https://github.com/rahamniabdelkaderseifelislem/prodigy_ml_02
Task 2 of the Prodigy InfoTech ML internship which involves Creation of K-means clustering algorithm to group customers of a retail store based on their purchase history.
https://github.com/rahamniabdelkaderseifelislem/prodigy_ml_02
clustering k-means k-means-clustering k-means-implementation-in-python machine-learning machine-learning-algorithms machine-learning-models plotly
Last synced: 2 months ago
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Task 2 of the Prodigy InfoTech ML internship which involves Creation of K-means clustering algorithm to group customers of a retail store based on their purchase history.
- Host: GitHub
- URL: https://github.com/rahamniabdelkaderseifelislem/prodigy_ml_02
- Owner: RAHAMNIabdelkaderseifelislem
- Created: 2024-11-06T07:22:06.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-11-07T09:47:06.000Z (7 months ago)
- Last Synced: 2025-03-24T23:35:13.535Z (3 months ago)
- Topics: clustering, k-means, k-means-clustering, k-means-implementation-in-python, machine-learning, machine-learning-algorithms, machine-learning-models, plotly
- Language: Python
- Homepage:
- Size: 18.6 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🛍️ Customer Segmentation Analysis - Mall Customer Data 🏪
## Task 2 - Prodigy InfoTech ML Internship
[](https://www.python.org/)
[](https://scikit-learn.org/)
[](https://streamlit.io/)
### 🎯 Project Overview
Transform raw customer data into actionable insights using K-means clustering! This project helps mall marketing teams identify distinct customer segments for targeted marketing strategies.
### ✨ Features
- 🔍 Advanced data preprocessing and scaling
- 📊 Automatic optimal cluster detection using elbow method
- 🎨 Interactive visualization of customer segments
- 🖥️ User-friendly GUI for real-time customer classification
- 📈 Detailed cluster analysis and insights
### 🛠️ Technology Stack
- Python 3.8+
- scikit-learn
- pandas
- numpy
- streamlit
- plotly
### 🚀 Quick Start
1. Clone the repository
```bash
git clone https://github.com/RAHAMNIabdelkaderseifelislem/PRODIGY_ML_02.git
cd customer-segmentation
```
2. Install dependencies
```bash
pip install -r requirements.txt
```
3. Run the GUI
```bash
streamlit run app.py
```
### 📁 Project Structure
```
customer-segmentation/
├── data/
│ └── mall_customers.csv
├── src/
│ ├── preprocessing.py
│ ├── model.py
│ └── visualization.py
├── app.py
├── requirements.txt
└── README.md
```
### 🎓 Model Details
- Algorithm: K-means Clustering
- Features used: Annual Income, Spending Score, Age
- Preprocessing: StandardScaler
- Optimal Clusters: Determined using Elbow Method
### 📊 Sample Visualizations
- 3D scatter plots of customer segments
- Feature importance analysis
- Cluster centroids visualization
### 🤝 Contributing
Feel free to submit issues, fork the repository, and create pull requests for any improvements.
### 📝 License
MIT License
### 🙏 Acknowledgments
- Prodigy InfoTech for the amazing internship opportunity
- Mall customer dataset contributors