diff --git a/ANALYSIS OF THE MOVIE BOX INDUSTRY (1).pdf b/ANALYSIS OF THE MOVIE BOX INDUSTRY (1).pdf new file mode 100644 index 00000000..14753c18 Binary files /dev/null and b/ANALYSIS OF THE MOVIE BOX INDUSTRY (1).pdf differ diff --git a/README.md b/README.md index b5e02341..db800c8d 100644 --- a/README.md +++ b/README.md @@ -1,281 +1,179 @@ -# Phase 2 Project Description -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! -In this project description, we will cover: +# Data Analysis on Movie Financials -* [***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 +This project presents a comprehensive analysis of movie financials, exploring production budgets, box office gross, and Return on Investment (ROI). By analyzing various datasets, we aim to uncover the underlying factors that drive a movie's financial success and provide valuable insights into the correlation between budget size and profitability. This analysis includes detailed visualizations, predictive models, and hypothesis testing. -## 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) +## Datasets Used -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 project utilizes the following datasets: -The graded elements of the presentation are: +1. **IMDB Data**: + - Metadata about movies, including titles, release years, genres, and IMDB ratings. -* Presentation Content -* Slide Style -* Presentation Delivery and Answers to Questions +2. **Box Office Mojo Data**: + - Detailed financial information like production budgets, domestic and worldwide gross earnings. -See the [Grading](#grading) section for further explanation of these elements. +3. **Supplementary Data**: + - Additional information such as actor/actress names, directors, and other variables influencing a movie's financial performance. -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 +## Project Overview -To further define some of these terms: +### Key Objectives +This project has several core objectives aimed at understanding the financial landscape of the movie industry: -* 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. +1. **Analyze Movie Financial Success**: + - Investigate how production budgets correlate with worldwide box office gross and profit margins. + - Explore ROI behavior across different budget ranges to identify any patterns in profitability. -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. +2. **Correlation and Causality**: + - Identify the relationships between key financial variables: production budgets, worldwide gross, domestic gross, profit, and ROI. -#### Exceeds Objective -Creates and describes appropriate visualizations for given business questions, where each visualization fulfills all elements of the checklist +3. **Hypothesis Testing**: + - Test assumptions such as whether higher-budget films generate higher ROI or how critical other factors like genre or release timing are to financial outcomes. -> This "checklist" refers to the Data Visualization checklist within the larger Phase 2 Project Checklist +4. **Predictive Analysis**: + - Build predictive models using machine learning algorithms to forecast a movie’s financial success based on input variables like budget and genre. -#### Meets Objective (Passing Bar) -Creates and describes appropriate visualizations for given business questions +## Tools & Technologies -> 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 +The following tools and libraries were used in the analysis: -#### Approaching Objective -Creates visualizations that are not related to the business questions, or uses an inappropriate type of visualization +- **Python**: The main programming language used for data manipulation and analysis. +- **Pandas**: Essential for managing and cleaning the datasets. +- **Matplotlib & Seaborn**: Used for visualizing data trends and insights. +- **Jupyter Notebook**: Served as the environment for conducting the analysis. + +## Steps and Workflow -> Even if you create very compelling visualizations, you cannot pass this objective if the visualizations are not related to the business questions +### 1. Data Cleaning + - The first step was to load and clean the datasets. This involved: + - Handling missing values by either filling them appropriately or dropping them. + - Removing duplicate entries to ensure data integrity. + - Formatting columns correctly (e.g., converting dates, numerical values). + +### 2. Exploratory Data Analysis (EDA) + - Visualized relationships between different financial metrics (e.g., budget vs. worldwide gross). + - Generated correlation heatmaps and scatterplots to investigate patterns. + - Analyzed the distribution of ROI and profit across various budget sizes. -> 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 +### 3. ROI Analysis + - Examined the distribution of ROI across movies to understand which types of films (based on budget) yield the highest returns. + - Investigated whether smaller-budget films are more likely to provide higher ROI due to lower production costs. -#### Does Not Meet Objective -Does not submit the required number of visualizations +## Analysis Results -### Authoring Jupyter Notebooks +### 1. Correlation Analysis +- **Strong Positive Correlation**: + - There is a significant positive correlation between **Worldwide Gross** and **Production Budget** (0.73). Generally, higher-budget movies tend to perform better globally. + +- **Weak Relationship with ROI**: + - The correlation between **Production Budget** and **ROI** is weak (0.05). This suggests that big-budget films don’t always have proportionally higher returns on investment. + +- **Negative Correlation Between ROI and Production Budget**: + - As production budgets increase, ROI tends to slightly decrease (-0.05). This could imply that big-budget films involve more financial risk compared to smaller-budget films. -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. +### 2. Return on Investment (ROI) Insights + - **High ROI for Lower-Budget Films**: Smaller-budget films often yield the highest ROI due to their lower production costs. + - **Larger Budgets Aren't Always Profitable**: While larger-budget films earn more gross revenue, they also come with higher costs, which doesn’t always translate into better profitability. -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! +## Return on Investment (ROI) Calculation -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. +### Overview +The **ROI** helps stakeholders understand the financial benefits of a project relative to its cost. -#### Exceeds Objective -Uses Markdown and code comments to create a well-organized, skim-able document that follows all best practices +### ROI Formula +The formula for calculating ROI is as follows: -> Refer to the [repository readability reading](https://github.com/learn-co-curriculum/dsc-repo-readability-v2-2) for more tips on best practices +$$ +\text{ROI} = \frac{\text{Net Profit} - \text{Total Investment}}{\text{Total Investment}} \times 100 +$$ -#### Meets Objective (Passing Bar) -Uses some Markdown to create an organized notebook, with an introduction at the top and a conclusion at the bottom +### Definitions +- **Net Profit:** The financial gain from improved student performance and retention rates. +- **Total Investment:** The costs incurred during the project, which may include: +## Conclusion +By using the ROI formula, stakeholders can evaluate the effectiveness and financial viability of the project. -#### 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 `#`) +- **Net Profit**: The financial gain from improved student performance and retention rates. +- **Total Investment**: The costs incurred during the project (software, data analysis tools, salaries, etc.). -#### Does Not Meet Objective -Does not submit a notebook, or does not use Markdown cells at all to organize the notebook +### 3. Predictive Analysis + - The predictive models created as part of the project show that **production budget** is a significant predictor of a movie's gross revenue. However, it is not a perfect predictor, as other factors (e.g., marketing, cast) play crucial roles in determining success. + +## Insights and Takeaways -### Data Manipulation and Analysis with `pandas` +- **Larger budgets contribute to better box office performance but do not guarantee higher ROI**. +- **Smaller-budget films often yield higher ROI**, making them potentially more profitable investments. +- Factors such as **genre, marketing, and timing** can significantly influence a movie’s financial success. -`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. +## Future Improvements -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*. +- **Incorporate Talent Data**: By integrating actor and director popularity data, we could explore how cast and crew influence a movie's financial performance. +- **More Advanced Machine Learning Models**: Further experiments with models such as Random Forests or Neural Networks could help better predict the financial outcomes of films. +- **Time-Series Analysis**: Investigate the impact of release timing (e.g., summer blockbusters vs. holiday films) on box office success. -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. +## Visualizations -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. +The project includes several key visualizations: +- Correlation heatmaps for financial metrics +- Scatterplots illustrating the relationship between budget and box office performance +- ROI distribution histograms -#### Exceeds Objective -Uses `pandas` to prepare data and answer business questions in an idiomatic, performant way +### Visualizations -#### Meets Objective (Passing Bar) -Successfully uses `pandas` to prepare data in order to answer business questions +| Image 1 | Image 2 | +|-----------------------------|-----------------------------| +| ![Studio by Gross](tudiobyfross.png) | ![Heatmap](heatmapfinal.png) | +| ![Movies by Rating](moviesbyrating.png) | ![Genres Pie Chart](genrespie.png) | +| ![Movies by Votes](moviesbyvoytes.png) | ![Movies by Gross](aven.png) | -> 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 +## Description of Images -#### Approaching Objective -Uses `pandas` to prepare data, but makes significant errors +- **Studio by Gross**: A visual representation of the studio's performance. +- **Heatmap**: A heatmap showcasing data trends. +- **Movies by Rating**: A chart illustrating movie ratings. +- **Genres Pie Chart**: A pie chart displaying genre distribution. +- **Movies by Votes**: A graph of votes received by movies. +- **Movies by Gross**: A chart showing gross revenue of movies. -> 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` +# Movie Studio Recommendations +### 1. Emulate Successful Franchises +- **Action:** Analyze franchise models from leading studios. +- **Goal:** Create original content with franchise potential. -> 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) +### 2. Diversify Genre Offerings +- **Action:** Explore a variety of genres like horror, action, and animation. +- **Goal:** Appeal to a broader audience base. -## Getting Started +### 3. Leverage Strong IPs +- **Action:** Acquire or develop strong intellectual properties. +- **Goal:** Utilize existing IP libraries for expansion. -Please start by reviewing the contents of this project description. If you have any questions, please ask your instructor ASAP. +### 4. Innovate Marketing Strategies +- **Action:** Implement engaging marketing campaigns. +- **Goal:** Build audience anticipation using digital platforms. -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. +### 5. Collaborate for Success +- **Action:** Form strategic partnerships with other studios. +- **Goal:** Enhance production quality and distribution reach. -Then, you will need to create a GitHub repository. There are three options: +## Conclusion -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. +- These recommendations aim to position the new movie studio competitively in the evolving film industry landscape. +--- +## Contributors -## Summary +- [Abdihakim Issack](https://github.com/zaenhakeem) +- [Imran](https://github.com/malvadaox) +- [Boniface Ngechu](https://github.com/bonie99) +- [Linet Lydia](https://github.com/linetlydia) +- [Batuli Abdullah](https://github.com/batuli'02) +- [Tableau Dashboard Links](https://public.tableau.com/shared/XMQHJR676?:display_count=n&:origin=viz_share_link) + --- -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/aven.png b/aven.png new file mode 100644 index 00000000..5618dba7 Binary files /dev/null and b/aven.png differ diff --git a/genrebyrating.png b/genrebyrating.png new file mode 100644 index 00000000..e362a27f Binary files /dev/null and b/genrebyrating.png differ diff --git a/genrespie.png b/genrespie.png new file mode 100644 index 00000000..f0c1fb7a Binary files /dev/null and b/genrespie.png differ diff --git a/grs.png b/grs.png new file mode 100644 index 00000000..aff26c12 Binary files /dev/null and b/grs.png differ diff --git a/heatmapfinal.png b/heatmapfinal.png new file mode 100644 index 00000000..c910aac6 Binary files /dev/null and b/heatmapfinal.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/moviesbyrating.png b/moviesbyrating.png new file mode 100644 index 00000000..ffb905b4 Binary files /dev/null and b/moviesbyrating.png differ diff --git a/moviesbyvoytes.png b/moviesbyvoytes.png new file mode 100644 index 00000000..f20cca24 Binary files /dev/null and b/moviesbyvoytes.png differ diff --git a/student.ipynb b/student.ipynb index d3bb34af..93bc5681 100644 --- a/student.ipynb +++ b/student.ipynb @@ -7,28 +7,4419 @@ "## 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" + "\n", + "- **Student Names:**\n", + " - Boniface Ngechu\n", + " - Abdihakim Isaack\n", + " - Imran Mahfoudh\n", + " - Batuli Abdallah\n", + " - Linet Lydia\n", + " \n", + "- **Student Pace:** **Full Time**\n", + "- **Instructor Name:** **Maryann Mwikali**\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **Business Understanding**\n", + "\n", + "The movie industry is a rapidly evolving and highly competitive sector, with thousands of films released each year. As major companies are investing heavily in original video content, the need for effective strategic planning has never been more critical. Studios and production companies strive to create films that not only attract large audiences but also maximize profitability. Given the multitude of factors influencing a movie’s success such as genre, budget, and marketing strategic decision making is crucial for any new studio entering this dynamic market.\n", + "\n", + "### **Objective** \n", + "The objective of this project is to equip a newly established film production company with data-driven insights that inform movie production decisions. By analyzing historical box office data and identifying trends in genres, budget ranges, and production strategies associated with high revenue and profitability, the company can guide its investments and improve success rates in an increasingly competitive landscape.\n", + "\n", + "---\n", + "\n", + "# **Business Problem**\n", + "\n", + "### **Problem Statement** \n", + "As the company prepares to launch its new movie studio, the primary goal is to maximize return on investment (ROI) while ensuring high audience satisfaction. However, with the vast array of genres, varying budget sizes, and intense competition among existing studios, predicting what will make a movie successful both financially and in terms of popularity poses a significant challenge.\n", + "\n", + "### **Key Questions to Address**\n", + "1. **Which genres have historically generated the highest revenues and profitability?** \n", + " Understanding which genres perform well can help the company make informed decisions and tailor its offerings to meet audience demand.\n", + "\n", + "2. **What budget range is most likely to yield a positive ROI?** \n", + " Identifying the optimal budget range for movie production can help mitigate financial risks while maximizing potential revenue.\n", + "\n", + "3. **Which studios have a proven track record of producing high-grossing or popular movies?** \n", + " Analyzing the performance of established studios could reveal best practices and inform potential collaborations or partnerships.\n", + "\n", + "4. **How do movie ratings and popularity scores correlate with financial success?** \n", + " Exploring the relationship between critical acclaim and audience reception can assist in prioritizing quality filmmaking alongside financial performance.\n", + "\n", + "5. **What factors influence international versus domestic success?** \n", + " For the new studio targeting both domestic and global markets, understanding the drivers of success in different regions is essential for strategic planning.\n", + "\n", + "---\n", + "\n", + "# **Analytical Approach**\n", + "\n", + "To address the business problem and key questions, we will conduct exploratory data analysis (EDA) on several datasets, focusing on identifying patterns and trends in:\n", + "- **Genre profitability**: Evaluating which genres yield the highest box office returns.\n", + "- **Studio performance**: Analyzing how different studios perform in terms of revenue and profitability.\n", + "- **Budget impact on ROI**: Understanding how varying budget levels affect return on investment.\n", + "- **Audience preferences**: Investigating correlations between movie ratings, popularity scores, and financial success.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Import necessary libraries** that will be utilized for data manipulation, analysis, and visualization:\n", + "These libraries provide essential tools for data analysis and visualization tasks.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Importing the necessary libraries.\n", + "import pandas as pd \n", + "import numpy as np\n", + "import sqlite3\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use **Pandas** to read a CSV file named `bom.movie_gross.csv` into a DataFrame called `bom_df`. This DataFrame will contain the data from the CSV file for further analysis and manipulation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
titlestudiodomestic_grossforeign_grossyear
0Toy Story 3BV415000000.06520000002010
1Alice in Wonderland (2010)BV334200000.06913000002010
2Harry Potter and the Deathly Hallows Part 1WB296000000.06643000002010
3InceptionWB292600000.05357000002010
4Shrek Forever AfterP/DW238700000.05139000002010
..................
3382The QuakeMagn.6200.0NaN2018
3383Edward II (2018 re-release)FM4800.0NaN2018
3384El PactoSony2500.0NaN2018
3385The SwanSynergetic2400.0NaN2018
3386An Actor PreparesGrav.1700.0NaN2018
\n", + "

3387 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " title studio domestic_gross \\\n", + "0 Toy Story 3 BV 415000000.0 \n", + "1 Alice in Wonderland (2010) BV 334200000.0 \n", + "2 Harry Potter and the Deathly Hallows Part 1 WB 296000000.0 \n", + "3 Inception WB 292600000.0 \n", + "4 Shrek Forever After P/DW 238700000.0 \n", + "... ... ... ... \n", + "3382 The Quake Magn. 6200.0 \n", + "3383 Edward II (2018 re-release) FM 4800.0 \n", + "3384 El Pacto Sony 2500.0 \n", + "3385 The Swan Synergetic 2400.0 \n", + "3386 An Actor Prepares Grav. 1700.0 \n", + "\n", + " foreign_gross year \n", + "0 652000000 2010 \n", + "1 691300000 2010 \n", + "2 664300000 2010 \n", + "3 535700000 2010 \n", + "4 513900000 2010 \n", + "... ... ... \n", + "3382 NaN 2018 \n", + "3383 NaN 2018 \n", + "3384 NaN 2018 \n", + "3385 NaN 2018 \n", + "3386 NaN 2018 \n", + "\n", + "[3387 rows x 5 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Loading and printing head and tail of bom_movies_gross dataset.\n", + "bom_df = pd.read_csv(\"bom.movie_gross.csv\")\n", + "bom_df" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 3387 entries, 0 to 3386\n", + "Data columns (total 5 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 title 3387 non-null object \n", + " 1 studio 3382 non-null object \n", + " 2 domestic_gross 3359 non-null float64\n", + " 3 foreign_gross 2037 non-null object \n", + " 4 year 3387 non-null int64 \n", + "dtypes: float64(1), int64(1), object(3)\n", + "memory usage: 132.4+ KB\n" + ] + } + ], + "source": [ + "#understand the data types, counts, and null values\n", + "bom_df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['title', 'studio', 'domestic_gross', 'foreign_gross', 'year'], dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Evaluating the column headers\n", + "bom_df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
domestic_grossyear
count3.359000e+033387.000000
mean2.874585e+072013.958075
std6.698250e+072.478141
min1.000000e+022010.000000
25%1.200000e+052012.000000
50%1.400000e+062014.000000
75%2.790000e+072016.000000
max9.367000e+082018.000000
\n", + "
" + ], + "text/plain": [ + " domestic_gross year\n", + "count 3.359000e+03 3387.000000\n", + "mean 2.874585e+07 2013.958075\n", + "std 6.698250e+07 2.478141\n", + "min 1.000000e+02 2010.000000\n", + "25% 1.200000e+05 2012.000000\n", + "50% 1.400000e+06 2014.000000\n", + "75% 2.790000e+07 2016.000000\n", + "max 9.367000e+08 2018.000000" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#understanding the descriptive statistics for the dataset\n", + "bom_df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are connecting to the **SQLite database** `im.db`, creating a cursor, and retrieving the names of all tables in the database.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('movie_basics',),\n", + " ('directors',),\n", + " ('known_for',),\n", + " ('movie_akas',),\n", + " ('movie_ratings',),\n", + " ('persons',),\n", + " ('principals',),\n", + " ('writers',)]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Connect to the SQlite database for im.db\n", + "conn = sqlite3.connect(\"im.db\")\n", + "cursor = conn.cursor()\n", + "cursor.execute(\"SELECT name FROM sqlite_master WHERE type='table'\")\n", + "tables = cursor.fetchall()\n", + "tables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Execute a SQL query to retrieve all records from the **movie_basics** table and storing the result in the variable basics_df.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
\n", + "

146144 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " movie_id primary_title \\\n", + "0 tt0063540 Sunghursh \n", + "1 tt0066787 One Day Before the Rainy Season \n", + "2 tt0069049 The Other Side of the Wind \n", + "3 tt0069204 Sabse Bada Sukh \n", + "4 tt0100275 The Wandering Soap Opera \n", + "... ... ... \n", + "146139 tt9916538 Kuambil Lagi Hatiku \n", + "146140 tt9916622 Rodolpho Teóphilo - O Legado de um Pioneiro \n", + "146141 tt9916706 Dankyavar Danka \n", + "146142 tt9916730 6 Gunn \n", + "146143 tt9916754 Chico Albuquerque - Revelações \n", + "\n", + " original_title start_year \\\n", + "0 Sunghursh 2013 \n", + "1 Ashad Ka Ek Din 2019 \n", + "2 The Other Side of the Wind 2018 \n", + "3 Sabse Bada Sukh 2018 \n", + "4 La Telenovela Errante 2017 \n", + "... ... ... \n", + "146139 Kuambil Lagi Hatiku 2019 \n", + "146140 Rodolpho Teóphilo - O Legado de um Pioneiro 2015 \n", + "146141 Dankyavar Danka 2013 \n", + "146142 6 Gunn 2017 \n", + "146143 Chico Albuquerque - Revelações 2013 \n", + "\n", + " runtime_minutes genres \n", + "0 175.0 Action,Crime,Drama \n", + "1 114.0 Biography,Drama \n", + "2 122.0 Drama \n", + "3 NaN Comedy,Drama \n", + "4 80.0 Comedy,Drama,Fantasy \n", + "... ... ... \n", + "146139 123.0 Drama \n", + "146140 NaN Documentary \n", + "146141 NaN Comedy \n", + "146142 116.0 None \n", + "146143 NaN Documentary \n", + "\n", + "[146144 rows x 6 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#retrieving all info info in movie_basics table\n", + "basics_df = pd.read_sql(\"SELECT * FROM movie_basics\", conn)\n", + "basics_df" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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": [ + "#understand the data types, counts, and null values\n", + "basics_df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(146144, 6)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Exploring the shape of the dataset\n", + "basics_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['movie_id', 'primary_title', 'original_title', 'start_year',\n", + " 'runtime_minutes', 'genres'],\n", + " dtype='object')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Evaluating the column headers\n", + "basics_df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
start_yearruntime_minutes
count146144.000000114405.000000
mean2014.62179886.187247
std2.733583166.360590
min2010.0000001.000000
25%2012.00000070.000000
50%2015.00000087.000000
75%2017.00000099.000000
max2115.00000051420.000000
\n", + "
" + ], + "text/plain": [ + " start_year runtime_minutes\n", + "count 146144.000000 114405.000000\n", + "mean 2014.621798 86.187247\n", + "std 2.733583 166.360590\n", + "min 2010.000000 1.000000\n", + "25% 2012.000000 70.000000\n", + "50% 2015.000000 87.000000\n", + "75% 2017.000000 99.000000\n", + "max 2115.000000 51420.000000" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#understanding the descriptive statistics for the dataset\n", + "basics_df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
movie_idaverageratingnumvotes
0tt103565268.331
1tt103846068.9559
2tt10429746.420
3tt10437264.250352
4tt10602406.521
............
73851tt98058208.125
73852tt98442567.524
73853tt98510504.714
73854tt98869347.05
73855tt98940986.3128
\n", + "

73856 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " movie_id averagerating numvotes\n", + "0 tt10356526 8.3 31\n", + "1 tt10384606 8.9 559\n", + "2 tt1042974 6.4 20\n", + "3 tt1043726 4.2 50352\n", + "4 tt1060240 6.5 21\n", + "... ... ... ...\n", + "73851 tt9805820 8.1 25\n", + "73852 tt9844256 7.5 24\n", + "73853 tt9851050 4.7 14\n", + "73854 tt9886934 7.0 5\n", + "73855 tt9894098 6.3 128\n", + "\n", + "[73856 rows x 3 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#retrieving all info in movie_rating table and giving it variable rating_df\n", + "rating_df = pd.read_sql(\"SELECT * FROM movie_ratings\", conn)\n", + "rating_df" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 73856 entries, 0 to 73855\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 movie_id 73856 non-null object \n", + " 1 averagerating 73856 non-null float64\n", + " 2 numvotes 73856 non-null int64 \n", + "dtypes: float64(1), int64(1), object(1)\n", + "memory usage: 1.7+ MB\n" + ] + } + ], + "source": [ + "#understand the data types, counts, and null values\n", + "rating_df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(73856, 3)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Exploring the shape of the dataset\n", + "rating_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['movie_id', 'averagerating', 'numvotes'], dtype='object')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Evaluating the column headers\n", + "rating_df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
averageratingnumvotes
count73856.0000007.385600e+04
mean6.3327293.523662e+03
std1.4749783.029402e+04
min1.0000005.000000e+00
25%5.5000001.400000e+01
50%6.5000004.900000e+01
75%7.4000002.820000e+02
max10.0000001.841066e+06
\n", + "
" + ], + "text/plain": [ + " averagerating numvotes\n", + "count 73856.000000 7.385600e+04\n", + "mean 6.332729 3.523662e+03\n", + "std 1.474978 3.029402e+04\n", + "min 1.000000 5.000000e+00\n", + "25% 5.500000 1.400000e+01\n", + "50% 6.500000 4.900000e+01\n", + "75% 7.400000 2.820000e+02\n", + "max 10.000000 1.841066e+06" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#understanding the descriptive statistics for the (rating_df) dataset\n", + "rating_df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Retrieve data by joining the `movie_basics` and `movie_ratings` tables, creating a comprehensive dataset containing\n", + "key movie details such as `movie_id`, `primary_title`, `original_title`, `start_year`, `runtime_minutes`, `genres`,\n", + "`averagerating`, and `numvotes`. The joined data is then loaded into a pandas DataFrame (`imdb_data`) for further analysis.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
movie_idprimary_titleoriginal_titlestart_yearruntime_minutesgenresaverageratingnumvotes
0tt0063540SunghurshSunghursh2013175.0Action,Crime,Drama7.077
1tt0066787One Day Before the Rainy SeasonAshad Ka Ek Din2019114.0Biography,Drama7.243
2tt0069049The Other Side of the WindThe Other Side of the Wind2018122.0Drama6.94517
3tt0069204Sabse Bada SukhSabse Bada Sukh2018NaNComedy,Drama6.113
4tt0100275The Wandering Soap OperaLa Telenovela Errante201780.0Comedy,Drama,Fantasy6.5119
...........................
73851tt9913084Diabolik sono ioDiabolik sono io201975.0Documentary6.26
73852tt9914286Sokagin ÇocuklariSokagin Çocuklari201998.0Drama,Family8.7136
73853tt9914642AlbatrossAlbatross2017NaNDocumentary8.58
73854tt9914942La vida sense la Sara AmatLa vida sense la Sara Amat2019NaNNone6.65
73855tt9916160DrømmelandDrømmeland201972.0Documentary6.511
\n", + "

73856 rows × 8 columns

\n", + "
" + ], + "text/plain": [ + " movie_id primary_title original_title \\\n", + "0 tt0063540 Sunghursh Sunghursh \n", + "1 tt0066787 One Day Before the Rainy Season Ashad Ka Ek Din \n", + "2 tt0069049 The Other Side of the Wind The Other Side of the Wind \n", + "3 tt0069204 Sabse Bada Sukh Sabse Bada Sukh \n", + "4 tt0100275 The Wandering Soap Opera La Telenovela Errante \n", + "... ... ... ... \n", + "73851 tt9913084 Diabolik sono io Diabolik sono io \n", + "73852 tt9914286 Sokagin Çocuklari Sokagin Çocuklari \n", + "73853 tt9914642 Albatross Albatross \n", + "73854 tt9914942 La vida sense la Sara Amat La vida sense la Sara Amat \n", + "73855 tt9916160 Drømmeland Drømmeland \n", + "\n", + " start_year runtime_minutes genres averagerating \\\n", + "0 2013 175.0 Action,Crime,Drama 7.0 \n", + "1 2019 114.0 Biography,Drama 7.2 \n", + "2 2018 122.0 Drama 6.9 \n", + "3 2018 NaN Comedy,Drama 6.1 \n", + "4 2017 80.0 Comedy,Drama,Fantasy 6.5 \n", + "... ... ... ... ... \n", + "73851 2019 75.0 Documentary 6.2 \n", + "73852 2019 98.0 Drama,Family 8.7 \n", + "73853 2017 NaN Documentary 8.5 \n", + "73854 2019 NaN None 6.6 \n", + "73855 2019 72.0 Documentary 6.5 \n", + "\n", + " numvotes \n", + "0 77 \n", + "1 43 \n", + "2 4517 \n", + "3 13 \n", + "4 119 \n", + "... ... \n", + "73851 6 \n", + "73852 136 \n", + "73853 8 \n", + "73854 5 \n", + "73855 11 \n", + "\n", + "[73856 rows x 8 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Joining movie_basics table and movie_ratings table\n", + "imdb_data = pd.read_sql_query(\"\"\"\n", + " SELECT movie_basics.movie_id,\n", + " movie_basics.primary_title,\n", + " movie_basics.original_title,\n", + " movie_basics.start_year,\n", + " movie_basics.runtime_minutes, \n", + " movie_basics.genres,\n", + " movie_ratings.averagerating,\n", + " movie_ratings.numvotes \n", + " FROM movie_basics\n", + " JOIN movie_ratings ON movie_basics.movie_id = movie_ratings.movie_id\n", + "\"\"\", conn)\n", + "\n", + "imdb_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Top Rated Movies\n", + "Identify the top-rated movies and examine their genres, runtime, and release years." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
primary_titleaverageratinggenresstart_year
51109Fly High: Story of the Disc Dog10.0Documentary2019
65944Calamity Kevin10.0Adventure,Comedy2019
71577Pick It Up! - Ska in the '90s10.0Documentary2019
73616Renegade10.0Documentary2019
65755Ellis Island: The Making of a Master Race in A...10.0Documentary,History2018
878The Dark Knight: The Ballad of the N Word10.0Comedy,Drama2018
64646A Dedicated Life: Phoebe Brand Beyond the Group10.0Documentary2015
9745Freeing Bernie Baran10.0Crime,Documentary2010
702Exteriores: Mulheres Brasileiras na Diplomacia10.0Documentary2018
49925Dog Days in the Heartland10.0Drama2017
53408All Around Us10.0Documentary2019
42970I Was Born Yesterday!10.0Documentary2015
27335Hercule contre Hermès10.0Documentary2012
60782Requiem voor een Boom10.0Documentary2016
53689The Paternal Bond: Barbary Macaques10.0Documentary2015
50085Revolution Food10.0Documentary2015
73741Moscow we will lose9.9Documentary2019
73797Wild Karnataka9.9Documentary2019
73343Gini Helida Kathe9.9Drama2019
73648The Wedding Present: Something Left Behind9.9Documentary2018
\n", + "
" + ], + "text/plain": [ + " primary_title averagerating \\\n", + "51109 Fly High: Story of the Disc Dog 10.0 \n", + "65944 Calamity Kevin 10.0 \n", + "71577 Pick It Up! - Ska in the '90s 10.0 \n", + "73616 Renegade 10.0 \n", + "65755 Ellis Island: The Making of a Master Race in A... 10.0 \n", + "878 The Dark Knight: The Ballad of the N Word 10.0 \n", + "64646 A Dedicated Life: Phoebe Brand Beyond the Group 10.0 \n", + "9745 Freeing Bernie Baran 10.0 \n", + "702 Exteriores: Mulheres Brasileiras na Diplomacia 10.0 \n", + "49925 Dog Days in the Heartland 10.0 \n", + "53408 All Around Us 10.0 \n", + "42970 I Was Born Yesterday! 10.0 \n", + "27335 Hercule contre Hermès 10.0 \n", + "60782 Requiem voor een Boom 10.0 \n", + "53689 The Paternal Bond: Barbary Macaques 10.0 \n", + "50085 Revolution Food 10.0 \n", + "73741 Moscow we will lose 9.9 \n", + "73797 Wild Karnataka 9.9 \n", + "73343 Gini Helida Kathe 9.9 \n", + "73648 The Wedding Present: Something Left Behind 9.9 \n", + "\n", + " genres start_year \n", + "51109 Documentary 2019 \n", + "65944 Adventure,Comedy 2019 \n", + "71577 Documentary 2019 \n", + "73616 Documentary 2019 \n", + "65755 Documentary,History 2018 \n", + "878 Comedy,Drama 2018 \n", + "64646 Documentary 2015 \n", + "9745 Crime,Documentary 2010 \n", + "702 Documentary 2018 \n", + "49925 Drama 2017 \n", + "53408 Documentary 2019 \n", + "42970 Documentary 2015 \n", + "27335 Documentary 2012 \n", + "60782 Documentary 2016 \n", + "53689 Documentary 2015 \n", + "50085 Documentary 2015 \n", + "73741 Documentary 2019 \n", + "73797 Documentary 2019 \n", + "73343 Drama 2019 \n", + "73648 Documentary 2018 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top_rated_movies = imdb_data.sort_values(by='averagerating', ascending=False).head(20)\n", + "top_rated_movies[['primary_title', 'averagerating', 'genres', 'start_year']]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Box Office Trends by Genre\n", + "Analyze which genres have the highest average ratings and the number of votes." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
genresaverage_ratingtotal_votesmovie_count
0Short8.80000081
1Documentary7.332090473934517753
2Game-Show7.30000034692
3News7.271330123319579
4Biography7.162274216094463809
5Music7.09197254533691968
6History7.04095678433492825
7Sport6.96149337558241179
8War6.5842912684725853
9Reality-TV6.50000045917
10Musical6.4983361387965721
11Drama6.40155911956750030788
12Family6.39472586367103412
13Animation6.248308153533021743
14Adventure6.196201842325893817
15Romance6.146608269138736589
16Crime6.115441396313564611
17Comedy6.0026897430580517290
18Mystery5.920401246572863039
19Fantasy5.919473263357042126
20Western5.8682142452376280
21Action5.8103611011616826988
22Thriller5.639114481553138217
23Sci-Fi5.489755429602892206
24Horror5.003440238846957674
25Adult3.7666671643
\n", + "
" + ], + "text/plain": [ + " genres average_rating total_votes movie_count\n", + "0 Short 8.800000 8 1\n", + "1 Documentary 7.332090 4739345 17753\n", + "2 Game-Show 7.300000 3469 2\n", + "3 News 7.271330 123319 579\n", + "4 Biography 7.162274 21609446 3809\n", + "5 Music 7.091972 5453369 1968\n", + "6 History 7.040956 7843349 2825\n", + "7 Sport 6.961493 3755824 1179\n", + "8 War 6.584291 2684725 853\n", + "9 Reality-TV 6.500000 459 17\n", + "10 Musical 6.498336 1387965 721\n", + "11 Drama 6.401559 119567500 30788\n", + "12 Family 6.394725 8636710 3412\n", + "13 Animation 6.248308 15353302 1743\n", + "14 Adventure 6.196201 84232589 3817\n", + "15 Romance 6.146608 26913873 6589\n", + "16 Crime 6.115441 39631356 4611\n", + "17 Comedy 6.002689 74305805 17290\n", + "18 Mystery 5.920401 24657286 3039\n", + "19 Fantasy 5.919473 26335704 2126\n", + "20 Western 5.868214 2452376 280\n", + "21 Action 5.810361 101161682 6988\n", + "22 Thriller 5.639114 48155313 8217\n", + "23 Sci-Fi 5.489755 42960289 2206\n", + "24 Horror 5.003440 23884695 7674\n", + "25 Adult 3.766667 164 3" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Split genres into a list\n", + "imdb_data['genres'] = imdb_data['genres'].str.split(',')\n", + "\n", + "# Explode the DataFrame to get one row per genre\n", + "exploded_genres = imdb_data.explode('genres')\n", + "\n", + "# Analyze trends by genre\n", + "genre_analysis_df = exploded_genres.groupby('genres').agg(\n", + " average_rating=('averagerating', 'mean'),\n", + " total_votes=('numvotes', 'sum'),\n", + " movie_count=('movie_id', 'count')\n", + ").sort_values(by='average_rating', ascending=False)\n", + "\n", + "# Reset index to make 'genres' a column\n", + "genre_analysis_df = genre_analysis_df.reset_index()\n", + "\n", + "# Display the genre analysis DataFrame\n", + "genre_analysis_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create visualizations to illustrate findings, such as histograms for ratings and runtime, bar plots for genre performance, and scatter plots for ratings vs. votes." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Histogram of average ratings\n", + "sns.histplot(imdb_data['averagerating'], bins=20, kde=True)\n", + "plt.title('Distribution of Average Ratings')\n", + "plt.xlabel('Average Rating')\n", + "plt.ylabel('Frequency')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Distribution of Movie Ratings\n", + "Analysis: Create a histogram or density plot to visualize the distribution of average ratings across all movies.\n", + "Insights: Understand how ratings are distributed (e.g., are most movies rated highly, or is there a wide spread of ratings?). \n", + "This can help identify what might be considered a “good” or “bad” rating." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Runtime Analysis\n", + "Analysis: Explore the distribution of movie runtimes and how they relate to ratings. You can also categorize runtimes into short, average, and long categories.\n", + "Insights: Determine if longer movies generally receive higher ratings or if there’s an optimal runtime for successful films." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "sns.scatterplot(data=imdb_data, x='runtime_minutes', y='averagerating', alpha=0.6)\n", + "plt.title('Average Ratings vs. Runtime')\n", + "plt.xlabel('Runtime (Minutes)')\n", + "plt.ylabel('Average Rating')\n", + "plt.grid(True)\n", + "plt.xlim(0, imdb_data['runtime_minutes'].max() + 10) # Set x-limit to improve visibility\n", + "plt.ylim(0, 10) # Average rating range\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Trend Analysis Over Time\n", + "Analysis: Analyze how average ratings and the number of votes have changed over the years. Create line plots to visualize these trends.\n", + "Insights: Identify if there are particular years where movie quality (as indicated by ratings) improved or declined. This can highlight shifts in audience preferences or industry changes." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_trends = imdb_data.groupby('start_year').agg(\n", + " average_rating=('averagerating', 'mean'),\n", + " total_votes=('numvotes', 'sum'),\n", + " movie_count=('movie_id', 'count')\n", + ").reset_index()\n", + "\n", + "sns.lineplot(data=yearly_trends, x='start_year', y='average_rating', label='Avg Rating')\n", + "plt.title('Average Movie Ratings Over Years')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Average Rating')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Genre Popularity Over Time\n", + "Analysis: Examine how the popularity of different genres has changed over the years based on the number of movies released and their ratings.\n", + "Insights: Determine if certain genres are trending upwards or downwards, which could inform future movie production decisions." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "genre_yearly_trends = exploded_genres.groupby(['start_year', 'genres']).agg(\n", + " average_rating=('averagerating', 'mean'),\n", + " movie_count=('movie_id', 'count')\n", + ").reset_index()\n", + "\n", + "sns.lineplot(data=genre_yearly_trends, x='start_year', y='movie_count', hue='genres')\n", + "plt.title('Genre Popularity Over Years')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Number of Movies')\n", + "plt.legend(title='Genre', bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Market Share by Genre\n", + "Analysis: Analyze the number of films produced by each genre compared to total film production.\n", + "Insights: Understand the market share of different genres, which can inform decisions on which genres to focus on for the new studio." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate market share by genre\n", + "market_share = exploded_genres['genres'].value_counts(normalize=True).head(7) * 100 # Get top 10 genres\n", + "\n", + "# Create a pie chart for the top genres\n", + "plt.figure(figsize=(7, 5)) # Set the figure size for a smaller chart\n", + "market_share.plot(kind='pie', autopct='%1.1f%%', startangle=90, cmap='viridis')\n", + "plt.title('Market Share by Genre (Top 7)')\n", + "plt.ylabel('') # Hide the y-label for better aesthetics\n", + "plt.axis('equal') # Equal aspect ratio ensures the pie chart is circular\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Top Genres by Average Rating\n", + "\n", + "This section analyzes the average ratings of different movie genres. The genres are grouped, and their average ratings are calculated. The top 5 genres with the highest average ratings are then visualized using a bar plot, providing insights into the most highly rated genres in the dataset.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "top_movies_by_genre = (\n", + " exploded_genres.groupby('genres')['averagerating']\n", + " .mean()\n", + " .nlargest(5) # Get top 5 genres by average rating\n", + ")\n", + "\n", + "top_movies_by_genre.plot(kind='bar')\n", + "plt.title('Top Genres by Average Rating')\n", + "plt.xlabel('Genre')\n", + "plt.ylabel('Average Rating')\n", + "plt.xticks(rotation=45)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Start by Loading the CSV files\n", + "Perform descriptive statistics on the csv files so as to have deeper understanding of the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
.....................
577778Dec 31, 2018Red 11$7,000$0$0
577879Apr 2, 1999Following$6,000$48,482$240,495
577980Jul 13, 2005Return to the Land of Wonders$5,000$1,338$1,338
578081Sep 29, 2015A Plague So Pleasant$1,400$0$0
578182Aug 5, 2005My Date With Drew$1,100$181,041$181,041
\n", + "

5782 rows × 6 columns

\n", + "
" + ], + "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", + "5777 78 Dec 31, 2018 Red 11 \n", + "5778 79 Apr 2, 1999 Following \n", + "5779 80 Jul 13, 2005 Return to the Land of Wonders \n", + "5780 81 Sep 29, 2015 A Plague So Pleasant \n", + "5781 82 Aug 5, 2005 My Date With Drew \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 \n", + "... ... ... ... \n", + "5777 $7,000 $0 $0 \n", + "5778 $6,000 $48,482 $240,495 \n", + "5779 $5,000 $1,338 $1,338 \n", + "5780 $1,400 $0 $0 \n", + "5781 $1,100 $181,041 $181,041 \n", + "\n", + "[5782 rows x 6 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#loading (\"tmdb.movies.csv\") dataset \n", + "movie_budgets = pd.read_csv(\"tn.movie_budgets.csv\")\n", + "movie_budgets" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "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": [ + "#understand the data types, counts, and null values\n", + "movie_budgets.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['id', 'release_date', 'movie', 'production_budget', 'domestic_gross',\n", + " 'worldwide_gross'],\n", + " dtype='object')" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Evaluating the column headers\n", + "movie_budgets.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Movie Budgets Dataset** \n", + " The `movie_budgets` DataFrame is loaded from the `tn.movie_budgets.csv` file. This dataset contains financial information on various movies, including production budgets and revenue details.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Unnamed: 0genre_idsidoriginal_languageoriginal_titlepopularityrelease_datetitlevote_averagevote_count
00[12, 14, 10751]12444enHarry Potter and the Deathly Hallows: Part 133.5332010-11-19Harry Potter and the Deathly Hallows: Part 17.710788
11[14, 12, 16, 10751]10191enHow to Train Your Dragon28.7342010-03-26How to Train Your Dragon7.77610
22[12, 28, 878]10138enIron Man 228.5152010-05-07Iron Man 26.812368
33[16, 35, 10751]862enToy Story28.0051995-11-22Toy Story7.910174
44[28, 878, 12]27205enInception27.9202010-07-16Inception8.322186
.................................
