Customer Churn Prediction Using Machine Learning | CodSoft Task 3 | Python Data Science Project

Customer Churn Prediction Using Machine Learning | CodSoft Task 3 | Python Data Science Project Customer Churn Prediction Using Machine Learning Excited to share my CodSoft Task 3 project, where I developed a Machine Learning model to predict customer churn and understand which customers may be likely to leave a service. In this project, I worked with customer information such as age, geography, credit score, account balance, and active membership status. I performed data preprocessing, exploratory data analysis, feature encoding, feature scaling, and model training. I implemented and compared Logistic Regression, Random Forest, and Gradient Boosting models. The models were evaluated using Accuracy, Precision, Recall, F1 Score, ROC AUC Score, and Confusion Matrix. The selected model was also saved using Joblib for future use. Technologies used Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit learn, Google Colab, and Joblib. This project helped me strengthen my practical skills in Machine Learning classification, data preprocessing, model evaluation, and predictive analytics. #MachineLearning #Python #DataScience #CustomerChurnPrediction #CodSoft #ArtificialIntelligence #DataAnalytics #ScikitLearn #Pandas #NumPy Customer Churn Prediction, Machine Learning, Python, Data Science, Customer Analytics, Churn Prediction Model, Logistic Regression, Random Forest, Gradient Boosting, Scikit Learn, Pandas, Predictive Analytics, CodSoft Internship, Machine Learning Project, Data Analytics