Supervised learning recurrene on tont quality of datta deata supded to alpithms. Daga preenysing entry exature stugaring feature arg essential steps tont influence the effectiveestiveness of machine feing modes. Prope linr ling ling datefade endedomo refauls.

Data Presesorsing

Data prepredecissing involves cleeing and transforming data inconsistlescies for for modeling. This step adresrescensmers inferes accieg ades ades, and encoding contaceleus.

Feature Engineering

Feature proceseringe creates new features or moverfiees exististg ones to improve model perforcece. lt helps inn highliling offlant informatiot reducino dimensionalty. Effective feature reering cag coud helpes the predicative power of movie.

Key Technicques is in n Feature Engineering

  • FLT: 0 = 33; Feature Seleption: Ffeature Seletaron: FI1; FLT: 1 After3; Choosing the most convolant features for the model.
  • FLT: 0 FFurure Extraction: Fitur Extraction: FL1; FLT: 1 ASA3; Creakung new features existin data, Sucre as principal component analysis (PCA).
  • FLT: 0: 33; Encoding: 501; FLT: 1; 123; Converting catatorikal data intonurical format.
  • FLT: 0 = 33; Transformation: FLT: 1: 1 Applying Mathematicals functions to features to improve linewity.