Ml Data Preprocessing — Free Machine Learning Tutorial
Learn Ml Data Preprocessing in Machine Learning with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Ml Data Preprocessing in Machine Learning with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
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Ml Data Preprocessing in Machine Learning
Raw data is messy: missing values, outliers, inconsistent formats, and categorical text. Preprocessing cleans and transforms data so algorithms work well. Common steps: (1) Handle missing values (drop rows, impute with mean/median), (2) Remove outliers (values far from typical range), (3) Scale features (normalize to similar ranges like 0-1 or mean=0), (4) Encode categorical variables (convert text to numbers).
Scaling is essential when features have different units (e.g., age in years, salary in rupees). StandardScaler centers data (mean=0, std=1) and MinMaxScaler scales to [0, 1]. Without scaling, algorithms like KNN and neural networks treat large-range features (salary) as more important than small-range ones (age). Tree-based models (decision trees, random forests) don't require scaling.
Categorical encoding: One-hot encoding (convert "city: Mumbai, Delhi, Bangalore" to separate 0/1 columns) works for algorithms like logistic regression. Label encoding (convert to 0, 1, 2) is simpler but implies order, so avoid for non-ordinal categories. For ordinal categories (low, medium, high), label encoding is fine.
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