Recipe Popularity Prediction

This project provides a machine learning model to predict the popularity of recipes based on their characteristics. It includes a Flask web application for user interaction and a machine learning pipeline for training and evaluating the prediction model.

Features

• Predict Recipe Popularity: Enter recipe details to get a popularity score.
• Flask Web Application: User-friendly interface for making predictions.
• Machine Learning Pipeline: Data preprocessing, feature engineering, model training, and evaluation.
• Hyperparameter Tuning: Uses GridSearchCV for optimizing the RandomForestRegressor model.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/recipe-popularity-prediction.git
   cd recipe-popularity-prediction

2. Install dependencies:

   pip install -r requirements.txt

Running the Web Application

1. Train the model and generate recipe_popularity_model.pkl:

   python recipe-popularity-prediction.py

2. Start the Flask app:

   python recipe-popularity-app.py

3. Open your browser and go to http://127.0.0.1:5000/

Project Structure

recipe-popularity-prediction/
│── recipe-popularity-app.py        # Flask web application
│── recipe-popularity-prediction.py # Machine learning pipeline
│── recipes_with_popularity.csv     # Dataset (replace with actual file)
│── recipe_popularity_model.pkl     # Trained model (generated after training)
│── templates/
│   ├── index.html                   # HTML template for web app
│── README.md                        # Documentation

Model Details

The model is trained using a Random Forest Regressor, with the following features:

• rating
• servings
• prep_time_minutes
• cook_time_minutes
• total_time_minutes
• ingredient_count
• calories
• cuisine_type

Performance Metrics

• Mean Absolute Error (MAE)
• Root Mean Squared Error (RMSE)
• R-squared Score (R²)

Example API Request

You can send a POST request to the /predict endpoint with the following data:

{
    "rating": 4.5,
    "servings": 4,
    "prep_time_minutes": 30,
    "cook_time_minutes": 45,
    "total_time_minutes": 75,
    "ingredient_count": 12,
    "calories": 350,
    "cuisine_type": "italian"
}

License

This project is licensed under the MIT License.

Author

Sanjay ram - GitHub Profile