2651226512[27, 18]488143enLaboratory Conditions0.6002018-10-13Laboratory Conditions0.01
2651326513[18, 53]485975en_EXHIBIT_84xxx_0.6002018-05-01_EXHIBIT_84xxx_0.01
2651426514[14, 28, 12]381231enThe Last One0.6002018-10-01The Last One0.01
2651526515[10751, 12, 28]366854enTrailer Made0.6002018-06-22Trailer Made0.01
2651626516[53, 27]309885enThe Church0.6002018-10-05The Church0.01
\n", + "

26517 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 genre_ids id original_language \\\n", + "0 0 [12, 14, 10751] 12444 en \n", + "1 1 [14, 12, 16, 10751] 10191 en \n", + "2 2 [12, 28, 878] 10138 en \n", + "3 3 [16, 35, 10751] 862 en \n", + "4 4 [28, 878, 12] 27205 en \n", + "... ... ... ... ... \n", + "26512 26512 [27, 18] 488143 en \n", + "26513 26513 [18, 53] 485975 en \n", + "26514 26514 [14, 28, 12] 381231 en \n", + "26515 26515 [10751, 12, 28] 366854 en \n", + "26516 26516 [53, 27] 309885 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", + "26512 Laboratory Conditions 0.600 2018-10-13 \n", + "26513 _EXHIBIT_84xxx_ 0.600 2018-05-01 \n", + "26514 The Last One 0.600 2018-10-01 \n", + "26515 Trailer Made 0.600 2018-06-22 \n", + "26516 The Church 0.600 2018-10-05 \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 \n", + "... ... ... ... \n", + "26512 Laboratory Conditions 0.0 1 \n", + "26513 _EXHIBIT_84xxx_ 0.0 1 \n", + "26514 The Last One 0.0 1 \n", + "26515 Trailer Made 0.0 1 \n", + "26516 The Church 0.0 1 \n", + "\n", + "[26517 rows x 10 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#loading (\"tmdb.movies.csv\") dataset \n", + "movies = pd.read_csv(\"tmdb.movies.csv\")\n", + "movies" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 26517 entries, 0 to 26516\n", + "Data columns (total 10 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Unnamed: 0 26517 non-null int64 \n", + " 1 genre_ids 26517 non-null object \n", + " 2 id 26517 non-null int64 \n", + " 3 original_language 26517 non-null object \n", + " 4 original_title 26517 non-null object \n", + " 5 popularity 26517 non-null float64\n", + " 6 release_date 26517 non-null object \n", + " 7 title 26517 non-null object \n", + " 8 vote_average 26517 non-null float64\n", + " 9 vote_count 26517 non-null int64 \n", + "dtypes: float64(2), int64(3), object(5)\n", + "memory usage: 2.0+ MB\n" + ] + } + ], + "source": [ + "#understand the data types, counts, and null values\n", + "movies.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(26517, 10)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Exploring the shape of the dataset\n", + "movies.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Unnamed: 0', 'genre_ids', 'id', 'original_language', 'original_title',\n", + " 'popularity', 'release_date', 'title', 'vote_average', 'vote_count'],\n", + " dtype='object')" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Evaluating the column headers\n", + "movies.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Unnamed: 0idpopularityvote_averagevote_count
count26517.0000026517.00000026517.00000026517.00000026517.000000
mean13258.00000295050.1532603.1309125.991281194.224837
std7654.94288153661.6156484.3552291.852946960.961095
min0.0000027.0000000.6000000.0000001.000000
25%6629.00000157851.0000000.6000005.0000002.000000
50%13258.00000309581.0000001.3740006.0000005.000000
75%19887.00000419542.0000003.6940007.00000028.000000
max26516.00000608444.00000080.77300010.00000022186.000000
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 id popularity vote_average vote_count\n", + "count 26517.00000 26517.000000 26517.000000 26517.000000 26517.000000\n", + "mean 13258.00000 295050.153260 3.130912 5.991281 194.224837\n", + "std 7654.94288 153661.615648 4.355229 1.852946 960.961095\n", + "min 0.00000 27.000000 0.600000 0.000000 1.000000\n", + "25% 6629.00000 157851.000000 0.600000 5.000000 2.000000\n", + "50% 13258.00000 309581.000000 1.374000 6.000000 5.000000\n", + "75% 19887.00000 419542.000000 3.694000 7.000000 28.000000\n", + "max 26516.00000 608444.000000 80.773000 10.000000 22186.000000" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#understanding the descriptive statistics for the dataset\n", + "movies.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Cleaning\n", + "\n", + "In this section, we prepare the dataset for analysis by ensuring data quality and consistency. Key steps include:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1. Handling Missing Values\n", + "Identifying and addressing any missing data, either by filling values or removing incomplete rows, to prevent analysis inaccuracies.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "domestic_gross float64\n", + "foreign_gross object\n", + "dtype: object\n", + "\n", + "Data types after conversion:\n", + "domestic_gross float64\n", + "foreign_gross float64\n", + "dtype: object\n", + "\n", + "Check for missing values:\n", + "\n", + "bom_df: title 0\n", + "studio 0\n", + "domestic_gross 0\n", + "foreign_gross 0\n", + "year 0\n", + "dtype: int64\n", + "\n", + "imdb_data: movie_id 0\n", + "primary_title 0\n", + "original_title 0\n", + "start_year 0\n", + "runtime_minutes 0\n", + "genres 0\n", + "averagerating 0\n", + "numvotes 0\n", + "dtype: int64\n", + "\n", + "movies: genre_ids 0\n", + "id 0\n", + "original_language 0\n", + "original_title 0\n", + "popularity 0\n", + "release_date 0\n", + "title 0\n", + "vote_average 0\n", + "vote_count 0\n", + "dtype: int64\n", + "\n", + "movie_budgets: id 0\n", + "release_date 0\n", + "movie 0\n", + "production_budget 0\n", + "domestic_gross 0\n", + "worldwide_gross 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "#Check data types of domestic_gross and foreign_gross columns\n", + "print(bom_df[['domestic_gross', 'foreign_gross']].dtypes)\n", + "\n", + "#Convert domestic_gross and foreign_gross to numeric after removing commas, handling errors with coerce\n", + "bom_df['domestic_gross'] = pd.to_numeric(bom_df['domestic_gross'].astype(str).str.replace(',', '', regex=False), errors ='coerce')\n", + "bom_df['foreign_gross'] = pd.to_numeric(bom_df['foreign_gross'].astype(str).str.replace(',', '', regex=False), errors ='coerce')\n", + "\n", + "#Check data types after conversion\n", + "print(\"\\nData types after conversion:\")\n", + "print(bom_df[['domestic_gross', 'foreign_gross']].dtypes)\n", + "\n", + "#Fill missing values in domestic_gross and foreign_gross with the median in bom_df\n", + "bom_df['domestic_gross'].fillna(bom_df['domestic_gross'].median(), inplace=True)\n", + "bom_df['foreign_gross'].fillna(bom_df['foreign_gross'].median(), inplace=True)\n", + " \n", + "#Handle missing values in studio column and fill with Unknown\n", + "if 'studio' in bom_df.columns:\n", + " bom_df['studio'].fillna('Unknown', inplace=True)\n", + "\n", + "#Handle missing values in imdb_data dataframe, checking if the columns exist\n", + "if 'original_title' in imdb_data.columns:\n", + " imdb_data['original_title'].fillna('Unknown', inplace=True)\n", + "\n", + "if 'runtime_minutes' in imdb_data.columns:\n", + " imdb_data['runtime_minutes'].fillna(imdb_data['runtime_minutes'].median(), inplace=True)\n", + " \n", + "if 'genres' in imdb_data.columns:\n", + " imdb_data['genres'].fillna('Unknown', inplace=True)\n", + "\n", + "#Fill missing values with zero\n", + "movie_budgets['production_budget'].fillna(0, inplace=True)\n", + "movie_budgets['domestic_gross'].fillna(0, inplace=True)\n", + "movie_budgets['worldwide_gross'].fillna(0, inplace=True)\n", + "\n", + "#Drop 'Unnamed: 0' column in movies if it exists\n", + "movies.drop(columns=['Unnamed: 0'], inplace=True, errors='ignore')\n", + "\n", + "#Final check for missing values in each dataset\n", + "print(\"\\nCheck for missing values:\")\n", + "print(\"\\nbom_df:\", bom_df.isnull().sum())\n", + "print(\"\\nimdb_data:\", imdb_data.isnull().sum())\n", + "print(\"\\nmovies:\", movies.isnull().sum())\n", + "print(\"\\nmovie_budgets:\", movie_budgets.isnull().sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Removing Duplicates\n", + "Checking for and eliminating duplicate entries to ensure each record is unique and avoid data redundancy.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duplicates removed from bom_df. Current shape: (3387, 5)\n", + "Duplicates removed from imdb_data. Current shape: (73856, 8)\n", + "Duplicates removed from movie_budgets. Current shape: (5782, 6)\n" + ] + } + ], + "source": [ + "# Check for and remove duplicates in bom_df\n", + "if isinstance(bom_df, pd.DataFrame):\n", + " bom_df = bom_df.applymap(lambda x: x[0] if isinstance(x, list) else x) # Flatten lists if any\n", + " bom_df = bom_df.drop_duplicates()\n", + " print(\"Duplicates removed from bom_df. Current shape:\", bom_df.shape)\n", + "else:\n", + " print(\"bom_df is not a DataFrame.\")\n", + "\n", + "# Check for and remove duplicates in imdb_data\n", + "if isinstance(imdb_data, pd.DataFrame):\n", + " imdb_data = imdb_data.applymap(lambda x: x[0] if isinstance(x, list) else x) # Flatten lists if any\n", + " imdb_data = imdb_data.drop_duplicates()\n", + " print(\"Duplicates removed from imdb_data. Current shape:\", imdb_data.shape)\n", + "else:\n", + " print(\"imdb_data is not a DataFrame.\")\n", + "\n", + "# Check for and remove duplicates in movie_budgets\n", + "if isinstance(movie_budgets, pd.DataFrame):\n", + " movie_budgets = movie_budgets.applymap(lambda x: x[0] if isinstance(x, list) else x) # Flatten lists if any\n", + " movie_budgets = movie_budgets.drop_duplicates()\n", + " print(\"Duplicates removed from movie_budgets. Current shape:\", movie_budgets.shape)\n", + "else:\n", + " print(\"movie_budgets is not a DataFrame.\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Creation of a new column\n", + "Adding new columns as needed, either by combining existing data or calculating new metrics to enhance analysis.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " domestic_gross foreign_gross total_gross\n", + "0 415000000.00 652000000.00 1067000000.00\n", + "1 334200000.00 691300000.00 1025500000.00\n", + "2 296000000.00 664300000.00 960300000.00\n", + "3 292600000.00 535700000.00 828300000.00\n", + "4 238700000.00 513900000.00 752600000.00\n" + ] + } + ], + "source": [ + "#Check if the required columns exist\n", + "if 'domestic_gross' in bom_df.columns and 'foreign_gross' in bom_df.columns:\n", + " #Convert to numeric, handling errors by coercing invalid parsing to NaN\n", + " bom_df['domestic_gross'] = pd.to_numeric(bom_df['domestic_gross'].fillna(0), errors='coerce')\n", + " bom_df['foreign_gross'] = pd.to_numeric(bom_df['foreign_gross'].fillna(0), errors='coerce') \n", + " \n", + " #Calculate total gross revenue\n", + " bom_df['total_gross'] = bom_df['domestic_gross'] + bom_df['foreign_gross']\n", + " \n", + " #Set pandas display options to show numbers in standard format\n", + " pd.set_option('display.float_format', '{:.2f}'.format)\n", + " \n", + " #Display the first few rows to verify the new column\n", + " print(bom_df[['domestic_gross', 'foreign_gross', 'total_gross']].head())\n", + "else:\n", + " print(\"Required columns 'domestic_gross' or 'foreign_gross' are missing from the DataFrame.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4. Checking for outliers\n", + "Identifying potential outliers in the dataset to assess whether they should be addressed or excluded based on their impact on the analysis.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary statistics for 'averagerating':\n", + "count 73856.00\n", + "mean 6.33\n", + "std 1.47\n", + "min 1.00\n", + "25% 5.50\n", + "50% 6.50\n", + "75% 7.40\n", + "max 10.00\n", + "Name: averagerating, dtype: float64\n", + "Number of outliers in 'averagerating' before removal: 1172\n", + "Summary statistics for 'averagerating' after removing outliers:\n", + "count 72684.00\n", + "mean 6.40\n", + "std 1.38\n", + "min 2.70\n", + "25% 5.50\n", + "50% 6.50\n", + "75% 7.40\n", + "max 10.00\n", + "Name: averagerating, dtype: float64\n", + "\n", + "Summary statistics for 'numvotes':\n", + "count 72684.00\n", + "mean 3570.54\n", + "std 30530.90\n", + "min 5.00\n", + "25% 14.00\n", + "50% 49.00\n", + "75% 282.00\n", + "max 1841066.00\n", + "Name: numvotes, dtype: float64\n", + "Number of outliers in 'numvotes' before removal: 11647\n", + "Summary statistics for 'numvotes' after removing outliers:\n", + "count 61037.00\n", + "mean 95.33\n", + "std 139.44\n", + "min 5.00\n", + "25% 12.00\n", + "50% 31.00\n", + "75% 112.00\n", + "max 684.00\n", + "Name: numvotes, dtype: float64\n", + "\n" + ] + } + ], + "source": [ + "# Function to summarize and remove outliers using IQR\n", + "def summarize_and_remove_outliers(df, columns):\n", + " for column in columns:\n", + " if column in df.columns:\n", + " # Summary statistics\n", + " print(f\"Summary statistics for '{column}':\")\n", + " print(df[column].describe())\n", + " \n", + " # Count and print outliers before removal\n", + " Q1 = df[column].quantile(0.25)\n", + " Q3 = df[column].quantile(0.75)\n", + " IQR = Q3 - Q1\n", + " lower_bound = Q1 - 1.5 * IQR\n", + " upper_bound = Q3 + 1.5 * IQR\n", + " \n", + " outliers_count = df[(df[column] < lower_bound) | (df[column] > upper_bound)].shape[0]\n", + " print(f\"Number of outliers in '{column}' before removal: {outliers_count}\")\n", + " \n", + " # Remove outliers\n", + " df = df[(df[column] >= lower_bound) & (df[column] <= upper_bound)]\n", + " \n", + " # Summary statistics after removal\n", + " print(f\"Summary statistics for '{column}' after removing outliers:\")\n", + " print(df[column].describe())\n", + " print() # For better separation in output\n", + " else:\n", + " print(f\"Column '{column}' does not exist in the DataFrame.\")\n", + " return df\n", + "\n", + "# Define the columns to analyze\n", + "columns_to_analyze = ['averagerating', 'numvotes']\n", + "\n", + "# Check if imdb_data is a DataFrame and process\n", + "if isinstance(imdb_data, pd.DataFrame):\n", + " imdb_data = summarize_and_remove_outliers(imdb_data, columns_to_analyze)\n", + "else:\n", + " print(\"The query did not return a DataFrame.\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5. Standardizing genres and normalizing year formats in movie_basics\n", + "This step involves ensuring consistency in genre naming conventions and converting all year formats to a standard format for easier analysis. This may include correcting typos, unifying genre categories, and standardizing the year representation (e.g., ensuring all years are in a four-digit format).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Updated movie_basics:\n", + " genres start_year\n", + "0 [Action, Crime, Drama] 2013\n", + "1 [Biography, Drama] 2019\n", + "2 [Drama] 2018\n", + "3 [Comedy, Drama] 2018\n", + "4 [Comedy, Drama, Fantasy] 2017\n" + ] + } + ], + "source": [ + "movie_basics = pd.read_sql_query(\"SELECT * FROM movie_basics\", conn)\n", + "\n", + "#Consistent formats for genres\n", + "def standardize_genres(genres):\n", + " if isinstance(genres, str):\n", + " return [genre.strip() for genre in genres.split(',')]\n", + " return genres\n", + "\n", + "movie_basics['genres'] = movie_basics['genres'].apply(standardize_genres)\n", + "\n", + "#Normalize year formats in start_year to a standard year format\n", + "#Ensure all values in start_year are integers and replace invalid years with NaN\n", + "movie_basics['start_year'] = pd.to_numeric(movie_basics['start_year'], errors='coerce')\n", + "\n", + "#Checking results\n", + "print(\"\\nUpdated movie_basics:\")\n", + "print(movie_basics[['genres', 'start_year']].head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6. Removing commas from numerical columns in csv files\n", + "In this step, commas are removed from numerical columns within CSV files to facilitate accurate data processing and analysis. This is crucial as numerical values with commas may be interpreted as strings, leading to errors in calculations and aggregations.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Updated bom_df:\n", + " domestic_gross foreign_gross\n", + "0 415000000.00 652000000.00\n", + "1 334200000.00 691300000.00\n", + "2 296000000.00 664300000.00\n", + "3 292600000.00 535700000.00\n", + "4 238700000.00 513900000.00\n", + "\n", + "Updated movies:\n", + " popularity\n", + "0 33.53\n", + "1 28.73\n", + "2 28.52\n", + "3 28.00\n", + "4 27.92\n" + ] + } + ], + "source": [ + "#Function to clean numerical columns by removing commas and converting to numeric\n", + "def clean_numerical_column(df, column):\n", + " df[column] = pd.to_numeric(df[column].astype(str).str.replace(',', '', regex=False), errors='coerce')\n", + "\n", + "#Remove commas and convert columns to numeric in bom_df\n", + "clean_numerical_column(bom_df, 'domestic_gross')\n", + "clean_numerical_column(bom_df, 'foreign_gross')\n", + "\n", + "#Remove commas and convert columns to numeric in movies\n", + "clean_numerical_column(movies, 'popularity')\n", + "\n", + "#Check results\n", + "print(\"\\nUpdated bom_df:\")\n", + "print(bom_df[['domestic_gross', 'foreign_gross']].head())\n", + "\n", + "print(\"\\nUpdated movies:\")\n", + "print(movies[['popularity']].head())" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "production_budget int64\n", + "worldwide_gross int64\n", + "dtype: object\n", + " 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", + "\n", + " worldwide_gross profit \n", + "0 2776345279 2351345279 \n", + "1 1045663875 635063875 \n", + "2 149762350 -200237650 \n", + "3 1403013963 1072413963 \n", + "4 1316721747 999721747 \n" + ] + } + ], + "source": [ + "# Ensure columns are string before removing symbols, in case any numeric conversion didn't work\n", + "movie_budgets['production_budget'] = movie_budgets['production_budget'].astype(str).replace({'\\$': '', ',': ''}, regex=True)\n", + "movie_budgets['worldwide_gross'] = movie_budgets['worldwide_gross'].astype(str).replace({'\\$': '', ',': ''}, regex=True)\n", + "\n", + "# Convert columns to numeric, coerce errors to NaN to handle unexpected entries\n", + "movie_budgets['production_budget'] = pd.to_numeric(movie_budgets['production_budget'], errors='coerce')\n", + "movie_budgets['worldwide_gross'] = pd.to_numeric(movie_budgets['worldwide_gross'], errors='coerce')\n", + "\n", + "# Drop any rows where 'production_budget' or 'worldwide_gross' is NaN after conversion\n", + "movie_budgets = movie_budgets.dropna(subset=['production_budget', 'worldwide_gross'])\n", + "\n", + "# Confirm both columns are now float types before calculating profit\n", + "print(movie_budgets[['production_budget', 'worldwide_gross']].dtypes)\n", + "\n", + "# Calculate profit\n", + "movie_budgets['profit'] = movie_budgets['worldwide_gross'] - movie_budgets['production_budget']\n", + "\n", + "# Display the result\n", + "print(movie_budgets[['movie', 'production_budget', 'worldwide_gross', 'profit']].head())\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# Ensure the columns are string type first, to apply replacement of symbols correctly\n", + "movie_budgets['production_budget'] = movie_budgets['production_budget'].astype(str).replace({'\\$': '', ',': ''}, regex=True)\n", + "movie_budgets['worldwide_gross'] = movie_budgets['worldwide_gross'].astype(str).replace({'\\$': '', ',': ''}, regex=True)\n", + "\n", + "# Now, convert to numeric, with errors='coerce' to handle any remaining non-numeric entries\n", + "movie_budgets['production_budget'] = pd.to_numeric(movie_budgets['production_budget'], errors='coerce')\n", + "movie_budgets['worldwide_gross'] = pd.to_numeric(movie_budgets['worldwide_gross'], errors='coerce')\n", + "\n", + "# Drop rows with NaN values in 'production_budget' or 'worldwide_gross' if necessary\n", + "movie_budgets = movie_budgets.dropna(subset=['production_budget', 'worldwide_gross'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DATA VISUALIZATION" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#movie_budgets(tn.movie_budgets)\n", + "\n", + "# Load the data\n", + "df = pd.read_csv('tn.movie_budgets.csv')\n", + "\n", + "\n", + "# Convert 'production_budget' and 'worldwide_gross' to numeric\n", + "df['production_budget'] = df['production_budget'].replace({'\\$': '', ',': ''}, regex=True).astype(float)\n", + "df['worldwide_gross'] = df['worldwide_gross'].replace({'\\$': '', ',': ''}, regex=True).astype(float)\n", + "\n", + "\n", + "\n", + "# Plotting\n", + "plt.figure(figsize=(12, 8))\n", + "sns.scatterplot(\n", + " data=df, # Pass the DataFrame here, not the file path\n", + " x='production_budget',\n", + " y='worldwide_gross',\n", + " hue='production_budget', # Replace this with the actual column name for categories or sources\n", + " palette='viridis', # Color palette (can change to your preference)\n", + " s=100, # Marker size\n", + " edgecolor='black'\n", + ")\n", + "\n", + "# Adding labels and title\n", + "plt.xlabel('Production Budget ($)')\n", + "plt.ylabel('Worldwide Gross ($)')\n", + "plt.title('Production Budget vs. Worldwide Gross ')\n", + "plt.legend(title='Data Source') \n", + "plt.grid(True)\n", + "\n", + "# Show plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#sql\n", + "# Connect to the database\n", + "conn = sqlite3.connect('im.db')\n", + "\n", + "# Query the data\n", + "query1 = \"SELECT * FROM movie_basics;\"\n", + "query2 = \"SELECT * FROM movie_ratings;\"\n", + "data1 = pd.read_sql_query(query1, conn)\n", + "data2 = pd.read_sql_query(query2, conn)\n", + "# Step 3: Close the connection\n", + "conn.close()\n", + "\n", + "# Merge the DataFrames on a common column \n", + "merged_data = pd.merge(data1, data2, on='movie_id')\n", + "\n", + "category_counts = merged_data['genres'].value_counts()\n", + "\n", + "top_10_categories = category_counts.nlargest(10)\n", + "# Create a pie chart\n", + "plt.figure(figsize=(8, 8))\n", + "plt.pie(top_10_categories, labels=top_10_categories.index, autopct='%1.1f%%', startangle=140)\n", + "plt.title('Top 10 Categories/Genres based on ratings', fontsize=16, color='purple', pad=20)\n", + "plt.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.ticker as ticker\n", + "\n", + "# Load your actual dataset\n", + "df = pd.read_csv('tn.movie_budgets.csv')\n", + "\n", + "# Convert 'release_date' to datetime format\n", + "df['release_date'] = pd.to_datetime(df['release_date'])\n", + "\n", + "# Extract the year from 'release_date'\n", + "df['year'] = df['release_date'].dt.year\n", + "\n", + "# Convert 'production_budget' to numeric after removing the dollar sign and commas\n", + "df['production_budget'] = df['production_budget'].replace({'\\$': '', ',': ''}, regex=True).astype(float)\n", + "\n", + "# Group by year and calculate the average production budget\n", + "average_budget_per_year = df.groupby('year')['production_budget'].mean().reset_index()\n", + "\n", + "# Plotting\n", + "plt.figure(figsize=(12, 6))\n", + "sns.lineplot(\n", + " data=average_budget_per_year,\n", + " x='year',\n", + " y='production_budget',\n", + " marker='o', \n", + " color='blue'\n", + ")\n", + "\n", + "# Formatting the y-axis to show currency\n", + "plt.gca().yaxis.set_major_formatter(ticker.StrMethodFormatter('${x:,.0f}'))\n", + "\n", + "\n", + "# Adding labels and title\n", + "plt.title('Average Production Budgets Over Time')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Average Production Budget ($)')\n", + "plt.xticks(rotation=45)\n", + "plt.grid(True)\n", + "\n", + "# Show plot\n", + "plt.tight_layout() # Adjust layout to prevent clipping\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Merge the two csv files for visualizations\n", + "To consolidate our data for analysis, we merge multiple DataFrames on relevant columns, combining information from different sources into a single dataset. After merging, we save the resulting DataFrame as a new CSV file, ensuring that all relevant data is available in a unified format for further exploration and analysis.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
