The theme of this paper was estimating ground state energies of a molecule using machine learning methods. At the start of the paper the reader is introduced into the field of quantum chemistry and the hypothesis was defined followed by a brief look at the state of the art. Some ground rules were set in the way of theory and formulas to help in understanding the process. This was followed by an analysis and visualisation of the dataset that was generated on the basis of density functional theory. Methods of machine learning were described and the process of model selection and training was defined. After setting up the dataset methods of dimensionality reduction via PCA was explained and the methods were applied with succesive training and optimisation of the models followed by displaying the results. When the dataset was sufficienty reduced and estimators were well optimised ensemble methods were defined. These were trained and cross valdiated in a few different combination and the most efficient one was selected. The methods were evaluated by the standard evaluation metrics R2, MAE, RMSE and their standard deviation. After selecting the best ensemble method a fresh dataset was loaded and applied to the estimator to confirm a succesful research. Last chapter was dedicated to discussion and commentary on the process and reasoning behind certain decisions.