Regression Model for Kaggle Tabular Playground Series 2021 August Using Python and Scikit-learn

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Kaggle Tabular Playground Series Aug 2021 dataset is a regression situation where we are trying to predict the value of a continuous variable.

INTRODUCTION: Kaggle wants to provide an approachable environment for relatively new people in their data science journey. Since January 2021, they have hosted playground-style competitions on Kaggle with fun but less complex, tabular datasets. The February dataset may be synthetic but is based on a real dataset and generated using a CTGAN. The original dataset tries to predict the loss from a loan default. Although the features are anonymized, they have properties relating to real-world features.

ANALYSIS: The average performance of the machine learning algorithms achieved an RMSE benchmark of 8.0771 using the training dataset. We selected ElasticNet and Gradient Boosting to perform the tuning exercises. After a series of tuning trials, the refined Gradient Boosting model processed the training dataset with a final RMSE score of 7.8563. When we processed Kaggle’s test dataset with the final model, the model achieved an RMSE score of 7.8416.

CONCLUSION: In this iteration, the Gradient Boosting model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground Series Aug 2021 Data Set

Dataset ML Model: Regression with numerical attributes

Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-aug-2021

One potential source of performance benchmarks: https://www.kaggle.com/c/tabular-playground-series-aug-2021/leaderboard

The HTML formatted report can be found here on GitHub.

Regression Model for Kaggle Tabular Playground Series 2021 August Using Python and XGBoost

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Kaggle Tabular Playground Series Aug 2021 dataset is a regression situation where we are trying to predict the value of a continuous variable.

INTRODUCTION: Kaggle wants to provide an approachable environment for relatively new people in their data science journey. Since January 2021, they have hosted playground-style competitions on Kaggle with fun but less complex, tabular datasets. The February dataset may be synthetic but is based on a real dataset and generated using a CTGAN. The original dataset tries to predict the loss from a loan default. Although the features are anonymized, they have properties relating to real-world features.

ANALYSIS: The performance of the preliminary XGBoost model achieved an RMSE benchmark of 7.8834. After a series of tuning trials, the refined XGBoost model processed the training dataset with a final RMSE score of 7.8463. When we applied the last model to Kaggle’s test dataset, the model achieved an RMSE score of 7.8324.

CONCLUSION: In this iteration, the XGBoost model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground Series Aug 2021 Data Set

Dataset ML Model: Regression with numerical attributes

Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-aug-2021

One potential source of performance benchmarks: https://www.kaggle.com/c/tabular-playground-series-aug-2021/leaderboard

The HTML formatted report can be found here on GitHub.

Regression Model for Kaggle Tabular Playground Series 2021 August Using Python and TensorFlow

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Kaggle Tabular Playground Series Aug 2021 dataset is a regression situation where we are trying to predict the value of a continuous variable.

INTRODUCTION: Kaggle wants to provide an approachable environment for relatively new people in their data science journey. Since January 2021, they have hosted playground-style competitions on Kaggle with fun but less complex, tabular datasets. The February dataset may be synthetic but is based on a real dataset and generated using a CTGAN. The original dataset tries to predict the loss from a loan default. Although the features are anonymized, they have properties relating to real-world features.

ANALYSIS: The performance of the cross-validated TensorFlow models achieved an average RMSE benchmark of 7.9119. When we applied the final model to Kaggle’s test dataset, the model achieved an RMSE score of 7.8865.

CONCLUSION: In this iteration, the TensorFlow model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground Series Aug 2021 Data Set

Dataset ML Model: Regression with numerical attributes

Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-aug-2021

One potential source of performance benchmarks: https://www.kaggle.com/c/tabular-playground-series-aug-2021/leaderboard

The HTML formatted report can be found here on GitHub.

Data Validation for Chicago Taxi Trips Using Python and TensorFlow Data Validation

SUMMARY: The project aims to construct a data validation flow using TensorFlow Data Validation (TFDV) and document the end-to-end steps using a template. The Chicago Taxi Trips dataset is a regression situation where we attempt to predict the value of a continuous variable.

INTRODUCTION: The City of Chicago collects taxi trip data in its role as a regulatory agency. This example notebook illustrates how we can use TensorFlow Data Validation (TFDV) to investigate and visualize datasets. The data validation process includes examining descriptive statistics, inferring a schema, checking for and fixing anomalies, and detecting drift and skew in the dataset.

Additional Notes: I adapted this workflow from the TensorFlow Data Validation tutorial on TensorFlow.org (https://www.tensorflow.org/tfx/tutorials/data_validation/tfdv_basic). I also plan to build a TFDV script for validating future datasets and building machine learning models.

CONCLUSION: In this iteration, the data validation workflow helped to validate the features and structures of the training, validation, and test datasets. The workflow also generated statistics over different slices of data which can help track model and anomaly metrics.

Dataset Used: Chicago Taxi Trips Dataset, with modifications by TensorFlow.org

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference: https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/chicago_data.zip

The HTML formatted report can be found here on GitHub.

Regression Model for Kaggle Tabular Playground Series 2021 Feb Using Python and AutoKeras

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Kaggle Tabular Playground Series 2021 Feb dataset is a regression situation where we are trying to predict the value of a continuous variable.

