Icon

L1-DS Final Assessment Workflow

There has been no title set for this workflow's metadata.

L1-DS Final Assessment Workflow

This workflow contains the final assessment of the L1-DS self-paced course. Solve the workflow and complete the quiz at the end of the course!

URL: KNIME Self-Paced Courses https://www.knime.com/knime-self-paced-courses

Task 3. Make sense of the dataThe columns edu_mother and edu_father represent theeducation level of a student's parents. Map the indices to thefollowing categories0 - none1 - primary2 - middle3 - secondary4 - higherGroup the data in order to obtain the percentage of studentsfor each combination of mother and father education level. Task 1. Read dataThe students.sqlite database stores studentpersonal info in 2 tables - GP and MS - correspondingto two different schools. Read the content of the twotables into the workflow.The transcript.csv file contains failures, absencesand grades for each student. Question: What is thepercentage of students withboth parents having highereducation? Task 2. Bring things togetherMerge all the data in a single table containing studentsfrom both schools and the relative transcript data. Question: What is thetotal number of studentsin both schools? Fill the gaps: The feature with the highestcoefficient is ____. Applied on the test data, thelinear regression shows a mean absolute error of___. Task 4. Linear RegressionTrain a linear regresion model to fit the grade_final category.Partition 70-30 with random seed 1.Apply the model to the test data and evaluate its performance. Question: Which of thefollowing nodes can beused to replace theeducation index? Question: For which ofthe following studentsthere is no transcriptavailable? students.sqlitetranscript.csvNode 34Node 36Node 37Node 38Node 39Node 40Node 41Node 42Node 43Node 45Node 46Node 47Node 48 SQLite Connector CSV Reader Joiner DB Table Selector DB Table Selector DB Reader DB Reader Concatenate GroupBy Rule Engine Rule Engine Partitioning Linear RegressionLearner RegressionPredictor Numeric Scorer Task 3. Make sense of the dataThe columns edu_mother and edu_father represent theeducation level of a student's parents. Map the indices to thefollowing categories0 - none1 - primary2 - middle3 - secondary4 - higherGroup the data in order to obtain the percentage of studentsfor each combination of mother and father education level. Task 1. Read dataThe students.sqlite database stores studentpersonal info in 2 tables - GP and MS - correspondingto two different schools. Read the content of the twotables into the workflow.The transcript.csv file contains failures, absencesand grades for each student. Question: What is thepercentage of students withboth parents having highereducation? Task 2. Bring things togetherMerge all the data in a single table containing studentsfrom both schools and the relative transcript data. Question: What is thetotal number of studentsin both schools? Fill the gaps: The feature with the highestcoefficient is ____. Applied on the test data, thelinear regression shows a mean absolute error of___. Task 4. Linear RegressionTrain a linear regresion model to fit the grade_final category.Partition 70-30 with random seed 1.Apply the model to the test data and evaluate its performance. Question: Which of thefollowing nodes can beused to replace theeducation index? Question: For which ofthe following studentsthere is no transcriptavailable? students.sqlitetranscript.csvNode 34Node 36Node 37Node 38Node 39Node 40Node 41Node 42Node 43Node 45Node 46Node 47Node 48SQLite Connector CSV Reader Joiner DB Table Selector DB Table Selector DB Reader DB Reader Concatenate GroupBy Rule Engine Rule Engine Partitioning Linear RegressionLearner RegressionPredictor Numeric Scorer

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: emilio.silvestri
Created at: 2022-12-20
On NodePit since: 2023-10-23
Last update: 2024-05-01
Created with KNIME version: v5.1.2
Tags: L1-DSeducation

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

Try NodePit Runner!