@datascibykashi: Missing values in numerical features are super common in real-world datasets ❌ Instead of dropping data, we can impute (fill in) the gaps 🛠️ 🔹 Popular Methods for Numerical Imputation: 1️⃣ Mean Imputation 👉 Replace missing values with the column’s mean. ✅ Simple, but sensitive to outliers. 2️⃣ Median Imputation 👉 Replace with the median value. ✅ More robust when data has outliers. 3️⃣ Mode Imputation 👉 Replace with the most frequent value. ✅ Works when data has repeated numbers. 4️⃣ Advanced Methods • KNN Imputer → finds “nearest neighbors” to estimate missing values. • Regression Imputation → predicts missing values using other features. ⚡ Key Takeaway: Imputing numerical data keeps your dataset complete ✅ and prevents data loss 🚀 But always choose the right method depending on the data distribution 📊 #️⃣ #NumericalData #MissingValues #DataPreprocessing #MachineLearning #DataScience #100DaysOfML

Data Scientist | Kashi

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Monday 08 September 2025 05:39:39 GMT