From Raw Data to AI-Ready Datasets
Transform unstructured, messy raw data into clean, feature-rich datasets that power accurate machine learning models
The Data Preparation Challenge
of a data scientist's time is spent on data preparation, not modeling
Inconsistent Formats
Raw data comes in CSV, JSON, XML, databases, logs, images, and text. Each source has different schemas, encodings, and quality levels.
Missing Values & Noise
Real-world data is dirty: missing fields, duplicates, outliers, encoding errors, and inconsistent naming conventions.
Feature Engineering Complexity
Raw data rarely has the right features for ML. You need domain expertise to create meaningful features from primitive data.
Scale & Performance
Transforming terabytes of data requires distributed processing frameworks and optimized code that most teams struggle to implement.
The Data Transformation Pipeline
We follow a systematic 7-stage process to convert raw data into production-ready ML datasets
Data Discovery & Profiling
Before transformation, understand your data: schema structure, data types, distributions, missing value patterns, relationships between tables, and quality issues. We use automated profiling tools like Pandas Profiling, Great Expectations, or Apache Atlas.
Outputs: Data quality report, schema documentation, statistical summaries, correlation analysis, recommendations for handling missing data and outliers.
Data Cleaning
Remove or fix corrupted, inaccurate, or irrelevant data. This stage handles missing values, duplicates, outliers, encoding issues, and inconsistent formatting.
Missing Values
- • Drop rows/columns if over 50% missing
- • Impute with mean/median/mode
- • Forward/backward fill for time series
- • ML-based imputation (KNN, MICE)
Outlier Handling
- • Z-score or IQR detection
- • Domain-based thresholds
- • Cap at percentiles (winsorization)
- • Remove or flag for investigation
Data Integration
Combine data from multiple sources into a unified dataset. This involves joining tables, resolving entity matching, handling schema conflicts, and managing temporal alignment.
Challenges we solve: Different granularities (daily vs hourly), schema evolution over time, fuzzy entity matching (e.g., "Apple Inc." vs "Apple Computer"), timezone handling across global sources.
Feature Engineering
Create new features from raw data that better represent patterns for ML algorithms. This is where domain expertise meets data science.
Numerical Transformations
Log transforms for skewed data, polynomial features for non-linear relationships, binning continuous variables, ratio features (e.g., debt-to-income)
Temporal Features
Extract day/month/year, create lag features, rolling window aggregations, seasonality indicators, time since last event
Categorical Encoding
One-hot encoding for nominal, ordinal encoding for ordered, target encoding for high cardinality, embedding layers for deep learning
Text Features
TF-IDF vectors, word embeddings (Word2Vec, BERT), sentiment scores, entity extraction, text length/complexity metrics
Feature Scaling & Normalization
Many ML algorithms require features to be on similar scales. We apply appropriate scaling based on algorithm requirements and data distribution.
Standardization (Z-score)
Mean 0, std 1. For algorithms assuming normally distributed data (linear regression, logistic regression, neural networks)
Min-Max Scaling
Scale to [0,1] range. For algorithms sensitive to feature magnitude (neural networks with sigmoid/tanh, KNN)
Robust Scaling
Uses median and IQR, robust to outliers. When your data has extreme outliers that you want to preserve
No Scaling
Tree-based models (Random Forest, XGBoost) don't require scaling as they split on feature values directly
Train/Validation/Test Splitting
Properly split data to enable accurate model evaluation and prevent data leakage. Split strategy depends on data type and business context.
Versioning & Documentation
Track dataset versions, transformation code, and metadata to ensure reproducibility and enable debugging when models fail.
We version: Raw data snapshots, transformation code (Git), processed datasets (DVC, MLflow), feature statistics, data lineage (what sources contributed to each feature), transformation parameters and decisions.
Accelerate Your ML Projects
Stop spending 80% of your time on data preparation. Our data engineers will build automated transformation pipelines that deliver ML-ready datasets.
Tools & Technologies We Use
Pandas & NumPy (Python)
The foundational tools for data manipulation in Python. Pandas provides DataFrame operations for cleaning, joining, and transforming tabular data. NumPy handles numerical computations and array operations.
Best for: Small to medium datasets (up to 10GB), exploratory data analysis, rapid prototyping, datasets that fit in memory.
Apache Spark & PySpark
Distributed processing framework for big data transformations. Handles datasets that don't fit in memory by distributing computation across cluster nodes.
Best for: Large datasets (100GB+), complex joins across multiple tables, parallel feature engineering, production pipelines requiring scale.
dbt (data build tool)
SQL-based transformation framework that enables analytics engineers to transform data in the warehouse. Provides testing, documentation, and lineage tracking built-in.
Best for: Warehouse-native transformations, teams preferring SQL over Python, complex transformation DAGs with dependencies, version-controlled transformation logic.
Feature Stores (Feast, Tecton)
Centralized platforms for managing, serving, and monitoring ML features. Ensure training-serving consistency and enable feature reuse across teams.
Best for: Organizations with multiple ML models, real-time prediction requirements, teams wanting to avoid feature engineering duplication, ensuring feature consistency.
