https://github.com/hk151109/customer-segmentation-using-k-means-clustering
This repository contains the implementation of K-Means Clustering to segment customers of a retail store based on their Annual Income and Spending Score. The goal is to group customers into clusters for targeted marketing strategies.
https://github.com/hk151109/customer-segmentation-using-k-means-clustering
internship-task k-means-clustering machine-learning
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This repository contains the implementation of K-Means Clustering to segment customers of a retail store based on their Annual Income and Spending Score. The goal is to group customers into clusters for targeted marketing strategies.
- Host: GitHub
- URL: https://github.com/hk151109/customer-segmentation-using-k-means-clustering
- Owner: hk151109
- Created: 2024-11-24T09:25:43.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-12-17T18:15:13.000Z (5 months ago)
- Last Synced: 2024-12-17T19:25:13.643Z (5 months ago)
- Topics: internship-task, k-means-clustering, machine-learning
- Language: Python
- Homepage:
- Size: 11 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
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README
# Customer Segmentation Using K-Means Clustering
## Project Overview
This repository contains the implementation of K-Means Clustering to segment customers of a retail store based on their Annual Income and Spending Score. The goal is to group customers into clusters for targeted marketing strategies.
## Index
- [Project Overview](#project-overview)
- [Features](#features)
- [File Structure](#file-structure)
- [Steps to Run](#steps-to-run)
- [Visualization](#visualization)
- [Dependencies](#dependencies)
- [Results](#results)
- [Dataset](#dataset)
- [License](#license)
## Features
### Data Preprocessing
- Standardizing the data (scaling the features)
- Handling missing values (if any)
### Clustering
- Applying K-Means Clustering to divide customers into 3 distinct clusters based on Annual Income and Spending Score
### Visualization
- Scatter plot to visualize the customer segments based on the two features
- Count plot to show the distribution of customers in each cluster
- Histograms to show the distribution of distances from each customer to the centroids
### Predictions
- Assigning a cluster to each customer
- Saving the results with the distances from the centroids to a CSV file
## File Structure
```
│ customer_clustering.py
│ mall_with_distances.csv
│ mall.csv
│
└───visualizations
└───scatter_plot.png
└───count_plot.png
└───distance_histogram1.png
└───distance_histogram2.png
└───distance_histogram3.png
```
## Steps to Run
### 1. Data Preprocessing
Run the `customer_clustering.py` script to load the data, preprocess it, and standardize the features for clustering:
```bash
python customer_clustering.py
```
This will process the customer data and apply K-means clustering. The results, including customer assignments to clusters and distances from each centroid, will be saved in the `mall_with_distances.csv` file.
### 2. K-Means Clustering and Prediction
The script will also plot and visualize the customer segments and the distances from centroids:
- A scatter plot will be generated to visualize customer segments
- A count plot will show the distribution of customers across clusters
- Histograms will display the distribution of distances to each of the centroids
All plots will be saved as PNG files in the `visualizations/` folder.
## Visualization
The project includes several visualizations that are automatically generated when running the `customer_clustering.py` script:
- **Scatter Plot**: Shows customer segmentation based on Annual Income and Spending Score
- **Count Plot**: Displays the distribution of customers in each of the 3 clusters
- **Distance Histograms**: Depict the distribution of distances of each customer from the centroids of the 3 clusters
These plots will be saved in the `visualizations/` folder and also displayed during execution.
## Dependencies
The following Python libraries are required for running this project:
- numpy
- pandas
- scikit-learn
- matplotlib
- seaborn
Ensure these libraries are installed in your Python environment:
```bash
pip install numpy pandas scikit-learn matplotlib seaborn
```
## Results
- The K-means clustering will assign each customer to one of the three clusters based on their Annual Income and Spending Score
- Results are saved in the `mall_with_distances.csv` file, which includes:
- Customer IDs
- Assigned cluster label
- Distances to each of the 3 centroids
The visualizations will help in understanding how well the customers are grouped, and the cluster assignments can be used for targeted marketing.
## Dataset
The dataset used in this project is the Mall Customer Segmentation Data, which contains information about customers' income and spending score.
**Source**: [Kaggle - Mall Customer Segmentation](https://www.kaggle.com/datasets/vjchoudhary7/customer-segmentation-tutorial-in-python?select=Mall_Customers.csv)
The dataset is included in the repository as `Mall_Customers.csv`.
## License
This project is licensed under the MIT License.