This repository implements a movie recommendation system utilizing various algorithms, including content-based and item-based collaborative filtering. The project includes data exploration, preprocessing, feature extraction, and model training.
- Project-Page -> Click Here
EDA_Preprocessing_and_Feature_Extraction/: Contains notebooks for exploratory data analysis, primary preprocessing, and feature engineering.EDA_and_Primary_Pre-Processing.ipynb: Notebook for exploratory data analysis and initial data cleaning.Feature_Engineering.ipynb: Notebook for extracting and transforming features necessary for model training.
Models/: Contains notebooks implementing different recommendation algorithms.Content_Based_Collaborative_Filtering/Classical_ML_Models.ipynb: Notebook implementing classical machine learning models for content-based filtering.NLP_based_Bag_of_Words.ipynb: Notebook implementing the Bag of Words model for text-based recommendations.
Item_Based_Collaborative_Filtering/Item_Based_Collaborative_Filtering.ipynb: Notebook implementing item-based collaborative filtering.
Raw_Data/: Contains the initial datasets used in the project.links.csv: Dataset containing links to movie information.movies.csv: Dataset with details of the movies.ratings.csv: Dataset containing user ratings for the movies.tags.csv: Dataset with user-generated tags for the movies.
Processed_Data/: Contains processed datasets after cleaning and feature extraction.Featured Data.csv: Dataset containing the features extracted for model training.Pre-Processed Data.csv: Dataset after primary preprocessing steps.
Reports/: Contains generated reports documenting the analysis and models used.Report_EDA_Pre-Processing.pdf: A detailed report on the exploratory data analysis and preprocessing steps.Report_Models.pdf: A report summarizing the model implementations and evaluations.