Principal Component Analysis (PCA) for Movie Recommender System

This repository contains code for performing Principal Component Analysis (PCA) on a movie dataset and implementing a recommender system function based on the PCA results.

Overview

Principal Component Analysis (PCA) is a dimensionality reduction technique commonly used in data analysis and machine learning. In this project, PCA is applied to a movie dataset to identify patterns and relationships among movies and users.

The steps involved in the project include:

1. Loading and preprocessing the movie dataset.
2. Performing PCA to reduce the dimensionality of the dataset.
3. Visualizing the results using scree plots, loading plots, and score plots.
4. Implementing a recommender system function based on the PCA results.

Dataset

The dataset used in this project consists of the following files:

• dataMatrix.csv: Matrix of user ratings for movies.
• selectedMovies.csv: Information about selected movies.
• usertype.csv: Information about user types.

Results

Upon running the main script, the following results will be displayed:

• Scree plot: Explained variance ratio of principal components.
• Loading plot: Visualization of feature loadings on principal components.
• Score plots: Visualization of projected data points onto principal components.
• Recommended movies: List of movies recommended for a specific user based on PCA.