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 Superconductivity Temperature dataset is a regression situation where we are trying to predict the value of a continuous variable.

INTRODUCTION: The research team wishes to create a statistical model for predicting the superconducting critical temperature based on the features extracted from the superconductor’s chemical formula. The model seeks to examine the features that can contribute the most to the model’s predictive accuracy.

In this iteration, we will establish the baseline mean squared error for comparison with the future rounds of modeling.

ANALYSIS: The baseline performance of the machine learning algorithms achieved an average RMSE of 16.33. Two algorithms (Random Forest and Gradient Boosting) achieved the top RMSE metrics after the first round of modeling. After a series of tuning trials, Random Forest turned in the best overall result and achieved an RMSE metric of 9.72. By using the optimized parameters, the Random Forest algorithm processed the test dataset with an RMSE of 9.40, which was even better than the prediction from the training data.

CONCLUSION: For this iteration, the Random Forest algorithm achieved the best overall results using the training and testing datasets. For this dataset, Random Forest  should be considered for further modeling.

Dataset Used: Superconductivity Data Set

Dataset ML Model: Regression with numerical attributes

Dataset Reference: https://archive.ics.uci.edu/ml/datasets/Superconductivty+Data

One potential source of performance benchmarks: https://doi.org/10.1016/j.commatsci.2018.07.052

The HTML formatted report can be found here on GitHub.