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ML Hyper-Trainer

gamified machine learning

6 Challenges

MVP

Iris Species

๐ŸŒธ

ยท Target: 82% accuracy

Data Preview

Features

Scaling

Train

Dataset Overview

150 rows

5

TOTAL COLUMNS

4

NUMERIC FEATURES

0

MISSING COLUMNS

1

OUTLIER COLUMNS

Data Quality Issues Detected

โ€ข Sepal Width: contains outliers

ColumnTypeSample ValuesDistributionMissingOutliersImportance

Species

TARGET

Target: Setosa / Versicolor / Virginica

Target
setosaversicolorvirginica

โ€”

None

No

Sepal Length

Sepal length in cm

Numeric
5.176.3

ฮผ=5.84 ฯƒ=0.83

None

No

79%

Sepal Width

Sepal width in cm

Numeric
3.53.23.3

ฮผ=3.06 ฯƒ=0.44

None

Yes

44%

Petal Length

Petal length in cm

Numeric
1.44.76

ฮผ=3.76 ฯƒ=1.77

None

No

95%

Petal Width

Petal width in cm

Numeric
0.21.41.8

ฮผ=1.2 ฯƒ=0.76

None

No

93%

๐Ÿ’ก Review the data carefully โ€” understanding your features helps you make better preprocessing choices.

โ”€โ”€ PIPELINE SCORE โ”€โ”€โ”€โ”€

C

63/100

Accuracy modifier: ร—1.01

Features

70

Scaling

70

Outliers

30

Architect

75

โšก You have outlier columns โ€” consider clipping or imputing them.

Step 1 of 3

Score: 63/100