Electricity Price forecast is a major task in smart grid operation. There is a massive amount of data flowing in the power system including the data collection by control systems, sensors, etc. In addition, there are many data points which are not captured and processed by the energy market operators and electricity network operators including gross domestic product, the price of fuel, government policy and incentives for renewable and green energy sectors as well as impacts on new technologies such as battery technology advancement and electric vehicles. In this study, data points from 2001 to 2017 were collected and 78 data points are considered for analyses to select the highly-correlated features which could potentially affect the electricity price. In the first step, a comprehensive correlation method using Pearson Correlation Coefficient is applied to find the points which are correlated with the electricity price. In the next step, the correlated data is fed to the machine learning algorithm for price forecast. The algorithm results were tested in the historical data in California and the outcomes were satisfactory for the three years forecast. The combination of featured selection and machine learning is giving superior outcomes than the traditional methods.