Movie Recommendation System

This project aims to build a movie recommendation system using content based machine learning technique. The system processes and analyzes movie data to recommend similar movies based on a given input.

Project Flow

1. Data Collection
2. Data Pre-processing
3. Building the ML Model
4. Creating the Website

Working

1. Data Import - Data is imported from the TMDB 5000 movie dataset (available on Kaggle).
2. Data Cleaning - Remove attributes such as revenue, release date, runtime, etc.
3. Data Preprocessing
   • Convert all text to lowercase.
   • Remove all punctuations and non-word characters (special symbols, etc.).
   • Remove stop words (are, the, is, etc.).
   • Apply stemming to reduce words to their root form.
4. Building the Model
   • Concatenate tags from the dataset.
   • Build a dataframe that records the frequency of tags.
   • Transform each row into a vector.
   • Use an N-dimensional vector space for cosine similarity to calculate distances between vectors.

Tech Stack

• Python: The primary programming language for data processing and model building.
• Streamlit: Used for creating the web application.
• DataSet: TMDB 5000 movie dataset from Kaggle.
• Development Tools: Jupyter Notebook and PyCharm.

Contributing

Feel free to submit issues and pull requests. Contributions are welcome!

Screenshot