| # Movie Profitability Analysis - EDA Summary |
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| ## Dataset Overview |
| This project explores the **“Movies Metrics, Features and Statistics”** dataset from Kaggle. |
| The dataset contains **6,569 movies** and **32 features**, including: |
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| - Production Budget |
| - Worldwide & Domestic Gross |
| - Running Time |
| - Genre |
| - Creative Type |
| - Production Method |
| - Ratings and Release Date |
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| The goal is to understand which **pre-release factors** influence a movie’s ability to generate **positive profit**. |
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| ## Prediction Question |
| **Can we predict whether a movie will be profitable using only pre-release features such as budget, runtime, genre, and production characteristics?** |
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| ## Target Variable |
| Binary classification target: |
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| - **is_profitable = 1** → if *Worldwide Gross > Production Budget* |
| - **is_profitable = 0** → otherwise |
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| # Exploratory Data Analysis (EDA) |
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| Below are the research questions and the insights based on the dataset’s visualizations. |
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| ## 1. How does production budget influence profitability? |
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| ### **Visualization:** Profit vs. Production Budget |
| *[Profit X Budget](https://huggingface.co/datasets/Leelu1002/Movie_Profitability_Analysis/resolve/main/Profit%20X%20Budget.png)* |
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| ### **Insight** |
| - A clear **positive correlation** exists between budget and profit. |
| - Higher-budget films tend to achieve **higher profit**, but with larger variance. |
| - Budget is a **strong predictor** of financial success. |
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| ## 2. How does genre affect profitability? |
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| ### **Visualization:** Profitability Rate by Genre |
| *[Profit X Genre](https://huggingface.co/datasets/Leelu1002/Movie_Profitability_Analysis/resolve/main/Profit%20X%20Genre.png)* |
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| ### **Insight** |
| The most profitable genres: |
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| - **Adventure** (highest) |
| - **Horror** |
| - **Romantic Comedy** |
| - **Action** |
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| Less profitable genres include Drama and Documentary. |
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| ## 3. How does running time correlate with profitability? |
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| ### **Visualization:** Running Time Density (Profitable vs. Not) |
| *[Profit X Running Time](https://huggingface.co/datasets/Leelu1002/Movie_Profitability_Analysis/resolve/main/Profit%20X%20Running%20Time%20.png)* |
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| ### **Insight** |
| - Profitable movies tend to be slightly **longer** (around 105–120 minutes). |
| - Extremely long films are less common but can still be profitable. |
| - Running time has a **weak-to-moderate** influence on profit. |
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| ## 4. How does production method influence profitability? |
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| ### **Visualization:** Average Profit by Production Method |
| *[Profit X Production Method](https://huggingface.co/datasets/Leelu1002/Movie_Profitability_Analysis/resolve/main/Profit%20X%20Production%20Method.png)* |
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| ### **Insight** |
| Highest profit methods: |
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| - **Animation + Live Action** |
| - **Digital Animation** |
| - **Hand Animation** |
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| Lower profitability: |
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| - **Stop-Motion**, **Live Action**, **Multiple Methods**, **Rotoscoping** |
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| # Key Insights Summary |
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| ### Strong Predictors of Profitability |
| - Production Budget |
| - Genre |
| - Creative Type |
| - Production Method |
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| ### Moderate Predictor |
| - Running Time |
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| # Final Summary |
| This analysis shows that **pre-release movie characteristics** can be used to meaningfully predict profitability. |
| The strongest indicators are **budget**, **genre**, and **production method**, while running time offers additional but weaker predictive value. |
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| # Project Files |
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| Below is a complete list of all files used throughout this project: |
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| ## Dataset Files |
| - **movies_dataset.csv** — Original dataset downloaded from Kaggle |
| - **movies_cleaned.csv** — Cleaned version after handling missing values and removing duplicates |
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| ## Notebook Files |
| - **Leelu_EDA_&_Dataset.ipynb** — Main notebook containing: |
| - Data loading |
| - Data cleaning |
| - Target variable creation (`is_profitable`) |
| - Full Exploratory Data Analysis (EDA) |
| - Visualizations and insights |
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| ## Visualization Outputs |
| (Images included in the README) |
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| - **Profit X Budget.png** — Profit vs. Production Budget |
| - **Profit X Running Time.png** — Running Time Density (Profitable vs. Not Profitable) |
| - **Profit X Genre.png** — Profitability by Genre |
| - **Profit X Production Method.png** — Average Profit by Production Method |
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| ## Documentation |
| - **README.md** — Project summary and final results documentation |
| - **[Presentation Video](https://www.youtube.com/watch?v=9TbwMmHUUXw)** |
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| # Author |
| **Leelu Alfi** |
| Reichman University - Data Science Track |
| 2025 |
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