Multi-Class Classification Model for Human Activity Recognition with Smartphone Using Python Take 1

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

SUMMARY: The purpose of this project is to construct a prediction model using various machine learning algorithms and to document the end-to-end steps using a template. The Human Activities with Smartphone Dataset is a multi-class classification situation where we are trying to predict one of the six possible outcomes.

INTRODUCTION: Researchers collected the datasets from experiments that consist of a group of 30 volunteers with each person performed six activities wearing a smartphone on the waist. With its embedded accelerometer and gyroscope, the research captured measurement for the activities of WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING. The dataset has been randomly partitioned into two sets, where 70% of the volunteers were selected for generating the training data and 30% the test data.

For this iteration, the script focuses on evaluating various machine learning algorithms and identify the algorithm that produces the best accuracy metric.

CONCLUSION: The baseline performance of the ten algorithms achieved an average accuracy of 84.68%. Three algorithms (Linear Discriminant Analysis, Support Vector Machine, and Stochastic Gradient Boosting) achieved the top three accuracy scores after the first round of modeling. After a series of tuning trials, Linear Discriminant Analysis turned in the top result using the training data. It achieved an average accuracy of 95.43%. Using the optimized tuning parameter available, the algorithm processed the validation dataset with an accuracy of 96.23%, which was even better than the accuracy from the training data.

From the model-building activities, the Linear Discriminant Analysis algorithm achieved the top-notch training and validation results. For the project, Linear Discriminant Analysis should be considered for further modeling or production use.

Dataset Used: Human Activity Recognition Using Smartphone Data Set

Dataset ML Model: Multi-class classification with numerical attributes

Dataset Reference:

One potential source of performance benchmarks:

The HTML formatted report can be found here on GitHub.