https://github.com/abdiasarsene/predictive-churn-management-data-driven-customer

Use unsupervised learning techniques to segment a company’s customers into distinct groups in order to personalize marketing campaigns. To ultimately propose specific marketing strategies for each customer segment based on the insights obtained.
https://github.com/abdiasarsene/predictive-churn-management-data-driven-customer

acp kmeans-clustering matplotlib pandas plotly python scikit-learn seaborn

Last synced: 3 months ago
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Use unsupervised learning techniques to segment a company’s customers into distinct groups in order to personalize marketing campaigns. To ultimately propose specific marketing strategies for each customer segment based on the insights obtained.

• Host: GitHub
• URL: https://github.com/abdiasarsene/predictive-churn-management-data-driven-customer
• Owner: Abdiasarsene
• Created: 2025-03-05T01:50:11.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2025-06-16T00:27:32.000Z (4 months ago)
• Last Synced: 2025-06-16T01:28:41.982Z (4 months ago)
• Topics: acp, kmeans-clustering, matplotlib, pandas, plotly, python, scikit-learn, seaborn
• Language: Jupyter Notebook
• Homepage: https://github.com/Abdiasarsene/Customer_segmentation_for_a_Marketing_Campaign/tree/main
• Size: 6.99 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

          
# Customer Segmentation for a Marketing Campaign

## 📌 Description

This project aims to segment a company's customers into distinct groups using unsupervised learning algorithms. The goal is to optimize marketing campaigns by offering tailored promotions to each customer segment.

## 📊 Objectives

- **Analyze** customer data to identify trends.

- **Segment** customers into homogeneous groups using clustering techniques.

- **Visualize** the results to interpret the segments.

- **Propose** marketing recommendations based on the insights.

![Customer_segmentation](./statics/centroids.png)

## 📁 Project Structure

```

Customer_segmentation_for_a_Marketing_Campaign/

│-- data/

|-- |--customer_segmentation.csv                  

│-- notebooks/

|-- |-- __init__.py           

|-- |-- exploratory.ipynb

|-- |-- clustering.ipynb

|-- statics/

|-- |-- numerous of images files

|-- __init__.py        

│-- clustering.py

|-- .gitignore           

│-- requirements.txt

|--  README.md            

```

![Customer_segmentation](./statics/elbow.png)

## 🛠️ Technologies and Libraries

- **Python**: Main language for data analysis

- **Pandas, NumPy**: Data manipulation and analysis

- **Scikit-learn**: Clustering algorithms and dimensionality reduction

- **Matplotlib, Seaborn**: Data visualization

## 📌 Project Steps

1. **Data Preparation**: Cleaning, normalization, and encoding of categorical variables.

2. **Data Exploration**: Visualization of distributions and correlations.

3. **Dimensionality Reduction**: PCA to simplify data.

4. **Choice of Optimal k** : Elbow and Silhouette Coefficient

5. **Clustering**: Applying K-Means and testing other methods.

6. **Interpretation and Recommendations**: Analysis of segments and tailored marketing strategies.

![Customer_segmentation](./statics/silhouette.png)

## 📊 Expected Results

- Identification of distinct customer segments.

- Visualizations of groups and their characteristics.

- Personalized marketing strategies for each segment.

![Customer_segmentation](./statics/histogram.png)

## 🤝 Collaborate With Me

Do you work in education, humanitarian tech, or social impact analytics?  

Looking to deploy smart dashboards in your organization?

📩 Reach out: [abdiasarsene@gmail.com]  

🔗 LinkedIn: [Abdias Arsène. Z 📊📈](https://www.linkedin.com/in/abdias-arsene)

## 📩 Contact

If you have any questions or suggestions, feel free to contact me via LinkedIn! 😊