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.

Univariate Time Series Model for Iron Production in Australia Using TensorFlow

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The project aims to construct a time series prediction model and document the end-to-end steps using a template. The Iron Production in Australia dataset is a univariate time series situation where we attempt to forecast future outcomes based on past data points.

INTRODUCTION: The problem is forecasting the monthly iron production in Australia. The dataset describes a time-series of weight (in thousand tons) over 40 years (1956-1995), and there are 476 observations. We used the first 80% of the observations for training and testing various models while holding back the remaining observations for validating the final model.

ANALYSIS: The baseline persistence model yielded an RMSE of 56.494. The MLP model processed the same test data with an RMSE of 41.415, which was better than the baseline model as expected. In an earlier ARIMA modeling experiment, the best ARIMA model with non-seasonal order of (1, 1, 1) and seasonal order of (1, 0, 1, 12) processed the validation data with an RMSE of 34.639.

CONCLUSION: For this dataset, the TensorFlow MLP model achieved an acceptable result, and we should consider using TensorFlow for further modeling.

Dataset Used: Monthly basic iron production in Australia January 1956 through August 1995

Dataset ML Model: Time series forecast with numerical attribute

Dataset Reference: Rob Hyndman and Yangzhuoran Yang (2018). tsdl: Time Series Data Library. v0.1.0. https://pkg.yangzhuoranyang./tsdl/.

The HTML formatted report can be found here on GitHub.

Binary-Class Model for Acoustic Extinguisher Fire Using Python and TensorFlow Decision Forests

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 Gradient Boosted Trees model achieved an accuracy benchmark of 99.26% on the training dataset. When we applied the finalized model to the test dataset, the model achieved an accuracy score of 97.82%.

CONCLUSION: In this iteration, the TensorFlow Decision Forests 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 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 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 average performance of the machine learning algorithms achieved an accuracy benchmark of 95.37% using the training dataset. Furthermore, we selected Extra Trees as the final model as it processed the training dataset with a final accuracy score of 97.07%. When we processed the test dataset with the final model, the model achieved an accuracy score of 97.55%.

CONCLUSION: In this iteration, the Extra Tree 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.

Roz Zander and Ben Zander on The Art of Possibility, Part 4

In the book, The Art of Possibility: Transforming Professional and Personal Life, Rosamund Stone Zander and Benjamin Zander show us the 12 things we can do to go on a journey of possibility, rather than living a life full of hurdles and constraints of our own making.

These are some of my favorite concepts and takeaways from reading the book.

The Fourth Practice: Being a Contribution

In this chapter, Roz and Ben discuss the concept of playing a contribution game rather than a success/failure game. They offer the following observations and recommendations for us to think about:

Too often, we treat life as a series of success/failure games. We notice mostly the obstacles and constraints in our lives. We warn others about the limitations of having too much to do, having too little time, not enough resources, and quality too hard to measure, just to name a few.

While we play the game of success/failure, we judge ourselves based on other people’s standards. We would often ask the questions of “Is it enough?” or “Would I be loved for what I have accomplished?” We desperately seek reassurance from others about our value and place in life.

Another angle to consider is exploring the possibility of playing a game of contribution. The contribution game is not about attaining other people’s standards or judgment. Instead, we hold ourselves accountable to the joyful question of “How will I be a contribution today?”

By switching the focus from a success/failure orientation to a contribution orientation, we can shift the context from survival to one of opportunity for growth. Ben suggests we take the following two steps for practicing:

  1. Declare ourselves to be a contribution.
  2. Throw ourselves into life as someone who makes a difference. Also, we embrace the reality that we may not understand how or why.

Naming oneself and others as a contribution produces a shift away from self-concern and engages us in a productive relationship with others. Rather be overly concerned with the superficial measurements of being cheap, good, and fast, we ask more questions like “Who is it for?” and “What is it for?”

Multi-Class Model for Kaggle Tabular Playground Series February 2022 Using 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 Kaggle Tabular Playground Series February 2022 dataset is a multi-class modeling situation where we are trying to predict one of several (more than 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 on Kaggle with fun but less complex, tabular datasets. For this dataset, we want to predict bacteria species based on repeated lossy measurements of DNA snippets. Each row of data contains a spectrum of histograms generated by repeated measurements of a sample, and each row contains the output of all 286 histogram possibilities.

ANALYSIS: The average performance of the preliminary TensorFlow models achieved an accuracy benchmark of 98.20%. When we processed the test dataset with the final model, the model achieved an accuracy score of 90.72%.

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

Dataset Used: Kaggle Tabular Playground Series February 2022

Dataset ML Model: Multi-Class classification with numerical attributes

Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-feb-2022/data

One potential source of performance benchmark: https://www.kaggle.com/c/tabular-playground-series-feb-2022/leaderboard

The HTML formatted report can be found here on GitHub.

Multi-Class Model for Kaggle Tabular Playground Series February 2022 Using Decision Forests

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 Tabular Playground Series February 2022 dataset is a multi-class modeling situation where we are trying to predict one of several (more than 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 on Kaggle with fun but less complex, tabular datasets. For this dataset, we want to predict bacteria species based on repeated lossy measurements of DNA snippets. Each row of data contains a spectrum of histograms generated by repeated measurements of a sample, and each row contains the output of all 286 histogram possibilities.

ANALYSIS: The performance of the preliminary Gradient Boosted Trees model achieved an accuracy benchmark of 99.36% on the training dataset. When we applied the finalized model to Kaggle’s test dataset, the model achieved an accuracy score of 91.55%.

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

Dataset Used: Kaggle Tabular Playground Series February 2022

Dataset ML Model: Multi-Class classification with numerical attributes

Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-feb-2022/data

One potential source of performance benchmark: https://www.kaggle.com/c/tabular-playground-series-feb-2022/leaderboard

The HTML formatted report can be found here on GitHub.