Customer Churn Prediction Project This project predicts whether a customer is likely to churn (stop using a service) based on historical data. It utilizes machine learning techniques to build a predictive model and allows users to input customer details for real-time predictions.

Project Features Data Preprocessing: Cleans and preprocesses data, including handling missing values, encoding categorical features, and scaling numerical data. Exploratory Data Analysis (EDA): Visualizes key relationships and insights using correlation heatmaps, bar plots, and descriptive statistics. Model Training: Implements a Random Forest Classifier to predict customer churn with high accuracy. Evaluation: Evaluates model performance using accuracy, confusion matrix, classification report, and ROC-AUC score. User Input: Accepts customer details for real-time churn prediction and provides a confidence score for the prediction. Technologies Used Programming Language: Python Libraries: Data Processing: pandas, numpy Visualization: matplotlib, seaborn Machine Learning: scikit-learn Dataset The project uses the Telco Customer Churn Dataset, which contains customer demographics, account information, and churn labels. Dataset Link: Telco Customer Churn Dataset on Kaggle