This project aims to classify mobile phones into different price categories using machine learning techniques. The model is trained on a dataset containing various features such as battery life, camera quality, internal storage, etc.
pc Primary Camera mega pixels
fc Front Camera mega pixels
sc_h Screen Height of mobile in cm
sc_w Screen Width of mobile in cm
m_dep Mobile Depth in cm
px_width Pixel Resolution Width
px_height Pixel Resolution Height
ram Random Access Memory in Mega Bytes
int_memory Internal Memory in Giga Bytes
four_g Has 4G or not
three_g Has 3G or not
dual_sim Has dual sim support or not
battery_power Total energy a battery can store in one time measured in mAh
touch_screen Has touch screen or not
clock_speed speed at which microprocessor executes instructions
n_cores Number of cores of processor
wifi Has wifi or not
blue Has bluetooth or not
mobile_wt Weight of mobile phone
talk_time longest time that a single battery charge will last when you are
price_range This is the target variable with value of 0(low cost), 1(medium cost), 2(high cost) and 3(very high cost).