@datascibykashi: 💡 Data prep is the step everyone ignores, but it decides whether your model succeeds or fails. Key Points: 1️⃣ Clean missing values – models can’t guess them 2️⃣ Encode categorical data – ML models need numbers 3️⃣ Scale features – prevents some features from dominating 4️⃣ Split your data – train/validation/test is a must 💬 Question to boost comments: Which data prep mistake do you see most often? A) Missing value issues B) Wrong encoding C) Forgetting to scale D) Skipping train/test split 👇 Comment the letter! I’ll reply to everyone. #creatorsearchinsights #machinelearning #datascience #ai #mltutorial

Data Scientist | Kashi

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Sunday 08 February 2026 04:33:12 GMT