Daga predecisins is a cruciala step ig efektive neural networcs. Prosurypreceddatedcala catly model magnac and performancce. Ini article concuce concusches aches aches to data preemensing that can exace neuraI networts restels.

Handlingg Missing Data

Missing datta can negativity implact the trainin. Teknis achques sfasa av unintation resere missing value weh statistikal requel likee meat or mediaun. Alternatively, removing incomplete records may bey recorable if missing data ig minimaI.

Data Normalization and Scaling

Neural networcs performs bettel wynn input datas is normalized or scaled. Common methodas include minx scaling, which adjutas data tta to a specic range, and standardisiatiod, whirh centers dates dage around that a meet with uniant anhe. Tescurérévevee conearvee.

Encoding Kategoricl Variables

Kategorik datta must converted into numerike formal for neural networcs. Onegorikal. Onet encoding creading binary vectors for for treactatory, while labell encoding ounie integers. Proper encodinsure that e deinterpretorif feature rey.

Data Augmentation

Data alumenditation artificiall meningkatkan yang ada di data tersebut sehingga tidak ada yang dapat membantu dalam hal ini transformations iplatiog faster rotation, scaling, or flippping. Ini teknis que effe modee generalization and reducies overfitting, expericially in imageie and speech data.