Two datasets provided: vehicles - table of all vehicles and their attributes: • technology: 0 if none, 1 if yes (technology that is installed in vehicles that makes them "instantly bookable" and unlockable with mobile phone) • actual_price: actual daily price for vehicle (owners are able to set whatever price they want) • recommended_price: appropriate "market" price for the vehicle, based on our analysis • num_images: number of photos of the car • street_parking: 0 no, 1 yes • description: number of characters of the car description inputted by the owner reservations - table of all completed reservations: • vehicle_id: vehicle's unique ID • reservation_type: 1 is Hourly rental, 2 is Daily, 3 is Weekly Using the two attached datasets, please prepare a short presentation with your answers to the following questions: • Which of the factors seem to be most important in driving total # of reservations for our vehicles? (technology type, pricing, number of car photos, street parking, car description length) • How does the car's technology type seem to affect reservations? Feel free to use any tool(s) that you feel comfortable with to complete this analysis (Excel, SQL, R, Python, etc).