The Movie Recommender System Website

This project is a content-based movie recommender system that suggests similar movies based on a selected title.
It uses natural language processing, vector similarity techniques, and a user-friendly Streamlit web interface for seamless interaction.

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About the Project

The aim of this project is to recommend similar movies using their metadata such as title, genre, overview, keywords, etc.
I deployed the app using Streamlit and utilizes The Movie Database (TMDB) API for fetching posters and movie details.

Key techniques used include:

• CountVectorizer for transforming text to vectors
• Cosine Similarity to find similar movies
• Porter Stemmer for text cleaning
• TMDB API to fetch real-time movie posters and metadata
• Kaggle dataset for historical movie data

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Topics Covered

• Text vectorization with CountVectorizer
• Cosine similarity for recommendation
• Text cleaning with NLTK's PorterStemmer
• REST API integration (TMDB)
• Streamlit Web App Development
• Data Cleaning and Feature Engineering
• Python libraries: Pandas, scikit-learn, NLTK, requests

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Dataset Source:

Kaggle movie metadata dataset (TMDB)

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Technologies Used in Project:

Python, Data Proprocessing Pandas, NumPy scikit-learn NLTK (PorterStemmer) Streamlit TMDB API CountVectorizer & Cosine Similarity

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Features:

1. Suggests top 5 similar movies based on selected title.
2. Fetches posters and movie data in real-time from TMDB.
3. Simple and fast web interface.
4. Easy to scale and deploy.