@datascibykashi: Missing values don’t just happen in numbers ❌ They also appear in categorical features (like Gender, City, Color). Instead of dropping rows, we can impute them smartly 🛠️ 🔹 Methods for Categorical Imputation: 1️⃣ Mode Imputation 👉 Replace missing values with the most frequent category. ✅ Simple & effective. 2️⃣ “Missing” Category 👉 Add a new category called “Unknown” or “Missing”. ✅ Keeps information about the fact that data was missing. 3️⃣ Random Sample Imputation 👉 Fill missing values with a random category from existing data. ✅ Preserves variation. 4️⃣ Model-Based Imputation 👉 Use ML algorithms (like Decision Trees) to predict the missing category. ✅ More accurate but computationally heavy. ⚡ Key Takeaway: For categorical data, the mode or an “Unknown” label are the most common strategies ✅ Use advanced methods only when accuracy is critical 📊 #️⃣ #CategoricalData #MissingValues #DataPreprocessing #MachineLearning #DataScience #100DaysOfML

Data Scientist | Kashi

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Tuesday 09 September 2025 15:59:43 GMT