software and tools required

1. Github
2. VSCode
3. Heroku

Movie Recommendation System

📋 Overview
The Movie Recommendation System is a Python-based application powered by Machine Learning to recommend movies based on user preferences. It combines content-based filtering and collaborative filtering techniques. The system includes a user-friendly interface built using Flask for interactive movie recommendations.

✨ Key Features

• Interactive Movie Search: Input a movie title to receive tailored recommendations.
• Content-Based Filtering: Utilizes movie features like genres, keywords, cast, and director for recommendations.
• Collaborative Filtering: Suggests movies based on user behavior patterns.
• Data Insights: Includes genre distribution, runtime distribution, and other visualizations for exploratory analysis.

Dataset The system uses a movie dataset (movies.csv) containing features like genres, keywords, tagline, cast, and director.

🛠️ Technologies Used

Category	Technologies
Programming Language	Python
Framework	Flask
Libraries	pandas, numpy, scikit-learn, nltk, matplotlib, seaborn, TF-IDF, Pickle
ML Techniques	TF-IDF Vectorization, Cosine Similarity, Collaborative Filtering

🚀 Installation
Follow these steps to set up the application:

1. Clone the Repository:

   git clone https://github.com/YourUsername/Movie-Recommendation-System.git  
   cd Movie-Recommendation-System  

2. Create and Activate a Virtual Environment:

   python -m venv venv  
   source venv/bin/activate    # On Windows use `venv\Scripts\activate`  

3. Install Dependencies:

   pip install -r requirements.txt  

4. Run the Application:

   python app.py  

⚙️ How It Works

Step-by-Step Guide

1. Upload Data: Load the movie dataset (pre-configured as movies.csv).
2. Preprocessing: Data is cleaned, vectorized using TF-IDF, and models are trained.
3. Input Search: Provide a movie name to get recommendations.
4. Explore Results: Analyze the recommendations with visualizations and download the results as needed.

💻 Code Highlights

Text Vectorization
Transforms text features using TF-IDF for similarity computation.

from sklearn.feature_extraction.text import TfidfVectorizer  

tfidf_vectorizer = TfidfVectorizer(max_features=5000)  
tfidf_matrix = tfidf_vectorizer.fit_transform(movie_data['combined_features'])  

Similarity Computation
Calculates cosine similarity between movies.

from sklearn.metrics.pairwise import cosine_similarity  

cosine_sim = cosine_similarity(tfidf_matrix)  

🎯 Applications

• Movie Streaming Platforms: Provide personalized recommendations to users.
• Entertainment Portals: Enhance user experience with tailored suggestions.
• Data Enthusiasts: Explore data-driven approaches to recommendation systems.

📊 Model and Features

• Dataset: Contains features like genres, keywords, tagline, cast, and director.
• Models Used: TF-IDF Vectorization for content-based filtering and collaborative filtering for user-based recommendations.

📦 Folder Structure

Movie-Recommendation-System/  
│  
├── app.py                      # Flask application file  
├── static/                     # Static assets (CSS, JS, images)  
├── templates/                  # HTML templates for the app  
├── data/                       # Dataset folder (e.g., movies.csv)  
├── models/                     # Saved models (e.g., TF-IDF vectorizer, Pickle files)  
├── requirements.txt            # List of dependencies  
├── exploratory_analysis.ipynb  # Jupyter Notebook with EDA  
└── README.md                   # Project documentation  

🏗️ Contributing
Contributions are welcome!

• Fork the repository.
• Create a new branch:

  git checkout -b feature-name  

• Make your changes and commit:

  git commit -m "Add feature"  

• Push the branch:

  git push origin feature-name  

• Open a pull request.

🙏 Acknowledgments

• Flask for the web application framework.
• scikit-learn for machine learning capabilities.
• pandas for data manipulation.
• nltk for natural language processing.

---