Binary-Class Model for KDD Cup 1998 Using Python and Scikit-Learn Take 6

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 Acoustic Extinguisher Fire dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: This is the data set used for The Second International Knowledge Discovery and Data Mining Tools Competition, held in conjunction with KDD-98, The Fourth International Conference on Knowledge Discovery and Data Mining. The modeling task is a binary classification problem where the goal is to estimate the likelihood of donation from a direct mailing campaign.

In the Take1 iteration, we built and tested models using a minimal set of basic features. The model will serve as the baseline result as we add more features in future iterations.

In the Take2 iteration, we built and tested models with additional features from third-party data sources.

In the Take3 iteration, we built and tested models with additional features from the US Census data.

In the Take4 iteration, we built and tested models with additional features from the promotion history data.

In the Take5 iteration, we built and tested models with additional features from the giving history data.

In this iteration, we will build and test models with additional features engineered from the giving history features.

ANALYSIS: In the Take1 iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 70.99% using the training dataset. Furthermore, we selected Random Forest as the final model as it processed the training dataset with a final ROC/AUC score of 77.23%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.42%.

In the Take2 iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 71.92% using the training dataset. Furthermore, we selected Extra Trees as the final model as it processed the training dataset with a final ROC/AUC score of 79.79%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.02%.

In the Take3 iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 72.72% using the training dataset. Furthermore, we selected Extra Trees as the final model as it processed the training dataset with a final ROC/AUC score of 85.02%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.20%.

In the Take4 iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 72.56% using the training dataset. Furthermore, we selected Extra Trees as the final model as it processed the training dataset with a final ROC/AUC score of 82.28%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.18%.

In the Take5 iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 72.80% using the training dataset. Furthermore, we selected Extra Trees as the final model as it processed the training dataset with a final ROC/AUC score of 82.39%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.27%.

In this iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 71.34% using the training dataset. Furthermore, we selected Extra Trees as the final model as it processed the training dataset with a final ROC/AUC score of 82.69%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.03%.

CONCLUSION: In this iteration, the Extra Trees model appeared to be suitable for modeling this dataset. However, we should explore the possibilities of using more features from the dataset to model this problem.

CONCLUSION: In this iteration, the Extra Trees model appeared to be suitable for modeling this dataset. However, we should explore the possibilities of using more features from the dataset to model this problem.

Dataset Used: KDD Cup 1998 Dataset

Dataset ML Model: Binary classification with numerical and categorical features

Dataset Reference: https://kdd.org/kdd-cup/view/kdd-cup-1998/Data

The HTML formatted report can be found here on GitHub.

Binary-Class Model for KDD Cup 1998 Using Python and Scikit-Learn Take 5

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 Acoustic Extinguisher Fire dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: This is the data set used for The Second International Knowledge Discovery and Data Mining Tools Competition, held in conjunction with KDD-98, The Fourth International Conference on Knowledge Discovery and Data Mining. The modeling task is a binary classification problem where the goal is to estimate the likelihood of donation from a direct mailing campaign.

In the Take1 iteration, we built and tested models using a minimal set of basic features. The model will serve as the baseline result as we add more features in future iterations.

In the Take2 iteration, we built and tested models with additional features from third-party data sources.

In the Take3 iteration, we built and tested models with additional features from the US Census data.

In the Take4 iteration, we built and tested models with additional features from the promotion history data.

In this iteration, we will build and test models with additional features from the giving history data.

ANALYSIS: In the Take1 iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 70.99% using the training dataset. Furthermore, we selected Random Forest as the final model as it processed the training dataset with a final ROC/AUC score of 77.23%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.42%.

In the Take2 iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 71.92% using the training dataset. Furthermore, we selected Extra Trees as the final model as it processed the training dataset with a final ROC/AUC score of 79.79%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.02%.

In the Take3 iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 72.72% using the training dataset. Furthermore, we selected Extra Trees as the final model as it processed the training dataset with a final ROC/AUC score of 85.02%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.20%.

In the Take4 iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 72.56% using the training dataset. Furthermore, we selected Extra Trees as the final model as it processed the training dataset with a final ROC/AUC score of 82.28%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.18%.

