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04_​Data_​Preprocessing_​for_​ML_​Models

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Data Preprocessing for ML Models

This workflow demonstrates the following standard preprocessing steps before training a machine learning model:
- Partitioning
- Outlier detection
- Missing value handling
- Dimensionality reduction
- Conversion
- Feature selection

URL: House Prices Dataset https://www.kaggle.com/marcopale/housing

This workflow demonstrates the standard preprocessing steps before training a machine learning model. Partition randomly80% for training20% for testHousingPrices Partitioning CSV Reader Outlier Detection Missing ValuesHandling Normalization DimensionalityReduction Conversion Feature Selection This workflow demonstrates the standard preprocessing steps before training a machine learning model. Partition randomly80% for training20% for testHousingPrices Partitioning CSV Reader Outlier Detection Missing ValuesHandling Normalization DimensionalityReduction Conversion Feature Selection

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Created by: Lada.Rudnitckaia
Created at: 2021-08-18
On NodePit since: 2023-10-26
Last update: 2024-12-25
Created with KNIME version: v5.1.1
Tags: preprocessingpartitioningoutlier detectionmissing valuePCAencodingconversionforward feature selectionmachine learningML

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