📊 Mean vs Median Imputation in Handling Missing Values with Python 🐍

📊 Mean vs Median Imputation in Handling Missing Values with Python 🐍 @FunCodingIndonesia In this tutorial, we explore one of the most important preprocessing techniques in Data Science and Machine Learning: Imputation for Missing Values. You will learn the difference between: ✅ Mean Imputation for normally distributed data ✅ Median Imputation for skewed distributions We generate synthetic datasets, introduce missing values, apply statistical imputation methods, and visualize the results using histograms and KDE plots. 🧠 Topics Covered: ✔️ Understanding missing data (NA / null values) ✔️ Normal Distribution vs Skewed Distribution ✔️ Mean, Median, and Mode concepts ✔️ Why median works better for skewed data ✔️ Using SimpleImputer from Scikit-Learn ✔️ Data visualization using Seaborn & Matplotlib ✔️ Comparing statistical changes before and after imputation 📚 Libraries Used: - NumPy - Pandas - Matplotlib - Seaborn - Scikit-Learn 🎯 This session is perfect for: - Data Science beginners - Machine Learning students - Python learners - Anyone learning data preprocessing & feature engineering 🚀 Learn how professional ML pipelines handle incomplete datasets before training predictive models! #Python #MachineLearning #DataScience #MissingValue #Imputation #MeanImputation #MedianImputation #FeatureEngineering #DataPreprocessing #Seaborn #Pandas #ScikitLearn #AI #PythonTutorial #FunCoding