Movie Recommendation System

This repository contains a movie recommendation system that predicts the next movie a user might want to watch based on their input. The system uses a dataset downloaded from The Movie Database (TMDB) and employs TMDB's API to fetch posters for the recommended movies. The recommendation engine leverages vectorization techniques to suggest similar movies.

Features

• Movie Recommendations: Suggests movies based on the input movie name.
• Movie Posters: Fetches and displays posters of the recommended movies using TMDB API.
• Exploratory Data Analysis (EDA): Includes detailed EDA to understand the dataset. = Vectorization Technique: Uses vectorization methods to compute similarities and recommend movies.

Project Structure

• notebooks/: Jupyter notebooks for EDA, model building, and evaluation.
• scripts/: Python scripts for data preprocessing, model training, and inference.
• data/: Directory containing the original and processed datasets.
• app/: Streamlit application for the frontend.
• requirements.txt: List of all Python dependencies required for the project.

Dataset

The dataset for this project is sourced from Kaggle. It includes information about various movies such as genres, ratings, overviews, and more. The TMDB API is used to fetch movie posters for the recommendations.

Usage

• Enter the name of a movie you like in the input box.
• The app will display a list of recommended movies along with their posters.
• Click on any recommended movie to get more details.

Methodology

• Exploratory Data Analysis (EDA): Conducted EDA to gain insights into the dataset, such as the distribution of genres, ratings, and popularity.
• Vectorization: Used vectorization techniques (like TF-IDF) to convert text data into numerical format for similarity calculations.
• Cosine Similarity: Computed cosine similarity between movie vectors to recommend movies that are most similar to the input movie.

Results

The recommendation model was able to suggest movies with high relevance based on user input. Below is a sample output of the movie recommendations:

Future Work

• Integrate user ratings to improve the recommendation quality.
• Explore deep learning techniques for more advanced recommendations.
• Add support for recommending TV shows and other media types.

Acknowledgments

• TMDB for providing the API and dataset.
• Streamlit for creating a simple and effective web application framework.

Contact

For any questions or suggestions, please feel free to reach out at my linkedin as mentioned in my profile.