Book Recommender System Using Machine Learning

This is a streamlit web application that can recommend various kinds of similar books based on an user interest.

Demo:

Dataset has been used:

• Dataset link

Concept used to build the model.pkl file : NearestNeighbors

1 . Load the data

2 . Initialise the value of k

3 . For getting the predicted class, iterate from 1 to total number of training data points

4 . Calculate the distance between test data and each row of training data. Here we will use Euclidean distance as our distance metric since it’s the most popular method.

5 . Sort the calculated distances in ascending order based on distance values

6 . Get top k rows from the sorted array

How to run?

STEPS:

Clone the repository

https://github.com/entbappy/Books-Recommender-System-Using-Machine-Learning

STEP 01- Create a conda environment after opening the repository

conda create -n books python=3.7.10 -y

conda activate books

STEP 02- install the requirements

pip install -r requirements.txt

#run this file to generate the models

Books Recommender.ipynb

Now run,

streamlit run app.py

Author: Utpal Paul
Data ML Engineer
Email: utpalpaul108@gmail.com