Data mining lab manual using weka

INDEX The objective of the lab exercises is to use data mining techniques to identify customer segments and understand their buying behavior and to use standard databases available to understand DM processes using WEKA (or any other DM tool) 1. Gain insight for running pre- defined decision trees and explore results using MS OLAP Analytics.5/5(1). 2. EXPERIMENT NO:1 Aim: Create an Employee Table with the help of Data Mining Tool WEKA. Description: We need to create an Employee Table with training data set which includes attributes like name, id, salary, experience, gender, phone number. Procedure: Steps: 1)Open Start Programs Accessories Notepad. COURSE NAME: DATA WAREHOUSING AND MINING LAB COURSE CODE: A COURSE OBJECTIVES: 1. Learn how to build a data warehouse and query it (using open source tools like Pentaho Data Integration Tool, Pentaho Business Analytics). 2. Learn to perform data mining tasks using a data mining toolkit (such as open source WEKA). 3. Understand .

Step1:Loading the data. We can load the dataset into weka by clicking on open button in preprocessing interface and selecting the appropriate file. Step2:Once the data is loaded, weka will recognize the attributes and during the scan of the data weka will compute some basic strategies on each attribute. The left panel in the above. Convert the data of these three days (i.e., Days ) into the ARFF format, and save it in the “play_tennis_www.doorway.ru” file. • Launch the WEKA tool, and then activate the “Explorer” environment. • Open the “play_tennis” dataset (i.e., saved in the “play_www.doorway.ru” file). - For each attribute and for each of its possible values, how many instances in each class. Learn to perform data mining tasks using a data mining toolkit (such as open source WEKA). 3. Understand the data sets and data preprocessing. 4. Demonstrate the working of algorithms for data mining tasks such association rule mining, classification, clustering and regression. 5. Exercise the data mining techniques with varied input values for different parameters. 6. To obtain Practical Experience Working with all real data sets. 7.

To apply the concept of Linear Regression for training the given dataset. Algorithm: 1. Open the weka tool. 2. Download a dataset by using UCI. 3. Apply replace. DATA WAREHOUSE LAB. OBJECTIVES. Learn how to perform data mining tasks using a data mining toolkit (such as open source WEKA),. Understand the data sets and. To obtain practical experience using data mining techniques on real world To remove an attribute, you can use the preprocess tab in Weka's GUI Explorer.