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Kaggle Kernels for Regression Tasks
************************************

The following Kaggle kernels show how to patch scikit-learn with |intelex| for various regression tasks.
These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with |intelex|.

.. include:: /kaggle/note-about-tps.rst

Using a Single Regressor
++++++++++++++++++++++++

.. list-table::
   :header-rows: 1
   :align: left
   :widths: 30 20 30

   * - Kernel
     - Goal
     - Content
   * - `Baseline Nu Support Vector Regression (nuSVR) with RBF Kernel
       <https://www.kaggle.com/kppetrov/tps-jul-2021-baseline-with-nusvr-model/notebook>`_
       
       **Data:** [TPS Jul 2021] Synthetic pollution data
     - Predict air pollution measurements over time based on weather and input values from multiple sensors
     - 

       - data preprocessing
       - search for optimal paramters using Optuna
       - training and prediction using scikit-learn-intelex
   * - `Nu Support Vector Regression (nuSVR)
       <https://www.kaggle.com/alexeykolobyanin/tps-aug-nusvr-with-intel-extension-for-sklearn>`__
     
       **Data:** [TPS Aug 2021] Synthetic loan data
     - Calculate loss associated with a loan defaults
     -

       - data preprocessing
       - feature engineering
       - training and prediction using scikit-learn-intelex
       - performance comparison to scikit-learn

   * - `Nu Support Vector Regression (nuSVR)
       <https://www.kaggle.com/alexeykolobyanin/house-prices-nusvr-sklearn-intelex-4x-speedup>`__
       
       **Data:** House Prices dataset
     - Predict sale prices for a property based on its characteristics
     -

       - data preprocessing
       - exploring outliers
       - feature engineering
       - filling missing values
       - search for optimal parameters using Optuna
       - training and prediction using scikit-learn-intelex
       - performance comparison to scikit-learn
   * - `Random Forest Regression
       <https://www.kaggle.com/pahandrovich/tps-jul-2021-fast-randomforest-with-sklearnex>`_
       
       **Data:** [TPS Jul 2021] Synthetic pollution data
     - Predict air pollution measurements over time based on weather and input values from multiple sensors
     - 

       - checking correlation between features
       - search for best paramters using GridSearchCV
       - training and prediction using scikit-learn-intelex
       - performance comparison to scikit-learn

   * - `Random Forest Regression with Feature Engineering
       <https://www.kaggle.com/alexeykolobyanin/tps-jul-rf-with-intel-extension-for-scikit-learn>`_
     
       **Data:** [TPS Jul 2021] Synthetic pollution data
     - Predict air pollution measurements over time based on weather and input values from multiple sensors
     - 

       - data preprocessing
       - feature engineering
       - search for optimal parameters using Optuna
       - training and prediction using scikit-learn-intelex
       - performance comparison to scikit-learn
   * - `Random Forest Regression with Feature Importance Computation
       <https://www.kaggle.com/code/lordozvlad/tps-mar-fast-workflow-using-scikit-learn-intelex>`_

       **Data:** [TPS Mar 2022] Spatio-temporal traffic data
     - Forecast twelve-hours of traffic flow in a major U.S. metropolitan area
     -

       - feature engineering
       - computing feature importance with ELI5
       - training and prediction using scikit-learn-intelex
       - performance comparison to scikit-learn
   * - `Ridge Regression
       <https://www.kaggle.com/alexeykolobyanin/tps-sep-ridge-with-sklearn-intelex-2x-speedup>`_
     
     
       **Data:** [TPS Sep 2021] Synthetic insurance data
     - Predict the probability of a customer making a claim upon an insurance policy
     -

       - data preprocessing
       - filling missing values
       - search for optimal parameters using Optuna
       - training and prediction using scikit-learn-intelex
       - performance comparison to scikit-learn

Stacking Regressors
+++++++++++++++++++

.. list-table::
   :header-rows: 1
   :align: left
   :widths: 30 20 30

   * - Kernel
     - Goal
     - Content
   * - `Stacking Regressor with Random Fores, SVR, and LASSO
       <https://www.kaggle.com/alexeykolobyanin/tps-jul-stacking-with-scikit-learn-intelex>`_
       
       **Data:** [TPS Jul 2021] Synthetic pollution data
     - Predict air pollution measurements over time based on weather and input values from multiple sensors
     - 

       - feature engineering
       - creating a stacking regressor
       - search for optimal parameters using Optuna
       - training and prediction using scikit-learn-intelex
       - performance comparison to scikit-learn

   * - `Stacking Regressor with ElasticNet, LASSO, and Ridge Regression for Time-series data
       <https://www.kaggle.com/alexeykolobyanin/predict-sales-stacking-with-scikit-learn-intelex>`_
       
       **Data:** Predict Future Sales dataset
     - Predict total sales for every product and store in the next month based on daily sales data
     -

       - data preprocessing
       - creating a stacking regressor
       - search for optimal parameters using Optuna
       - training and prediction using scikit-learn-intelex
       - performance comparison to scikit-learn