Welcome to the next 6 months of my data science learning journey! After being heavily inspired by Daniel Bourke’s AI masters curriculum, I have created my own learning curriculum that I will be following for the next 6 months. Will this be a challenge for me? Yes. Will the content be difficult and hard to […]
The sheer volume of data available is staggering, presenting both opportunities and challenges. Our ability to harness and interpret this data can have a direct impact on the direction and success of businesses. There is increasing pressure on businesses to not only adapt to this new world of AI and data but to thrive. Right […]
One of the most important aspects of machine learning classification models is evaluating how well they predict the target. For this, it’s essential to have a solid understanding of the confusion matrix and ROC curves. The confusion matrix breaks down a model’s predictions by showing true positives, true negatives, false positives, and false negatives. On […]
In data science, there’s an often-underestimated hero working behind the scenes: data preprocessing. Imagine analyzing a dataset filled with gaps, inconsistencies, and outliers. The results would be like deciphering a blurred photograph—it’s frustrating and usually just a total waste of time. That’s where data preprocessing comes in. It’s the process of cleaning, transforming, and structuring […]
This project is based on Season 3, Episode 2 of the Kaggle Playground Series. The title of this episode is: “Tabular Classification with a Stroke Prediction Dataset”. Our task is to predict the probability that a patient will have a stroke. The target, stroke, is a binary variable and so classification methods are needed to predict the […]
This project is based on Season 3, Episode 1 of the Kaggle Playground Series. The title of this episode is: “Tabular Regression with the California Housing Dataset”. Our task is to predict the median housing value of a block group of housing. In this project, my goal is to use the Julia programming language for […]
Voting Ensembles Voting ensembles combine diverse machine learning models using techniques like majority voting or average predictions. The individual models used in the ensemble could be regression or classification-based algorithms. Once the individual models have been trained, the ensemble can be constructed in a couple of different ways. In regression, ensembles are created by averaging […]
In this post, we’ll be learning about decision trees, how they work and what the benefits are for using them. We’ll also use this algorithm in a real-world data to predict diabetes. So, what are decision trees? Decision trees are a machine learning method for classification or regression. It works by segmenting the dataset through […]
I first discovered the Goldilocks Rule in the book Atomic Habits by James Clear where he says: “The Goldilocks Rule states that humans experience peak motivation when working on tasks that are right on the edge of their current abilities. Not too hard. Not too easy. Just right.” James Clear When you take on projects […]
According to Gartner, “by 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated.” I don’t know about you but I was shocked by that statistic and it shows that synthetic data is definitely a rising trend. In this blog post I will be exploring synthetic data […]
In this first project of 2022, I will be fitting linear and polynomial regression models to some randomly generated dataset where there is a nonlinear relationship between the target and the predictor. The purpose of this project is to observe some of the red flags for a model that is severely underfitting to the data […]