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What a Used Car Prediction Can Teach You About Machine Learning
Machine Learning
Machine learning, a form or artificial intelligence, has been used for many years to help with prediction by analysing historical data, recognize patterns, and forecast future outcomes.
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The Importance of Exploratory Data Analysis
Most data science projects fail. And when I say most, I mean 90% of them. More concerning is that only 20% of analytics insights deliver business outcomes.
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Is AI Replacing Data Scientists?
AI has created a world where people with no knowledge have suddenly become a subject matter experts.
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Can a Human Being Live to 300 Years? A Machine Learning Exercise
The answer has been long sought by more than scientists and doctors. In fact, the first person who had the idea to record death was neither a scientist or medical doctor. It was a statistian: John Graunt
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Why is Central Tendency Important? Why Do We Use It?
Central tendency is one of the basic tools to help us understand essential information about the values of a dataset.
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Creating Tables for Descriptive Analysis
EDA
When conduction EDA, multiple steps are required. When using python, other tools can be used to facilitate visualizations and other commands.
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Creating Relevant Synthetic Data
One of the greatest obstacles maintaining a blog about data is the data itself. Simply sharing information as a blog entry can come across as boring. Or even irrelevant.
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The Future of Biological Data Analysis
A deep-learning framework reveals whole-body perturbations at cell level, 2026
Machine Learning (ML) is often associated with predictions. And that is the case most of the time. However, its use expand beyond predictions. A new paper published in Nature caught my attention.
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Precision, Recall, and F1 Score Metrics. Which one Matters Most?
In the statistical world, in my simple mind, precision was the golden metric for gauging whether your model was performing as it was supposed to. That is partially correct. But precision alone will only give you a partial image of the model's performance. For a full picture, you will need to check other gauges — Recall and F1 Score.
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Simple vs. Stratified Sampling What's the Difference?
In research, the best data you can feed a model is the whole population. After a decade of working with data, working with the entire population is almost never the case. For most of the analyses we create, we end up working with a sample.
About
I am a veteran. I spent years in intelligence analysis, where being confidently wrong carried a cost, and that is where I learned to separate what evidence shows from what it suggests. I have a master's in journalism and communications, and I am finishing a doctorate in data science on how findings get communicated. I work in English and Portuguese.