The car dataset was found on Kaggle https://www.kaggle.com/datasets/lepchenkov/usedcarscatalog
For this assignment the first dataset I analyzed was hotel reservations, the first probability I looked at was P(Canceled) which was 32.76%. Or there is a 32.76% chance of a hotel reservation canceling. Next was P(Canceled, Not selected meal plan) so the probability that they canceled and did not select a meal plan is 4.68%. The probability they canceled out of the not selected meal plan group is 0.331%. Next, I analyzed the market segment type or why are they staying at the hotel. First was the probability they canceled their reservation out of the corporate group or work travel group over the canceled is 0.018%. The probability they are staying at the hotel for free or complementary out of the canceled group is 0%… makes sense.
Now to the other dataset which was on vehicles. I chose this dataset because of the interesting features like color, transmission type, and car brand. I was curious to see which of the car types had higher percentages of being a certain transmission type. First I wanted to see the probability of a car being diesel because it’s black which was found to be 0.195%. On the contrary, I tested the probability a car was gasoline because it was blue which is 0.149%. My next set of probabilities is on the make of the car. Firstly what is the probability of a car being mechanical because it is a Toyota, which is 0.025%, and a car being automatic if it is a honda has a probability of 0.0310% both of which I thought to be very low? I suppose a lot of Toyotas are automatic cars. Next was if the car had a warranty based on if it was new or owned. The probability of a car having a warranty if it is new was 0.856% and the probability of a car not having a warranty because it was previously owned was 0.998% therefore the probability of a used car having a warranty was 0.00196%, so concluding used cars do not have a warranty over 99% of the time and new cars do have a warranty 85% of the time.