Customer Churn Prediction App

Project Overview

This project predicts customer churn using a Random Forest Classifier. Customer churn refers to the likelihood of a customer leaving a service provider. The prediction model utilizes various customer attributes such as demographics, subscription details, and service usage.

A web application built with Flask allows users to input customer details and receive predictions along with the probability of churn.

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Features

• Machine Learning: Random Forest Classifier for churn prediction.
• Data Preprocessing: Encoders and scalers for categorical and numerical data transformation.
• Web Application: User-friendly interface for predictions.
• Scalable Design: Easily adaptable to new features.

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Dataset Features

The model uses the following features for prediction:

Demographics:

• gender
• SeniorCitizen
• Partner
• Dependents

Service Details:

• PhoneService
• MultipleLines
• InternetService
• OnlineSecurity
• OnlineBackup
• DeviceProtection
• TechSupport
• StreamingTV
• StreamingMovies

Contract Information:

• Contract
• PaperlessBilling
• PaymentMethod

Subscription Metrics:

• tenure
• MonthlyCharges
• TotalCharges

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Requirements

• Required Libraries:
  • Flask
  • pandas
  • scikit-learn
  • pickle
  • matplotlib
  • numpy

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How to Run the Application

1. Clone the Repository

git clone 

2. Start the Flask Server

python app.py

3. Access the Application

Open your browser and go to:http://127.0.0.1:5000/

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Folder Structure

|__ data
├── app.py                     
├── model/
│   ├── best_model.pkl         
│   ├── encoder.pkl            
│   ├── scaler.pkl             
├── templates/
│   └── index.html             
── README.md                  

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Application Flow

1. Input Data: User provides customer details in a web form.
2. Data Preprocessing:
   • Encodes categorical features.
   • Scales numerical features (tenure, MonthlyCharges, TotalCharges).
3. Prediction:
   • Outputs whether the customer is likely to Churn or Not Churn.
   • Provides the probability of churn.
4. Output: Results are displayed on the web interface.

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Sample Input

Feature	Example Value
gender	Male
SeniorCitizen	0 (No)
Partner	Yes
Dependents	No
tenure	24
PhoneService	Yes
MultipleLines	No
InternetService	Fiber optic
OnlineSecurity	No
OnlineBackup	Yes
DeviceProtection	No
TechSupport	No
StreamingTV	Yes
StreamingMovies	Yes
Contract	Two year
PaperlessBilling	No
PaymentMethod	Credit card
MonthlyCharges	85.75
TotalCharges	2075.5

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Future Enhancements

• Add functionality to train models directly through the app.
• Support additional machine learning models for comparison.
• Deploy the app on cloud platforms like AWS, GCP, or Heroku.
• Improve the user interface for better usability.

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