A heart disease data set contains the medical information of 100 patients of two classes. The first row contains column names, and other rows contain the information of the patients. The last column num contains the classes of the patients. Use the perception module in Python package sklearn to find the weight vector w for linear classification. Use the LDA module in Python package sklearn to find the weight vector w for linear discriminant analysis. The python code of the analysis should be provided.

heart_disease_data - Notepad File Edit Format View Help age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal, nu 63.0,1.0,1.0,145.0,233.0,1.0,2.0,150.0,0.0,2.3,3.0,0.0,6.0,0 67.0,1.0,4.0,160.0,286.0,0.0,2.0,108.0,1.0,1.5,2.0,3.0,3.0,1 67.0,1.0,4.0,120.0,229.0,0.0,2.0,129.0,1.0,2.6,2.0,2.0,7.0,1 37.0,1.0,3.0,130.0,250.0,0.0,0.0,187.0,0.0,3.5,3.0,0.0,3.0,0 41.0,0.0,2.0,130.0,204.0,0.0,2.0,172.0,0.0,1.4,1.0,0.0,3.0,0 56.0,1.0,2.0,120.0,236.0,0.0,0.0,178.0,0.0,0.8,1.0,0.0,3.0,0 62.0,0.0,4.0,140.0,268.0,0.0,2.0,160.0,0.0,3.6,3.0,2.0,3.0,1 57.0,0.0,4.0,120.0,354.0,0.0,0.0,163.0,1.,0.6,1.0,0.0,3.0,0 63.0,1.0,4.0,130.0,254.0,0.0,2.0,147.0,0.0,1.4,2.0,1.0,7.0,1 53.0,1.0,4.0,140.0,203.0,1.0,2.0,155.0,1.0,3.1,3.0,0.0,7.0,1 57.0,1.0,4.0,140.0,192.0,0.0,0.0,148.0,0.0,0.4,2.0,0.0,6.0,0 56.0,0.0,2.0,140.0,294.0,0.0,2.0,153.0,0.0,1.3,2.0,0.0,3.0,0 56.0,1.0,3.0,130.0,256.0,1.0,2.0,142.0,1.0,0.6,2.0,1.0,6.0,1 44.,1.0,2.0,120.0,263.0,0.0,0.0,173.0,0.0,0.0,1.0,0.0,7.0, 52.0,1.0,3.0,172.0,199.0,1.0,0.0,162.0,0.0,0.5,1.0,0.0,7.0,0 57.0,1.0,3.0,150.0,168.0,0.0,0.0,174.0,0.0,1.6,1.0,0.0,3.0,0 48.0,1.0,2.0,110.0,229.0,0.0,0.0,168.,0.,1.0,3.0,0.0,7.0,1 54.0,1.0,4.0,140.0,239.0,0.0,0.0,160.0,0.0,1.2,1.0,0.0,3.0,0 48.0,0.0,3.0,130.0,275.0,0.0,0.0,139.0,0.0,0.2,1.0,0.0,3.0,0 49.0,1.0,2.0,130.0,266.0,0.0,0.0,171.0,0.0,0.6,1.0,0.0,3.0,0 64.0,1.0,1.0,110.0,211.0,0.0,2.0,144.0,1.,1.8,2.0,0.0,3.0,0 58.0,0.0,1.0,150.0,283.0,1.0,2.0,162.0,0.0,1.0,1.0,0.0,3.0,0 58.0,1.0,2.0,120.0,284.0,0.0,2.0,160.0,0.0,1.8,2.0,0.0,3.0,1 58.0,1.0,3.0,132.0,224.0,0.0,2.0,173.0,0.0,3.2,1.0,2.0,7.0,1 60.0,1.0,4.,130.,206.,0.,2.,132.,1.,2.4,2.,2.,7.,1 50.0,0.0,3.0,120.0,219.0,0.0,0.0,158.0,0.0,1.6,2.0,0.0,3.0,0 58.0,0.0,3.0,120.0,340.0,0.0,0.0,172.0,0.0,0.0,1.0,0.0,3.0,0 66.0,0.0,1.0,150.0,226.0,0.0,0.0,114.0,0.0,2.6,3.0,0.0,3.0,0 43.0,1.0,4.0,150.0,247.0,0.0,0.0,171.0,0.0,1.5,1.0,0.0,3.0,0 40.0,1.0,4.0,110.0,167.0,0.0,2.0,114.0,1.0,2.0,2.0,0.0,7.0,1 69.0,0.0,1.0,140.0,239.0,0.0,0.0,151.0,0.0,1.8,1.0,2.0,3.0,0 60.0,1.0,4.0,117.0,230.0,1.0,0.0,160.0,1.0,1.4,1.0,2.0,7.0,1 64.0,1.0,3.0,140.0,335.0,0.0,0.0,158.0,0.0,0.0,1.0,0.0,3.0,1 59.0,1.0,4.0,135.0,234.0,0.0,0.0,161.0,0.0,0.5,2.0,0.0,7.0,0 44.0,1.0,3.0,130.0,233.0,0.0,0.0,179.0,1.0,0.4,1.0,0.0,3.0,0 42.0,1.0,4.0,140.0,226.0,0.0,0.0,178.0,0.0,0.0,1.0,0.0,3.0,0 43.0,1.0,4.0,120.0,177.0,0.0,2.0,120.0,1.0,2.5,2.0,0.0,7.0,1 57.0,1.0,4.0,150.0,276.0,0.0,2.0,112.0,1.0,0.6,2.0,1.0,6.0,1 55.0,1.0,4.0,132.0,353.0,0.0,0.0,132.0,1.0,1.2,2.0,1.0,7.0,1 61.0,1.0,3.0,150.0,243.0,1.0,0.0,137.0,1.0,1.0,2.0,0.0,3.0,0 65.0,0.0,4.0,150.0,225.0,0.0,2.0,114.0,0.0,1.0,2.0,3.0,7.0,1 40.0,1.0,1.0,140.0,199.0,0.0,0.0,178.0,1.0,1.4,1.0,0.0,7.0,0 71.0,0.0,2.0,160.0,302.0,0.0,0.0,162.0,0.0,0.4,1.0,2.0,3.0,0 59.0,1.0,3.0,150.0,212.0,1.0,0.0,157.0,0.0,1.6,1.0,0.0,3.0,0 61.0,0.0,4.0,130.0,330.0,0.0,2.0,169.0,0.0,0.0,1.0,0.0,3.0,1 58.0,1.0,3.0,112.0,230.0,0.0,2.0,165.0,0.0,2.5,2.0,1.0,7.0,1 51.0,1.0,3.0,110.0,175.0,0.0,0.0,123.0,0.0,0.6,1.0,0.0,3.0,0 50.0,1.0,4.0,150.0,243.0,0.0,2.0,128.0,0.0,2.6,2.0,0.0,7.0,1 65.0,0.0,3.0,140.0,417.0,1.0,2.0,157.0,0.0,0.8,1.0,1.0,3.0,0 53.0,1.0,3.0,130.0,197.0,1.0,2.0,152.0,0.0,1.2,3.0,0.0,3.0,0 41.0,0.0,2.0,105.0,198.0,0.0,0.0,168.0,0.0,0.0,1.0,1.0,3.0,0 65.0,1.0,4.0,120.0,177.0,0.0,0.0,140.0,0.0,0.4,1.0,0.0,7.0,0