https://github.com/coder5omkar/logistic-regression-customer-churn-prediction

This project uses Logistic Regression to predict customer churn in the telecom industry. To run, clone the repository, install dependencies, and run the Jupyter notebook for full analysis and predictions.
https://github.com/coder5omkar/logistic-regression-customer-churn-prediction

logistic-regression ml pandas scikit-learn seaborn statistics

Last synced: 13 days ago
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This project uses Logistic Regression to predict customer churn in the telecom industry. To run, clone the repository, install dependencies, and run the Jupyter notebook for full analysis and predictions.

• Host: GitHub
• URL: https://github.com/coder5omkar/logistic-regression-customer-churn-prediction
• Owner: coder5omkar
• License: mit
• Created: 2024-12-25T11:22:39.000Z (14 days ago)
• Default Branch: master
• Last Pushed: 2024-12-25T11:46:16.000Z (14 days ago)
• Last Synced: 2024-12-25T12:25:19.415Z (14 days ago)
• Topics: logistic-regression, ml, pandas, scikit-learn, seaborn, statistics
• Language: Jupyter Notebook
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        


















# 📊 Logistic Regression - Customer Churn Prediction 😊 

## 📋 General Information

``` 

This project uses Logistic Regression to predict customer churn in the telecom industry. The dataset includes churn data, customer data,

internet data. We preprocess the data, perform exploratory analysis, and build a Logistic Regression 

model using Scikit-learn. The model predicts whether a customer will churn or stay. Evaluation metrics like accuracy, precision, and 

recall are used to assess performance. Visualizations and insights help interpret model results. The project is implemented in Python with 

libraries such as Pandas, NumPy, and Matplotlib. To run, clone the repository, install dependencies, and run the Jupyter notebook for full 

analysis and predictions.

```

## 🛠️ Technologies Used

- [Python](https://www.python.org/) version: 3.12.4

- [Numpy](https://numpy.org/) version: 1.26.4

- [Pandas](https://pandas.pydata.org/) version: 2.2.2

- [Seaborn](https://seaborn.pydata.org/) version: 0.13.2

- [Matplotlib](https://matplotlib.org/) version: 3.9.2

- [scikit-learn](https://scikit-learn.org/) version: 1.5.1

- [statsmodels](https://statsmodels.org/) version: 0.14.2

## 🚀 **Getting Started** (In Anaconda PowerShell Prompt)

1. Clone the repository:

   ```bash

   git clone https://github.com/coder5omkar/Logistic-Regression-Customer-Churn-Prediction.git

   ```

2. Navigate to the project directory:

   ```bash

   cd Logistic-Regression-Customer-Churn-Prediction

   ```

3. Open the notebook:

   ```bash

   jupyter notebook LRG.ipynb

   ```

---

## 🤝 Acknowledgements

- This project was inspired by IIT-B AI-ML program at Upgrad

Developed as part of the ML-1 Module assignment required for Post Graduate Diploma in Machine Learning and AI - IIIT,Bangalore.

This project is open source and available under the [MIT License](https://github.com/coder5omkar/Logistic-Regression-Customer-Churn-Prediction/blob/master/licence.txt).

## Contact

Created by [@in/omkaramale](https://github.com/coder5omkar) - feel free to contact me!