Please go through the readme to get a background of the problem.
Start by segregating the methods into different categories based on their functionality. This will provide a clearer understanding of the purpose and usage of each method.
Here are some examples of categories:
Exploratory Analysis Helpers:
matplotlib.pyplot: This module is commonly used for creating visualisations and plots to explore and analyse data.
Data Analysis - Classification:
sklearn.tree.DecisionTreeRegressor: This module is used for implementing decision tree-based regression algorithms, which are commonly used for classification tasks.
By categorising the methods, we can better understand the specific functionalities they serve in the context of data analysis. This analysis will help us uncover patterns and trends in the usage of different modules and functions.
Note: You are free to choose your own categories, but keep in mind this is a very important step for the analysis.
Please go through the readme to get a background of the problem.
Start by segregating the methods into different categories based on their functionality. This will provide a clearer understanding of the purpose and usage of each method.
Here are some examples of categories:
matplotlib.pyplot
: This module is commonly used for creating visualisations and plots to explore and analyse data.sklearn.tree.DecisionTreeRegressor
: This module is used for implementing decision tree-based regression algorithms, which are commonly used for classification tasks.By categorising the methods, we can better understand the specific functionalities they serve in the context of data analysis. This analysis will help us uncover patterns and trends in the usage of different modules and functions.
Note: You are free to choose your own categories, but keep in mind this is a very important step for the analysis.