Modeling
Transform your data into powerful machine learning models. No coding required - just upload your CSV file and let Heimdall ML automatically build the best model for your use case.
What You Can Build
- Classification Models - Predict categories (e.g., spam/not spam, product categories)
- Regression Models - Predict numerical values (e.g., prices, scores, quantities)
- Custom Features - Use any structured data from your business
How It Works
Heimdall's platform will guide you through uploading your data and picking your target variable through an intuitive UI. Once you select your target, it will start building models that are best fit for your problem.
The entire machine learning pipeline is handled automatically:
- Data Preprocessing - Clean and prepare your data for training
- Feature Engineering - Extract relevant features automatically
- Algorithm Selection - Test multiple algorithms to find the best fit
- Model Training - Train and optimize your model
- Validation - Test performance and ensure accuracy
- Deployment - Get a production-ready API endpoint
No coding or data science expertise required - just upload your data and get results!
Data Requirements
To build a machine learning model, you need to upload your training data in the correct format.
File Requirements
- File types: CSV or Excel (.xlsx) format
- Headers: First row must contain column headers
- Size: Minimum 200 rows, maximum 10 MB
- Quality: Clean, structured data works best
Other Data Sources: For database connections and other data sources, see our Database Connections section.
Example Data Format
Your data should include both feature columns (the variables you'll use to make predictions) and a target column (the value you want to predict). Feature columns can be numerical, categorical, or text data. The target column should contain the actual values you want your model to learn to predict.
| MSSubClass | MSZoning | LotFrontage | LotArea | Street | SalePrice |
|---|---|---|---|---|---|
| 60 | RL | 65 | 8450 | Pave | 181500 |
| 60 | RL | 68 | 11250 | Pave | 223500 |
| 60 | RL | 70 | 9550 | Pave | 140000 |
This example shows a real estate dataset where:
- MSSubClass: Building class type
- MSZoning: Zoning classification
- LotFrontage: Linear feet of street connected to property
- LotArea: Lot size in square feet
- Street: Type of road access
- SalePrice: Target variable (what we want to predict)
Training
Models take different amounts of time to complete depending on your data size and complexity. Don't worry - you can come back anytime to check in on your model's progress. Once training is complete, you'll be prompted to save the model to your inventory. Heimdall will thoroughly split and test your data in different ways to find the best possible model for you problem.