Classification in Machine Learning | Logistic Regression, KNN, SVM | Day 19 Data Science in 30 Days

Can a machine learning model tell malignant from benign? In Day 19 of Data Science in 30 Days we solve exactly that with 5 classic classification algorithms in Python, on one real dataset, with one fair train/test split, and compare the real results. ✅ You will learn: What classification is (binary, multi-class, multi-label) Logistic Regression, KNN, Naive Bayes, Decision Tree and SVM, with visuals Why feature scaling can drop accuracy from 97.4% to 90.4% Precision vs recall: which metric to trust, and how thresholds change it Cross-validation, data leakage and the accuracy trap A cheat sheet and 5 interview questions CODE (everything in the video, runs top to bottom): https://drive.google.com/file/d/1CnbR... DATASET: Breast Cancer Wisconsin (Diagnostic), included in scikit-learn (also on the UCI Machine Learning Repository). 📚 RESOURCES MENTIONED IN THIS VIDEO Algorithms (scikit-learn docs) • Logistic Regression: https://scikit-learn.org/stable/modul... • K-Nearest Neighbors: https://scikit-learn.org/stable/modul... • Naive Bayes: https://scikit-learn.org/stable/modul... • Decision Trees: https://scikit-learn.org/stable/modul... • Support Vector Machines: https://scikit-learn.org/stable/modul... Metrics and evaluation: • Model evaluation (accuracy, precision, recall, F1): https://scikit-learn.org/stable/modul... • Cross-validation: https://scikit-learn.org/stable/modul... • Google ML Crash Course, accuracy, precision and recall: https://developers.google.com/machine... • Google ML Crash Course, thresholds and the confusion matrix: https://developers.google.com/machine... • Google ML Crash Course, ROC and AUC: https://developers.google.com/machine... Mistakes to avoid: • Feature scaling (StandardScaler): https://scikit-learn.org/stable/modul... • Pipelines: https://scikit-learn.org/stable/modul... • Common pitfalls and data leakage: https://scikit-learn.org/stable/commo... Dataset and practice: • Breast Cancer dataset in scikit-learn: https://scikit-learn.org/stable/modul... • Original dataset (UCI repository): https://archive.ics.uci.edu/dataset/1... • Classifier comparison example: https://scikit-learn.org/stable/auto_... • Free Kaggle course, Intro to Machine Learning: https://www.kaggle.com/learn/intro-to... Coming next in Day 20 (Random Forest and Boosting) • Ensemble methods: https://scikit-learn.org/stable/modul... TOOLS: Python, scikit-learn, NumPy. DISCLAIMER: Educational content using a public research dataset. These models are for learning and are not medical advice or a diagnostic tool. FULL SERIES: Data Science in 30 Days Next: Day 20, Ensemble Learning (Random Forest and Boosting). ▶️ CHAPTERS: 00:00 Intro: malignant or benign? 01:09 Quick recap (Days 12-18) 01:39 What is classification? 02:26 The dataset 03:09 The classification workflow 03:47 1. Logistic Regression 05:24 2. K-Nearest Neighbors 07:20 3. Naive Bayes 09:29 4. Decision Tree 11:20 5. Support Vector Machine 13:07 Decision boundaries side by side 13:56 Python code (scikit-learn) 15:04 Results and cross-validation 16:19 Choosing the right metric 17:20 3 mistakes to avoid 18:34 Cheat sheet 19:02 Interview questions 19:56 Recap and Day 20 preview ♥️ Subscribe to The Data Key for more such content related to data science and artificial intelligence world ! ----------------------------------------------------------------- #machinelearning #machinelearningfullcourse #machinelearningwithpython #datascience #dataanalytics #datascienceroadmap #datascienceforbeginners #dataanalytics #datastructures #trendingvideo #thedatakey #classification #predictivemodeling #artificialintelligence #aicourses #logisticregression #decisiontree #knn #foryou #fyp #datasciencecourse #aibasics