To read the given data and perform Feature Encoding and Transformation process and save the data to a file.
STEP 1:Read the given Data. STEP 2:Clean the Data Set using Data Cleaning Process. STEP 3:Apply Feature Encoding for the feature in the data set. STEP 4:Apply Feature Transformation for the feature in the data set. STEP 5:Save the data to the file.
- Ordinal Encoding An ordinal encoding involves mapping each unique label to an integer value. This type of encoding is really only appropriate if there is a known relationship between the categories. This relationship does exist for some of the variables in our dataset, and ideally, this should be harnessed when preparing the data.
- Label Encoding Label encoding is a simple and straight forward approach. This converts each value in a categorical column into a numerical value. Each value in a categorical column is called Label.
- Binary Encoding Binary encoding converts a category into binary digits. Each binary digit creates one feature column. If there are n unique categories, then binary encoding results in the only log(base 2)ⁿ features.
- One Hot Encoding We use this categorical data encoding technique when the features are nominal(do not have any order). In one hot encoding, for each level of a categorical feature, we create a new variable. Each category is mapped with a binary variable containing either 0 or 1. Here, 0 represents the absence, and 1 represents the presence of that category.
• Log Transformation • Reciprocal Transformation • Square Root Transformation • Square Transformation
• Boxcox method • Yeojohnson method
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