https://github.com/analyticalnahid/feature-engineering-tutorial-for-ml

Feature Engineering Tutorial based on more projects specific
https://github.com/analyticalnahid/feature-engineering-tutorial-for-ml

feature-detection feature-engineering feature-extraction feature-selection machinelearning

Last synced: about 1 year ago
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Feature Engineering Tutorial based on more projects specific

• Host: GitHub
• URL: https://github.com/analyticalnahid/feature-engineering-tutorial-for-ml
• Owner: analyticalnahid
• License: mit
• Created: 2022-08-26T01:36:52.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2022-08-31T14:07:07.000Z (almost 4 years ago)
• Last Synced: 2025-02-02T12:37:57.458Z (over 1 year ago)
• Topics: feature-detection, feature-engineering, feature-extraction, feature-selection, machinelearning
• Language: Jupyter Notebook
• Homepage:
• Size: 2.99 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Feature-Engineering-Tutorial-For-ML

This repo designed on basis of my learning experienced.

## 1. Feature Transformation

#### 1.1 Encoding Variable

         1. Encode Categorical Variable (Nominal -> One Hot Encoding, Ordinal -> Ordinal Encoding and Label Encoding)

         2. Encoding Numerical Variable (Binning, Binarization)

#### 1.2 Feature Scaling

         1. Standardization

         2. Noramlization (MinMaxScaling,Mean Normalization, Robust Scaling)

#### 1.3 Handling Outlier

         1. Trimming

         2. Capping (Z Score, IQR, Percentile)

         3. Imputing

#### 1.4 Handling Missing Data

         1. Numerical Variables (Mean or median imputation, Arbitrary value imputation, End of tail imputation)

         2. Categorical Variables (Frequent category imputation, Add a missing category)

         3. Hybrid (Complete case analysis, Random sample imputation)

         4. Multivariate (KNN Imputer, Interative Imputer)

#### 1.5 Variable Transformation

         1. Function Transformer (Logarithmic transformation, Square root transformation, Reciprocal transformation, Power transformation)

         2. Power Transformer (Box-Cox transformation, Yeo-Johnson transformation)

#### 1.6 Handling Mixed Variable

#### 1.7 Handling Date Time Variable

#### 1.8 Handling Geospatial Data

## 2. Feature Construction

        1. Feature Construction

## 3. Feature Extraction

        1. Feature Extraction

## 4. Feature Selection

        1. Filter Method (Pearson Correlation Coefficient, Spearman’s Rank Correlation Coefficient, Kendall’s Rank Correlation Coefficient)

        2. Wrapper Method (Forward Feature Selection, Backward Feature Elimination, Exhaustive Feature Selection, Recursive Feature Elimination)

        3. Embedded Methods (Regularization, Feature Importance)

        

## 🔗 Links

Connect with me: