We saw indispensable commands of R and Python for DataScience. Ofcourse what we saw is just a Wave and the Ocean to be crossed is still left out. But let us cross wave by wave, which will undoubtedly make us cross the ocean.

In this page we are going to see Data Preprocessing Techniques. Data preprocessing is nothing but preparing the data in proper format for further analysis. Like handling missing values etc. Below are some of the techniques.

• Missing Value Analysis
• Outlier Analysis
• Feature Selection
• Feature Scaling
• Sampling Techniques

Table of contents

• Missing Value Analysis - Introduction
• Missing Value Analysis - R
• Outlier Analysis - Introduction
• Outlier Analysis - R
• Feature Selection Numerical Values - Introduction
• Feature Selection Numerical Values - R
• Feature Selection Categorical Values - Introduction
• Feature Selection Categorical Values - R
• Feature Selection Numerical & Categorical Values - Python
• Feature Scaling - Normalization
• Feature Scaling - Standardization
• Feature Scaling - R Implementation
• Sampling Techniques - Introduction
• Sampling Techniques - R