With the growing number of freely accessible databases and open source tools(eg. Python , RDKit , Scikit Learn , Matplotlib and Seaborn ) it is easy to learn and apply machine learning or at least conduct data driven research. The cheminformatics gain also from openness of researchers that publish their code and data pipeline . Such attitude is simplifying the knowledge acquiring process and making QSAR/QSPR and machine learning adaptable to other problems. making predictions of maximum emissions of compounds from test sets. The predictions were checked with real values from test sets and the errors were studied. The process of splitting, training and testing was repeated tenfold in cross validation. Best performing machine learning models were chosen based on mean absolute error, mean squared error, maximum error and R 2 parameter. Chosen models are to be validated with compounds from laboratory.