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Introduction to Feature Engineering
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Classroom Contents
Complete Feature Engineering in OneShot - 5 Hours
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- 1 Introduction to Feature Engineering
- 2 Raw data Vs Processed Data
- 3 Types of FeaturesNumerical, Categorical, Ordinal, Binary, Date & Time,Text, Image & Signal
- 4 Feature Engineering Vs Feature Selection Vs Feature Extraction
- 5 Data Leakage
- 6 Understanding Missing Values
- 7 Dropping Missing Values
- 8 Mean and Median Imputation
- 9 Forward Fill and Backward Fill
- 10 KNN Imputation
- 11 Regression Based Imputation
- 12 Missing Indicator Feature
- 13 Label Encoding and OneHot Encoding
- 14 Ordinal Encoding
- 15 Frequency Encoding
- 16 Target Encoding
- 17 Why Scaling is needed?
- 18 Standard Scaling
- 19 MinMax Scaling
- 20 Robust Scaling
- 21 MaxAbs Scaling
- 22 Log Transformation
- 23 Power Transformation
- 24 Understanding Outliers
- 25 Z-Score
- 26 IQR Method
- 27 WinsorizationCapping
- 28 Isolation Forest
- 29 Local Outlier Factor
- 30 Remove Vs Keep Vs Cap Decision
- 31 Creating Ratio Features
- 32 Aggregation based features
- 33 Difference from Group Mean
- 34 Polynomial Interaction Features
- 35 Boolean Rule Based Feature
- 36 Cumulative Feature
- 37 Rank Based Feature
- 38 Understanding Feature Transformation
- 39 Square Root Transformation
- 40 Binning using equal width
- 41 Binning using equal frequency
- 42 Principal Component Analysis
- 43 Explained Variance Analysis
- 44 Linear Discriminant AnalysisLDA
- 45 Feature Agglomeration
- 46 Understanding Feature Selection
- 47 Variance Threshold
- 48 Correlation Based Feature Selection
- 49 SelectKBest
- 50 Recursive Feature Elimination
- 51 L1 RegularizationLasso
- 52 Tree Based Feature Importance
