Feature engineering is an important area in the field of machine learning and data analysis. It helps in data cleaning process where data scientists and analysts spend most of their time on

1. Outlier detection and removal
2. One hot encoding
3. Log transform
4. Dimensionality reduction using principal component analysis (a.k.a. PCA)
5. Handling missing values
6. Scaling

• Outlier Introduction
• Percentile
• Use of Standard Division and Z-Score to remove outliers
• Remove Outliers from simple dataset
• Remove outliers from complex dataset