What is Feature engineering?Feature engineering kya hai?

🧸 Think of it likeAise samjho

Like a cook turning raw ingredients into ready masalas: the model gets inputs that are much more useful than the raw data.Jaise cook kachche saamaan ko ready masale mein badalta hai: model ko raw data se kahin zyada kaam ke inputs milte hain.

📘 ExplainedVistaar se

Examples: EMI to income ratio, days since last purchase, number of failed logins in the last hour, one hot encoded city. Good feature engineering often beats a fancier model on tabular data. Compute features the same way in training and serving, and check them for leakage.Examples: EMI to income ratio, last purchase se kitne din, pichhle ghante mein fail hue logins, one hot encoded city. Tabular data pe accha feature engineering aksar fancy model ko harata hai. Training aur serving mein features ek hi tarike se banao, aur leakage check karo.

💡 Real exampleAsli example

Creating 'average order value in the last 30 days' from raw order rows.Raw order rows se 'pichhle 30 din ka average order value' banana.

📖 Learn it step by stepStep by step seekho

• Feature engineering: turning raw data into useful inputsFeature engineering: raw data ko kaam ke inputs mein badalna

🔗 Related termsJude hue shabd

• FeatureOne input the model uses to make a prediction, like age, income or number of logins.Ek input jo model prediction ke liye use karta hai, jaise umar, income ya logins ki ginti.
• Feature storeA shared system that stores ready-made features for both training and live predictions.Shared system jo training aur live predictions dono ke liye taiyaar features rakhta hai.
• Training serving skewWhen features are computed differently in training and in production, quietly hurting the model.Jab training aur production mein features alag tarah se bante hain, aur model chupke se kharab hota hai.