Tag: scikit-learn

Binary Class Tabular Model for Kaggle Playground Series Season 3 Episode 3 Using Python and Scikit-Learn

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 average performance of the machine learning algorithms achieved an AUC/ROC benchmark of 0.7566 after training. Furthermore, we selected Random Forest as the final model as it processed the training dataset with an AUC/ROC score of 0.8380. When we processed the test dataset with the final model, the model achieved an AUC/ROC score of 0.8797.

CONCLUSION: In this iteration, the Random Forest 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.

Binary Class Tabular Model for Kaggle Playground Series Season 3 Episode 2 Using Python and Scikit-Learn

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: The average performance of the machine learning algorithms achieved an AUC/ROC benchmark of 0.7836 after training. Furthermore, we selected Logistic Regression as the final model as it processed the training dataset with an AUC/ROC score of 0.8735. When we processed the test dataset with the final model, the model achieved an AUC/ROC score of 0.8662.

CONCLUSION: In this iteration, the Logistic Regression model appeared 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 Scikit-Learn

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: The average performance of the machine learning algorithms achieved an RMSE benchmark of 0.5855 after preliminary training runs. Furthermore, we selected Extra Trees Regressor as the final model as it processed the training dataset with an RMSE score of 0.3083. When we tested the final model using the test dataset, the model achieved an RMSE score of 0.5873.

CONCLUSION: In this iteration, the Extra Trees model 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 Scikit-Learn

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: The average performance of the machine learning algorithms achieved an accuracy benchmark of 88.39% after training. Furthermore, we selected Linear Discriminant Analysis as the final model as it processed the training dataset with an accuracy score of 91.42% using the 10-fold cross-validation method.

CONCLUSION: In this iteration, the Random Forest model 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 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 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: The average performance of the machine learning algorithms achieved an accuracy benchmark of 78.30% after training. Furthermore, we selected Random Forest as the final model as it processed the training dataset with an accuracy score of 79.55%. When we processed the test dataset with the final model, the model achieved an accuracy score of 79.58%.

CONCLUSION: In this iteration, the Random Forest model appeared 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.