🧹 Data Preprocessing for Machine Learning

Before you teach your model, you must prepare your data! Let's dive in! 📊

Good machine learning models start with good data. Preprocessing means cleaning, formatting, and organizing your data to make it perfect for algorithms! 🛠️

Step	Meaning	Example
Handling Missing Data 📋	Fill or remove missing values in the dataset.	Replace missing age with average age.
Encoding Categorical Data 🔤	Convert text labels into numbers.	"Male" ➔ 1, "Female" ➔ 0
Feature Scaling 📈	Standardize or normalize data ranges.	Bring ages from 0-100 into 0-1 scale.
Splitting Dataset ✂️	Divide data into training and testing parts.	80% for training, 20% for testing.

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📋 Handling Missing Data

- Use techniques like mean imputation or deletion.
- Machine learning models cannot handle blanks!

Example:
🔹 Fill missing salary with the average salary of the group.

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🔤 Encoding Categorical Data

- Algorithms prefer numbers, not text.
- Label Encoding or One-Hot Encoding techniques are used.

Example:
🔹 Turn "Yes" and "No" into 1 and 0.

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📈 Feature Scaling

- Features with large values can dominate others.
- Scaling keeps everything balanced.

Techniques:
🔹 Min-Max Scaling
🔹 Standardization (Z-score)

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✂️ Splitting Dataset

- Train your model on one part, test it on another.
- Prevents "overfitting" (memorizing instead of learning).

Tip: 🔹 Typical split is 80% train, 20% test.

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🎯 Quick Challenge!

Why do we scale features?

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By Darchums Technologies Inc - April 28, 2025