Highlights
The variable definitions are provided below
|
VAR |
DEFINITION |
|
Ad ID |
SERIAL NUMBER |
|
Car Name |
NAME - SUBJECTIVE |
|
Make |
CAR COMPANY |
|
Model |
CAR NAME |
|
Year |
YEAR OF MAKE |
|
KM's driven |
SELF EXPLANATORY |
|
Price |
PRICE QUOTED IN THE PLATFORM |
|
Fuel |
SELF EXPLANATORY |
|
Registration city |
SELF EXPLANATORY |
|
Car documents |
SELF EXPLANATORY |
|
Assembly |
LOCAL vs IMPORTED |
|
Transmission |
SELF EXPLANATORY |
|
Condition |
SELF EXPLANATORY |
|
Seller Location |
SELF EXPLANATORY |
|
Description |
SUBJECTIVE |
|
Car Features |
SUBJECTIVE |
Question 1
Perform a clustering on the
How many clusters do you think the data has?
What are the cluster characteristics?
Question 2
Develop a predictive model that predicts the price of the car quoted in the platform
What is the R-Square and Adjusted R-Square of the Model?
How do I build the model and what are the list of explanatory variables for the model?
Suppose I want to re-phrase the above problem as predicting the price for an ‘Expensive’ vs ‘non-expensive’ car. How do I change the model specification and how would I define the cut-off and why?
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