Assessing predictive performance of supervised machine learning algorithms: an alternative model for diamond pricing - Kigo, Samuel Njoroge (Strathmore University, 2022)
The world’s hardest mineral is a diamond, which is 58 times harder than any other mineral, and its beauty as a jewel has long been appreciated. The diamond is popular due to its optical property as well as other causes such as its durability, custom, fashion, and strong marketing by diamond producers. Diamond demand, on the other hand, is not directly related to such inherent characteristics, but rather to their perceived value as rare and expensive objects. Forecasting diamond pricing is challenging due to non-linearity in important features such as carat, cut, clarity table, and depth. Given this, we conducted a comparative analysis and implementation of multiple supervised machine learning models in predicting diamond price in both classification and regression approaches. We evaluated eight different supervised algorithms in our work, including Multiple Linear Regression, Linear Discriminant Analysis, eXtreme Gradient Boosting, Random Forest, k-Nearest Neighbors, Support Vector Machines, Boosted Regression and Classification Trees, and Multi-Layer Perceptron, and showcased the best suitable model given selected evaluation metrics. The analysis in this work is based on data preprocessing, exploratory data analysis, training the aforementioned models, assessing their accuracy, and interpreting their results. Based on the performance metrics values and analysis, it was discovered that eXtreme Gradient Boosting was the most optimal algorithm in both classification and regression, with a R2 score of 97.45% and an Accuracy value of 74.28%. As a result, the eXtreme Gradient Boosting method was recommended for forecasting the price of a diamond specimen.
Submitted in total fulfilment of the requirements for the degree of Master of Science in Statistical Sciences of Strathmore University