Customer Churn Prediction

📌 What is this project?

This project predicts whether a customer is likely to leave (churn) a telecom service using machine learning.

👉 Churn means when a customer stops using a service.
Companies use churn prediction to identify customers who might leave and take actions to retain them.

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🎯 Problem Statement

Given customer data such as tenure, monthly charges, and contract type, the goal is to predict:

➡️ Will the customer churn? (Yes / No)

This is a binary classification problem.

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📊 Dataset

The dataset contains customer information such as:

• Tenure (how long the customer stayed)
• Monthly Charges
• Total Charges
• Contract Type
• Internet Service
• Payment Method
• Churn (Target Variable)

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⚙️ Technologies Used

• Python
• Pandas & NumPy → data handling
• Matplotlib & Seaborn → visualization
• Scikit-learn → machine learning models
• XGBoost → advanced model

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🔄 Project Workflow

1. Data Loading

Loaded the dataset using pandas.

2. Data Cleaning

• Converted incorrect data types (e.g., TotalCharges)
• Handled missing values using errors='coerce'

3. Exploratory Data Analysis (EDA)

• Analyzed churn distribution
• Studied relationships between features (e.g., tenure vs churn)
• Used visualizations for better understanding

4. Feature Engineering

• Converted categorical variables into numeric form
• Used encoding techniques like label encoding / one-hot encoding

5. Train-Test Split

• Split data into training and testing sets (80:20)

6. Model Training

Trained multiple models:

• Logistic Regression
• Decision Tree
• Random Forest
• XGBoost

7. Model Evaluation

Used multiple metrics:

• Accuracy
• Precision
• Recall
• F1 Score
• ROC-AUC

8. Result

XGBoost performed the best due to its ability to handle complex patterns and nonlinear relationships.

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📈 Key Insights

• Customers with low tenure are more likely to churn
• Month-to-month contracts have higher churn rates
• Higher monthly charges may increase churn probability

🚀 How to Run the Project

1. Clone the repository
2. Install dependencies
3. Run