Credit Risk Assessment

Welcome to the Credit Risk Assessment repository! 🏦📊 Here, we're all about predicting whether customers are likely to churn using some fancy machine learning models. Get ready to dive into a world of data, predictions, and a sprinkle of fun! 🎉

Table of Contents

• Introduction
• Files Included
• Setup Instructions
• How to Use
• Project Structure
• Contributing

Introduction

Are you ready to predict customer churn with the power of Python and machine learning? This project provides a complete pipeline from data preprocessing to model training and evaluation. With this toolkit, you'll be able to assess customer credit risk and predict churn with confidence. Let's get started!

Files Included

Here's a quick rundown of the magic you'll find in this repository:

1. churn_predictor.py

The heart of our project. This script collects user input, loads the pre-trained model, and predicts customer churn. It's like a crystal ball but more tech-savvy! 🔮

2. churn_predictor_app.py

Similar to churn_predictor.py, but with added awesomeness for deployment as a Streamlit app. Bring your predictions to life on the web! 🌐

3. dataset_splitter.py

Splits your dataset into training and testing sets with a sprinkle of randomness. Perfect for when you need a fresh split every time. 🎲

4. svm_classifier_train.py

This script preprocesses data, trains an SVM model, and saves the pipeline. It's the engine room of our machine learning journey. 🚂

5. svm_test.py

Another version of the training script for testing purposes. Because who doesn't love some extra validation? ✅

6. pipeline_model.pkl

Our pre-trained model pipeline, ready to make predictions. It's the secret sauce! 🌟

Setup Instructions

Before you can start predicting churn, you need to set up your environment. Here's how:

1. Clone the repository:

   git clone https://github.com/yourusername/credit-risk-assessment.git
   cd credit-risk-assessment

2. Install the required libraries:

   pip install -r requirements.txt

3. Ensure you have the necessary data: Make sure you have the customer_churn.csv dataset in the root directory.

How to Use

Running the Churn Predictor

To predict customer churn using the command-line interface:

python churn_predictor.py

Follow the prompts to enter customer data and get instant predictions! 📈

Running the Streamlit App

To launch the web app:

streamlit run churn_predictor_app.py

Open the provided URL in your browser and enjoy the interactive predictions. 🚀

Splitting the Dataset

To split the dataset into training and testing sets:

python dataset_splitter.py

Training the Model

To preprocess the data and train the SVM model:

python svm_classifier_train.py

Testing the Model

To validate the model with additional testing:

python svm_test.py

Project Structure

Here's a peek into the project's structure:

credit-risk-assessment/
│
├── churn_predictor.py
├── churn_predictor_app.py
├── dataset_splitter.py
├── svm_classifier_train.py
├── svm_test.py
├── pipeline_model.pkl
├── customer_churn.csv
├── customer_churn_test.csv
├── customer_churn_train.csv
└── README.md

Contributing

We welcome contributions with open arms! If you have ideas for improvements or new features, feel free to fork the repo, make your changes, and submit a pull request. Let's make this project even more awesome together! 💪

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Happy predicting! May the churn odds be ever in your favor! 🍀