# RoboIntern > Describe your data as a list of columns; RoboIntern ranks the right machine-learning models, explains the maths, and writes the Python to train and predict. RoboIntern is a field guide to machine-learning models for predicting from historical data, with the Python included. You describe your data the way it already exists, as a list of columns: paste a header row and mark the target, the time column, series ids and your key drivers. RoboIntern ranks the models that fit with a visible scoring formula, explains the maths of each, and writes train.py, predict.py and requirements.txt that you run on your own machine. Nothing runs on the site: it is a lookup dictionary with a code writer. Who it's for: Analysts, developers and students who need to predict from historical data ## What it does - Paste a header row; column types and roles are guessed - Forecasting, regression, classification, anomaly detection and clustering - A visible scoring formula that ranks every model that fits - Plain explanations of the maths behind each model - train.py, predict.py and requirements.txt, or a Jupyter notebook - Optional cross-validated tuning with Optuna ## Questions ### Does RoboIntern see my data? No. You describe the columns, not the rows; the code it writes runs on your own machine. ### Which tasks does it cover? Forecasting, regression, binary and multiclass classification, anomaly detection and clustering. ### How are models ranked? With a published score that weighs accuracy, interpretability and speed by the priorities you set, plus listed adjustments for your data. ## Links - [Open RoboIntern](https://robointern.com/) - [About RoboIntern](https://robointern.com/about) - Made by [Arkvital](https://arkvital.com), Montreal, Canada (arkvital@outlook.com) Keywords: which machine learning model, model selection guide, time series forecasting python, regression vs classification, machine learning code generator, train.py predict.py, anomaly detection model, ML for beginners, forecasting model comparison