Technologies Used:

• Backend: PHP / Java / Node.js

• Frontend: HTML, CSS, Bootstrap, JavaScript

• ML Tools: Python, Pandas, Scikit-learn, Matplotlib, Seaborn

• Database: MySQL / PostgreSQL / MongoDB

• Optional: REST APIs for data integration from CRM systems

Project Objective:

To develop a machine learning-powered web system that predicts which customers are likely to stop using a product or service (i.e., churn), helping businesses proactively retain them.

Key Features:

1. Customer Data Input Module:
   Upload datasets manually or connect to live CRM data (via API).

2. Churn Prediction Engine:
   Uses ML models trained on historical data to classify customers into:

   •  High Churn Risk

   •  Medium Risk

   •  Low Risk

3. Visual Dashboard:

   • Churn probability chart per customer

   • Risk segmentation (pie/bar charts)

   • Trend analysis over time

4. Customer Profile Analysis:
   Detailed view of what factors (like complaints, usage, payment delays) contribute to the churn prediction.

5. Admin Panel:

   • Upload new data

   • Trigger model retraining

   • Manage users

How ML Works in This Project:

 Dataset Includes:

• Customer demographics (age, gender, location)

• Usage patterns (login frequency, transaction volume)

• Subscription type, tenure

• Support complaints or ticket history

• Payment delays or issues

 ML Workflow:

1. Preprocessing:
   Handle missing data, encode categorical values, normalize numeric features.

2. Model Building:
   Algorithms like:

   • Logistic Regression

   • Decision Tree / Random Forest

   • XGBoost

   • Neural Networks (optional)

3. Model Output:

   • Predicts churn probability

   • Feature importance (why the customer might churn)