This project is delivered as part of my Masters in Big Data Science (MSc BDS) Program for the module named “Data Mining” in Queen Mary University of London (QMUL), London, United Kingdom. This project covers the Implementation of the Outlier Detection using the proximity-based method of k-nearest neighbors to calculate the outlier scores on the”house prices” dataset; with the inclusion of the data pre-processing steps of z-score normalisation and PCA dimensionality reduction techniques. The implementation is executed using Python’s libraries namely pandas, numpy, matplotlib, sklearn and scipy. The solution includes the computation of the Euclidean distance to further detect the top 3 outlier houses with the highest prices when compared with the average house price. **NOTE:** Due to the data privacy and the data protection policy to be adhered by the students; the datasets and the solution related code are not exposed and updated in the GitHub public profile; in order to be compliant with the Queen Mary University of London (QMUL) policies.