Icon

Solution 7 Practicing with Data Preprocessing Techniques

This workflow shows a solution to a hands-on exercise in the L4-ML Introduction to Machine Learning Algorithms self-paced course

Task 2: Normalize data using two alternative methods and compare the statistics ofthe normalized columns1. Min-max normalize column 0 into the range [0,1]2. Normalize column 0 using the decimal scaling method in a parallel workflow branch3. Calculate the statistics of the normalized columns Task 1: Perform outlier detection, missing value imputation, dimensionalityreduction, and encoding1. Visualize the numeric columns in a box plot2. Replace the numeric outliers with missing values. Apply k=1.5.3. Replace the missing values in the numeric columns with the column mean4. Remove rows with missing values in the class column5. Transform the numeric columns into principal components. Preserve 90% of theinformation. 6. Perform category and one-hot encoding on the “category” column in parallelworkflow branches Readpreprocessing-data.tablenumeric: meanclass: remove row90%informationReadnormalization-example.tablemin-maxdecimal scaling Table Reader Numeric Outliers Box Plot Missing Value Category To Number One to Many PCA Table Reader Normalizer Normalizer Statistics Statistics Task 2: Normalize data using two alternative methods and compare the statistics ofthe normalized columns1. Min-max normalize column 0 into the range [0,1]2. Normalize column 0 using the decimal scaling method in a parallel workflow branch3. Calculate the statistics of the normalized columns Task 1: Perform outlier detection, missing value imputation, dimensionalityreduction, and encoding1. Visualize the numeric columns in a box plot2. Replace the numeric outliers with missing values. Apply k=1.5.3. Replace the missing values in the numeric columns with the column mean4. Remove rows with missing values in the class column5. Transform the numeric columns into principal components. Preserve 90% of theinformation. 6. Perform category and one-hot encoding on the “category” column in parallelworkflow branches Readpreprocessing-data.tablenumeric: meanclass: remove row90%informationReadnormalization-example.tablemin-maxdecimal scalingTable Reader Numeric Outliers Box Plot Missing Value Category To Number One to Many PCA Table Reader Normalizer Normalizer Statistics Statistics

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: maarit.widmann
Created at: 2022-03-01
On NodePit since: 2023-09-25
Last update: 2024-11-04
Created with KNIME version: v4.5.2
Tags: educationL4-ML

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!