🎬 Movie Recommendation System Using Python

📌 Project Overview

This project aims to build a Movie Recommendation System using Python and Machine Learning techniques. The system provides personalized movie suggestions to users, helping them discover films they are likely to enjoy.

🧠 Recommendation Techniques Used

The system combines two major approaches:

1. Collaborative Filtering

   • Learns from user interactions (like ratings).
   • Finds patterns among users with similar tastes.
   • Recommends movies based on similar users' preferences.

2. Content-Based Filtering

   • Uses movie features like genre, cast, and plot.
   • Recommends movies similar to those the user liked before.

By integrating both techniques, the system delivers more accurate and personalized recommendations.

Features

• Simple text-based input and output
• Recommends similar movies using TF-IDF and cosine similarity
• Uses a dataset of movies with metadata
• User-friendly CLI interface

How It Works

1. Loads movie data from a CSV file.
2. Combines relevant movie features into one text field.
3. Transforms this text using TF-IDF vectorization.
4. Calculates similarity between movies using cosine similarity.
5. Asks the user for a movie title and recommends similar ones.

🛠️ Technologies & Libraries

• Python
• Pandas – Data manipulation
• Scikit-learn – Machine learning models & similarity metrics
• Surprise Library – Collaborative filtering models
• Difflib – For matching close movie titles
• TfidfVectorizer – To analyze textual features of movies
• Cosine Similarity – To measure similarity between movies

📈 Model Evaluation

• The model performance is evaluated using:
  • Root Mean Square Error (RMSE)
    To ensure reliable accuracy in recommendations.

🎯 Project Goals

• Deliver relevant and engaging movie suggestions.
• Simplify the movie discovery process for users.
• Show the power of Machine Learning in building real-world recommendation systems.