An automotive service chain is launching its new grand service station this weekend. They offer to
service a wide variety of cars. The current capacity of the station is to check 315 cars thoroughly per
day. As an inaugural offer, they claim to freely check all cars that arrive on their launch day, and
report whether they need servicing or not!
Unexpectedly, they get 450 cars. The servicemen will not work longer than the working hours, but
the data analysts have to!
Can you save the day for the new service station?
How can a data scientist save the day for them?
He has been given a data set, ServiceTrain.csv that contains some attributes of the car that can be
easily measured and a conclusion that if a service is needed or not.
Now for the cars they cannot check in detail, they measure those attributes and store them in
ServiceTest.csv
What is the accuracy range (in %) of the predictions made over test data?
After applying logistic regression I think I got the observations from the resultant confusion matrix as true positive=29, true negative=94 & false positive=5, true negative=94
In the process of accuracy range prediction after couple of lines into data preparation by tagging the categorical values Yes as 1 and NO as 0
train_data = pd.read.csv("ServiceTrain.csv)
test_data = pd.read_csv("ServiceTest.csv)
train_data.head()
After this I am not sure if I need to check the size of the input and output features or I need to separate the those features.
>Solution :
Great to see the data and made me curious to work on this. Obviously I don’t have all the data required to do the process but I will just assume couple of things to clarify the issue.
You need to separate the input and output features of train data first then check the size of input and output.
If I am you I would be creating an instance of the logistic regression class next.
Glad to help.