https://github.com/nirmaldeepponnada/codeclauseinternshipproject1

This project involves Customer Segmentation using K-Means clustering to group customers based on Recency, Frequency, and Monetary (RFM) analysis from the Online Retail dataset. It also performs Sentiment Analysis on Amazon Product Reviews using Natural Language Processing techniques & Logistic Regression to classify reviews as positive or negative.
https://github.com/nirmaldeepponnada/codeclauseinternshipproject1

kmeans logistic-regression numpy pandas python3 regular-expressions scikit-learn tf-idf-vectorizer

Last synced: 5 days ago
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This project involves Customer Segmentation using K-Means clustering to group customers based on Recency, Frequency, and Monetary (RFM) analysis from the Online Retail dataset. It also performs Sentiment Analysis on Amazon Product Reviews using Natural Language Processing techniques & Logistic Regression to classify reviews as positive or negative.

• Host: GitHub
• URL: https://github.com/nirmaldeepponnada/codeclauseinternshipproject1
• Owner: nirmaldeepponnada
• Created: 2024-11-08T08:52:06.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-11-08T09:20:56.000Z (3 months ago)
• Last Synced: 2024-11-23T07:07:30.219Z (2 months ago)
• Topics: kmeans, logistic-regression, numpy, pandas, python3, regular-expressions, scikit-learn, tf-idf-vectorizer
• Language: Jupyter Notebook
• Homepage:
• Size: 21.3 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# CodeClauseInternshipProject1

### Summary of the Project Code

1. **Customer Segmentation:**

  * Cleans and preprocesses the online retail data.

  * Computes Recency, Frequency, and Monetary values for customer segmentation.

  * Applies K-Means clustering to create customer segments.

2. **Sentiment Analysis:**

  * Cleans and preprocesses customer review text.

  * Uses TF-IDF for feature extraction.

  * Trains a logistic regression model to classify reviews as positive or negative.