Data Preprocessing та попередня обробка даних

🎓 Data Cleaning

Duplicate Detection

Methods: Exact duplicates, fuzzy matching.

Processing: Removal, preservation of one.

Importance: Critically for quality.

Inconsistency Detection

Types: Format inconsistencies, logical errors.

Processing: Standardization, validation rules.

Examples: Date formats, categorical values.

Data Validation

Checks: Range checks, type checks, constraints.

Goal: Detecting errors in early stages.

Application: Data quality assurance.

🔧 Transformation

Scaling & Normalization

StandardScaler: Mean equals zero, standard deviation equals one.

MinMaxScaler: Range between zero and one.

RobustScaler: Median-based, for outliers.

Encoding

One-Hot: For categorical variables without order.

Label: For categorical variables with order.

Target: Mean target per category.

Feature Transformation

Log: For skewed distributions.

Square Root: For count data.

Box-Cox: For normalization.

📚 Practical Examples

Example 1: Complete preprocessing pipeline

Cleaning: Remove duplicates, inconsistencies.

Missing: Impute missing values.

Outliers: Detect and handle outliers.

Transformation: Scaling, encoding.

Example 2: Handling missing values

Analysis: Identify patterns of missing values.

Imputation: Use mean/median/mode.

Indicator: Add indicator variables.

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Data Preprocessing: Data preparation

Try it live

Everything above runs in your browser — open Dimensionality Reduction: PCA, t-SNE & UMAP and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.