🛠️ Data Preprocessing – Handling Missing, Wrong & Duplicate Data | Class 11 AI (CBSE Code 843)

Learn how to clean and prepare raw data for Artificial Intelligence models in this Class 11 Artificial Intelligence (CBSE Code 843) lesson! We’ll explore the steps of Data Preprocessing including handling missing values, outliers, inconsistent data, and duplicates, making your dataset ready for accurate model building. 📌 What You’ll Learn: ✅ Data Cleaning → Fix missing, wrong, and duplicate data ✅ Data Transformation → Convert text to numbers, create new features ✅ Data Reduction → Reduce dataset size/features for efficiency ✅ Data Integration & Normalization → Merge sources + scale values equally ✅ Feature Selection → Keep only the important data for modeling 🔧 Python Tools (if coding included): ✔ Pandas – Handle missing & duplicate values ✔ NumPy – Work with arrays & data transformations ✔ Scikit-learn – Scaling & feature selection basics 📊 Why Watch This? 🔹 Aligned with Unit 2 – Data Modeling & Evaluation of Class 11 AI CBSE Curriculum 🔹 Builds a strong foundation in Data Preprocessing & Cleaning 🔹 Beginner-friendly examples with step-by-step explanation 🔹 Real-world connections: how clean data improves AI model accuracy 👩‍🏫 Who Should Watch? Class 11 AI (CBSE 2025–26) Students Beginners in Python, AI, or Data Science CBSE Teachers & Educators Anyone wanting to learn the first step in AI projects – Preprocessing 🔍 Top Search Keywords: data preprocessing class 11 ai, handling missing data python, outliers preprocessing ai, data cleaning pandas python, duplicate data handling class 11, cbse ai code 843 data preprocessing, normalization scaling python, feature selection ai class 11 👍 Don’t forget to Like, Share & Subscribe for more AI, Python & Data Science tutorials! #DataPreprocessing #Class11AI #PythonForAI #CBSEAI #Unit2AI #DataCleaning #Normalization #FeatureSelection