AI Data Preprocessing: The Complete Guide to Preparing Your Data

Essential Cleaning Steps:

• Handling Missing Values: Imputation (mean/median), deletion, or prediction
• Outlier Detection: Z-score, IQR, or isolation forests
• Duplicate Removal: Exact and fuzzy matching
• Noise Reduction: Smoothing techniques

Python Implementation:

from sklearn.impute import SimpleImputer
from sklearn.ensemble import IsolationForest

# Missing value imputation
imputer = SimpleImputer(strategy='median')
X_clean = imputer.fit_transform(X)

# Outlier detection
clf = IsolationForest(contamination=0.05)
outliers = clf.fit_predict(X)
X_clean = X[outliers == 1]
        

Impact on Models:

Proper cleaning can reduce error rates by 15-30% in most supervised learning tasks

Key Techniques:

• Normalization: MinMaxScaler (0-1 range)
• Standardization: StandardScaler (μ=0, σ=1)
• Log/Power Transforms: For skewed data
• Encoding: One-hot, label, target encoding

Scaling Comparison:

Method	Formula	Best For
MinMax	(x - min)/(max - min)	Neural networks
Standard	(x - μ)/σ	Distance-based algorithms
Robust	(x - median)/IQR	Outlier-prone data

Performance Tip:

Tree-based models often don't need feature scaling, while neural networks require careful normalization

Advanced Techniques:

• Polynomial Features: x², x³ interactions
• Binning: Converting continuous to categorical
• Date Features: Day-of-week, holidays
• Text Processing: TF-IDF, word embeddings

Automated Feature Engineering:

from featuretools import dfs

# Automated feature generation
feature_matrix, features = dfs(
    entities=entities,
    relationships=relationships,
    target_entity="customers"
)

# Select top features
from sklearn.feature_selection import SelectKBest
selector = SelectKBest(k=20)
X_new = selector.fit_transform(X, y)
        

Real-World Impact:

Good feature engineering can provide 2-5x more model improvement than algorithm selection