In the tutorial, we learn how to built ANN using Sklearn python library. For this example, we classify input data as an array of feature into the Male class or Female class. From the previous information you must have figured it out then we need data for Male class and Female class. For simplicity, let’s assume that we have 1000 (N) data for Males and 1000 data set for Females and for each data we have Age, Weight and their income. For this example we don’t have real data so, we will generate data (X) before we proceed for actual ANN model. Along with that, we need a numerical class tag for each data (y). Now, we have data we will use MLPClassifier to build our classification model.
The most important 3 lines in this tutorial are:
1. from sklearn.neural_network import MLPClassifier 2. clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(100, 20), random_state= 1) 3. clf.fit(X, y)
All you need have is these three lines! That’s it.
from sklearn.neural_network import MLPClassifier
For this step, we need a multi-dimensional array (n x m), where n is the number of dataset and m is the number of feature per data.
Male = [ ] Female = [ ]
For this example, we will generate 1000(N) random dataset for each class(sex). For each data (row) we have the value of age, weight and income column-wise.
Male dataset
import random N = 1000 Male_age = [40,45] Male_wgt = [70,100] Male_inc = [6,10] Male_age = [random.randint(Male_age[0],Male_age[1]) for x in xrange(N)] Male_wgt = [random.randint(Male_wgt[0],Male_wgt[1]) for x in xrange(N)] Male_inc = [random.randint(Male_inc[0],Male_inc[1]) for x in xrange(N)] Male_class = [1 for i in xrange(N)] ## 0 for class Male Male_class
for i in xrange(N): Male.append([j*1. for j in [Male_age[i],Male_wgt[i],Male_inc[i]]]) print Male
[[41.0, 94.0, 9.0], [45.0, 94.0, 8.0], [42.0, 70.0, 10.0], [40.0, 93.0, 7.0], [45.0, 80.0, 7.0], [42.0, 92.0, 8.0], [41.0, 93.0, 8.0], [40.0, 90.0, 8.0], [44.0, 99.0, 8.0], [42.0, 89.0, 6.0], [44.0, 87.0, 9.0], [43.0, 88.0, 6.0], [42.0, 81.0, 8.0], [43.0, 81.0, 9.0], [40.0, 84.0, 6.0], [44.0, 72.0, 7.0], [41.0, 76.0, 6.0], [44.0, 97.0, 7.0], [45.0, 99.0, 8.0], [43.0, 100.0, 7.0], [40.0, 91.0, 8.0], [42.0, 96.0, 6.0], [40.0, 93.0, 7.0], [45.0, 86.0, 10.0], [44.0, 95.0, 9.0], [41.0, 73.0, 6.0], [40.0, 100.0, 7.0], [41.0, 86.0, 8.0], [42.0, 