Movie Recommender System 🎥

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A hybrid movie recommender system using content-based and collaborative filtering techniques to provide personalized movie suggestions.

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🔹 Key Features:

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✔️ Implements TF-IDF Vectorization for content-based filtering.✔️ Uses Cosine Similarity to measure movie similarities.✔️ Integrates Collaborative Filtering (User-Based and Item-Based) using KNN.✔️ Processes and cleans data using pandas and NumPy.✔️ Visualizes recommendation insights with Matplotlib and Seaborn.

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🛠 Technologies Used

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🐍 Python📊 Pandas & NumPy (Data Processing)📈 Scikit-learn (Machine Learning)🎯 TF-IDF & Cosine Similarity (Content-Based Filtering)🤝 KNN (Collaborative Filtering)📊 Matplotlib & Seaborn (Data Visualization)

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🎯 Challenges Addressed:

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✅ Handling missing data and noisy movie metadata.✅ Balancing recommendation diversity and accuracy.✅ Scaling collaborative filtering for larger datasets.✅ Evaluating model performance using precision-recall metrics.

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🔗 Interactivity & Links:

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GitHub Repository (https://github.com/Yonatan8475/movie-recommenders.git)