INTRODUCTION: Kaggle wants to provide an approachable environment for relatively new people in their data science journey. Since January 2021, they have hosted playground-style competitions on Kaggle with fun but less complex, tabular datasets. The February dataset may be synthetic but is based on a real dataset and generated using a CTGAN. The original dataset tries to predict the amount of an insurance claim. Although the features are anonymized, they have properties relating to real-world features.

ANALYSIS:  The performance of the best, preliminary AutoKeras model achieved an RMSE benchmark of 0.8625. When we applied the final model to Kaggle’s test dataset, the model achieved an RMSE score of 0.8648.

CONCLUSION: In this iteration, the AutoKeras model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground Series 2021 Feb Data Set

Dataset ML Model: Regression with numerical and categorical attributes

Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-feb-2021

One potential source of performance benchmarks: https://www.kaggle.com/c/tabular-playground-series-feb-2021/leaderboard

The HTML formatted report can be found here on GitHub.

Regression Model for Kaggle Tabular Playground Series 2021 Feb Using Python and TensorFlow

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Kaggle Tabular Playground Series 2021 Feb dataset is a regression situation where we are trying to predict the value of a continuous variable.

INTRODUCTION: Kaggle wants to provide an approachable environment for relatively new people in their data science journey. Since January 2021, they have hosted playground-style competitions on Kaggle with fun but less complex, tabular datasets. The February dataset may be synthetic but is based on a real dataset and generated using a CTGAN. The original dataset tries to predict the amount of an insurance claim. Although the features are anonymized, they have properties relating to real-world features.

INTRODUCTION: Kaggle wants to provide an approachable environment for relatively new people in their data science journey. Since January 2021, they have hosted playground-style competitions on Kaggle with fun but less complex, tabular datasets. The February dataset may be synthetic but is based on a real dataset and generated using a CTGAN. The original dataset tries to predict the amount of an insurance claim. Although the features are anonymized, they have properties relating to real-world features.

ANALYSIS: The performance of the cross-validated TensorFlow models achieved an average RMSE benchmark of 0.8642. When we applied the final model to Kaggle’s test dataset, the model achieved an RMSE score of 0.8642.

CONCLUSION: In this iteration, the TensorFlow model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground Series 2021 Feb Data Set

Dataset ML Model: Regression with numerical and categorical attributes

Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-feb-2021

One potential source of performance benchmarks: https://www.kaggle.com/c/tabular-playground-series-feb-2021/leaderboard

The HTML formatted report can be found here on GitHub.

Regression Model for Kaggle Tabular Playground Series 2021 Feb Using Python and XGBoost

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Kaggle Tabular Playground Series 2021 Feb dataset is a regression situation where we are trying to predict the value of a continuous variable.

INTRODUCTION: Kaggle wants to provide an approachable environment for relatively new people in their data science journey. Since January 2021, they have hosted playground-style competitions on Kaggle with fun but less complex, tabular datasets. The February dataset may be synthetic but is based on a real dataset and generated using a CTGAN. The original dataset tries to predict the amount of an insurance claim. Although the features are anonymized, they have properties relating to real-world features.

ANALYSIS: The performance of the preliminary XGBoost model achieved an RMSE benchmark of 0.8531. After a series of tuning trials, the refined XGBoost model processed the training dataset with a final RMSE score of 0.8434. When we applied the last model to Kaggle’s test dataset, the model achieved an RMSE score of 0.8443.

CONCLUSION: In this iteration, the XGBoost model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground Series 2021 Feb Data Set

Dataset ML Model: Regression with numerical and categorical attributes

Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-feb-2021

One potential source of performance benchmarks: https://www.kaggle.com/c/tabular-playground-series-feb-2021/leaderboard

The HTML formatted report can be found here on GitHub. [https://github.com/daines-analytics/tabular-data-projects/tree/master/py_regression_kaggle_tabular_playground_2021feb]

Regression Model for Kaggle Tabular Playground Series 2021 Feb Using Python and Scikit-learn

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Kaggle Tabular Playground Series 2021 Feb dataset is a regression situation where we are trying to predict the value of a continuous variable.

INTRODUCTION: Kaggle wants to provide an approachable environment for relatively new people in their data science journey. Since January 2021, they have hosted playground-style competitions on Kaggle with fun but less complex, tabular datasets. The February dataset may be synthetic but is based on a real dataset and generated using a CTGAN. The original dataset tries to predict the amount of an insurance claim. Although the features are anonymized, they have properties relating to real-world features.

ANALYSIS: The average performance of the machine learning algorithms achieved an RMSE benchmark of 0.8790 using the training dataset. We selected Random Forest and Gradient Boosting to perform the tuning exercises. After a series of tuning trials, the refined Gradient Boosting model processed the training dataset with a final RMSE score of 0.8447. When we processed Kaggle’s test dataset with the final model, the model achieved an RMSE score of 0.8455.

CONCLUSION: In this iteration, the Gradient Boosting model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground Series 2021 Feb Data Set

Dataset ML Model: Regression with numerical attributes

Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-feb-2021

One potential source of performance benchmarks: https://www.kaggle.com/c/tabular-playground-series-feb-2021/leaderboard

The HTML formatted report can be found here on GitHub.