Tag: XGBoost

Regression Tabular Model for Kaggle Playground Series Season 3 Episode 8 Using Python and XGBoost

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 Playground Series Season 3 Episode 8 Dataset is a regression modeling 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 to give the Kaggle community a variety of reasonably lightweight challenges that can be used to learn and sharpen skills in different aspects of machine learning and data science. The dataset for this competition was generated from a deep learning model trained on the Gemstone Price Prediction dataset. Feature distributions are close to but different from the original.

ANALYSIS: The performance of the preliminary XGBoost model achieved an RMSE benchmark of 580. After a series of tuning trials, the final model processed the test dataset with an RMSE score of 584.

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

Dataset Used: Playground Series Season 3, Episode 8

Dataset ML Model: Regression with numerical features

Dataset Reference: https://www.kaggle.com/competitions/playground-series-s3e8

One source of potential performance benchmarks: https://www.kaggle.com/competitions/playground-series-s3e8/leaderboard

The HTML formatted report can be found here on GitHub.

Binary Class Tabular Model for Kaggle Playground Series Season 3 Episode 7 Using Python and XGBoost

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 Playground Series Season 3 Episode 7 dataset is a binary-class modeling situation where we attempt to predict one of 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 to give the Kaggle community a variety of reasonably lightweight challenges that can be used to learn and sharpen skills in different aspects of machine learning and data science. The dataset for this competition was generated from a deep learning model trained on the Reservation Cancellation Prediction dataset. Feature distributions are close to but different from the original.

ANALYSIS: The performance of the preliminary XGBoost model achieved a ROC/AUC benchmark of 0.9099 after training. When we processed the test dataset with the final model, the model achieved a ROC/AUC score of 0.8973.

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

Dataset Used: Playground Series Season 3, Episode 7

Dataset ML Model: Binary-Class classification with numerical features

Dataset Reference: https://www.kaggle.com/competitions/playground-series-s3e7

One source of potential performance benchmarks: https://www.kaggle.com/competitions/playground-series-s3e7/leaderboard

The HTML formatted report can be found here on GitHub.

Regression Tabular Model for Kaggle Playground Series Season 3 Episode 6 Using Python and XGBoost

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 Playground Series Season 3 Episode 6 Dataset is a regression modeling 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 to give the Kaggle community a variety of reasonably lightweight challenges that can be used to learn and sharpen skills in different aspects of machine learning and data science. The dataset for this competition was generated from a deep learning model trained on the Paris Housing Price Prediction dataset. Feature distributions are close to but different from the original.

ANALYSIS: The performance of the preliminary XGBoost model achieved an RMSE benchmark of 147,772. After a series of tuning trials, the final model processed the test dataset with an RMSE score of 257,339.

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

Dataset Used: Playground Series Season 3, Episode 6

Dataset ML Model: Regression with numerical features

Dataset Reference: https://www.kaggle.com/competitions/playground-series-s3e6

One source of potential performance benchmarks: https://www.kaggle.com/competitions/playground-series-s3e6/leaderboard

The HTML formatted report can be found here on GitHub.

Binary Class Tabular Model for Kaggle Playground Series Season 3 Episode 4 Using Python and XGBoost

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 Playground Series Season 3 Episode 4 dataset is a binary-class modeling situation where we attempt to predict one of 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 to give the Kaggle community a variety of reasonably lightweight challenges that can be used to learn and sharpen skills in different aspects of machine learning and data science. The dataset for this competition was generated from a deep learning model trained on the Credit Card Fraud Detection dataset. Feature distributions are close to but different from the original.

ANALYSIS: The performance of the preliminary XGBoost model achieved a ROC/AUC benchmark of 0.9167 after training. When we processed the test dataset with the final model, the model achieved a ROC/AUC score of 0.8127.

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

Dataset Used: Playground Series Season 3, Episode 4

Dataset ML Model: Binary-Class classification with numerical features

Dataset Reference: https://www.kaggle.com/competitions/playground-series-s3e4

One source of potential performance benchmarks: https://www.kaggle.com/competitions/playground-series-s3e4/leaderboard

The HTML formatted report can be found here on GitHub.

Binary Class Tabular Model for Kaggle Playground Series Season 3 Episode 3 Using Python and XGBoost

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 Playground Series Season 3 Episode 3 dataset is a binary-class modeling situation where we attempt to predict one of 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 to give the Kaggle community a variety of reasonably lightweight challenges that can be used to learn and sharpen skills in different aspects of machine learning and data science. The dataset for this competition was generated from a deep learning model trained on the IBM HR Analytics Employee Attrition & Performance dataset. Feature distributions are close to but different from the original.

ANALYSIS: The performance of the preliminary XGBoost model achieved a ROC/AUC benchmark of 0.8511 after training. When we processed the test dataset with the final model, the model achieved a ROC/AUC score of 0.8969.

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

Dataset Used: Playground Series Season 3, Episode 3

Dataset ML Model: Binary-Class classification with numerical and categorical features

Dataset Reference: https://www.kaggle.com/competitions/playground-series-s3e3

One source of potential performance benchmarks: https://www.kaggle.com/competitions/playground-series-s3e3/leaderboard

The HTML formatted report can be found here on GitHub.