Data Preprocessing Techniques for Machine Learning

 Data preprocessing is a critical step in the machine learning (ML) pipeline. It helps prepare raw data into a suitable format for modeling, which improves model accuracy and performance. Here's a comprehensive overview of common data preprocessing techniques:

🔹 1. Data Cleaning

Ensures the dataset is free from errors, noise, and inconsistencies.

Techniques:

Handling Missing Values

Removal: Delete rows/columns with missing values (if sparse).

Imputation:

Mean/Median/Mode imputation

KNN imputation

Regression imputation

Handling Outliers

Z-score or IQR method to detect and remove

Capping or transformation (log, Box-Cox)

Noise Removal

Smoothing techniques (e.g., binning)

Clustering-based or regression-based approaches

🔹 2. Data Integration

Combining data from multiple sources to form a unified dataset.

Techniques:

Entity resolution (identify duplicates)

Schema integration (unify attributes with different names or formats)

🔹 3. Data Transformation

Converting data into appropriate formats or scales for modeling.

Techniques:

Normalization / Scaling

Min-Max Scaling: Rescales features to [0, 1]

Z-score Standardization: Centers data with mean 0 and SD 1

Robust Scaler: Uses median and IQR for scaling

Encoding Categorical Variables

Label Encoding: Converts categories to integers

One-Hot Encoding: Binary vector representation

Ordinal Encoding: Preserves order in categories

Discretization: Converting continuous features to categorical (e.g., age into age groups)

Log Transformation: Reduces skewness in distributions

🔹 4. Feature Engineering

Creating new features or modifying existing ones to improve model performance.

Techniques:

Polynomial features

Interaction terms

Domain-specific feature extraction (e.g., time-based features)

Text vectorization (TF-IDF, Bag-of-Words, word embeddings)

🔹 5. Dimensionality Reduction

Reduces the number of input features to avoid overfitting and improve speed.

Techniques:

PCA (Principal Component Analysis)

t-SNE / UMAP (mainly for visualization)

Feature Selection (filter, wrapper, embedded methods)

🔹 6. Data Splitting

Splitting data into training, validation, and test sets to evaluate model generalization.

Typical splits:

Train/Test: 80/20 or 70/30

Train/Validation/Test: 60/20/20 or 70/15/15

🔹 7. Balancing the Dataset

Handling class imbalance in classification tasks.

Techniques:

Undersampling: Reduce majority class

Oversampling: Duplicate or synthetically create minority samples (e.g., SMOTE)

Class weights: Modify the algorithm to penalize misclassification of minority class

Summary Table

Technique Type Example Techniques

Cleaning Imputation, outlier removal, noise smoothing

Integration Schema matching, entity resolution

Transformation Scaling, encoding, normalization

Feature Engineering Polynomial features, time extraction

Dimensionality Reduction PCA, t-SNE, feature selection

Splitting Train/test/validation split

Balancing SMOTE, undersampling, class weighting

Learn Data Science Course in Hyderabad

Read More

Introduction to Neural Networks and Deep Learning

The Bias-Variance Tradeoff in Machine Learning

How to Choose the Right Machine Learning Algorithm

Supervised vs. Unsupervised Learning Explained

Visit Our Quality Thought Training Institute in Hyderabad

Get Directions