not be to simply maximize international revenue share. The strategy should be to maximize total worldwide revenue. For large-budget films, this will inherently require international success. The focus should be on creating a product that resonates globally to achieve the high absolute grosses needed for profitability, rather than targeting a specific international revenue percentage.\"" + ] + }, + { + "cell_type": "markdown", + "id": "10a492ac-9db1-4fb9-8fe4-61dec183ebbf", + "metadata": {}, + "source": [ + "# End\n", + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (ray-env)", + "language": "python", + "name": "ray-env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/movie-data-analysis.pdf b/movie-data-analysis.pdf new file mode 100644 index 00000000..61a505ad Binary files /dev/null and b/movie-data-analysis.pdf differ diff --git a/movie_data_erd.jpeg b/movie_data_erd.jpeg deleted file mode 100644 index 0fa01bda..00000000 Binary files a/movie_data_erd.jpeg and /dev/null differ diff --git a/student.ipynb b/student.ipynb deleted file mode 100644 index d3bb34af..00000000 --- a/student.ipynb +++ /dev/null @@ -1,48 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Final Project Submission\n", - "\n", - "Please fill out:\n", - "* Student name: \n", - "* Student pace: self paced / part time / full time\n", - "* Scheduled project review date/time: \n", - "* Instructor name: \n", - "* Blog post URL:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here - remember to use markdown cells for comments as well!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}