The project investigated mobile phone pricing with an emphasis on the wide range of variables impacting consumer decision. The project analyzed how phone features such as screen size, camera properties and so on affect the price of a phone, I did an exploratory data analysis on the features to explore the relationship between the explanatory variables and the response variable.Explored the mathematical relationship between the explanatory variables and response variable, this was achieve by fitting a multiple linear regression on the dataset. A model was fitted using the best subset strategy based on the least AIC,least BIC and least cp mellow criteria, the statistical significant of the variables and the least square assumptions was checked and then the response variables was transformed so that the model will meet the least square assumptions.The project reveals that variables that could be useful in predicting mobile phone price are Storage, RAM, Screen size, and Camera