I'm building a model to predict the flight delay. My dataset contains the following columns:
FL_DATE (contains months(1-12)), OP_CARRIER (One hot encoded data of Carrier names), ORIGIN(One hot encoded data of Origin Airport), Dest(one-hot encoded data of Dest Airport), CRS_DEP_TIME(Intended time of departure ex: 1015), DEP_TIME(Actual time of departure ex: 1017),DEP_DELAY(the difference between crs-dep ex: -2), ARR_DELAY(arrival delay ex: -2)
My target variable is ARR_DELAY. After checking my data, I have decided it is a regression problem. However, I'm not sure what method do I need to use for selecting the appropriate columns. On the other hand, I was plotting each column with ARR_DELAY to check their relation and got something like this: FL_TIME vs ARR_DELAY
In such scenarios, if I have to build a model for such data which regression technique should I use?
PS: I'm new to Machine Learning. Please correct me If I'm heading in the wrong direction