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 Sensorless Drive Diagnosis is a multi-class classification situation where we are trying to predict one of the several possible outcomes.
INTRODUCTION: The dataset contains features extracted from electric current drive signals. The drive has both intact and defective components. The signals can result in 11 different classes with different conditions. Each condition has been measured several times by 12 different operating conditions, such as speeds, load moments, and load forces.
In this iteration, we will establish the baseline accuracy measurement for comparison with future rounds of modeling.
ANALYSIS: The baseline performance of the machine learning algorithms achieved an average accuracy of 85.53%. Two algorithms (Random Forest and Gradient Boosting) achieved the top accuracy metrics after the first round of modeling. After a series of tuning trials, Random Forest turned in the top overall result and achieved an accuracy metric of 99.92%. After applying the optimized parameters, the Random Forest algorithm processed the testing dataset with an accuracy of 99.90%, which was even better than the prediction from the training data.
CONCLUSION: For this iteration, the Random Forest algorithm achieved the best overall training and validation results. For this dataset, Random Forest could be considered for further modeling.
Dataset Used: Sensorless Drive Diagnosis Data Set
Dataset ML Model: Multi-class classification with numerical attributes
Dataset Reference: https://archive.ics.uci.edu/ml/datasets/Dataset+for+Sensorless+Drive+Diagnosis
The HTML formatted report can be found here on GitHub.