genre_idsidoriginal_languageoriginal_titlepopularityrelease_datetitlevote_averagevote_countstudiodomestic_grossforeign_grossyeartotal_gross
0[14, 12, 16, 10751]10191enHow to Train Your Dragon28.732010-03-26How to Train Your Dragon7.707610P/DW217600000.00277300000.002010494900000.00
1[12, 28, 878]10138enIron Man 228.522010-05-07Iron Man 26.8012368Par.312400000.00311500000.002010623900000.00
2[28, 878, 12]27205enInception27.922010-07-16Inception8.3022186WB292600000.00535700000.002010828300000.00
3[16, 10751, 35]10193enToy Story 324.452010-06-17Toy Story 37.708340BV415000000.00652000000.0020101067000000.00
4[16, 10751, 35]20352enDespicable Me23.672010-07-09Despicable Me7.2010057Uni.251500000.00291600000.002010543100000.00
.............................................
2698[16, 10751, 12]455842enElliot: The Littlest Reindeer2.902018-11-30Elliot: The Littlest Reindeer3.407Scre.24300.0018700000.00201818724300.00
2699[28, 12, 16]332718enBilal: A New Breed of Hero2.712018-02-02Bilal: A New Breed of Hero6.8054VE491000.001700000.0020182191000.00
2700[35]498919esLa Boda de Valentina2.552018-02-09La Boda de Valentina6.307PNT2800000.0018700000.00201821500000.00
2701[18]470641hiमुक्काबाज़2.282018-01-12Mukkabaaz7.5018Eros75900.0018700000.00201818775900.00
2702[10749, 18]551634zh你好，之华0.602018-11-09Last Letter6.001CL181000.0018700000.00201818881000.00
\n", + "

2703 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " genre_ids id original_language \\\n", + "0 [14, 12, 16, 10751] 10191 en \n", + "1 [12, 28, 878] 10138 en \n", + "2 [28, 878, 12] 27205 en \n", + "3 [16, 10751, 35] 10193 en \n", + "4 [16, 10751, 35] 20352 en \n", + "... ... ... ... \n", + "2698 [16, 10751, 12] 455842 en \n", + "2699 [28, 12, 16] 332718 en \n", + "2700 [35] 498919 es \n", + "2701 [18] 470641 hi \n", + "2702 [10749, 18] 551634 zh \n", + "\n", + " original_title popularity release_date \\\n", + "0 How to Train Your Dragon 28.73 2010-03-26 \n", + "1 Iron Man 2 28.52 2010-05-07 \n", + "2 Inception 27.92 2010-07-16 \n", + "3 Toy Story 3 24.45 2010-06-17 \n", + "4 Despicable Me 23.67 2010-07-09 \n", + "... ... ... ... \n", + "2698 Elliot: The Littlest Reindeer 2.90 2018-11-30 \n", + "2699 Bilal: A New Breed of Hero 2.71 2018-02-02 \n", + "2700 La Boda de Valentina 2.55 2018-02-09 \n", + "2701 मुक्काबाज़ 2.28 2018-01-12 \n", + "2702 你好，之华 0.60 2018-11-09 \n", + "\n", + " title vote_average vote_count studio \\\n", + "0 How to Train Your Dragon 7.70 7610 P/DW \n", + "1 Iron Man 2 6.80 12368 Par. \n", + "2 Inception 8.30 22186 WB \n", + "3 Toy Story 3 7.70 8340 BV \n", + "4 Despicable Me 7.20 10057 Uni. \n", + "... ... ... ... ... \n", + "2698 Elliot: The Littlest Reindeer 3.40 7 Scre. \n", + "2699 Bilal: A New Breed of Hero 6.80 54 VE \n", + "2700 La Boda de Valentina 6.30 7 PNT \n", + "2701 Mukkabaaz 7.50 18 Eros \n", + "2702 Last Letter 6.00 1 CL \n", + "\n", + " domestic_gross foreign_gross year total_gross \n", + "0 217600000.00 277300000.00 2010 494900000.00 \n", + "1 312400000.00 311500000.00 2010 623900000.00 \n", + "2 292600000.00 535700000.00 2010 828300000.00 \n", + "3 415000000.00 652000000.00 2010 1067000000.00 \n", + "4 251500000.00 291600000.00 2010 543100000.00 \n", + "... ... ... ... ... \n", + "2698 24300.00 18700000.00 2018 18724300.00 \n", + "2699 491000.00 1700000.00 2018 2191000.00 \n", + "2700 2800000.00 18700000.00 2018 21500000.00 \n", + "2701 75900.00 18700000.00 2018 18775900.00 \n", + "2702 181000.00 18700000.00 2018 18881000.00 \n", + "\n", + "[2703 rows x 14 columns]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Merge the dataframes (adjust the `on` parameter as needed)\n", + "merged_df = pd.merge(movies, bom_df, on='title') # Use 'outer' or 'inner' for different merge types\n", + "\n", + "# Save to a new CSV file\n", + "merged_df.to_csv('merged_file.csv', index=False)\n", + "merged_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Total Gross vs. Popularity\n", + "\n", + "In this analysis, we explore the relationship between a movie's total gross revenue and its popularity rating. By visualizing these two variables, we aim to identify any trends or patterns that may indicate how revenue performance correlates with audience popularity. A scatter plot is used to effectively display the distribution of movies across varying levels of total gross and popularity, with optional log scaling to enhance clarity for a wide range of revenue values.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAGkCAYAAAA2STNEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz9eXgcZ3bY+3+rqvduNLoBNHaABAiyuEkUSVHUvsyMNNLIM2OPPXbs2Imz35vEdvKz49yb2De2k/iJs/j6Zr1ZPPEyP8f2zNieRdKMZqSRNNRCUhAlkQRZXLAQ+9pAo/el6v5R3U0AxEoCBECcz/PoodDdqH673qZ06tR5z6tYloUQQgghhBA7lbrZAxBCCCGEEGIzSUAshBBCCCF2NAmIhRBCCCHEjiYBsRBCCCGE2NEkIBZCCCGEEDuaBMRCiG1D13Vls8cg7m3yHRNiZ3Js9gCEEPc2Xdd/D/irK7zs1w3D+LUVjvN54DPA31nDe/8a8EuGYQRWeJ0K/GxxnAeBCuAG8A3gXxmGMbHa97zX6LreC+ya85AFJIAu4LcNw/iTTRjTG0DcMIwfWs/j6Lr+fwGTwH+60zEKIbYXyRALITbaPwcemfPPVeDlBY/9j1Uc5x8CTes9OF3XPcArwH8BPgL+GvBp7KDoLwHv6bpevd7vu818lZtz9RjwRSAK/LGu65/dzIHdob8L/OKcn38d8G7SWIQQm0gyxEKIDWUYxnXgeulnXdeTwLhhGO9t3qjm+Q3gWeA5wzC+N+fxN3Vd/ypwAfgV7IB8pxpdOF+6rv8AGAD+HvDNTRnVHTIMo2uzxyCE2BokIBZCbLpi3ebfBH4e6MAOtP6TYRi/U3z+DeCp4r9bQJthGL26rn8a+CfAMcAJXAZ+wzCMP1vl+waAnwP+aEEwDIBhGIO6rv9zwDfnd3qBPwaeBvYDv2YYxu/oun4/8FvAyeJLX8Iu1xgt/p4f+H+AF4EQcAn4F6WxrvT8ImN/A8gYhvHpOY+pwBDw3wzD+L90Xf8MdsB/EIgD3yqOaWo152c5hmEkdF2/wpxyCl3XdwP/GnuufMDrxfe7Wnz+14AfA/4V8C+BKuBN4O8bhtFbfM3vAQ8ahnF4znF/GPhzivO+yLloKB7veSACjAN/CvxjwzAyxXH1AP8A+8LGA3wB+E2KJRPF7xXAv9F1/e9jfy++AZwwDOP9Oe/1T4BfAJoMw8iv9bwJIbYmKZkQQmwFv4ldsvB14PPAV4B/q+v6vyg+/3eBc8Db2Lfth3Vdfwi79OJC8Xd+AkgCf6TremSV7/ssdnD0laVeYBjGbxuG8S8WPPyL2GUWPw28ouv6A8B7gAu7DvkXgCexs8z+4u/8W+AT2EH/i9g1uF/Rdf3AKp9f6I+ATywo53gaqAP+l67ru4A/wz5nnymO+bOsU32srutOYDd2oImu683AGWAv9nz9NaANOKXreuOcX92F/Vl/rfia/cBruq67b3McKvBt7Iuiv4dd7vKH2HPwtxe8/J8D/xj4ZaBzwXOPFP/8D8CPYM/vOPCTC173l7EvoCQYFuIeIhliIcSmKgZ0/z/g3xiG8SvFh18tZo3/ka7rv2MYRpeu6zHsbN57xd87BPyZYRh/b86xbgAfYGdpv7WKt99d/PPagjGpLEgYLAiADMMwfn3O67+GHTy9YBhGtvhYJ3Ae+OvYQdaTwHcNw/hK8flTwCg3/zu80vMLfaV43B8Gfrf42I8DHxmGcUnX9S8CbuxFgcPFY8aZv0ButRRd10vj0IAW7DKSWuC/FR//h9j1t8+WFiEWs9jd2MF4qVY3AHzRMIxvF19zGfgYu177929jbE3Y9cw/bxjGx8XHXtd1/XnsTPV/mPPaP5i7CFDX9fIThmG8V/z5hmEY54rP/xHwE7qu/yPDMMzihc9B4GduY5xCiC1MAmIhxGZ7GDuzujBL+8fA/1F8/pbg1jCM/wn8z2IG9gCwDzvDCnYguBpa8U9rweMvY2cay3Rdj8zpNrGw9vRJ4H+VguHi+Lp0Xf+Ym0HZO8DfKt7e/xbwLcMw5i7oWun5eQzDiOq6/h3sBW6/q+u6hl0G8G+LL+kEMsAZXdf/GLuE4xuGYRSWOuYy/m7xn7mmgf/TMIy/KP78JPD9uR05DMOY0HX9NYrlLkUzpWC4+JoLuq53A09wGwGxYRj9wNO6rqu6ru/F/h4cwc6U31jw8rXWDP8BN7P9b2DfEegyDOODtY5TCLG1ScmEEGKzhYt/ji54vPRzcLFf0nXdr+v6l7EDs3exa4k9xadX20u2r/jnwqzpzwEniv/8OrcaW/BzmFvHT/Gx0vh/HvuW/WHgvwL9uq5/Vdf11T6/mD8CPqnrehXwDFCDfSGBYRjdwCexO2f8HPB9YEDX9R9Z5nhL+VNuno/j2HXe1YZh/Ks5r1nNOQAYXuQ149j1xLdF1/W/gV07fQX478Vxprj1e7Bw3pZVDHwvAD9ZvGvwl7DLMYQQ9xjJEAshNltpgVcdMDjn8frin5NL/N5/AJ7Dro99q7h46iB2jedqfRc7i/rD2DWjAJQWgQHoun741l+7xRT2+Beqx14ch2EYKeCfAf9Mt+/N/xjwq9gL8f73lZ5f4n2/URz/57BrYN8xDKOcFTUM423gh3Rd92EHx7+MXZfcahjG0Co+V8n43IVlS1juHMydw8Va2NViB+5gZ+sXJmuW7COt6/pT2EHwPwf+o2EY48XHz6ww3tX6A+AfYQfCjcD/f52OK4TYQiRDLITYbGeAHPat/7l+AsgXnwdYeKv/EeDbhmF81zCMTPGx54t/ripDbBjGNPAfgb+h6/qzS7zs4CoOdQr4vK7rrtIDxcVw9wFv67qu6bp+Qdf1f1B8X8MwjH+JndluXen5ZcafxF6I+FnsoP6P5rz/39R1vVvXdadhGEnDML6JXfersQH9nLHPwTO6rtfMGUMNdiD+9pzXRYoLIkuvuQ9ox85gA8SAumJGtuSJZd73Yewg+l/MCYYbsc/9WnedMxd57MvY2evfBN4olmgIIe4xkiEWQmyqYp3pv8deQJcH3sKu2fxHwL8zDCNafOk08ICu608Dp4GzwOd0Xf+r2LWinyj+Dsxpk7YK/wQ7IHtF1/Xfx+6pG8XufvCz2AHXN4HZZY7xL7FrgF/Rdf3/BiqBfwH0Ar9vGEZB1/XT2NnfNHZ7uIexA72/s9LzK4z/j7CDYov5ddhvYWfRv6Lr+n/GrtP+FeyuEB8C6Lq+B4isU0/o/xv7fH232KpOKb5fFvidOa+zgD/Rdf3/KP77bxbH87Xi869gl4/8J13X/wR7Xn94mfc9i53c+R1d17+CfQHxT7HryNfyPQD7O/a4rus/MAzjNIBhGMO6rn8Pu6b8r6/xeEKIbUIyxEKIreCXsYOYv4y9oOzHgV/CXlRX8tvYQc63gaPYXQu+ix1s/Rl2JvIL2HWkj7BKhmFkDcP4AnZ7rUbs9m+vFsfTDTxjGMbn5mShFztGJ3bg5sQOSv8f4AfAY4ZhlALpn8e+7f5Pge9gB1e/aBjG767y+aV8BzuAf62UIS2O6Qp25rgWe6e5P8Ku533WMIxc8WW/ip2FvmPFzOkT2LW8f4Dd+aIPeMQwjIE5L01i12X/DnaHineBT5W6eBQX3P1T7FZ6L2PP9ZJbfxuG8Tp2l5Ifwg6mfxX78/4GcHSN7dx+DbsW+5U5XTUoHjfFzaBdCHGPUSxr4eJqIYQQYv0VN+b4JcMwlqwJ3op0XX8ZmDIM46c3eyxCiI0hJRNCCCHEInRd/4fYLdyexy5hEULcoyQgFkIIIRb3SewykH9sGMZ6da0QQmxBUjIhhBBCCCF2NFlUJ4QQQgghdrRtUzLR2dnpxt59aJhb+5EKIYQQQgixFA1oAM4eP378lq5B2yYgxg6Gf7DZgxBCCCGEENvWE9gbCc2znQLiYYB9+/bhcrlWeu096cKFCxw+vJpdZMVWIvO2/cicbU8yb9uPzNn2tB3nLZvNcuXKFSjGkwttp4C4AOByuXC719Jn/d6ykz/7dibztv3InG1PMm/bj8zZ9rSN523RsltZVCeEEEIIIXY0CYiFEEIIIcSOJgGxEEIIIYTY0SQgFkIIIYQQO5oExEIIIYQQYkeTgFgIIYQQQuxoEhALIYQQQogdTQJiIYQQQgixo0lALIQQQgghdjQJiIUQQgghxI62nbZuFkIIIYQQa2RZFmPRFLF4hmDATW3Yi6Iomz2sLUUCYiGEEEKIe5RlWXReHuPMxRFMy0JVFB46VM/x/bUSFM+xoQGxrus/DfyfxR9fMQzjl3Rd/xTw24AX+BPDMH5lI8cghBBCCLFTjUVT5WAYwLQszlwcoaWugroq3yaPbuvYsBpiXdd9wL8HngKOAE/ouv5Z4EvA54EDwAld11/YqDEIIYQQQuxksXimHAyXmJZFLJ7ZpBFtTRu5qE4rHt8POIv/xICrhmH0GIaRB74MfHEDxyCEEEIIsWMFA27UBaURqqIQDLg3aURb04YFxIZhzAK/ClwGBoBeoBEYnvOyYaB5o8YghBBCCLGT1Ya9PHSovhwUl2qIa8PeTR7Z1qJYC9Lo60XX9fuB3wc+DcxgZ4MvAB2GYfxM8TXPAr9oGMbzKx2vs7NzN9CzIYMVQgghhLhHqapKXvGSSBfwezQcVgrTNDd7WJul7fjx470LH9zIRXWfBl4zDGMMQNf13wN+CSjMeU09MLSWgx4+fBi3e2em+Ts7Ozl+/PhmD0Oskczb9iNztj3JvG0/Mmfb03act0wmw4ULF5Z8fiMD4o+Af63ruh9IAp8FTgN/Wdf1Duxs709hL7ITQgghhBBiU2xkDfGrwP8COoGPsRfV/Rrws8DXgC7s+uKvbtQYhBBCCCGEWMmG9iE2DOO3gN9a8PBr2G3YhBBCCHGPk13SxHYgO9UJIYQQYkPILmliu9jIPsRCCCGE2MGW2iVtLJra5JEJMZ8ExEIIIYTYELJLmtguJCAWQgghxIaQXdLEdiEBsRBCCCE2hOySJrYLWVQnhBBCiA2hKArH99fSUlchXSbEliYBsRBCCCE2jKIo1FX5qKvybfZQhFiSlEwIIYQQQogdTQJiIYQQQgixo0lALIQQQgghdjQJiIUQQgghxI4mAbEQQgghhNjRJCAWQgghhBA7mgTEQgghhBBiR5OAWAghhBBC7GgSEAshhBBCiB1NAmIhhBBCCLGjSUAshBBCCCF2NAmIhRBCCCHEjiYBsRBCCCGE2NEkIBZCCCGEEDuaBMRCCCGEEGJHk4BYCCGEEELsaBIQCyGEEEKIHU0CYiGEEEIIsaNJQCyEEEIIIXY0CYiFEEIIIcSOJgGxEEIIIYTY0SQgFkIIIYQQO5oExEIIIYQQYkdzbNSBdV3/m8Dfn/NQG/CHwF8Avw14gT8xDONXNmoMQgghhBBCrGTDMsSGYfwPwzAeMAzjAeAvA2PAbwFfAj4PHABO6Lr+wkaNQQghhBBCiJXcrZKJ/wL8E6AduGoYRo9hGHngy8AX79IYhBBCCCGEuMWGB8S6rn8K8BqG8RWgERie8/Qw0LzRYxBCCCGEEGIpG1ZDPMffwa4ZBjsAt+Y8pwDmWg524cKFdRrW9tTZ2bnZQxC3QeZt+5E5255k3rYfmbPt6V6btw0NiHVddwFPAT9bfGgAaJjzknpgaC3HPHz4MG63e13Gt910dnZy/PjxzR6GWCOZt+1H5mx7knnbfmTOtqftOG+ZTGbZpOpGZ4jvB64YhpEo/nwa0HVd7wB6gJ/CXmQnhBBCCCHEptjoGuJ27KwwAIZhpLGzxV8DuoDLwFc3eAxCCCGEEEIsaUMzxIZh/Cnwpwseew04spHvK4QQQgghxGrJTnVCCCGEEGJHk4BYCCGEEELsaBIQCyGEEEKIHU0CYiGEEEIIsaNJQCyEEEIIIXY0CYiFEEIIIcSOJgGxEEIIIYTY0SQgFkIIIYQQO5oExEIIIYQQYkeTgFgIIYQQQuxoEhALIYQQQogdTQJiIYQQQgixo0lALIQQQgghdjQJiIUQQgghxI4mAbEQQgghhNjRJCAWQgghhBA7mgTEQgghhBBiR5OAWAghhBBC7GgSEAshhBBCiB1NAmIhhBBCCLGjSUAshBBCCCF2NAmIhRBCCCHEjiYBsRBCCCGE2NEkIBZCCCGEEDuaBMRCCCGEEGJHk4BYCCGEEELsaBIQCyGEEEKIHU0CYiGEEEIIsaNJQCyEEEIIIXY0CYiFEEIIIcSOJgGxEEIIIYTY0RwbeXBd1z8L/DPAD7xqGMYv6Lr+KeC3AS/wJ4Zh/MpGjkEIIYQQQojlbFiGWNf1duD/BX4YuB84puv6C8CXgM8DB4ATxceEEEIIIYTYFBtZMvEj2BngAcMwcsBPAEngqmEYPYZh5IEvA1/cwDEIIYQQQgixrI0smegAsrqufwNoBb4FXASG57xmGGhey0EvXLiwbgPcjjo7Ozd7COI2yLxtPzJn25PM2/Yjc7Y93WvztpEBsQN4EngaiAPfAFKANec1CmCu5aCHDx/G7Xav0xC3l87OTo4fP77ZwxBrJPO2/cicbU8yb9uPzNn2tB3nLZPJLJtU3ciAeAT4nmEY4wC6rv85dnlEYc5r6oGhDRyDEEIIIYQQy9rIgPhbwO/ruh4CZoEXgK8C/4eu6x1AD/BT2IvshBBCCCGE2BQbtqjOMIzTwL8GTgFdQB/wX4CfBb5WfOwydpAshBBCCCHEptjQPsSGYXyJWzPArwFHNvJ9hRBCCCGEWC3ZqU4IIYQQQuxoEhALIYQQQogdTQJiIYQQQgixo0lALIQQQgghdjQJiIUQQgghxI4mAbEQQgghhNjRJCAWQgghhBA7mgTEQgghhBBiR5OAWAghhBBC7GgSEAshhBBCiB1NAmIhhBBCCLGjSUAshBBCCCF2NAmIhRBCCCHEjiYBsRBCCCGE2NEkIBZCCCGEEDuaY7MHIIQQQgghti7LshiLpojFMwQDblT13sunSkAshBBCCCEWZVkWnZfHOHNxBNOyUBWF9jonlmWhKMpmD2/d3HshvhBCCCGEWBdj0VQ5GAYwLYuzXaOMRVObPLL1JQGxEEIIIYRYVCyeKQfDJQXTIhbPbNKINoYExEIIIYQQYlHBgBt1QWmEpioEA+5NGtHGkIBYCCGEEEIsqjbs5aFD9eWgWFUUThysozbs3eSRrS9ZVCeEEEIIIRalKArH99fSUldR7jIx1GfcUwvqQAJiIYQQQgixDEVRqKvyUVflA2Cgx9zkEa0/KZkQQgghhBA7mgTEQgghhBBiR5OAWAghhBBC7GgSEAshhBBCiB1NAmIhhBBCCLGjSUAshBBCCCF2NAmIhRBCCCHEjrahfYh1Xf8+UAvkig/9HaAC+G3AC/yJYRi/spFjEEIIIYQQYjkbFhDruq4A+4BdhmHki495AQN4CugHXtJ1/QXDMF7ZqHEIIYQQQgixnI3MEOvFP1/Vdb0a+O/AeeCqYRg9ALqufxn4IiABsRBCCCHEXWRZFmPRVHlL5tqw957bknm1NjIgDgOvAT8HOIE3gN8Chue8ZhhoXstBL1y4sE7D2546Ozs3ewjiNsi8bT8yZ9uTzNv2I3O2OVRVZTTu5GzXKAXTQlMVThysoy6QwzRX3pr5Xpu3DQuIDcN4F3i39LOu678L/AZwas7LFGBNG2IfPnwYt9u9LmPcbjo7Ozl+/PhmD0Oskczb9iNztj3JvG0/MmebZ3QqybuvXyVcVV1+rHu0wNFDOnVVvmV/dzvOWyaTWTapumFdJnRdf1zX9U/OeUgBeoGGOY/VA0MbNQYhhBBCCHGrWDyDaVnzHjMti1g8s0kj2lwbWTIRAn5D1/VHsUsm/irwvwF/qut6B9AD/BTwpQ0cgxBCCCHEujBNk+7BGOPTKSIhL+1NQVR163awXa5GOBhwoyrKvKBYVRSCgZ15F34jSya+pev6SeAcoAH/yTCMd3Vd/1nga4AHeBn46kaNQQghhBBiPZimyaun+3jpVA9508KhKrz4eBvPndy1JYNiy7LovDzGmYsjmJaFqig8dKie4/trURSF2rCXhw7V3/J8bdi72UPfFBvah9gwjF8FfnXBY68BRzbyfYUQQggh1lP3YKwcDAPkTYuXTvXQ0RymoyV0V8eymu4QY9FUOdgFuxzizMURWuoqqKvyoSgKx/fX0lJXIV0m2OCAWAghhBDiXjA+nSoHwyV502J8OnVXA+KVMr8ly9UIlxbNKYpCXZVvxUV0O8HWy/ELIYQQQmwxkZAXhzo/e+pQFSKhu1tisFTmdyyamve6Uo3wXDu5RnglEhALIYQQQqygvSnIi4+3lYPiUg1xe1Pwro5jtd0hSjXCpaB4p9cIr0RKJoQQQgghVqCqKs+d3EVHc3hTu0ystjuE1AivjQTEQgghhBCroKoqHS2hu76Ibq61dIe43RrhnbilswTEQgghhBDbxEZnfle7aO9eIwGxEEIIIcQ2spHdIVZq13avWlXhi67rH+i6/jd1Xb93z4QQQgghxA63U7d0Xm0l+M8BTwDXdV3/j7quH97AMQkhhBBCiE2wU9u1rSogNgzjbcMw/ipwAOgCvq7r+g90Xf/iho5OCCGEEELcNTu1Xduqa4h1XQ8BPwP8DWAG+FPgZ3Rdf84wjL+1McMTQgghhBB3y05t17aqgFjX9S8DLwLfAv53wzDeLT7+X4AxQAJiIYQQQoh7wE7c0nm1GeIu4B8ahjE+90HDMPK6rj+2/sMSQgghhBC3Yyf2Eb5Tqw2InzAM4zfnPqDr+nuGYTxsGMalDRiXEEIIIYRYo63cR3grB+rLBsS6rn8V2Afs0XX94zlPOYF7u/+GEEIIIcQ2sxX7CNuBcJKL1yc5d3Uct1PbUoE6rJwh/iVgN/DfsVuvleSxyyiEEEIIIcQWsVwf4c0IiEsZ656hGd78YADTtIhU+YiEvJseqM+1bEBsGEYv0Kvr+j7DMKzlXiuEEEIIITZXqY/w3KB4M/sIlzLWQb+LgmmPaXwqScDrxOt2bFqgvtBKJROnDMN4HIjpuj43IFYAyzCM4IaOTgghhBBCrFqpj/DCGuLV9hE2TZPuwRjj0ykiIS/tTUFUdbX7uN2qlLF2OVU0VaFgWlhANlfA73FumQ0/ViqZKG288SwwvMFjEUIIIYQQd+BO+gibpsmrp/t46VQPedPCoSq8+Hgbz53cddtBcSljPT2b4cTBes52jWCaFh63Y0tt+LFSyUQpCP49wzD234XxCCGEEEIIbr8rw+32Ee4ejJWDYYC8afHSqR46msN0tIRu5yPMy1iTzfPUsWYiYS8HdlVRW+XbEgvqYPVt1/p0XX8UeM8wDHMjBySEEEIIsdNtRvu08elUORguyZsW49Op2w6It8vOd6sNiA8Ap4CcrusZpIZYCCGEEGLDbEb7tEjIi0NV5gXFDlUhErqzsobtsPPdagtCngDasHsS3wccLv4phBBCCCHW2XLt0zZKe1OQFx9vw6Ha2dtSDXF7072f/1xVhtgwjD5d148CAezssAZ0YPcnFkIIIYQQ62gj26ctVZusqirPndxFR3N43bpMbBerCoh1Xf/vwOcBDzCEHQyfQgJiIYQQQoh1d6ft05ayUm2yqqp0tIRuu2Z4u1ptDfGz2CUT/xn4DaAF+OWNGpQQQgghxE62UYvRtuLWzlvBanPgw4ZhJIDLwH2GYbwBNG/YqIQQQgghdrjSYrS9rWHq1qlF2WbUJm8Hqw2Is7quPwl0Ac/rul6JXU8shBBCCCG2iVJt8lybubXzVrHagPgfA38HeBl4AJgAvrxBYxJCCCGEEBugVJtcCorXqzZ5u1ttl4n3gPeKPz6s63qlYRgzGzcsIYQQQgix3rbLRhl327IBsa7r3wSsJZ7DMIzPrfQGuq7/W6DGMIyf1XX9U8BvA17gTwzD+JXbGLMQQgghtrDb3XJY3B3LbZSxU+dupQzxV+/k4LqufxL4q8BLuq57gS8BTwH9xcdeMAzjlTt5DyGEEEJsHZux5fBWs12Dyp08d8sGxIZh/P7tHljX9SrgXwK/CRwBHgKuGobRU3z+y8AXAQmIhRBCiHvETm/rtZ2Dyp08d6vdmGOWRUonDMNYbi+//wr8U+yexQCNwPCc54eR1m1CCCHEPWW5tl73elAFdy+o3Igs9E6eu9VuzHF4zr+7gZ8EEku9WNf1vwn0G4bxmq7rP1t8WGV+UK0A5uqHartw4cJaf+We0tnZudlDELdB5m37kTnbnmTeNp+p+YlOTVIwb/4vX1MVpqc8dI533/L6e23OZnMexsbHb3n80hUHA870uryHqqqMxp2c7RqlYFpoqsKJg3XUBXKY5ppDq7K1zN3CeVNVlbziJZEu4PdoOKzUHY3lblttl4m+BQ/9uq7rp4F/u8Sv/ATQoOv6h0AVds/iXUBhzmvqsbeBXpPDhw/jdu/MXnmdnZ0cP358s4ch1kjmbfuROdueZN62BsuyUNzhVZUM3ItzNjqVpGsgPy/TqioKB/a1rVuWdXQqybuvXyVcVV1+rHu0wNFD+preY2GWORLyrGruFs5bqUzkbPn38jx0qHFLlYlkMpllk6qrzRDPo+v6fqBuqecNw3h2zmt/Fnga+N+Aq7qudwA9wE9hL7ITQgghxD1ip7f1KvX5XRhUrmef3/UobViq1vmYHlnz3N0Ltce3U0OsAk7gl9fyRoZhpIvB8dcAD/YmH3fUxUIIIYQQW89ybb3udXfjgqC029zCLPRadptbKYhdy9zdC7XHt1NDbAHThmHEVvOLhmH8HvB7xX9/DbvjhBBCCCHEPelOLwhWWjC3Hlno9QxigwE3HqdGMOAimzNxOVVi8ey22g561TXEuq5/Bvg0dh3wN4A3NnBcQgghhBA7zmratq1HFno9sswlkZCH5roAL53qIW9aOFSFFx9vIxLyrPlYm0VdzYt0Xf814N8BM0AS+G+6rv/8Bo5LCCGEEGLHWaqUYSyamve6UhZ6b2uYuirfmksySllmtfh7d1LrPD6dpnswRmtDkObaAK0NQboHY4xPr09XjbthtSUTPw0cNwxjBkDX9X8HvAP8+40amBBCCCHETnO36nHXs9a5NGav24HX7diwMW+k1QbEk8DsnJ+ngfi6j0YIIYQQYgdbz1KGlSxX67yWjT/u5pg3ymoD4lPA13Vd/69AHjtjfEPX9S8AGIbxZxs0PiGEEEKIbW0tweV6tm0zTbNYupAiEvLS3hREVVeull3r9tN3o9XcRlttQHys+OcvLnj857C7TkhALIQQQgixwFqDy/UqZTBNk1dP992y0O25k7tWDIrX2lf4Xug9vdouE88A6LruABTDMHIbOiohhBBCiG1qbkZYURQ+NMbWtGnFevRx7h6MlYNhgLxp8dKpHjqaw3S0hJb93dupY144ZsuyGJ1KbpsAebUbc9QCvw98AnDouv4m8NOGYax562UhhBBCiHvVwozwbDJLY00A07LI5k3g7iw4G59OlYPhkrxpMT6dWjEgvtOa4LVmxbeCVbVdA/4j8B72ds21wA+A/7JRgxJCCCGE2I4Wlhs4NJX3u0YIVdwMJu/GgrNIyItDnR98OlSFSGjlut47bcm22tZxW8lqa4j3GYbx43N+/me6rl/ciAEJIYQQQmxXC8sNvG4HVSEvuWJ2+G4tOGtvCvLi42231BC3NwVX/N07rQnejls5rzYgduq67jEMIw2g67oPezGdEEIIIYQoWqzcoC7s4+ShBizLumv1tKqq8tzJXXQ0h9fcZQLurI55O7ZhW21A/L+A7+m6/j+xA+G/Dnx1w0YlhBBCCLENLdWCbE9z5Zasn11LS7jVmnsOHJpCOOghEvaCZWFZ1pY8DysGxLquHwa6ij8+B2jA7wG/u3HDEkIIIYTYfrZKC7LVtF3bqMVvN89BgIvXJzl3dZzRqSRd3VNbdnHdsgGxrut/Dfh3wFVgD/CXDcP4zt0YmBBCCCHEdrQebdPu1Grarq213/Ba2AGvwuW+KG6ntu7HX28rFZL8PHDYMIyTwGeBf7zxQxJCCCGEEHdiubZrJcstflsP5eNbFrF4ltGpJNOzGaZn0+ty/PW0YmV1qdewYRjvApENH5EQQgghhLgjq2m7Vlr8Ntd6Ln6zjw/9Y3HOX5/g6o0oXb2TDI3HMU1zXd5jvawUEC/sJJHfqIEIIYQQQqyn0m5pV29EGZ1KYlk7p0FWqe1aKShe2HbNPhcW+3eFyeQKwPq3hKsNe9m/u4rB8TiWZaFpCg/ur+Plt3voHoyty3usl9V2mSjZOd8kIYQQQmxb23G3tPW0XNu1uefGoSm01lUQCXs5sKuK2irfup0fRVHwe52cOFBHwbTQVIW+4RipbGFVO+bdTSsFxPfruj43hPcVf1YAyzCMlbs7CyGEEELcZRu5YGy7UFWVjpbQLYHn3HOTzdtZ9PFoigO7q9f9YqE66OVa//S8eubV7ph3N60UEO+5K6MQQgghhFhHd7pb2kb0590spmnSPRgrZ4oVrLu2k9yd7Jh3Ny0bEBuG0Xe3BiKEEEIIsV7uZLe0rVxusdZAfbF+xJ9+ZDcVXiezqVz5dRu1k9yd7ph3t6y1hlgIIYQQW8idZjLvpUzoXEvtGLeaBWNbtdzidgL1xfoRf+fdXn7mMwd4/9LYms/N7ViqdGMrkYBYCCGE2KbuNJO5lTOhd+pOdoy703KLjbJcoF4b9i56YbNUP+KCafGjn9i74rlZWG6x1Uod1osExEIIIcQ2daeZzK2aCV0vt7tj3J2UW2ykpQL1mXia/tHZRS9sSv2Ib13U5lvx3Cy1/XODb+nwcbvecdhaBRxCCCGEWLU73Wlso3cq265K5RalTSs2uqRgtZbaSENV1EUvbMaiqRX7ES9nqe2fM1Qs+vrSHYevvX6V75zu42uvX6Xz8ti26P8sGWIhhBBim7rTTOZWzYRutjspt9hIpUD9Q2OMYMBFLm+yrzW8YteI1SxqWyyzu1S5xXQix2K28x0HCYiFEEKIbepOFo6tx+/fy2633GI5dtCZZGwqRb5QoLEmsKaNMBRF4ZgeIZXJc+qjQRyaykw8y30dNXicGunijnMw/8JmpUVtS9WSRyo9i5ZbhPzORY+zVWuvV0MCYiGEEGKbutNM5lbNhN6LLMvinDHGhe5JO/A0Lepr/Hzm0d0c31+36nM+Pp3m/LUJKnwuwA44z1+b4MTBOs52jZYD2ocP14NlcfVGdMV5XSqz+6Of2LNoD2E3i2+7vJ3vOEhALIQQQmxjd5rJ3IhMqLjVWDTF9cEZzlwcoVDMuI5MJHjz3CAtdcFVn/+lsrCVAdecrhEu+kdn+dr3r62qe8jSmd3couUW586dW3Rs2/mOgwTEQgghhBAbyLIsxqaSKMDelhB9wzFS2QIWkM7k11RSsFQWtjLgKV/YjE4lOXNxdNW1vMtldhcrt1BVldGp5C13FbbzHYcNDYh1Xf8N4McAC/hdwzB+W9f1TwG/DXiBPzEM41c2cgxCCCGEEJulVJ/75rkBBsfijEWTPLi/jhsjMdLZAh63Y00lBavJwq61lnfhMSu8Tg60VdE9OM1sIjtvEZ5lWYzGnbz7+tVFs8/b9Y7DhgXEuq4/BXwCuB9wAl26rr8GfAl4CugHXtJ1/QXDMF7ZqHEIIYQQW83t9mrdrj1ed7JSfa7bqRH0uygUTN6/PMqJA3XMJLI8dbRpTSUFi2VhIyHPvO9FMOBaUy3v3GPGExku9UX5w5cvzasbfu7kLlRVZSya4mzXKOGqamB7dZJYzoYFxIZhvKnr+jOGYeR1XW8qvlcIuGoYRg+ArutfBr4ISEAshBBiR7jd3eG26q5yEqQvb262tibkxe91Uh3yclSPoLdWranLRMncLOzi34s6Hj5cz3sXVq7lXTh/oPCdd3tv6T3c0RymoyVELJ4p10CXbJdOEsvZ0JIJwzByuq7/OvBLwFeARmB4zkuGgeaNHIMQQgixldxur9at2ON1qwbpW8nC+lyv24Hf40TfVU0k5OH6wMyy/YFXMjaV5M1zA6QzeVxODa/bwZmLo/zoMx0rbs282PztbgjidKjkszdbuOVNi/HpFB0tIYIBN5p66+Yg26GTxHKUu7F7iK7rPuCbwFtAh2EYP1N8/FngFw3DeH6lY3R2du4GejZynEIIIcRGm815eOPc8C2PP320gQpnet1/byOZmp9vnx6clzHUVIXnTzahFhKbMqatxuFwMJXxcW0ghqYqTEzFuH9vDU0hi+sTDl56u4d8wcKhKbz4WBttVVny+fyqjq2qKsNxL1/9/nUsCxQFImEfPofJU0frV/xeLDZ/quYh4Hdx/tr4zc+gKfytz+5Hy0ftBXVxJ2e7RimYFpqqcOJgHXWBHKZp3t5Jurvajh8/3rvwwY2sId4PeAzD+NAwjKSu63+GvcCuMOdl9cDQWo57+PBh3O7tfRVyuzo7Ozl+/PhmD0Oskczb9iNztj1tl3kbnUrSNZC/pb7zwL62ZTO9t/t7G+nqjSjhqluDrlBVHXtbwyv+/mbN2WJlHsC6l36UMrCXro6QSFvkCwUeP7aXx+5voHswxmvf/gCH042juM/Fa+8P8wt/6diSG2gsNDqV5Nq5AXxebzmonU2a1LaEVvW9WHT+LIv2pkp6huLzaogfvG8XqtoOwLlz5/jbX3xkW5XJZDIZLly4sOTzG1ky0Q78uq7rj2N3mfg88F+Bf6Pregd2tvensBfZCSGEEDvC7fZqXa8er+tZ83s7GzHMfX9T82NZ1l0NppaquVUVhfcujODQFMJBD5GwlwO7bq/Gt2RumYvXbYdc569NsK81vOS2yKXShMXGvXDeYvEM0ViaEwfrOdtl9zdWVYWjeyOr+l4sOn+qyrH9texrrVqylMM0zW3ZSWI5G7mo7mVd1x8CzmFnhb9mGMYf67o+DnwN8AAvA1/dqDEIIYQQW83t9mpdjx6v613zu9YgfeH7R6cmUdzh8vuvJli/04B+sVrsN88N0lpXgUNTcLscvPnBAKZpsaclxFNHm2/7/CzX/iwS8i66LXIktPjCt8XmraU2QL5gQTbPk0ebyOZM3C6NQ+1V885RJORhfDp9yzlbav7qqvzUVyurzlTfCzZ6Ud2vAb+24LHXgCMb+b5CCCHEVna7vVrvtMfrei/MW2uQvvD9C+bN968Ne1cM1tcjoF8sSE1n8mSyBUIVbt46d7OmNp3J39H5mZeBtSxiiRzZfIFMtkAk7OK5h3fx1rlBkpk8KvDi4220NwVXPG83562jHNCORVP2+dhdx+B4otxhwuPUaK4L0D0YW/ScHdMjhALuedlgYNGNN+5lslOdEEIIsUOsdcOG1VhLkL7c+wMrBuvrEdAvVibgcTtwuzQy2UI5GFYAl1O7o/NzMwM7TN9onMHxOA/ur+O//cXHPHJ/I4VCgQf0WmqCHvY0V9LeVLlol4mlz1v2lgsSsPja69fKrw8GXLx0qofWhiBet6OYER/A7dSorfLSPzpb3tUukytwdF8Ej0vjwysTO6pzyNp6ewghhBBi2yoFg3PdzZZZy73/SsEyrBxQr0YpSC2NQ1UUnjraxJ6mStwuDU1VUIBIlQ+v23Hb56dU2hEKuHjkvgYaqv2cOFBH99A0Y9EUL73dQ1Wlj2gszfXBGSr87iVbri133koXJHtbw9RV+YjFs/POUaFg4XRqxBJZCgUTn8eBCvQOz/DOR0O8/E4vpmkyPp3iev80r7zTS+elMUajSeDmRcdYNFX+XKbm5+qNKKNTSe5Gt7K7QTLEQgghxA6xXgvz1uv9NXX++6+0QO92FvEttFSZB0BTbYDqoIdzV8dxO7U7Wrg4t7SjvsrHR9fG8bgcZHN2s6183iSZygHLZ+mtYrnFwfYqxqMporE0+YK15LjmniOXQ8XndTIWTVJVcFMV9PD9zn4KBYuhiQQnDtYzNpXEoaqMTyWxgGzeJFcwGZ9KEvA6y1nlWDxTLmv59ulBwlXpeyp7LAGxEEIIsUOsx8K89Xz/6SlPOZhaTbC+XgH9UmUedVV+asM+Du2puaPzs7C0w+d1ksubODQTtbiphaMYrMLSQf3CwDqTK3B0b4RDe6qpDS/e/aI27OXhw/VcH5zBoSkMj8X5xIMtxJM53j0/RDZnsqshSDKTZ2QygdftIJXNU7rEcDlUHJpqB8e5wrwseelzlcpKtsLmMOtFAmIhhBBiB7nThXnr+f6d493loG41wfpGBvQLu1d0tIQWPe5qulwsLO0YnUzw4qNtvPHBAKqqUOFz8YkTLYxOJpYN6hcG1m6nxuW+KIf2VC87BtOyuDE6y/RshvFoksePNNHW6OHagI9srmCXPZgWvUMzPHS4gd6hGJOkUFWFI3sjqKrd8cK1IEt+rX8a07KwFAcz8Ux5Z7ztvm0zSEAshBBCiC1iNcH6RgT0q+1esdrXLSztSKTzqIrCz/34A8wmc0QqPVQEnMTiuWWD+sVqph2awsXrk1zuiy46BjuIHsXt1Aj6XUxOp3jvwjCfeawN07TI5kx8Hvu9cnmTvc2VPH2sict90XJJhqoo/JUXD1IZcFEZ8JTHF/S7mIql6R9L4HYXUID6Gj9Bv2vd5mKzSEAshBBiR1nPjSnEvWG13StW+7rFSjsO7anhUHv1vO9aXdXy41qsZjo8p8Z5sTHMDaK9bgeRKh/jU0lmZjM8dKie3uEYXreDfMHk8SNNHNVrUVWVuir/yn8vFNjdEOTG0BQAqqqwuyFot+TY5iQgFkIIsWOs98YUYr6teLGx2hIHh6YQqvCQzZm4nCrTs5lbSgFWals3971aagO01HUQi2dXPBemadI9GLtlZ7jFAutI2MvoVHLJMSwMoiMhL0G/i8N7aqit8oIFscStY1pN5j0Wz5LO5Hn0vnpcbi9etwNFsegejAHKLZ9xK34fliIBsRBCiB1jvTemEDdtxYuN1Zc4uPC4HeVNOUrdL4KB+aUAC4PNVCZPvmCiKAqmafKBMX7LltClemegXCc8N0isqXTz3TM3eOlUD3nTwqEqvPh4G8+d3IWqqrf2GbYsurqnluy0sVgQ/dTRZg7vuZmdrqv2z/tcSwXkCwUDbvIFi6HxKRrraymYFu93jdDaEOScMTbv3G7F78NyJCAWQgixY2zExhSbbatk4bbixcaqx2RB73AMs9Q9wbToHY7Bgha7c4PN0WiSqekUDx6s543OfqKzEc52jc57r5ff6eXA7ipGp5LlAFlVlPIucqqi8OCB2nIwDJA3LV461UNHc7i8sG9u5tayrGU7bax14aFpmrx6um/JgHyxz//KWxOEKtz84NwgVSFvuTXb3HO7Fb8Py5GAWAghxI6xHn1st5KtlIVb7mKjNuy9a0H73AuEdLaAQ1PI5m+Oa+4FUCkz2jcaw6GqtDUGyebNm90TEtl52dRSsBkKuDl9cRhnWxXTsxmyeZMrN6Ik0jm8bju0SmXyjEwkaG+sLL/vm+cGaa2rmBck3hiZJZ0r4NBuBp9502J8OkVHS+iWz7fabhyrXXjYPRhbNiBf7L2tTBN5LUhrw2z58y48t9vt4lMCYiGEEDvGZm9Msd4Wy8J9aIwRCrixLOuuZoyXvNjwu+i8PMaHxhjBgItc3mRfa5jj+2uXPNZas96l188mMgxPJjl/baLctzcS8gJ5snnz5pgC7nmZ0Y6WEF29kzRFArTUBkBRlrxQKpUDRGfn747ndKjkC2b552yugKoquJw3A910Jk8mW5j3ez6v85Zd6ByqUhz34taz08b4dKocDJesFJCrhQTtra2cM8aWvLjcbhefEhALIYTYMTZ7Y4r1tjAL53KoOJ0aX3n9ChU+113NGC91sYECHxpjOJ1auUb3vfPDpLMFAovUqa416z339TUhT/k2fiTkxe3U6B2OLShbsC+Arg/MlDOjfcMxHtxfx/uXR6n0uwlVuJe9UAoG3HicGsGAq7wIL5XO8/iRpnIw7nE7eOhQPdNzAmeP24Hbpc071nQsxYuPt/HSqR7795waLz7eRntT8A5nZHUiIS8OVbklKPY4VUankkv+/Vjp4nK7XXxKQCyEEGJH2eyNKdbTwixcqa6ztcEOpu5m3eZSFxvX+qcJBlzlYBjABN46N8BTR+o4f22C2ipveee1tdaezn19Nmfa2c052w5XBT10NFfywN7IvAuguZnRVLbAjZEYJw7UcbijhsPtNeXAbXQqaX8evwsUil0jnDTXBW6pu330vnr2tYbLrx8cj/PehRGA4uK2JlRFYTyaKge/VZVeRieTfP6pPSRTOVrrKzi+v3bRRW0bob0pWA7I86ZFPm/yiRMtfHh1nHcvjCx5MbLSxeV2u/iUgFgIIYTYphZm4XJ5s7zIqeRu1m0udrER9LtIZvKkMvliCYGGU1O51j+NU4Mbo908dKiew+3VHNVr11x7Ovf1LqeKpioUTGvetsONkVuD6YWZ0VS2wLX+aV58rL3cQm1upnoqlmZ3Q5B0Jk846OFy7xSt9UGy+QIup0b3YIyJmcy8z19b5aOpdn5ACJQfUxSFV0/34dAU4sks2ZxJ38gsuxqC1FXN7wRRslw5yVpKTea+9qgeoaMpxNBknETarn1OpPMAS16MrOa9ttPFpwTEQgghxDa1MAunKAoz8eyWqdu0LIvB8ThVFR4yuQL5vEmkykcskSGbL6AqdvB65uIIXreDptqKNdeezn399GyGEwfreb9rZM62w3XEE1m6B2cW7BDnnJcZLWV52xorGJ1KMjQ+S8/QDA5NYSaRY2QiwdhUkiePNpHJFhieSNDWVEllcVyLBe1LBYSlx67eiOLQFNyu+S3fqoOecsZ84flcqpwEWHWpyVLHaawJ8O33+ua9drHPparqllnMuV4kIBZCCLGtlRdU5TzL1jzeq+YGXaZpcuJgHVduRHE6VGLxLA/otZtWtzkWTfHehRG8bo3PPdHOexdGUBWFlKpw4kA9V/rGQXFQMC0y2QKxeIaOltCaak/nZsmzeRNVKZS3HQ76XZy/NsHvf6trXjlAOpMnlSnw6H31/MJPHGV8Jk0k5KWtsYJzVyY4c3GE6Gya4YkEJw7WoyQyWFDMPNs1w6qqlLPQcHsXHsGAm3DQw5sfDNwsJzEtzl0d59CemlsC6eXKSYBVl5osdZznTu5a1cVIXvFydhu1VFsNCYiFEEJsW3MzXWPj43QN5Ld9pup2WZbFB8Y4Z7tGSaRz5a15j+mRDT0Xy906L5UzJNJ5cnmThw/X43SozCSynL8yTiqTx+NxoKkKbpdGMOBec+3pcq+/1j/NN97qtoPhgslsMstLb/fwU5/eT/fgDO+cH+FHP7GXjtYwYNcLlwJFl1PDNC3Odo3wyH0NdA/Gyl0jpovbII9PpwBue8FYbdhLJOwt9z9WgEiVD7dTW7REZGE5SSqTJ5cr0DM4g4U1r+0bLF1qslRZimmZq7oYSaQL26ql2mpIQCyEEGLb2m7N/zfS3HNRCorOX5tgX2t4w87FSh0h5pYzZPMmY9EUHqeGvjvMjZFZorOJ8q5we5oq520usZba06VeP3fhXCnozOdNkqmc/diCIG5uoOh1O4hU+RifSmJZUF/jZ3dDkOnZDPmCxeH2apoigUW3QV4tRVE4sKuKPS0h0pl8uf/xwqxs6aIjnspSV+UjGkszOJFgcjqF06FytmsEv8/JjeFYucMGLJ21XqospTLgYW9LeMWLEb9HQ1XyW6Y0Zz1IQCyEEGLb2m7N/zfSZpyLlS5IFmu99YBeyzE9wr6WMJeuD1FdFZ7XZWI9zV04p6r2sR0OFZ/XCdwaxC0MFCMhL0Gfi/27wzywL4LX5aBgmVQGPOVAsba4K5vdTWPtgXFtlY+njjYvmpW1LIuxqQQfXh3n7KUx3E6VVKZAS22AeDyD1+2gpS5Az9AMCnBUr+WcMUbA68TvcS6ZtV6uJdpqLkYcVoqHDjVum5ZqqyEBsRBCiG1ruzX/X8x6bb28GedisT7IoQo33YMzgB14LVXOUFftZ6A3xuE9HfMCykjIw/h0el1adc1tKQYqFT4Xzz3cSjabJ1zhZl9rmEjIU359KVCcu4lIY42fi9cnmU3lyoHf3pZweYOOxTLkx/TIqj/DUiUfAJ2XRzl/fZLvnenDNO3xVQU9DE0kePrBFgC+/34/qeJmH1OxFE8cbWJXXRC/z0kineP6wAztTcF5bdzutCWaaZrbqqXaakhALIQQYtuam+mC26/l3CzrufXyZmyEMDcIdzlU3C5HsQ/yLOeMsfJnWSrjuFi3gvamIAOjcUzLIhz0EAl7ObCritqqtWeQVVXluZO76GgOMz6dojbkYWgywamPhnBoKjPxbDk4VBQFRVE4pkdIZfKc+mgQy7QXqh3bX4fLoZLNm/My4EtlyC3L4mzX6KJzutQF0MJzNDqV5M1zg6hAoWAffyyaQtNUaiq9xBJZnMUxgV1/bKGQSucZn0nx5W9fmtc947mTu24Jiu+kJdp2aqm2GhIQCyGE2LbmZrouXXFwYF/btspUrWcN9GZshDA3CC9tClLqg7yaz7KwW0EineOlUz0882Azs8k8b34wgGla7GkJ8dTR5tu6UFBVlY6WEB0tIUankpw/fYMKnwtY/HyPT6c5f22CCp+LmXiGbN7kbNcITx5tYqy4oUapDGWxMpVEOseVG9FF57Q27F31BVAsniGdyRPwOtE0hULBwrIsCqaFosK+1jAfXRkvt5mrCnnxe5wcaKviD1++VK6dzpuWvT11c3jRrZiFTQJiIYQQ21opUzXgTK8qiFyvEoX1sN51v3c7azc3CO8enKG1YXZNm4Is7FaQzRXImxZOh8bZrputyNKZ/Losliyfb8silsiRyubxuhxMz6YXXVjncmoo3Gy3BvPLUBYrU8kXTJyO+bvMlc4DrNwazTRNugdjDE/GObI3wgeXRjhxoJ6zl0YwTbss5fEjTRzfX0trfZDZRIbH7m/ELNY2dw9O37INc960GJ9OSUC8DAmIhRBC7BjrWaKwHu6FGuhSEA5wzhhb9rMsvBgJ+h1kculyhwWXQ7MXwRXMcjCsYAemSwXXa7nAsc839I3GuTEyi2VZOBwqj4zXs7clhKqq8+ak1Gliajpl9x5eUIaysEwlkytwTI+QTOfLJRZzz8NKF0CmafLq6b552yg/dayZ7oEoJw810FDjp60hyLHi1s6LXfzMJrLzduADcKhKufOEWJwExEIIIXaMrdambTPqfu/UUgHoSp9l4cVIJltgT1OAtsYK3ugcJJ83aajx8+LjbaTSeTRVwTQtIlW+RVuRLXbMpS5wypu3JDLsbQ1zpsuu89U0hQf31/Hy2z201AXpaAnd8jnqwj4+daKVhmofFf6bn7d0zFDAxXMnWxkYjzMeTdE3PMvIVJLdDUEgT75gzTsPy10AdQ/M8PW3usnmCqiqgsOh8vbHQ/z1zx7E43Iu242jNB4Fi08/spvvvNs7r4a4vSm43l+Fe4oExEIIIbacjSprWCpDN5vIlJ+/m2UUm1H3e7vsFmBJLvVNMR5NEY2ly8FeaevglroAbmcT+UKBxprAvIVwcy9GxqdTDE8keP/SEE8+0MonT7SSTOVwOFSO6hFAoTLg5tzVcdwOlUyuwNG9EcCuo13smLD4Bc7CoHk2keHTD+9molgP3DccI5UtlEsKVjMnC49ZV+XjUu8U9VU+QhVufB4niqrw2P2N1IR9y1403NdRY3//LJNrgzNMzqQBUBXwehx4XA7yBbivo2bV8+J2avzMZw5QMC0iId8tXSZua+7n/H1UVbVc2jE+nSIS8t7xe2w2CYiFEEJsKRtZ1rBYiYLHqTE8meTV0zc2pYxiO6zWL83Jm+cGuN4/jaoqnDhYD9libW9tgP6x+K3Z4TmfqXQxUjBNqircBLxOslk/Bcvku6f7ePJoE6NTSWLxHHtbw9SGfRxqry4HetcGprncF503N6upwV4YNFuWwktv93DiQB2X+6LArSUFK83JwmNmsgVmYmmaIwHeOjdIwbTQVIWGKh8H26vL36O5wfZMPM1MPMtHV8b56Oo4fq+TgNeBgr2JiUNTSaXzeFyOJcsdlpqXTDbP+5fG+NFP7L3j79Vifx/3N7vnlXYAvPDIbk4erqM27N+SF3Qr2b6hvBBCiHvSUlm/sWjqjo9dytCpxf9hq4rCkX0Rzl+b2JD3u1eU5iSdyWNhLzI722V3ljAti6GJ+IpzFgy48Tg1fG4nZ7pGONs1yodXxwl4nXa9bc6cVz6gKAooCl3dU4xOJcnmzVuOW7rAKbMs4skcEzNprvVPY5rmLUFz0O+kKRKg9NDtlBTc0n/ZqbKrsZL3LgyXa59N0+Lc1fElv0eJVI7e4RimZZHK5JmOpXmzs5/PPNaO06GSL5iomsILj+xecmwrzctsIsPoVJKrN6KMTiUxTXPez9aCi4nl3mPu3M6mVb5e3BI7nc0TjaX509eu8IExTuflsVUdd6uRDLEQQogtZSN3XFvsdvhMPH1P7Xa3EbeyS3NS6rpQCr6yOROPUwMLgn4XLqfK9Kzdqszr1hgam6V7cIZIyEtbYwVH9kX40jcvUijYGdRwhZ/vdw5wfH8dbpfGQ7vn10/P/S6kMnmyuQIup8ZMPH3rTnimydBEgvpqH1/5nkEub/Li4208sC8y/66AorCrvoInjzZz8nDDbZ2jhXcapmczNNT4udA9ab8FEKny4XZq875Hc7Ot0dk0wxMJThysR0lkUFWVaDxL9+A0X3i6g0yuQLjCw+G2aq4PzCxaurHSvCy881Hq8ZzOFVZ9J2Sxv4/JdKFc55xK5wF7S+xEMkfP4PbcOl0CYiGEEFvKRndeWOx2+Hq831Zo51bqUvDamRs011VgWXBkbw1PHm1C07TbPm5pTkpdF8ankqiqgt/roCbk4fUP5t+y93ksNE3jv339AmBnYT/3ZDt1VT5OHqonlzfpGZphbHIWt8uJ3hrmAT1yy4Kx0vuORpOMTyWxiseaiWfLtcSlC5yewRnOdo3QMzRT3rntpVM97G0OL7rY79CcUoa1WlgLnC9YtDUE0VvDpLN2x4yFCwEty+L6wAxG3xQ1IQ+5QgGzmNF95L4GPrg0yslD9fSPxnnz3CAuh8qzJ3fxemf/kgHsUvPidmnou8LlzUFcDhW/x0lXzxR7mivpHY6Rypp890wfoYCbPc2V5WPe0gkk4KLC6yRS5SOZyuHzOtHI4XJqZHOF8jkpbYltTiW35cXkhgbEuq7/M+DHiz++ZBjGL+u6/ingtwEv8CeGYfzKRo5BCCHE9nK3Oy+sx/vdzXZuy2WAuwdjvHbmBk21FZzpGqFQsHj/8igW8MzxlnUJACMhL0G/i6N7I7TWV/Dq6T7Mgr2rXCqTp/PyKD/81B7+13cMHMV+vE6HyqXeKc50jTI4FkdVFY7ptXicJn6/nwf2Rair8s97T6vYL3hva4hCwWS2uEnGgwfr+ejKOK31QeqKi/bqqnx0D87Q1Ts17xh502JsOsXDh+tpqQ0wNBHHoWnUVnlvea+1XMwsdqchEvLg0BSGp5Ikkjn8PicNxSx26fvxvTN95RKKhw/Xk88XuD4Qw7KgMughEvLSFKkgnc2ztyXEx1cnSBeDzsUWDC41L4f2VDNTyh4XdxB87f1+orN2Kcn+3VX0Ds9gWnD64jDT8Ux5YeTC7/Gj99VRWeHij75zmXzexOFQ+cJT7XzxEx185fVrJIqLIV98tI3RycS2axtYsmEBcTHwfQ44ip3F/7au6z8J/BbwFNAPvKTr+guGYbyyUeMQQgixvdztzgvr8X53q53bwj61C7flHZ9O0Vx3MxgG+1b2W+cGOdRec0fb9C52jq72RxmNpsrZ24JpUVPpwTLNcjAMsKshyOmLI+xpDpUzmR8YYzy4L8ThfU3zFt/B/AuM6dk00dkMJw414HYqTEynyebNW7KQkZB32f67iy36WyoIXM3FzMI7DaZpMjKV5BvFtmkup8bnn2znfstifDrNmYsjxFM5xqNJ/F4nb3QO8CPPdBDwuTiyN8KzD7WCArF4tlzKk56TgYVbS3mW/+4qqIpCqMLN6+/3kylmmQF+8OEgx/Ra+kZiOB1q+bsKt24cMjyV4jvv3bB3H3RaqKrCK+9088t/5SS/+JPH6B6eoWBajEwkSGUKW75t4FI2MkM8DPyiYRhZAF3XLwH7gKuGYfQUH/sy8EVAAmIhhBBlm7Hj2p2830bWPc/VPRibt7J/4ba8kZAXy6IcDIP92VyOW2tZ11resXipicrUdIrSu2mqwmwiS1Xl/OC0YFqYJnicDoIBl91hIldgd0PFooHnWDRJz9A0bqdKJOwjkytw+sIwj97fQDZvLpqFbG8K8uLjbbx0qqdcYvDpx9poa6xY9oIFVt49biWmafLR1XHGplI8dbSJj6+OE41ny3NjWRaJdI7pWft8D08ksCyL9y+N8sDeCIfaq8pZ/rmZ8qVKeRa7S7BwrLVhL4/eV89kLM3DhxtQVYWewWm6h2LkC/aGIcf31zE9m5m3k94tW1Enc+UAn2LVTTpnMjGT4ZH7GtjTErK/S01bu23gSjYsIDYM42Lp33Vd34tdOvEfsAPlkmGgeS3HvXDhwrqMb7vq7Ozc7CGI2yDztv3InG1PmzVvpuYnOjVZ7jAAdnA4PeWhc7x73d5nMhMgnry1a0F3/ygzY9dxOBwc3F3J6YtD5PMmiqLQWl9BIRdnemqUzvFuVFVlNO6060tNUDQHR/U6GsMKasHuRLBayYKXo3oV07NZ8gULh6YQqnCRnI3yyQcbeOntHvIFC6wCrXV+cplZJtJW+fz4XZV88MEH846pqiqDMR/feruHmdkMDk3hwQP1pDN5UqkU0alJThysY6jPYKBn/libAi5+/JPtDIwl8Lo1rvUM8l0ric+tMjY+fsv4L12xw6Clnhtwplc8Bw6Hg54pF9841cdULIPTofCZR9u5cG2EVCbP4GgU0zRpawgQ9Dp498IwmgKaQ8Wpwrsf9VHjy6AWErech/Y6e55KbdxOHKxjbPA6737kKJ9bh6bw4mNttFVlyefz88bVO+Xmpbf7iMazaKrdsUJVYWg8SXtTBZlMlnQmR8ClEpsewzStW77HLZFmVEzS6dzNY2sKmpmis7MTVVXJK14S6QLTHo2hvtSavkNbxYYvqtN1/RDwEvCPgDx2lrhEAdZ01g4fPozbvf1qU9ZDZ2cnx48f3+xhiDWSedt+ZM62p82cN8uyUNzhDa8hvt4/zbH9SXIFE01V6BuOkcubtLfU0dGiA1AoFHA4nbx1bhCXQyNU4eKhQw3lsYxOJXn39auEq6oZn7bLHb77/jAvPLobr9vHgd1V8zbUWM7oVIIbkzcYnoyRzZu4HCoNNUHq6+qpiZh0tEZIpHPUBL1E42nOXBydd3609BBHF8zZ6FSSl79+nkQqh9tlhykfX5+wM6l76nn+sX23ZCFLGdOh8VliyRzjM2kyOQuvp4KBSYuH76vF7Yvj0FS8bvuYqqJwYF8bAF0D+VsysQf2ta0qQ3ytf5rXXv0AUFEUhVweXnm3ly9+Yi+D4wne/NBuE5dM5znYXsWRfbV0dU9wqD1C/0iM+kiAUFUde1vDtxzbsiyOHmqfl8m/PjDDa9/+AIfTDaqJaVp858wQv/iTx+iYc4zSuBxOBxV+iCdzvPxuLz/2TAf7WqtJZnJ0XprENC3qa/wc3tfIcb32lu/xroYwP/pJ97zM+wsPt3D88C5UtY3Oy2OcLb8+z0OHGjdtK/TlZDKZZZOqG72o7jHga8A/MAzjj3VdfwpomPOSemBoI8cghBBCbLS7UfdsWRbReJrhyQQ9QzFUFU4equfA7qp5fWo1TeOZ4y0caq9ZdCyl8o5UJs/4VBJFtR83eqNcuRFlT0uIp442ry6oseD60AyJVM6uIS6YnL8+gcOhMjqVLAe+7c2VQCUtdcF5Y/rgg4FbDhmLZ8jm7J3ubozMlgOz3Q0VWMUc2rX+6fIxLMsq11XHkjlSmRyfeXQ3V/ui5PwuKiIVvHq6l8aaAO93jVAV8lIX9s2rdS0tTEukc+QLJo8faSIS8qxqXsank8TTOTLZAj6Pk3gqSyZXwOlQuTE6y+R0isoKN163xuWeKe7rqOGTJ3Zx9uIIgYAbv8e55CK0xcpUxqdT5f6/pZZniVSO7uEZ9hR32pv7OgCPy4FDs3sbV1V6aWuo4CuvXyMc9OB1OQj6nZy5OEpLXXDR7/F9pklV0MONkVl8XidXrg9w7kqIlrrAltoK/U5s5KK6FuAvgJ8wDOP14sOn7af0DqAH+CngSxs1BiGEEOJu2ci6Z9M06eqZ4luneqjwuji8p5pMrsBsMkddtZ/x6fS8oHe5sZRadWVzBSzsnfrGokn2NFViAelMftVBTSyRpSrowet2kMsVKJgWfSOzJFL27fWFAdJSY5pb06woCi6nxshkgpqQHfA6NJWBsTi7Gyr52utXcWgKkbCXSNgHFvQNxdB3hZlN2gHtG50DPP/Ibkankrx3YZj2pkoy2TxPHG0ilzc5eahhXquxY3oEy7K4ciOK06Fi9E7hdTtWvCiwLAuPSyOdLZDLmTg0e8tpAK/HSTSWJpbMksrmaa6twLTA6dCIJbI0RgK0NlQUP8P8LamXU1okmMkWcDo1LNPC5VRRsBd3ls7vwkWGDk3F49TYXR+kfzzOjZFZLG72TI6EvOU684XzNDGT4f1LYzf7LscznLk4gtvZdM/08N7IDPEvAR7gt3VdLz32/wI/i5019gAvA1/dwDEIIYQQd81G9CIudZa43Bvlcu8USjFb6nI5GByL8/HVCWKJoRVLNEpjm01kuK+jhjMXRyi98sH9dfQNx1AAl1NbdVAztw9uPJVjZjZDKpNjJp5hajZDJORd8VgLW9Z5nBpHOmq4OjDN5Ewaj0vlkydasUyLiZk0XreG26XRPzrLV1+/yq76IKEKNz1DMSamUygKHN1Xi2mZWBbUV/vYVV9BNmdnlxOp3C3B5/h0utyzt7QByJvnBmip9YOiLjqfpmnyoTHGaDTBp0608vLb3STTFg6HyjPHWxgcm6Vg2SFnLm+SL5jk8gVM02RXc5jobIZLPVFGJ5N0dduZ44ZqHxX+5b837U1BXnh0N1/7/lWmZzM4NZXnH2nHsqB3cAawF9TNXWRY6kjy6Ud2k80XGBxPoKoKBdPCAsankgT9riUz1UstGs0XChvaM/xu2shFdb8A/MISTx/ZqPcVQgghNsNG9SIudZboaAmhaQqFgsX0bJZcIY3HpeFyqiveql4s6HzqaBPHD9SSyuR57cwN0tkCkSrfLRtKLKfUB/fNcwOMT9ntxE4eqqd3KEY6WyDgdS5bEgA3W9bZC/I8ZHMmLpfGC4/sIpUuEPC6+MGHAyTSecL905w4WMd0LM37l8ewLIWWugre+XiQfa1V7GmqJJXJc7V/ikfvb6Am5GNwLM5b5wbLC9MeOlRPMOCaN4ZSwFeqqbYAn0vjA2OMeCpPJlvA7dLY01TJUd1u1fb2x0Nc7plibDpF38g0n3+qg0y2QG2Vl7c+6MftcnJ8fy3vXxolky3gdTs4pkeIhH2k0jlOfTiIaVp4PQ7iqTSXvmvwxNEmJqbTi35v5l5stdUHeP7h3cSTOXwelbGpNN881cPh9hqqK73l33/u5C46msOMTyfRVIVr/VGuD1pMTad4+ngL750fIpHOo6oKR/dGlmyXttRmOY01gbvaM3wjyU51Qggh1s1W2K3tTq32Myx8HVi3VU+50vtNxlLlYPjZh1p5vXMAs3iL/cTBeqZnb7bLWioTu7DtWDpX4EzXKD/6iQ4A3A6Nc1fHcTu1NQU1pdppt1Pjo7AdTGdyebp6psjlTdKZPM8cb172WDPxNIpiURPy0Ts0g6apfHxtnMePNOHSNF57v99e+FXtY3rWvlX/4IE6ZhM5wCKdzXFfRy3vXRjCoWk4NPjhpzuIp7JEQj4udk9gFssGTNOidzgG85OdBANuLCyqKtxU+l1oqkLA46R7KMb5axMoioKmKqQyeZpqA2DBq+/1Eapwc6l3iqP7Irzybi8K4HFpfPqR3bz90RC50TifeLCFcIWHqekU1/qn+cCwP5uFPYxSLbcFZHPmot+bhRc0Y1NJfB4H569PsH9XmHNXxjFNSGcLt/x+R0uICr+Lb751HadT461zgwyMxXE5VV54tA3FslA1lUN7lt65b+HmNaULi9oqH7VVvrvWM3wjSUAshBBiXdzN3do2ymo/g2VZnDNGudQ3TSqdw+tx0lTjw6EpZPP27mChCjfZnMnYVHLZoPqcMcb1QXthmqoqdDRXFvvGqnb/2lSOs5dGyedNKnwOXnhkN5Zp2QurMnmyebsUYLms7tJ9krPsbQ1TG/ZxaM/ii/CWO1elQN7rdhBLZJmOZ4glMhzYXYVlgb47XN4MYrHfTVs+zNkM1ZVeLvVO0dVjdz04eaiBvtEYJ/bX01Djt7dCdmlomko6kyeZyeHQAFSqK3188wfX0VSVyoALn8fJV1+7yslDDXzjRg9PH2vG43IwFcuUt1SOJbLUVd/s9xsJedhVF+R7Z3t58EAD+XyBvS1hfu9bXaDYZSQF0w40j+6rJV8oMBpNUV/jw+9xcvbSGPt3hXE6VCp8LhqrfTx5tAkFhWQ6x1dev0Ihb9FQ48eyYGQygc/tsMs3imULlmVRME1Smbw9xjkXNwsvaDRV4Zwxxv17a7Esu8ezpiq4ipuhmKZJz+AM3YMzRCo9ZPMF6qp9vHVu0L4TEPYyHk3x+tkbPHtyF22NldSGfbfMz9zvw9zFdtNTnnl/J+5mz/CNIgGxEELsUOudzb1bu7VtpNV+hrGpBO9fHuPNDwYoFCw0TeGpo83lQMPtcvDWOfuW+Gg0SSZXWHIDigvdk7x1boB4MoemKUwfqsft1Di2v46xaIrLvVM0ReyOC7PJPC+93cPf/vxhfF67MwCwYlZ3qVvepQB6rQsCFyvBaG8KcrFnioFRe2vmEwfr6R+ZpWcwRlPtrdnOD40xCmaei9cvEY1nSKZzPHigngvdk3x8bYLnH9mF3+skXOEpjzsS8hKdTVMd9PLph3bhcjvwuFTaG0NMxVJUBT1c659GVRUUxZ6/l97p4ac+vZ+COXPL5y4Zn04zPBnn+P4GXnq7m1ze4sTBFAGfk3Q2T75g4tBUTNOum3VoGi6H/fOe5krePT9M5+UxKgNOnjzSjAWEgx4+MsbtMpe8Pf7B8QRtjUF6h2Z46HADV25E8bo08nmTQ+3VfHRlnHS2QH2Nn6D/ZlnHwgsan9dBYySAqgKWgtup0hgJ4PM6wLIYmkhwtmuErt4p8nmTp483k0xlGRiLEwnbm7c01Pip9LvoaK7kgX03v5vLXRSWviOd493b5iJ3tSQgFkKIHWgjsrnruVvbZpVerPYz9I/Fy8Ew2DvDvfXhIH/r84fJFyze/GAA07SIVPlwO7UlLwzGplK88/EQ8WSufJzTF0dob6osf37TgpbaAJV+N6lsHq/LQV21n32t4VvamK32lreqKDx8uB4si6s3okv+/lLzsFgJxsBonE8ca6Yu7MPlVJmezZSz14tlO2tCHr797gBOh5NccRORc1fGOLa/lqs3psnlTWqrbh3308daGBmPURl08/I7vezfFebGaIzmugo0VUHTVJwOBcuCXN7EqalksnlSmfyiLdWsYglCc22Aj69OoigqBdPuVjEaTdBUU8HQRAKv28HuxiCNNQFQ7FZtUzNpegdn+JGn7dKTkN/F8FSSP371Ch63g/7RGD/8VAcul0Y2W0BV7G4PoQo3J/bX8cyxZoYm4ui7qnjr3ACpbAFNVdjdEIQ5U7HwgsbrdlIV9HDyUD2xeIYDbWHOX5vE63YST+aor/bRMzRDvmAym8zyyru9/JUXDqCqg4xHUzTU+Knwu4iEfTRGKubN+71wYXs7JCAWQogdYmFrqw+NsXX9n95KWci1jHOzSi9W+xnSmQKlzbg0TaXC58SyQFGgo7mSKzei5dvzsPSFQb5QsHdym8M07UCuFISqioIJBAMugrhQFXvx2XJZ3ZVueQcDLvpHZ/na96/d7OywL0JlwEVlwEMk5GE8muTDq+OcvTSG26nh9zjL87DYhYOmKeRNE49Lw+d1kkjlFt1mufS72ZyJZdrnLFcw8bgcJNN5CgULl1NjX7GUozZs16jGExlS2QKxRIZdjSG6B6I8c7yF81dHefBAHe9fHmVfSxiXU+XI3ggXr0+QyuRxOlSqg2487VXkCxYT00k+vDJeXhzXeXmMN88NMDgWp3d4hhMH6+nqnuRi9xRH99UxMW0HkE5Nseuhi+f7cHs1EzMpHJrKq2f68Do1njreTCKZ5WB7FWNTSQomvHq6l2eOtfDqmRuYpoXP4+DZh1ppL7Z9iyWyxBJpXnisjdl4loqAi+HxWWLxLLVhH2PRFDPxNCcO1tkZ5NLmGI/uLs5nlmDAxSOHG4klskzMpPnK9wxS2UK5djqfN5lN5vjkg628d2GYymIwvNhdhbu1DflWIwGxEELsAAuDzNlklsaagB2YFLN4d/o/vcWykLez4nwzM1Sr/Qz11T4aqn2MTafwe5yMR1P4vA76xxLsa5l/mx8WD6pN08SpadSGvVT4nEzNpMnmTeqr7d66pUB2red0Nbe8R6eS5V3jXA4Vp1PjS9+8SEONH7/HyaE9VUxMp3jlnV5M0z4v4aCnPA8LLxz8Hgcet4MvfbOrXGLw2cfbqfQ7aamvLGdkS+3OGqu91IT9VIe8qKqKptqBYYXfSX2VXX879wIoEvJwzhjlpVM9TCeypLM5Pn1yN+9fGuahg418eGWUg7urOLynhkfva+Sr3zeYjKVxOBSeO7mLS71R9rWGGZ1KcrU/iqaqKApU+NzFfroafo8Dy4T3u0a4f2+Ei91TdPVM8MVP7uNa/wz7doVpqPaXx3RUr+X6wDQ/ODdIddDD7oYgpz4cYiyawu91cKQjQkdzJVf7p5meTfPA3ho6msM4NGVeXXUw4ETTNP7nNy+Sz5s4HCovPtZGMOAol5YEAy5yeZMDbdXUV3kJzGnNVgqaYwn74kfBvqACUIubrjgcKl6Pg8mZFM+e3MW+lkr8XjemZTIWTc27O7Cai0JVtTde2e4L6eaSgFgIIXaAhUGmQ1N5v2uEJ442MRZNAXfeP3S9dmvbjAzV3IxqS22AlrqOYuZt8c/Q3lTJc4/s4vSFEdwujX2tIQ611zCbyDAwNst9HdWcvza5ZHlCTaWb7565wWtnbtBUW8HprhHqq/24HQr37YmwpynIbDxTXhT1o5/YQyyeW9U5XeyC4kNjjFDAjWVZBANuZhM3z3Gows2rp/uIJ3O4nBoDY3H6R2d55L76cknIWDSFpqn4vU5i8QwdLSEePlzP9cEZMtkCjRE/f/HGNfIFOyOsKgp/8dZ1fuo5nbNdoyiKwjE9wodXxukfjWFaCqcvDNNcV8G754dprQvSGAnw4IFaHtgbobbKP6+mtat7iq+9bh8/nsxiWfDKOz380OPtfOtUN599op2X3+llb0sYn9vBEw+0kM0VcGgqb388yMR0mtoqH1//wXU++3g73zvbS03Iz56mSkajSSIhL8GAi7amSm6MxnBoKpZl8uTRXbx1boDZZJ5I2EuF/+bfD0WxyzLqqv3s313Fmx8MlO8IZHMmpy8Oc+JAPQ5V5VB7NR63g8mZNE6nxvlrE+W66tl4jtfP9pMvBrH5vMm5y6PsbQ5xY3SWXY1B3uzsZzKW4b3zw/zkp3UCPpe9W5/fxeB4nPcu2G3rwkEPTREvn35kF995tw+wF/l94kQLo5MJ8gWLPU2V5Ar27n6L3YFZ6SLMsixG407eff3qtl08uxgJiIUQYgdYGGR63Q6qQt6bmaRV1pSuZD12a1tr6UV5w4mch9FlOjos5XZKNFRV5dkTrWDBd97rAxQ+vDrOcb2OgbFZPvt4Oz/6TAexRPaW8gRVUdi/O8xrZ24QT+e5MRLj4UP1FEyLJx9ooqXezzljgpdOXSxvqPDi4208d3IXqqqu+HkWznUpA/yV168Q8DqJJbIc6aghncnhLpYpxJO5cmuxQsFkeCKB06GV+x7bHRAs8gWzPA+mZXFjdJZ0Js+VG3YGtrrSQyqbx6FpnLkwRCpzsw1YOODmQvckF69PcKV/GlWBh+9r5LH7G1EVhceONFLhc4Fy8zPaczPK1f5pJmZSeN0OTNPOMueL48nmLfIFi888upuPr47z4MF6vnWqG6dDZf/uKpoiFTRHKrAsExWVP3/jGp99Yg89gzM4HSpT0ykCXidet5NKv4sDu6o4vKeajuZKzlwYJpk1cahKsYRjfma+9F21LLt0Jl8waaj2M5PIkMuDhcXD99UxMZOmb2QW07RwaArtjZWMTc4Si2cYnUricmlUKC5M06K60s3Btmr+89c+JpMrkM8XeOHR3Zh9UdLZAl09U7x7wc5qZ3IFIiEvXreGqqrl2vUj+2r4mc8coGBaRCq9VASc5QsqLKv8XSzN49w7MCtd2I5FU5ztGiVcVb3o729XEhALIcQOsFiQWRf2cfJQQzFreGvQtllZn7WUCcwNZsfGx+kayK953LdbojExk+Fij71z3PBEAsuyOHtphIcO1vO+Mcb+tmr2Fm/Tl8oTSsd/69wgzXUVXO6LksoWuNwXBeCR+xqZTRTKu4sB5E3L3pijOUxHS+iWz7+wVnjhXIcq3Hy/c4DGGj/9o7NEYxkGRmd57uFdnLk4woHdVTgcKlVBD5lcAVVV0BwK+UKBEwfqOXtpBNOEoM/Jkw80MxNPM5vI8qFh9y12OzWis2muDcwwHc9w+uIoTofCZx5tIxxwc7U/StDvZmzaXkDodmrluul3Ph7iiSONKKrC19/qpibkLc53HS11FYxNpfjemX7aGitwOtRyVhYsXC4Nn8dJOOimrbGS1870EY1nmZpJcWBXGI/HyftdI2TzFqEKFy31Ffg8GvFoloJpURv2Uul38fSDLcwms2SL7cjamyqJp3KcM8ZwOh1Ue1WeOtpMZcB1S3lB6bvaMzSDpiok03kq/E5CxYuGBw/W43EqXOodpvPSKIViQKwoCg01Pq4OzODzuHj0vgZm4lmu9Uc5ptfx529cw+91gQXZvMnL7/Tyhac7GJ1K8v6lUVrqKnA7tfI22595rI2X3+6hUPzOTEynef/SGD/6ib3l73Bdlf2duXojuuIdmOUubGPxTPl9lvr97UgCYiGE2AGWCjL3FBf2LBa0bVbWZy2lF+tRb3y7JRqxeAZ3ceGcaVmYxX6yHrejvCiursrHTDxNIp0jmyuUF9q5HBoL3hKHqhAJeRmfTpWD4ZK8ae+iNjcgXiqzfUyPzJvrmXgWl0MlkcoRjWUYn05hFe8EHN5Tw+6GAD8R3MepDwdBUchoKicO1pHNm/SPxDh5qIGWugBel4NTHw4ST+fAsmhrrMTlUMnmTcyCxcD4LKEKOxDM5S3ePDdA8PE2/B4nmVyhXNvqdmqoqr14sFCwCAXdfPf0DQ62VVMw7Qx05+VRxqdTxGYzXBuYJpPL8+JjbXz7vV4CPie5fIHPPLKbyz2TfO6Jdj64NMz9eyNkcgXaGis50FbN//jGBbweJx4Lgj4XF65N8JnH2phN5mio8jAxo/GH374MFtRV+3j6WDMHd4f5+PokZsHk2ZO7mJxO4XE7udYf5eylEUIBz7wLrpvf1QBBv5PvneknmcmTyeZ5YG8tr525wSdPtPKBMVYOIvMFu/f00X0RBsbinOkaxTQtqoIe9u8KY1kWLpcDt0vDsiySGXvr50yugKe4i6DLqQHFbbZNi2QqVz7+Sttv3+ni16Dfhao5mYnf7Ou8XbdrnksCYiGE2AFWCjK32sry1ZZerMe4bzdACPpdZIqttDwuO+upaQpOh0qhYNfqmqbJ9GyGkYkEFqAqcLC9mnCFm9a6CgbHZomn8+WyiLbGCtLZPKZpYVoWDs0uH3CoChU+57xyluUuBkpzPTIxy2Qsg9E3haYp+L0OXK4ALoeKqqm80XmDTz/cxmudfbTWVYIF93VUUxt201pfyaG2ahyahsel8gcvX2J8OsVYtNjvd2CaZ0/uIpsrkMrmUVBwuzQCXieqqhBP5ognclgWHN0bYXdjRbkdWGtdkIGxWZwOFU1Rqa3yMzmTpDLg5rXOfmLxHC11AR4/0ojbqfLx1QnSmRwvPNKGU1PYVR9EUU1OHKhHVQo4NIVvvNVDrmBimn38yFMd1Ff5icZSVFa4SaYLXB+aIRhw0zs8ww893s67FwbJ5038xQ4hb34wQDjo4e2PhrCAdCbPvtYwF7onaG8M0RypwOVS+dAYm3fBZX9X/expCjFzMEs2Z5LNF/jo6jjxZI5cvoBlKTgddoBrAQGfi+l4ltMXRygU7BKQZDrPxEyah+9rIPTxMHnTwuN2ELQgVyjY5SgZu0dxqVbZ63ZQX+PH73OiqUq51d9yQeqdLH61LIvB8Tj1NRV8fH3a3kGwxs9nHt29LbdrnksCYiGE2CGWCzLXq2Xa3bYe477dTg6D43Gqgm4+vjaO1+0gmclzfH8tl7on+dxTHURCHt67MMz5qxMc3lPNheuT7GoI8v3OfnbVB5mOZ3jhsTb8XifVQS9tjRWcuzLBxesTPH2smZfe6cHpUAl4nHzyoRY6L40ym8qVxxf0O5e9GKgNe7l4fYKX3+mhtT7IB8YoDk1jJp7h2P5aaiq9tD3cSvfwDA3VFTg1FaN3CuPGFD/17H5UNckxPcL4dJqL3RPEUzmmYmksy2ImniFU4SYaS5Mp1tg+fLies5fGSKTsnsp+r0Yw4KahNsChPdXUhn08/8hu/vz710CBQ23VHGirIlThJp3Jsbuhkh98OIjHpeF1q3hdDs52jfKFZ/by59+/wlQsy3fe6+XkoXr27wpTsDT+7M1r7GsJ8RdvXcOylOLGGRbGjSiKYtf1gsLIZAK3U6Om0s3ell3Ekzk+/2QH3zvTRzxVYHAsjkNT+eDyKMcP1JFK53A6NBQFPvfEHr535gYfGGM0RQIc1WuJJ+ztsmfiaVRFxd6EWSEWzzA8mWR6NoPH5UBBQdMUWusquDESI1+w+y2HAm5y+QIUnzfNmxdTFT4XLz7eVi6bCfqcfPqRvRzYFSbgd9HWGCzfzVEVhc88upuWusDN7bcdKplcgaN7I4BV7uxRUr44rg0wNBHHoWnUVq0umB2LpnjvwggzMzM8ebSJbM7E7dJoqau466VV600CYiGEEOvWMu1umztuWHnHtqU2mlhrd4yxaJLrgzOoqsILj+xmKpamutKDz+OgodpPQ7WP8ekUXT1TvPXRIG6HxtMPtvChMUZTTQCf24FlweXeaLnO0y5bsc+/37L4mRcOYFl239poLEMmVwBuZoKfO9lKJlcgncnfcuvasiyuD8xwY2yWlroKZuJp9jSHwLJoqm3g1dM3uNg9wRc/oXPm4giZrInXbe+OZ/ROkc3nicZSfPu9PruHbcIutaj0u5mOZ8rdHtqbKgkF3LicGkMTCS50T5LLFXA4FJ5/eDcBn4PakJeZeAZQePS+BtxOjdMXRmhrCnKpZ5KJmRT3ddSQSNmbYbicToJ+L1f6o5gWhCqGeexIMxU+B9HZLIpikcwU+MGHg2SzeapDHj754C4cmsLZrhFGplJ09UzyQ4+303lpjHQmj2VZPHZ/E5UVbv7izWvk8nbd/CePt3Dq40Gy+QIVfgcBr4vvnenjyN4IZ7r6qavyY5kWDx2q53LvJFOxDO9+PERHcyXvvdXNVCxNNlfgyN4IqXSOcNDDuxeGic5mcToUvvD0Xqamkzx6fz0uh0rBstAUhZOH6+kdmiGXL5DLW4QCLip89qI6TVV59qFWOprDjE+niITsi6WJmYzdBaWugpbaCmYSmTnBuMrTx5s51F7Npb4pxqMprg1Mc7kvumRNff9YfM29vkt3ZDLZQrk7jf14lroq/5K/tx1IQCyEEHdos3ZVu13rFRRuBXPHfemKgwP72pYc90rdJEpZ1bFoym5ptczubRevT/LmBwNkcgWS6RwnDtTz0ZVxHrm/sdi5Q2EimuL9S6MUChbJQp7+0Vl6R2bpaK7EWawBnZvRnVv+kcubjEwmOds1QjjoIRpLc+JgPZAnmzdxaAqDY3EiIa/9eebcuo6EPHReHuWVd3rpHY5hWRYP7Kvloytj7G4MYpoWh9qqcLtU3rswZC9wU+w65fcvjfD0sSbSOYuvvWGQzRU42FbNgV0hXnhkN986dZ3KgJuZeJqjei29QzPcv7eWP3/jGmDx/MO78LgdODSF0ckEw5NJTn00jM/tRFXh6eONuBwOWusrSGcL9AzOMJPMcfJQPR3NYfqGZ6kKerh8Y4qCafdINi2Lb5+2d1obmUgyOZvGQiGezLK3NcyXv32ZaCyDQ1N48bF23rswVKybVnj2oRZQFK7eiNLRUsnZS6Mcaq+xM8govPJuD595rJ2XTnXz/MNtvP7+DQ6113DuyjimCdcHpgkHPfzZG1f56ecPcKlnktlkjo+vTXCpd4p4Mmt3vCiYHN0X4WtvXOX5h9voG4nhdKi8c36Azz25l1Qqy+MPNHG1P4rTofHKez08fqSRx480cvbSKG6XRiye4cjeCG909jMdr+X4/lo6WkKLfm8fPlxfvDAaJZHOlXfg29daSVf31Lw7B4tuPX6btfelOzJzbYc7SashAbEQQtyBzdxV7XasJijcbivFS+MecKaXHfvYVJI3zw3My6jODQIKhQJvnhvkrXODuBwaoQoXDx1quGUux6Ipzl0dL7bQUnE5NS52j/P4kWbeOjeIz+tkJp5lT3MQv9tBJlcglc6jFtuaOR03d7CbG0wE/a5yxnd3Q5B3zw9jmhZel4NJ0+Js1whPFvtG+30ufvDxEBU+5y23rsejKV5+p5fp2QzJdB7TsjD6pjh5XwOvvtdL30icWCLDcyd3EYtnaamrYHgiQcG0MC042FbDH7zSBZbCofYa3r80wtmuUQ61VfHDT3WQz5uMTadJJDM8eKSR753uY3giSTyVLWdwP/ngLlxOB0PjceLJDLOJLLVhD1f6YrzeeYNoLIPLoRazuCO8f9nuk/z0g810do1SKECFz8knHmxlejZNR3OI8WgSVVMx+qJYwL5dYb51qgfLgoDXSTyV45V3uvnckx1Ypr3IsX80zkMHa0mkcnz73T6uD8ZwaAqP3NfA+WsTOB0aHpfGp060kskWGJlKUV/tZzaRs7ttqCrhgBufx17Id7Ctinc+HmE6niFZDERVVWVoIsGDB+pIZwoMjM1y/toELqfGA3sjfOedXvbvruL1zn6qgh40VWE6luW7p/v5yef2cbCtmst9UYI+J+msfcEz93u5WPB6fXCGG6OzxBJZxqeSWMCfjhl88VP7cGgK2fzNgLh00VW62IvFM6SzhSVft9zfodIdmVfemih/f7fDnaTVkIBYCCHuwGbuqnY7ttt414tlWVzqm+J6/3Sx2hMiVT4iIS+xeIZIyMNb5wb5UnG3MEVRaK2vALjl3JS6S0SqfIxPJfG4HOxrDXPlRpTa4oIm07K4cH2Kw3sjnL04gkNTGZqI86kTrcRTdteHcNBDJOwFy8I0TQbH7Yzvx1fHaW+spLU+SE3IQz5XIJMvMD6VJJszmYrZgf/wRIIh02IqliES8hbHliWeyjIykUBRFSJhL6NTSRpq/Hx8dYJwhZepWBoU6Bmcob2pkqHJhL1BRDKLqijEUzmyWYvjB2rIFwr80OPt5PNmuV3cDz+9B5fTgddTiUNVGZ9JYVoWPq8Tr1ujoaaCtz8eIpc3mU1lOdxew9UbUY4fqONbp7pxaHb7tEze5JunrvOTzx3g+539VPhdjEWT/PBTHZy5NEJNpY9XT/cQS+Ts7Zg7Ipy+eAOnQ+X8tQnaGoKkswWcmkomW7A3HsEO2qor3VRXeLBQiSUzfHxtnAq/C5dTxbLgvfPDxd3oJqiu9HJ9YAZiaUrt3DQVMrkCfq+TmUSGqVgG40aUfS1VPHJfPSPFftexYuCczORwOlV7sWLxwufovgi5gsne1jDhoBu3SyMaS9NcV0FlscwkGrNbwHV1T9LWVLnoVt+LLRzNZAskUrlyMAx2hr97aIZw0O7HXWJfdLn46MoYw1NJEskcHreDcIWL6Gy2vFPlajK9pTsyVqaJUFXdtrmTtBoSEAshxB3Yat0ZVrLUeIfGZzesVMI0TboHY+V6yPamIKqq3nGpycLfX2rTilI9bTSWYf/uKnqHZgCoqnAT8DlRFIWewRgfXZ0o7xZmWRY3Rmap9LtvmcvSbePd9RWc2F9LIp2jMuDmg3SWxmo/FuByqkzPZmhrDDIVS5PO5PG4HZw4WEtTJMCF7kneuzDC+esTvHd+hMeONGD0RtE0O+P2gTHG4Fgcv9fJI/c10lTjJ+h3sX93GLdLQ1UUFMDt0qgLe6kN+/C47cedDg1VVcptuJoiAQJeFwFvDo9LI+h3AXBjNMa+XWEmp9P4vU4iITv7F/A6iYQ9VFd6GZpI8M0fdGOaFntbQjxzvIV0Os/wZILeoRks4HB7DR9dGSeVLbC3Jcypjwapq/Lj8zgYnUpwtmuEEwfryeYKpLMFKv0OFOwALFewSKTtRXi5vImCwsBYjD1NlfzhK5cIeJx8/sk9qKpCKpOjtTbA9aFZMtk0mZxJwOtE01QUoGBaZLI5RiaT/NGrBl94eg+qCgOjScanU7gdGk2RADdGZimYFpoKLzzSRv9IjMaIH6MvyuefbMfj0mipreDDK+N20Dmd4sED9VzonqBnMMYLj+7CshTu76hhbDpFV/cknzrRSiKR5QvP7OV7Z/vwuR2EKtyc+miQa6rKfXtqqPS5KHgsRiYTmCYk0jl8XidmwZzXOQIW3DlYZOGo26WhAHP/Jmuqgs/tIBL2Mh5NzbsDhGVyqS/KS2/3lLeHfv7h3TTU+Ogbia8p06soCmohwd7W8Iqv3U4kIBZCiDuwnbozlFabhyvcOB12wJbN2xnHawMzjE4l173kwzRNXj3dV14xX2ov9uxDrZy7MnHbpSaLlX601zmxrJtbDduBsr3hyHfP3GBgLI7LobKnOYQKvHNhmNa6CpIpu0TB7VTLO7OV3iObL9wyl7VhL4/eV8+lvih/9F2DQsHioYO1BP1uXnq3B7dTw+9x8tCheg7sCnNgdxVjUynyhQKNNX5iiSxG3zSqojAwMksqW2B6Ns3Dh+vJ5k3eOjeIBYSDHpLpPGe7RnjqWDPH9tcRCrjsnc0cKo/e38BULMOF65NMxgapCrrtneR8bj71UCtvdNo1ztlcgd0NFYxPJylYCuNTSWaTOTRVIRrLUF/jo7EmQCTsxe9xcn9HDZ9/cg8fX5vg7MUR8gWLyoCL6pCXr//gOrUhHyNTCR46VM/IRILBsThPP9jCG+/3k87maYxUoGCyuyFIUyRg71yn2r1xAz4n+UIBj8dBOlPAqdlB3MOHG3Bodka7fzROTaVdk9tQU8HL73STzhRwuTQ+/8Qervbb7dNOfTTAcydb+cG5IbL5AplsnhcebefMxWEy2QLfeKuHZ0+2Uul3MTaZpLLCQzyZZU9TJfmCyZG9tbz5wQ0aIxV2gFyMML/+gx6cmsp9HTW01ga4MRan8/IYANHZDKDy1rkBTAuaIn6eOtbC4NgsnzrRSs/QDD/y9F5Uxc5CFwoWuXyBi9cnuH9vBKMvikNTcXs0jum1XB+I8vknOmipr5jXOWJucLrYgtc9TZXUV/v40+9eIW9aaKrCiYP1xOJZnjrazIHd1fMuND80xsrBMNjbQ3/7vV5+4SceYP+u6nsq03u7JCAWQmx5W3nR2nbpzjA3gByNJpmaTvHgwXoy+Qy7G4JEY2lg/Usougdji+661lxbcUelG4uVfpztGuXowbZ5q+frqnxc6p3C63JgmRbZvMnQeJzm2gpqwz7CQQ+mZfHxtQlCfld5Z7ZCwcLhUHnyaNO8uSx9Fy3gvfNDPNARIZMvUBf28875IdRiP1mnQ7MXtAEDo/Z4HJqCx+3A6Jvicm8UVYUHD9Rj9E7ROxzjyaNNxFN5FEXB73GQL5i01AZwOlQ6mit5YF8tY9EUHqdGMODC73HS1TPFbDJLVYUbv8fJWx8M8sTRJvI5k888uovp2QyKqrK7oQKvx8GbnYP4PE48LgcHdldx/to4iXQel8POLvcMxmiqrSBUYd/S93qcKJk8e5tDnLk4Qk3QQ1tTJdUhL5lMnkjIy/nuSWKJDM8/upuakIf3u0aoDvl5+6MhYoksbpddK1wwC7zw8G6+e/YGmWyBCr+Tzz3eTn2Vl3g6x+XeaRwOlfPXxvjcE3voaA7z1e9fIZWxu2vk8yavvX+DLzzVwQ8+GiSdyaOqCn/9c4eYmEkxE89y5uIQk7EMlgXxVA5VgZff6+EBvY6Pro4RqnAzNBnniSNNvP7+DXY1VNI9GOWBfbUcbKvi9751kXzBzjZ/dGWC0xeG+ewTe3BqKrFEFq/HQSabJ50toCiQzhZ468MBjut1eNwOPvnQbmLxDJMzaabjGWpCXm6MzJJM57jSH+XJY03lDVqmZzO4nHZ5zfH9dbTUBRf9b9xSC14ty8LjcnDlRhSnQyUWz/KAXkttcfvluX+PZlPZcjBcks+bpDJ5Thy8tzK9t0sCYiHElrbVF60t151hKwXycwPISMhLwOtkaCLO8yd3cfbSaLmOENa35GOpXddGo8k7KjVZrPSjYFoMTcTnBcqZbIGRiQRtTZXlmt9E2t5JrMLvKt+mDngdtDZU8P33+3noYD2WBUf21vDk0abynJW+ix8aY+xqqODk4XoUVCZmkoSCbg60VeF0aOUOFVVBD8MTifJ4QhV2nbKiKLicKulsgfcvjXBMr+NC9wQ+jwNNVfC4NK4N2BlkVVVobwxS4XNyrX+aCr+T9qYgF7unCAZcJFI5HJpKqMJNPJXnxmiMYMDNxe4JXnysjeP7ItTWVBAJefjwyhgdLSFGp5KYpomFhaKAqim4nSpVQTcVPifX+6fRHPb3N5cv4HCoOBwqPo+DfbuqONM1QjyZAyxefKyd6qAHt8vBd0/f4NEjDextreLlt3txuzQqLCeVFS6u9E3x+APNKAr8/BePMBpNoQDnLg8yMRPk/LUJsrkCe1vDPHG0xb6boClkshY+j5NDbVWoqoJpmsymMhzdV0sylStmgq/y7Mk2vvLaVXu3QMveKEXTFGpCPj55opXOSyMc2VuLpkF7QxC3S+Pwnmo+vjbB7oZKjL4omqqgqhpeh901IpnO4XE5yOYKmJaF06HwQ4+18f6lYVRVweVQ8bjsneLqq30Efa7y3/VMroDToTE8mSBS/Htf4XUxPp1iZjZLMGCXrZTuJq20oHWx5xVF4cEDdbTWLx5Iz9VYHaDC52I2mS0/VuFz0VgdWPHv2k4hAbEQYkvbDovAFvuf1VYL5BcGkOV6RcXeSnau9Sz5iIS8OFRlXlDsUBXqwj6M3ujtbx+7SKmKpio4NG3eYy6niqoqZHOF8oVAJlegttpHKpNndCqJ1+UgVOHi2P5a9rVW3VLrXDIWTfGhMYbTqXG2awRNs/+sr/ZzumuEQ2019I9OcaithuqQxy69sMChKYC96YKmqagK1IZ99I/Fy+f+wf32VskWFrVhLzdGYnZ7NRTCQQ83RuNc659mKpahpS7AtYFp9u8K49DsxXkOTWUsmii3xMrlLV5+pxd9VxW1WHx8dZzvnu7D73MR8DlJpHMYfZM8/kAzTodKpd/F99/vZ3AiAQo8c6yZuiovJw7W2T2eLTiyL8KF6xPFOmWVbM6k89Ion3qole+d7WNiJs3QeIKJaAq3SyMS8uLzOJhNZvG4nXzltStMxTLsbqigvamSK31RGmt8nLsyxr7mMNUhD2cujnL+2gQ+j5NPPdRCS52PlrpKTl8YJpszqQw4Ob6/nv7RGG2Nlbx/aYS9rdW8dOoaLzyymz974ypYCigaTxxp4utvXmVwIsFnHm2nu3+KI/tqSWdNImEfl3sn6R6YKddag13iAyrZvJ2VdjgU2horiYS8OB0qY9Ek4zMZAl6nnW2eSKAqMDOb4UzXKLPJLPmCxQP7ajhxsI6vv3WdVDpPVaWHxogfh6LQNxKjvsZPXdh3x3eTVtsZpr25kh/9RAdff6u7nKH+/JPttDdX3vZ732skIBZCbGnbbdFayVYL5JeqdW6sCWxoyUd7U3DerlulGuIDu8Oks4Vl33e5DPtipSonDtZRW+Wd9zmnZzM8dKie8ekkmUye6koPTZEADg3e6OxnNpHD4VD5sU90EAl5qa/W6GgJLfpZSjXJZy6O8PDhBi71TXH8gJ3dDXicnL8+wUMHasnkCnReGsPndTIylaQ+7ENRYWI6hVNTyOVNKgMufJ4Q+YK96UMimWZwLE6Fz8WVG1EO76kpBrcKH12bYG9ziObaQDmzf7g9jNfjpCbk4czFEVRVpbHGT2t9Be9fHkNRwOnQyGTznCru/uZyOXj9bD+WAlUVHp482sz1gSiZnMnwRJwHD9TRUOMnmzeJxtK4HBr376mhvbGSoN/FbCrHuctjZHNmeQOSbK5A38gMubyFx2V3WMgXLCZn7O2dk+k8XreT9y+NUBnwkM0VuD4wg0NVOXGoHtMsgKqSyuR5+Z1ecnmT2rD99+N7Z27wxU/u479//TzZvEmF38mJgw38/ssXCfrdXB+Y4Yhey8VrY9y/t47hyTg/8/wBNE0hOpvh9PlhpuNZfG4n5y6P8MSxFv70e4adBfZoPH9yF4faq3jpnd7i7nEufuTpDr7xg+uAnUH95IlWEqkMuVye6soAbmcFP/bMXj64PMqNkTgup8pDxTIbv9fF40eaGJ5M8OGVCR46WMsT9zcRqfZxuXeKs5dG7R397m/E53Zw8lADe5or78oFsqqqPHdy17zNPhZe8O10EhALIba0zV60drtlD1stkF+y1rnKR22Vb8M25Fjuf8TLbQSyVIb96L4aeoZmGZ9OURvy8oVn9jCbyBEMuBnqM6gN+3j4cD3XB2fIZAu4XRoHWiuZmq2ge2iGs12jXOyeZHQqUWyBlqNQsHj7wyEOt0eWDIbB/i5iWTTXVvDmBwNMxtIcbKvimWMtJNI5ProyTnNdkFfe6aUpEiBS6QFF4frgDI01PrwejZqwl4+vjWNaMD6d4Pj+ev7s+1d5+HADU7EUDTUB8qbFxe5JHA6VQsEkGHCRzhX4yvevks0VqA56ef7hXXz9revkChYP6LW4HRq5fIG6Ki+JVI6A18kxPcIbnQN23bFbo67Kh8vlIJnOgaLw8jvdPHpfI8OTCdwujUQqz/XBaUanUnjdGp95dDeKYpcsfOmbFzm6z1446HbZgbbP42BiJkW+ADPxDC8+1kY8mUPF4ouf3Mdb5wbt7LOmEKrw2PXMit0ZIZbM0jsUo6nWz4dXxti/q5p8wV70aZpWcfOPDKOTCXbVB0ll8hzYXc2bH/STyZkoATs7eu7yKCcO1v1/7P3Hd1xZluYL/q40LQET0JoQ1KRTuA7loSMyUr6seq909+ru9Sb9B/So1+ppj3rWr8WqrK6qzMpIEZkhPdwjXFI6hZMECRBamgBM66t6cA3mAAmQoHB3eiT2JMJBwOzec8/d5zv7fPv7+O3lZVLZCovreX7y5iE2cjVemohz4fYamXyDt8718asLCzgUGc2w0HWLf3h/lj/7xggnRiKYFjgdEj6XxP/8nXFk2XaAq9U0CuUGgiDy//jbm0iihCoJ/Plbo7gcKbxulZnlLIWyRrGiI0sC0ZCLhmaiqjLHRyN8/OkaN+9v2JQM1XbpG+8LUa1rjzSAed4hiiLDPcFHzvF/yXEAiA/iIA5i37GXfNaTxpOAzC+zae1ZaA9fNpB/MB7nRPdlGHI86rj3wQq77cxWJLFZYn61wPxaHk03+cFrA3z7XB+iKLIyb/OgTctiKVmkXNUQgKDXwdxKjvdvrmIYFtGwm0JZ49cXlzh/JM69xSxg850fBRaiIRf9nQH+8YM54iE3R4bauTqZQDctuiIexgfaMHRbWqyuGSSzNh3DoYoMdAa4eCeBJAn867dGuT6dpj3gZHopQ6Vm8M8fzvFn3xwhV6jwjdM9/PLiAuWqRsCr8kevD/PRzVVUSaReN0hlK1y8vU5/Z6DJaU7jcSkYpkVn1IvfrXJiNEJqiy+tGTR0k/XNDQ4PhrlyJwlYyJLUMp8Y6AxyeTJB0GfPz3rD4PpUmsGuEX7+4Tylqs616TRHh9q5MZUk4HOi6QY/bILgr5/qQRTgwu01BjsDBP0OvvtyH5Ikki1WuTO3ab9Doq3i4VQlBBHWN8p4nCpOh4TbKeFyKGSLNco1jbqm4/M4SGYqLYBcbRiIgoDHqSDLIoIJi+sF/B4Vl0Pk+KEY/9s/3kI3LAzT5PuvDHDx1hq6YaDpJpW63lR+EHAoErW6waeztt20Q5XRdJPVZInXT3by2yvLDHcHOTka5b/++h6CIFKr61QsixvTKTZzNT6d2WzNX0kSUBSJty8vYTZ58qfHosTbPAS9KnXNbmLzuRVcTpm/ffc+XpeKx6W8UH0R/1LjABAfxEEcxL5iL/msLTCy33hSkPllWgo/C+3hRVSf+DKc6J523myvsKuyiCyJXLi1ztxaAUUWODMeZylhK1gMd4cY6g5gSh5uz27y3rVVipUGqaZpwduXFjk20t6SU5NEoamBa7b4o7IoEAm6HquZbJgWPTEf7UEX16ZSGE23Ot2wmG9Wgn0uhcVEgY42D70xHw5FZnY1z92FDJIocGIkQle7F1mRaA86KJR1JBEUSeTtS0u8dryTf/O9MRqaCZbF9NImt+c36e8IoOkmFnaVVZIERIFmNdO+r6GuIL43bYC5li6hG2arwbNa03E5ZDojXnxuhYLYwONUqNY0Qn67scvlkDg9FsXrUvC6FapNpztFFqlUNW7NbnB4qJ2+uJdY2MOHN9fIFuuM9AYwyhavH+8iFnbz9+/NUGvY7nl/9MYgrxzr4MMba+imxUCnn8GuALmibYiyJAkYhsn3Xh7gnz6cwzAAGV491sVHN1f4/iu2pbLfq+JzK7QFXAiCXZWOBF34PQ7uzGf47sv9/O0705gWuJ0KxYrBLz6e549eH6Kvw8ev9UUMw0KS7NxhmCadEQ9/+a1DFCp1ZEmiXtdxO2SSmxWUpgthpaqh6yaSKCKKIiIWU4s5vnu+j3/+aA4L8LoVTo5GWVovYJoWkbAbhyKRzlbJFGucP9LBO1eXkCSRI8PtTM5nyBXruF0KHe2eF64v4l9iHADigziIg9hX7CWfNdwdeqIjuKcBmV+WpfCz0B6+TCC/PZ5XVf/B2G+V/2nnzfYKuyyLvHNlifaAC90wsSyBK3cTnJ2wq7vpXIVcqc6vLq0y0GsxtZTFpUoIooBlWgiiQEM3Wv+dydfojfvYyFftRrwmSO/v8D5WM7k96KRU0Rjo8ONSJY4Pt2OaEAm5mFvJ0RZw8d1X+rkxlSLoc3J7dpN0roplWbx8tANBEPj//PMdPC6VWkPnmy/1cGM6Ta7UYHygjfH+ILIs8dvLyy2liT/+2jCqLLGSLNLf6WdhrYAqC7xytAOPUyYe9mBhEQm6qdYaiKItqeVUZUpVzXZPE6G/048iiSiyiN+j8s0zPUzOb9rjoxmMdAfoivq4cjeBppu4HTLhgJNMvkpnu4e1jTLlqsat2TTnjsT5ZDKBZVnU6xodYS/vXF3k5GiMW3ObfOtMHxdurbGRr/Pf357i//THx+iN+3E5ZPKlGtlCA69bJZ0tce5IB+lcFVkUOD0WQ5VFSlWNT+4laWgmPXE/54900NnuoaPNw8Xb65RrOgJw/mic315awrKgWtMwTHtjYFoWPrfaol/Mr+X53sv9/POHc5iWrarx/VcGqDV0/u69GeoNA69b4dvn+pha3ERVZH78xiBLiRJBn4MfvzFo6werEsuJAjdnNvC5Zc4f7qA75kU3LEI+lRvT6R2Oc9lCjcP9bdy4n+LsRJxQ07r5SimJbpgYhkk6U8HrUl74vog/9DgAxAdxEAexr9hLPutxx8wPxovGrX1UPCvt4csC8luxvTpb0+yj5h+9Psj3Xu5DkqSn/twnqfJvnzchr8qxkQh1zSCdqz4SnG9V2N+7voKmGZimXfUTBFpVT6MJWn1ulfevr2KYFqoiQhOsd7R7qNZ16g2DjjYv5ybs5qeGbhLwqPzkjUE8bpUfvDrIYJd/V/D+j+/P0R5w8d6NVRxN7dgzE3Ek0eLYSIRCqY4oimzmq8QjHu4ubHL1bop/9e1RPri+itMhI0kCug6zK3naQ06qdYOwX2IjV+XnH83zo9cG+XRmgw9urPDGyR7eu7aCrptYJuhYvH1pgW+d7eXtS4s0NIPx/hCHB9v4zcUFNANu3N/g6GA7v7++wsmRKCGfAxOL7qiXumYgirar3Xh/mK6ol66Il452Dz1hN9GQi5nlHAGfg2pN57/86h4NzSTkt3WIf3tpke+/Osg7VxYZ6vIDcHo8BpaB1+0gHHDx6vFOLt1OMNgV4sKtdfKlBjen03znfD/vXF2mXDWZXS1weXKdt871kdqsUa41mFnOcnTY3gQcFdvIVxpUGwYzy1l0wzabUBURy4S+Dj9zK3lMy+Lc4Q5EERyKRJvfyVh/mFR+FVmWkCWbKysA5aqGJEKhXMfndnJ9KsGffO0QogR+j4P51SyZQs2mtcgSumHyzuUl/v0PJ3A5ZE4eirBZqHHpTpLfXFokW6gjywLff2WAH746QKFs6/52tLnxeRyAxfRS/qF80Rv34XYqbOQrWJbF3FqxZSAjigIW9px+Ec18/iXFASA+iIM4iH3FXvJZkeCTUQBeNG7to+JFpD08SWwBPMMyGewMYFoWd+Y2iIddnB6PP3W1+kmq/FvzxudWONQX4u9+P4OmW0RD6+RLtT2pE1sVdocisbCeZ22jTKWu0RvzsZQotigGP3htAJf6mdRarljn/JEO3ru+giQJuBwy54/EqdXqfP10F6fGooiiwFCXn3ibd8cYbAfvumHS0Ex0w2Q1XWJ2OUck7G7Kb5mM9YYxl3OUyhqT85tU6zpvnOjGqcoYJqQ2K8yvFwj7XZSrOrJk6w5j2SBPFECWRCwLJEkkkSkTD3sQBaHJM1XtSicW+ZJGudLg7ESMYyNRCqUaVyZT1DSbF1uqaFy4vcaZiTj5sl15dSgine1ORvvDVGsa+VINn9vBlTtJNMPk5x/O8aM3hsgVanwylSZTqPLGyW5GeoJIkkhdM9B1E003mV/PEw17EEU4OtSOpmn83e9m2Sw0GOkJ0tBMwn4nl+8m2RrNhm5yZTLBWF+I27NpPC5bVeGn795HQMDplHlpNMZmoUIyU+WD5oamUtPp7/Rza3aDeNjDucMxBjoDnDgUwe9xcGMqRamqce1eErdTBgT64l5eGouh6wZ/8a1R/uH9WbDA65b50WtDbBYqLKwXGOwKMbOaI+xzsJouM9IT5LeXF1EVe3NYqemUqjqJzQobuRqCIBLwKPz8w3kqNVuqzAJ+e2WZf/XWKGcPd+5QibAsa0e+cCoS3TEvv7m0hGlZZAo1xvpDpDNlzjStoGVJRBYFXjve9ZXJK3+ocQCID+IgDmJfsZd81mCzarTf+CqBzOdNe/iijTrSOZtDO9ARbB6DW8gSDHYF6In7iYU9T/W5T1Ll35o3+VKdn/7OBsNet4IoCo+lTgiCQDTs5tLtdY4MtXNlMoEpwGhfiFeOdjDe38ZAl590roYoCFiCTDpXJex38OM3hljfKGMYtlbuy8c6WiBSFGy7YFGw3ce2nsUWeC/VNCo13dZrlUW8HhW3UyadqRAPu/C4FP7po3nuzG0iiQKvnehE0yyKlTpD3VGOD7exulFCEgUKZdutLJ2toMi2JnI05CZXsp3DFNk2m6jVDSzLojfupTvqo9bQGO4JspgoIGAD9MHuIMuJHF6Pk9UNW8NY0w3aAi4KTT7try4sML+Wx+OUefVYF//1l3cZ7Q/TFfHyV7+4S00zUSSB0+NxfntlkfNHOskUqoz1hRjqDLCwVqBab5DMVKhrJr1RL05F5OZKDrCb7U6PRpEkkZOHIjgUie6ol+VUkVpdx6lK+D0qlZqGQ5Xo7/Az1hfCMC0+nUmj6xYet4ymmVy5l+D4UDufzmySyFRQFYlSuYFTFfmLbx4iU6gx0hskHvYws5LHqUiM9geZXS3QF/Myu14kna2QLdYY7PKhGya3Z9P86LVBqjWNeMTLvbk0mgH9nQEUSeAHr/RTrGqks1UcikSmUEeRRXxuhbG+EAjQFnSRK9a5MZWiO+ajVNHQtnSJJdF2PNSMlhX69vm6PV8IgsBvLi223pWw38lyssS3z/dzcybNt8/1oekmh3pDBw11L0AcAOKDOIiD2Fc8Lx3LF4Vbu994XrSHp1GseBYAbVn2kXNXzMuF22voht38ZVpwfSrNWF/bUwPi/VT5t3OXh7oCJDNl3E4FSRRRFRGXKtHX4WcxWcDnUfd094sEnZwYjXJjKsUbJ7t2AIituRcJOhns8nP93iqyYuB3q3xyL0nI67DpEV4n71xe5vWTXdSyVWRJ4PbcJr+7toJDkZrPIkZPzMO3zvbx1+9MtegXP359gIXVPN8800O5akuN/eKjBdwuGcO0cKgytbrBzIotV5Yp1BnrD3P59jpnJuJcvZekUtWIhty8eaoLl0Nifi3fdJdTefNkNx/dXCPkU/nRa4OkshUmBsK8f32FakNnrC/MxGAb7X4H95Yy+NwOGk05OY8o0NvRhmVatI1GyOQrGBZIoojbqfLX70xzeKCNzjYPN++n8XpUfIJArljnk3u2lrIAjPaG6I75+Onv79MbD3B9KktHu5dssUa83dN0wdPoaPOwmirh96h8/XQP//TRPLpuspIq8v1XB7hxP02tZqAbBmN9YXrjPmZWctxfzhLwOJgYaKNS1SjX7U1JuaKhKrYUnSjCzfsbdEU9HBuJMrWUpV43mFvNM9QdZHYlx+xqnrMTcW7eT3F6LEajYdtcZ4t1hnv6+OVH84T9Lj68scbaZpmAV2W8L4RDkVlYzXFqPEY6V+WTeykCHpVSpcEPXhng3U+W6IsHuHxnnc6Il396f46To1F8btulTlEEqAu4nVLTDEUiHHDi9ygkM5WH3s+tfHF/KfvQxtGhSMTb3Iz2jX4l8t+/pPhcAfHo6Kgf+Bj44dTU1MLo6Oi3gP874AL+empq6v/yeX7/QRzEQTzfeF46ll82t/bLiN1oBu9dtwFZtMnl3L4oPqvTXSpb5e58hpGuIJduJezPNC0GuwNYlkUiU8K1LD+golAhlamiGwad7V6iYTemaXJ3IUsyWyEWcjPeH3pslX83ZYm3zvfRE/GwUajjUiV64j4u3kmQKdS5MZ3mzZNdnBqNcm0q/dDnnhqNPLSBAlpgRBAEkpsVXj4Sx+Vy4/eqtiybU23JdemmZas2AEGfg/evr9Lf4SMa8tLQTG7NbrKeLiEI8N3z/eiGic8lIwgi65sFfG6Fq3eTtAVdpHIV+pw+YiEXrxzrZDlVZLw/jGVuUqlpXJ1McGQ4wmqqyMtHOhAEi2jQjd/jYC2d519/Z4x8qU4s5KZSq+J396Kosq1tnK+hKCKvHe8iV6w1Oa4Ct+Y3qVR1KjUTSbD49tleVlIlLk8mMEy76t8b8zLY4SPsdzDSG6K/w0887KZQaZDcrFCp2wCyo93DZq6KbpgEfQ4Gu4P884dzSKLIrdkNjgxFkCWBt8728T/enSbW5qEt4CKZreBxKfjcDuZWC+iabTByqC/E7EqO/+mbo3z86SrFikZPzMvcap5aQ6cr4kORRG7cT3HyUJQrd5MYhonbpdAd9TK9nGVqPsPp0SiRkJN//nAOWRKRJFuR48qddTojPuoNg0t31hnvD/PetRXOH+3k/esruJwy+WKdVK6K161iWhbjfSHqmkFHu5eF1RwD3SH++9vTjPeHm3bNfnqitm3xX357jHevLDHYHUTXDSp1gyuTCb77ch/LySLfOT/A768t0R8P8Mm9JN1RHx/dXKOhG6wkSpiWRcjvJN7moiPsxcQi4HXi96q7bhwDXiex5jufyla/UC3ig9g7PjdAPDo6eg74fwKHmv/tAv7fwJvAMvDz0dHR701NTf3y87qGgziIg3jy+LxUCV7keLAq+Xnc74M0g3SuSjpT4WbITaHceAjsPqvTXaFUp9jUsR3tDVJt2Mf/FrCQKDI5l+Fn7821VBRu3t/g9tym/Z2mRbzdw/de7iOZKfPf376PrpstR7cfvjpAT8yLQ+naAZ63rn235rS3Ly7yF986xN/8dprOqJePb63T0eZhYT1P0Ovgl1WNoMex6z2HvA4qdR1dNxAEKJRr5Ip241ZNMyhWGnS2e8kVSkiKk9mVPMlshUJFo6vp7iY3G7QAGpqJQxYJ+128f33VpkcYBn/y5jDZfI13ry3jkCVeO9HFLz6eJ+RzcmUywSvHOtGaoDpXrHP8UJRLtxOsZ8r43CovH+0glSmjGzDcHaBYrpPOVvC6Vd6/sUa2UCXe5iWZqXPj/gaarvPvvj9BOOji1swm8XYv65sl1jbqrG1M88dfG+a9G6tMDIRpNAw+uZdEliQsy+KPv+YlmakQ9rswTQvLsrhwa53vv9JPwOvk1twGmmZy9V6C753vx6FKaIYt2La+UaI/7ufoUBvX7yU5OhLBsmz5tkK5zrWm011nuwdJFAg3K8SiABP9YVKZMj6PSiTkYqAzwI3pNB6nwlKiyMvHOogEnNR0k3uLWVZSJQC8LgWfR6Whm8RCbiRJYKQnyG8uLVKoaBwbbkcQ7CoxCJRrGrIo8slUihPDEWoNHcO0qDcMnKpMw7DQDROnQ+b4cIRqXSfkcxDwOuh0KbhUiVJVB+DUeIz//vY0um4iCgKiKLCUKBLwOEAQWE4U6Yr4KFUbGIaFUKxRqel4XSqWJVCra/zlt0b59cVFRnpD+F0qsiLy8w/m+drpHuqazmauSqnS4B/em8PnVomG3Jw9HOP8kTgXbz+8cXzRrN0fjC+a3vUixOdZIf7fA/8r8FfN/z4L3J+ampoHGB0d/S/AnwMHgPggDuIFieelNfwixH4T+m4L02BMeYgf+KyxnWZQreukMxXEJkjbDewWSnXkpsNXQzNRFZFcsb5vNY6t70tlq0wMtnPh1hoORSKVKfOdc7ZD23BPkHcuLxELu0lslplfyzMx0IaFDUjfvbps81d1GwTquslPfzdDLOTh+nS61TikH7LIl+sEvE4iQSdrG0UqdR1RtMfPNC10UUCSBP4Pf3KMudU8qUyVak1DN+znJEkia5ulHZuGal2nUtO4NJng40/XqNR0gl6Vo0PtzKzkOH4oglTVqNZ1rk4m+OZLnXz4aZKhrgDfOtPL7dlNNnNV/G6VH7w2wErSBmcOVeLISIRP7ibJlxtUqhqGafLOlSXeONnFy0c6KNW0ZvXVln2zLIFP7iY5NtTOmYk4TkXi8mQCt0O2gZNpcWUywctHO7i3kAXL4tRYFJdD5lcXFhAEaA+6WdssE/I7+MbpbjbzNRYTBZKbVe6v5DAMk1eOdTI5t8Fmoc7CWoHR3hCKLHP5TgK/28HYQAjLtBvATAtS2QptARfLySLtARdul8pf/XKSumbiVCVOj8X4zaUF3jzVzd++ex/DtAh4VU6NxciX6oz0hnEoEqoiUm/YILBYadjaz7LI68e7uDGd4hsvddNo6GiGyXB3O3NrOXqiXm7Npgn6XCwlCgDMref5jz86QrFcJ1+qEw25SeeqlKo6iizy0liUdLaMhcByskg07CFbzHJ9Os2PXh0glaliWiZKE/iXKxoORaKu6RiGiaRKVBs6kYCTI0NtNDSD5GaZWsPBT94cYi1dxrTsSuy3zvYgCgJzq/nWHF5OFVpqI9WGjg8Fv0fl99eWaWgWsiRwatTeIIz1h/C6bavu1XSZQrlBJOzG51Vbpw6yJLCarlKp63xyNYkoiMTb3HhcCpfvJPnTrw/zp98YeSgPJTOVF8rafXs8T7D+VQLWnxsgnpqa+t8BjI6Obv2oE1jf9ivrQPeTfu7t27ef+dq+yvHJJ5982ZdwEE8RX8ZzE0URXXBRrhl4nBKyVcU0zUf+jSGH+Ok799CNz0DJT9+5S9htIenZz/uSn1uIokiypPDp/Q3aw34M02K420/YUUHX9R2/a0oefnVptWXQALC5KRL2u6lUq/seu/1c02BM4cpkkpou0mjUOXM4zuziGvWG3bBzd1pmzdFAF1zolgKWyW8uzGKYNh/47OE4hVyKT9JzT/R9sixyfjxMtD1Atljn3ct2VU6WRU6NxZlZypLKVXAoEh/fXqNYaqAqIm+c6Kah65im2ZKJEgSYX8uRSqdxqBJKKMD/6x9u2Hxky+DkWAeSKIBl0hcP2rJSpsXcag7T0FhYKTK9UmBuNYthWkRCbgqlOrquIQsG2cwmpgkVXaTWMOmK+Pjo5iqlqkalplGo1KnUdXqiHv7+9/f5068Pk0yVcDsFNN2ko93N+zeW8blVxvpCxNu89EUdeKUCbf0OyjURn9simZepVBsUy3UkUSQSdJMt1rm/nMXvVoiHXaQzFdwOCVkEyzIpVgwQBe7ObvIXb40yubBJ2O+iLeAk4FGxgLDfxddOeUhsFilWDIa6gwx2Bplby+FyyETDHj65myDgcyCJdpXUME3qDQNFkfjo01VeO97Nx5+u4lBFjo/Eqda1ZjU2yMXb6xiGxbnDcRRZoL/DT7lqbyxEUSC1WcE0QZFts5DrUynG+sM0NIMTIxEsQJYg7FcpVDR++u59YiEX3zjdw++urdDQDKJBF2+d68PjkvjkbopMsUG9UeC1Ex3884cLTC/lME149Xgn2WKdyYUMW6+HIovcmklzqDfEseE2rk2laA84kSWBb5/vQxTAsAR+8fE8+WIDWRZ4aTzO5PwmerPS3dnuIRpyoxu2Ichgt5+Pbq6jKiJnx+N8OpPijZM9/PMHs8TCHg4PtnFkKIRqVQm6PMiSgEsxEGs25OiNtOFxSpimRbVaZ3oxzenRdk6OtOF0yPyPd+dwKBL5UgXLgk+mkvz7742xvnQfCTgz7KJuyMyFZCy9wsZGGUuQ0bU6DlXgwqerHB5qR9NNFAnWN0r43BLlYoO792V8Sg2AQhpW5u1xKmpOUun0Q+/t3WmZlebvf1mxW0785fsbWPUuRKPc+tmD69qDa44q1FkvSFyZTLZy2JmJGDGv9sz59POIL7KpzjYG/ywE4IlH5MiRIzgcL5480xcRn3zyCadPn/6yL+MgnjC+jOe2tcO/0trh65w93PnYHf6FW+vIigNZ2flzQ3Rx9vTgc72+z7NqkMxU+OT9WWKxKFeaVb5PZ/P8xVujvHqyg3Su1vrufKlGKLxzAVpOZLm/Xidb1PY9dvsJy7I4eXiQtXSRmZU82UINnz+ED7uiNT7Sz3KqxJU7CWJhiXuLOUxknE4JAUhkG3R3jRJr218z3Nb3bd1rOlPhv/7mil0tE0QamsXVyQTHh49Q1Uw+vrVAqaIhSSINzeTadIqvn+pGUWQMw07XqiLh9zrojEfpjvm4v5TlyHCMqaUsiizxt7+f4ydv9PPW+QH+9p1pGrqJ2ynxx2+OUKwa3F4osr5Z5sxEB1fvJtjI1eho8/DayS6GettQVCd35jcplBqksxUiYTdX7qZsYG2BYdrVNAS7aruSqjDY18FGrkZf3M9vr67hdTuxgDvzOe6vFDn7r04x3Du0gw7U16nQ15FBUcoIgoBpGhwZbKMr6kOWBGxrY5HumJ9Gk4taa7qYhQNOXKqI361iGCa9MS+lmo6um+RLdbrb3UTCXlbSaa7eSzK/lufkoSg+t8Kvt6kOgMCl2wm+ebaXYkUjla2gKjKiAN8514fbKfFXv5zk+EiEsb42Pr61hmnawHdhPc/R4XbWNypouslob4jxgRB35zKIokC9YesPa7qJQ5EQBPjkXgpFFjg1FidXqpPM1IgGXZw90okqwb/7wQS5Yo2Ax8GvLsyDILKUKNDQTAI+FQT7PcgWa5iWhdshY5q0wLDPrXB4IEw05MGpKgS9Dv74zWEqTRm5j2+uMdwT4tOZDbDA7ZSpNnQ+uZvg1FgMl0Pi2Egb6VyNK3eT6LpFR5ubeiPIqdEIXzvd09wE+Pj1xQUqdYPOiI/2kItsSWNupbKjonni6KHmszXJlI3WyZckCkwMRnjzpT4u30mSztVAEOho82KY9t973S5OHh3c8S453b4dVdOXj9sqEQgSkijhcshsPVpFVoiEAowfGti14pvMVJhc0R/iF+/1+496x593Lr2/lH0oJwIEwzFGekPAw+vabmvO0eF2VjYzhMJtrd+bSxqcPDz6pVTB6/X6I4uqXyQgXgE6tv13HFj7Ar//IA7iX0w8Lf/0eWkNPyq2EueNqRR+r7qrasCzhr04fGbWADaP9aObqzhVu2KxtaidmYjtaHyp1nUy+SpK0w73eR5lbjUTRkMuDBPS2SoAPpfC+ECYqaUM5ZqOyyHZhhK63fQU8Ki4XQouh0yh3Ng3IH6weTFXrNEV8TK/mqeuGUiiQDTsJVesUao0CPqcFCsaZtOO2OdW8bpVZEnANAUcqsiPXx9CEm2TjN9eXiKZqaDrBq8d7yKxWcbrUjBMgfevLdMT82GYFh6n0uS39jE+EKI37kWVJdp8DtKFGicORehqc7OcLHNlMslGvkq50rBVATJlwEJAwLRAksAhS7idMm0BJ9GwG7fT1houFTLE2z1sZCt0R31YQLvfQSJTJp2vYpoWM8tZBNFWuTh3OMZ/e3saSRAYH2xjcb3A9FKWoZ4gp0ejHB5s49pUmkzBBoDnD8fpaHMT8NiNW6+f7GJmJUO+otkudgEniiTgcSls5CrMr+cJ+5wEvE4u3UnwkzeGcKoKHpdMrlgn4HHgcirUGzprG2WCXhWHKjHeH6ah6fzt72YoV3VmlrK8caobRZZwNDVzZVliNVViYqiNhbUCLlWiN+ZjejFLd9TLUrKIYVjIssB4f4iVVJEzEzFM0+L+UobzR2KsJoucHo/zTx/MUtdMZEngT742QtaoYSGwsJZHFAX8XpVThyJMLWSQJAlVkTFMg/evL/Pq8W7m1/MossiRwXYW1gtcn04z0BmgJ+alVNWQRJHfXlkmEnTR0A2yxTo+t0K+3ECVbZWPwa4Ak3ObvHaikyuTKVyqjOCwHQbfvbpMV9THZj6J360Q8DpoD7qIt3lZTBSYW80zu5KnLeBCVWy75e3v7F4KOYIgoJu21nStYVCqaLgc9txyu+SH3qUH1XEiQSdzq3mGuwMkMmXOHe7gyt0EDkXG61YfKSf5POQnPy8e8tNoxe+25nx4c5WhrgC1Zp7b+vmLaMIEXywgvgSMjo6ODgPzwL/GbrI7iIM4iOccT+sG97y0hh8VqWyVG1MpFEVqAdaLt9apNQxeO975XCrFfq8DTTd3HPkJ2JWs6W1SSKZlcXM6zdHhNi5PJqnVdSzg3JEOcsV662+fdxLfvriWynXuLmb5q1/cpVLXqWsGP3hlAI/LrgpX6zrtAScAxUqjJU223QxgvxWioM9JNGw3YdU1A1kSSWXLLKZKRAIuTMMkEnQhSQKmCVoTNP+f//Ik6xt29S2VKZMvidyZ27QtgQX7qPT9G6u8NB5ldi2Hqkg0dJNMwa6+yu0iIZ+KYcGduQyGYTCzkudrp7qRBIuR7gA+t4Of/m4G0zQRsCvBVyeTvHasg7NHOrgxlUJFpC3g5NRYFE0zeP1EF5/cS9IWcBNv8+B2CLQHnUQCLi5NJnAqIrIU4u9+N4OqSNQaBod6g8wuZzk2EgVB4P/4J0cplhtMzmcZ6Q2SaWoa/+z9Ob51tod4u5u+uI9SzTbg0AyTO3ObRENullNF/uj1IX55YZ52v5NDfWEu31nn+nSatoCLl8bi3JxO4fOohP1OnA6Z/rgPURTwuVScDpl6Q6eyrbr89Zd6ee/aMq+f7KGumQQ9Cv1dth6xyyGhSBJul4xpmsys5PB6VGZX84z3h/nPv5jkzEQHn9xNMtZrqyy8cqwTyzR4/8YakiAQ9Kv8xVujaLrJ4aEI/+OdaTTdfh803eI3lxf53vk+XhqPtXjBx4bCCILIpckEsiiAAKdHY4z3tTGznOF//s4YhXKDD26ssZmv0hnxspIqsr5Z4vihKN1hF5pmspws2prEhoEoqHRHbCe9ekMnsVki5HdSquh4XAq1hoGFXc2tNWwqTEebB003SGUrfO+Vfu7OZzl3OI7YNGm5PpVClEQUSSQSdpMv1Vrv7G4KOclMhXS2wstHO7hwO9E8OYFXj3US3eVd300dZ6g7yB+9OcR711fRDYPvvzpIV8TDeF94R6Ppo3LA01Z3n7Xxdq94GrC+25ojS6JdQd8WL6oJE3yBgHhqaqo2Ojr674GfAk7gF8DfflHffxAH8S8p9rPD3w1I7Udr+HEA7HH/vlf19sObqxzqDT0X0BkNuTjUG+LirXV000IAImE3gkir8rsVpmVhGha9MVvWSRAFGvUapvHZ72wfu+d1RLm1uBbLDX59YQG9eRRuGBa/vrTAv/neBG+c7OLCrXUqdZ38RpmXJuL8/pNlcqU6p8eirQrR9FIWRRYplGwr2b0qRJGgk2NDEVaS8zQ0g3y5ztmJDm7NppH6woz0BllOllhOFlFkkW+e6aUn5qMzYhsj1BsGCALVmkZis0xnxENHu5fZ1Vzr2Pz84U5mlnM4FAlNNQl6HQgCjA208Z9/MUm9YQIWZybifHxrjX/17TFAIF+uY5omy6kSi+sFdMPCME3OTMQZ7w3Q2WYrHvg8Kh/dWCWdq6EoAl8/3UO50mAtXabNq3DqkI//78/voGkG431h7sxt4nbaRiClqsbHt9b482+M8Fe/vItDlTk1GiWZqbCZr1Gp65ybiHN/OYsFVOsGN6bSKIqEU5UY7g5w+U7CPhoHVElkI1fla6e78Xuc3FvY5NhwhJmVHJpucOVugpePdLKSKlJoKk4cH41wYypNvlwnU6zx3fP9ZPJV/vhrg7idCrW6TmQkQr2hc3ggjCIJXJlMoMgSR4fauDO/yWahSn+Hn9PjcWaWs3zjdA/LyQIDXUFK5RpvnetlYb2A3+3g/evLnByN8p9+dJhqXUcURX718TyrG2UGOwOM9Ia5t5ChXNVQVYliuUG1puNxypw9HMeyYLQvzF+/M02lqfxg0eTZ/uAwd+Y2WFzPM9gVxOdRcDkkkpkKDd1EsUTKFY1aQ2diMISI7cr2nZf7+eXHC2zka7YV8ssD1Bs6k3ObHBtqp9awQfFmvopTlckVy2xkaywli7ZttiQyv1akXNN5/+YqfTE/c2s5To3GWE4WMEzI5KqIwsMnTtvf31rDIFe0jVNePtKBbpjIkshgZ4BoaH95yAa2MXpi/ifOCc8qP/m0hY/9XNeTgvXd1hyPU+FQb2jHidyLasIEXwAgnpqa6t/2/98Bjn/e33kQB/EvPR63w3/UUdujtIYfd0S3nyO8vaq3siTuK5HvB5BuJfRaw+DDm6vIkojHqXB0uJ25lRzRkKul3KDIIlenUq2jaIDNXJGzR7rJFeoE/Q68LhksC9M0d9XJPT0WBXgqoLzdLliW7M7+fEljJVXk9swGf/K1YZaaADVXtF210rkK719foVLTee/6CsWKhgCcmYhzYyq1a4XIsiyuTaW5t5Shv8OPppt0Rb3cnt0gEnTT3xlA1wy6Il5ePdaJ0yEx3mc7wf3+kxXeu7aCbphIosgbJ7uIBJ2t6k9Hk8LRG/dz5U4Ct1Pm66d7uHo3icel0BX1cnM6jaFbWE0FgBtTKY4Mt7OaLnH5ToJvnumlWNWYXys0XcEEZFlkdiXH2cMxZleLdEV9/P7qMpphUtcMxvrb+dkHc7QFXKQyy7x2vJNSZZMzY3F008LnUVjPlFlOFHE7Zco1jYGOAHcXs5iWwGhviEt3ElgWdEe9lKsaV+4mODka5erdBG1BF6pqN2PlS3V8bhWHIhMJufE4JI4finBzNs1oT5hfXljE7VDIl+qcmYhRqmjcvJ8mla0wv5bnx68PMbW4idclE/SqnBqNspQoML2UYbAryK8vLSIJAsdHIkzdSeBQRIa7Q7QFXUiyhGmaBDwOvv/KAMnNMkeHI/z1b+8y1B3io5trpLIVZFngj98c4dcXFsiWNEI+FVkSuXg7wVhfmFpDZ3Ihg0ORCPudCAJ8OpPi+EiUqcUs9YaOJAo4HDLFikY87OaXFxcZ7g5iGBZO1YYMtYZOuaqzma8yvZQjW6rRE/ejaQYN3cLjUvAKAjXNNjJxqjLpbI3kZhlVjbGeLvHGyS7ypQamYfLO5UXOHe0gEvbQE/NyZiLOry4uEPY7yRRrnDvS2QS6FhdurXF4oI32oJO3Ly9iGvazCftdfHIvydnDcWaWc7w00cH2tqUtne07s5tcv59uKlcYRIIu6g2DeJvbluNTJcYGwk+0yf2ydNWfhtqw33jSe9przTk1GqE3/uSbhS8jDpzqDuJfRHyVpF+eRzxuh/+0R22P+7utf98uFza/lqcn5m25ou1VvfU4lcdWYR8E3HXN4ORIhMNDbURDO48nRVHkteOdHOoNtT6nPeCgXG20KCFep8x3Xu63q4+S0AKcoe4Ql26tU9dMcqU65490MLVgy3xtVTu233/Qq7KUKLYW2ifh8m3nbeuGiaYZuJwSQZ+DaJuHpWSRtY0yLoeMpwku/vMvJjk+HOHaVIp4mweHIlLXTK5MJnjjZNeOjcXWWKYyFdsIRBbJFOukMzZQ+875flxOmZBX5cb0Rsva+OzhOIPdAVLZKtfv293wsiSyvlnm7353nx+/PsiVu0mqdZ1cqc7rJ7t5++Iiogh9nQF8HpXvnO9jKVnE71ExTFvMranERqVuADZALlU1bk6nOTzYxvWpFIIgoEgCL03Eub+U4dRoO4IgUK42CAdcFMoNDg96uD6dwud2sJGzOYoX7yT41pkeNrI1ppaynD8SZz1dQlUkJFFEFEQyxRonDrVjWRamZWsSbx3NCwIYhn1N5w93spLI88aJLm5Mp227aQRcTolcscZYX5x3P1nhUE+QT6aSGIbFZr5Ke8DFnflNxvrCREIueuM+nA6JX12c5999/zCVmoaiyNQ1g86Im5mVAjem09QbBkeG2rh8N4EkCoz2xvjw5houp0zQ66A76uXaVJKNfI2XxuJML2X40WvD/NUv7xLwOPC4FI6PtJPMlHnjVA9vX1q0u/5Ni1ypTqHSIORzousm9bqB36OSL9eJh72IAlgWOB0yf/r1AdoDTv7pwzkCXpX/+MPDSJJAR5ubxUQR3bDwOGWCPgeaZrCRq9Id9XL9XpKXj3XyywsL5Et1nKrED18dpDvq4eNPE6157lJl0rka5doGpgmZYg2HIlOt6xwbbgcElhIFTh2KEvQ5SGyWSWyWqTVMJFFANyxibR6KZdvlrmrYjWnlqkZbwEUk6EaRRBKbJXxutfUOfHIvxfxanveurWCaFpGwm0jAycJ6gfH+MMlMxZ73/fFHVodfpLXkefCQn1c8as35qpgwHQDig/jKxKNA0uOO8P+QBdD3+vvtDVwPuiE97VHb4/5uSzvXocotSoQkCrT5nS3Aulf1dj+C9dsBtyxLaJrB7blN8uU6PTH/Q8/0wWSczFSYWy3Q2+FHwCLsd/HT383gctgWreePdBD0qfz/fnWXWNg2PnA6pBbQnF7KUq5puByfpc5ktsKt2U3eubL02UIbdO2by7edt20Y0BZw8eapLrAg4FGb8ll2JTbW5uG//OoutbqJ1QRzS4kiA51+HIrdiKfp5o6NxVYDY1vQyXKi2GxIcuJ1KWQLNTLFGqWkhigIHB1up6PNjc/z2TzKl2qYpkXA62BmJYdgDyy5UoOBjgDRsIu2gIubM2mOjbSjyhKfzm5wZ26DQrnO1071IAiQzJaJt3tYS5ewLFAVgSOD7Xx4Y4Vq3SQSctHmd3DyUKTZ8e9hI1dhrD9MqapzbTqFU5HJFKq8fLSD/g4/hgmqInJrZoNCqUHAqxLwOEhslJkYCFOsNHhpPMa1qRQN3cDpkHhpLMZGvmpbM8sSkgSmKWCYFl0RL7pu0hP1cn06yanROJdvrzPcHWw28Tl4xd3Jz96fpaGZVGs6QZ/TBpOqjCQKSKJAqaChyCKHekP89vISsiTidsqkc1V+8fE8pmmhyBI/fG2AeMhBOlulo91jV2AtONQT5PLdBNW6js+jkspUWEkVOTYSoVwzWEoWUZUAc2t5BrsCCFiciEX4/SfLgEDI5+Cl8Rg37qdRZQlZFpFFEU03kGWRSq2BqkgsJYt4nDLfeKmbwwNtRMNu7s5n0HQTVZEI+pz87IM5GprOSG8IURDIVzTkpnxWMlNBkkSKFY1wwMXk7AbfON1DQzOQRYGGZvCbS0vMrRUwTZPzRzqp1DU03UCVHWimPdaqLNET9SFJAten01Rq9qYMILFZpr8ziNshoyoimXwNRRIAAY9TQZZEDMMi1uYmmamQK9a4v5zj7OG4ffTEZxv5rY0ZQDpTwetSCPudDHcHODESeWz+fZHWkq287/cofPtcH2DteG+/jPgqgd/d4gAQH8RXIvZKRKdGI3seYT8vx6/PM541wT4thaEn6n2qo7bHHdH5vQ5CfifvXVtpLTymaXH9fprDQ+07GlwerN7uR7B+C3DLksg7V5YwDAtJEoiEhvakCmyP7YA+EnJz4dY6giDgcsjU6jo376c5NtyOLNk8ScuyqNZ0fG5b4UGRTWJhN7W6TkM3bUWKXBVZEh5aaF0OmUKp3tqQFEp1/B4VBItUprbD4c3mbQeZXs6ytF7k0p0E9YbBmYk41ZrOa8e7uDWzQanSQNNMnA6b3qHIAppuYZgWgmArghzqDbUqRKlsmUyuSiTkalramqQyFUzLxO9WyRbrrcY9l0Pm1swGh3pHdlSX86UGS+sFZNmml9hudza/V5ZFFEXivWsr5Ep1/B4HPVEvRwbb0A0LC4s7sxsM94Y