Keeping a customer is far cheaper than winning a new one. Churn prediction uses past customer behavior, such as contract type, monthly charges and tenure, to estimate which customers are likely to leave. A classification model learns those patterns so a business can step in before the customer is gone.
The lessons start with a full end-to-end build covering data analysis, feature engineering and a scikit-learn model, finishing with a small Streamlit app. Next is a classification walkthrough on the Telco dataset, then a focused XGBoost model. The last lesson builds a neural network (ANN) with Keras and reads the results with a confusion matrix, precision and recall.
Watch the lessons in order and code along in a Jupyter notebook or Google Colab. If a video runs long, treat it as a reference you can dip back into while you build your own version.
Build a churn model from scratch: analyze the data, engineer features, train a model with scikit-learn, then deploy it in an interactive Streamlit app.
Video 50minWalk through the Telco churn dataset: import and load the data, explore it, preprocess it, then train and test classification models to find customers likely to leave.
Video 52minBuild a churn model with the XGBoost classifier: preprocess categorical and numerical values, scale the data, split train and test sets, then train and evaluate.
Video 38minBuild a neural network for churn on the Telco dataset, then read its precision, recall and accuracy using a confusion matrix and classification report.
Video 40min