@datascibykashi: When working with real-world datasets, we often face missing values ❌ One of the simplest ways to handle them is Complete Case Analysis (CCA). 🔹 What is Complete Case Analysis? 👉 CCA = Remove all rows that contain missing data. Only “complete” records are kept for training the ML model. ✅ Advantages: • Very simple to apply • Keeps the dataset clean • Works fine if missing values are rare ❌ Disadvantages: • Can cause data loss if many rows are dropped • May lead to bias if missing data isn’t random • Not suitable for small datasets 💡 Example: If a dataset has 1,000 rows and 50 contain missing values → after CCA, you only train on 950 rows. ⚡ Key Takeaway: Complete Case Analysis is quick & easy, but use it only when missing data is minimal and random. Otherwise, you risk losing valuable information 📉 #️⃣ #MissingData #CompleteCaseAnalysis #DataPreprocessing #MachineLearning #DataScience #100daysofml

Data Scientist | Kashi

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Sunday 07 September 2025 09:02:46 GMT