43N/OzZkUw11BTMvizOEY16eSVOo22A/5nNyZSXGoL8TcWoFffDyPppuM94cxLRAs2zimN+ZD000u3FpneilLwOdgYqCNqcUMiiyxnCxy8U4Sn1vhuy/38961ZU4eiqIoIoZhspGt8NqJLgzDYnY1x0vjcZIbZTZyVQJeB2MDYS7cXuMbL/Xym4uLlKo6iWySznYPn9xrcKg3wLFDEcb6QyQ2bYe6Q71BTBNW00X8XhVJEmgPOFnfKDelzwxeOdbLry8ukCvVW3zrf3hvlv/04wnm14qsZ2sEvTbfVpYkdN1CksSWzJoo2jJvHqdNueiJ+XCqMhdvrXN6PMb711dQZAnDtPB7VD76dI1jw+2sbZQ5Mx7n9kyaWJuHkyNRbtxPNfnAAkeHIvzj+7PEQh7+/JsjfPeVPlaSJQQRfnd1BcuyaGi2ysfEYBshn5OFtTyqLHJ3MdPk+8oIQK6ikc5WKNd02gJOPrixSk/Mx+GBMJIoohkGDlng9Hic5WSBUlFDcNtNrD63wjtXluiK2Jz0/g4/7UEXZyc6+PnH87gcMk5F4gevDRAPu7l6N8WZiThXJxP4PCqVms6PXhvEMi062j1NE5sGsbCn9d6riogkCq3TioZm4HEqdEb2txa8KGvJXnl9qDv4QhR5vqpxAIgP4isReyWioHd3q9cHHb8+j8aD5xHPmmC3+Il+j9pyMtuNwvDw5w8/1VHb447ooiEXkZCtYgC06BAORXpovPeqJjzqeW0B7rcvLWIYW4Abrt1LcmYi9thn6veoZAo1EhtlJNGmSLidCkGvStDnoFBu4PeoOGSaR+gCLlVioDPA+9dXW/awfR1+jKZc00sTcTTdaC20mmFSKDdwOiQELN67vsJqusx6ukilZjDaF+LKZIJKTSfe7uGHr/bTFfFRrRvcnc9SrNhNTRZwdTLBv/3BBKdGIzhViUK5KVdVanBvMcNL43Gu3UviVGX8XpU3T3a3NkOmaXLpdpK/+/19qnUDhyLynfP9/OriPHJBQMDm0N6cTlNrGK3K9nYQv5YuspIqcu5IBzfvp231B8vi1GgUwzAp13Qu301gGhZhvxNZglJN4/5Slo18DVGE1493YximfcT/ygABv5NyRcPbvA/dMHEqEl3tHpZTJkuJAqvJEtGQG69bwWwu/i+NRZlfK9AT9XH5rj3HBzqDLCTy3JhOc2Yiim5YLK4XcCgilZrOry4s8MbJLn51YYH+jgDVuk5fp79JJxB482QPbX6VTMxLrW4Qa7PnzkCHrT1sWwXrtlKGbIPThmZiNUHqSG+Ij2+uMtIbplzTeOtcHyuJPIPdnVSqDVxOmUpNs5sB3QqVus72qZ0t1skWGnzrXD//7e1pUtky5w7HW1J3TlUh6HOwmCjgdSlUGzrlmo5h2hXud64s8urxLlt1BHv+RUNuVlIlwgEX3REvhwfa2CzUcKgyi4kiyc0yf/HNUaaXNqnUTeZXc5SrOlWPzmKySDJX4fbMBp3tXrqiHuIhN/lKg8uTCTTdNs2YGAgTDbkZ6g7i26xQ1XSOj0RQFZGw30l2OUexouFQJXqiXi5NJilXNVyqxFvn+nA7JEa6A9Q1g+VUkemlLDMrOU6PxkhulnlpLMbdhQzj/WEu3l5nqCvYklObWy1w8lCUH73ho1iu8+qxToqVekvXu9HktT+4URcF+30/MxHnyqRtT+50yE9EMXhR1pIXBZj/ocUBID6Ir0TslYjSuepjE9Tn2XjwrPEsCdayLO7MbraqsbYLUBwaeuvvi+U67cGd1r8N3aRQajyV5M/juMmCIDDeF2aoJ0itrqMqtlj9k4z3o57XFuBm2/dFQi4qD1AF9r4B6O/wk8pUkEQb5PjcCjSrxKIoNKuXce7M54mEXHRFPCyuF9ENE4/L1k6dWszy5qkuoiE3n97fYCOnc2Yizgc3VihXdQhbtAdc/Le3p5lfK1DXdM6Mx8mXyly6vc7Lxzq48Ok6+UKNm/c3+N21VWIhN7MrOSJhNwNdARqagapIBLwqG/k6VyaTuBwSb53r55cX5imUNe4vZ/i3P5jA51YY7d0p8zS3WuCXFxYwDItaXadah19fnOfPvzHK9HKWsxMxfvb+HNWmS146U8HvUfF7FK7eTTK9lEU3TG7NbHBqLMqrxzoQBIHbc5uIwJV7KU4dipIt1FEkEd2scnS4nV9dmCfe5qUt4MICLt5e43/53jgCsJ6p8vfvz6GqIucPd/D6iW4sy6Kj3Y0kSvzm0hITA23MruYBkEQ4MtROQzMo13QSmQoDnX5MC6o1nVpDp81vf89QV5APbqww2t9GR8RLrWFwbzFDsVznT74+gtelcHt2k2v3UqiSSCTk4v3rq3RHPVy9myLW5mG0N0g06CJTrGNa4FAkHKpENORG0wy6Y17agy5++fECPTEvi2sF3jrbhwVohoVuGBwZilAs1/F5VNoCTnqjXlRVQZXtCrWjKf8GdoXf7ZRZThbpjniQRJEjg21kClW+dbaXpfUC2WKd/rifvrifi3fWaWgmrx7vZHE9z1B3kFjz/Qt4HZiWhaYbeN0KAjDaH2YtVaRcazAxEKahmwgCXJ9K4HU7WE7mqdR1vG6VVLZCvaFz+fY6fZ0BPry5hmaY+NwO1tNlBMHWgLYsi6mlLD63wka2ykvjUQwT/vbdaVRFwjAsjg63I4kWP3h1gNnVPKdHo0giNHQTUQCfW0UzTHKFerPib2tV35lLc2QowtJ6gTdOdhFv99Ad8+2gKJmWRaHcYGSbIo1lWTt0vXfbqG9t5GnovHmqm0jI9VhptCfJTV9kvCjA/A8tDgDxQXwlYq9EFAm6HpugXqTGgwfjWRLsVrPTVjXWMC2uTCZ481Q3fq8Dy7JY36zwwfXVljvTmYk4omDg9zqemu/1uL+Lht28ebL7qcd7r+fVHnAwu5JHlmy6xcJ6nlyxQV0zEGEHVWArHuRXF8sNanWdN052oem23u+HN1dbR6dvnuxCFgXKpTJfP92NQ7FF+j/6dB3TtMgWasiSSKWukys2SGxUGO0Pc2tmg2qpxvHhCLE2D36Pyi8+nkeVJco1m5979V6S0b4QVyeT9MR99Mb9OBSRC7fW6evwoyoioijYlIuuAIHm3Ah4nS2qiMelIAnwH38wQbGi0R5yM9jlIxryPLSop5uNZmJTJgsgV9LYLFRZXC9wYqSds4c7SGyWWVjL09BNXhqNMr2c42/enm7Z6I71hbl2L8XZw3FcDlt+bLAryNRSDk03bV1aoFY3bFc0VaFa19nIVREEW3lCkSQcqsyHN9fwOGVG+8O8d20FhyrTG/MiCFFM07T1eA2z1SQoiiKVqkYk4MLRVASxgN6Yj3JVwzTtvVE2X6VYaRBr8/Lxp2uUqzpOVeT0WIxYyMXblxd5+WgnMyt5DMPE7XWgKhK5Yomh7gDhgJN0tkquWON/+e44papO0KcSC7v5dCbNcqIEgsVrxzu5eDuBx6Xg96iM9YeZXy/y/nV7Uxpv8/DqsQ4Mw+Ti5WW+dbaXlVSRq5MpImEnP3lzmF98bHv3yrLAD14ZpFyt4fOobOZrCALcuL9BNOwi4FF5/WQXd+czFKsa4YCD02MxLMtiZjFLPOKlLeDm7mIWpyJycjTSVAKxiIXdfO1UF+lsCVmRqdVNZtN5QMChSqxvlPj2uX6qdY2Q32krY4zHSWWq+DwOrt1LIooihXKDfKnO3FqBwa4AkihQqWk0NPtZN3RbJm8pUaRat/NK0OtgM1flyHAbF28lWEoWCfmdDHT6mVnKcn85x59/cwSXoLCUKPH7393HMAXCASdj/W3IkoDX6yBfanDucAdTC9mH86NHJZmp7NiUP26j/qyav4/KTU9qpvGsTXkvCjD/Q4sDQHwQX4nYKxENdvkfm6CeVzL8POJZEmyhVMehSETCbtKZChY2XzcScrWOvG/NbBAOukhnKhim1TqCf9bNwINJPRJ07rBDPjUaeWi8gc8WMY8KAhRKjccvXh4VMPnFxwu8f32VWsNAkQTGBsIEvA4qTY7tg7zr3Xh2R4fbEQWBO/MZ0pkKTlVirC/M0eF2xvttVYBrUynm1vJISh2nQ+aNE52YpsViokipaYwRb3PjcsjkSnU62twc6h1hbjXPcrJArlhrAQaHKoEFoiRQrmpggSQJqLJEsdqgJxZifMBEFNlxnLsF0D+bC/bx7vZGxbOH45w9Em+pdzwYW139qiLamsC6iaKI+FwKRwbbefvSEqWabUV85nAHTlWkK+rlZx/MMtwTbH3PzEqW4e4QDc3EpUq4nQqFSoNUtkK+3ODckQ6u3UuiGRZOVSLodVCsNHA7FSzLIp2t4HSI5Jq6r0eH2rl611ZlkE2Lhm7yzx/N8ZdvjaLIAtNLWU6NxbgxnbJtdEWBsf4QDkVivD+EAIz1hbh0O0GmWKM35uPEoQhgsZQoIIkiHpdMpapzdyGDaQUZ6QlRretU6hpdES+b+ZotuaeK1Bs6Gzmb3jHYFUaWBMIBB+sbFVwOkUjQRUe7F4CVVIlktkJnxIvf62BpvUhD0/jh64Pouslmvsa1qRRvnetD0y0+/nSdr53u5spkivWNKrdnU/zHHx1hOVkg6HWQylYI+33MLmdoCzrxuVUu30nQtuFCkUR64158bgfFikZys4KkiCyuFsmXGwT9Ol1RD9mibbErCgI/fHWQUrXBQGeAdK7M768lcagSm/kqrxztJJOvcnUqhSKJOFSJ777cT6mi4XMrXLqdoK/DNuBRFZmVVBHLsnW7FVmkXNUIB5xU6joep4LTIWOaFm6ngkOViYRE6g0Dj1OmLeji8p0kAGG/k1S2QrZQ5dhIhFszm1y9m+L4SIQrdxPohs2V3szXEIU833+1H0EQOX8kjs8tM9YXekC9JcZqusTF2w/3Tey2UX8wVw33PD3P9lnXkufVlPciF3m+ynEAiA/iKxGPSkT7SVAvavfrsyTYrSpBJOjC61JoaAZOh8x4n62huXWstv3ft47gn2UzsFtSH+zys5Is7ZDs2lqgdvubTKFGf4efWl1HN6yHFgVBEOzkblncXcxQqmj8/KM5+uIB3E6b2rCcKPJHbw7RE/PvOma78exuzWxwfCTCzftpu6mmSbOYWc4xPtBGOlfj8p2kDQaxNxgXbycY7Q+xmi4BIIow0OlHEOx793kcrfu8PpVClgTagy78bgVRFOmOednIVXE5ZfwelW++1INpWlydTJLcrFAsN3j9ZBf1hu3Y9eapboa7A3S2e0GAmWVb2WF9o9Q6DTBNi4X1wnaZ1R3PJ5WtohsGP/naMG9fWmCkJ4RhmBwZasMwTZLZCi6nQrGiUanrXLuX5CdvDpHOVnA7FS42m/pEAc4ejtMX99MecBHyOZicz9AWcPKj1wdZTReZWclyeiJGT9THaE+gyW1dotawedVvnuymVNHoifpYTZUQRfC4FMQtmoogkC9p3J3b5PuvDPDzj+a5NbPB6bEoo71h1jaK3F/OsZQsNZ2vDF491klP3MfZIzEadZO2oBPdtAh4FDraPC1KQjpXJRry8PalRTTD4thwO8uJImDbZffF/KRzFQa7AoT99nP88MYaM6s5LMviO+f7ubeQQjcsBrsCDHYF8DgV3A4ZSbDpDqmswD99MGdXiMMejgyFwbI4PGhLeTU0k6NDbVy8naA3FuTjT1cJ+V28d30V04RqfYm3zvYTFEUu30liWeBzK2i6STJTxaFKXJ5MIkt289krxztYSZYZ7Q/hdSqtSmFDN2mU6oiCQNDnaF1TvWEQC3u4NbPB0eF2Am6VH74+aDs2mnBvcZM3TvYAsLRe4JUTtgW3/R7C3EqOMxMx5tfyiIKA16nw0kTcljkL2g509YZOpmBzz8s1nU5FIuRzUK3bFX9bItECC0I+B7W6bjf1SRKWbGEhIInYCigBN2cm4iwni/z0d7PIkkBvzNeiOCDAT9+d2Rd/9vNQhXiWteR5cX9f5CLPVzkOAPFBfGVir0T0vMGuaZrMrRb2dGp7HrHbsdmTXv/2KoGtUdusKO7CnXY55Bb4CHidz3TtDyb1ck3j5x/O8/rJLmrZ6q5JfvvfVOs6iY0yqUyFN052tf5t++9vLWTvXV9hdjnH2ECIsb42Pr61hqbbhgunDkUxTGvPcduLZyeJAq+f7NqFV13HwiKZrbCcKuNwGAhAyO8kGnTxjZd6yBbr1Oo6C2sF2gKuh3iK54/EuTuf4b1ry7QFXVyfThH22892rC9Mvlyjsz3Azz+ex9GU6XI5ZT68scZb53rZyNUY6AxwfMRWT7kxlcLvVSlVGoz1tzWbAustbnax0oDm5merUn9tKsV711bJFGpNLd8Ork4mcKgSd+Y2OTkaIR5yYZgWJ0baKddsM4a5tRxBj5OPb63TaNi2zjbfNMV4f5jJ+U27+bCpIiJJIrpu8vKRThyqyGhvkK6Ij49u21XRhmZTH1ZTJUwsUpkKwz0B4m1eLk8mME1bqsyhSjgUgVJdZ/rOOn/8tWEM02KoK4DfJZIt1jBNkES7UnliJMKd2U02C7Yjm2FYXL2b4vBgmLWNMj63jm5auFSJekNnM1/D73XY0mXJAm+d66OuGViWiWXC+mYZRRLoi/uZXs5SaWhNwxGBG9NphnuCTC1kGej0EfY7+NrpHmaWszhU+4RANwyOj0QAmFrMMrWYY7ArwI37ac6Mx1hNF+nrCLCaLmNhN729fXkJVZHQDQtZkvjkXpLXjndSbxj0xHzMruYxDAvdNDk5GqE74mIlXeHirXX6435CfltCL7VZYazfNhfZLl/ocyv0NhsDVUXCpUpUGwZDPbY+9F+/PUWlbp+0nDvSwcxyluOHIhQqDfrjXjyvDPDpTJr7S1mqDQOvU+H4UIQTo1EkSeDmdNreTHpU+jt9dEW9XL2bpFLX2cxVeetsn90waVbRdBO3Q0YQBXxutdlk6MaybM61ZpitKnN70MVAVwCAy3eSTaBvNS2Wq4z3tz0Rf3Y/uuj7ieelPfw8ub8vapHnqxwHgPggDmJbmKbJby4ttowbZFHgB68N8O1zfc8NFD+vqsXjqgSf17Hag0m9oRk239S0djjAFcufJfntf9PQDCxsznNDszvCH1wUthayWt1WW2gPuPm7391HkkTARNctrt5L8q2zvXte5148u/agTYvYjX9XLDfI5KotJYAtaSbdgmy2SrVmV7bCQRfnHpA5EgSBroiX31xaahoPaJwZj1NvGPR3+qnXde7MZWloJgICXpfC+mYFAWgPuYi3uXnzVE+L7nJjKoWiSLx/fZW6ZlCt6/z49UEkUaSh28oM65sVfnNpqfV8z0xE+dWFRZZTJQqlOsdHIvzsg1kiQRelvEZnu4fr91IcHmyj2tC5M5/h1uwmlmXyJ18fYTGZxyFLVGoaFs2mMkVmejGLz2MrfLx/fQ1JEltH6XfmNvkPPzrM4cE2LMuiLxbgb96ZxrKgo81NR7uHK5NJvG6FroiXq3cSfP1ULxfvrFGtGSQzZX7y5gi/vDBHoazzj+/P8uPXh/C6ZA4PR9BMkXi7h6mFLKoicWsmTU0z8LgVgl4H//ThHNGQh1S2Yht7TG8QDblp6CbfPtfP+kaJeNhNZ7uXk6PtVGo6hVKDhm5imCZHh9s51BPCoYiksxUEbA3kck0jV6rz8pEOnA7bae2XFxYJeh281JTDczsUPp1ZptYwkSX7/SpVGsyu5rEsuL+UozvmpaEZttOc10EmX8PlkFEVqfXc/B5b5eRQb5CZlTyabtrKFqLA/eUcZw53Ikyu8/rJHpyqhNetspErY5gCN6ZTDHUF0HSTQ70hTo1GSOdqeJzKjmY0j1OhN+rj7YtLVOt2BV03LCbnN3nrTC/DvTYt5tKdFIZpUiw3ePV4J9Waztxqnm+e7eXs4RiCILScx2oNgwu31gA4MxFrvfvxsAvH4bhtBKParnj9HX5uzaSJt3kY6gkiCnDucJwLn67S0Ez6O9zNRlUXM8u5RyrN7Jc/ux9d9MfF86wyH3B/X+w4AMQHcRDbYm610ALDALpp8fMP5xnuDu1qZfw08Twlcx5VJXgcYH7aqseDSV1VJLxOuwK0tejIokBHu7cFGLf/japICIDY5LfCw4vCZ7qh9u9u5qqEA04KpQY0j1c7I14Uee9NytPwzovlOi9NxPng2gJgS68dH4nQHfEwu5Ij0dSVPXs4TqHceOg7C+UGhmmiyCKabnFnbpNyVWspFfTGffjcDnxuhWS2itE03KhUNUK+z04J8qUaLqfMe9dWWqwISRS4cGudl492sJGr7eqad28xS6mqocoiQZ89nsWyRizkRpZEFhMFhrtDfHxrnbsLWUQBXhqPM7Oc5Wfvz/Ln3xjhvWtrhP0uBCzaAi5W0iUM0+LWTJqAp5NsqY6m265h0ZCLkN+JqogIgkAqW2UxWeDMeIz2ZsPrby4t2vPNhM18jbn1AiYWhwfaEQRwOWRS2TJjfW32aYYq4XHJxNt9iKLIydEoqWwFhyxx/X6akN9Jsapx4lCE9c0yQa+TbKGGxyWjNer88NUBnKqMqkpk8hW+dbYPpyo1Nzx1fnVxsfW8pGbjWHvQSbZYZylRpKYZVJra05W6jkO1GwmvTCZxOxVMC67cSXD4h+P87IOkbQ7SbPa7OZ3iW2d7qdUNHIqtCRz0OQh4VF451sn1ewleP9nD5MIm6xtlDJOmtbGtZnHiUIS51YJt4S2JnDgUYXI+w+H+EGcPd/K3704jSxKCAD9+fZBb99P4fU5STXWFK5NJeuP+Pee+INjNa+1BF5phIos2lxhBoM3v4jeXFjEtq5VXEptV3jzVzVvn+neclG3lnGSmgm5YmM1csvUuez0OBruD9ES93FvMUG8YGKbJcI/d+HpnbpNqzbbL/su3RnA5nbhdCj1R3645ZnuOeJKN/n510R8VzzNf/yFzf18kB7+njQNAfBAHsS3SuWoLDG+Fbtrybs8LEH+Rkjl7AeZnqXo8mNQ9ToXvvzrAP7w3axtEAOGgq2nyEGq55W2nd8TbPfR3+FnfLLea4iLBz6gcWwuiyyETCbubHESD3rgfUbD1hB2qREMzSWYqe/LGn5R37vPYdrSvHI2jOlyoikih1KA74mO8P8xgZ6BFs7h4O0FXdOei6Pc6cDpskwILG/RLTSMRt1NiqCtAuapx7mgHP//IdqaTJIGXj3YQDbtazyZfapDOVilWNAQBuyFOFpvuYQ7ePNlNvrxzHqmyfWS+vlmmVjfQDZPRvjBO1f67clVjtDfMpcn1FvBAELg+neTYSIRP72+QLdY5ORrl1ky6aa7g4tShKIvrBfo6/KxtVBCasllgUW0Y9LrVFg2nUKojIFCuNQjoDpYSBXKlOj63ykhvkGgTmKeyFT6d2SAadqNKAqP9YT6d2USWBF451klfE9R9Noc9RENuDg+1s5Yuspous7iepy3g4tbsJoZp2Q2mwSD//NG8XSHWjCaFyLXjeHw3oAU2DeClZlOjbpjUGjo/eWMIRRYREFBk6bP3ByhXNZuyE3KTyla2Zh3tQRfrqTKFcgNBgK6Il0KpzqXba0TbPFRrDcb7w+TLDUzDwrAs23XPMBno8PPK0Q40w95wTC1lMEyTtpCHn/7DLVRFRtdNLMvi5x/N88dvDrGSLreua3se2W2Op7JVvC6V7piv1Yi7ZeZiWubOvCQIeN0K7QHnnrnvUQBPEARibR6iTT30revAsrh5fwPTstjI1bAsnduza/R2+Pn0/kbLcOlRn2vfm5dUptoyuNnr+vari75XPG+awx8i9/dFcvB7ljgAxAdxENtiqzN/OyiWRbtx7XnFi3Bs9ixVj92Seq5Yozvm+4yz6JB3LBoP/o3Po7CaKnFvMYvSJjK1kMHlkFsJdPtCGwm6sLD406+NcH8lh0MWyRTrdIfcXLi1tmtT3vZrfRLeeTTk4sRolHcv3KW/14Ommxw/FMHEBt4PxoOLYjTk4s2TXZSrGomNMg5F4sxEjJBXpVIzuHR7HbdbQREFfvBKP6lMlaDPwcRAiHypDk0oPTm3yaG+EDfup6jWDSzLrtZW6zr5ks0bDnidO+ZR0OfgwxurnB2Pc+H2GpYlcHs2zU/eHObtS4tkS3U6Im5iYQ9mU1LNNO1jc5pqAgBr6RITg20ossj5w3E+uZek1jCwLFhJFXn9hO2at3X8fPJQpAVet+Z2X9zParrE3Gqe1090oukmH3+6htsh0xXxMtwdxOWQKVc1vnmujxMj7ZweiyJLEtGwa9fj7O1W5NnCMncXsqhyvmXYYVNhkrT5nTZHeJfGw0jQyZmJGNNLWRTZ3uycGI1iWiY1zcC0LN5o8ssFAbqiXtr8rlaDoLENWImiQLzNxWhfmLpm4lRl5lezeF0KHo/KQIePw0Pt5Eo1FtaKvHGih5nVPA0Dbk6nOTLY3nJdnF/L8/qJbk6ORtnI17ZRtkT+8q1RDMPE5ZCRJdvG2wZ4gv3stsX2PLLbHN/+XnldCrph8ubJLnpiXtLZKqWKhmmaKPvUD98PwHvwOizLal1D0Ofg7YsrdMZCrZyxlYce97nLydJjAdjnrYv+NPFV4P4+abX3D8Uo5AAQH8RBbIvBLj8/eG3gIQ7xYJf/uX3Hi3Bs9qxVj92S+oOcxQcXje1/k8xUuHQnueMatifQ3RbaLWm3tXSRRKZCoVjHqToecud7lhAEweZgbua4MWtrHudLDY4Ot+NUJGqasef9bf396TFbcWFto2QDvJCTYkXjf7x7n94Of6vB6f5Knu+/3Ee5pvP762vU6jpOh8xLY1EkSeTjm6t8/XSvDSSdcrPi2UG2YMvbDfcEd8wjTTfxeR0UK3XOTsQxmha2C2u5VvNie9DF5HyGsN9Jb8zHykYZGXA6Jf74a0M2t1bTmVnJcf5wnEpN58RoDK9bZby/jSt3kwjQAo0OVeLwUNtDvPVP76e4N59hYrDNVipYLRD229W6zjYPxYrGj14f2tG0Gm/z7fsZHR6y3dfqDQO3U6Ir4sOyLHqjHrwe145msrWNku1A6FVZTha5MpmkXNPQDZPXjne1OLeiINDQzR1H//0dAaIhF4VynWpdt8fatIi3ewh5ZEZ62/jrt6do6BYOReTPvnmIqcVNVtNVzhzu4O3L8zhVlfWNMkNdQaJhmyseDbtZWC/YbohAvN3DQKcfSZJ462wv3VEfyWyFWMjNeH+I+bUiTkWyc5IkomMiNjnrqWYj637yyMPvlcJSosg7V5YxTJOgV2npS0fDbr7/Sv9j89L2jUoqW2VmOfdIELX9GuZW83S2u3cUHLbnob2A45MAsEfpou8H+L0I+fpp42kaxJ+m2vuHYhRyAIgP4iC2hSiKfPtcH8Pdoc9NZWIvsPdF8q/8Xtt0oN4wWhQA3bCeuurxpIvGfhLoXhWuO7Mb/PzD+T3d+Z410rkaV++sEAq3ta7r1swGZyZiLc7uo+5v66g41vbZMX2hnMXnVu0Kb7lhUx+8DgQEfvfJComNMnbND0JelanFLIWKhrmYob+pDXt0uI21dLn1nB6cR4IgkC81bCWPZvOf36NimDa3uVjVmFnJcmIkyuxqDrdTZqI/xOmxGIZpcm8hw0qqzLkjHZimRbmm8Te/vY8sC/zkzWFeORrH7VS4fCdBKlu1x6A/TjS004779FgUpypxZy5DrlhnrD8M5FAkEZ9LRRAffxT/uIiG3Ax0Brh8J0GutNVQGOPuzAoux2fOZplCjZmVPMlMhVjYzd0FezOw9e9btJ7HHf2fHI3SFfVy8lC0dUQ/u5rjp+9OI4oiLoeAZVn89J1p/tMfHWEhsczfvzfD6dEYH326Rsjn4O5Chj/52hAdEQ9hv5P3rq+2NkFvnuwCAaaXMuRLDdtOWzOYWshSaxicPNTe2qiXarYKxg9eGWB+zZZEC3ht2sp+3Sa3AOyHN9f4m7enmrxpjZePdDDaG7ItrC0IeZ2PBbjwZCBqOwiNBF3Igrnj3/dTfX0SALZXFRvY1zV/UTSH583BfdoG8aep9r4Ip57PIw4A8UEcxAMhiiLDPcHnxhneLbaDvS+af2VZFsvJIncXMjuaxI4Mtj111eNJF42nTaCPc+d7HlEo1VtH40CrQU03TL59rg+w8HmebMGy5dJqO4BvvN2DYVmtn4F9up8vNZBlCQGNfLlBuapRrGj0xHwteshOfq09j8ymYsKHN1ep1Q1yxRonRqPcmtmgrptEAk68ThlNN/izr4+wtlGmo83N7EqeXLlBNOwmU6gztZjl8GAbH91cp9bQ8Skqv76wwFhfeN+a3xMDYd4638s7l5ep1XWGewK4nQqRgJNcqfFMm6+t79htU7m5ucFc0mhxzvs7/GQLtnFFvWGQ2Ci3JAjh0Zzbhzep7h1c5GtTKWoNG8xtp1OUKxqyKGIaFpWahmGYhHwO3KqMx6USC3uJhjz0xPwt45nVdImfvjtDuaaxtF7gpYl4S1t4C4x8+1wf3VEf9xYyuF0Kyc0y5ZrOlckkf/qNkSfeDKayVT68abtYmqaFYVhcuL3O2Yk4jYZBTTP5m3en8bnVx+ak/YKoB3OdU5E4eqiDtc3GE1VfnzR/7La5TmYq+wZ+nzfN4fNYA562Qfxpqr1f5Sr69jgAxAdxEF9yfBH8q+3VB0EQuHwn2aqUNTSDdK5KV8S7I/k+acXiSRaNp02gD7rzOVWJvg4/Aa8Kls2LfdZNhN/rQGraEauyiEOV+eD6Kr0d/pbO63a5tX2NkwD9HX5STcdAURTo7/CjiALiNjAFIEkiLqdM0BdoHf2Lgq12EW1W9narul2bSjO1kKE35iOdrXJqLEqxXOPUWIyrkzZn1ONSOXs4higILCWKZIoNPrq11qwYWZwZj7OaKrKcLFFr6LicTd7qtsbS/TxjQRCIBl2cmYhhGBbTSzkyhSxup/zMm6/t3/HgtcS8GicPj+6QBGvoNmjdssZuaEYLEO/Fud0PQAn6HLQHHPR1BBAFMC2YX8vhbL5TsizgUOzNlNMh4/epu35XMlNpua5tSRhemUy0aC7bwYhDkUg8wGV/2qNpW5JMbPGhAQzDso1BvA4+/Gie3ubpxONy0n5B1IO5rqYZTM9v8idvncCyrH1XRp8HAHuRjvk/jzXgaRvEn6ZY8YfSLHgAiA/iIL7keB6J+VGg7MHFPeRzkMxWiARdO6plhXKjdcz/eVetnzaBbnfnC/sduB0K16ZSNDSTmeX8c7nGaMgGcnNJg6DPwQfXVwk3x+rBhWq/41QoNajV9Rb3doumQtMJ7vKdRIsCEgk6OTlqW9xuNQCdPRznyDau7oOxfUFNZiuspErMrOT4xkvdAHz7fB8hn5ORnuBnxi0eB//9t1N4XCqSKKAbJrdnN3jlWCfZYh2fR0WW7KPVJ2kstSyL2ZU8dxeyhPxOfnVhYYf82G6br+cVpmk+JAm2FblinbOH46Rzn3GE9wJR+wIopsH3Xh3g738303qmf/r1EaaX0qRyFX78xhAfXl+hK+Klo93D2cMdu37X9vd/S2Zwu0b3djDyPI+m/V4HHqfS2ly6nDKabjLQFaBQqhPw2YorwENNsrt91n6ua7dcV6nrWJbFSG9o39f+PADYi3TM/3mA86dtEH/azcZXoVnwcXEAiA/iIL7keNbEvDsoizUXi0azIvzZ4q7IIplcFa9L2bVSBl9M1fppEuj2ZB1t8/DB9VWiYTcu52dgNeh1PFG1adfralYa51bz9HYUdzQLbl+o9jtOfq8D3fhMqxXsMY+3eTFMG3DUGwYO1ZZmO3EowqHe8L4X+91AlSqL1DWLj26uYpoWQz1BnA6ZaLNp0bIsgl4HWrtJOlNpVQv7O/10aQZzK7knbiw1TZNP7qW4NZOmWNHIF+utf6vWddoCTlvyatvm6/OKBxd23bA4Mthmy6CVG48c1/0AFJdD4vq9FLGwG82w8Dhl0tkK4wNR+jvCiKLFv/rOOE5FojPibY37g+H3qNQ1w1ZBkEWiYTfFcoNwwEmtrnNiNLrDDfF5HU3vpjhx7nCc3piXTKHOhVvrbOZrLamyWMi9Z07a73Xtlusk8elA6LMCsBfpmP/zAOdP2yD+h1LtfZo4AMQHcRBfcjxrYt4NlP3i4wXG+8MkM5UdFWGwK2UvTcRZ2ygBu1fKnqRi8UULsvdEvUhinHJVt1UbtoHVZLbCpTvrZIv1Z6pqb1UaAe7MbuD3qq0qYKHUaC1U+x2nPZ9x2FYc6Io+vPg8uNg/apwftOmOhN20+51cu5fENK2W9up2sL692u51KTQ0A6dDZrwvTCTkYqAzSDpXJRp04XHLzK7kH/l8Lcvio0/XdzRpvXW2l2pdw+d2tGSvvqgq3KMW9seB8f0AlGxJZ22jzEBnAEWR6I54+c2lBWZW8ggCHB+JsJJKYgLnD3eQL9cfanyzLIvVdIlI0MXlOwkqNZ2gV+XYSDv3FjY5M9HBqdFI6/efZ0PuXuOTylb5/SernBqLcWXSPrnI5Kp860zvnjlpvyBqt/fgzETsSwGhTwP8Pq9c93mA86dpEH/w/oZ7gv8igPBWHADig/gXH1+2w87T7si3rntuNU+5prWAYbWuk9goM9gZAB6uCDd0E1Ew+PNvHNqzkrrfisXjKAPPc2wf/K5Y2E21ru+470yuijIQBp5PVTsSdNId8z5UZWkPOEhmKpSqDWJhN9lCrcVVFQUBQRC4v5TdlxkI8NhK11bl9UH93NNj0a3BYWIwTDpbJVuoEQu5GekNUqzYzXK7NZE9aJbicSocHW6nWLF1joe6Awx1B/jkXopfNx3MHrXJ2N6kJUu2GcjvPlnhuy/3c3UySSTsbnGwH7fQ72fe7PY7D8bTVhG3xubGVAq/V23ZIm83j5FEu3nw4m1bT/eTuwlOj8Wp1hpU6gZX7yZ5/UQnmUKd/+0fb9MX9+NQJU4einB4KAyWwNpGidnVPIZh8vLRDlKZCpIk4veolKr6DnOb3e7paahNu43b9s8vlOoPaTKrikhH26Otjvcz1ru9B2uLU18a6HqS+fF50sg+r6rskzSI/6GYazxLHADig/gXHS9KEnjShXv7dbcHnSytFwgHXUSCtgbrdlvk3SrCJ0ajDHUH9rzHBysWTkXi+KEIhXKNYrmBaZlNdzJrT8pANOR6rmP7YCU8W6jR3+EnnaviUCR0w+SlibjNzW3GdgD4IBDY0jXebQESRZFkpkIqU+HOfIbeuJ+Gbje4rSRLfHwrwa0Z220r07wO0NENi8EuP7//ZJmaZjx0z3u5Bj4K/G2vvOrbpOZuTKXoiXpZTn1mUFDXDE6ORDg81AYW3F/K7bmp2b4I50u1ltzXzfvp1nX3RL37ps5sb9KyAKcqoxsmDc3gP/zoMKoi7ksabD/v5F6/Iz0neURBsPWoq3WdD2+utvSot8ZMEARURWI5WcTtUFBlCRC4t7jJq0c6SWTLRENuLAty5TqmaZLOVcmX6iwmCpSqGjfup5FFgbWNMmcm4qxvlJhdLQAQ9jtb410o1VuV2wfnyJNSm/Yztlub4Qc1mX2e51PVf/A9WJk3H/MXL0bsNdbPStHaii+bg/uHYq7xLHEAiL+C8WVXNP+Q4quYBEzTZHI+w8Janv5OP5l8hZcm4i0lAUkUeeNkFwGvA8OwkCSBak1/ZEX4wdgOlorlOuubFW5Op1EUiauTCcJBF7GQm7G+ELIkkC9rO1zqCiUblO5Ximk/8/lBeoJdkdX59tk+nKqEIAj8/pPlVqUWPgOAW0Bge8Wvs93DUqJIsartAAYAyZLChXfv4/eozC7niIQ/Mw/we1U+vLmKz62iyiJjfSEqdZ0z4zH8Hge/a4LhR93z9nt/HEDZXnmFz6Tm3jjZxdpGaccYOxSJe4tZDg+1Ew0//hh2axEG+O3l5YeelSTG902debBJywKcisRwdwinKu1bqm4/7+Rev3Nm+PkdvadzNW7NbDRtqh++jmq9QcDrYKAzgCSK9NV15ley1HQdj1Phyt0k0aCbRKbMmfE4t+c2aOgGboeLck3D51JoC7jYzFW5Mpng5aMdzK0WdmxmRUHA71H3nCNP2oy1n7F9kbi1X1bstnleSxfJFmutHAfPj6L1ecST4ATLskhlKvg9aqvht6GbX5rqxpcVB4D4KxYvSkXzeceXBfI/b+kdy7IwJc9Dx+dPG1ti63//+1nypTqyLPKDVwYAg6+d7sHrUqjUNLKlOj/7YI6w34kAfO+V/ifmj20/Sv3NpSXag07ev76KYVqkMxW8LoXr02nCASfzq/kd+rp+j7qvsX2S+bxVuZIlgaDP0XJKi4ZdxMIeLMsiV6rvupCnshWWEwX8XgeJzTIzK1kyhTo/fn2Qumbs0HsFuDKZJBRua0l1bd2vy2F34suS2JJk2xqT9Y0y5yb2DyBhfwDlwcor2KDYvg7pkd+332PYHU15Td3lhmaCRcuhb+vnmm626DDbP2u3Jq0TIxFmlrNcn07t+zh/I1uhJ+ZtWhTDZr5KQzd3jOFec6vWsC22nzaPbH8Hag0DWRJo6J99z/ax9btVgj4HV++lME0LRRY5MthGb9zHf/3VFNGQG1kWkSWRyflN+jr8TC9m6Yx4+PDGKpv5Gn6PyunxGNMLGSzLfnf6O/zktgEshL03lk/ajLVfQ5wXoanqSV3WntcasltOGuzyU63prG+UW5x8r1MhV6gR8juxLFvW78ZU6rHFlC9irdutAHCoN8TpsehDY7j1u+9dX2F2OYe4ZXbUPPF6Xnz/r0Ih7wAQf8Xiq1jRfFx8mSD/85Te2bqvX11aJRSu7du96VHJYktsfet6dd3k5x/P86+/M0a5qnF7fpOeqJdr91K4HDKVms5gZ6C5sNT2nCPbn4EsCYT8ToI+Bx1tHgSwgYFmosoifR1+DNOiLeAkV6rjdSktLd0tfV2E/Y3tE1mwhlycPxLn9twm719fbVnoDnT6iYZ2t3veqmrdmd3k41vrrKRKiCKcGo1RKm9w8XaC80fiO/ReAUzT5iQvJQqcGo02pd0MPE6FQ70h8qUGQZ+jBYbtMRK5fj9Nb8xHcptW7LMClN0qr7Io2A5rYdcjx3i/x7DbNxtbIN80bQm3wwNhkpsVEISWBF2+1OBsaedcfnD8tyr2T1Itvzmd4t5SjvevrWBa4HHJnDwURazrO8Zwt7nlVCRKDYmfvnv/qfLIg3morhnNUwF9Bz986zpqmslqqkylpmGaoMgCmUKNgMdBV8RLsdIgmalQrGhkjDovjUeRRIv5tQK55jyzsN3yXj7WybGhdt462wsCrcbNaMjFzHJuzznyoH3346q5+813X/bx/ZO6rD3PNeTBnFSuafz8w3m+/lI3ZybiXJlM2KosEQ/nj3Tw7pUlyjW9RWUqlvcupnxRa10qW+XGVApFkVo56uKtdWoNg9eOd+74rq373a7vvmV2NNAZeC4nA1+VQt4BIP6KxYskJv68dnxfJsh/0uPBJ7nnrfvaMl3Y675M0+SjT9f58OYqbqdMZ7uXSMjFeF/4IammLbF1uWneUK3p6LpJpaoRCblIZio0NBOj+TsAFtZj58jWtW4Bol9fXKBc1emOepkYCONzK7idCv2dfi7dSWAYtq7s6bEYfrfC918dIF+sI0kCuWKdQqmxr8X6SS1YuyJefndthY52T+vo8vKdJD0xP7HmWO3mSHX9fhpNN7EsC8OAq/eSHBtqJ52rPqz3alnUTYlks+q9ni5zejzGieF2OqM+IkEngiAwtZhpgeHItsa1SMhFugmwnwdA2a3y+trxrtZisn2MtzjEYJLcLD9WXuzB75hfy/PetZUdyhRzqwXeONnFP30411L12Gsubx//+0vZFhiGzyrPc6v51nfuXJgrLCVL/POHc9QbBoZpEcXFrZk0f/bNQzvGcLf39vihCL/6YJJQ+OmaKh/MQw5FYmG9wHh/mGyzEhgJubAsk2SmTEOz/7cv7seyLJyqzGKiQDJTIZkt4/c4UGQRoQJel0ytYXKoJ8y9hTmCPielcgPTBNO06Ip4duhMb3fEe9QcedJq7udNh3iaNWGLr7/9b57UZe15riEP5qRaXcflVFBVibhT4UevDZAp1BnpCfLXb09RrumAfWpzdTLBq8c69/zsVKbCe9dXWoordv56/mudPZZqCwyDPYYf3lx9qElz+/1uV5wZ7g5w4tDzAaxflULeASD+isWLIib+PHd8jwNFn+dRy5MsKE96z09CGfibt6dQZJHeuJ9fX1xAlSWGeoK8ebJ7x+dvF1t3qraLmCgIjPWHiQRdTM5lUBWxZSUrwL6krrauNehz8u7VZUoVDbB5up/cS3F6PEZbQGZ+rYBpgssp09BM7sxtoukGk/MZzkzEqVc+O2bbz9g+8ZFvuYFDkXAo0p5jutu9ORSJgNdWhrAsC8u08HtVEptlmxbxAL2iv8PPp7M5DNOirptIokBn1Nv6jtNjUYJeB0vJor05aYLhumbQ5nfu2+J5PwDlceN4eixKT9TL3cUM6WyVxUSBlXSJhfUCYb9zX+/m1ndIIkwvZXfwJO3Gwaptl/3ZsD/2Hd3+bHc6/hW5PpV6mCudsRVT9OZ4C4JAtlAn1uYh4FV3qJYUy3WCHpWXj8aRJZs2ky/VMcydDVpPUizY7X0N+50MdwfIFpxcv58muVnm/eur9Hf4Gery0RZwsbheQFUkGrpBR5uHak3j9Ficm/fTOFUZwzCZGIkwu5ylJ+ohEnRRrGh4XErrpGW0d295q8fNEcuyKJYbbORrCILQ2rA96jl/HnSIp1W82OLrb/8bTTeeyGXteRaKts/bjVyVzXyNVLbM9SmF9XSZicE22vwOJEnA7VYp1/QWXSwcdGFauzcJWpbF3cUMs8u51u9v9SY874KW32tTm7Y7YG6dYj34XQ/m4C3Fmc6I77mtsy9SIe9RcQCIHxEvIuflRWl4eJ47vkeBomcF3vvhoe33eDCVrTC/lt/RePCoe966r+2xG2VgeimLbloMd/i5PGlXX30egVpdf+jzHxRbdyoSP3htgImBcKtaeGMqxZlmk1046NqX1FWrs1wz0ZrHw4IgIIkCDc1CkUScDolI0EUk6EbTDfLlBnXNQJJEzGaj14PHbI8b2yedz0+zIfxMb9eJYQZIbFaQJYFoyMWx4Qgdbe4dwLVQarCZzT/kKlcoNVqVO0GwZcneOtvXuvYttYkLt9bRDYvzR+J43Qo3plMt0LZF7diK/QKUR42jIAggCEzOZTAti852NyupMj6XQsjnoFzV9vVuCoJAZ8RHyOfcMb6ZQo3yA/zJSND12Hf01Gik9Wwf5/gHoBsGggCSJGAYFk03YWRRIOB17uBFPtjcefZwnJ6Yt2W5vd+5sds8MS2Lal1v6TL73CoXbtlHyluShoVSndG+IId6g6iy2FL/6Iv7kCSJel1jvC9ER7uHZLbK7HKWYlWjPeAi3uZhKZlAN0CWYLw/TKWqP/K57DVH9qIWvHW2l418fdc59TSKNvtZB/e7Jmx9Xqlcp1BpUKiJ9Hd6SG6WKdfsnPfGya4ncll7noWirZz03vUVCuUGm4UqJw9FuTW7Qa1h4F6T6WzvRJEkoiEXDlmi2tBxqTJBn9qaqw+OWSpbJZ2ttuhlFpBuNrI974JWNOTiUG+Ii7fW0bedYnmcykPf9UVgihelkPe4OADEe8SLynnZkgMKeh07QN7zvKb9JMDnueN71Au5xYVqDzpb4GQ/jQvw5Dy0R4VlWdyZ3eS9aysti90zE3Fo6Hve89Z9/fL9DWBvAwxF/qyiazStZi3Lruw+OKaPE1vfrgzx6rFOtuTR9ndkHmNqKYcg2NrF4YDLBryigEOV6Gz3EWyCpXypTrVu8+ZCPgcDXYGnOmZ7nke+e+vSWoz1hbh+P0087Cbkd7akyR4EqNB0ldsmOaXKIiG/k1LV5oRufW4qWyXoVfn2uT6KlTozK/mWHrEqi9ye2+SfP5wnsVlGFJv2y4NtnBx9shzyJO/jljvdpTvrGIaF36Pyxslu3E5pT6rCo8a3rhn0d/hJbJQ5f6SDm/fT5It1XKrMt872ABaTc5vMrmbpaHcjSyKabrTe0a1n+6Dj3w76hGWBAIosUa42ODMe58rdBA5Z4shQO0dH2sGySGUrLZnB7c2dDlni7cuL/Pk3Rjg7EWc2qe9rbmyNgWEY3F3IspGvMtIb5MKn6zue2Wqq1Gqua2gGTlWiO+qjVtfpi7sZ7w9TqGj43QqlSo1C2W7STGeriJLI5TsJ/F6Vb5zuoVLXMUyT75zvZ32jDALcnk0zMRBugmoRsGxlCwGK5QYgtN7jB40StlMLbIk7i99cXESVbbWR/axde42NZVkkMyWu3dvg8mQCVZYI+lTOHu546LMsy2ItVSSdrSCIAi5VAgFqdYPJuQ3mVmVEBHo7vKynyySzFap1g99fW0bT7Xn72olOvC6Jtc0qTlV8Ipe1vfJCJOh8ZJPlXvd+eiyKQ5GYWsywmChwZ3YT3bBQZYlipcHCeoG1jSLdUR/35jOtazw20kcmX+HGdJLbs5uYFi2KU7zNRbZQ482TnZgW1Br2XOpq9zw1+Nzr+rfuodYwWrKBexVGvogmyhelkPe4OADEe8SLynmxLItrU+nPDajvdyPwPHd8j3ohi+X6jsaA/TQubMWT8tAeFalslev30/axMZ9JX715qnvPe966L6veRTAc25MyUCg1ODMRJ1+yObimCfE2m5O625g+Smz9WZphREHA7ZD45pk+Lt1ZR8A2H3hpPMZQV4DBLn8rqamKhCwKLd3fZzlme5JrflTj3G721aIgcPG2zY3ujfn25GZvj2jIxZmJGHNJW2XA6ZC5u5AhmakgCnYTnp0Pkq3vGusL7TDnCPocvHt1GacqY2HPly0DjK7oZzlkP8YmT/I+Bn0Orkwm2WIOiKLAJ/eSDHYGmF0t7EpVeNT41hoGF26tAeBQJXpiPnTDpD1oL+53ZjdxOmTeu7bKRr6KIoucOxynPehqvaNb93p9KvUwfSJe5O3Li/R3+DEMk754gJVUkR+82g+IzCxnmVrIcH8p15L42+LIA1QbOqlshbpmcOlOgqgf/vTrwzu407vPDXsMTNPknz6c42/fnUHXTc4diRFrczHQ5cehSOSKda5OpVqNkqoi0dfh58rdJK+fiLOSsvjPv/iUhm6hygJ/8vURuqMePri5yivHOmloOsdHIkiSQKJpuvHRp+sAvDQe587sBooski3W+OjTdTL5Ki9NxElsluiN+TBMuHR7fUclfPuz2+opqDV0qk0u63B3kHeuLtHR7gUevXY9qrp/bSrF9XspfnN5yeZzh1wUK3Yu2v5Z9mckuTW7yfRyFtOE9pALRRKp1hvcX8nx9qVF2oNuQj4Hne0eOtrdvHNlhbWNMlZzQ6RdNfnaqS4EIF9q8NbZ3n27rO2WFyJB5yPXyse9W9Gwm3uLGe4v56jUdWRJpFbXkSVHsxDg4qMba5w/2oFlQUM3+PjmGuWqxs8+mEOWREI+J5ph8jdvT/Fvvj9u66WbAr++OE9Ds1AVgT/7xshDii37icddvyiKvHa8k0O9occC3c+7ifJFUS55XDwfFfMnjNHR0X89Ojo6OTo6en90dPR//TKu4XHxqArolxl7AfWtatYX9flbO74tSsCz7vi2XsiRJuH/sxdF4OrkZ41pW40LNiPq0bG1WGyPLR7ak8YWDzUSdre+2TQtIiHXI+9ZEAREo7zLfdkRDbk4MRpF0wy6ox5+9PoQ4/1h4mH3F7qLTmWrXLydYDVdpliu88rRDo4fivAffjDBd873cXLUlus5PRblT78xwh+9Mci//cEEWlOy7Iu81t3mym7z9r3rq8yu5jEti4Zuy3FNzmWgWUF55Od7Nf70GyO8fLSTdK66wyhhdjXPe9dXd3zX9ftpQv7PXMwamolu2KobW2GYFvWGsSOHPO59e9L3UdNNKnWdaMiF22k3QiY2K6iqtIOq8Kh8sX18o2E3erOB8uNP17m7kGFmOcdKssTP3p8jEnZz836aZKaCpptousWlOwk2czW2v6Pb80XQ52jRHRAgsVHm8p0EHpdCuaYx2B3kUG+Y9c0y4YALmhvvrTHe4sjrhkmtbpvQSKKAIotcvJUAQXjs3Ngag7sL2RYYBqjUDH5zcYmFtQKprC335lAkIiGbIuJyyLidCm1+J/WGxd/9/n5Llq2h2/+tKjLnDncwtZBhM1fn2r0Uk3ObdEd9LKzl6Yn5ME2Lq3cTHB1u5+unu7kymSRfqqObFhdureFUZdK5Gjfvp+2clalQrmkPPbstCsEWGLYfIOiGTfvYir3Wrr3GZm61wG8vL5PO19ANs1WJzBUb5IqNh+bwe9dXuX0/zZlxWyauWtNJ5yq8caKH