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https://github.com/juliusmarkwei/customer-churn-eda-balancing-and-ml
Bank customer churn prediction using multiple ml models
https://github.com/juliusmarkwei/customer-churn-eda-balancing-and-ml
algorithm application data data-science machine-learning python streamlit
Last synced: 7 days ago
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Bank customer churn prediction using multiple ml models
- Host: GitHub
- URL: https://github.com/juliusmarkwei/customer-churn-eda-balancing-and-ml
- Owner: juliusmarkwei
- License: mit
- Created: 2023-06-29T02:42:07.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-09-22T11:49:27.000Z (over 1 year ago)
- Last Synced: 2023-09-23T14:44:09.625Z (over 1 year ago)
- Topics: algorithm, application, data, data-science, machine-learning, python, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 144 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Customer Churn Prediction App
## Overview
This project is a machine learning classifier for predicting whether a bank customer is likely to churn (leave) or not. It includes a Streamlit web application that allows users to interact with the predictive model and visualize the results.
## Table of Contents
- [Demo](#demo)
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Streamlit App](#streamlit-app)
- [Model Training](#model-training)
- [Contributing](#contributing)
- [License](#license)
## Demo
Click [here](https://customerchurnpredict.streamlit.app/) to view the prediction app in your web browser.
Here are some pictures of what the app looks like:
1. Prediction Page
2. Visualization Page
## Getting Started
### Prerequisites
List the prerequisites that users need to have installed or set up before using the project.
```bash
python>=3.8
requirements.txt
```
### Installation
To use my application, follow this steps below to successfully install and run the program.
```bash
# Clone the repository
git clone https://github.com/juliusmarkwei/Customer-Churn-EDA-Balancing-and-ML.git
# Change directory
cd Customer-Churn-EDA-Balancing-and-ML/
# Install dependencies
pip install -r requirements.txt
```
## Streamlit App
Carefully type the command below in your teminal of the "Customer-Churn-EDA-Balancing-and-ML/" directory to run the app.
```bash
# Run the Streamlit app
streamlit run app.py
```
## Model Training
Our machine learning model was trained using a dataset containing [describe your dataset]. The training process involved the following steps:
- **Data Preprocessing:** We performed data cleaning, handled missing values, and encoded categorical features as part of data preparation.
- **Model Selection:** We selected the Random Forest model for the prediciton app after evaluation as the base model due to its suitability for our problem.
- **Model Evaluation:** The model's performance was evaluated using metrics accuracy and F1-score. Cross-validation was used to assess its generalization ability.
- **Hyperparameter Tuning:** We fine-tuned the model's hyperparameters to optimize performance.
For detailed information on the model training process, please refer to the [training notebook](https://github.com/juliusmarkwei/Customer-Churn-EDA-Balancing-and-ML/notebooks/main.ipynb).
## Contributing
We welcome contributions to improve this project! Whether it's bug reports, feature suggestions, or code contributions, we appreciate your help.
- **Reporting Issues:** If you encounter a problem or have a suggestion, [open an issue](https://github.com/juliusmarkwei/Customer-Churn-EDA-Balancing-and-ML/issues) with details.
- **Making Pull Requests:** Feel free to submit pull requests for fixes or enhancements. Follow common coding standards and provide clear descriptions for your changes.
Thank you for your contributions!
---
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.