The aim of this research is to develop a model to predict the price of a mobile when the specifications of a mobile are given and to find the ML algorithm that predicts the price most accurately. The usage of archival data to accurately forecast forthcoming instances is the essence of Predictive Analytics. One of the ways Predictive Analytics can be performed is by using Machine Learning. Predictive Machine Learning works by taking in data as input to develop and train a prediction model and the trained model is used to predict the outcome of future data instances. Supervised Machine Learning algorithms make use of data that contains a pre-defined class label, which is the attribute that needs to be predicted. The class label is the price of a mobile in our case. The Mobile Price Class dataset sourced from the Kaggle data science community website (https://www.kaggle.com/iabhishekofficial/mobileprice-classification) that categorizes mobiles into price ranges was used to train the prediction model. Python is used due to its readily accessible ML libraries. Various classification algorithms were used to train the model to try and find the algorithm that is able to predict the mobile price class most accurately. Metrics like accuracy score, confusion matrix, etc. are were used to evaluate the trained model to determine the algorithm most suitable among the ones used.