Movie Recommendation System
Project Overview
Project Purpose
The purpose of the Movie Recommendation System is to help users discover movies that align with their preferences efficiently. By leveraging machine learning algorithms, the system automates the recommendation process and provides personalized suggestions, reducing the effort needed to browse large movie databases. It also demonstrates how data-driven approaches can enhance user experience in entertainment platforms.
Objectives
To provide a responsive and visually appealing web interface using Flask and HTML/CSS.
To allow users to input a movie and receive personalized suggestions.
To analyze movie features such as genres, keywords, tagline, cast, and director for better recommendations.
To create a system that can handle multiple recommendation methods and display results efficiently.
My Role
I worked as a Full-stack Developer and Machine Learning Engineer on this project. I implemented both Decision Tree and Cosine Similarity recommendation algorithms, integrated them into a Flask web application, and designed the responsive HTML templates. I also processed and analyzed the movie dataset, created the Jupyter Notebook workflow, and ensured the system provides accurate and interactive movie recommendations.
Key Highlights
Dynamic AI Content
Utilized GPT-4 for generating project descriptions and case studies.
Thematic Design Engine
Created a system for applying different visual themes.
One-Click Deployment
Integrated with Vercel and Netlify for seamless publishing.
In-Depth Narrative
Conception & Challenge
The main challenge was designing a system that could provide accurate and meaningful recommendations using different machine learning approaches while maintaining a fast and responsive web interface. Handling a large dataset of movies, preprocessing features, and ensuring both algorithms produced reliable suggestions required careful planning and testing. Additionally, creating two different visually appealing templates that integrate seamlessly with the recommendation logic posed a design challenge.
Architecture & Solution
The Movie Recommendation System uses a client-server architecture with a Python Flask backend and a responsive HTML/CSS frontend. It processes a CSV movie dataset and integrates recommendation algorithms—Decision Tree and Cosine Similarity—to provide real-time movie suggestions based on features like genres, keywords, tagline, cast, and director. A Jupyter Notebook was used for data preprocessing and model development, ensuring accurate and efficient recommendations.
Unique Features
The Movie Recommendation System offers dual recommendation methods using Decision Tree and Cosine Similarity algorithms, providing feature-based suggestions that consider genres, keywords, tagline, cast, and director. It has an interactive web interface with two gradient-themed templates, ensuring a responsive and user-friendly experience, and delivers real-time, personalized movie recommendations efficiently.
Technologies Utilized
Technologies and tools used in this project