Overview:

The Customer Churn Prediction System is a machine learning-based project designed to predict whether a customer is likely to discontinue using a company’s products or services (i.e., churn).

Churn prediction helps businesses—especially in sectors like telecom, banking, insurance, and e-commerce—to identify at-risk customers before they leave, allowing the company to take proactive measures such as offering discounts, improving service quality, or launching targeted marketing campaigns.

The system uses historical customer data (e.g., demographics, purchase history, service usage, feedback, and complaints) to train a predictive model that classifies customers as “churn” or “non-churn.”

Objectives:

• To analyze customer data and identify patterns that lead to churn.

• To predict the probability of customer churn using machine learning algorithms.

• To help companies retain customers by providing actionable insights.

• To reduce business losses and increase customer satisfaction.

Key Features:

1. Customer Data Analysis: Visualizes patterns of active vs. churned customers.

2. Predictive Modeling: Uses ML algorithms (Logistic Regression, Random Forest, etc.) to forecast churn risk.

3. Churn Probability Score: Assigns a churn likelihood percentage to each customer.

4. Feature Importance Analysis: Shows which factors (tenure, usage, complaints, etc.) contribute most to churn.

5. Dashboard & Reports: Displays churn rate, retention rate, and prediction accuracy.

6. Admin Panel: Allows data upload, model retraining, and performance monitoring.

7. Responsive Interface: Designed with HTML, CSS, Bootstrap, and JavaScript for clean visualization.

Tech Stack:

Frontend:

• HTML, CSS, Bootstrap, JavaScript

• Chart.js / D3.js for data visualization

Backend:

• Python (Flask / Django)

• Node.js (alternative backend)

Machine Learning Libraries:

• Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn

Database:

• MySQL / MongoDB

System Workflow:

1. Data Collection:
   Customer-related data such as demographics, subscription details, usage patterns, and support history are collected.

2. Data Preprocessing:
   Handles missing values, encodes categorical data, and normalizes features for ML input.

3. Model Training:
   Machine learning models (e.g., Logistic Regression, Random Forest, XGBoost) are trained on labeled data (churn or not churn).

4. Prediction:
   For each customer, the system predicts the likelihood of churn.

5. Visualization:
   The dashboard displays churn probability, customer retention trends, and insights about key churn factors.

6. Decision Support:
   The business team can use these predictions to launch retention campaigns or personalize offers.