All about Machine learning
Building a performing Machine learning Model * data preparation > feature enginerring>data modeling>performance measure> performance improvement * this is highly iterative process , to repeat until your model reaches a satisfying performance * Let's see the steps 1. Data preparation * query your data - basically you can query your data using pandas , this will give you a dataframe with your raw data * clean your data - 1st step is to deal with missing values, and if a colum contain too much missing value remove that coloum ,and 2nd step is remove the outlliers ,and you need to remove them , you can remove it by using your mind or you can use a robust methods to remove the outliers * Format data - this is basically a encoding of categorical variables , you can use label encoding or one hot encoding 2. Feature Engineering * A feature is an individual measurable property of a phenomenon being observed * like for to predict the price o...