📊 Customer Segmentation & Churn Prediction Dashboard

An interactive Streamlit + Plotly dashboard to analyze customer transactions using RFM segmentation, clustering, and churn prediction. Built for marketing and business strategy teams to understand customer behavior, identify valuable segments, and reduce churn.

---

🚀 Features

• 🔍 RFM Analysis: Calculate Recency, Frequency, and Monetary value for each customer.
• 🔄 Customer Segmentation: Cluster customers using K-Means.
• ⚠️ Churn Prediction: Predict churn risk using a machine learning model.
• 🌐 Interactive Dashboard: View insights, filter segments, and simulate churn.

---

📂 Folder Structure

Customer_Segmentation_Sales_Analytics/
├── data/
│   ├── raw/
│   └── processed/
├── notebooks/
│   ├── 1_data_cleaning.ipynb
│   ├── 2_rfm_segmentation.ipynb
│   ├── 3_clustering.ipynb
│   └── 4_churn_prediction.ipynb
├── model/
│   └── churn_prediction_model.pkl
├── streamlit_app.py
└── README.md

---

🛠️ Technologies Used

• Python (Pandas, Scikit-learn, Matplotlib, Seaborn)
• Machine Learning: K-Means, Logistic Regression
• Visualization: Plotly, Streamlit

---

📈 How to Run

1. Clone the repository:

git clone https://github.com/your-username/customer-segmentation-churn-dashboard.git
cd customer-segmentation-churn-dashboard

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Launch the Streamlit app:

streamlit run streamlit_app.py

---

📌 Dashboard Sections

• Introduction: Overview of goals and dataset
• Overview: KPIs and churn stats
• RFM Analysis: Distribution plots
• Clusters: Segment insights
• Churn Analysis: Risk visualizations
• Predict Churn: User input + model output

---

📊 Dataset

This project uses a public e-commerce transactions dataset with ~25,000 records:

• InvoiceNo, CustomerID, InvoiceDate, Quantity, UnitPrice, Country

---

📄 License

This project is licensed under the MIT License.

---

🙋‍♀️ Author

Priyanka Malavade

---

Made with ❤️ using Streamlit and Plotly.