It is clean and regularized, designed to be usable right away. Obfuscated, so it can be given out for free.

Apply machine learning to predict the stock market.

Build a model using the example Python and R scripts. Everything you need to get started in one package.

!/usr/bin/env python Example classifier on Numerai data using a xgboost regression. import pandas as pd from xgboost import XGBRegressor training data contains features and targets training_data = pd.read_csv(numerai_training_data.csv).set_index(id) tournament data contains features only tournament_data = pd.read_csv(numerai_tournament_data.csv).set_index(id) feature_names = [f for f in lumns if feature in f] train a model to make predictions on tournament data model = XGBRegressor(max_depth=5, learning_rate=0.01, \ n_estimators=2000, colsample_bytree=0.1)[feature_names], training_data[target]) submit predictions to predictions = model.predict(tournament_data[feature_names]) predictions.to_csv(predictions.csv)

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