So we created a categorial algorithm to simplify the category description and easy to query. The reason behind this is that categories are added by business owners. There are always a long text to describe the business' category. Categories in Yelp dataset is very complicated.The detailed result will be geomapping into the map with detailed contact information(phone,address,rating and etc.) Using Yelp Fusion API, the application can query the business in any category and from any location.Also the application provides the full data tables to display the whole business information(Totla 33,412 businesses included) Provide Yelp GTA business overview dashborad,which includes the total business number,rating number and reviewed number and also adds key filters to provide the detailed information.Deploy the system and provide the APIs capabilities (Python Flask Web Server).Select the recommendation algorithm(Item-based Collaborative filter).Select the machine learning models,train and fine-tune the models (Logistic Regression,XGBoost,Light-GBM and Ensemble Models).Feature Engineering - Numeric Features,Categorical Features,Time Series Features,Text Features and Handling the missing data.Data Visualization and EDA - Discover and visualize the data to gain insights ( Matplotlib, Seaborn, JavaScript, D3, plot.ly and leaflet mapping).Data Preprocessing, Extract-Transform-Load (JSON to CSV, Database: PostgreSQL 10).This project is a full-stack data analytics application.We will use the newly updated dataset from Yelp Dataset Website. It is aggregated check-ins over time for each of the 192,609 businesses. The Yelp dataset includes 1,223,094 tips by 1,637,138 user.There are over 1.2 million business attributes like hours, parking, availability, and ambience.Yelp Business Data Analytics, Recommendation and Rating Prediction Table of Contents
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |