In Machine Learning, these methods are known as Data Preprocessing.
📊 What is Data Preprocessing?
Data Preprocessing involves cleaning and transforming raw data into a structured and usable format so that machine learning models can perform effectively.
📌 Key Techniques:
Handling Missing Values
👉 Filling (imputation) or removing incomplete data
Normalization / Scaling
👉 Adjusting values to a common scale (e.g., 0–1 range)
Encoding Categorical Data
👉 Converting text labels into numerical form (e.g., One-Hot Encoding, Label Encoding)
Removing Noise & Outliers
👉 Eliminating irrelevant or extreme values that can distort model learning
🎯 Why is it important?
Improves model accuracy
Ensures consistent data format
Reduces errors and bias
Helps models learn meaningful patterns
🔖 Hashtags:
#MachineLearning #DataPreprocessing #DataScience #DataCleaning #Normalization #Encoding #AI #DataAnalytics #FeatureEngineering #ModelPerformance