Hi Experts, friends
I have a business problem where I need to find out through some predictive models or techniques which Airline should I choose and what will be my max and min fare depending on a particular Origin and Destination and for the particular season or date range. I am not sure which analytics technique to follow for this ? Can the experts please suggest ?
Hi Experts, friends
Can you give some more details about your target variable and features? Probably share the dataset or a few rows from the dataset.
In this business problem I am trying to predict which Airline should be considered on a particular date or date range and what should be the optimum fare that should be paid on a particular route.
So as an example for route or O&D Airport pair BOS (Boston) - LGA (New York) I have lot of Airline options like United Airlines, Delta Airlines,American Airlines on a particular date or date range . Which airline should be chosen for that route on a date which will give me the optimum fare ? Attached the data set. I am not sure which predictive analytical method I should choose?
Many thanks for your suggestion
VishnuAirspend_O&D_US.zip (116.8 KB)
The size of the dataset is too small for any kind of predictive analytics to work. Your best bet is to group by airlines, weekday, source and destination and use mean/median fare within the historical data to find out the estimated fare and then choose the minimum within a date.
The dataset i have provided i actually few rows from the actual data set because of the size constraint. I need to predict optimal timing for purchase of the tickets with some regression models. I see that a similar model was created by computer science department of University of Minnesota!!
The problem is that they have not used R, they have conceptually derived the problem.
So basically my aim is to create a regression model for predicting optimal timing for purchase of airline ticket. Any help from experts here will be welcome !!