A machine learning-based Movie Recommendation System that uses K-Nearest Neighbors (KNN) and clustering techniques to suggest similar movies to users based on their preferences or selected titles.

• 📌 Content-based Filtering using KNN
• 🧠 Clustering of similar movies using KMeans
• 🎯 Personalized movie recommendations
• 📊 Based on genres, ratings, popularity, and other metadata
• 🧹 Clean and scalable code structure for further improvements

• Python
• Pandas, NumPy
• Scikit-learn (KNeighborsClassifier, KMeans)
• Matplotlib / Seaborn (optional: for visualizations)
• Jupyter Notebook / Streamlit (if there's a UI)

You can use any of the following:

• TMDB 5000 Movie Dataset
• MovieLens Dataset

Make sure the dataset includes features like:

• Movie title
• Genre
• Keywords
• Ratings
• Popularity / Vote Count

1. Preprocessing: Clean the dataset, handle missing values, and extract relevant features.
2. Feature Vectorization: Use TF-IDF or CountVectorizer on text data (like genres and keywords).
3. Clustering: Group similar movies using KMeans (optional).
4. KNN Algorithm: For a given movie, find its top-N nearest neighbors in the feature space.
5. Recommendation: Return the most similar movies.

1. Clone the repository:

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