#regression

Mar 6 · 3 min read · Have you ever noticed that your life already runs on a decision tree? If it’s raining → take an umbrella Else if it’s sunny → take sunglasses Else → just pray and go out Congratulations! You just

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PPPranoti Patilai-for-multidisciplinary-application.hashnode.dev

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22.3 Illustration of K-Means Clustering Using a Sample Dataset

Mar 6 · 4 min read · To understand how the K-Means clustering algorithm works, let us consider a small customer dataset.Each customer is represented using two attributes: Annual Income and Spending Score.The objective of

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PPPranoti Patilai-for-multidisciplinary-application.hashnode.dev

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21.3 Numerical example of decision tree using ID3

Mar 6 · 3 min read · Numerical example of decision tree using ID3Suppose, we have, 2 featuresFeature 1,(Feature = F1)Feature ExplanationRoot Node:Feature → 9 Yes / 5 No First Split (C1):6 Yes / 2 No → More pure node

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22.2 How to Apply K-Means Clustering Algorithm

Mar 6 · 3 min read · Suppose we have 8 data points on a graph.Our goal is to group these points into clusters using the K-Means algorithm. Step 1: Choose the Number of Clusters (K) First, we decide how many clusters we

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22. K-Means Clustering

Mar 6 · 5 min read · Introduction to Cluster Have you ever noticed that customers in a mall don’t behave the same?”Some people earn high salary but spend less,some earn less but spend more,and some are in between. Can we

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PPPranoti Patilai-for-multidisciplinary-application.hashnode.dev

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21.2 ID3 (Iterative Dichotomiser 3)

Mar 6 · 4 min read · ID3 (Iterative Dichotomiser 3) is a popular algorithm used to construct a decision tree for classification problems. It selects the best attribute for splitting the data based on Information Gain, whi

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ARAjiboye Raphaelweek16.hashnode.dev

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Week 16 at DataraFlow: Random Forest Regression, The Single Feature Paradox, and Why Visualization Beats Blind Metrics

Feb 12 · 2 min read · Hello everyone! 👋 Ajiboye here from Nigeria — just completed Week 16 of the intensive 6-month Data Science, Machine Learning & GenAI program at DataraFlow. This week’s module took us deep into ensemble methods, specifically Random Forest Regression....

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ARAjiboye Raphaelweek15dataraflow.hashnode.dev

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Week 15 at DataraFlow: Building a Used Car Price Predictor – Why Simple Linear Regression Beat Everything Else

Feb 12 · 2 min read · Hi everyone! 👋 Ajiboye here — currently in Week 15 of the 6-month Data Science, ML & GenAI Intensive at DataraFlow. This week’s assignment was hands-down one of the most practical and eye-opening tasks so far: build and compare multiple regression m...

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IEIfeanyi Ezeriohagodwithus.hashnode.dev

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The Regression Playbook: From Polynomial Curves to Ensemble Methods (and Why the Simplest Model Won)

Feb 11 · 10 min read · Introduction Hey there! This week, I went full detective mode on regression models during my DataraFlow internship. What started as a simple polynomial curve-fitting exercise on a tiny salary dataset evolved into building a production-ready used-car ...

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OMOnwupelu Miraclemy-soft-launch-into-tech-blogging.hashnode.dev

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🚗 From Guesswork to Goldmine: How Machine Learning Predicts Car Prices With 93.5% Accuracy

Feb 8 · 4 min read · The Problem: A $2,349 Mistake Ever bought a used car and wondered, “Did I pay too much?” You’re not alone. The average American overpays by $2,349 when buying a used car—that’s the exact error rate our baseline model started with. But by the end of t...

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#regression

21.1 Introduction of Decision Trees

22.3 Illustration of K-Means Clustering Using a Sample Dataset

21.3 Numerical example of decision tree using ID3

22.2 How to Apply K-Means Clustering Algorithm

22. K-Means Clustering

21.2 ID3 (Iterative Dichotomiser 3)

Week 16 at DataraFlow: Random Forest Regression, The Single Feature Paradox, and Why Visualization Beats Blind Metrics

Week 15 at DataraFlow: Building a Used Car Price Predictor – Why Simple Linear Regression Beat Everything Else

The Regression Playbook: From Polynomial Curves to Ensemble Methods (and Why the Simplest Model Won)

🚗 From Guesswork to Goldmine: How Machine Learning Predicts Car Prices With 93.5% Accuracy

#regression

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#regression

21.1 Introduction of Decision Trees

22.3 Illustration of K-Means Clustering Using a Sample Dataset

21.3 Numerical example of decision tree using ID3

22.2 How to Apply K-Means Clustering Algorithm

22. K-Means Clustering

21.2 ID3 (Iterative Dichotomiser 3)

Week 16 at DataraFlow: Random Forest Regression, The Single Feature Paradox, and Why Visualization Beats Blind Metrics

Week 15 at DataraFlow: Building a Used Car Price Predictor – Why Simple Linear Regression Beat Everything Else

The Regression Playbook: From Polynomial Curves to Ensemble Methods (and Why the Simplest Model Won)

🚗 From Guesswork to Goldmine: How Machine Learning Predicts Car Prices With 93.5% Accuracy