🎬 Movie Recommender System This project is a Movie Recommender System built using Python and machine learning techniques. It suggests movies to users based on their preferences using content-based filtering.
📌 Features Recommends movies based on title similarity.
Uses NLP techniques to extract and compare movie metadata.
Built using a dataset of movies with information like genres, keywords, cast, crew, and more.
Interactive and user-friendly interface (Jupyter Notebook).
🔍 Technologies Used Python
Pandas, NumPy
Scikit-learn (TfidfVectorizer, cosine_similarity)
NLTK / spaCy (if used)
Jupyter Notebook
📂 Dataset The dataset includes the following features:
Title
Overview
Genres
Keywords
Cast & Crew
(You can link the dataset source here if it's public, e.g., Kaggle.)
🚀 How It Works Loads and cleans the dataset.
Combines important features into a single string.
Converts the text into vectors using TF-IDF.
Calculates similarity between movies using cosine similarity.
Returns the top N similar movies based on user input.
🛠️ How to Run Clone the repo:
bash Copy Edit git clone https://github.com/yourusername/movie-recommender-system.git cd movie-recommender-system Install dependencies:
bash Copy Edit pip install -r requirements.txt Run the Jupyter Notebook:
bash Copy Edit jupyter notebook 📈 Future Improvements Add collaborative filtering.
Build a web app using Flask/Streamlit.
Include user ratings for better recommendations.
🤝 Contributing Feel free to fork the repo and submit a pull request. Suggestions and improvements are welcome!