https://github.com/parag000/customer-churn-prediction
An end to end ML solution to predict customer churn, aiding businesses in identifying at-risk customers. This repository features a tuned LightGBM model, custom preprocessing, SMOTE for class balancing, and a user-friendly Streamlit app for predictions, emphasizing model optimization and deployment.
https://github.com/parag000/customer-churn-prediction
gridsearchcv hyperparameter-tuning lightgbm-classifier pipeline random-forest-classifier smote-sampling streamlit winsorization
Last synced: 2 months ago
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An end to end ML solution to predict customer churn, aiding businesses in identifying at-risk customers. This repository features a tuned LightGBM model, custom preprocessing, SMOTE for class balancing, and a user-friendly Streamlit app for predictions, emphasizing model optimization and deployment.
- Host: GitHub
- URL: https://github.com/parag000/customer-churn-prediction
- Owner: Parag000
- Created: 2024-11-14T03:35:44.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-11-14T04:15:21.000Z (6 months ago)
- Last Synced: 2025-01-21T05:28:23.952Z (4 months ago)
- Topics: gridsearchcv, hyperparameter-tuning, lightgbm-classifier, pipeline, random-forest-classifier, smote-sampling, streamlit, winsorization
- Language: Jupyter Notebook
- Homepage:
- Size: 1.07 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Customer Churn Prediction Using LightGBM Model 📉
This repository presents a comprehensive machine learning solution for customer churn prediction, designed to help businesses identify at-risk customers and proactively optimize retention strategies. The project employs a tuned LightGBM model within a tailored data pipeline, effectively handling class imbalance through SMOTE and engineering features to improve model performance.
This project showcases an end-to-end, production-ready approach to churn modeling, highlighting skills in model optimization, and deploying interactive machine learning applications—ideal for demonstrating expertise in building impactful, business-driven ML solutions.
---
## Project Notebook 📓
Explore the code and methodology in the [Google Colab Notebook](https://colab.research.google.com/drive/1wN07WXLO-mUPvVJQV64q-6casNfe3lZv?usp=sharing). This notebook includes all the steps for data preprocessing, feature engineering, model training, and evaluation.
---
## Web Application 🖥️
*Streamlit interface that allows for interactive customer churn predictions.*
---
## Approach and Steps 🛠️
1. **Data Preprocessing**:
- Created new features (`CustomerTenureEngagement`, `ContentConsumptionScore`) to improve model accuracy.
- Encoded `SubscriptionType` using ordinal encoding and applied one-hot encoding to other categorical features.
- Handled outliers and skewness with Winsorization and log transformations.
2. **Model Training**:
- Utilized LightGBM for its efficiency with large datasets
- Addressed class imbalance with SMOTE to improve recall for the churn class.
- Tuned hyperparameters with GridSearchCV to optimize recall and F1 score.
3. **Deployment**:
- Built a Streamlit app for churn predictions, processing user inputs through the full pipeline to deliver customer-specific churn probabilities.
---
## How to Run the Streamlit Application 🚀🚀
Follow these steps to set up and run the application locally:
### Step 1: Clone the Repository
First, clone the repository and navigate to the project directory:
```bash
git clone https://github.com/Parag000/Customer-Churn-Prediction.git
cd customer-churn-prediction
```
### Step 2: Set Up Environment and Install Dependencies
```bash
python -m venv venv
source venv/bin/activate # For Linux/MacOS
venv\Scripts\activate # For Windows
pip install -r requirements.txt
```
### Step 3: Download Model and Pipeline Files
Download the following files and place them in the project root:
- smote_lgbm.pkl: The pre-trained LightGBM model.
- preprocessing_pipeline.pkl: The custom preprocessing pipeline.
These files are be provided as part of the repository assets.
### Step 4: Run the Streamlit App
Start the Streamlit application by running:
```bash
streamlit run app.py
```
This will open the application in your web browser