Portfolio optimization is a process in which the capital is allocated among the portfolio assets such that the return on investment is maximized while the risk is minimized. Portfolio construction and optimization is a complex process and has been an active research area in finance for a long time. For the portfolios with highly correlated assets, the performance of traditional risk-based asset allocation methods such as, the mean-variance (MV) method is limited because it requires an inversion of the covariance matrix of the portfolio to distribute weight among the portfolio assets. Alternatively, a hierarchical clustering-based machine learning method can provide a possible solution to these limitations in portfolio construction because it uses hierarchical relationships between the covariance of assets in a portfolio to distribute the weight and an inversion of the covariance matrix is not required. A comparison of the performance and analyses of the difference in weight distribution of two optimization strategies, the traditional MV method and the hierarchical risk parity method (HRP), which is a machine learning method, on real price historical data has been performed. Also, a comparison of the performance of a simple non-optimization technique called the equal-weight (EW) method to the two optimization methods, the Mean-variance method and HRP method has also been performed. This research supports the idea that HRP is a feasible method to construct portfolios with correlated assets because the performance of HRP is comparable to the performances of the traditional optimization method and the non-optimization method.