Data Preparation

3.1 →

Data Selection

Select the most relevant data subsets for modeling based on business requirements, data quality, and feasibility assessments.

Feature Selection Sampling Filtering

3.2 →

Data Cleaning

Handle missing values, remove duplicates, correct errors, and address outliers to improve data quality for modeling.

Missing Values Outliers Deduplication

3.3 →

Data Construction

Create new features through aggregation, derivation, and domain-specific transformations to enhance model performance.

Feature Engineering Aggregation Derivation

3.4 →

Data Integration

Combine data from multiple sources into a unified dataset, resolving schema conflicts and ensuring consistency.

Data Merging Schema Mapping ETL

3.5 →

Data Formatting

Transform data into the required format for modeling, including encoding, normalization, and train/test splitting.

Encoding Normalization Splitting

3.6 →

Data Pipeline Development

Build automated, reproducible data pipelines to ensure consistent data preparation across development and production.

Automation Orchestration Reproducibility