Embedded Files
Classification of online shopping review based on Machine Learning approach.
About_Shopping
About_Shopping
Visiting_Issue
Visiting_Issue
Complacency
Complacency
Column Name= ["Time","Name","Age","Gender","About Shopping","Shopping For","Finding For","Visiting Issue","Compared to OS","Complacency","Find ERP","Payment Complacency", "CheckOut Experience","Product Receive TimeLine","Rider Review","CS Experience"]
Categorical Features= Gender,About_Shopping ,Shopping_For, Finding_For,Visiting_Issue,Compared_to_OS,Complacency,Find_ERP,Payment_Complacency,Product_Receive_TimeLine
Numerical Features= Age,CheckOut_Experience,Rider_Review,CS_Experience
Dataset Contain : (4026, 14)
Data Pre-Processing
Separate Categorical and Numerical Features
Explority Data Analysis ( EDA )
Separate X and Y
Feature Selection
Dropping Less Importants Column
Normalization of the Dataset
Model Implementation
In this project, I have implement a machine learning model to classify online shopping reviews based on their complacency level. The target column has five unique values, 'Neutral', 'Very satisfied', 'Satisfied', 'Dissatisfied', and 'Very dissatisfied'. I have used the following machine learning algorithms:
Support Vector Machine,
K-Neighbors Classifier,
Gaussian Naive Bayes,
Decision Tree Classifier,
Random Forest
Deep Learning-Based sentiment Analysis of Bangla Text for Football Leveraging LSTM Models on Social Media Data
Data Set Summary
Data Set Summary
WordCloud
WordCloud
Student Result Management System
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