4d ago · 2 min read · Most predictive analytics work looks solid in isolation. Models are trained. Accuracy is high. Visualizations are clean. Reports get shared. And yet, nothing in the business changes. That is not a mod
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Dec 5, 2025 · 7 min read · Introduction AMIHAN was a project made a year ago focusing on air quality monitoring in compliance with the requirements of our 2nd year course last year, CS158-1L: Artificial Intelligence Laboratory; where we created, academically, our ‘first’ machi...
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Nov 4, 2025 · 1 min read · 📊 istatistik.ai — IntroductionIn the age of information, numbers are more than digits — they tell stories.Istatistik.ai explores how artificial intelligence transforms raw data into meaningful insights. BodyBusinesses, governments, and researchers a...
Join discussionSep 3, 2025 · 3 min read · 📈 Predicting the S&P 500 Using Multiple Linear Regression 📝 Problem Statement This project aims to predict the S&P 500 stock index based on two key macroeconomic indicators: Interest Rate (%) Employment Numbers (in thousands/millions) Using thi...
Join discussionJul 7, 2025 · 2 min read · When analyzing sports for betting, bettors use two main perspectives: quantitative and qualitative. These are fundamentally different ways of assessing a game, and understanding them is key to building a sound, data-driven strategy. The Quantitative ...
Join discussionFeb 27, 2025 · 2 min read · Introduction Customer churn is a critical issue for businesses, as retaining customers is often more cost-effective than acquiring new ones. In this analysis, we leverage data science techniques to identify key factors driving customer churn and buil...
Join discussionJan 10, 2025 · 19 min read · 1. Introduction In the financial lending industry, companies evaluate applicants by analyzing their financial backgrounds to determine risk levels and make informed decisions on loan approvals and terms. While companies can rely on direct applicants ...
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Nov 7, 2024 · 14 min read · Suppose we have a dataset containing information about passengers on the Titanic and whether they survived. The features include class ( \( x_1 \) : Pclass), sex ( \( x_2 \) ), age ( \( x_3 \) ), and fare paid ( \( x_4 \) ). Based on these features, ...
Join discussionSep 19, 2024 · 6 min read · Part 1 In this blog post, we will explore various regression models used in machine learning. Regression is a fundamental concept used to predict continuous outcomes. We'll dive into simple linear regression, multiple linear regression, polynomial re...
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