Feature engineering for machine learning
Code snippets and examples of how to prepare your data for modelling and create models typically used in data science.
This is a topic page, where our content is gathered into helpful groupings
📋
This topic page covers the following:
1. Feature encoding
2. Feature scaling
3. Data cleaning
4. Groupby operations
5. Selecting and changing values
1. Feature encoding
2. Feature scaling
3. Data cleaning
4. Groupby operations
5. Selecting and changing values
Feature encoding
📊
Transform categorical feature into sequential numeric features using feature encoding
Label encode multiple columns in a Pandas DataFrame
Label encode unseen values in a Pandas DataFrame
One hot encoding vs label encoding
Feature scaling
🔢
Transform a numerical column through scaling to help improve model training
Scale multiple columns in a Pandas DataFrame
Data cleaning
❌
Clean up your datasets to improve your model's ability to learn
Remove outliers from Pandas DataFrame
Groupby operations
⬆️
Aggregate a dataset to another level by using the Groupby function
Pandas groupby aggregate functions
Pandas groupby column and sum another column
Selecting and changing values
🤔
Manipulate and analyse your dataset using the wide range of functions available in Pandas
Pandas loc vs iloc
Set value for multiple rows in Pandas DataFrame
Divide two columns in Pandas DataFrame
