- lasso.py : Predict with a Lasso Regression model
- knn.py : Predict with a k-Nearest Neighbors model
- bayes.py : Predict with a Bayesian model
- gaussian.py : Predict with a Gaussian Process model
- svm.py : Predict with a Support Vector Machine model
- tree.py : Predict with a Decision Tree model
- forest.py : Predict with a Random Forest model
- xgboost.py : Predict with a XGBoost Tree model
- keras.py : Predict with a Neural Network model
- subsemble.py : Predict with an Ensemble of models and partitions
- blend.py : Predict with an Ensemble of models
- pipe_lasso.py : Predict with a Lasso Regression pipeline
- pipe_nnet.py : Predict with a Neural Network pipeline
- tune_knn.py : Tunes a k-Nearest Neighbors model with a random grid search
- tune_svm.py : Tunes a Support Vector Machine model with a random grid search
- tune_tree.py : Tunes a Decision Tree model with a random grid search
- tune_forest.py : Tunes a Random Forest model with a random grid search
- tune_xgboost.py : Tunes a XGBoost Tree model with a random grid search
- doe.R : Selects an optimal subset of a grid search
- kmeans.py : Cluster with a k-Means model
- hclust.py : Cluster with a Hierarchical Clustering model
- birch.py : Cluster with a Birch model
- mixture.py : Cluster with a Gaussian Mixture model
- mean.py : Cluster with a Mean Shift model
- pca.py : Embed with a Principal Component Analysis model
- isomap.py : Embed with a Isomap model
- lle.py : Embed with a Locally Linear Embedding model
- clean.py : Fill in missing values, make all values numeric
- outliers.py : Remove outliers
- features.py : Generate features and select features
- features2.py : Generate features and select features
- timeLag.py : Add time-lagged features to features
- lstm.py : Forecast with a Long Short Term Memory Neural Network model
- hmm.py : Forecast (states) with a Hidden Markov Model
- arima.py : Rolling forecast with an Autoregressive Integrated Moving Average model (Regression Only)
- exp.py : Rolling forecast with a Simple Exponential Smoothing model (Regression Only)
- holt.py : Rolling forecast with a Holt-Winter's model (Regression Only)
- words.py : Rank words on how well they predict text clusters (topics)
- plots.py : Plot with seaborn
- plotting.py : Plot with plot.ly