Result Detail – Preprocessing

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Result Information

Result ID: 34
Type: Preprocessing
Status: completed
Dataset ID: 690
Created: 2026-09-21 02:11:20.564609

Preprocessing Summary

Steps Applied: missing value imputation normalization
Rows (in → out): 400 → 400
Features: 40
Target Column: PC_SHOOTMASS

Step Parameters

Step	Parameters
missing value imputation	strategy = median
normalization	method = z-score

Figures

Before vs after Download PNG

Missing values Download PNG

Derived Dataset — Ready for Downstream Analysis

The output of this run was saved as a new dataset, gallery — cat_g_field_data_plots.csv (preprocessed), which you can select in any downstream analysis (machine learning, statistical tests, further preprocessing, or feature selection).

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Pathway Analysis (same dataset)

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Method & Provenance

Method: Preprocessing
Method key: preprocessing (result type: preprocessing, result #34)
Engine: Python · pandas/scikit-learn v3.0.3
Dataset(s): #690 gallery — cat_g_field_data_plots.csv
Status: completed
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Created: 2026-09-21 02:11:20.564609

Parameters used

Parameter	Value
`normalization`	{'method': 'z-score'}
`steps`	missing_value_imputation → normalization

Execution trace

Reconstructed from the parameters stored with this result — represents the Python/R calls the analysis service made.

# app.services.preprocessing_service.PreprocessingService

# step: missing_value_imputation

# step: normalization

df_out = service.apply(df, steps=steps, params=params)