Telco Customer Churn Classification

Telco Customer Churn Classification

This project focuses on building a machine learning classification model to predict whether a customer will leave a telecommunications company (churn) or not. The dataset contains customer demographic information, subscribed services, payment methods, contract type, tenure, and the actual churn status.

The workflow includes:

* Exploratory Data Analysis (EDA) to understand patterns and relationships in the data.

* Data cleaning and preprocessing, including handling missing values and encoding categorical features.

* Splitting the data into training and testing sets.

* Training multiple classification models such as Logistic Regression, K-Nearest Neighbors (KNN), and Random Forest.

* Evaluating model performance using metrics like Accuracy, Precision, Recall, and F1-score.

* Selecting the best-performing model for churn prediction.

This model enables telecom companies to identify customers who are likely to churn in advance, allowing them to take proactive actions such as offering personalized promotions or improving services to retain customers and increase profitability.