This paper demonstrates the usage of machine learning algorithms in the prediction of Real estate/House prices on two real datasets downloaded from Kaggle from Boston created by Harrison, D., and Rubinfeld, D.L. and from Melbourne created by Anthony Pino. To this day, literature about research on machine learning prediction of house prices in India is extremely limited. This paper provides a review of the usage of existing machine learning algorithms on two extremely different datasets and tries to implement this prediction engine for real-life usage by users. The findings indicate that using different algorithms can drastically change accuracy. Also, a poor dataset can negatively affect the predictions. Furthermore, it provides sufficient proof of what algorithm is best suitable for this task.