Handling Missing Values in Python (helped by Chat GPT) | Data Cleaning for Beginners

📊 Handling Missing Values in Python (Pandas) | Data Cleaning for Beginners @FunCodingIndonesia In this video, with the help of ChatGPT we learn how to handle missing values (NaN) in datasets using Python and Pandas — a crucial step in any Data Science & Machine Learning project. Real-world data is often incomplete, and knowing how to clean it properly will improve your analysis and model performance. 🧠 What You’ll Learn: ✅ How to detect missing values using .isnull() and .notnull() ✅ Inspect dataset structure with .info() ✅ Filter rows with null / non-null values ✅ Create dataset copies to preserve original data ✅ Method 1: Remove rows using dropna() ✅ Method 2: Fill missing values using different strategies: Fill with 0 Forward Fill (ffill) Backward Fill (bfill) Fill with minimum value Fill using interpolation (linear) 💡 Understand when to delete vs fill missing data, and how each method affects your dataset. 🎯 Perfect for beginners in Python, Data Analysis, and Machine Learning who want to master data preprocessing step-by-step. 🚀 Clean your data the right way and get ready for better insights!