Lecture 5 covers Classification in Data Mining with detailed explanations of Decision Trees, Naive Bayes, KNN, and Logistic Regression. Includes formulas, diagrams, examples, coding, real-world applications, and comparisons. Classification is one of the most widely used techniques in Data Mining…
Lecture 4 explains Association Rule Mining, including Apriori, FP-Growth, support, confidence, lift, frequent itemsets, market basket analysis, diagrams, formulas, step-by-step examples, and Python implementation perfect for BS CS, BS AI, BS IT, and Data Science students. Association Rule Mining is…
This lecture explains Data Preprocessing in Data Mining with in-depth coverage of data cleaning, handling missing values, integration, transformation, reduction, diagrams, case studies, and Python examples ideal for BS AI, BS CS, BS IT & Data Science students. Data Preprocessing…
Lecture 2 explains the types of data in Data Mining, including structured, semi-structured, unstructured, spatial, temporal, and multimedia data. It also covers data patterns, practical examples, case studies, diagrams, and tools used in industry perfect for BS CS, BS AI,…
Lecture 1 provides a complete introduction to Data Mining, including definitions, KDD process, data mining tasks, tools, step-by-step examples, diagrams, real-world applications, and Python demonstrations for students and teachers of BS CS, BS AI, BS IT, and Data Science. Data…