https://github.com/muhammadadilnaeem/customer-segmentation-unsupervised-learning
This project explores customer segmentation using various clustering techniques on a dataset of mall customers. The goal is to identify distinct customer groups based on demographic and behavioral attributes, enabling businesses to tailor their marketing strategies more effectively.
https://github.com/muhammadadilnaeem/customer-segmentation-unsupervised-learning
customer-segmentation dbscan-clustering-algorithm hirarchical-clustering kmeans-clustering unsupervised-machine-learning
Last synced: 3 months ago
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This project explores customer segmentation using various clustering techniques on a dataset of mall customers. The goal is to identify distinct customer groups based on demographic and behavioral attributes, enabling businesses to tailor their marketing strategies more effectively.
- Host: GitHub
- URL: https://github.com/muhammadadilnaeem/customer-segmentation-unsupervised-learning
- Owner: muhammadadilnaeem
- License: apache-2.0
- Created: 2024-07-11T09:59:47.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-07-11T10:41:49.000Z (11 months ago)
- Last Synced: 2025-01-17T15:52:53.207Z (5 months ago)
- Topics: customer-segmentation, dbscan-clustering-algorithm, hirarchical-clustering, kmeans-clustering, unsupervised-machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 213 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
---
# Customer Segmentation Using Clustering Techniques
This project explores customer segmentation using various clustering techniques on a dataset of mall customers. The goal is to identify distinct customer groups based on demographic and behavioral attributes, enabling businesses to tailor their marketing strategies more effectively.
## **Project Overview**
The objective of this analysis is to segment customers to better understand their demographics and spending behavior, which can help businesses improve their marketing strategies and customer satisfaction.
## **Dataset Description**
The dataset consists of the following attributes:
- **CustomerID**: Unique identifier for each customer.
- **Gender**: Gender of the customer.
- **Age**: Age of the customer.
- **Annual Income (k$)**: Annual income of the customer in thousands of dollars.
- **Spending Score (1-100)**: Score assigned by the mall based on customer behavior and spending nature.
## **Data Exploration and Cleaning**
1. **Checked for missing values**: Ensured the dataset is complete with no missing values.
2. **Summary statistics**: Provided an overview of the data distribution.
3. **Feature Engineering**: Encoded the 'Gender' attribute and scaled the features to ensure they are on a comparable scale.
## **Clustering Techniques Employed**
### **K-Means Clustering**
Tried different numbers of clusters (k = 2 to 5) and selected the best one based on silhouette scores.
### **Agglomerative Clustering**
Experimented with various cluster counts and selected the best model based on silhouette scores.
### **DBSCAN**
Explored different epsilon values for density-based clustering and identified the best model based on silhouette scores.
## **Key Findings and Insights**
- Identified distinct customer groups based on age, income, and spending habits.
- Uncovered patterns that can drive personalized marketing efforts and enhance customer experiences.
## **Recommendations**
- Further exploration with additional features could refine the segmentation.
- Diving deeper into individual clusters for more targeted strategies.
## **Project Structure**
- `data/`: Contains the dataset used for the analysis.
- `notebook/`: Jupyter notebooks with the data exploration, cleaning, and clustering models.
## Usage
To reproduce the analysis, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/muhammadadilnaeem/Customer-Segmentation-Unsupervised-Learning.git
cd Customer-Segmentation-Unsupervised-Learning
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Run the notebooks or scripts to perform the analysis:
```bash
jupyter notebook notebook/data_exploration.ipynb
```
## Contributing
If you have suggestions for improvements or would like to contribute, feel free to open an issue or submit a pull request.
## License
This project is licensed under the Apache License. See the [LICENSE]([LICENSE](https://github.com/muhammadadilnaeem/Customer-Segmentation-Unsupervised-Learning/blob/main/LICENSE)) file for details.
---