🎬 Content-Based Movie Recommendation System

This project implements a content-based recommendation system that suggests movies based on user preferences and movie genres using IMDB dataset.

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📌 What is a Content-Based Recommendation System?

A content-based recommender system suggests items to users based on the features of the items and the preferences shown by the user. Unlike collaborative filtering (which relies on user interactions and similarity between users), content-based systems use only item features and the user’s own history to make recommendations.

✅ Key Characteristics:

• Does not require data from other users
• Works well with sparse data
• More interpretable, as recommendations are based on specific item attributes

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🧠 Project Goal

The goal of this project is to build a recommendation engine that suggests movies based solely on their genres and the movies a user has rated. We use the IMDB dataset, including movies.csv and ratings.csv.

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🔧 Implementation Steps

1. Data Loading

   • Load movies.csv and ratings.csv into pandas DataFrames

2. Data Preprocessing

   • Extract the year from movie titles
   • Drop the timestamp column
   • Split genres into lists and apply one-hot encoding

3. User Input

   • Simulate user ratings by creating a list of favorite movies and scores

4. User Profile Generation

   • Build a genre-based profile of the user using their ratings

5. Recommendation Scoring

   • Score all movies by comparing their genres to the user's profile

6. Output Recommendations

   • Display the top 20 recommended movies

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📁 Dataset Files

• movies.csv — contains movieId, title, genres
• ratings.csv — contains userId, movieId, rating, timestamp

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🧪 Requirements & Execution

This project uses the following Python libraries:

pandas
numpy
matplotlib

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🚀 To Run:

1. Open the .ipynb notebook in Google Colab or Jupyter Notebook
2. Run the cells in order
3. Provide user input (movie titles and ratings) when prompted
4. View the recommended movie list at the end

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📊 Sample Output

If a user rates these movies:

• "Toy Story" ⭐⭐⭐⭐⭐
• "Jumanji" ⭐⭐⭐⭐
• "Pulp Fiction" ⭐⭐⭐⭐

The system might recommend:

1. "Aladdin" (Similarity: 92%)
2. "The Lion King" (Similarity: 89%)
3. "Reservoir Dogs" (Similarity: 85%)

Recommendations are based on genre similarity to rated movies

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🤝 Contributions

Have an idea to improve the system? Want to add features like:

• 🎭 Actor/Director analysis
• 📝 Plot summaries (NLP processing)
• 💬 User reviews integration
• 🎞️ Movie poster visualization