Errors are common in coding. Exception handling can help our code to 1) keep running despite of any errors and 2) at the same time produce informative alerts to help identify relevant problems.

This is a demo of the workflow of customer churn modeling. Techniques used include Tidymodels (Data preprocessing), H2o.ai (Model training), and LIME (Model explanation).

A R user's python learning note. This one on the very basic topic of loading modules and functions.

Demonstrate FOR and WHILE loops in Python, with a focus on FOR loops.

It is interesting to learn that there are two special types of strings, raw strings and f-strings in Python.

One of the most prevalent characteristics of real-world data is arguably the presence of missing values. While the best strategy to treat missing values largely depends on the purpose of our analysis and the algorithm to be used, the first step is always to identify the magnitude and patterns of missing in our data. Missing value identification is therefore important: oversight or negligence could easily bias our analysis. Here I will share my workflow and favorite tools in this “detective work”. Hope it is of some help and would love to hear your thoughts too. :)

Mutable objects can be changed after they have been created, while immutable objects can't. In this note, this distinction is demonstrated by attempts to change a list (mutable) vs a tuple (immutable).

Something weird, interesting and worth noting about Python lists.

Another R user's learning note on Python.

This is a short note about Scikit-Learn in Python versus Tidymodels in R. Throughout the note, R and Python code are compared side by side. I keep it mainly as a study note, but hopefully it might be of interest to some R users learning Python, and Python users learning R.

This is a short note about DataFrames in R and Python. Throughout the note, R and Python code are compared side by side. I keep it mainly as a study note, but hopefully it might be of interest to some R users learning Python, and Python users learning R.

This is a short note about IF, ELSE IF and LOOPS in R and Python. Throughout the note, R and Python code are compared side by side. I keep it mainly as a study note, but hopefully it might be of interest to some R users learning Python, and Python users learning R.

This is a short note about data structure and how sometimes they behave differently in R and Python. Throughout the note, R and Python code are compared side by side. I keep it mainly as a study note, but hopefully it might be of interest to some R users learning Python, and Python users learning R.

This is a short note about data types and how sometimes they behave differently in R and Python. Throughout the note, R and Python code are compared side by side. I keep it mainly as a study note, but hopefully it might be of interest to some R users learning Python, and Python users learning R.

This is a study note sharing an R user's reflection on learning Python. Where relevant, I produced some R code to compare with Python code. I hope the discussion is of interest to both R users learning Python and python users learning R.

There are 25 subdivisions of criminal incidents recorded in the data. What is the best way to visualize them? This is a temporary draft for discussion purposes.

Process to explore and prepare the data for Decision Tree and GBM modeling.

A simple data engineering project that combines multiple excel files in different formats to enable visualization and analysis.

Code for data preparation. The main issue being dealt with here is the imputation of a large amount of missing values. A link also provided to download the data at GitHub.

Four graphs based on the same data, seek to shed light on the comparison between log and linear graphs.

Analyzing the effect of Covid-19 vaccine rate on new cases and deaths through the Many-Models approach, where the same model was estimated among each and every entity then summarized to gain a comprehensive picture of vaccine effectiveness worldwide.