https://github.com/murugavl/customer_churn_prediction
Customer churn prediction is a crucial task in business analytics, helping companies identify customers who are likely to leave and take preventive actions. This project utilizes Machine Learning (ML) techniques to analyze customer behavior and predict churn.
https://github.com/murugavl/customer_churn_prediction
jupyter-notebook machine-learning matplotlib numpy pandas pickle python scikit-learn streamlit
Last synced: 3 months ago
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Customer churn prediction is a crucial task in business analytics, helping companies identify customers who are likely to leave and take preventive actions. This project utilizes Machine Learning (ML) techniques to analyze customer behavior and predict churn.
- Host: GitHub
- URL: https://github.com/murugavl/customer_churn_prediction
- Owner: Murugavl
- License: mit
- Created: 2025-03-01T06:48:12.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-01T07:22:21.000Z (over 1 year ago)
- Last Synced: 2025-03-01T07:27:59.464Z (over 1 year ago)
- Topics: jupyter-notebook, machine-learning, matplotlib, numpy, pandas, pickle, python, scikit-learn, streamlit
- Language: Python
- Homepage: https://customer-churnprediction.streamlit.app/
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Customer Churn Prediction
## 📌 Overview
Customer churn prediction is a crucial task in business analytics, helping companies identify customers who are likely to leave and take preventive actions. This project utilizes **Machine Learning (ML)** techniques to analyze customer behavior and predict churn.
## 🔍 Features
- **Data Preprocessing:** Handles missing values, encodes categorical variables, and scales numerical data.
- **Exploratory Data Analysis (EDA):** Visualizes key trends in customer churn.
- **Machine Learning Models:** Implements multiple ML algorithms (Logistic Regression, Random Forest, SVM, etc.).
- **Model Evaluation:** Uses accuracy, precision, recall, and F1-score to assess performance.
- **Deployment:** Deploys the model using **Streamlit** for an interactive user interface.
## 🛠️ Technologies Used
- **Python**
- **Pandas, NumPy** (Data Manipulation)
- **Matplotlib, Seaborn** (Visualization)
- **Scikit-Learn** (Machine Learning)
- **Streamlit** (Deployment)
## 📂 Project Structure
```
Customer_churn_Prediction/
│── dataset/ # Contains the dataset (CSV file)
│── notebooks/ # Jupyter notebooks for EDA & training
│── app.py # Streamlit app for prediction
│── requirements.txt # List of dependencies
│── README.md # Project Documentation
```
## 📊 Dataset
- The dataset includes customer information such as **demographics, account details, and usage patterns**.
- The target variable is **Churn** (0: Not Churned, 1: Churned).
## 🚀 Installation & Usage
1. **Clone the Repository**
```bash
git clone https://github.com/Murugavl/Customer_churn_Prediction.git
cd Customer_churn_Prediction
```
2. **Create a Virtual Environment** *(Optional but recommended)*
```bash
python -m venv venv
source venv/bin/activate # For macOS/Linux
venv\Scripts\activate # For Windows
```
3. **Install Dependencies**
```bash
pip install -r requirements.txt
```
4. **Run the Streamlit App**
```bash
streamlit run app.py
```
## 📌 Model Training
To train the model, run the Jupyter notebook available in the `notebooks/` folder:
```bash
jupyter notebook notebooks/Customer Churn Prediction.ipynb
```
## 💡 Future Improvements
- Enhance feature engineering for better model performance.
- Implement deep learning models for improved accuracy.
- Integrate customer segmentation for targeted retention strategies.
## 🤝 Contributing
Contributions are welcome! If you’d like to improve the project, fork the repository and submit a pull request.
## 📜 License
This project is licensed under the MIT License.