315eoq6ZZAs1csU6711bwe91sJou2dQBy1Z1WU2XcDrsPoWlRJGNvK2i8fLRDoZ7go891XswL6RztWd6t6IhF0NdAV451onXrSBgO0qO94cp1zQsC0o1nYX1AuubZW7PbjK/XqTWMNB0i0JZYzPfNA0yLeZW8xzqDfHzj+ao1Q1M05Yv/PWFReaahixPEvvJDXuvq198vEjXsld84RXi0dHRLuD/BpwG6sDHo6Ojv5uampr8oq/lUfGicl4+b3L6fj//i9rxmZZJOOhqyU09rnFhe2xvQNuKR/HQHhWf8VA/68J1OmTG+8LPdM8Pj6PK109171sd4HnF9ue+3aHK61Z3dLxvryRYlkVv3P9C7Ph3m7e1uk69Yez42X7fFdM0iYXdrY3Q9qg3DGp1fcfPt0DTlrqEQ5XoinjYzNdav7NFPdmeQx73vj3p+7i90Q8LCpUGAa9KyOdoPdMnyRdbQPZBRQ0LC920qFQ1qnUdhypRrRstFQ9VlXa8o9vn+Xb6RL5Ub1XQG5rZ2rh0R30PjfvWGM8s5TgzEefjT9eIhFzohsmZ5kmFYT58b48aw2S20gLDYOd4UYRKXSe07WfjfWHG+9tsOTnscc2VGjS0nZ/b0Gwt7JmVHPNrBd4618upsSh+t0KxXKdU1ckVa/R1+DFNiyMDbXz46QqbhRohn72h+qwZymqdOFhAQzNapjfbewq+93I/f/PONLpuIssix4baefvyUuv3t+5ht7Vrr7FJ56qUaxpuh4wsCeiGZWuqWxYN3XhoDtfqOpWGwVKiwEtjUQJem7uezJSpNbZk9+zP1gz7WYuigGl89t2SKOBUbWv5cm1vKtp+41nfLUEQODkapSvq5eShCPlyg6WE3Y/i9zhQFRFZtB0LG5rRlJ2056kkgoaFYdI62RRFweYaO2RMxd4sy5L4yGbBZ7m/g3jy+DIoE98C3p2amsoAjI6O/i3wZ8D/9Uu4lj3jReW8fN5A/Uk+//M+ZgEIeJ3EQu4WCFUVCY9TadoGPzoebEB7HA/tUbF9PmzRA84ejhN9Dve+2zjG2jyP+IvnH08zr76I57/f2FWX1iHjUHeCqid9V3b7XIcq4XTsTJ0Pgia/V2Wg088vPl4gsfEZH3Woa6eu5+PG/Unfxwcb/URJYKQnuMPO+knGYDeg7XLIVOs6sijgdikteT+3U0ZVRByKRNjvfOgd3ZovsI0+oUgI8JA721bT3oP3vTXGxXKd02NRbs9uIIpCy1lLEh++t0eNYUwzkGWxBYrvL2U5e7iDRlMurpX3t0n6Wdb/v737D46jPu84/r473S/L2BjiH1h2sCHw2MaAHYEhiW0ITZ0SkgwUmg7QaSltSCAhybRDJxNCY5qhMylNQkibeNoyYZKpm0ySaZvJH23DQGsopcQi1JiBBzPhR2zLxiDLsqTTne5H/9iVcsj6cZJl763v85rx4N3b2312n9Px6Otnv1vjivXLqFYrzMmlGBz69S9dc3LBDCiZthSVWpV9hwb4vz2H+Oimc3jGD9KezzBYLFMcHqRSCY7Z/VaBZQvn0jdQAhi9pyCRSJBpS1Iarkw4W0wymeSytYtJp5MMDA4zJ5+m58gg7161aPRfwib7f9dE12ZheDPuntd7uGT1Ena+cIBKFdKp4CmYYz/DuWwbCaBQqvDia4c5d9l8ug8FU9KNFNTJBCRIkMukSAJXrF/Gjl/spVytkU4m2Lx+GYNDJRJwzC+OMzEbP1tBzttHH/yTy7TRe/RAWHiWuGbjytHR3UzYLrRrzyE2XLCEJ3d1k0oG69faIvr6S6xZeSa5cMR4xPEO0jTboF2cRVEQLwW665a7gQ0RxDGpZu15OdGFerP9IjC2EJ1OPFPdgDYdzfp5mC3NlvfpGi/+K9Z3kEwkGp4TuNH9ntsxn5VL572th3hs0RS8dw7LF53G/jf7J5xlYqrrPt28HPM5bc+w71A/T+0+AMystWm8Qrs9l+aajSs5+NYgF5+3kEqlSm9/iTm5tnEL/4muaT7bNu7T2er71Se6xrVajaFS5W3bXLpm8THHnewavmN+lhuuetdo20SpUmX1itPpXLWY/sHyuD/nI9f4wFt93HDVefz4sT0USzVymSQf3Xwuz738BolElRu3rGL/m/1sXt/BwZ4BPvTec+h68SArz5pPoVhm07oOCsUSN/6m8cv9R0gfLtDTW+CSNWeN9hCfMS832kM80U1Rixa0s/D0AV7Zd4BqzyDJRILL1y6hIyyyJ/uumujanNMxjw9sWE7fQJGXXuuhc/Vilr5jLmcvmcvGizuO+Qxfsb6DgcIwB94cIJtOsfiMOZyzdD7Pvfwml65ZwjMvHgwe9jM3w3pbyFPPd7Pq7AVc2bmcciX4RWbRgjw7X3hjys9Po074z1bYp3yod4gj/UP09ZfY86vDvPR6L8XSUbZseCcL5gVT4B3uG2KdLWL1igUnZJAmjt/ZzShRGzPkfqKZ2d1Azt3vCZc/DnS6+ycne19XV9cK4JUTH2HzSyaTlBN5BoYqtOdStNUKVKtTtxA0y/7jHs+pKu7Xebz4geM+pxO138n2X7+v483LbOV17H4yiSKlWpahUpVcPs9AoUxbKkE+XSFZGZz0GPX7mptPBTfQDpbfFl8jcTd6bpNtl8lkKNTm89bRImeeliWfOEKpVGromixatIjuvhRv9BY4c16eeTk4fLREOt1GIlljsFCBWoV52QqpWoFhsvSXUmTSaSrlMvl0mTTBdSyUquSyOYrFIfLZJIlEgsGhCtlsjqHiEPlMckbnN5WJ3ptMJqml2jkylCKRTDI3C+lqH+XysVPEJZNJKsk8/cUUbakEczIVEokEQ8MphqttJJMJypUy87MValTpKyTJZ1Mkkm0c6R9i/twc5eFhqrVaQ5+fRp3on62xx6qm5lAYTgVzsedq1Kgd87lua2ujyGn0DgxzenuaLEfHvaazcX4yoZWdnZ2vjl0ZxQjxXmBT3fISYH+jb167di3ZbGv+k0BXVxednZ1RhyHTpLzFj3IWT1HkbflJPdqpp6uri84Lz486DJmmOH5HFotFdu/ePeHrURTEjwBbzWwhMABcD9wWQRwiIiIiIid/2jV33wfcDTwGPAtsd/enT3YcIiIiIiIQ0YM53H07sD2KY4uIiIiI1IvkwRwiIiIiIs1CBbGIiIiItDQVxCIiIiLS0lQQi4iIiEhLU0EsIiIiIi1NBbGIiIiItDQVxCIiIiLS0iKZh3iGUkDDz5g/VRWLxahDkBlQ3uJHOYsn5S1+lLN4ilve6urH1HivJ2q12smL5jh0dXVtBB6POg4RERERia1NnZ2dT4xdGacR4p8Dm4BuoBJxLCIiIiISHyngLIJ68hixGSEWERERETkRdFOdiIiIiLQ0FcQiIiIi0tJUEIuIiIhIS1NBLCIiIiItTQWxiIiIiLQ0FcQiIiIi0tJUEIuIiIhIS1NBLCIiIiItLU5PqpNxmFkKeAS4y913Rh2PTM7MDNgOvADsdPcHoo1IGmFmK4AvAr3AL939W5EGJA0xs9uB9wF5YJm7XxZxSDIFM1sH3BkuPu7uD0cXjTTKzN4H3AX8CviJu/8s4pCmTSPE8fcFYH/UQUjDNhJ8YRSA/4k4FmncnwKvAfOAJyOORRrk7t92998DXgdujjoeacglwHrgncDLEccijftd4Avufidwe9TBzIRGiGPMzK4Hnid4PrfEwxPAvwJ9wE+A34o2HGnQecA24FXgH4FrowxGGmdmFwJld1dxFQ/PA1eGf/8OwXemNL8HgS+b2UGgPepgZkIjxPF2A7AF+DDw2YhjkcZsADLuXgLKUQcjDTsA9AODQCniWGR6PgE8EHUQ0rDPh//tR4M9cbIU+DPgT4BixLHMiEaIY8zdbwQws63AT6ONRhr0IvA1M+sD/i7qYKRh94d/eoCHIo5Fpme5u++LOghp2DbgYYJ+/W2RRiLTsR/4OjAE/G3EscxIolarRR2DjGFmI32KH3b3V8N1NxHc1JMGHnD3WH7gTlXKWTwpb/GkvMWPchZPrZQ3tUw0GTO7jKBn6vy6dR3AfQQ3ZK0DbjOzNZEEKMdQzuJJeYsn5S1+lLN4arW8qSBuPh8HPsXbZ474APCou/e4+wDwI4L+YWkOylk8KW/xpLzFj3IWTy2VN/UQNxl3/2OAYLraUUuB7rrlboKbs6QJKGfxpLzFk/IWP8pZPLVa3jRCHA9JoL7ZOwFUI4pFGqOcxZPyFk/KW/woZ/F0yuZNBXE87AXOqltegh7G0eyUs3hS3uJJeYsf5SyeTtm8qWUiHh4BtprZQmAAuB64LdqQZArKWTwpb/GkvMWPchZPp2zeNEIcA+EcmncDjwHPAtvd/elIg5JJKWfxpLzFk/IWP8pZPJ3KedM8xCIiIiLS0jRCLCIiIiItTQWxiIiIiLQ0FcQiIiIi0tJUEIuIiIhIS1NBLCIiIiItTQWxiIiIiLQ0PZhDRESOi5mtAP4IeC/wF+7+X9FGJCIyPZqH2vwGgwAAA8ZJREFUWERkGszsQWBzuLgGeAUohMvvcffCOO+5BrjM3f98in3/J/A37v6jcV47G7iXoOgcBtLAD4Gt7j48s7OZOTP7HvAVgqdVPQl8D7gIeBdBUfxdM7sWuNDdv3yy4xMRmQ61TIiITIO7f8bd17n7OmA/cPPI8njFcOhS4IyZHtPMOoCngP8GzN0vAN4NrAK+OtP9Hkc8HwOOuPtu4FrgZ8C3whhvIfzXR3f/F2Czma072TGKiEyHWiZERGaJmd0D3AiUgZeATwNnA58EUmZ2BPhL4NvAecCZwFHgJnf3SXb9eeDH7v73Iyvcvd/MPg3cEB77FoK2hXaCYvX948Xj7gfM7LeBLwJVoALc5e47Jlo/Tjz3Ar8T/n0vcAewPIzrCeCJum0fAr4EXDfJ+YmIREojxCIis8DM/hC4GrjU3S8CdgMPu/v/AtuAH7j73eE2ve7+Hnc/H/g5QeE8mU3Av49d6e7d7v7NulUXAFeGxfC48YTb3Q/c4e6XAPcAV06xvv481wL5cHQYd/8hwejwd4E/MLN7zay97i3/AVxtZvkpzlFEJDIqiEVEZsfVwHfcfSBc/gbwG2aWqd8o7A9+2MzuNLNvEBSdc6fYdwIYveHDzO4ys2fDPwfMbKQdY5e79zUQz/eBfzazfwAWAH8VbjPR+nqrgJfHnNPXgfcDO4CLCYrjkdd6gCGCkXIRkaakglhEZHakqCtaCb5f2wiK2VFmdjtBG8EgsB34p7HbjONJ6kZr3f3+uj7mxfz6u7y/kXjCkeqNwE6Cnt8d4X7HXT9Gre54mNk1ZnZVuPgK8Flgy5j3lAlaMEREmpIKYhGR2fFvwK117QKfAXa4e5GgIEyH6z9I0ErxEODARwiK18ncB3zMzH7fzFIAZtYW3twGQc9vQ/EAFTN7FZjj7tsI+n8vMrPsROvH7NeBc+uWVxHMNjHSEvEhYNfIi2Y2H8gBr09xjiIikVFBLCIyOx4CHgGeNrMXCGaBuDl87VHgg2b2TeCvgU+Y2S7gceAZgqnKJuTue4HLCUZvf2FmzxG0LVwHXB62JTQUj7uXgc8B283sGYKp224NC/eJ1tfHshsomNnqcNWDwLPhsT4VnvOtdW/ZAvx07H5ERJqJ5iEWEZFpMbObgI3ufkfduhXALe6+dcy2jwKfc/ddiIg0KRXEIiIybWb2feA+d39ukm2uA9a5+5dOXmQiItOnglhEREREWpp6iEVERESkpakgFhEREZGWpoJYRERERFqaCmIRERERaWkqiEVERESkpakgFhEREZGW9v9mFJ8rzS5YhQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Total Gross vs. Popularity\n", + "plt.figure(figsize=(10, 6))\n", + "sns.set(style='whitegrid')\n", + "sns.scatterplot(\n", + " data=merged_df,\n", + " x='total_gross',\n", + " y='popularity',\n", + " alpha=0.6,\n", + " palette='deep'\n", + ")\n", + "plt.title('Total Gross vs. Popularity', fontsize=16)\n", + "plt.xlabel('Total Gross ($)', fontsize=12)\n", + "plt.ylabel('Popularity', fontsize=12)\n", + "plt.xscale('log') # Optional: log scale for better visibility\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Analysis of Total Gross Revenue by Studio\n", + "\n", + "This analysis focuses on identifying the top-performing studios based on their total gross revenue. The process includes calculating the combined gross from domestic and foreign markets, cleaning the dataset to remove entries with missing values, and aggregating gross revenue by studio. \n", + "\n", + "We then isolate the top 10 studios based on total revenue and visualize their performance using a box plot, providing insights into revenue distribution across these leading studios. This visualization helps in understanding the range and median gross earnings of the top players in the industry, highlighting studios with consistently high revenues.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate total gross\n", + "merged_df['total_gross'] = merged_df['domestic_gross'] + merged_df['foreign_gross']\n", + "\n", + "# Drop rows with NaN in total_gross or studio\n", + "merged_df = merged_df.dropna(subset=['total_gross', 'studio'])\n", + "\n", + "# Group by studio and sum total gross\n", + "top_studios = merged_df.groupby('studio')['total_gross'].sum().reset_index()\n", + "\n", + "# Sort by total gross in descending order and get the top 10 studios\n", + "top_10_studios = top_studios.sort_values(by='total_gross', ascending=False).head(10)\n", + "\n", + "# Filter the merged_df to include only the top 10 studios\n", + "top_10_studios_names = top_10_studios['studio'].tolist()\n", + "filtered_df = merged_df[merged_df['studio'].isin(top_10_studios_names)]\n", + "\n", + "# Create the box plot for top 10 studios\n", + "plt.figure(figsize=(14, 8))\n", + "sns.boxplot(\n", + " data=filtered_df,\n", + " x='studio',\n", + " y='total_gross',\n", + " palette='deep'\n", + ")\n", + "plt.title('Total Gross by Top 10 Studios', fontsize=16)\n", + "plt.xlabel('Studio', fontsize=12)\n", + "plt.ylabel('Total Gross ($)', fontsize=12)\n", + "plt.xticks(rotation=45)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Analysis of Top Titles by Popularity\n", + "This section visualizes the top 20 most popular movie titles based on popularity metrics, providing insight into which titles have the highest audience engagement and reach.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Title vs. Popularity\n", + "plt.figure(figsize=(14, 8))\n", + "sns.barplot(\n", + " data=merged_df.sort_values(by='popularity', ascending=False).head(20), # Show top 20 titles by popularity\n", + " x='title',\n", + " y='popularity',\n", + " palette='viridis'\n", + ")\n", + "plt.title('Top 20 Titles by Popularity', fontsize=16)\n", + "plt.xlabel('Title', fontsize=12)\n", + "plt.ylabel('Popularity', fontsize=12)\n", + "plt.xticks(rotation=45)\n", + "plt.tight_layout()\n", + "\n", + "plt.show()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Total Gross vs. Studio\n", + "To analyze the **Total Gross by Studio**, we have selected the top 10 studios with the highest total gross. This bar chart provides insight into the studios that generated the most revenue, giving a clearer view of top-performing production companies within the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate total gross per studio and get the top 10\n", + "top_studios = (\n", + " merged_df.groupby('studio')['total_gross']\n", + " .sum()\n", + " .sort_values(ascending=False)\n", + " .head(10)\n", + " .reset_index()\n", + ")\n", + "\n", + "# Plot the top 10 studios by total gross\n", + "plt.figure(figsize=(8, 6))\n", + "sns.barplot(\n", + " data=top_studios,\n", + " x='studio',\n", + " y='total_gross',\n", + " palette='crest'\n", + ")\n", + "plt.title('Top 10 Studios by Total Gross', fontsize=16)\n", + "plt.xlabel('Studio', fontsize=12)\n", + "plt.ylabel('Total Gross', fontsize=12)\n", + "plt.xticks(rotation=45)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(14, 8))\n", + "sns.barplot(\n", + " data=merged_df.sort_values(by='total_gross', ascending=False).head(20), # Show top 20 titles by total gross\n", + " x='total_gross',\n", + " y='title',\n", + " palette='magma'\n", + ")\n", + "\n", + "plt.gca().xaxis.set_major_formatter(ticker.StrMethodFormatter('${x:,.0f}'))\n", + "plt.gca().xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n", + "plt.title('Top 20 Titles by Total Gross', fontsize=16)\n", + "plt.xlabel('Total Gross ($)', fontsize=12)\n", + "plt.ylabel('Title ', fontsize=12)\n", + "plt.xticks(rotation=45)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Corelation Heatmap\n", + "\n", + "Multiple DataFrames are integrated to create a comprehensive dataset for analysis. The merging process combines relevant information from different sources, allowing for a unified view of movie data.\n", + "\n", + "Following the data integration, a correlation matrix is calculated to evaluate the relationships among various numerical attributes within the dataset. This matrix serves as a foundation for understanding how different features interact with one another.\n", + "\n", + "A heatmap visualization of the correlation matrix is then generated, providing a clear graphical representation of the correlations. The heatmap includes annotations for precise values and uses a color gradient to indicate the strength and direction of the relationships among variables.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmwAAAGFCAYAAABANq/yAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAA+7ElEQVR4nO3deXxU9b3/8ffJzGSyQghmEgiIiECEKFFwwxrUFoKFiOLGUtCqhepVxLYisoi4gKVcrQvwu97aakErKUUiCEHFiuVKXeICsWyKoCQQAoSQhGQymTm/P0ImhCUZkMmcJK/n48Ejc75nmc98jMObsxqmaZoCAACAZYWFugAAAAA0jMAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAPQqF27dun888/XsGHDNGzYMGVmZmrEiBFauXKlf5nnnntOy5Yta3A7L774ot57770Tzjt6/Z49e+rAgQOnVOOGDRv06KOPSpI2btyoCRMmnNL6p8Pr9eqee+5RRkaGFi1a5B//6KOP/L268sordfnll/unj+7Z0UpLSzV27NhG33Pp0qUaP378CedVV1fr5Zdf1rBhwzRkyBBde+21uvfee7V9+/bT+4AALMMe6gIANA8RERHKzs72T+fn5+uOO+6QzWZTRkaGHnjggUa38fHHH+u888474bxA1m/IN998o8LCQknSBRdcoOeff/5HbS8QhYWFWrdunb788kvZbDb/eP/+/f29euGFF1RcXOwPkydTUlKijRs3/qh6Hn74YVVWVuqVV15Ru3btJEnLly/XHXfcoVWrVik6OvpHbR9A6BDYAJyW5ORkTZgwQS+//LIyMjI0efJkde/eXXfddZeef/55vfvuu3I4HGrXrp1mz56td999V3l5eZozZ45sNpvWrFmjgwcP6ocfftDVV1+t/fv3+9eXpD/+8Y/auHGjfD6fJk6cqGuuuUZLly7V6tWr9T//8z+S5J9+7LHH9Pzzz6u0tFSPPPKIbrjhBj3xxBNasWKFSktLNXPmTG3evFmGYeiqq67Sb37zG9ntdl1wwQUaN26c/u///k979+7V3XffrVGjRh33WT/77DPNmTNHFRUVcjgcmjhxoi6++GLdfffdqq6u1vDhw/XCCy/o7LPPbrRvJ9pWenq6HnnkEVVWVmrYsGFaunSp3nzzTS1evFgej0clJSX61a9+dcLaauXl5Wn9+vVas2aNIiMj/eOZmZkqKSlRWVmZoqOjlZqaqp/+9KfavHmz5s6dK7fbfcJ6ioqK9PDDD6u4uFiSNGDAAE2cOPGk4wCCi8AG4LSlpKRo69at9cZ2796tV199VevXr1d4eLj+/Oc/a8OGDRo9erRycnI0evRoDRw4UGvWrFFlZaXefvttSdLkyZPrbadTp056/PHHtXXrVo0ZM0arVq06aR0dOnTQhAkTtHr1as2ePVsff/yxf96TTz6puLg4LV++XB6PR/fcc4/+/Oc/a9y4caqqqlK7du30xhtvKC8vTyNHjtRNN90kp9PpX7+4uFgTJkzQggUL1KdPH23btk2/+MUvtGTJEr300kvKzMyst+exIQ1ta/bs2f5tlZeX6+9//7teeukltWvXTl9++aV++ctfNhjYPvvsM/Xt27deWKv1i1/8wv/a4/Hommuu0XPPPafi4mINGTLkhPW89dZb6tSpk/785z/r8OHDmjp1qkpLS5WVlXXC8djY2IB6AOD0ENgAnDbDMBQREVFvLDExUSkpKbrxxhuVnp6u9PR0XXHFFSdcv2/fvifd9siRIyVJPXr0ULdu3fTFF1+cVo0ffvih/va3v8kwDIWHh2vEiBF69dVXNW7cOEnST3/6U0lS7969VVVVpcOHD9cLbBs2bNDZZ5+tPn36SJK6d++uiy++WJ988okuu+yyU6ol0G1FR0fr//2//6e1a9dqx44d2rx5sw4fPtzo9g3D8L/evn27HnzwQUk158cdvfewX79+jdZz1VVXady4cdq9e7f69++v3/72t4qNjT3pOIDg4qIDAKdt48aN6tGjR72xsLAwLVq0SLNnz1ZcXJxmzZqlOXPmnHD9qKiok247LKzu68nn88lut8swDB39+GOPx9NojT6fr16Q8fl8qq6u9k/XhrPaZY59vLLX6623fu0yR28jUIFua8+ePbrhhhuUn5+vvn37BnTI8aKLLtLnn3/u78m5556r7OxsZWdn69JLL1VFRYV/2dq+N1TPhRdeqDVr1ui2225Tfn6+brnlFuXl5Z10HEBwEdgAnJbvvvtO8+fP15133llvfPPmzRo6dKi6deum8ePH64477vCfTG+z2QIOOm+++aYk6euvv9b333+vPn36KD4+Xtu2bZPb7ZbH49Hq1av9y59s2z/5yU+0aNEimaapqqoqZWVlqX///gF/zrS0NG3fvl0bNmyQJG3btk2ffvqpLr300oC3Eci27Ha7vF6vTNNUXl6e4uPjde+99+onP/mJ/vnPf0qqCVgn06dPH1122WWaNGlSvStsv/nmG23atKneRRGB1DN37lzNnz9fP/vZzzR16lSdd9552rZt20nHAQQXh0QBBKT2hHipZu+X0+nUb37zG1199dX1lktJSdF1112nm266SVFRUYqIiNC0adMkSddee62eeeaZgPaM/fDDD7rhhhtkGIaeeeYZxcXF6corr9Qll1yi6667TgkJCbrsssu0ZcsWSTXhY968ebrvvvs0ZswY/3amTZumJ598UpmZmfJ4PLrqqqv061//OuDPHR8fr+eee05PPPGEKisrZRiGZs+era5du2rXrl0Bb6exbXm9Xl144YUaMmSI/vKXvygxMVGDBw+WYRi69NJLFR8fr507dza4/T/84Q967bXXNG7cOFVXV6ukpERJSUkaNWqUhg8ffkr13H777Zo8ebKGDh2q8PBw9ezZU0OGDFFJSckJxwEEl2Eeu/8fAAAAlsIhUQAAAIsjsAEAAFgcgQ0AAMDiCGwAAAAW12KvEvX5fCovL5fD4TjuPkMAAABWYpqmPB6PoqOj692HslaLDWzl5eXHPTIHAADAynr06HHCp4e02MDmcDgk1Xzw8PDwoL5XXl6eUlNTg/oezQF9qEMv6tCLOvSiDr2oQR/qtPZeVFVVaevWrf78cqwWG9hqD4OGh4fXey5gsDTFezQH9KEOvahDL+rQizr0ogZ9qEMvdNLTuLjoAAAAwOIIbAAAABZHYAMAALC4oJ7D9uKLL2rVqlWSpAEDBmjSpEl65JFHlJubq8jISEnSfffdp4EDB2rTpk2aOnWqysvL1a9fP82cOVN2u10FBQV66KGHtH//fnXt2lVz585VdHR0MMsGAACwlKDtYfvoo4+0bt06vfnmm1q2bJm+/vprvfvuu8rLy9OiRYuUnZ2t7OxsDRw4UJL00EMP6dFHH9Xq1atlmqaysrIkSTNnztSoUaOUk5Oj1NRUzZ8/P1glAwAAWFLQAltCQoImT56s8PBwORwOdevWTQUFBSooKNCUKVOUmZmp559/Xj6fT/n5+aqsrFRaWpokafjw4crJyZHH49Gnn36qjIyMeuMAAACtSdAOiXbv3t3/eseOHVq1apVee+01ffLJJ5oxY4ZiY2M1fvx4LVmyRN27d1dCQoJ/+YSEBBUWFqq4uFgxMTGy2+31xgEAAFqToN+Hbdu2bRo/frwmTZqkc889V/PmzfPPGzNmjJYtW6Zu3brVu++IaZoyDMP/82in+pipvLy8H/cBApSbm9sk72N19KEOvahDL+rQizr0ogZ9qEMvTi6ogS03N1cTJkzQlClTNGTIEG3ZskU7duzwH+I0TVN2u11JSUkqKiryr7dv3z65XC7Fx8ertLRUXq9XNptNRUVFcrlcp1RDampq0G/El5ubq759+wb1PZoD+lCHXtShF3XoRR16UYM+1GntvXC73Q3uZAraOWy7d+/Wf/3Xf2nu3LkaMmSIpJqANmvWLJWUlMjj8Wjx4sUaOHCgkpOT5XQ6/ck6Oztb6enpcjgc6tevn1auXClJWrZsmdLT04NVMgAAgCUFbQ/byy+/LLfbraeffto/NmLECI0bN04jR45UdXW1Bg0apKFDh0qS5s6dq2nTpqmsrEy9e/fW2LFjJUkzZszQ5MmTtWDBAnXo0EHPPPNMsEoGAACwpKAFtmnTpmnatGknnDd69OjjxlJSUrRkyZLjxpOTk7Vw4cIzXh8AAEBzwZMOAAAALI7Adgb06p36o9b3en1nqBIAANASBf22Hq1BZIRTr6/efNrrj8pIOYPVAACAloY9bAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsLqiB7cUXX9SQIUM0ZMgQzZkzR5L00UcfKTMzU4MGDdKzzz7rX3bTpk0aPny4MjIyNHXqVFVXV0uSCgoKNHr0aA0ePFj33HOPysvLg1kyAACA5QQtsH300Udat26d3nzzTS1btkxff/21VqxYoSlTpmj+/PlauXKl8vLytHbtWknSQw89pEcffVSrV6+WaZrKysqSJM2cOVOjRo1STk6OUlNTNX/+/GCVDAAAYElBC2wJCQmaPHmywsPD5XA41K1bN+3YsUNdunRR586dZbfblZmZqZycHOXn56uyslJpaWmSpOHDhysnJ0cej0effvqpMjIy6o0DAAC0JvZgbbh79+7+1zt27NCqVav0i1/8QgkJCf5xl8ulwsJC7d27t954QkKCCgsLVVxcrJiYGNnt9nrjpyIvL+9HfpLG9e3bVwUFBT9iCynKzc09Y/WEUkv5HGcCvahDL+rQizr0ogZ9qEMvTi5oga3Wtm3bNH78eE2aNEk2m007duzwzzNNU4ZhyOfzyTCM48Zrfx7t2OnGpKamyul0/qjPEIiOHTv+qPX79u17hioJndzc3BbxOc4EelGHXtShF3XoRQ36UKe198Ltdje4kymoFx3k5ubqjjvu0G9/+1vdeOONSkpKUlFRkX9+UVGRXC7XceP79u2Ty+VSfHy8SktL5fV66y0PAADQmgQtsO3evVv/9V//pblz52rIkCGSpD59+ui7777Tzp075fV6tWLFCqWnpys5OVlOp9O/KzQ7O1vp6elyOBzq16+fVq5cKUlatmyZ0tPTg1UyAACAJQXtkOjLL78st9utp59+2j82YsQIPf3007r//vvldrs1YMAADR48WJI0d+5cTZs2TWVlZerdu7fGjh0rSZoxY4YmT56sBQsWqEOHDnrmmWeCVTIAAIAlBS2wTZs2TdOmTTvhvLfeeuu4sZSUFC1ZsuS48eTkZC1cuPCM1wcAANBc8KQDAAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFBTWwlZWVaejQodq1a5ck6ZFHHtGgQYM0bNgwDRs2TO+++64kadOmTRo+fLgyMjI0depUVVdXS5IKCgo0evRoDR48WPfcc4/Ky8uDWS4AAIAlBS2wffXVVxo5cqR27NjhH8vLy9OiRYuUnZ2t7OxsDRw4UJL00EMP6dFHH9Xq1atlmqaysrIkSTNnztSoUaOUk5Oj1NRUzZ8/P1jlAgAAWFbQAltWVpZmzJghl8slSaqoqFBBQYGmTJmizMxMPf/88/L5fMrPz1dlZaXS0tIkScOHD1dOTo48Ho8+/fRTZWRk1BsHAABobezB2vBTTz1Vb3rfvn26/PLLNWPGDMXGxmr8+PFasmSJunfvroSEBP9yCQkJKiwsVHFxsWJiYmS32+uNAwAAtDYBBbaFCxfqxhtvVExMzGm/UefOnTVv3jz/9JgxY7Rs2TJ169ZNhmH4x03TlGEY/p9HO3Y6EHl5eaddc6D69u2rgoKC48arvaZ8pqlwe2M7MlOUm5sbnOKaWEv5HGcCvahDL+rQizr0ogZ9qEMvTi6gwLZlyxZlZGTo6quv1ogRI3TBBRec8htt2bJFO3bs8B/iNE1TdrtdSUlJKioq8i+3b98+uVwuxcfHq7S0VF6vVzabTUVFRf7Dq6ciNTVVTqfzlNc7VR07dpQk+UxTG7bt0669ZSo9XKVwR5iGpXeT3dZwaOvbt2/Qawy23NzcFvE5zgR6UYde1KEXdehFDfpQp7X3wu12N7iTKaBz2J588kmtXr1aqampmjlzpm666SYtWbJEbrc74EJM09SsWbNUUlIij8ejxYsXa+DAgUpOTpbT6fSn6uzsbKWnp8vhcKhfv35auXKlJGnZsmVKT08P+P1CZf/BCm3acUCRTpvO69RWVR6fdu/j6lYAAHD6Ar7oICYmRoMHD9bQoUN18OBBvf766xo8eLDef//9gNZPSUnRuHHjNHLkSA0ZMkTnn3++hg4dKkmaO3euZs+ercGDB+vw4cMaO3asJGnGjBnKysrSz3/+c3322WeaOHHiqX/CJpa/r1yGIV2Vlqy+5ycqItymnXsOhbosAADQjAV0SHT9+vVavHix1q9fr4yMDM2bN08pKSn6/vvvNWrUKF177bUnXffoQDd69GiNHj36uGVSUlK0ZMmS48aTk5O1cOHCQEq0jN1F5UqIi1S4wyZJ6pwYq+35JfJU++Ro9Fw2AACA4wWUIGbOnKmLL75Y7733nh5//HGlpKRIks4++2zdeuutQS2wOSmv9OhgmVsdz6q7OKNLUqy8PlP5RWUhrAwAADRnAQW2t956S3FxcYqNjVVRUZFeeeUV+Xw+SdKECROCWmBzUnuuWseEaP/YWXGRinLa9T2HRQEAwGkKKLA98cQT+uCDD2pWCAtTbm6uZs2aFcy6mqWConJFR9jVJjrcP2YYhs5OitXufeWq8nhDWB0AAGiuAgpsX3zxhZ555hlJUvv27fXcc8/p448/DmphzY3X59Oe/eXqmBBz3P3izk6Klc+UftjLYVEAAHDqAgpsHo9HVVVV/unah7Ojzt7iCnl9pjqcFX3cvPg2EXI6bNp3sCIElQEAgOYuoKtEr776at11110aNmyYDMPQihUrNGDAgGDX1qzs3lcuW5ihxPio4+YZhqG4WKcOllaGoDIAANDcBRTYJk2apNdee01r1qyR3W7XwIEDNWLEiGDX1qwUH6pUu1jnSZ9oEBfr1Dc/HJTPZyos7NQfsQUAAFqvgAKbzWbT2LFj/Te0xfFKyquUnHDyZ63GxTjl9Zkqq6hSm+jgPyoLAAC0HAEFtvfee8//WCnTNP3jn3/+edAKa05KD1fJXeWtd3XoseJia0JacambwAYAAE5JQIHtD3/4gyZPnqxevXoddwUkpF2FNVd/NhTY2saEyzCkg6VudUlqqsoAAEBLEFBga9OmjQYNGhTsWpqtH/aWSmo4sNnCwtQmKlwHS91NVRYAAGghArqtR58+fbR27dpg19Js/VBYqrAwQ9GRjgaXi4t16mAZgQ0AAJyagPawrV27VosWLZLD4ZDD4ZBpmjIMg3PYjti1t0yxUQ6FNXK4OC7WqZ17SlXl8fofDg8AANCYgALbK6+8EuQymrdde0vVNoALCeJiapY5WOqW6wT3awMAADiRgA6JJicna+PGjcrKylJ8fLy++OILJScnB7u2ZsHt8arwwOEGz1+rVXulKIdFAQDAqQgosL300kv629/+ppycHFVWVurFF1/UvHnzgl1bs1BQVCbTlNrENB7YIp12hTvCuPAAAACckoAC29tvv63//d//VWRkpNq1a6esrCytWLEi2LU1C4Hc0qOWYRiKi4kgsAEAgFMSUGCz2+0KD68LJG3atJHdHtDpby3err2lMgwpNqrxwCbVXSnqO+oGxAAAAA0JKLB16NBBH3zwgQzDUFVVlRYsWMA5bEf8sLdMifFRJ32G6LHaxoTL6zN1uLI6yJUBAICWIqCUMX36dP3lL3/Rli1blJaWpg8//FDTp08Pdm3Nwg+Fperkig14+Zgj92orO1wVrJIAAEALE9BxzcTERL366quqqKiQ1+tVTMzJH3Lemnh9pgqKypTWIyHgdWoPnZZVeIJVFgAAaGECCmx/+ctfTjj+y1/+8owW09zsP1ihqmqfOifGat/BioDWiYywK8yQyg4T2AAAQGACCmxbt271v66qqtKnn36qK664ImhFNRft20bo5mu7q/8FHfTWv7YHtE6YUfMIKw6JAgCAQAUU2GbPnl1vurCwUFOnTg1KQc2JzRam24f0OuX1YqLCVcohUQAAEKDALm08RmJiovLz8890La1GTKRDZYc9Mrm1BwAACMApn8Nmmqby8vLUvn37oBXV0sVEOVTt9cnt8SoinPvZAQCAhp3yOWxSzX3ZJk2aFJSCWoPYyCNXih72ENgAAECjTuscNvw4MVFH7sVW4dFZcZEhrgYAAFhdQIFtzJgxMgzjpPP/+te/nrGCWoNobp4LAABOQUCBLTU1Vd9++61uvfVWORwOZWdnq7q6WkOGDAl2fS2S3RamSKedm+cCAICABBTYPv/8c73++uuy2WySpKuuukq33nqrMjIyglpcSxYT5eDmuQAAICAB3dbjwIEDcrvd/uny8nJVVlYGrajWICbSobIKDokCAIDGBbSHbejQobrttts0cOBAmaapVatWaezYscGurUWLjQrXdwWHVO31hboUAABgcQEFtgceeEC9evXSv//9bzmdTj3++OO69NJLg11bixbjv/CAw6IAAKBhAT/pIDExUd27d9fEiRPlcDiCWVOrUHdrDw6LAgCAhgUU2P7xj3/okUce0Z/+9CeVlpbq3nvvVVZWVrBra9FioupungsAANCQgALbokWLtHjxYsXExKh9+/ZaunSpXn311WDX1qI5HTY57GHc2gMAADQqoMAWFhammJgY/3SHDh38t/jA6YuOdKicwAYAABoRUGCLi4vTpk2b/E87eOutt9S2bdugFtYaxBDYAABAAAK6SnTKlCl64IEH9P333+snP/mJnE6n5s+fH+zaWryoCIf27C+XaZoNPvoLAAC0bgEFtsrKSmVnZ2vHjh3yer3q2rUrV4qeAdGRdlV7TZUe9qhNdHioywEAABYV0CHR3/3ud7LZbOrWrZt69OhBWDtDoiNq+ri3+HCIKwEAAFYWUGDr2bOnli9froKCAh08eND/Bz9O9JGb5xYR2AAAQAMCOiS6Zs0a5eTk1BszDEObNm0KSlGtRe0etsIDFSGuBAAAWFlAgW3jxo3BrqNVCneEyW4z2MMGAAAa1OAh0enTp/tfHzhwIOjFtDaGYSg6wsE5bAAAoEENBra8vDz/67vuuivoxbRG0ZEO7eWQKAAAaECDgc00zRO+xpkTxR42AADQiICuEpXEjV2DJDrSrrIKjw5X8sQDAABwYg1edODz+VRSUiLTNOX1ev2va8XFxQW7vhav9tYee4srdE4H7m8HAACO12Bg27p1qy6//HJ/SLvsssv887itx5lx9M1zz+nQJsTVAAAAK2owsG3evLmp6mi1/DfPPcB5bAAA4MQCPocNwRERbpPDHqbCYq4UBQAAJ0ZgCzHDMJQQF8mVogAA4KSCGtjKyso0dOhQ7dq1S5L00UcfKTMzU4MGDdKzzz7rX27Tpk0aPny4MjIyNHXqVFVXV0uSCgoKNHr0aA0ePFj33HOPysvLg1luyLjio3jaAQAAOKmgBbavvvpKI0eO1I4dOyRJlZWVmjJliubPn6+VK1cqLy9Pa9eulSQ99NBDevTRR7V69WqZpqmsrCxJ0syZMzVq1Cjl5OQoNTVV8+fPD1a5IeVqF8XNcwEAwEkFLbBlZWVpxowZcrlckqQNGzaoS5cu6ty5s+x2uzIzM5WTk6P8/HxVVlYqLS1NkjR8+HDl5OTI4/Ho008/VUZGRr3xlsjVLlIHy9xye7yhLgUAAFhQQA9/Px1PPfVUvem9e/cqISHBP+1yuVRYWHjceEJCggoLC1VcXKyYmBjZ7fZ64y1RQrsoSVJR8WF1csWGuBoAAGA1QQtsx/L5fPWelmCapgzDOOl47c+jnc7TFo5+Hmqw9O3bVwUFBae9fp/ufSRJ//fJBp3XIeJMlRUSubm5oS7BMuhFHXpRh17UoRc16EMdenFyTRbYkpKSVFRU5J8uKiqSy+U6bnzfvn1yuVyKj49XaWmpvF6vbDabf/lTlZqaKqfTeUY+Q0M6dux42usmtIuUJLVtn6y+fc85QxU1vdzcXPXt2zfUZVgCvahDL+rQizr0ogZ9qNPae+F2uxvcydRkt/Xo06ePvvvuO+3cuVNer1crVqxQenq6kpOT5XQ6/ak6Oztb6enpcjgc6tevn1auXClJWrZsmdLT05uq3CbVvk2EwsIMbu0BAABOqMn2sDmdTj399NO6//775Xa7NWDAAA0ePFiSNHfuXE2bNk1lZWXq3bu3xo4dK0maMWOGJk+erAULFqhDhw565plnmqrcJmWzhemsuEgVcfNcAABwAkEPbO+//77/9RVXXKG33nrruGVSUlK0ZMmS48aTk5O1cOHCoNZnFa52kSrk8VQAAOAEeNKBRbjacfNcAABwYgQ2i3C1i9KBQ5XyVPtCXQoAALAYAptFuNpFymdK+0s4jw0AANRHYLMI15Gb53KlKAAAOBaBzSJc8UcCG88UBQAAxyCwWcRZcZEyDPawAQCA4xHYLMJhD1O72AgCGwAAOA6BzUIS46O4eS4AADgOgc1CErh5LgAAOAECm4W42kVp38EKeX1mqEsBAAAWQmCzEFe7SHl9pooPVYa6FAAAYCEENgvx39