Add my analysis & notebook (cleaned large DB files)

.gitignore

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+*.db
+zippedData/*
+*.db
+zippedData/*

Movie_README.md

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+# 🎬 Movie Studio Investment Analysis
+
+This notebook explores movie performance data to help our company decide **what types of films to create**.  
+We will use exploratory data analysis and statistical modeling to answer business questions about ROI.
+
+
+> **Deliverables included (starter set):**
+> - `README.md` — Notebook summary and guide
+> - `presentation.pptx` (or `presentation.md`) — Non-technical presentation for Management
+> - `student-checkpoint.ipynb` — analysis notebook developed by the project team
+> - `data/` — expected location for raw inputs (e.g., `zippedData/...`), usually **gitignored**
+
+---
+
+## 1) Overview
+
+**Business Problem.** The company is launching a **new movie studio** and needs evidence-based guidance on **what kinds of films to green-light**.
+
+**Objective.** Use exploratory data analysis to identify patterns linked to **Return on Investment (ROI)** and produce **three concrete recommendations** for slate strategy.
+
+**Current Scope.** We begin with:
+- **The Numbers** (`tn.movie_budgets.csv.gz`): budgets and grosses
+- **IMDB** (`im.db` → `movie_basics`): title, year, runtime, genres
+
+The workflow is **modular** so teammates can add Rotten Tomatoes, TMDB, Box Office Mojo, etc.
+
+---
+
+## 2) Business Understanding
+
+**Stakeholder.** Head of the new studio (green-lighting decisions).
+
+**Key Questions.**
+1. **Genres vs ROI** – Which genres yield the best returns?
+2. **Budget vs ROI %** – Are bigger budgets more or less profitable?
+3. **Runtime vs ROI** – Does movie length impact profitability?
+
+**Decision Use.** Prioritize genre slate, design budget bands, and define runtime guardrails by genre.
+
+---
+
+## 3) Data Understanding & Preparation
+
+**Sources.**
+- **The Numbers** — production budgets, domestic/worldwide grosses, release dates.
+- **IMDB** — movie metadata (titles, start_year, runtime_minutes, genres).
+
+**Join Logic.** Normalize titles and match on **title + year** to merge The Numbers with IMDB.
+
+**Target Metric.** `ROI = (worldwide_gross − production_budget) / production_budget` (requires budget > 0).
+
+**Cleaning.**
+- Cast currency strings to numeric.
+- Drop missing/non-finite ROI.
+- **Multi-genre policy (simple):** explode genres; each film contributes to every genre it’s labeled with. Results are **directional** because genres overlap.
+
+---
+
+## 4) Methods (aligned to syllabus)
+
+- **Descriptive statistics:** counts, mean/median ROI, % profitable (ROI > 0).
+- **t-based confidence intervals** (n ≥ 30; CLT justification).
+- **Optional one-sample t-tests:** compare each genre’s mean to the overall mean.
+- **Simple linear regression** (StatsModels): ROI ~ log10(budget), ROI ~ runtime.
+- **Diagnostics:** QQ plot (normality) and residuals vs fitted (homoscedasticity).
+- **Outlier awareness:** distributions plotted; optional winsorization/log transforms for sensitivity.
+
+---
+
+## 5) Analysis Summary (current iteration)
+
+### 5.1 Genres vs ROI (Simple Multi-Genre)
+- Per-genre **n, mean ROI, median ROI, % profitable**.
+- Visuals: Top 10 by **Avg ROI**, Top 10 by **% Profitable**, **boxplots** (outliers hidden).  
+- Interpretation: Favor genres above overall Avg ROI **and** with high % profitable; medians close to means suggest less outlier risk.
+
+### 5.2 Budget vs ROI
+- Regression **ROI ~ log10(budget)** with diagnostics; informs budget bands and slate mix.
+
+### 5.3 Runtime vs ROI
+- Regression **ROI ~ runtime_minutes** with diagnostics; checks for diminishing returns with very long runtimes.
+- Visuals: Runtime vs Return on Investment with rehression line.
+- Interpretation:  The graph shows that runtime has no meaningful effect on profitability.
+
+> Teammates can extend with Rotten Tomatoes/TMDB (ratings, votes), cast/star power, franchise flags, marketing proxies.
+
+---
+
+## 6) Recommendations (draft; refine with added evidence)
+
+1. **Slate focus:** Prioritize the top 2–3 genres that are above the overall Avg ROI **and** show high % profitable with medians close to means.
+2. **Budget discipline:** Concentrate budgets in bands where ROI is resilient. Use a **tiered slate** (a few mid–high budget bets plus a steady pipeline of mid/low budgets).
+3. **Runtime guardrails:** Avoid extreme runtimes unless the genre historically sustains them; target the runtime range with stable ROI in the regression.
+
+**Next iteration:** Add confidence intervals to slides, perform sensitivity (winsorized/log ROI), and enrich with ratings and cast variables.
+
+---
+
+## 7) How to Run
+
+**Environment:** Python 3.x; `pandas`, `numpy`, `matplotlib`, `scipy`, `statsmodels`, `sqlite3` (stdlib).
+
+**Data layout (example):**
+```
+data/
+└── zippedData/
+    ├── tn.movie_budgets.csv.gz
+    └── im.db
+```
+
+**Notebook:**
+1. Open `student-checkpoint.ipynb`.
+2. Run all cells (update any paths if needed).
+3. Export charts to the `figures/` folder for the deck.
+
+**.gitignore suggestions:** `/data/`, `*.zip`, `*.gz`, `*.db`, `*.DS_Store`
+
+---
+
+## 8) Repository Structure (suggested)
+
+```
+.
+├── README.md
+├── presentation.pptx            
+├── student-checkpoint.ipynb
+├── notebooks/                   
+├── figures/                     
+├── data/
+│   └── zippedData/              # raw inputs (not tracked by git)
+├── .gitignore
+└── LICENSE (optional)
+```
+
+---
+
+## 9) Team Collaboration Notes
+
+- Add new analyses as **separate notebooks** (e.g., `notebooks/ratings_analysis.ipynb`).
+- Export figures to `/figures` and reference them in `presentation.pptx`.
+- Use **clear commit messages** (`feat:`, `fix:`, `viz:`, `doc:`) and keep commits small.
+
+---
+
+## 10) Limitations & Next Steps
+
+**Limitations:** multi-genre overlap (directional results), ROI skewness (blockbusters), imperfect title-year joins.
+
+**Next Steps:** add t-CIs to slides; winsorized/log ROI sensitivity; integrate ratings/votes/star power/franchise; consider multivariate/regularized regression when feature set grows.
+
+---
+
+## 11) Contact
+Owner: _Your Name_ · [email protected]_ · _LinkedIn URL_  
+Collaborators: _Teammate A, Teammate B, …_

Untitled.ipynb

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