Customer Churn Prediction

This repository contains a machine learning project focused on predicting customer churn. The objective is to identify customers who are likely to stop using a product or service. The project follows a step-by-step process that includes data exploration, analysis, model building, and performance evaluation.

Table of Contents

1. Project Overview
2. Dataset
3. Project Structure
4. Installation
5. How to Use
6. Exploratory Data Analysis (EDA)
7. Model Building
8. Evaluation Metrics
9. Hyperparameter Tuning
10. Results
11. Future Work

Project Overview

Customer churn is a key business problem, where the goal is to predict if a customer is likely to churn (leave the service) based on historical data. This project builds a machine learning model that classifies customers based on features such as demographics, usage patterns, and interaction history.

Dataset

• Source: [Mention the dataset source here (e.g., Kaggle, UCI Repository, or internal data)].
• Description: The dataset contains information about customer demographics, account information, and their usage of the service.
• Features: A brief overview of key features used in the analysis.

Project Structure

The repository is organized as follows:

├── data/                                          # Folder containing dataset files
├── Customer_Churn_Prediction.ipynb /              # Source code for data preprocessing, feature engineering, data analysis and modeling etc
├── saved_models/                                  # Saved machine learning models
├── images/                                        # Source code for data preprocessing, feature engineering, etc.
├── results/                                       # Folder containing images
├── README.md                                      # Project overview and instructions
└── requirements.txt                               # Required packages and libraries
├── DockerFile                                     # Docker file

Installation

To run the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/customer-churn-prediction.git

2. Install the required libraries:

   pip install -r requirements.txt

3. Launch Jupyter Notebook:

   jupyter notebook

How to Use

1. Load the dataset and perform data preprocessing.
2. Run exploratory data analysis (EDA) to understand the data distribution and relationships between features.
3. Build, train, and evaluate the machine learning models using the provided notebook.

Exploratory Data Analysis (EDA)

EDA is performed to analyze the dataset's distribution, identify missing values, and understand relationships between features. Key steps include:

• Data Visualization: Histograms, scatter plots, and box plots.
• Correlation Analysis: Analyzing relationships between features and the target variable.
• Outlier Detection: Identifying and handling outliers.

Model Building

Multiple machine learning models are trained and evaluated to predict customer churn. The models include:

• Logistic Regression
• Decision Trees
• Random Forest
• Gradient Boosting

Evaluation Metrics

The model performance is evaluated using the following metrics:

• Accuracy
• Precision
• Recall
• F1-Score
• ROC-AUC Curve

Hyperparameter Tuning

Hyperparameter tuning is performed using techniques like Grid Search and Random Search to optimize model performance.

Results

The final model achieved an accuracy of X% and an AUC score of Y. The tuned model is able to predict churn with high precision and recall, making it effective for business decision-making.

Future Work

• Implement advanced techniques like ensemble learning and deep learning models.
• Explore additional features that could improve prediction accuracy.
• Deploy the model using a web framework for real-time predictions.

Contributing

Contributions are welcome! If you have suggestions for improvement, feel free to open a pull request.