Tag: scikit-learn
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ML Model Evaluation: Beyond Accuracy
First of all, let me apologize for this being such a long post. But, if you can truly grasp the material presented here, it means you can distinguish a truly useful model from one that needs more work. And isn’t that really the point of what we do? Accuracy is a trap You could deploy…
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Regression Models: When You Need to Predict Actual Numbers
Learn when to use regression models instead of classification. Build customer lifetime value predictions using Linear Regression, Ridge, and Random Forest. Understand MAE, RMSE, and R² metrics with practical Python code.
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ML Fundamentals: Stop Overthinking, Start Building
Learn machine learning fundamentals by building a spam classifier in 30 minutes. Same techniques apply to customer churn and employee attrition prediction. No PhD required—just Python and practical code.
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Classification Models: Pick the Right Tool
Compare four classification algorithms on the same churn dataset. Learn when to use Logistic Regression, Decision Trees, Random Forest, or XGBoost. Understand the performance vs interpretability tradeoff and make principled choices based on business context.
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Data Prep: Where ML Projects Actually Live or Die
Master the 80% of ML work that happens before modeling. Handle missing values, scale features, avoid data leakage, and build proper train/test splits. If your model performs terribly, it’s probably your data prep.
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Random Forest: The Swiss Army Knife of Machine Learning
So you’ve heard about Random Forest and you’re wondering what all the fuss is about? Well, buckle up because we’re about to dive into one of the most reliable and versatile algorithms in the machine learning toolbox. What’s This Random Forest Thing Anyway? Think of Random Forest as that friend who always gives solid advice…
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Multiple Regression with Scikit-learn: When One Variable Isn’t Enough
So you’ve mastered simple linear regression and you’re feeling pretty good about yourself. You can predict house prices based on square footage, estimate salaries from years of experience, and impress your friends at parties with your newfound ML skills. But then reality hits: the real world is messy, and one variable rarely tells the whole…