https://github.com/abdiasarsene/customer_segmentation_for_a_marketing_campaign
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/customer_segmentation_for_a_marketing_campaign
acp kmeans-clustering matplotlib pandas plotly python scikit-learn seaborn
Last synced: 9 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/customer_segmentation_for_a_marketing_campaign
- Owner: Abdiasarsene
- Created: 2025-03-05T01:50:11.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-03-05T02:05:58.000Z (9 months ago)
- Last Synced: 2025-03-05T03:18:40.771Z (9 months ago)
- Topics: acp, kmeans-clustering, matplotlib, pandas, plotly, python, scikit-learn, seaborn
- Homepage: https://github.com/Abdiasarsene/Customer_segmentation_for_a_Marketing_Campaign/tree/main
- Size: 47.9 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
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.
## 📁 Project Structure
```
Customer_segmentation_for_a_Marketing_Campaign/
│-- data/ # Contains the datasets used
│-- notebooks/ # Jupyter notebooks with analyses and visualizations
│-- src/ # Python scripts for preprocessing and segmentation, Stores visualizations and segmentation results
│-- README.md # Project documentation
```
## 🛠️ 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 (if necessary)**: PCA to simplify data.
4. **Clustering**: Applying K-Means and testing other methods (DBSCAN, hierarchical clustering).
5. **Interpretation and Recommendations**: Analysis of segments and tailored marketing strategies.
## 📊 Expected Results
- Identification of distinct customer segments.
- Visualizations of groups and their characteristics.
- Personalized marketing strategies for each segment.
## 📜 Deliverables
- A **Jupyter notebook** with all analysis and segmentation steps.
- A **detailed report** explaining the results and recommendations.
- A **PowerPoint presentation** summarizing key findings.
## 🚀 How to Use This Project?
1. Clone the repository:
```bash
git clone https://github.com/your-username/Customer_segmentation_for_a_Marketing_Campaign.git
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Run the notebook in `notebooks/` to explore the analysis and results.
## 📩 Contact
If you have any questions or suggestions, feel free to contact me via GitHub! 😊