🎬 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!