How to choose the right scaling method #DataScience #ML #InterviewTips

🚀 Day 18/200 – Min-Max Scaling vs Standardization | Machine Learning Why should we scale features like ₹1,00,000 salary, age, and years of experience before training a Machine Learning model? 🤔 In this video, you'll learn the fundamentals of Feature Scaling in Machine Learning, including: 📌 What is Feature Scaling? 📌 Min-Max Scaling 📌 Min-Max Scaling Formula 📌 Standardization / Z-Score Scaling 📌 Standardization Formula 📌 Min-Max Scaling vs Standardization 📌 Effect of Outliers 📌 When to use each technique 🔹 Min-Max Scaling X' = (X − Xmin) / (Xmax − Xmin) It typically transforms values into a 0–1 range. 🔹 Standardization Z = (X − Mean) / Standard Deviation It generally produces a feature with approximately Mean = 0 and Standard Deviation = 1. 🔥 Key Difference: Min-Max Scaling → Fixed range Standardization → Mean 0 and Standard Deviation 1 There is no single best scaling technique. Your choice depends on the algorithm, data distribution, outliers, and model requirements. ⚠️ Remember: Min-Max Scaling is sensitive to outliers. 📊 Next: Day 19 – Outliers: Detection, Impact & Treatment 🚀 Subscribe @tech_jroshan for the complete 200 Days Data Analysis & Data Science Series covering Python, Statistics, Machine Learning, Data Science and AI. Feature Scaling, Min-Max Scaling, Standardization, Machine Learning, Z-Score, Feature Engineering, ML Preprocessing, MinMaxScaler, StandardScaler, Machine Learning Algorithms, Data Science, Python, Scikit-learn Min-Max Scaling vs Standardization Feature Scaling in Machine Learning Standardization using Z-Score When to use Min-Max Scaling When to use Standardization Feature scaling with outliers Machine Learning preprocessing techniques DataAnalysis ,FeatureEngineering ,MLPreprocessing,MachineLearningTips ,DataScientist ,DataAnalyst, DataAnalytics ,Python, ScikitLearn ,Statistics ,AI, DeepLearning ,ML, TechJroshan, 200DaysChallenge, Day18 🔎Tags min max scaling, min max normalization, standardization machine learning, feature scaling, feature scaling in machine learning, min max scaling vs standardization, normalization vs standardization, z score standardization, z score machine learning, machine learning preprocessing, preprocessing techniques machine learning, feature engineering, machine learning tutorial, data science tutorial, data science, machine learning, python machine learning, scikit learn, sklearn preprocessing, outliers machine learning, standard scaler, minmaxscaler, data analyst, data scientist, AI, ML, Tech Jroshan #MachineLearning #FeatureScaling #MinMaxScaling #Standardization #DataScience #DataAnalysis #MachineLearningTutorial #FeatureEngineering #MLPreprocessing #Python #ScikitLearn #DataScientist #DataAnalyst #Statistics #AI #DeepLearning #ZScore #TechJroshan #Day18 #200DaysChallenge In this video, we will be discussing the key differences between Min-Max Scaling and Standardization, two essential techniques used in feature scaling in machine learning. Feature scaling is a crucial step in the data preprocessing phase of machine learning, as it helps to prevent features with large ranges from dominating the model. We will explore how Min-Max Scaling and Standardization work, and when to use each technique. This video is part of our machine learning full course and playlist, designed to provide a comprehensive understanding of machine learning concepts, including data analytics and data science. Whether you are a data analyst or an aspiring data scientist, this video will provide you with valuable insights and interview tips to help you tackle common problem-solving questions. We will also touch on the importance of standardization vs normalization, and how these techniques can impact the performance of neural networks and other machine learning models. By the end of this video, you will have a clear understanding of what is feature scaling, and how to apply it in your own machine learning projects. Our goal is to provide a data science roadmap that is easy to follow, and to help you become proficient in machine learning and artificial intelligence. So, let's dive in and explore the world of feature scaling in machine learning.