Pattern discovery terminologies and concepts in data mining — Pattern discovery tries to uncover patterns in a massive dataset, which forms the foundation for many data mining tasks such as association, correlation, and causality analysis, cluster analysis, to name a few. This article introduces common terminology in association rule mining, followed by association rule mining techniques for frequent patterns…

6 feature encoding techniques to consider for your data science workflows — Feature Encoding converts categorical variables to numerical variables as part of the feature engineering step to make the data compatible with Machine Learning models. There are various ways to perform feature encoding, depending on the type of categorical variable and other considerations. This article introduces tips to perform feature encoding…

In-depth explanation with Python examples of unsupervised learning evaluation metrics — In Supervised Learning, the labels are known and evaluation can be done by calculating the degree of correctness by comparing the predicted values against the labels. …

Comparison of free-tier Docker deployments — With the end of Heroku's free tier era, I was scrambling to find an alternative to host my web application. My previous article on deploying Docker using Heroku certainly did not age well. There are many Heroku alternatives for various workflows, but in my case, it was specifically for the…

Introduction to reinforcement learning terminologies, basics, and concepts (model-free, model-based, online, offline RL) — Machine Learning (ML) is split into three branches: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Supervised Learning (SL): Concerned with getting the correct output given labeled training data Unsupervised Learning (UL): Concerned with discovering patterns in the data without pre-existing labels

Logging crash course with common logging issues addressed — In a Python project, it is easy to track the code progress and debug using print statements and stack trace. However, logging provides additional features of tracking and debugging with timestamps, file name, the line number of code, and more, differentiating between different types of print statements, and even the…

Choose your lists wisely — We all know lists as a collection of items, but there exist alternative list-like data structures that can optimize your Python code depending on how you intend to use lists. The default Python list does have its charm — it is simple, easy to understand, and use. However, after spending…

Development Workflow Framework made easy — explained with Python examples — In a data science project, various coding components can be thought of as a flow of data — data flow from the source, to feature engineering, to modelling, to evaluation, etc. This flow is made more complex with training, evaluation, and scoring pipelines where the flow for each pipeline can…