Train the age model on all rows (age_years → price_lakh) and predict every car.
The error of a car is its real price minus its predicted price. Store the car_id of the car with the biggest error in either direction in result. These are the listings a reviewer should look at first.
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.