End To End Project Customer Churn

Abstract

1. Introduction

Churn prediction means detecting which customers are likely to cancel a subscription to a service based on how they use the service. It is a critical prediction for many businesses because acquiring new clients often costs more than retaining existing ones. Once you can identify those customers that are at risk of cancelling, you should know exactly what marketing action to take for each individual customer to maximise the chances that the customer will remain.

Customer churn is a common problem across businesses in many sectors. If you want to grow as a company, you have to invest in acquiring new clients. Every time a client leaves, it represents a significant investment lost. Both time and effort need to be channelled into replacing them. Being able to predict when a client is likely to leave, and offer them incentives to stay, can offer huge savings to a business.

2. Objective

End to end projects-- Customer Churning prediction using Gradient Boost Classifier Algorithm perform pre-processing steps then fit data into the Algorithm and Hyper Parameter Tunning to reduce TN & FN value to perform our model to works with a new data. Finally deploying the model using Flask API

This project demonstrates how Jupyter Notebook can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/satz2000/End-to-end-project---Customer-churn.git
cd End-to-end-project---Customer-churn

1. Data Analysis (EDA)
2. Data Preprocessing.
3. Feature Engineering.
4. Feature Selection (SelectKBest)
5. Fit into Algorithm (ML Algorithm)
6. Hyper Parameter Tunning (RandomSearchCV)
7. Dump model (Pickle)
8. Creating Web Application using Flask

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by satz2000 and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.