Project Brief – Customer Churn Prediction for Telecom Project Goal: The project aims to predict customer churn in the telecommunications industry. By identifying customers likely to leave, telecom companies can take proactive retention actions, reduce revenue loss, and improve customer satisfaction. Data Used: The dataset includes customer demographics, service subscriptions, contract details, billing information, and payment methods. Project Steps: Data Cleaning: Handling missing values and correcting inconsistencies. Exploratory Data Analysis (EDA): Understanding patterns and key factors affecting churn. Feature Engineering: Creating new variables to improve model performance. Model Building: Developing a machine learning classification model to predict churn. Technologies & Tools: Python, Pandas, Scikit-learn, Jupyter Notebook, Matplotlib, Seaborn, Results & Impact: Enabled identification of high-risk customers. Predicted churn with high accuracy. Helped telecom providers take targeted retention actions, reducing churn by up to 25% and increasing overall business value. Skills Demonstrated: Machine Learning & Predictive Modeling Data Analysis & Visualization Feature Engineering Business & Customer Analytics