Customer_Churn_prediction_Classification

This project focuses on building a machine learning classification model to predict customer churn — the likelihood that a customer will stop using a company's service. By analyzing historical customer data, the model identifies key factors that influence customer retention and provides insights to help businesses take proactive actions.

Objectives

Predict whether a customer will churn or stay.

Identify important features that contribute to churn.

Provide actionable insights to reduce churn and improve customer satisfaction.

Dataset Overview

The dataset typically includes customer demographics, service usage behavior, account information, and past interactions. Common features include:

Customer tenure

Monthly charges

Contract type

Payment method

Internet service usage

Customer support calls

? Techniques Used

Data preprocessing and feature engineering

Exploratory Data Analysis (EDA)

Model building with classification algorithms such as:

Logistic Regression

SVM

Random Forest

Decision Tree

AdaBoost

Gradient Boost

XGBoost

Artificial Neural Network(ANN)

Model evaluation using accuracy, precision, recall, F1-score, and Confusion matrix