Data-Analytics

Every AI, machine-learning and data post on this site in one place, in an order that builds from the basics to agents, MCP and the cost of tokens.

Whether you’re preparing for a quiz or just brushing up on fundamentals, this guide distills the key concepts from Data Mining into bite-sized, memorable chunks. Let’s dive in!

Imagine you’re trying to find the best solution to a problem, but the problem is really complex and has a lot of randomness involved.

Linear programming is a mathematical technique used to find the best possible outcome (maximum profit or minimum cost) in a situation where the relationships between the factors involved are linear.

In the context of forecasting and business analytics, a model is a simplified representation of a real-world situation or process.

Forecasting methods are the techniques used to predict future outcomes based on past data and trends. They’re like having a crystal ball, but instead of magic, they use data and statistics!

How to tell which forecasting model is better: MAE, MSE, RMSE, MAPE, sMAPE, bias and Theil's U, explained with an ice-cream-sales example.

Imagine you’re a business owner trying to figure out why your sales are down. You could guess, or you could use business analytics.

Business Analytics (BA) is like a GPS for businesses—it uses data to guide decisions, solve problems, and predict future outcomes.

From a Business Analytics perspective, understanding correlation and covariance is crucial for several reasons:

RFM (Recency, Frequency, Monetary) Analysis is a customer segmentation technique that uses three key metrics:

Artificial Intelligence (AI) has revolutionized many aspects of our lives, from virtual assistants like Siri and Alexa to self-driving cars and personalized content recommendations.