🎬 Movie Recommender System 🍿

Welcome to the Movie Recommender System! This project uses content-based filtering to suggest movies similar to the ones you love.

🌟 Features

• 📊 Utilizes TMDB dataset for comprehensive movie information
• 🔍 Content-based recommendation using cosine similarity
• 🖼️ Displays movie posters fetched from IMDb
• 👥 Shows top cast members for each recommended movie
• 🚀 Built with Streamlit for a smooth user interface

🛠️ Technologies Used

• Python 3.9+
• Streamlit
• Pandas
• Scikit-learn
• IMDbPY

🚀 Getting Started

Dataset

This project uses the TMDB 5000 Movie Dataset from Kaggle. To get started:

1. Download the following files from Kaggle TMDB 5000 Movie Dataset:

   • tmdb_5000_credits.csv
   • tmdb_5000_movies.csv

2. Place these files in your project directory.

3. Use Jupyter Notebook to open and process the data. You can start with the provided Untitled.ipynb notebook or create a new one.

Data Processing

In your Jupyter Notebook, you can use the following code to load the data:

import pandas as pd

# Load the movies data
movies_df = pd.read_csv('tmdb_5000_movies.csv')

# Load the credits data
credits_df = pd.read_csv('tmdb_5000_credits.csv')

# Merge the dataframes if needed
merged_df = movies_df.merge(credits_df, on='id')

# Now you can start processing and analyzing the data

Make sure to explore the data, handle any missing values, and perform necessary preprocessing steps before building your recommendation system.

Installation

1. Clone the repository:

   git clone https://github.com/Ashutoshdas-dev/Movie-Recommender-System
   cd movie-recommender-system
   

2. Install the required packages:

   pip install -r requirements.txt
   

3. Run the Streamlit app:

   streamlit run app.py
   

🎯 How It Works

1. Select a movie from the dropdown menu.
2. Click "Show Recommendation" to get similar movie suggestions.
3. Browse through the recommended movies, complete with posters and cast information.

📁 Project Structure

• app.py: Main Streamlit application
• movie_list.pkl: Preprocessed movie data
• similarity.pkl: Precomputed similarity matrix
• credit_cast.pkl: Cast information for movies

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check [issues page] https://github.com/Ashutoshdas-dev/Movie-Recommender-System.

📝 License

This project is MIT licensed.

🙏 Acknowledgements

• TMDB for the movie dataset
• IMDb for movie posters and additional information
• Streamlit for the awesome web app framework

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Made with ❤️ by Ashutosh