Tag: AutoKeras

Binary Class Tabular Model for Kaggle Playground Series Season 3 Episode 2 Using Python and AutoKeras

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 2 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 Stroke Prediction Dataset. Feature distributions are close to but different from the original.

ANALYSIS: After 100 trials, the best AutoKeras model processed the training dataset with the best ROC/AUC score of 0.9539. When we processed the test dataset with the final model, the model achieved a ROC/AUC score of 0.7423.

CONCLUSION: In this iteration, AutoKeras did not appear to be a suitable algorithm for modeling this dataset.

Dataset Used: Playground Series Season 3, Episode 2

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

Dataset Reference: https://www.kaggle.com/competitions/playground-series-s3e2

One source of potential performance benchmarks: https://www.kaggle.com/competitions/playground-series-s3e2/leaderboard

The HTML formatted report can be found here on GitHub.

Regression Tabular Model for Kaggle Playground Series Season 3 Episode 1 Using Python and AutoKeras

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 1 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 California Housing Dataset. Feature distributions are close to but different from the original.

ANALYSIS: After 100 trials, the best AutoKeras model processed the training dataset with a loss rate 0.6705. When we tested the final model using the test dataset, the model achieved an RMSE score of 0.7341.

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

Dataset Used: Playground Series Season 3, Episode 1

Dataset ML Model: Regression with numerical features

Dataset Reference: https://www.kaggle.com/competitions/playground-series-s3e1

One source of potential performance benchmarks: https://www.kaggle.com/competitions/playground-series-s3e1/leaderboard

The HTML formatted report can be found here on GitHub.

Multi-Class Tabular Model for Date Fruits Classification Using Python and AutoKeras

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 Date Fruits Dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Many factors determine the type of fruit with external appearance features such as color, length, diameter, and shape. However, classifying the variety of fruits simply by looking at their outward appearance requires expertise and a great effort and is also time-consuming. This study aims to classify the types of date fruit using machine-learning methods. The research team obtained 898 images of seven different date fruit types for this project via a computer vision system. The research team was hopeful that machine learning methods could successfully classify date fruit types.

ANALYSIS: After 70 trials, the best AutoKeras model processed the training dataset with an accuracy score of 91.87%.

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

Dataset Used: Date Fruits 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/10.1155/2021/4793293

The HTML formatted report can be found here on GitHub.

Binary-Class Tabular Model for Kaggle Spaceship Titanic Using Python and AutoKeras

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 Spaceship Titanic dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: In 2912, the Spaceship Titanic, an interstellar passenger liner, launched with almost 13,000 passengers on board. The vessel set out on its maiden voyage transporting emigrants from our solar system to three newly habitable exoplanets orbiting nearby stars.

While rounding Alpha Centauri en route to its first destination, the unwary Spaceship Titanic collided with a spacetime anomaly hidden within a dust cloud. Sadly, it met a similar fate as its namesake from 1000 years before. Though the ship stayed intact, almost half of the passengers were transported to an alternate dimension!

This incident presented a challenge to the data scientists of the future. To help rescue crews and retrieve the lost passengers, the data scientists tried to build a model that predicted which passengers were transported by the anomaly using records recovered from the spaceship’s damaged computer system.

ANALYSIS: After 30 trials, the best AutoKeras model processed the training dataset with the best accuracy score of 79.96%. When we processed the test dataset with the final model, the model achieved an accuracy score of 50.68%.

CONCLUSION: In this iteration, the AutoKeras model did not appear to be a suitable algorithm for modeling this dataset.

Dataset Used: Spaceship Titanic Dataset

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

Dataset Reference: https://www.kaggle.com/competitions/spaceship-titanic

One source of potential performance benchmarks: https://www.kaggle.com/competitions/spaceship-titanic/leaderboard

The HTML formatted report can be found here on GitHub.

Binary-Class Tabular Model for Liver Disease Patients Using Python and AutoKeras

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 Liver Disease Patients dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: This dataset contains over 30,000 cases of liver disease diagnosis results. The researcher trained machine learning models using this dataset to test the feasibility of applying machine learning techniques for making diagnostic predictions.

ANALYSIS: After 50 trials, the best AutoKeras model processed the training dataset with the best accuracy score of 99.78%.

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

Dataset Used: Liver Disease Patients Dataset

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

Dataset Reference: https://www.kaggle.com/datasets/abhi8923shriv/liver-disease-patient-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/abhi8923shriv/liver-disease-patient-dataset/code

The HTML formatted report can be found here on GitHub.