Simple Linear Regression
Hopefully, after the previous post, you have had a good idea of what regression analysis is. With that, we will start looking into the big collection of regression models. And…
Regression Analysis
After the previous post on data processing pipeline, we are now ready to move on to the analysis in data science! And just like I introduced in an old post,…
Data Pipeline
The pipeline that we have learned previously is very useful but only performs a fixed sequence of transformation on the input. However, more often than not, we want to apply…
Processing Pipeline
At this point, we have gone through quite some preprocessing methods for different issues in data such as handling outliers, scaling, imputation, encoding, etc. So, I think it is now…
Train-test Split
Predictive analysis is a major branch of data analytics where we want to apply knowledge learned from historical data on new data. However, there is one potential issue in this…
Encode Categorical Data
Categories are a big part in tabular data. You will see them more often than not, and it is just inevitable. However, a lot of analytical models cannot handle categorical…
Scale Numeric Data
It is very often that we have numeric columns with very different scales in the same data set. For example, a data set may have people's income in the range…
Handle Missing Data
Missing data is prevalent in analytics. They are fields in your data without a valid value, and they must be addressed. Otherwise, most analytical models would omit data that has…
Handling Outliers
Now that we have had a good idea on what to do or can be done during an exploratory analysis, it is time to move on to data preprocessing! So,…
Handle Skewed Data
A while ago, we discussed distributions of numeric columns. Depending on the types of analysis, sometimes a symmetrical distribution is preferred over a skewed one. So, in this post, I…