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https://github.com/steveee27/customer-churn-prediction

A machine learning project to predict customer churn for a bank using XGBoost and Random Forest models. The project includes data preprocessing, feature engineering, model training with hyperparameter tuning, and deployment using Streamlit for real-time predictions.
https://github.com/steveee27/customer-churn-prediction

customer-churn-prediction machine-learning modeldeployment streamlit xgboost-classifier

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A machine learning project to predict customer churn for a bank using XGBoost and Random Forest models. The project includes data preprocessing, feature engineering, model training with hyperparameter tuning, and deployment using Streamlit for real-time predictions.

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README 

          
# Customer Churn Prediction



This project predicts customer churn for a bank by analyzing customer data using machine learning techniques. It includes data preprocessing, such as handling missing values and encoding features, and model training with Random Forest and XGBoost to identify patterns associated with churn. The best model is deployed using Streamlit, allowing users to input customer details and receive real-time churn predictions. This solution helps banks proactively address customer retention challenges.



You can try the deployed Streamlit app here: [Customer Churn Prediction App](https://customer-churn-prediction-machine-learning.streamlit.app/)



---



## Table of Contents



- [Overview](#overview)

- [Technologies Used](#technologies-used)

- [Project Structure](#project-structure)

- [Setup Instructions](#setup-instructions)

- [Usage](#usage)

- [Features](#features)

- [Model Evaluation Results](#model-evaluation-results)

- [Contributing](#contributing)

- [License](#license)



---



## Overview



The **Customer Churn Prediction** project aims to predict whether a customer will churn based on historical data. The project includes:



- Data preprocessing (handling missing values, feature encoding, and scaling).

- Model training with Random Forest and XGBoost classifiers.

- Evaluation of model performance.

- Deployment of the best-performing model using Streamlit.



---



## Technologies Used



- **Python 3.10**

- **Pandas** for data manipulation.

- **Scikit-learn** for preprocessing and machine learning.

- **XGBoost** for gradient boosting.

- **Streamlit** for creating a web-based UI.

- **Joblib** for model serialization.



---



## Project Structure



```plaintext

├── data/

│   └── Customer-Dataset.csv          # Dataset

├── src/

│   ├── Training-Model.ipynb          # Notebook for training models

│   ├── Training-Model_OOP.py         # OOP-based training script

├── models/

│   ├── xgb_classifier_model.pkl      # Trained XGBoost model

│   ├── gender_encoder.pkl            # Gender encoder

│   ├── hasCrCard_encoder.pkl         # HasCrCard encoder

│   ├── isActiveMember_encoder.pkl    # IsActiveMember encoder

├── README.md                         # Project documentation

└── requirements.txt                  # Python dependencies

└── main.py                           # Streamlit deployment script

```



---



## Setup Instructions



### Prerequisites



- Python 3.10 or higher

- Pipenv or virtualenv for environment management



### Installation



1. Clone the repository:

    ```bash

    git clone https://github.com/steveee27/Customer-Churn-Prediction.git

    cd Customer-Churn-Prediction

    ```



2. Create a virtual environment:

    ```bash

    python -m venv env

    source env/bin/activate    # On Windows: env\Scripts\activate

    ```



3. Install dependencies:

    ```bash

    pip install -r requirements.txt

    ```

    

---



### Usage



#### Model Training



1. Run the OOP-based training script to train the model and save artifacts:

    ```bash

    python src/Training-Model_OOP.py

    ```



2. Ensure that the trained model and encoders are saved in the `models/` directory.



#### Deployment



1. Start the Streamlit app:

    ```bash

    streamlit run main.py

    ```



2. Open the URL provided by Streamlit (e.g., `http://localhost:8501`) in your browser.



#### Prediction



Enter customer data in the web interface to get a prediction on whether the customer will churn.



## Features



- **Data Preprocessing**: Cleans and prepares raw data for modeling, including handling missing values, encoding categorical variables, and scaling numerical features.

- **Model Training**: Implements two machine learning models (Random Forest and XGBoost) with hyperparameter tuning to optimize performance.

- **Evaluation**: Provides detailed classification reports and other metrics to evaluate model performance.

- **Deployment**: Features a user-friendly web application built with Streamlit to predict customer churn interactively.



---



## Model Evaluation Results



The table below compares the performance of the Random Forest and XGBoost models based on their hyperparameters and evaluation metrics:



| **Model**        | **Hyperparameters**                                                                                      | **Precision (Churn)** | **Recall (Churn)** | **F1-Score (Churn)** | **Accuracy** |

|-------------------|---------------------------------------------------------------------------------------------------------|-----------------------|--------------------|----------------------|--------------|

| **Random Forest** | `n_estimators=100`, `criterion='gini'`, `max_depth=10`                                                 | 0.77                  | 0.52               | 0.62                 | 86%          |

| **XGBoost**       | `n_estimators=100`, `learning_rate=0.1`, `max_depth=3`, `subsample=0.5`                                 | 0.76                  | 0.55               | 0.64                 | 86%          |



---



### Insights



1. **XGBoost Model**:

   - Achieved slightly higher recall (**0.55**) and F1-score (**0.64**) compared to Random Forest.

   - This makes XGBoost more suitable for scenarios where capturing more true churn cases is critical.



2. **Random Forest Model**:

   - Has a slightly higher precision (**0.77**) than XGBoost, making it better for reducing false positives in churn prediction.



3. Both models achieved an accuracy of **86%**, indicating comparable overall performance, but their suitability depends on the specific business use case.



---



### Conclusion



Based on the evaluation, **XGBoost** is selected as the final model due to its higher F1-score and recall, which are crucial for minimizing churn cases missed. The trained XGBoost model has been saved as `xgb_classifier_model.pkl` and is deployed for predictions in the Streamlit application.



---



## Contributing



Contributions to the project are welcome! To contribute, follow these steps:



1. Fork the repository.

2. Create a new branch for your feature or bugfix:

    ```bash

    git checkout -b feature/your-feature-name

    ```

3. Make your changes and commit them:

    ```bash

    git commit -m "Add your message here"

    ```

4. Push your changes to your forked repository:

    ```bash

    git push origin feature/your-feature-name

    ```

5. Create a pull request in the main repository.



For major changes, please open an issue first to discuss what you would like to change.



---



## License



This project is licensed under the [MIT License](LICENSE). You are free to use, modify, and distribute this project as long as proper attribution is given to the original author.