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 predictive model using various machine learning algorithms and to document the end-to-end steps using a template. The Human Activity Recognition Using Smartphones dataset is a multi-class classification situation where we are trying to predict one of several (more than two) possible outcomes.

INTRODUCTION: Researchers collected the datasets from experiments that consist of a group of 30 volunteers, with each person performing six activities by 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.

In previous iterations, the script focused on evaluating various classic machine learning algorithms and identify the algorithm that produces the best accuracy metric. The previous iterations established a baseline performance in terms of accuracy and processing time.

In iteration Take1, we constructed and tuned an XGBoost machine learning model for this dataset. We also observed the best accuracy result that we could obtain using the XGBoost model with the training and test datasets.

In iteration Take2, we constructed several Multilayer Perceptron (MLP) models with one hidden layer. These simple MLP models would serve as a benchmark as we build more complex MLP models in future iterations.

In iteration Take3, we constructed several Multilayer Perceptron (MLP) models with two hidden layers. We also experimented with the dropout layers as a regularization technique for improving our models.

In iteration Take4, we constructed several Multilayer Perceptron (MLP) models with three hidden layers. We also experimented with the dropout layers (25% or 0.25) as a regularization technique for improving our models.

In iteration Take5, we constructed several Multilayer Perceptron (MLP) models with four hidden layers. We also experimented with the dropout layers (25% or 0.25) as a regularization technique for improving our models.

In this Take6 iteration, we will construct MLP models with four hidden layers of 2048/1024/512/256 nodes. We also will fine-tune the model with different dropout rates at each layer.

ANALYSIS: From iteration Take1, the XGBoost model achieved an accuracy metric of 99.45% in training. When configured with the optimized parameters, the XGBoost model processed the test dataset with an accuracy of 94.94%, which indicated a high variance issue. We will need to explore regularization techniques or other modeling approaches before deploying the model for production use.

From iteration Take2, the one-layer MLP models achieved an accuracy metric of between 98.8% and 99.3% after 50 epochs in training. Those same models processed the test datasets with an accuracy metric of between 93.0% and 95.9%.

From iteration Take3, the two-layer MLP models achieved an accuracy metric of between 96.2% and 98.5% after 50 epochs in training. Those same models processed the test datasets with an accuracy metric of between 93.6% and 96.2%.

From iteration Take5, the four-layer MLP models achieved an accuracy metric of between 91.9% and 98.3% after 100 epochs in training. Those same models processed the test datasets with an accuracy metric of between 85.0% and 95.8%.

For this Take6 iteration, the best four-layer MLP model with dropout layers of (0.75, 0.75, 0.25, 0.25) achieved an accuracy metric of 93.11% after 100 epochs in training. The same model processed the test datasets with an accuracy metric of 93.00%.

CONCLUSION: For this iteration, the four-layer MLP models produced mixed results with smaller but still noticeable variance. However, the model with 2048/1024/512/256 nodes with dropout layers of (0.75, 0.75, 0.25, 0.25) produced a balance of high accuracy and low variance. For this dataset, we can explore other modeling approaches to reduce variance before deploying the model for production use.

Dataset Used: Human Activity Recognition Using Smartphones

Dataset ML Model: Multi-class classification with numerical attributes

Dataset Reference: https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

The HTML formatted report can be found here on GitHub.