In this iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 72.80% using the training dataset. Furthermore, we selected Extra Trees as the final model as it processed the training dataset with a final ROC/AUC score of 82.39%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.27%.

CONCLUSION: In this iteration, the Extra Trees model appeared to be suitable for modeling this dataset. However, we should explore the possibilities of using more features from the dataset to model this problem.

Dataset Used: KDD Cup 1998 Dataset

Dataset ML Model: Binary classification with numerical and categorical features

Dataset Reference: https://kdd.org/kdd-cup/view/kdd-cup-1998/Data

The HTML formatted report can be found here on GitHub.

Binary-Class Model for KDD Cup 1998 Using Python and Scikit-Learn Take 4

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 Acoustic Extinguisher Fire dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: This is the data set used for The Second International Knowledge Discovery and Data Mining Tools Competition, held in conjunction with KDD-98, The Fourth International Conference on Knowledge Discovery and Data Mining. The modeling task is a binary classification problem where the goal is to estimate the likelihood of donation from a direct mailing campaign.

In the Take1 iteration, we built and tested models using a minimal set of basic features. The model will serve as the baseline result as we add more features in future iterations.

In the Take2 iteration, we built and tested models with additional features from third-party data sources.

In the Take3 iteration, we built and tested models with additional features from the US Census data.

In this iteration, we will build and test models with additional features from the promotion history data.

ANALYSIS: In the Take1 iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 70.99% using the training dataset. Furthermore, we selected Random Forest as the final model as it processed the training dataset with a final ROC/AUC score of 77.23%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.42%.

In the Take2 iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 71.92% using the training dataset. Furthermore, we selected Extra Trees as the final model as it processed the training dataset with a final ROC/AUC score of 79.79%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.02%.

In the Take3 iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 72.72% using the training dataset. Furthermore, we selected Extra Trees as the final model as it processed the training dataset with a final ROC/AUC score of 85.02%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.20%.

In this iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 72.56% using the training dataset. Furthermore, we selected Extra Trees as the final model as it processed the training dataset with a final ROC/AUC score of 82.28%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.18%.

CONCLUSION: In this iteration, the Extra Trees model appeared to be suitable for modeling this dataset. However, we should explore the possibilities of using more features from the dataset to model this problem.

Dataset Used: KDD Cup 1998 Dataset

Dataset ML Model: Binary classification with numerical and categorical features

Dataset Reference: https://kdd.org/kdd-cup/view/kdd-cup-1998/Data

The HTML formatted report can be found here on GitHub.

Binary-Class Model for KDD Cup 1998 Using Python and Scikit-Learn Take 3

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 Acoustic Extinguisher Fire dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: This is the data set used for The Second International Knowledge Discovery and Data Mining Tools Competition, held in conjunction with KDD-98, The Fourth International Conference on Knowledge Discovery and Data Mining. The modeling task is a binary classification problem where the goal is to estimate the likelihood of donation from a direct mailing campaign.

In the Take1 iteration, we built and tested models using the minimal set of basic features. The model will serve as the baseline result as we add more features in future iterations.

In the Take2 iteration, we built and tested models with additional features from the third-party data sources.

In this iteration, we will build and test models with additional features from the US Census data.

ANALYSIS: In the Take1 iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 70.99% using the training dataset. Furthermore, we selected Random Forest as the final model as it processed the training dataset with a final ROC/AUC score of 77.23%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.42%.

In the Take2 iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 71.92% using the training dataset. Furthermore, we selected Extra Trees as the final model as it processed the training dataset with a final ROC/AUC score of 79.79%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.02%.

In this iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 72.72% using the training dataset. Furthermore, we selected Extra Trees as the final model as it processed the training dataset with a final ROC/AUC score of 85.02%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.20%.

CONCLUSION: In this iteration, the Extra Trees model appeared to be suitable for modeling this dataset. However, we should explore the possibilities of using more features from the dataset to model this problem.

Dataset Used: KDD Cup 1998 Dataset

Dataset ML Model: Binary classification with numerical and categorical features

Dataset Reference: https://kdd.org/kdd-cup/view/kdd-cup-1998/Data

The HTML formatted report can be found here on GitHub.

