Managers think in rupees, not lakh. Train the age model (all rows, age_years → price_lakh) and store how many rupees a car loses every year, as a positive whole number (1 lakh = 1,00,000 rupees), in result.
import io
import pandas as pd
cars = pd.read_csv(io.StringIO("""car_id,age_years,km_driven_thousand,fuel,owners,price_lakh
1,5,32,Diesel,1,6.77
2,1,3,Petrol,1,8.59
3,2,11,Petrol,2,7.28
4,2,23,Petrol,1,7.17
5,4,37,Petrol,1,5.86
6,5,39,Diesel,1,6.68
7,6,74,Diesel,1,5.01
8,5,57,Diesel,1,6.00
9,7,68,Petrol,1,4.04
10,7,77,Diesel,2,4.41
11,3,37,Petrol,1,6.29
12,7,59,Petrol,3,4.12
13,1,11,Petrol,2,7.47
14,2,18,Petrol,1,7.34
15,11,113,Petrol,3,1.12
16,6,75,Diesel,1,5.24
17,7,77,Diesel,2,4.72
18,10,128,Petrol,1,2.01
19,1,3,Petrol,1,7.74
20,11,143,Diesel,2,2.17
21,8,95,Petrol,2,3.41
22,8,96,Petrol,1,3.27
23,3,46,Diesel,2,6.90
24,3,30,Petrol,1,7.01
25,1,17,Diesel,1,8.77
26,1,15,Petrol,1,8.07
27,11,101,Petrol,1,1.94
28,6,78,Petrol,1,4.17
29,11,136,Diesel,2,1.79
30,8,99,Petrol,2,3.11
31,5,56,Petrol,1,5.76
32,7,77,Petrol,1,4.60
33,5,60,Petrol,1,5.08
34,3,33,Diesel,1,7.18
35,2,29,Petrol,1,7.59
36,7,94,Diesel,1,4.62
37,6,57,Diesel,1,5.47
38,6,54,Diesel,1,5.88
39,3,38,Petrol,1,7.21
40,9,114,Petrol,2,2.84
41,9,125,Petrol,3,2.14
42,9,94,Petrol,1,2.47
43,11,97,Diesel,1,2.99
44,1,15,Diesel,1,9.08
45,10,90,Diesel,3,2.82
46,11,112,Petrol,2,1.03
47,4,44,Petrol,1,6.53
48,4,52,Petrol,1,6.23
49,2,19,Diesel,1,8.23
50,8,87,Diesel,1,4.03
51,2,34,Petrol,1,7.31
52,6,85,Diesel,2,5.18
53,8,87,Petrol,3,2.80
54,5,66,Diesel,1,6.06
55,11,104,Petrol,2,0.90
56,7,69,Petrol,1,4.79
57,9,126,Petrol,2,1.95
58,10,108,Petrol,1,2.34
59,5,67,Petrol,2,5.40
60,4,40,Petrol,2,6.04"""))Store your final answer in a variable named result.
Each hint gives a little more help. Try on your own first.
Run your code to see the output here.