https://github.com/5h3r10k/cs210-final

https://github.com/5h3r10k/cs210-final

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• Host: GitHub
• URL: https://github.com/5h3r10k/cs210-final
• Owner: 5h3r10k
• Created: 2024-12-10T05:05:21.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-12-26T16:43:50.000Z (5 months ago)
• Last Synced: 2025-02-07T22:26:47.459Z (4 months ago)
• Language: Jupyter Notebook
• Size: 938 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# CS210-final

## **Movies Data Analysis and Prediction**

This project is a comprehensive analysis of a movies dataset, with visualizations and a predictive model to forecast movie revenue based on specific features. It leverages Python libraries like pandas, matplotlib, and scikit-learn to clean data, visualize trends, and build predictive models.

---

### **Project Features**

1. **Data Cleaning**:

   - Removed missing or invalid entries for critical features such as `runtime`, `budget`, and `revenue`.

   - Filtered out invalid release years.

2. **Exploratory Data Analysis**:

   - **Key visualizations**:

     - **Average runtime of movies by release year**.

     - **Distribution of revenue across years**.

     - **Budget trends over time**.

   - Used matplotlib to present trends and insights.

3. **Predictive Model**:

   - A **Linear Regression model** built using `scikit-learn` predicts movie revenue.

   - Features used for prediction:

     - `release_year`

     - `runtime`

     - `budget`

     - `vote_average`

   - Model evaluation using the R² score.

4. **Prediction Example**:

   - Predicted revenue for a hypothetical movie with:

     - Release year: 2022

     - Runtime: 120 minutes

     - Budget: $10,000,000

     - Vote average: 7.5

---

### **Installation**

1. **Clone the repository**:

   ```bash

   git clone 

   ```

2. **Install required packages**:

   ```bash

   pip install -r requirements.txt

   ```

   Key dependencies include:

   - `pandas`

   - `matplotlib`

   - `scikit-learn`

3. **Run the Jupyter Notebook**:

   ```bash

   jupyter notebook CS210_final.ipynb

   ```

---

### **Usage**

1. **Run the notebook** to execute all cells sequentially for data analysis and visualization.

2. **Modify predictive features** in the model section to predict revenue for custom movie scenarios.

---

### **Example Prediction**

```python

# Example features

release_year = 2022

runtime = 120

budget = 10_000_000

vote_average = 7.5

# Predicted revenue

predicted_revenue = model.predict([[release_year, runtime, budget, vote_average]])

print(f"Predicted revenue: {predicted_revenue}")

```

---

### **Output Highlights**

- **R² Score**: The model achieved a score of `0.5509`, indicating moderate predictive power.

- **Predicted Revenue for Example**: $26,244,374.

Feel free to run and modify the notebook for further insights or enhancements to the predictive model.