Ai Data Preprocessing — Free AI & ML Tutorial
Learn Ai Data Preprocessing in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Ai Data Preprocessing in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
Written & reviewed by the Syllab.in Academic Team (CBSE/NCERT subject experts) · Updated
Ai Data Preprocessing in AI & ML
"Data preprocessing" is preparing raw data for ML models. In real AI projects, 70–80% of the time is spent on data preprocessing — not building models. Raw data is messy, incomplete, and inconsistent.
Common issues: Missing values (some records have blank fields), Outliers (extreme values like a student with 200/100 marks), Inconsistent formats ("Male", "male", "M" all mean the same thing), Duplicates (same record entered twice), Wrong data types (age stored as text "25" instead of number 25).
Feature Engineering: Creating new features from existing ones to help the model learn better. Example: From "date of birth", create "age" and "age_group". From "purchase time", create "is_weekend" and "hour_of_day".
Normalisation vs Standardisation: Normalisation scales all values to 0-1 range (useful for neural networks). Standardisation converts to mean=0, std=1 (useful for most ML algorithms).
Ai Data Preprocessing — Syntax
# Key preprocessing steps: # 1. Handle missing values: fill with mean/median/mode, or drop # 2. Remove duplicates # 3. Fix data types # 4. Encode categories: "Male"→0, "Female"→1 # 5. Normalise/Standardise numerical features # 6. Feature engineering: create new useful features
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