Binary-Class Model for KDD Cup 1998 Using Python and Scikit-Learn Take 2

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 Acoustic Extinguisher Fire dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: This is the data set used for The Second International Knowledge Discovery and Data Mining Tools Competition, held in conjunction with KDD-98, The Fourth International Conference on Knowledge Discovery and Data Mining. The modeling task is a binary classification problem where the goal is to estimate the likelihood of donation from a direct mailing campaign.

In the Take1 iteration, we built and tested models using the minimal set of basic features. This model will serve as the baseline result as we add more features in future iterations.

In this iteration, we will build and test models with additional features from third-party data sources.

ANALYSIS: In the Take1 iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 70.99% using the training dataset. Furthermore, we selected Bagging Classifier as the final model as it processed the training dataset with a final ROC/AUC score of 77.23%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.42%.

In this iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 71.92% using the training dataset. Furthermore, we selected Extra Trees as the final model as it processed the training dataset with a final ROC/AUC score of 79.79%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.02%.

CONCLUSION: In this iteration, the Extra Trees model appeared to be suitable for modeling this dataset. However, we should explore the possibilities of adding more features to model this problem.

Dataset Used: KDD Cup 1998 Dataset

Dataset ML Model: Binary classification with numerical and categorical features

Dataset Reference: https://kdd.org/kdd-cup/view/kdd-cup-1998/Data

The HTML formatted report can be found here on GitHub.

Binary-Class Model for KDD Cup 1998 Using Python and Scikit-Learn Take 1

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 Acoustic Extinguisher Fire dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: This is the data set used for The Second International Knowledge Discovery and Data Mining Tools Competition, held in conjunction with KDD-98, The Fourth International Conference on Knowledge Discovery and Data Mining. The modeling task is a binary classification problem where the goal is to estimate the likelihood of donation from a direct mailing campaign.

In this Take1 iteration, we will build and test models using the minimal set of basic features. The final model will serve as the baseline result as we employ more features in future iterations.

ANALYSIS: In this iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 70.99% using the training dataset. Furthermore, we selected Random Forest as the final model as it processed the training dataset with a final ROC/AUC score of 77.23%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.42%.

CONCLUSION: In this iteration, the Random Forest model appeared to be suitable for modeling this dataset. However, we should explore the possibilities of using more features from the dataset to model this problem.

Dataset Used: KDD Cup 1998 Dataset

Dataset ML Model: Binary classification with numerical and categorical features

Dataset Reference: https://kdd.org/kdd-cup/view/kdd-cup-1998/Data

The HTML formatted report can be found here on GitHub.

Binary-Class Model for Acoustic Extinguisher Fire 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 Acoustic Extinguisher Fire dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Fire is a disaster that can have many different causes, and traditional fire extinguishing methods can be harmful to people. In this study, the research team tested a sound wave flame-extinguishing system to extinguish the flames at an early fire stage. The researchers conducted 17,442 extinguishing experiments using different flame sizes, frequencies, and distance ranges in their study. The goal is to create an environmentally friendly system with innovative extinguishing methods.

ANALYSIS: The performance of the preliminary TensorFlow model achieved an accuracy benchmark of 96.85%. When we processed the test dataset with the final model, the model achieved an accuracy score of 97.74%.

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

Dataset Used: Acoustic Extinguisher Fire Dataset

Dataset ML Model: Binary classification with numerical and categorical features

Dataset Reference: https://www.muratkoklu.com/datasets/

The HTML formatted report can be found here on GitHub.

Binary-Class Model for Acoustic Extinguisher Fire 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 Acoustic Extinguisher Fire dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Fire is a disaster that can have many different causes, and traditional fire extinguishing methods can be harmful to people. In this study, the research team tested a sound wave flame-extinguishing system to extinguish the flames at an early fire stage. The researchers conducted 17,442 extinguishing experiments using different flame sizes, frequencies, and distance ranges in their study. The goal is to create an environmentally friendly system with innovative extinguishing methods.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 97.74%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 97.86%. When we processed the test dataset with the final model, the model achieved an accuracy score of 98.58%.

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

Dataset Used: Acoustic Extinguisher Fire Dataset

Dataset ML Model: Binary classification with numerical and categorical features

Dataset Reference: https://www.muratkoklu.com/datasets/

The HTML formatted report can be found here on GitHub.