https://github.com/skeyboarder123/customer-segmentation-churn-dashboard
Analyze customer behavior with the Customer Segmentation & Churn Prediction Dashboard. Use RFM analysis and machine learning to enhance marketing strategies. 🌟💻
https://github.com/skeyboarder123/customer-segmentation-churn-dashboard
airflow athena aws churn data data-science glue-job machine-learning pandas prediction python quicksight redshift rfm rfm-analysis streamlit tableau visualization
Last synced: 3 months ago
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Analyze customer behavior with the Customer Segmentation & Churn Prediction Dashboard. Use RFM analysis and machine learning to enhance marketing strategies. 🌟💻
- Host: GitHub
- URL: https://github.com/skeyboarder123/customer-segmentation-churn-dashboard
- Owner: skeyboarder123
- License: mit
- Created: 2025-07-02T06:38:02.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-02T19:49:27.000Z (3 months ago)
- Last Synced: 2025-07-02T20:38:18.968Z (3 months ago)
- Topics: airflow, athena, aws, churn, data, data-science, glue-job, machine-learning, pandas, prediction, python, quicksight, redshift, rfm, rfm-analysis, streamlit, tableau, visualization
- Language: Jupyter Notebook
- Size: 382 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Customer Segmentation & Churn Dashboard 📊
[](https://github.com/skeyboarder123/customer-segmentation-churn-dashboard/releases)
## Overview
The **Customer Segmentation and Churn Dashboard** is a powerful tool built using Streamlit and Plotly. This dashboard allows businesses to analyze customer behavior, perform RFM (Recency, Frequency, Monetary) analysis, and predict churn using machine learning techniques. By visualizing customer segments and churn probabilities, organizations can make informed decisions to enhance customer retention and optimize marketing strategies.
## Table of Contents
- [Features](#features)
- [Technologies Used](#technologies-used)
- [Installation](#installation)
- [Usage](#usage)
- [Data Analysis](#data-analysis)
- [RFM Analysis](#rfm-analysis)
- [Churn Prediction](#churn-prediction)
- [Visualizations](#visualizations)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## Features
- **Customer Segmentation**: Group customers based on purchasing behavior.
- **RFM Analysis**: Calculate Recency, Frequency, and Monetary values to identify high-value customers.
- **Churn Prediction**: Use machine learning models to predict customer churn.
- **Interactive Visualizations**: Utilize Plotly for dynamic charts and graphs.
- **User-Friendly Interface**: Built with Streamlit for easy navigation and interaction.
## Technologies Used
- **Python**: The primary programming language for data manipulation and machine learning.
- **Pandas**: For data analysis and manipulation.
- **Scikit-learn**: For machine learning algorithms.
- **Streamlit**: For building the web application.
- **Plotly**: For creating interactive visualizations.
- **NumPy**: For numerical operations.
## Installation
To get started with the Customer Segmentation and Churn Dashboard, follow these steps:
1. **Clone the Repository**:
```bash
git clone https://github.com/skeyboarder123/customer-segmentation-churn-dashboard.git
cd customer-segmentation-churn-dashboard
```
2. **Install Required Packages**:
Ensure you have Python installed. Then, install the necessary libraries:
```bash
pip install -r requirements.txt
```
3. **Run the Dashboard**:
Execute the following command to start the Streamlit app:
```bash
streamlit run app.py
```
## Usage
Once the application is running, navigate to `http://localhost:8501` in your web browser. The dashboard will present various options for customer segmentation, RFM analysis, and churn prediction.
### Key Sections:
- **Home**: Overview of the dashboard and its capabilities.
- **Customer Segmentation**: Analyze and visualize customer segments.
- **RFM Analysis**: Dive into RFM metrics and see how they affect customer behavior.
- **Churn Prediction**: View predictions and insights on customer churn.
## Data Analysis
The dashboard utilizes a dataset containing customer transactions. Key data points include:
- Customer ID
- Transaction Date
- Transaction Amount
- Product Categories
### Data Preparation
Data cleaning and preprocessing steps are crucial for accurate analysis. The following steps are typically performed:
1. **Handling Missing Values**: Remove or impute missing data.
2. **Data Transformation**: Convert categorical variables into numerical formats.
3. **Feature Engineering**: Create new features that may enhance model performance.
## RFM Analysis
RFM analysis helps businesses understand customer behavior. The three metrics are:
- **Recency**: How recently a customer made a purchase.
- **Frequency**: How often a customer makes a purchase.
- **Monetary**: How much money a customer spends.
### RFM Segmentation
Customers are segmented into groups based on their RFM scores. This segmentation helps identify:
- High-Value Customers
- At-Risk Customers
- New Customers
The dashboard visualizes these segments using charts, allowing for quick insights.
## Churn Prediction
Churn prediction uses machine learning to forecast which customers are likely to leave. The process involves:
1. **Data Preparation**: Similar to RFM analysis, but focuses on churn-related features.
2. **Model Selection**: Choose appropriate algorithms (e.g., Logistic Regression, Decision Trees).
3. **Training the Model**: Use historical data to train the model.
4. **Evaluating Performance**: Assess model accuracy using metrics like precision, recall, and F1-score.
### Churn Insights
The dashboard displays churn probabilities for each customer segment. This information can guide marketing strategies to retain at-risk customers.
## Visualizations
Visualizations play a key role in understanding data. The dashboard includes:
- **Bar Charts**: Show customer segments and their characteristics.
- **Line Graphs**: Display trends over time, such as sales growth or churn rates.
- **Heatmaps**: Illustrate correlations between different variables.
### Interactive Features
Users can interact with the visualizations to filter data by specific criteria, such as date ranges or customer segments. This interactivity enhances the analysis process.
## Contributing
Contributions are welcome! If you want to improve the dashboard, please follow these steps:
1. Fork the repository.
2. Create a new branch (`git checkout -b feature/YourFeature`).
3. Make your changes.
4. Commit your changes (`git commit -m 'Add some feature'`).
5. Push to the branch (`git push origin feature/YourFeature`).
6. Open a pull request.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## Contact
For any inquiries or feedback, feel free to reach out:
- **Email**: example@example.com
- **GitHub**: [skeyboarder123](https://github.com/skeyboarder123)
[](https://github.com/skeyboarder123/customer-segmentation-churn-dashboard/releases)