srivarshithdaladuli / customer-churn-prediction

Customer Churn Prediction Project

This repository contains the code and resources for a customer churn prediction project. The goal of this project is to build a machine learning model that can predict customer churn, i.e., identify customers who are likely to leave a service or subscription. Customer churn prediction is crucial for businesses as it allows them to proactively take actions to retain customers and improve their overall customer retention rates.

Table of Contents

• Introduction
• Prerequisites
• Getting Started
• Data
• Data Preprocessing
• Model Training
• Evaluation
• Deployment
• Contributing
• License

Introduction

Customer churn is a significant problem for businesses, and predicting it accurately can help companies allocate resources more efficiently and devise strategies to reduce churn rates. This project aims to demonstrate how to build a churn prediction model using machine learning techniques. The repository includes code, data, and instructions to guide you through the process.

Prerequisites

Before getting started, make sure you have the following installed:

• Python (>=3.6)
• Jupyter Notebook (for running the provided notebooks)

You can install the required Python packages by running:

pip install -r requirements.txt

Getting Started

To get started with the project, follow these steps:

1. Clone this repository to your local machine.
2. Install the required dependencies as mentioned in the Prerequisites section.
3. Open the Jupyter Notebook Churn_Prediction.ipynb to walk through the project code step by step.

Data

The dataset used for this project is located in the data directory. The file customer_data.csv contains customer information and historical churn data. The dataset is already preprocessed and ready for training.

Data Preprocessing

In the Jupyter Notebook, you will find the data preprocessing steps, including handling missing values, feature engineering, and data transformation.

Model Training

The Jupyter Notebook also covers the model training phase. We will use popular machine learning algorithms such as Random Forest, Logistic Regression, and Gradient Boosting to build and train the churn prediction model.

Evaluation

The performance of the trained models will be evaluated using various metrics such as accuracy, precision, recall, and F1 score. The evaluation results will help us compare the models and select the best performing one.

Deployment

The deployment folder contains resources and instructions to deploy the trained model into production. We will use a simple Flask web application to showcase how the model can be used to predict churn on new data.

Contributing

If you'd like to contribute to this project, feel free to open an issue or submit a pull request. We welcome contributions from the community.

License

This project is licensed under the MIT License, which means you are free to use, modify, and distribute the code as you see fit. Please refer to the LICENSE file for more details.

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We hope you find this project insightful and educational. If you have any questions or suggestions, please don't hesitate to reach out.