75.0, 10.0], [41.0, 82.0, 10.0], [43.0, 89.0, 8.0], [43.0, 85.0, 9.0], [45.0, 94.0, 6.0], [43.0, 71.0, 7.0], [42.0, 73.0, 7.0], [41.0, 74.0, 8.0], [45.0, 71.0, 10.0], [40.0, 86.0, 6.0], [44.0, 90.0, 8.0], [45.0, 95.0, 10.0], [43.0, 84.0, 10.0], [41.0, 96.0, 6.0], [42.0, 70.0, 9.0], [44.0, 86.0, 9.0], [44.0, 72.0, 9.0], [42.0, 78.0, 7.0], [42.0, 100.0, 10.0], [45.0, 76.0, 9.0], [43.0, 90.0, 6.0], [40.0, 95.0, 6.0], [43.0, 92.0, 9.0], [44.0, 92.0, 8.0], 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7.0], [45.0, 84.0, 7.0], [41.0, 88.0, 8.0], [45.0, 81.0, 8.0], [42.0, 99.0, 10.0], [41.0, 89.0, 10.0], [44.0, 85.0, 6.0], [45.0, 84.0, 7.0], [43.0, 84.0, 10.0], [41.0, 95.0, 6.0], [41.0, 78.0, 7.0], [42.0, 83.0, 10.0], [43.0, 84.0, 9.0], [43.0, 92.0, 7.0], [41.0, 80.0, 6.0], [40.0, 77.0, 6.0], [44.0, 82.0, 9.0], [44.0, 91.0, 7.0], [40.0, 85.0, 10.0], [40.0, 90.0, 6.0], [44.0, 95.0, 9.0], [43.0, 75.0, 8.0], [43.0, 82.0, 6.0], [40.0, 78.0, 6.0], [44.0, 72.0, 6.0], [43.0, 82.0, 7.0], [43.0, 77.0, 7.0], [45.0, 79.0, 8.0], [41.0, 90.0, 7.0], [44.0, 76.0, 6.0], [43.0, 71.0, 7.0], [43.0, 75.0, 10.0], [42.0, 88.0, 9.0], [41.0, 91.0, 6.0], [45.0, 72.0, 6.0], [40.0, 75.0, 8.0], [40.0, 97.0, 6.0], [40.0, 75.0, 8.0], [41.0, 95.0, 6.0], [40.0, 73.0, 8.0], [42.0, 81.0, 7.0], [45.0, 91.0, 10.0], [42.0, 91.0, 7.0], [44.0, 76.0, 9.0], [40.0, 98.0, 6.0], [41.0, 83.0, 10.0], [40.0, 70.0, 8.0], [42.0, 94.0, 6.0], [44.0, 97.0, 9.0], [45.0, 74.0, 10.0], [45.0, 97.0, 7.0], [42.0, 99.0, 9.0], [44.0, 89.0, 6.0], [45.0, 87.0, 10.0], [45.0, 91.0, 8.0], [40.0, 90.0, 9.0], [42.0, 89.0, 10.0], [44.0, 97.0, 8.0], [44.0, 96.0, 10.0], [43.0, 79.0, 9.0], [44.0, 70.0, 6.0], [44.0, 79.0, 8.0], [43.0, 94.0, 6.0], [41.0, 89.0, 9.0], [44.0, 96.0, 10.0], [44.0, 92.0, 7.0], [42.0, 98.0, 7.0], [42.0, 77.0, 7.0], [44.0, 84.0, 6.0], [43.0, 95.0, 6.0], [41.0, 88.0, 9.0], [45.0, 95.0, 10.0], [41.0, 84.0, 10.0], [45.0, 74.0, 10.0], [43.0, 90.0, 7.0], [40.0, 82.0, 6.0], [43.0, 84.0, 9.0], [42.0, 97.0, 10.0], [41.0, 91.0, 6.0], [40.0, 86.0, 9.0], [45.0, 71.0, 9.0], [42.0, 94.0, 10.0], [43.0, 72.0, 10.0], [40.0, 83.0, 8.0], [44.0, 87.0, 7.0], [45.0, 80.0, 10.0], [41.0, 83.0, 8.0], [40.0, 82.0, 9.0], [42.0, 80.0, 6.0], [41.0, 72.0, 7.0], [44.0, 74.0, 8.0], [44.0, 88.0, 8.0], [40.0, 83.0, 10.0], [43.0, 76.0, 7.0], [40.0, 98.0, 