Multi-Class Tabular Classification Model for Avila Bible Identification 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 Avila Bible Identification dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The Avila dataset includes 800 images extracted from the “Avila Bible,” a giant Latin copy of the whole Bible produced during the XII century between Italy and Spain. The paleographic analysis of the manuscript has identified the presence of 12 transcribers; however, each transcriber did not transcribe the same number of pages. The prediction task is to associate each pattern to one of the 12 transcribers labeled as A, B, C, D, E, F, G, H, I, W, X, and Y. The research team normalized the data using the Z-normalization method and divided the dataset into two portions, training and test. The training set contains 10,430 samples, while the test set contains 10,437 samples.

ANALYSIS: The average performance of the machine learning algorithms achieved an accuracy benchmark of 85.51% using the training dataset. Furthermore, we selected Bagging Classifier as the final model as it processed the training dataset with a final accuracy score of 98.53%. When we processed the test dataset with the final model, the model achieved an accuracy score of 99.20%.

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

Dataset Used: Avila Bible Dataset

Dataset ML Model: Multi-Class classification with numerical features

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

One source of potential performance benchmarks: https://www.sciencedirect.com/science/article/abs/pii/S0952197618300721

The HTML formatted report can be found here on GitHub.

Binary-Class Tabular Classification Model for Raisin Grains Identification 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 Raisin Grains Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: In this study, the research team developed a computerized vision system to classify two different varieties of raisin grown in Turkey. The dataset contains the measurements for 900 raisin grain images. The image further broke down into seven major morphological features for each grain of raisin.

ANALYSIS: The average performance of the machine learning algorithms achieved an accuracy benchmark of 84.11% using the training dataset. Furthermore, we selected Logistic Regression as the final model as it processed the training dataset with a final accuracy score of 86.29%. When we processed the test dataset with the final model, the model achieved an accuracy score of 91.11%.

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

Dataset Used: Raisin Dataset

Dataset ML Model: Binary classification with numerical features

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

One source of potential performance benchmarks: https://doi.org/10.30855/gmbd.2020.03.03

The HTML formatted report can be found here on GitHub.

Binary-Class Tabular Classification Model for Rice Cammeo Osmancik Identification 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 Rice Cammeo Osmancik Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Rice is one of the most widely produced and consumed cereal crops globally. The crop is also the main sustenance for many countries because of its economic and nutritious nature. However, before rice reaches the consumers, it must go through many manufacturing steps such as cleaning, color sorting, and classification. In this study, the research team developed a computerized vision system to classify two proprietary rice species. The dataset contains the measurements for 3,810 rice grain images. The grain image broke down into seven major morphological features for each grain of rice.

ANALYSIS: The average performance of the machine learning algorithms achieved an accuracy benchmark of 91.78% using the training dataset. Furthermore, we selected Logistic Regression as the final model as it processed the training dataset with a final accuracy score of 92.97%. When we processed the test dataset with the final model, the model achieved an accuracy score of 92.91%.

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

Dataset Used: Rice Dataset Cammeo and Osmancik

Dataset ML Model: Binary classification with numerical features

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

One source of potential performance benchmarks: https://doi.org/10.18201/ijisae.2019355381

The HTML formatted report can be found here on GitHub.

Multi-Class Tabular Classification Model for Durum Wheat Identification 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 Durum Wheat Identification dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Wheat is the main ingredient of most common food products in many people’s daily lives. Obtaining good quality wheat kernels is an essential matter for food supplies. In this study, the research team attempted to examine and classify type-1252 durum wheat kernels to obtain top-quality crops based on their vitreousness. The researchers used a total of 236 morphological, color, wavelet, and gaborlet features to classify durum wheat kernels and foreign objects by training several Artificial Neural Networks (ANNs) with different amounts of elements based on the feature rank list obtained with the ANOVA test.

ANALYSIS: The average performance of the machine learning algorithms achieved an accuracy benchmark of 98.65% using the training dataset. Furthermore, we selected k-Nearest Neighbors as the final model as it processed the training dataset with a final accuracy score of 99.56%. When we processed the test dataset with the final model, the model achieved an accuracy score of 99.66%.

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

Dataset Used: Durum Wheat Dataset

Dataset ML Model: Multi-Class classification with numerical features

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

One source of potential performance benchmarks: https://doi.org/10.1016/j.compag.2019.105016

The HTML formatted report can be found here on GitHub.

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.