🧹 Why Clean Data Matters
Understand why data quality determines model success—garbage in, garbage out is the rule of machine learning
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Training Your First Model
Why Data Preparation Matters
"Garbage in, garbage out" - This principle is fundamental in machine learning. No matter how sophisticated your model is, poor quality data will produce poor results. Data preparation is often 80% of the ML workflow!
📊 The Impact of Clean Data
❌
Poor Data Quality
- •Model learns incorrect patterns
- •Low accuracy and unreliable predictions
- •Training fails or takes forever
- •Biased results and fairness issues
✅
High Quality Data
- •Model learns meaningful patterns
- •High accuracy on unseen data
- •Faster training convergence
- •Fair and generalizable results
🔍 Common Data Quality Issues
❓
Missing Values
Null, NaN, or empty entries in your dataset
age: null, income: NaN
📏
Different Scales
Features with vastly different ranges
age: 25, income: 50000
🎯
Outliers
Extreme values that skew the distribution
ages: [25, 30, 28, 999]
📝
Inconsistent Format
Same data in different representations
"USA", "US", "United States"
🔤
Categorical Data
Text labels that need numerical encoding
color: "red", "blue", "green"
📊
Class Imbalance
Unequal distribution of target classes
95% negative, 5% positive
🔄 Data Preparation Pipeline
1
Data Cleaning
Handle missing values, remove duplicates
2
Normalization
Scale features to similar ranges
3
Feature Engineering
Create new meaningful features
4
Encoding
Convert categorical to numerical
5
Train-Test Split
Separate data for training and validation