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Group2_​Essential_​AI_​Project

1. Data Loading & Preprocessing

2. Data Analysis

3. Train/Test Split (75/25, seed 261)

4. Random Forest (50 / 150 / 200 trees)

5. MLP Neural Network (Min-Max scaling; 1–2 layers, 100/300 iter)

Scorer
2 Layers, 300 Iterations
RProp MLP Learner
Data Explorer
Diabetes status by Gender
Bar Chart
CLASS counts
Bar Chart
50 Trees
Random Forest Learner
Load diabetes.csv
CSV Reader
MultiLayerPerceptron Predictor
Drop ID, No_Pation
Column Filter
Gender x CLASS counts
Pivot
Scorer
Color Palette Designer
Encode Gender
One to Many
HbA1c vs BMI
Scatter Plot
75/25 stratified, seed 261
Table Partitioner
Scorer
Fit Min-Max on train
Normalizer
Random Forest Predictor
Scorer
150 Trees
Random Forest Learner
Scorer
1 Layer, 100 Iterations
RProp MLP Learner
2 Layers, 100 Iterations
RProp MLP Learner
Random Forest Predictor
Scorer
1 Layer, 300 Iterations
RProp MLP Learner
Gender to uppercase
String Manipulation
200 Trees
Random Forest Learner
Scorer
Random Forest Predictor
Column Resorter
MultiLayerPerceptron Predictor
MultiLayerPerceptron Predictor
Apply to test
Normalizer (Apply)
MultiLayerPerceptron Predictor

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: Snoke_IO
Created at: 2026-10-09
On NodePit since: 2026-10-13
Last update: 2026-10-13
Created with KNIME version: v5.12.0

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