$$

The variable definitions are provided below:

$\backslash$table$[[$VAR$,$DEFINITION$],[$Ad ID$,$SERIAL NUMBER$],[$Car Name,NAME $-$ SUBJECTIVE$],[$Make$,$CAR COMPANY$],[$Model$,$CAR NAME$],[$Year$,$YEAR OF MAKE$],[$KM$\text{'}$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$]]$

Please Note: $-$

There is lot subjective information in the file which can act as important explanatory variables in your model. Understand the data and clean the data and convert them to suitable numerical variables.

Question $1$

a$.$ Perform a clustering on the Data. $($CLO$-1,$ PLO$-1,$ Marks: $05)$

b$.$ How many clusters do you think the data has? $($CLO$-2,$ PLO$-2,$ Marks: $05)$

c$.$ What are the cluster characteristics? $($CLO$-1,$ PLO$-1,$ Marks: $05)$

Question $2$

a$.$ Develop a predictive model that predicts the price of the car quoted in the platform? $($CLO$-1,$ PLO$-1,$ Marks: $05)$

b$.$ What is the R$-$Square and Adjusted R$-$Square of the Model? $($CLO$-2,$ PLO$-2,$ Marks: $05)$