auto-sklearn

Load customer data into a Pandas DataFrame.

Split the data into training and testing sets.

Instantiate AutoSklearnClassifier and fit it to the training data.

Make predictions on the testing data.

Evaluate the model's performance using metrics like accuracy, precision, and recall.

Load housing data into a Pandas DataFrame.

Split the data into training and testing sets.

Instantiate AutoSklearnRegressor and fit it to the training data.

Make predictions on the testing data.

Evaluate the model's performance using metrics like Mean Squared Error (MSE) and R-squared.

Load applicant data into a Pandas DataFrame.

Split the data into training and testing sets.

Instantiate AutoSklearnClassifier and fit it to the training data.

Make predictions on the testing data.

Evaluate the model's performance using metrics like AUC-ROC and F1-score.

Load sensor data into a Pandas DataFrame.

Split the data into training and testing sets.

Instantiate AutoSklearnClassifier and fit it to the training data.

Make predictions on the testing data.

Evaluate the model's performance using metrics like precision and recall.

Load sales data into a Pandas DataFrame.

Preprocess the time series data to create lagged features.

Split the data into training and testing sets.

Instantiate AutoSklearnRegressor and fit it to the training data.

Make predictions on the testing data.

Evaluate the model's performance using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).