https://github.com/rohitinu6/customer-segmentation-ml
Customer segmentation using K-Means clustering and t-SNE visualization for better customer insights.
https://github.com/rohitinu6/customer-segmentation-ml
customer-segmentation data-science kmeans-clustering machine-learning python unsupervised-learning
Last synced: 2 months ago
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Customer segmentation using K-Means clustering and t-SNE visualization for better customer insights.
- Host: GitHub
- URL: https://github.com/rohitinu6/customer-segmentation-ml
- Owner: rohitinu6
- License: mit
- Created: 2025-03-22T12:52:08.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-22T12:56:12.000Z (2 months ago)
- Last Synced: 2025-03-22T13:39:36.117Z (2 months ago)
- Topics: customer-segmentation, data-science, kmeans-clustering, machine-learning, python, unsupervised-learning
- Language: Python
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Customer Segmentation using Unsupervised Learning
This project implements customer segmentation using unsupervised learning techniques, specifically K-Means clustering. The dataset contains various customer attributes which are used to identify distinct customer groups.
## Table of Contents
- [Overview](#overview)
- [Technologies Used](#technologies-used)
- [Dataset](#dataset)
- [Project Structure](#project-structure)
- [Installation](#installation)
- [Usage](#usage)
- [Results](#results)
- [Evaluation Metrics](#evaluation-metrics)
- [License](#license)
## Overview
Customer segmentation is a key strategy for businesses to understand customer behavior and personalize marketing. In this project:
1. We preprocess and clean the dataset.
2. Perform exploratory data analysis (EDA) and visualize key insights.
3. Apply t-SNE for dimensionality reduction.
4. Use the K-Means algorithm to cluster customers.
5. Evaluate the model using the elbow method and silhouette score.
## Technologies Used
- Python 3.13
- Libraries:
- Pandas (data manipulation)
- Numpy (numerical computations)
- Matplotlib & Seaborn (data visualization)
- Scikit-learn (machine learning)
## Dataset
The dataset contains customer information such as:
- Marital Status
- Income
- Number of Items Purchased
- Date of Joining (Dt_Customer)
Ensure that the dataset is placed in the following directory before running the code:
```
D:/VIT Bhopal/GitHub/ML Projects/Customer Segmentation using Unsupervised Learning/Data.csv
```
## Project Structure
```
├── Customer Segmentation (Main Directory)
├── Data.csv (Dataset)
├── main.py (Main Python Script)
└── README.md (Project Documentation)
```
## Installation
Ensure Python 3.13 is installed. Clone this repository and install the required libraries:
```bash
# Clone the repository
git clone https://github.com/rohitinu6/Customer-Segmentation-ML.git
cd customer-segmentation
# Create a virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
```
## Usage
Run the main Python script:
```bash
python main.py
```
The script performs:
1. Data cleaning and preprocessing.
2. Exploratory data analysis (EDA).
3. Dimensionality reduction (t-SNE).
4. K-Means clustering.
5. Model evaluation with the silhouette score.
## Results
### Data Visualization
- Distribution of categorical columns.
- Feature correlation heatmap.
- t-SNE projection for dimensionality reduction.
### Clustering Analysis
- Elbow method to determine the optimal number of clusters.
- Visualized customer segments using t-SNE projection.
## Evaluation Metrics
1. **Inertia (Within-Cluster Sum of Squares)**: Measures how tightly data points are grouped within a cluster.
2. **Silhouette Score**: Measures how well clusters are separated; a higher score indicates better-defined clusters.
The silhouette score for K=5 (optimal clusters) is printed during execution.
## License
This project is licensed under the MIT License.