https://github.com/rohithmacharla11/costumer-churn-predction

https://github.com/rohithmacharla11/costumer-churn-predction

Last synced: 17 days ago
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• Host: GitHub
• URL: https://github.com/rohithmacharla11/costumer-churn-predction
• Owner: RohithMacharla11
• Created: 2024-02-05T12:09:36.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-12-31T16:07:49.000Z (23 days ago)
• Last Synced: 2024-12-31T17:19:03.198Z (22 days ago)
• Language: Jupyter Notebook
• Size: 582 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Customer Churn Prediction

## Overview

This project focuses on predicting customer churn using machine learning techniques. Customer churn refers to when customers stop using a company's product or service. By identifying at-risk customers, businesses can take proactive measures to improve retention and reduce churn rates. 

## Key Features

- **Predictive Analytics**: Uses machine learning algorithms to predict the likelihood of customer churn.

- **Exploratory Data Analysis (EDA)**: Provides visual and statistical insights into customer behavior and trends.

- **Feature Engineering**: Extracts and optimizes key attributes for better predictive performance.

- **Model Comparison**: Evaluates multiple machine learning models to identify the best-performing one.

- **Actionable Insights**: Generates insights to support strategies for customer retention.

## Dataset

- **Name**: Churn_modelling.csv

- **Description**: The dataset contains customer demographic data, account information, and behavioral indicators.

## Technologies Used

- **Programming Language**: Python

- **Libraries and Tools**:

  - Pandas

  - NumPy

  - Scikit-learn

  - Matplotlib

  - Seaborn

  - Jupyter Notebook

## Approach

1. **Data Preprocessing**:

   - Cleaned and prepared the dataset by handling missing values.

   - Encoded categorical features into numerical formats.

2. **Exploratory Data Analysis (EDA)**:

   - Analyzed trends and correlations in the data using visualizations.

3. **Feature Engineering**:

   - Identified and transformed key variables influencing customer churn.

4. **Model Training**:

   - Implemented machine learning algorithms such as Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting.

5. **Model Evaluation**:

   - Assessed performance using metrics like accuracy, precision, recall, and F1-score.

6. **Insights and Recommendations**:

   - Highlighted actionable insights to reduce churn and improve customer satisfaction.

## Results

- Achieved high accuracy in predicting customer churn.

- Delivered meaningful insights to help businesses retain at-risk customers.

- Demonstrated the effectiveness of machine learning in solving real-world problems.

## Project Structure

├── data/ │ 

└── Churn_modelling.csv # Dataset file 

├── notebooks/ │ 

└── EDA.ipynb 

### Jupyter notebook for exploratory data analysis 

│└── Model_Training.ipynb 

### Jupyter notebook for model training and evaluation 

├── src/ 

├── preprocess.py

### Script for data preprocessing │ 

├── train_model.py 

### Script for training machine learning models │

└── evaluate.py

 ### Script for evaluating model performance 

├── README.md 

### Project documentation

## Conclusion

The Customer Churn Prediction project demonstrates the power of machine learning in addressing critical business challenges. By accurately predicting churn and providing actionable insights, the project supports data-driven decision-making and enhances customer retention strategies.