The socio-economic level of an individual is an indicator of the education, purchasing power and housing. Accurate and proper prediction of the individuals is of great significance for market campaign. However, the previous approaches estimating the socio-economic status of an individual mainly rely on census data which demands a great quantity of money and manpower. In this paper, we analyse two datasets: App usage records and occupation data of individuals in a metropolis of China. We divide the individuals into 4 socio-economic levels according to their occupations. Then, we propose a low-cost socio-economic level classification model constructed with machine learning algorithm. Our predictive model achieves a high accuracy over 80%. Our results show that the features extracted from user’s App usage records are valuable indicators to predict the socio-economics levels of individuals.