Book Recommender Engine made with Python for Training Machine Learning Model & Flask for making Web Application to represent the Data.
It is made by using DataBase of 271360 Books & 1149780 ratings of 278858 users.
Website Link: Book Recommender
Algorithms used are:
- K-Nearest Neighbour (KNN) to create Pivot Table of Users & Books Relationship
- Cosine Similarity is used to calculate the similarity between the Books
Libraries used are:
- For Training Model:
- Numpy
- Pandas
- SciKit Learn
- Pickle
- For Web Development:
- Flask
- Bootstrap
- For Integrating ML Model to WebApp:
- Jinja2 Templating Engine
IDE Used:
- Jupyter Notebook for Exploratory Data Analysis (EDA)
- VS Code for developing Flask Web App
Recommendation System Type Used:
- Popularity Based
- Collaborative Filtering Based
Server Used for Deployement: Gunicorn
DataSet Link: https://www.kaggle.com/datasets/arashnic/book-recommendation-dataset
CDN Provider for Images:
- Amazon Images
- Weserv.nl