This repository contains the work we did for the course Business Analytics (CSE352). We applied various techniques to the MovieLens dataset to gain insights and make predictions.
- Machine Learning Model Prediction: We used machine learning algorithms to predict user ratings for movies.
- Associative Rule Mining: We applied associative rule mining to find relationships between movies and user ratings.
- Collaborative Filtering: We implemented collaborative filtering to make personalized movie recommendations to users.
- Content-Based Filtering: We used content-based filtering to recommend movies to users based on their past preferences.
- Social Network Analysis: We analyzed the social network of users to understand how their connections influence their movie preferences.
- Text Mining: We applied text mining techniques to extract useful information from movie descriptions and user reviews.
- Time Series Analysis: We performed time series analysis to understand trends and patterns in movie ratings over time.
The MovieLens dataset is a widely used dataset for movie recommendation systems. It contains user ratings for movies, as well as information about the movies such as their genres and release dates.
Business Analytics (CSE352) is a course that teaches students how to use data analysis techniques to make informed business decisions. In this course, we learned about various techniques such as machine learning, data mining, and predictive modeling, and applied them to real-world datasets.