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.

Binary Classification Model for Bondora P2P Lending 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 Bondora P2P Lending dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: The Kaggle dataset owner retrieved this dataset from Bondora, a leading European peer-to-peer lending platform. The data comprises demographic and financial information of the borrowers with defaulted and non-defaulted loans between February 2009 and July 2021. For investors, “peer-to-peer lending” or “P2P” offers an attractive way to diversify portfolios and enhance long-term performance. However, to make effective decisions, investors want to minimize the risk of default of each lending decision and realize the return that compensates for the risk. Therefore, we will predict the default risk by focusing on the “DefaultDate” attribute as the target.

ANALYSIS: The performance of the preliminary XGBoost model achieved a ROC-AUC benchmark of 0.9712. After a series of tuning trials, the refined XGBoost model processed the training dataset with a final ROC-AUC score of 0.9849. When we applied the last model to Kaggle’s test dataset, the model achieved a ROC-AUC score of 0.9307.

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

Dataset Used: Kaggle Bondora P2P Lending Loan Data

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference: https://www.kaggle.com/sid321axn/bondora-peer-to-peer-lending-loan-data

Dataset Attribute Description: https://www.bondora.com/en/public-reports

One potential source of performance benchmark: https://www.kaggle.com/sid321axn/bondora-peer-to-peer-lending-loan-data/code

The HTML formatted report can be found here on GitHub.

Binary Classification Model for LendingClub Loan Data 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 LendingClub Loan Data dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: The Kaggle dataset owner derived this dataset from the publicly available data of LendingClub.com. Lending Club connects people who need money (borrowers) with people who have money (investors). An investor naturally would want to invest in people who showed a profile of having a high probability of paying back the loan. The dataset uses the lending data from 2007 to 2010, and we will try to predict whether the borrower paid back their loan in full.

ANALYSIS: The performance of the preliminary XGBoost model achieved a ROC-AUC benchmark of 0.8103. After a series of tuning trials, the refined XGBoost model processed the training dataset with a final ROC-AUC score of 0.8491. When we applied the last model to Kaggle’s test dataset, the model achieved a ROC-AUC score of 0.6039.

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

Dataset Used: Kaggle LendingClub Loan Data

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference: https://www.kaggle.com/itssuru/loan-data

One potential source of performance benchmark: https://www.kaggle.com/itssuru/loan-data/code

The HTML formatted report can be found here on GitHub.

Multi-Class Model for Kaggle Tabular Playground Series 2021 June 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 June 2021 dataset is a multi-class modeling situation where we attempt to predict one of several (more than 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 a real dataset and generated using a CTGAN. The original dataset deals with predicting the category on an eCommerce product given various attributes about the listing. Although the features are anonymized, they have properties relating to real-world features.

ANALYSIS: The performance of the preliminary XGBoost model achieved a logarithmic loss benchmark of 1.7534. After a series of tuning trials, the refined XGBoost model processed the training dataset with a final logarithmic loss score of 1.7497. When we applied the last model to Kaggle’s test dataset, the model achieved a logarithmic loss of 1.7483.

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

Dataset Used: Kaggle Tabular Playground 2021 June Data Set

Dataset ML Model: Multi-Class classification with numerical and categorical attributes

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

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

The HTML formatted report can be found here on GitHub.

Multi-Class Model for Kaggle Tabular Playground Series 2021 May 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 May 2021 dataset is a multi-class modeling situation where we attempt to predict one of several (more than 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 a real dataset and generated using a CTGAN. The original dataset deals with predicting the category on an eCommerce product given various attributes about the listing. Although the features are anonymized, they have properties relating to real-world features.

ANALYSIS: The performance of the preliminary XGBoost model achieved a logarithmic loss benchmark of 1.0995. After a series of tuning trials, the refined XGBoost model processed the training dataset with a final logarithmic loss score of 1.0936. When we applied the last model to Kaggle’s test dataset, the model achieved a logarithmic loss of 1.0933.

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

Dataset Used: Kaggle Tabular Playground 2021 May Data Set

Dataset ML Model: Multi-Class classification with numerical and categorical attributes

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

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

The HTML formatted report can be found here on GitHub.

Regression Model for Diabetes 130-US Hospitals 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 Diabetes 130-US Hospitals dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: The data set is the Diabetes 130-US Hospitals for years 1999-2008 donated to the University of California, Irvine (UCI) Machine Learning Repository. The dataset represents ten years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks. It includes over 50 features representing patient and hospital outcomes.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 63.72%. After a series of tuning trials, the refined XGBoost model processed the training dataset with a final accuracy score of 64.94%. When we processed the test dataset with the final model, the model achieved an accuracy score of 65.39%.

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

Dataset Used: Diabetes 130-US Hospitals for years 1999-2008 Dataset

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference: https://archive-beta.ics.uci.edu/ml/datasets/296

One potential source of performance benchmarks: http://www.hindawi.com/journals/bmri/2014/781670/

The HTML formatted report can be found here on GitHub.

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.

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.