Data Preprocessing: Cleaning and Normalizing
🧹 Data Preprocessing: Cleaning and Normalizing
Data preprocessing is a crucial step in any AI or machine learning project. It involves preparing raw data so that models can learn effectively. Two key tasks in preprocessing are cleaning and normalizing data.
1. Data Cleaning
What is Data Cleaning?
Removing or correcting errors, inconsistencies, and noise in the data to make it accurate and usable.
Common Cleaning Tasks:
Handling Missing Values
Remove rows/columns with missing data
Fill missing data using methods like mean, median, or interpolation
Removing Duplicates
Identify and delete repeated records
Fixing Inconsistencies
Standardize formats (e.g., date formats, text capitalization)
Correct typos or mislabeling
Filtering Outliers
Detect and decide whether to remove or correct extreme values
Noise Reduction
Smooth data or remove irrelevant data points
Why It Matters:
Dirty data can mislead models, cause errors, or reduce accuracy.
2. Data Normalization
What is Data Normalization?
Transforming data into a common scale without distorting differences in the ranges of values.
Why Normalize?
Features measured in different units or scales can bias a model.
Many algorithms assume data is on a similar scale (e.g., neural networks, k-means).
Common Normalization Techniques:
Technique How It Works When to Use
Min-Max Scaling Scales data to a fixed range (usually 0 to 1) using:
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