One of the challenges that Airbnb hosts face is determining the optimal nightly rent price. In many areas, potential renters are presented with a good selection of listings and can filter by criteria like price, number of bedrooms, square footage, and more. Since Airbnb is a marketplace, the amount a host can make per night is closely tied to market dynamics. The main objective of this project is to be able to predict, using supervised machine learning techniques, the ideal price that a host can charge, in such a way that we do not overcharge and neither undercharge. The main question of this project is : How can we hit the sweet spot in the middle ? How do we predict the optimal rent price for an apartment based on its characteristics through a supervised machine learning model ? To answer this question, we have used a supervised learning algorithm called the K-Nearest Neighbors or KNN for short, to automate the strategy applied by hosts to determine the optimal renting price. Several versions of the model have been implemented, from the univariate model to the multivariate model in order to make comparisons between their different predictions, and to evaluate each one of them to find the most efficient model of all as a solution to our problem. The result of this project could be integrated into the back-end part of the Airbnb website, which will make it easier for owners to determine the ideal price for renting their home/apartment based on its features.