Project status: In progress

Project Objective: The purpose of this project was to understand what exactly goes behind the inbuilt functions which we use to build a model. So I have coded each and every step instead of using the build in function. This code can help anyone who wants to understand the theory behind the basic recommendation engine

Method used

1. Exploratory analysis
2. Collaborative filtering
   • Neighbourhood formation using Cosine Similarity formula
   • User/Item based recommendation

Technology: Python

Project Description: The dataset used for this project is available at the dataset of LastFM. This project was build to understand the basic of collaborative filtering, the two phase of collaborative filtering i.e. the Neighbourhood formation using Cosine Similarity formula and User/Item based recommendation phase. This project also goes through some basic data exploration to understand the database well. For each product we have given the top 10 recommendations based on Item – Item Similarity matrix.