Welcome to the Book Recommender System GitHub project! This repository is dedicated to the development of a recommendation system that suggests books to users based on their reading preferences and behaviors. The goal is to provide personalized book recommendations, enhancing the reading experience and introducing users to books they might enjoy.
Key Steps and Achievements:
1.Data Collection and Preparation: Gathered a dataset containing information about books, users, and ratings. Cleaned and preprocessed the data to handle missing values and ensure consistency.
2.Exploratory Data Analysis (EDA): Conducted EDA to gain insights into the dataset's distribution, relationships, and patterns. Visualized data trends and explored correlations between user ratings and book attributes.
3.Collaborative Filtering: Implemented collaborative filtering techniques to recommend books based on similar users' preferences. Calculated user-user and item-item similarities to identify potential recommendations.
4.Content-Based Filtering: Developed a content-based filtering approach to recommend books based on their attributes and characteristics. Created feature vectors for each book and calculated cosine similarity to identify relevant book suggestions.
5.Model Training and Evaluation: Trained recommendation models using appropriate algorithms and techniques. Evaluated the models using relevant metrics such as precision, recall, and F1-score.
Impact: Through this project, you built a functional Book Recommender System that empowers users with personalized book recommendations. By combining collaborative filtering and content-based filtering you achieved a comprehensive solution that can enhance users' reading experiences.