Impute+Encoding+Scaling+Hyperparam tuning

Data Cleaning & Feature EngineeringImputation:

Don't just delete missing values; fill them with the median or use a KNN

imputer.Encoding:

Use One-Hot Encoding for categories with no order (colors) and Label Encoding for order (sizes like S, M, L).

Scaling:

Always use StandardScaler or MinMaxScaler for SVMs and Linear models; otherwise, large numbers will "bully" small ones.

Hyperparameter TuningGrid Search:

Tests every possible combination (Slow but thorough).Random Search: Tests random combinations (Faster, often just as good).Bayesian Optimization: Uses math to "guess" which parameters will work best next based on previous results.