https://github.com/alphacrypto246/customer-churn

This project predicts customer churn using machine learning. It includes data preprocessing, exploratory analysis, model training, and evaluation to identify key factors driving churn and provide actionable insights for retention.
https://github.com/alphacrypto246/customer-churn

knn-classification machine-learning machine-learning-algorithms python scikit-learn scikitlearn-machine-learning

Last synced: 4 months ago
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This project predicts customer churn using machine learning. It includes data preprocessing, exploratory analysis, model training, and evaluation to identify key factors driving churn and provide actionable insights for retention.

• Host: GitHub
• URL: https://github.com/alphacrypto246/customer-churn
• Owner: alphacrypto246
• Created: 2024-12-19T10:48:47.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-12-21T10:03:42.000Z (6 months ago)
• Last Synced: 2024-12-27T01:12:25.234Z (6 months ago)
• Topics: knn-classification, machine-learning, machine-learning-algorithms, python, scikit-learn, scikitlearn-machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 708 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Customer Churn Prediction

This project focuses on predicting customer churn using machine learning techniques. Churn prediction helps businesses identify customers likely to leave, enabling proactive steps to improve retention and enhance customer satisfaction.

---

## Table of Contents

- [Introduction](#introduction)

- [Features and Objectives](#features-and-objectives)

- [Technologies Used](#technologies-used)

- [Dataset](#dataset)

- [Implementation](#implementation)

- [Results](#results)

  

---

## Introduction

Customer churn occurs when customers stop doing business with a company. This project aims to:

1. Analyze factors influencing churn.

2. Build predictive models to classify customers as churners or non-churners.

3. Provide actionable insights based on model outcomes.

---

## Features and Objectives

- **Data Preprocessing**:

  - Handle missing values, outliers, and categorical encoding.

  - Normalize/scale numerical features for better model performance.

- **Exploratory Data Analysis (EDA)**:

  - Visualize trends, correlations, and distributions.

  - Identify key features impacting churn.

- **Model Development**:

  - Implement machine learning models such as Logistic Regression, Random Forest, and others.

  - Evaluate models using metrics like accuracy, precision, recall, and F1-score.

- **Insights**:

  - Highlight patterns and actionable recommendations to reduce churn.

---

## Technologies Used

- **Languages**: Python

- **Libraries**:

  - Data Manipulation: Pandas, NumPy

  - Data Visualization: Matplotlib, Seaborn

  - Machine Learning: Scikit-learn

- **Environment**: Jupyter Notebook

---

## Dataset

The dataset used for this project contains customer information such as demographics, subscription details, and usage patterns. 

- **Source**: [Specify the source if publicly available, or note if proprietary]

- **Key Features**:

  - Customer ID

  - Monthly Charges

  - Contract Type

  - Tenure

  - Churn Indicator

---

## Implementation

1. **Data Preprocessing**:

   - Data cleaning and transformation.

   - Encoding categorical variables.

   - Splitting the dataset into training and testing sets.

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

   - Visualize feature distributions and correlations.

   - Derive meaningful insights about customer behavior.

3. **Model Training and Evaluation**:

   - Train multiple machine learning models.

   - Evaluate and compare model performance.

4. **Final Model Selection**:

   - Choose the best-performing model for predictions.

---

## Results

- Achieved a high accuracy in predicting customer churn.

- Identified key factors contributing to churn.

- Provided actionable insights to reduce churn rates effectively.

Key results and visualizations can be found in the Jupyter Notebook.