@datascibykashi: Before building any model, 80% of your time is spent cleaning, transforming, and understanding data. Here’s what often gets skipped: 1️⃣ Handling missing values – don’t just drop them blindly. 2️⃣ Scaling & normalization – models like gradient descent need consistent ranges. 3️⃣ Encoding categorical features – yes, your models can’t read words. 4️⃣ Detecting outliers – strange data can ruin predictions. 5️⃣ Feature engineering – small changes can supercharge your ML models. 💬 Comment Question (Boosts Engagement): Which preprocessing step do you struggle with most? A) Missing Values B) Scaling / Normalization C) Encoding Categories D) Feature Engineering 👇 Comment your letter — I’ll reply to everyone! #creatorsearchinsights #machinelearning #datascience #dataanalysis #mltutorial

Data Scientist | Kashi

Open In TikTok:

Region: PK

Monday 02 February 2026 12:42:08 GMT