@datascibykashi: Missing values? Don’t just drop them ❌ Instead, let’s predict them using other features in the dataset 🤖 That’s exactly what the Iterative Imputer does! 🚀 🔹 How Iterative Imputer Works? 👉 Treats each column with missing values as a target variable 🎯 👉 Uses the other columns as predictors 👉 Builds a regression model to estimate the missing values 👉 Repeats the process multiple times (iterations) until values stabilize 📌 Example: If “Age” is missing → Iterative Imputer uses Salary, Education, Experience, etc. to predict Age. ✅ Advantages: • Very powerful & accurate • Considers relationships between features • Works well when missing data is not random ❌ Disadvantages: • Computationally expensive 🐢 • Risk of overfitting if not careful • More complex to tune compared to mean/median 💡 Key Takeaway: Iterative Imputer = Predict missing values like a mini machine learning model. It’s smarter, but heavier—best for datasets where accuracy matters most 📊 #️⃣ #IterativeImputer #MissingValues #DataPreprocessing #MachineLearning #DataScience #100DaysOfML

Data Scientist | Kashi

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Saturday 13 September 2025 11:56:59 GMT