Yelp Recommendation System

This repository contains work done as part of DS-1 course by Univ.ai.
Our team - Himanshu, Chaitanya, Aayush, Samyak

Project

Resaurant recommendation on Yelp data, based on collaborative filtering

• Recommender systems are an integral part of many online systems.
• From e-commerce to online streaming platforms. Recommender systems employ the past purchase patters on it's user to predict which other products they may in interested in and likey to purchase.
• Recommending the right products gives a significat advantage to the business

Data

• Yelp.com, is a crowd-sourced local business review and social networking site.

• The site has pages devoted to individual locations, such as restaurants or schools, where Yelp users can submit a review of their products or services using a one to five star rating scale.

• The data: https://www.yelp.com/dataset

• Please read about the dataset here.

Exploratory Data Analysis

• Following notebook contains the EDA of the yelp data.

Collaborative Filtering

• The collaborative filtering algorithm is very popular in online streaming platforms and e-commerse sites where the customer interacts with each product (which can be a movie/ song or consumer products) by either liking/ disliking or giving a rating of sorts
• Following notebook contains colaborative filtering based recommendation system model training and results

Presentation

• The followinng poster summarizes the whole project along with results.
• Click here to view the presentation.