End to End e-commerce book recommendation machine learning application with deployment based on collaborative filtering ML algorithm
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
- streamlit
- Machine learning
- sklearn
Clone the repository
https://github.com/daameya/E-commerce-Book-Recommender-System.gitpython -m venv booksbooks\scripts\activatepip install -r requirements.txt