Introduction: When I started to explore the datasets, one thing I’ve noticed is that static charts are useful only up to a certain point; i.e., the raw numbers will not give the proper story. They help us understand the data initially, but as soon as someone wants to explore the information in more detail, we often end up creating multiple versions of the same chart. Why does interactivity matter? Let me explain with a simple example from the heart disease dataset. Suppose I

Introduction Whenever we work with real‑world data, especially medical data, the first thing we need to do is understand what the data is and how it look like. Before building any model, it’s important to check whether the dataset has unusual values, missing values, or anything that might affect the results later. Outliers are simply values that don’t fit in with the rest of the data — they may be much higher or lower than what we normally expect. In healthcare datasets, thes

I. The Goal: Testing the Hype In my last post, I documented how I built a custom dataset by pulling live data directly from Wikipedia. It was a process of turning messy web tables into a structured CSV file. Today, I’m putting that data to work to answer a question many of us have: Are record-breaking box office numbers a consistent new trend or are we just seeing a few lucky hits? By looking at the last 30 years of cinema, I wanted to see if the "Billion Dollar Club" is actu

Hi team! Check out my latest blog on why data cleaning is critical for accurate visualizations and better business decisions. I also show a real-world COVID-19 dataset case study using Python.

Introduction I recently built my first LLM-powered chatbot using the OpenAI API, and it was a truly rewarding experience! In this blog,...

STEP 1: Imports and reading data When working with Python, importing libraries is essential for extending the functionality of your code....