🎬 Movie Recommender with Content-Based Filtering and NLP

This is a simple movie recommendation system that suggests 5 similar movies based on a movie selected by the user. It uses content-based filtering and genre-based similarity, along with natural language processing techniques, to compute recommendations.

Built using Python and Streamlit, the app is interactive and easy to use — just select a movie and get recommendations instantly.

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🚀 Features

• 🔍 Content-Based Filtering using movie metadata like title, overview, keywords, cast, and genres
• 🎭 Genre-Based Similarity scoring
• 🧠 NLP Techniques like lemmatization for text preprocessing
• 🧰 Uses cosine similarity for vectorizing and comparing movie descriptions
• 🖥️ Streamlit UI for an interactive web app
• 💾 Preprocessed data and model serialized with pickle for fast deployment

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🛠 Tech Stack

• Python
• Streamlit
• scikit-learn
• pandas
• NLTK (for lemmatization)
• Jupyter Notebook (for preprocessing and model building)

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📁 Datasets Used

• TMDb 5000 Movie Dataset

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▶️ How to Run Locally

1. Clone the repository

   git clone https://github.com/tahirkorma/movie-recommender.git
   cd movie-recommender
   

2. Install the required packages

   pip install -r requirements.txt
   

3. Run the app

   streamlit run app.py
   

🌐 Live Demo

• Click here

📸 Screenshot

🙌 Credits

• TMDb for metadata