Updating : I guess the algorithm doesn't matter because that's the whole question. The important thing is the use of Fischer Discriminant Analysis.

The pre-processing and dimensionality reduction techniques are crucial in machine learning. The data set consists of 30 vectors of 3 dimensional each with their corresponding classes as given below. Although the data set consists of three distinct features, it is thought that the set has been reduced to two dimensions. Using Fischer Discriminant Analysis, find reduced two dimensional data set of 30 vectors. Here apply the technique step by step and show every details of your calculations. Z Z 3.1 8.6 14.5 -3.1 18.8 2.7 11.9 14.3 15.9 Class 1 4.1 3.4 1.6 5.1 5.4 2.6 4.5 14.7 4 Z 0.3 0.1 0.5 2.3 -0.4 -2.5 1.3 0.3 1.8 -0.5 0.4 -10.7 -11.7 -1.6 -2.6 -8.8 2.1 -3.9 Class 2 -3.9 0.5 7.5 -8.8 -22.1 2.8 -14.8 4.9 -11.8 1.4 0.8 4.9 0.3 4.4 3.9 2.7 2.3 7.1 -0.8 -3.8 -3.3 -2.3 6.8 -1.6 -2.8 -1.8 2.9 Class 3 3.8 -8 -0.8 -11 3.2 4.2 -4.1 1.9 1 -5.7 -3.9 -6.2 -7.3 -6.1 -6.4 -4.2 -6.1 -7.7 The pre-processing and dimensionality reduction techniques are crucial in machine learning. The data set consists of 30 vectors of 3 dimensional each with their corresponding classes as given below. Although the data set consists of three distinct features, it is thought that the set has been reduced to two dimensions. Using Fischer Discriminant Analysis, find reduced two dimensional data set of 30 vectors. Here apply the technique step by step and show every details of your calculations. Z Z 3.1 8.6 14.5 -3.1 18.8 2.7 11.9 14.3 15.9 Class 1 4.1 3.4 1.6 5.1 5.4 2.6 4.5 14.7 4 Z 0.3 0.1 0.5 2.3 -0.4 -2.5 1.3 0.3 1.8 -0.5 0.4 -10.7 -11.7 -1.6 -2.6 -8.8 2.1 -3.9 Class 2 -3.9 0.5 7.5 -8.8 -22.1 2.8 -14.8 4.9 -11.8 1.4 0.8 4.9 0.3 4.4 3.9 2.7 2.3 7.1 -0.8 -3.8 -3.3 -2.3 6.8 -1.6 -2.8 -1.8 2.9 Class 3 3.8 -8 -0.8 -11 3.2 4.2 -4.1 1.9 1 -5.7 -3.9 -6.2 -7.3 -6.1 -6.4 -4.2 -6.1 -7.7