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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:
- Production Budget
- Worldwide & Domestic Gross
- Running Time
- Genre
- Creative Type
- Production Method
- Ratings and Release Date
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:
- **is_profitable = 1** → if *Worldwide Gross > Production Budget*
- **is_profitable = 0** → otherwise
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# Exploratory Data Analysis (EDA)
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?
### **Visualization:** Profit vs. Production Budget
*[Profit X Budget](https://huggingface.co/datasets/Leelu1002/Movie_Profitability_Analysis/resolve/main/Profit%20X%20Budget.png)*
### **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?
### **Visualization:** Profitability Rate by Genre
*[Profit X Genre](https://huggingface.co/datasets/Leelu1002/Movie_Profitability_Analysis/resolve/main/Profit%20X%20Genre.png)*
### **Insight**
The most profitable genres:
- **Adventure** (highest)
- **Horror**
- **Romantic Comedy**
- **Action**
Less profitable genres include Drama and Documentary.
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## 3. How does running time correlate with profitability?
### **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)*
### **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?
### **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)*
### **Insight**
Highest profit methods:
- **Animation + Live Action**
- **Digital Animation**
- **Hand Animation**
Lower profitability:
- **Stop-Motion**, **Live Action**, **Multiple Methods**, **Rotoscoping**
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# Key Insights Summary
### Strong Predictors of Profitability
- Production Budget
- Genre
- Creative Type
- Production Method
### 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
Below is a complete list of all files used throughout this project:
## Dataset Files
- **movies_dataset.csv** — Original dataset downloaded from Kaggle
- **movies_cleaned.csv** — Cleaned version after handling missing values and removing duplicates
## 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
## Visualization Outputs
(Images included in the README)
- **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
## 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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