Introduction

What is Rassket?

Rassket is an automated machine learning (AutoML) platform enhanced with decision intelligence capabilities. It transforms raw data into actionable insights and production-ready models without requiring deep technical expertise in machine learning or data science.

Unlike traditional AutoML tools that treat all problems generically, Rassket uses domain-aware intelligence to understand the context of your data—whether it's energy consumption patterns, research datasets, or econometric studies—and applies specialized feature engineering and model selection accordingly.

Who is Rassket For?

Technical Users

• ML Engineers & Data Scientists: Accelerate model development and reduce repetitive work
• Researchers: Focus on insights rather than implementation details
• Analysts: Build production-ready models without extensive ML training

Non-Technical Users

• Founders & Executives: Make data-driven decisions without hiring ML teams
• Business Analysts: Transform spreadsheets into predictive models
• Domain Experts: Leverage your expertise while Rassket handles the ML complexity

What Problems Does Rassket Solve?

1. Time-to-Insight

Traditional ML workflows require weeks or months of data preparation, feature engineering, model selection, and hyperparameter tuning. Rassket automates these steps, reducing the time from data to insights from weeks to hours.

2. Technical Barriers

Building production-ready ML models typically requires expertise in Python, scikit-learn, XGBoost, hyperparameter optimization, and model evaluation. Rassket abstracts away this complexity while still providing transparency into what's happening under the hood.

3. Domain Context Loss

Generic AutoML tools don't understand that energy forecasting requires different approaches than econometric analysis. Rassket's domain-aware feature engineering and explanations ensure your models are not just accurate, but also interpretable within your specific context.

4. Production Readiness

Many ML projects fail at deployment. Rassket generates export-ready model packages, comprehensive reports, and decision-ready outputs that integrate seamlessly into your existing workflows.

Why Energy and Research?

Rassket focuses on two primary domains: Energy and Research. This focused approach allows us to provide deeper, more meaningful automation than generic AutoML platforms.

Energy Domain

Energy data has unique characteristics: time-series patterns, seasonality, grid constraints, and market dynamics. Rassket understands these patterns and applies specialized feature engineering for:

• Energy consumption forecasting
• Renewable energy generation prediction
• Grid operations and demand management
• Energy market pricing and economics

Research Domain

Research datasets often require careful statistical treatment, experimental design considerations, and domain-specific validation. Rassket provides:

• Time series forecasting for research contexts
• Econometric modeling and analysis
• Statistical modeling with proper diagnostics
• Experimental analysis and observational studies

How AutoML Fits Into the Platform

AutoML is the foundation of Rassket, but it's enhanced with several layers:

1. Automated Data Processing

Rassket automatically handles missing values, duplicates, data type detection, and schema validation. No manual data cleaning required.

2. Domain-Aware Feature Engineering

Based on your selected domain (Energy or Research), Rassket applies specialized transformations:

• Time-series features (lags, rolling statistics, seasonality)
• Domain-specific aggregations
• Contextual feature interactions
• Statistical transformations appropriate to your domain

3. Intelligent Model Selection

Rassket automatically selects and trains multiple models, comparing their performance and selecting the best one for your specific problem type (regression, binary classification, multi-class classification).

4. Automated Hyperparameter Tuning

Using Optuna, Rassket automatically optimizes hyperparameters for each model, finding the best configuration without manual intervention.

5. Comprehensive Evaluation

Beyond basic metrics, Rassket provides:

• SHAP-based interpretability
• Domain-aware explanations
• Model diagnostics and reliability assessments
• Real-world implications analysis

6. Decision Intelligence

Rassket doesn't just produce models—it produces insights. The platform explains what the model learned, why certain features matter, and how to use the predictions in your decision-making process.

Next Steps

Or read the Getting Started guide to learn more before you begin.