Regression Model for Kaggle Tabular Playground Series 2021 Apr 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 Apr 2021 dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

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 dataset used for this competition is synthetic but based on the real Titanic dataset and generated using a CTGAN. The statistical properties of this dataset are very similar to the original Titanic dataset, but there is no shortcut to cheat by using public labels for predictions.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 0.7707. After a series of tuning trials, the refined XGBoost model processed the training dataset with a final accuracy score of 0.7725. When we applied the last model to Kaggle’s test dataset, the model achieved a ROC score of 0.7832.

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

Dataset Used: Kaggle Tabular Playground Series 2021 Apr Data Set

Dataset ML Model: Regression with numerical and categorical attributes

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

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

The HTML formatted report can be found here on GitHub.

Regression Model for Kaggle Tabular Playground Series 2021 Apr 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 Apr 2021 dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

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 dataset used for this competition is synthetic but based on the real Titanic dataset and generated using a CTGAN. The statistical properties of this dataset are very similar to the original Titanic dataset, but there is no shortcut to cheat by using public labels for predictions.

ANALYSIS: The average performance of the machine learning algorithms achieved an accuracy benchmark of 0.7253 using the training dataset. We selected k-Nearest Neighbors and Random Forest to perform the tuning exercises. After a series of tuning trials, the refined k-Nearest Neighbors model processed the training dataset with a final accuracy score of 0.7699. When we processed Kaggle’s test dataset with the final model, the model achieved an accuracy score of 0.7780.

CONCLUSION: In this iteration, the k-Nearest Neighbors model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground Series 2021 Apr Data Set

Dataset ML Model: Regression with numerical and categorical attributes

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

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

The HTML formatted report can be found here on GitHub.

Binary Classification Model for Kaggle Tabular Playground Series 2021 Mar 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 Mar 2021 dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

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 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 ROC benchmark of 0.8842 after running for 15 epochs. When we applied the final model to Kaggle’s test dataset, the model achieved a ROC score of 0.8861.

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

Dataset Used: Kaggle Tabular Playground Series 2021 Mar Data Set

Dataset ML Model: Regression with numerical and categorical attributes

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

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

The HTML formatted report can be found here on GitHub.

Binary Classification Model for Kaggle Tabular Playground Series 2021 Mar 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 Mar 2021 dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

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 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 a ROC benchmark of 0.8813. After a series of tuning trials, the refined XGBoost model processed the training dataset with a final ROC score of 0.8936. When we applied the last model to Kaggle’s test dataset, the model achieved a ROC score of 0.8946.

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

Dataset Used: Kaggle Tabular Playground Series 2021 Mar Data Set

Dataset ML Model: Regression with numerical and categorical attributes

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

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

The HTML formatted report can be found here on GitHub.

Binary Classification Model for Kaggle Tabular Playground Series 2021 Mar 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 Mar 2021 dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

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 a ROC benchmark of 0.8430 using the training dataset. We selected Logistic Regression and Random Forest to perform the tuning exercises. After a series of tuning trials, the refined Random Forest model processed the training dataset with a final ROC score of 0.8901. When we processed Kaggle’s test dataset with the final model, the model achieved a ROC score of 0.8902.

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

Dataset Used: Kaggle Tabular Playground Series 2021 Mar Data Set

Dataset ML Model: Regression with numerical and categorical attributes

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

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

Feature Selection for Kaggle Tabular Playground Series 2021 Jan Using Python and Scikit-learn

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

SUMMARY: Feature selection involves picking the set of features that are most relevant to the target variable. This can help reduce the complexity of our model and minimize the resources required for training and inference. The Kaggle Tabular Playground Series Jan 2021 dataset is a regression situation where we are trying to predict the value of a continuous variable.

INTRODUCTION: In this notebook, we will run through the different techniques in performing feature selection on the dataset. We will leverage the Scikit-learn library, which features various machine learning algorithms and has built-in implementations of various feature selection methods. We will compare which method works best for this particular dataset.

ANALYSIS: The feature selection technique that yielded the best RMSE score was Recursive Feature Elimination (RFE). Its RMSE for the training dataset was 0.7082.

CONCLUSION: In this iteration, the RFE technique appeared to be suitable for modeling this dataset. We should follow up on the feature selection exercise by modeling the whole dataset using the selected attributes.

Dataset Used: Kaggle Tabular Playground Series 2021 Jan Data Set

Dataset ML Model: Regression with numerical attributes

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