Data Mining Using Python Lab is a part of III B.Tech for R20 Regulation CSE-DS students. This lab offers practical exposure to core data mining concepts and techniques using Python programming. It enables students to extract useful patterns and knowledge from large datasets through implementation of data preprocessing, classification, clustering, and association rule mining. The lab emphasizes the use of libraries such as pandas, NumPy, matplotlib, scikit-learn, and mlxtend.

The expected outcomes from the students are:

• Understand and apply data preprocessing techniques like handling missing values, normalization, and data transformation.
• Implement classification algorithms such as Decision Trees, Naive Bayes, and k-Nearest Neighbors using Python.
• Perform clustering using algorithms like K-Means and DBSCAN.
• Generate and evaluate association rules using algorithms like Apriori.
• Visualize data and mining results using appropriate Python libraries.
• Gain practical knowledge in interpreting data mining results and applying them to real-world datasets.