Feature Scaling in Python: StandardScaler & Train Test Split Explained

Learn how to apply Feature Scaling and Data Splitting in Python using Scikit-Learn! In this step-by-step tutorial (Section 4 of our Machine Learning series), we walk through data preprocessing, splitting features into independent (X) and dependent (y) sets, performing train_test_split, and applying StandardScaler to normalize feature ranges. 📌 What You’ll Learn: Why feature scaling matters in machine learning models Extracting X (independent) and y (target/diagnosis) features with .iloc Splitting datasets using train_test_split (80:20 ratio & random_state) Implementing StandardScaler (fit_transform on X_train and transform on X_test) Viewing standardized numpy array outputs ⏱️ Video Timestamps & Chapters: 00:00 - Introduction & Recap of Previous EDA/Visualizations 00:52 - Defining Independent (X) & Dependent (y) Variables 02:25 - Extracting X & y with Pandas iloc 03:25 - Splitting Data: Train Test Split (80:20 Ratio) 04:58 - What is random_state in Scikit-Learn? 05:25 - What is Feature Scaling & Standardization? 06:05 - Applying StandardScaler to X_train and X_test 08:08 - Inspecting Standardized Output & What's Next 💡 Dataset Used: Breast Cancer Wisconsin Diagnostic Dataset 🛠️ Tools: Python, Google Colab, Scikit-Learn, Pandas, NumPy If this video helped you understand feature scaling, don't forget to LIKE, SUBSCRIBE, and turn on notifications for the next part on classification algorithms! Subscribe: [   / @freeplacementcourse  ] #MachineLearning #Python #DataScience #FeatureScaling #ScikitLearn #FreePlacementCourse #FeatureScaling #MachineLearning #PythonDataScience #StandardScaler #DataPreprocessing