Movie Recommendation System using Machine Learning

WE HAVE DONE THE TWO PROJECTS

1. Movie Recommendation System (Hollywood Dataset)

We have developed a recommendation system using the Hollywood Movie Dataset. The system suggests movies based on user preferences and leverages machine learning techniques for accurate predictions.

Kaggle link: https://www.kaggle.com/code/bandimohitha/movie-recommendation-using-m-l

2. An Incremental Approach for the Selection of Bias in Recommendation System (Tollywood Dataset)

This project focuses on building a recommendation system for the Tollywood Movie Dataset using Collaborative Filtering and the PIP (Proximity, Impact, Popularity) measure.

Features:

• Content-Based and Collaborative Filtering Systems: Implemented to provide personalized recommendations.
• PIP Measure: Enhances the recommendation accuracy by considering proximity, impact, and popularity.
• Process Workflow:
  • Data Collection using Web Scraping
  • Data Preparation and Cleaning
  • Exploratory Data Analysis (EDA)
  • Data Visualizations
  • Machine Learning Model Application

Group Members

• Mohitha Bandi (22WU0105037)
• Satya Vaishnavi Gumpally (22WU0106017) - @vaishnavi0307
• Hemanth Bandi (22WU0106028)

Note

We are in the process of writing a research paper based on this project. As a result, no code or additional details have been shared in this repository. Please stay tuned for updates!