This project contains the Module 11 assignment and activities files.
In order for us to understand what factors make a car more or less expensive, I have explored the car price in relation to its year of make, condition and other key features. Since the dataset holds many type of cars by manufacturer, model, fuel efficiency, cylinders, etc', i've chose to explore only one type of vhicle first, so not to compear apples to orenges, then explored the entire dataset. The cingular car exploration is not included in the scop of this submission.
There are 4 key feature which have the higest corralation to the price. Our customer weigh the price in correlation to the car make, model and year, functionality as it refer to the size, type, cylinder and fuel and condition as it refer to odometer (which customer consider as condition indicator).
- The top selling car profile is a white Ford F-150 in good condition with a clean title.
- The top 3 selling colors are silver, white and black when silver salls about 2.5 times more then white and over 7 times more then black.
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Cars in excellent condition are sold over 3 times more then cars in good condition.
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Most desired cars are a pickup trucks and SUV.
Given the finding and additional observations my recomendations are to park the desired cars on the out skirts of the parking lot so they become visible to potential customers as they drive by. The cars I recomend to park are silver pickup trucks at first, then white / black SUVs.
The link to the jupyter notebook used, supporting these findings can be found here