A used-car website wants to suggest a fair price for every car a seller lists. cars holds 60 hatchbacks already sold (price_lakh is the selling price in ₹ lakh).
Before building a model, check the pattern: store the correlation between age_years and price_lakh, rounded to 2 decimals, 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.