🎬 Movie Revenue Prediction 💰

This project is a Machine Learning-based application that predicts a movie's gross revenue based on budget, number of votes, runtime, and other important factors.

The project applies Linear Regression, Polynomial Regression, Random Forest, and XGBoost models to find the best predictions. We use GridSearchCV to fine-tune hyperparameters and deploy an interactive Streamlit web app for real-time predictions.

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🚀 Features

✔ Data Analysis & Visualization – Correlation heatmaps, bar plots, scatter plots, etc.
✔ Machine Learning Models – Linear Regression, Polynomial Regression, Random Forest, and XGBoost.
✔ Hyperparameter Optimization – Uses GridSearchCV for the best model settings.
✔ Feature Importance Analysis – Identifies the most important factors affecting revenue.
✔ Interactive Web App – A Streamlit-based UI where users can enter movie details and get predictions.
✔ Model Persistence – Saves the best-trained model for future use.

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📂 Project Structure

Movie_Revenue_Prediction/
│── 📁 data/ # Contains raw dataset
│ ├── movies.csv # Raw movie dataset
│
│── 📁 models/ # Contains scripts for ML training
│ ├── analysis_and_charts.py # Data analysis & visualization (correlation heatmaps, graphs)
│ ├── model.py # Machine Learning model implementation
│ ├── trained_model.py # Script for model training & tuning
│
│── 📄 app.py # Streamlit web app for predictions
│── 📄 requirements.txt # Dependencies needed for the project
│── 📄 README.md # Documentation

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📊 Machine Learning Models Used

• Linear Regression 📉
• Polynomial Regression 🔵
• Random Forest 🌳
• XGBoost ⚡ (Best performing model)

We applied hyperparameter tuning using GridSearchCV to optimize performance.

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⚙️ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/Ra638/Movie_Revenue_Prediction.git  
cd Movie_Revenue_Prediction  
pip install -r requirements.txt  
python models/analysis_and_charts.py  
python models/trained_model.py  
python models/trained_model.py

🔥 Model Performance

Model                           MAE   MSE        R² Score  
---------------------------------------------------------  
Linear Regression               70M   1.77e+16   0.50  
Polynomial Regression (Degree 2) 49M   1.16e+16   0.67  
Polynomial Regression (Degree 3) 49M   1.16e+16   0.67  
Random Forest                   49M   1.09e+16   0.69  
XGBoost (Tuned)                 49M   9.69e+15   0.77  

✅ XGBoost performs the best with R² = 0.77!

🎨 Web App Preview

The project includes a Streamlit Web App where users can enter details and get predictions.

🛠 Future Improvements

🚀 Enhance Feature Engineering – Add more relevant features 📈 Try Deep Learning Models – Test Neural Networks for better accuracy 🌐 Deploy Online – Host the app on AWS/GCP/Heroku

🤝 Contributing

Want to improve this project? Feel free to fork and submit a Pull Request 🎯