Category Archives: Machine Learning

Top 8 Books to Kick Start Your Data Scientist Career

To kickstart your career as a data scientist, there are several highly recommended books that cover a range of foundational topics and skills in the field. Here’s a curated list based on recent recommendations: Each of these books addresses different aspects of data science, from the technical skills required in Python and R programming to… Read More »

From Zero to Data Hero with ChatGPT

📢 Attention Data Science Enthusiasts! 🌐 I recently came across an exceptional book that I believe is a game-changer for anyone interested in data science: “From Zero to Data Hero with ChatGPT.” 📘 About the Book: This book is a comprehensive guide, perfect for both beginners and those looking to deepen their understanding of data… Read More »

Data Exploration with Some Great R Packages

One of my previous posts introduced effective data exploration methods. This post will introduce some great R packages for exploratory data analysis. We will still use the same customer churn data for demonstration purposes. The first package is the DataExplorer package. The package is extremely easy to use. Almost everything could be done in one… Read More »

Maximizing Incremental Impact of Your Marketing Campaigns

Maximizing the marketing campaign’s incremental impact is more important than ever during this challenging time. We want to get the maximum return out of the marketing dollars spent. Therefore, we need to target people who are ONLY likely to buy if they are included in a campaign. Targeting people who are going to buy anyways… Read More »

Dealing with Imbalanced Data

As we’ve seen from the previous post that the target variable Churn is imbalanced. We have about 27% churn records in the Telco Customer Churn data. It is not a highly imbalanced dataset. I typically would deal with the imbalance issue if the minority class is 10% or less. But want to use this data… Read More »

Effective Exploratory Data Analysis

When we first look at some data, we will want to understand them. It’s an essential step before any type of modeling to explore the data and understand the data first. From exploratory data analysis, we can discover patterns. Patterns in the data provide clues about relationships. If a systematic relationship exists between two variables… Read More »