Scikit-learn Preprocessing
Comprehensive suite of preprocessing utilities: scalers, encoders, imputers, and transformers. Integrates seamlessly with scikit-learn ML pipelines.
Best for: Standardizing preprocessing steps, creating reproducible transformation pipelines, ensuring train-test consistency, prototyping feature transformations.
Advanced Data Preparation Techniques
Automated Feature Engineering
Tools like Featuretools enable automated feature generation using deep feature synthesis. The algorithm automatically creates features by applying aggregation and transformation operations across related tables.
For example, given customer and transaction tables, it can automatically create features like "average transaction amount in last 30 days," "number of transactions per month," and "days since last transaction" without manual coding.
Handling Imbalanced Datasets
When one class dominates (e.g., 99% non-fraud, 1% fraud), standard ML algorithms perform poorly. We employ several strategies:
- Oversampling minority class: SMOTE creates synthetic minority examples
- Undersampling majority class: Randomly remove majority examples or use Tomek links
- Class weights: Penalize misclassifying minority class more heavily
- Ensemble methods: Combine multiple models trained on different class distributions
Time Series Feature Engineering
Time series data requires specialized feature engineering: lag features (values from previous time steps), rolling window statistics (moving average, std over last N periods), seasonal decomposition (trend, seasonality, residuals), and time-based features (hour of day, day of week, holidays).
Libraries like tsfresh automatically extract hundreds of time series features using statistical tests to select only relevant ones.
Dimensionality Reduction
High-dimensional data (thousands of features) can suffer from the curse of dimensionality. We use dimensionality reduction to create more compact representations:
- PCA (Principal Component Analysis): Linear transformation to uncorrelated components
- t-SNE, UMAP: Non-linear reduction for visualization and clustering
- Autoencoders: Neural networks that learn compressed representations
- Feature selection: Keep only statistically significant or high-importance features
Cross-Validation Strategies
Simple train/test splits can be misleading. Cross-validation provides more robust performance estimates:
- K-Fold CV: Split data into K folds, train on K-1, validate on 1, repeat K times
- Stratified K-Fold: Maintain class proportions in each fold for imbalanced data
- Time Series CV: Use expanding or sliding windows to respect temporal order
- Group K-Fold: Keep related samples together (e.g., all images from same patient)
Case Study: E-Commerce Recommendation System
The Challenge
A Nordic fashion retailer wanted to build personalized product recommendations but had messy data across 5 different systems: e-commerce platform (Shopify), CRM (Salesforce), analytics (Google Analytics), inventory (custom SQL), and customer service (Zendesk).
Data Preparation Process
1. Data Integration (2 weeks)
Built Airflow pipelines to extract data from all 5 sources daily. Implemented fuzzy matching to link customer entities across systems (email, phone, customer ID). Created a unified customer profile table with 120 attributes.
2. Cleaning & Quality (1 week)
Used Great Expectations to validate data quality. Removed duplicates (8% of records), imputed missing size/color attributes using product hierarchy, corrected encoding issues in product descriptions.
3. Feature Engineering (3 weeks)
Created 200+ features: customer lifetime value, purchase frequency, average order value, product category preferences, seasonal patterns, brand affinity, price sensitivity, browsing-to-purchase conversion rate, return rate by category. Used PySpark for distributed feature computation.
4. Dataset Preparation (1 week)
Built training dataset with 5M user-product interactions. Created positive samples (purchases) and negative samples (viewed but not purchased). Split by time: train on first 10 months, validate on month 11, test on month 12.
Frequently Asked Questions
How much data do I need for machine learning?
It depends on problem complexity and model type. Simple problems with clean features: 1,000-10,000 samples. Complex classification: 10,000-100,000 samples. Deep learning: 100,000-1M+ samples. More data helps, but quality matters more than quantity. Start with what you have, establish a baseline, then collect more systematically.
Should I handle missing values before or after splitting data?
Split first, then handle missing values. If you impute before splitting, information from the test set leaks into training. Fit imputation strategies (mean, median, model-based) only on training data, then apply to validation and test sets. This ensures realistic evaluation of production performance.
How do I choose which features to engineer?
Start with domain knowledge: What factors drive the outcome you're predicting? Create features representing those factors. Then use automated techniques: feature importance from tree models, correlation analysis, automated feature engineering (Featuretools). Iterate: train a baseline model, analyze errors, engineer features addressing those errors. Remove low-importance features to reduce overfitting.
What's the difference between feature engineering and feature selection?
Feature engineering creates new features from raw data (e.g., creating "age" from "birth_date"). Feature selection chooses which features to keep (removing redundant or low-importance features). Both are important: engineering expands your feature space with informative features, selection reduces it to prevent overfitting and improve performance.
How do I ensure my data preparation is reproducible?
Version everything: raw data snapshots (DVC), transformation code (Git), processed datasets, feature statistics. Use configuration files for parameters instead of hardcoding. Document assumptions and decisions. Build automated pipelines (Airflow, Prefect) that reproduce transformations from raw data. Test your pipeline on different time periods to verify consistency.
Stop Wasting Time on Data Preparation
Our data engineering team will build automated pipelines that transform your raw data into ML-ready datasets, freeing your data scientists to focus on modeling.