https://github.com/shyamkrishna466/e-commerce-ml
Machine learning projects in ecommerce and retail domain
https://github.com/shyamkrishna466/e-commerce-ml
classification clustering machine-learning regreession
Last synced: 3 months ago
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Machine learning projects in ecommerce and retail domain
- Host: GitHub
- URL: https://github.com/shyamkrishna466/e-commerce-ml
- Owner: shyamkrishna466
- Created: 2025-02-01T08:42:21.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-03-06T10:05:59.000Z (3 months ago)
- Last Synced: 2025-03-06T11:22:54.250Z (3 months ago)
- Topics: classification, clustering, machine-learning, regreession
- Language: Jupyter Notebook
- Homepage:
- Size: 487 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Machine Learning Projects in E-commerce
This repository contains a collection of Machine Learning (ML) projects tailored for the E-commerce domain. These projects aim to enhance various aspects of online retail, such as product recommendations, customer segmentation, sales forecasting, fraud detection, and more.
##🚀 Projects Included
### 1️⃣Advertisement Sales Prediction using Regression
This project explores how machine learning regression models can be used to predict advertisement-driven sales. By analyzing advertising expenditures across different channels (TV, radio, social media, etc.), we can determine their impact on sales and optimize marketing budgets effectively.
#### 2️⃣Customer Churn Prediction using Machine Learning
This project focuses on predicting customer churn using classification models. By analyzing customer behavior and usage patterns, businesses can identify customers likely to leave and take proactive measures to retain them
##### 3️⃣ Mall Customer Segmentation using Machine Learning
This project focuses on customer segmentation using unsupervised learning techniques, specifically clustering algorithms. By analyzing customer spending behavior and demographics, businesses can group similar customers and tailor marketing strategies accordingly