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.

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.

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 Pumpkin Seeds Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Pumpkin seeds are frequently consumed as confection worldwide because of their adequate amount of protein, fat, carbohydrate, and mineral contents. The research team carried out a study on the two most important types of pumpkin seed, “Ürgüp Sivrisi” and “Çerçevelik,” generally grown in Ürgüp and Karacaören regions in Turkey. Furthermore, the morphological measurements of 2500 pumpkin seeds of both varieties were captured using the gray and binary forms of threshold techniques.

ANALYSIS: After 100 trials, the best AutoKeras model processed the training dataset with an accuracy score of 87.88%.

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

Dataset Used: Pumpkin Seeds 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/10.1007/s10722-021-01226-0

The HTML formatted report can be found here on GitHub.