Kan will be introducing Decision Tree, which is one of the machine learning algorithms that build prediction models based on the patterns inside the data, by demonstrating it with Exploratory’s Analytics view.
Random Forest is known as one of the ensemble machine learning algorithms that build ‘decision tree’ based models to predict either categorical or numerical outputs based on the patterns inside the data. It can be often used as ‘Variable Importance’ to find which variables are more important to predict the target output. Kan will be showing how to use it with Exploratory’s Analytics view along with various methods like Boruta, EDARF, and SMOTE (adjusting imbalanced data).
Kan will be introducing various data wrangling techniques to clean and transform Text data. Also, he’ll be introducing the basics of Regular Expression, with which you can manipulate your text data in a much more flexible way.