Classification algorithms are supervised learning methods used to categorize data into predefined classes. They analyze patterns in labeled data to make accurate predictions for new inputs. Popular techniques like Logistic Regression, Decision Trees, KNN, SVM, and Naive Bayes help solve tasks such as spam detection, customer churn prediction, and medical diagnosis with high accuracy.
Learn how Logistic Regression predicts binary outcomes using probability-based decision boundaries. This lesson covers theory, implementation, and practical use cases like spam detection and churn prediction.
Understand how the KNN algorithm classifies data based on similarity. This lesson explains distance metrics, choosing the right K value, and building accurate classification models.
Explore Decision Trees and how they split data into meaningful decision rules. This lesson teaches tree-building, visualization, and practical classification applications.
Master Support Vector Machines for high‑accuracy classification. Learn how SVMs create optimal boundaries and handle linear and nonlinear data with kernel functions.
Discover the Naive Bayes classifier, a fast and powerful algorithm based on probability and Bayes’ theorem. This lesson shows how it excels in text classification and other high‑dimensional tasks.
Apply classification algorithms to a real Customer Churn Prediction project. Learn to preprocess data, evaluate models, and build insights that help businesses reduce customer loss.
