Write a MATLAB/Python code for this.
DATASET
LABEL
case
Engine Speed (cycle average); Part Engine
BMEP - Brake Mean Effective Pressure; Part Engine
BSFC - Brake Specific Fuel Consumption; Part Engine
Indicated Efficiency; Part Engine
Brake Efficiency; Part Engine
Brake Torque; Part Engine
Brake Power (HP); Part Engine
target
1.00
14.70
6000.00
-5.25
0.00
0.00
0.00
-131.27
-110.61
1.00
2.00
14.70
6000.00
-2.43
-303.46
3.55
-27.14
-60.73
-51.17
1.00
3.00
14.70
6000.00
2.39
550.27
32.55
14.97
59.70
50.30
1.00
4.00
14.70
6000.00
4.59
366.11
36.60
22.50
114.85
96.77
1.00
5.00
14.70
6000.00
5.24
333.41
38.47
24.71
130.93
110.32
1.00
6.00
14.70
6000.00
5.37
329.58
38.62
24.99
134.30
113.16
1.00
7.00
14.70
6000.00
5.41
328.17
38.70
25.10
135.23
113.94
1.00
8.00
14.70
6000.00
5.41
327.84
38.73
25.13
135.37
114.06
1.00
9.00
14.70
6000.00
5.41
327.83
38.73
25.13
135.37
114.06
1.00
Project Statement: You are given engine performance parameter data per attached file MS-21+PAI_Project Data. The data consists of seven engine parameters and target output. The target output is mentioned in the last column. The project will be done in a group of two students (selection of group members is as per your choice).
Use of Software: You can use either MATLAB or Python for programming. Use of toolbox is allowed, however, writing your own code will have more credit.
Requirements: Design your own backpropagation neural network using this data. You may take all or some of the engine parameters as your input, predict the output, and compare it with the target outputs. Using the backpropagation neural network, you can use some of the parameters as your input and use the most suited parameter (in your opinion) as output and compare your predicted value with the selected output. In this case, you will have more than two target outputs. In this case, you have to justify your selection of the most suited parameter as output. Write a 1500-word report including all the graphs generated by the software.