README.md

PERSONAL PROJECT

Project Title : BOOK RECOMMENDATION USING COLLABORATIVE FILTERING

Technologies : Machine Learning Technology

Domain : Recommendation System

What actually is Recommendation System?

A recommendation engine is a class of machine learning which offers relevant suggestions to the customer. Before the recommendation system, the major tendency to buy was to take a suggestion from friends. But Now Google knows what news you will read, Youtube knows what type of videos you will watch based on your search history, watch history, or purchase history. A recommendation system helps an organization to create loyal customers and build trust by them desired products and services for which they came on your site. The recommendation system today are so powerful that they can handle the new customer too who has visited the site for the first time. They recommend the products which are currently trending or highly rated and they can also recommend the products which bring maximum profit to the company. For this particular project we have used collabrotive filtering.

What is collaborative filtering?

Collaborative based filtering recommender systems are based on past interactions of users and target items. In simple words here, we try to search for the look-alike customers and offer products based on what his or her lookalike has chosen. Let us understand with an example. X and Y are two similar users and X user has watched A, B, and C movie. And Y user has watched B, C, and D movie then we will recommend A movie to Y user and D movie to X user. Youtube has shifted its recommendation system from content-based to Collaborative based filtering technique. If you have experienced sometimes there are also videos which not at all related to your history but then also it recommends it because the other person similar to you has watched it. :- Analytics Vidhya

Problem Statement:

-> The traditional book ordering system is a manual and time-consuming process wherethe customer has to visit a bookstore to search and purchase the books. In this tightschedule, problems arise in finding specific books due to the inadequate distribution of books through the bookshop. The buyer could not get a recommendation for the correctselection of books.

Aim/Goal :

To create an end to end api for book recommendation system using the collaborative filtering.

Dataset

The dataset is taken from kaggle.

Link :- https://www.kaggle.com/rxsraghavagrawal/book-recommender-system

It consists of three csv files -

1. Books – first are about books which contain all the information related to books like an author, title, publication year, etc.

2. Users – The second file contains registered user’s information like user id, location.

3. ratings – Ratings contain information like which user has given how much rating to which book.

So based on all these three files we can build a powerful collaborative filtering model. let’s get started.

Tools Used:

1. Python

2. Pandas

3. Numpy

4. Sklearn

5. Flask

7. Pickle

8. HTML

9. Heroku

Platforms Used:

Jupyter Notebook, VS Code, Github, Heroku

Deployment Link

https://book-recommendation-an.herokuapp.com/

User Interface