This is a streamlit web application that can recommend various kinds of similar books based on an user interest.
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
Clone the repository
https://github.com/entbappy/Books-Recommender-System-Using-Machine-Learning
conda create -n books python=3.7.10 -y
conda activate books
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