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M2_​SAE_​JOSHLYNN

Cleaning up of data

Classification (Decision Tree, Minimum Records per Node = 45)

Clustering k=5

Regression (Linear Regression – Target: Torque [Nm])

dataset witherror
CSV Reader
Remove Kfrom Air Temp
String Manipulation
Selected predictor variables and regression target.
Column Filter
Remove rpmfrom Rotational Speed
String Manipulation
Change Air Temp and Rotational Speed to Number
String to Number
Change Process Temp& Rotational Speedto Mean (Missing Values)
Missing Value
Selected numerical features for clustering
Column Filter
Normalised data
Normalizer
Split data into training (80%) & testing (20%) datasets.
Table Partitioner
Visualised cluster separation across selected features.
Parallel Coordinates Plot
Built a Linear Regression model to predict torque.
Linear Regression Learner
perform clusteringk=5
k-Means
Assign colors to clusters
Color Manager
Converted Machine a categorical
Number to String
Visualised cluster distribution using torque and rotational speed.
Scatter Plot
Predicted torque using the trained Linear Regression model.
Regression Predictor
Selected predictor variables and target variable.
Column Filter
Evaluated regression performance using RMSE, MAE and R² metrics.
Numeric Scorer
Predicted machine failure
Decision Tree Predictor
Evaluated classification performance using the confusion matrix.
Scorer
Split data into training (80%) and testing (20%) datasets using stratified sampling.
Table Partitioner
Built a Decision Tree classifier using Gain Ratio (minimum records per node = 45).
Decision Tree Learner

Nodes

Extensions

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Links

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Created by: joshl
Created at: 2026-07-26
On NodePit since: 2026-07-27
Last update: 2026-07-29
Created with KNIME version: v5.11.0

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