qDCw8AAMBRCGwW4r95LuexAQCAoxDYLCQhLlKStJcrRQEAwFEIbBYS4bSrbUw4h0QBAEA9BDaLSWgXxa09AABAPQQ2i0mKJ7ABAID6CGwWk9Q+WnsPHOZebAAAwI/AZjFJ7aPl9Znaf5ALDwAAQA0Cm8Ukta+5tceeA+UhrgQAAFgFgc1iktpHS5L27Oc8NgAAUIPAZjFntY2QLczQnv3sYQMAADUIbBZjs4XJ1S5KhexhAwAARxDYLCixfRTnsAEAAD8CmwUltY/W7n3sYQMAADUIbBaUFB+l0sNVKq/whLoUAABgAQQ2C6q9UpQnHgAAAInAZkmJtfdi40pRAAAgApslcS82AABwNAKbBcVEOhQT6eBKUQAAIInAZllJZ0VzLzYAACCJwGZZSfFRnMMGAAAkEdgsK6l9tPYWH5bXZ4a6FAAAEGIENotKah+laq+p/SUVoS4FAACEGIHNopLia64U3b2Pw6IAALR2BDaLSnbFSJIKispCXAkAAAg1AptFtW8boYhwm3btJbABANDaEdgsyjAMJbtiCGwAAIDAZmWdEmK1i0OiAAC0egQ2C0t2xaio+LAqq6pDXQoAAAghApuFdXLFyDS5UhQAgNaOwGZhnY5cKcp5bAAAtG4ENgvrmBAjwyCwAQDQ2hHYLMzpsCmhXZR27S0NdSkAACCECGwW18kVo3yuFAUAoFUjsFlcp4Sae7H5eAg8AACtFoHN4jq5YuSu8mp/SWWoSwEAACFCYLO4Tq5YSVJ+EeexAQDQWhHYLI5bewAAAAKbxcXFOhUVYSewAQDQihHYLM4wDHVyxXBrDwAAWjECWzNwdmIb7dxdKtPkSlEAAFojAlsz0DW5jQ6WuVVc6g51KQAAIAQIbM1At+Q4SdL2/JLQFgIAAELCHoo3HTNmjA4cOCC7vebtH3/8cZWXl2v27Nlyu9267rrr9OCDD0qSNm3apKlTp6q8vFz9+vXTzJkz/eu1Fl07tpEkfZt/UP3OTwxxNQAAoKk1efIxTVM7duzQP//5T3/wqqys1ODBg7Vw4UJ16NBB48eP19q1azVgwAA99NBDevLJJ5WWlqYpU6YoKytLo0aNauqyQyoqwqEO7aPZwwYAQCvV5IdEt2/fLkm68847df3112vRokXasGGDunTpos6dO8tutyszM1M5OTnKz89XZWWl0tLSJEnDhw9XTk5OU5dsCecmt9V3+YdCXQYAAAiBJg9shw4d0hVXXKF58+bplVde0RtvvKGCggIlJCT4l3G5XCosLNTevXvrjSckJKiwsLCpS7aEc5Pbavf+cpVXeEJdCgAAaGJNfkj0oosu0kUXXeSfvvnmm/X888+rb9++/jHTNGUYhnw+nwzDOG78VOTl5f34ohvRt29fFRQU/IgtpCg3N7fBJXwVFZKknA8+1Tku5494r+Bq7HO0JvSiDr2oQy/q0Isa9KEOvTi5Jg9sn332mTwej6644gpJNSEsOTlZRUVF/mWKiorkcrmUlJRUb3zfvn1yuVyn9H6pqalyOoMfcDp27Pij1j86sJ5I1+6Veu2D1XLEJKpv324/6r2CJTc3t9HP0VrQizr0og69qEMvatCHOq29F263u8GdTE1+SLS0tFRz5syR2+1WWVmZ3nzzTf3mN7/Rd999p507d8rr9WrFihVKT09XcnKynE6nP3FnZ2crPT29qUu2hPg2EYqLdXLhAQAArVCT72G75ppr9NVXX+mGG26Qz+fTqFGjdNFFF+npp5/W/fffL7fbrQEDBmjw4MGSpLlz52ratGkqKytT7969NXbs2KYu2TLOTW5LYAMAoBUKyQ3NJk6cqIkTJ9Ybu+KKK/TWW28dt2xKSoqWLFnSRJVZ27kd2+rNrUXyVHvlsNtCXQ4AAGgiPOmgGTk3ua28PlPf7+FB8AAAtCYEtmakW6e2kqRtPxwMbSEAAKBJEdiakQ7to9Uu1qmvt+8PdSkAAKAJEdiaEcMw1Pvc9sr7dp9M0wx1OQAAoIkQ2JqZ1HPba19JpfYWV4S6FAAA0EQIbM1MarezJEl53+4LcSUAAKCpENiamc6JsYqNcnAeGwAArQiBrZkJCzPUq2t75X1LYAMAoLUgsDVDqd3O0u795dpfwnlsAAC0BgS2Zij13PaSxGFRAABaCQJbM9S1YxtFOu0cFgUAoJUgsDVDNluYenWN10auFAUAoFUgsDVTF/V0adfeMhUUlYW6FAAAEGQEtmbqigs6SJLWb9wd4koAAECwEdiaKVe7KJ3XOY7ABgBAK0Bga8b6X9BBW74v1r6D3N4DAICWjMDWjHFYFACA1oHA1ox1csWqc2IsgQ0AgBaOwGYBXq/vtNftf2EHfb19nw7w1AMAAFose6gLQM191V5fvfm01i0pdctnSp/8p1CDrzjnzBYGAAAsgT1szVxcrFNtY8K16qMdMk0z1OUAAIAgILA1c4ZhqMfZ7bS9oET/+e5AqMsBAABBQGBrAc7p0EYxkQ4t/9f2UJcCAACCgMDWAthtYRp0WRetz9utomIuPgAAoKUhsLUQP7+yq2SaWrX+u1CXAgAAzjACWwuRGB+ly1I7KGf9Th2u9IS6HAAAcAYR2FqQW3/aQ2UVVcp6b2uoSwEAAGcQga0FOa9znK7t11nZH27X7n3loS4HAACcIQS2FmbMdefLbjP0lxVfh7oUAABwhhDYWpj2bSN180+7a/3G3fpqW1GoywEAAGcAga0FumHAeUqMj9Jzi79Q6eGqUJcDAAB+JAJbC+R02DRpTD8VH6rUH//2BY+sAgCgmSOwtVA9zm6nXw7trU/+s0fZH34b6nIAAMCPQGBrwTKvOldXXNBBr6z4jz75ek+oywEAAKeJwNaCGYahiSMu0rnJbTX71U/1+Za9oS4JAACcBgJbCxcV4dDMcVeoc2KMnvrzx/pqK1eOAgDQ3BDYWoHYqHA9Mb6/ks6K1oz/Xa+c9TtCXRIAADgFBLZWom2MU7+/7yr16ZGgeUu+0v8s3SBPtS/UZQEAgAAQ2FoIr7fx8BUT6dCjd12uGwZ004r/+06/fW6tvisoOaVtAACApmcPdQE4M2y2ML2+enNAy0Y67boqraM+/U+hHnjmA/Xq2l69zonX2CG9glwlAAA4HQS2VqqTK1YJcVHK3Vyor7fv1/b8EnVMiNE1/TrLFmaEujwAAHAUDom2Ys5wm/pf2FE/u/RsRTntem7xF7r392v07sc7Ob8NAAALIbBBCXGRGnjZ2Zp8+yWKjLDr+awvdfdT7+j11Zu1v6Qi1OUBANDqcUgUkmpusnvlhR3V/4IO+nzLXi3/13a98e4WLX5vq9K6J+iqtI66PLWDYqLCQ10qAACtDoEN9RiGob4pieqbkqg9+8v1zsc79eEX+Xpu8Zeat+QrpfVw6aq0ZF3SK1GxhDcAAJoEgQ0nldQ+WmN/3ktjrjtf3+w6qH99WaB1X+Xrs78VyjCk8zrF6aKeLqX1SFBKl/hQlwsAQItFYIOf1+uTzXb8aY2GYah753bq3rmd7hjSS9t+KNbnm/fqi61FWvL+NmW9t1UR4Tadf068NhdtUq+u7dWzSztFRThC8CkAAGh5CGzwO5V7uUlSWo8E9eoar73Fh7Vn/2Ft/X6/vtxWJNOUDEPqktRGKefE6/xz2imlS7w6nBUtw+CWIQAAnCoCG36UcIdNnVyx6uSKVUFbr+4deZW2fl+sTTuKtXnHAX34xS7/s0ujIx3q3ilO3Tq1VffO7XRe5zi52kUS4gAAaASBDWeU02FTWg+X0nq4JElen6ldhaXatOOAvtl1UN/sOqhla7+V12dKqnkw/Xmd2qpbpzh16dBGXZJi1aF9tCKc/GoCAFCLvxVxRjV0WDW+TYQu7ZWkvj1dOljm1oFDbh04VKnvCg75D6VKki3MULIrRp0Ta8JbUvtoJbWPUof20WofF8mTGAAArQ6BDU3OZgtT+7aRat820j/m9ZkqLa/SwTK3OrlitHN3qbbnl+jfG3f798ZJUliYobgYp9q1capdbITaxTrVNsap6EiHoiPsiopwKDrSoUinXdGRDkUdGYsMt53wggoAAJoDAhsswRZmKC7WqbhYp277WQ9/uPJ6fdpXUqk9+8q150C5Cg8c1sFSt4pL3SourdT2/BKVlLnrhbqTCXfYFOW0K8JpU6TTfvyfCLsiw2tex0aHKy7GqbYx4WobUxMKI8JtnG8HAAgJAhssp6HDqnZbmM6Ki9RZcXV750zTlNdrqqrap2qvV1Uen/pf2EGVVV6VV1Srwu1RRWW1DrurVeGuVqXbq4ojr0vK3Nqzv9w/XeH2nrSucIdNcUcCXHybCLVrE6H4WKfi2x55feRP2xjnGe8JAKB1axaBbfny5VqwYIGqq6t1++23a/To0aEuCRZiGIbsdkN2e5hqf6X7nZ90XOir3ZPWENM0Ve01VeXxasDFnVRS5lZJWZVKytw6WObWofIqHSx1q/DAYW3eeUAlZVXHbSPMkKIiwpT4Yak/xLWLjVB827qAF98mQnExTg7TAgACYvnAVlhYqGeffVZLly5VeHi4RowYocsuu0znnXdeqEtDC2QYhhx2Qw57mLolt200UHmqfTpY6taBQxU6cKjmMO2BkkodOFTzZ9/BCm37/qBKyt3+iyqOFhFu859nFx1Rd85dVIRdznCb7LYw2W1hstkM/2u7zZAtrKYu0zTlM2t+Hv3a5zPlrf3j9cnrqxmrPup1zTxTXp/Pf0jZFmb4388WVve+trCanjjsNoXbw+RwhCncbpPDfuTnUdP1XtvDVFrhVUmZW4ZhKMyo6bFx1M8ww6g3VrsMAKCO5QPbRx99pMsvv1xxcXGSpIyMDOXk5Oi+++4LbWFo8U71RsK1wsIMVR0uVo+zO/rPx6v2+lRS5tb+kkoVHwlzB8uqdLjSo/IKjw67q3X4yM99JRUqr6hWlccrr88nT3VN0DodYWGGbEf9CQurDXyGwo4Esdo/klR9VIDzen01016fqn2mqqt9AZ0reEJv7j7lVY4NcIZhyJAUFlZ/2jAM/1jNxziyTthR6xwdCMOOWsYwFBZ27Ouan7Vhst70MfMNw5DtSC21NdjCDP+2DEM184+st3fvQW3c/XVdWD3B+9XvQU39tf3QkanaxU44pprfXXtt2LaFyVEb9Gtf24//R0Dd67plj83NxwZpo9684/4LHvff8+g5tb9jJ1r52E3VW5cwj1bK8oFt7969SkhI8E+7XC5t2LCh0fXMI7szqqqOP2QVDPaw0/sLVZLcbvePWt8q24hwGJaowwrbiHAYsof5VF3t0T/e//aEy9gMKTYyTLGRTkknP+9tWHo3Za/9RqYp+UxTPtP0762r+4u7/l/shmoCQO0Cw9K7KfvDE9cRqKFXdpVUE+o81T55vF5VV/uOvPbVvD7ys6raVzev2uuvecM3+2TKlEyp9ockmbWvTbNuTHX/H9f8MHVux7YyDMO/PdNnyqcj6xy1d1FS3TK1f1Qz7fX56t7TV7Ouv6++unW8R/Zamj6fTJ9U7d+Ladat56t9z5rt186rfW+fz6wZ90m+mg+kbwp2ymeqpm7zqCa0RstXnvltHhMMa/7fOBLmjfo/DaPu/5ewmhcn3Ntbk4uP3UOsunV07HYNf7AMCzvmHw1HCqsN8WWlpfrnfz6uqfVIcK91or3yx8442TJ1/x8FsOzR2zvRRo5Z5sQLy9/72s9mGFJY7T8ojvpHTl0/jKO+w6SSkoP6bMfnx++Fr2ly/XUM1fvvU3+duu36y679LjmqbrPuRf2enaAfhqQr+yQrMT7q+D6cIbV55YS9lmSYJ5tjEQsWLJDb7dbEiRMlSVlZWcrLy9Pjjz/e4HqlpaXaunVrE1QIAABwZvTo0UOxsbHHjVt+D1tSUpI+++wz/3RRUZFcLlej60VHR6tHjx5yOBzsQgcAAJZmmqY8Ho+io6NPON/yga1///564YUXdODAAUVGRuqdd97RE0880eh6YWFhJ0yoAAAAVhQREXHSeZYPbImJiXrwwQc1duxYeTwe3XzzzbrwwgtDXRYAAECTsfw5bAAAAK0dd+0EAACwOAIbAACAxRHYAAAALI7ABgAAYHEENgAAAIsjsAVo+fLl+vnPf65BgwbptddeO27+pk2bNHz4cGVkZGjq1Kmqrq4OQZVNo7FevPfeexo2bJiuv/563XvvvSopKQlBlU2jsV7U+uCDD3Tttdc2YWVNr7FebN++XWPGjNH111+vu+66q1X/Xnz99de66aabdP3112v8+PE6dOhQCKpsGmVlZRo6dKh27dp13LzW9L0pNdyL1vS9KTXci1qt4XvzlJho1J49e8xrrrnGLC4uNsvLy83MzExz27Zt9ZYZMmSI+cUXX5imaZqPPPKI+dprr4Wg0uBrrBelpaXmlVdeae7Zs8c0TdP84x//aD7xxBOhKjeoAvm9ME3TLCoqMgcPHmxec801IaiyaTTWC5/PZw4aNMhcu3ataZqm+Yc//MGcM2dOqMoNqkB+L0aOHGl+8MEHpmma5uzZs81nnnkmFKUG3ZdffmkOHTrU7N27t/nDDz8cN7+1fG+aZsO9aE3fm6bZ+O+FabaO781TxR62AHz00Ue6/PLLFRcXp6ioKGVkZCgnJ8c/Pz8/X5WVlUpLS5MkDR8+vN78lqSxXng8Hs2YMUOJiYmSpJ49e2r37t2hKjeoGutFrWnTpum+++4LQYVNp7FefP3114qKilJ6erok6de//rVGjx4dqnKDKpDfC5/Pp/LycklSRUVFg3c3b86ysrI0Y8aMEz5OsDV9b0oN96I1fW9KDfeiVmv43jxVln/SgRXs3btXCQkJ/mmXy6UNGzacdH5CQoIKCwubtMam0lgv2rVrp4EDB0qSKisr9dJLL2nMmDFNXmdTaKwXkvTXv/5VvXr1Up8+fZq6vCbVWC++//57nXXWWZoyZYo2bdqkc889V9OnTw9FqUEXyO/F5MmTdeedd2rWrFmKjIxUVlZWU5fZJJ566qmTzmtN35tSw71oTd+bUsO9kFrP9+apYg9bAHw+X70HyJumWW+6sfktSaCftbS0VOPGjVNKSopuvPHGpiyxyTTWi61bt+qdd97RvffeG4rymlRjvaiurtYnn3yikSNH6s0331Tnzp319NNPh6LUoGusF5WVlZo6dapeeeUVrVu3TqNGjdLDDz8cilJDqjV9bwaqNXxvNqY1fW+eKgJbAJKSklRUVOSfLioqqrcr99j5+/bta3BXb3PWWC+kmn85jxo1Sj179mz0X1LNWWO9yMnJUVFRkW666SaNGzfO35eWqLFeJCQkqEuXLrrgggskSUOHDj1ur1NL0Vgvtm7dKqfT6X8m8m233aZPPvmkyesMtdb0vRmI1vK92ZjW9L15qghsAejfv7/Wr1+vAwcOqKKiQu+8847/XBxJSk5OltPpVG5uriQpOzu73vyWpLFeeL1e/frXv9Z1112nqVOntuh/MTfWiwkTJmj16tXKzs7WSy+9JJfLpddffz2EFQdPY7246KKLdODAAW3evFmS9P7776t3796hKjeoGutFly5dtGfPHm3fvl2StGbNGn+QbU1a0/dmY1rT92ZjWtP35qniHLYAJCYm6sEHH9TYsWPl8Xh0880368ILL9SvfvUrTZgwQRdccIHmzp2radOmqaysTL1799bYsWNDXXZQNNaLPXv26D//+Y+8Xq9Wr14tSUpNTW2R/2IM5PeitQikF/PmzdO0adNUUVGhpKQkzZkzJ9RlB0UgvZg9e7YmTpwo0zTVvn17zZo1K9RlN5nW+L15Mq3xe/NkWuP35qkyTNM0Q10EAAAATo5DogAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABwBgTyUHtJWrt2rTIzM5WZmanf/va3/kfVNYTABgDNxK5du/THP/5Rt99+e6u82S5gZV999ZVGjhypHTt2NLjcoUOHNHnyZD377LNavny5UlJS9Oyzzza6fQIbAEt68sknNWzYMA0bNkypqanKyMjwT1dWVp5wnQ8++EDPPfdco9seM2bMSR80np+fr8mTJysjI0NDhgxRRkaGnn32WXk8nh/1eU7XQw89pK1bt+qHH37QiBEjVFVVJbvdrqlTp2rZsmWSpPfee0/z5s0LSX0AapzoofbLli3TjTfeqGHDhmnKlClyu93asWOHOnbsqPPOO0+SdM011+i9995rdPsENgCWNG3aNGVnZys7O1sul0tz5871T0dERJxwnY0bN6qkpOS037OwsFC33XabLr74YuXk5Ojtt9/W0qVLtX37dv3+978/7e2erpUrVyo2NlY9evTQmjVr1L9/f40aNUppaWmaPXu2qqurJUk/+9nP9Nlnn2nTpk1NXiOAGk899ZT69evnn962bZuysrL0xhtvKDs7W+3bt9fLL7+sc845R3v27PE/+WXVqlXat29fo9vnSQcAmp158+bp7bffls1mU9euXTV9+nQVFBTojTfekNfrVWxsrMaPH6/HHntMO3fu1MGDBxUdHa25c+fq3HPPPel2X3rpJQ0aNEi33nqrfyw6OlrTp0/334F+6dKlWrJkiSoqKhQTE6OFCxeesJ6EhAS98847WrBggQzDkM1m06RJk3TJJZecdPxYL7zwgn+PYWJiol5//XXt2bNHktSvX796fzncfPPNevHFF9nTBljExx9/rJ07d/q/Tzwej3r16qU2bdro97//vaZPny6fz6dbb71VDoej0e0R2AA0K//4xz/0r3/9S0uWLFFUVJReeOEFTZ48WS+//LJGjBih4uJiPfjgg8rJyVGbNm20ePFiSdKjjz6q1157TdOnTz/ptj/77DM98MADx427XC6NGTPGP/3NN9/o/fffV0xMTIP1zJkzR3PnzlVaWprWrVunjz/+WJdccslJx4+2detWud1u9ejRQ5J03XXXqbCwUJMmTZIkmaapu+++W1FRUZKkK6+8UpMnT1ZlZeVJ90ACaDper1fXXXedpk2bJkkqLy+X1+uV1+tVUlKS/v73v0uSNmzYoM6dOze6PQ6JAmhWPvzwQw0fPtwfVMaOHat///vfqqqqqrfc4MGDdeONN2rhwoV68skn9cknn+jw4cMNbts0zXoP3v7Tn/7kP2/uyiuv1MGDByVJPXv2VExMTKP1DBkyRPfdd5+mTp2qQ4cO6Ve/+pUknXT8aNu3b9fZZ59db+yOO+7QX//6V11yySXavHmzHn74Yf+8uLg4OZ1O5efnB9JGAEF22WWX6d1339X+/ftlmqYee+wxvfrqqzIMQ3feeacKCwtlmqZeeeUV/fznP290ewQ2AM2Kz+erF6p8Pp//XK6jvf7665o6daoiIiKUmZmpoUOHqrFHJ1900UX1rr68++67/efN7du3Tz6fT5L84ayxeh588EG9/vrrSk1N1dKlSzV69OgGx49mGIb//aSaCyrWr18vSerUqZOmTp2qdevW1VvHZrPJZrM1+BkBNI2UlBTdd999uv322zVkyBD5fD6NGzdOYWFhevzxx3X33Xdr8ODBatOmje66665Gt0dgA9CsXHXVVfrHP/7h31u2cOFCXXLJJQoPD5fNZvOHpXXr1unGG2/ULbfcoq5du+r999+X1+ttcNv33HOPVq1apWXLlvmXra6u1sqVKyVJYWHHf2WerJ6wsDBde+21qqio0MiRIzVjxgxt2bJFVVVVJx0/WteuXfXDDz/4p7dv3665c+fK7XZLqrmPU8+ePf3zS0tLVVVVpY4dO55SPwGcWe+//746deokSbrlllu0YsUKrVy5Uv/93/8tp9MpSbr66qu1fPlyrV69Wo899hjnsAFoeW6++Wbt3r1bt9xyi3w+n7p06aK5c+dKki6//HL97ne/0xNPPKE777xTjz76qJYsWSJJSktL09atWxvcdlJSkhYvXqwXX3xRL7/8sqSa807S0tKUlZWluLi4gOux2+2aMmWKfve738lut8swDM2aNUvh4eEnHT9ajx495HQ69e2336pbt24aM2aMtm/frjvuuEMej0ddu3bVrFmz/MuvW7dOV1999XHbAdAyGGZjxwgAACGxfPly5ebm6rHHHvOP7dq1S2+++abuv//+esuOHTtWU6ZMUUpKShNXCaApcEgUACwqMzNTJSUl2rJli3+sU6dOx4W1d999V/369SOsAS0Ye9gAAAAsjj1sAAAAFkdgAwAAsDgCGwAAgMUR2AAAACyOwAYAAGBxBDYAAACL+//sOwCjv9jUyAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot total gross distribution\n", + "plt.figure(figsize=(10, 6))\n", + "sns.histplot(bom_df['total_gross'], bins=30, kde=True)\n", + "plt.title('Distribution of Total Gross')\n", + "plt.xlabel('Total Gross ($)')\n", + "plt.ylabel('Frequency')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Movie Budget Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert 'domestic_gross' and 'foreign_gross' to numeric\n", + "bom_df['domestic_gross'] = pd.to_numeric(bom_df['domestic_gross'], errors='coerce')\n", + "bom_df['foreign_gross'] = pd.to_numeric(bom_df['foreign_gross'], errors='coerce')\n", + "\n", + "# Impute missing domestic_gross and foreign_gross with their respective means\n", + "bom_df['domestic_gross'].fillna(bom_df['domestic_gross'].mean(), inplace=True)\n", + "bom_df['foreign_gross'].fillna(bom_df['foreign_gross'].mean(), inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Top 10 Movies by Total Gross\n", + "\n", + "This plot displays the top 10 movies based on total gross revenue. The movie titles are rotated for better readability and aligned to the right to prevent misalignment on the x-axis. Each bar represents the total gross earnings of the respective movie, providing a clear comparison among the top-performing films.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Top 10 Movies by Total Gross\n", + "top_movies = bom_df.nlargest(10, 'total_gross')\n", + "\n", + "# Plot Top 10 Movies\n", + "plt.figure(figsize=(16, 14))\n", + "top_movies.plot(x='title', y='total_gross', kind='bar', color='orange')\n", + "plt.title('Top 10 Movies by Total Gross', fontsize=16)\n", + "plt.xlabel('Movie Title', fontsize=12)\n", + "plt.ylabel('Total Gross ($)', fontsize=12)\n", + "\n", + "# Adjust x-ticks for better alignment\n", + "plt.xticks(rotation=30, ha='right')\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ - "# Your code here - remember to use markdown cells for comments as well!" + "# Calculate profit by subtracting 'production_budget' from 'worldwide_gross'\n", + "movie_budgets['profit'] = movie_budgets['worldwide_gross'] - movie_budgets['production_budget']\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculating Return on Investment (ROI)\n", + "\n", + "**Return on Investment (ROI)** is a financial metric used to evaluate the profitability of an investment relative to its cost. Here’s how we calculated ROI for each movie in the dataset:\n", + "\n", + "1. **Calculate Profit**: \n", + "2. **Define ROI Formula**: \n", + " - ROI is calculated as:\n", + " \n", + " $$\n", + " \\text{ROI} = \\left( \\frac{\\text{Profit}}{\\text{Production Budget}} \\right) \\times 100\n", + " $$\n", + "\n", + "3. **Calculate and Add ROI to DataFrame**: \n", + " - Using this formula, we created a new column `roi` in our DataFrame to store the ROI values for each movie, expressed as a percentage.\n", + "\n", + "4. **Display ROI**: \n", + " - The final DataFrame now includes the **ROI**, providing insight into the relative success of each movie based on its budget and gross revenue.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
movieproduction_budgetworldwide_grossprofitroi
0Avatar42500000027763452792351345279553.26
1Pirates of the Caribbean: On Stranger Tides4106000001045663875635063875154.67
2Dark Phoenix350000000149762350-200237650-57.21
3Avengers: Age of Ultron33060000014030139631072413963324.38
4Star Wars Ep. VIII: The Last Jedi3170000001316721747999721747315.37
\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", + "\n", + " worldwide_gross profit roi \n", + "0 2776345279 2351345279 553.26 \n", + "1 1045663875 635063875 154.67 \n", + "2 149762350 -200237650 -57.21 \n", + "3 1403013963 1072413963 324.38 \n", + "4 1316721747 999721747 315.37 " + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate ROI\n", + "movie_budgets['roi'] = (movie_budgets['profit'] / movie_budgets['production_budget']) * 100\n", + "\n", + "# Display a sample of the result\n", + "movie_budgets[['movie', 'production_budget', 'worldwide_gross', 'profit', 'roi']].head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Heatmap for Movie Budgets and Returns\n", + "\n", + "A heatmap provides a visual representation of the correlation between different variables in our dataset, specifically focusing on `production_budget`, `domestic_gross`, `worldwide_gross`, `profit`, and `roi`. Here are the steps involved:\n", + "\n", + "1. **Compute Correlation Matrix**: We created a correlation matrix for the relevant columns to understand how closely these variables are related.\n", + "2. **Visualize Correlation**: Using a heatmap, we plotted these correlations, where:\n", + "\n", + "This visualization helps us see patterns in movie budgets and returns, such as whether higher budgets generally correlate with higher gross revenues and profits.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate correlation matrix for relevant numerical columns\n", + "correlation_matrix = movie_budgets[['production_budget', 'domestic_gross', 'worldwide_gross', 'profit', 'roi']].corr()\n", + "\n", + "# Plot heatmap\n", + "plt.figure(figsize=(10, 6))\n", + "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=0.5)\n", + "plt.title('Correlation Heatmap for Movie Budgets and Returns')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis of Movie Budgets and Returns Heatmap\n", + "\n", + "This heatmap visualizes the correlation between different financial aspects of movies: production budget, worldwide gross, profit, and return on investment (ROI). Here's a breakdown of the findings:\n", + "\n", + "**Strong Positive Correlations:**\n", + "\n", + "- **Worldwide Gross & Profit (0.98):** This strong positive correlation is logical. Higher worldwide gross generally leads to higher profits, assuming production and marketing costs are managed effectively. \n", + "- **Worldwide Gross & Production Budget (0.75):** This suggests that movies with larger budgets tend to earn more at the box office. This could be due to several factors, including bigger marketing campaigns, attracting higher-profile actors, and affording better production values.\n", + "- **Profit & Production Budget (0.61):** While a positive correlation exists, it's weaker than the previous two. This implies that a larger budget doesn't guarantee a proportionally larger profit. Other factors like story, marketing, and audience reception play a significant role.\n", + "\n", + "**Weak Correlations:**\n", + "\n", + "- **ROI & Production Budget (0.05), ROI & Worldwide Gross (0.07), ROI & Profit (0.07):** These weak correlations indicate that ROI is not strongly influenced by the scale of the movie's budget, gross earnings, or even profit. This suggests that smaller budget films can be just as profitable (in terms of ROI) as big-budget blockbusters.\n", + "\n", + "**Negative Correlation:**\n", + "\n", + "- **ROI & Production Budget (-0.05):** This slight negative correlation suggests a potential trend where higher-budget movies might yield slightly lower ROI compared to lower-budget films. This could be attributed to the higher risk associated with large investments and the potential for significant losses if the movie doesn't resonate with audiences.\n", + "\n", + "**Key Takeaways:**\n", + "\n", + "- While a larger budget can contribute to higher box office earnings and potentially higher profits, it doesn't guarantee a higher ROI.\n", + "- Other factors beyond budget, such as story quality, marketing effectiveness, and audience reception, play a crucial role in a movie's financial success and ROI.\n", + "- Smaller budget films can be just as profitable, if not more so, than big-budget movies when considering ROI.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Business Recommendations for the New Movie Studio\n", + "\n", + "Based on an analysis of box office trends and successful film genres, the following recommendations are proposed to guide the new movie studio in its decision-making process:\n", + "\n", + "### 1. **Focus on High-Demand Genres**\n", + " - **Recommendation:** Prioritize the production of films in genres that have consistently performed well at the box office, such as:\n", + " - **Action/Adventure**\n", + " - **Fantasy/Sci-Fi**\n", + " - **Drama**\n", + " - **Animation**\n", + " - **Comedy**\n", + " - **Justification:** These genres attract larger audiences and generate higher revenues. Aligning the studio’s production strategy with popular genres can lead to greater box office success.\n", + "\n", + "### 2. **Embrace Diverse Storytelling and Representation**\n", + " - **Recommendation:** Develop films that feature diverse casts and storytelling perspectives, including:\n", + " - **Inclusive Characters:** Focus on stories that represent different cultures, backgrounds, and experiences.\n", + " - **Unique Narratives:** Explore unconventional plots that captivate audiences and set the studio apart from competitors.\n", + " - **Justification:** Modern audiences are increasingly seeking representation and unique storytelling. Producing films that resonate with varied demographics can help build a loyal viewer base.\n", + "\n", + "### 3. **Prioritize Large-Scale Productions**\n", + " - **Recommendation:** Allocate a significant budget towards high-potential movies with proven directors, popular franchises, or successful intellectual properties (IPs).\n", + " - **Justification:** Studios that frequently release blockbuster films typically see greater returns on investment. Investing in large-scale productions can significantly enhance profitability.\n", + "\n", + "### 4. **Invest in Quality Production and Talent**\n", + " - **Recommendation:** Allocate resources towards high-quality production and hiring experienced talent, including:\n", + " - **Skilled Directors and Writers:** Collaborate with established filmmakers who have a track record of successful films.\n", + " - **Top-Notch Production Values:** Ensure that production quality meets audience expectations, particularly in visual effects and cinematography.\n", + " - **Justification:** High-quality films tend to perform better at the box office and receive positive reviews, leading to increased viewer interest and repeat viewings. Investing in talent and production quality can bolster the studio’s reputation and success.\n", + "\n", + "### 5. **Leverage Data Analytics for Decision Making**\n", + " - **Recommendation:** Utilize data analytics to inform production and marketing strategies.\n", + " - **Justification:** Analyzing past performance, audience preferences, and market trends can provide valuable insights for making informed decisions on project selection, marketing campaigns, and release strategies. This data-driven approach can enhance success rates.\n", + "\n", + "### 6. **Enhance Marketing and Distribution Strategies**\n", + " - **Recommendation:** Develop robust marketing campaigns and strategic distribution plans for film releases.\n", + " - **Justification:** Effective marketing can generate buzz and anticipation for new releases, while a well-planned distribution strategy ensures that films reach the target audience efficiently. This can significantly impact box office performance.\n", + "\n", + "### Conclusion\n", + "By implementing these recommendations, the new movie studio can capitalize on current market trends, enhance audience engagement, and ultimately drive box office success. Prioritizing high-demand genres, diverse storytelling, quality production, and data-driven decision-making will position the studio for long-term profitability and growth in the competitive film industry.\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python (lean-env)", "language": "python", - "name": "python3" + "name": "learn-env" }, "language_info": { "codemirror_mode": { @@ -40,7 +4431,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/tudiobyfross.png b/tudiobyfross.png new file mode 100644 index 00000000..5d2ebd3b Binary files /dev/null and b/tudiobyfross.png differ