6.0], [42.0, 92.0, 10.0], [41.0, 99.0, 10.0], [40.0, 82.0, 10.0], [42.0, 88.0, 7.0], [42.0, 84.0, 9.0], [43.0, 79.0, 10.0], [43.0, 94.0, 7.0], [43.0, 86.0, 7.0], [42.0, 91.0, 8.0], [45.0, 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[41.0, 85.0, 7.0], [41.0, 73.0, 7.0], [40.0, 80.0, 6.0], [41.0, 84.0, 6.0], [45.0, 96.0, 8.0], [41.0, 76.0, 8.0], [40.0, 71.0, 9.0], [41.0, 89.0, 7.0], [45.0, 97.0, 6.0], [40.0, 80.0, 10.0], [42.0, 83.0, 9.0], [42.0, 78.0, 9.0], [45.0, 78.0, 9.0], [40.0, 77.0, 7.0], [41.0, 90.0, 7.0], [41.0, 82.0, 7.0], [43.0, 85.0, 8.0], [45.0, 93.0, 8.0], [40.0, 88.0, 8.0], [41.0, 77.0, 10.0], [42.0, 93.0, 9.0], [42.0, 85.0, 7.0], [44.0, 96.0, 10.0], [43.0, 76.0, 7.0], [40.0, 75.0, 10.0], [45.0, 88.0, 6.0], [45.0, 80.0, 10.0], [44.0, 86.0, 7.0], [45.0, 97.0, 6.0], [44.0, 72.0, 6.0], [41.0, 85.0, 6.0], [41.0, 78.0, 7.0], [45.0, 82.0, 6.0], [40.0, 81.0, 6.0], [44.0, 80.0, 9.0], [41.0, 99.0, 6.0], [41.0, 90.0, 9.0], [40.0, 76.0, 10.0], [40.0, 100.0, 7.0], [42.0, 94.0, 7.0], [42.0, 81.0, 9.0], [43.0, 70.0, 8.0], [44.0, 81.0, 10.0], [45.0, 98.0, 10.0], [45.0, 80.0, 8.0], [42.0, 82.0, 10.0], [45.0, 84.0, 9.0], [42.0, 87.0, 10.0], [45.0, 81.0, 6.0], [41.0, 73.0, 8.0], [41.0, 82.0, 8.0], [42.0, 99.0, 7.0], [42.0, 96.0, 7.0], [43.0, 75.0, 7.0], [41.0, 94.0, 8.0], [40.0, 89.0, 10.0], [44.0, 91.0, 9.0], [41.0, 88.0, 9.0], [41.0, 72.0, 6.0], [43.0, 88.0, 6.0], [43.0, 88.0, 6.0], [40.0, 83.0, 6.0], [41.0, 72.0, 7.0], [41.0, 82.0, 8.0], [43.0, 92.0, 6.0], [40.0, 91.0, 8.0], [43.0, 81.0, 9.0], [42.0, 77.0, 9.0], [44.0, 76.0, 6.0], [45.0, 80.0, 7.0], [40.0, 83.0, 8.0], [45.0, 92.0, 6.0], [43.0, 99.0, 8.0], [43.0, 90.0, 8.0], [43.0, 96.0, 9.0], [40.0, 76.0, 8.0], [40.0, 98.0, 8.0], [41.0, 96.0, 6.0], [45.0, 85.0, 6.0], [41.0, 80.0, 7.0], [44.0, 85.0, 6.0], [43.0, 70.0, 10.0], [42.0, 88.0, 8.0], [40.0, 82.0, 7.0], [41.0, 95.0, 6.0], [45.0, 70.0, 9.0], [41.0, 91.0, 6.0], [41.0, 95.0, 10.0], [43.0, 88.0, 9.0], [44.0, 73.0, 6.0], [42.0, 70.0, 9.0], [42.0, 99.0, 10.0], [45.0, 77.0, 10.0], [43.0, 98.0, 9.0], [45.0, 78.0, 9.0], [42.0, 82.0, 9.0], [45.0, 90.0, 7.0], [40.0, 70.0, 7.0], [44.0, 83.0, 10.0], [41.0, 80.0, 7.0], [45.0, 74.0, 7.0], [42.0, 93.0, 8.0], [40.0, 94.0, 8.0], [43.0, 74.0, 7.0], [45.0, 96.0, 8.0], [41.0, 93.0, 8.0], [44.0, 86.0, 8.0], [43.0, 94.0, 6.0], [44.0, 95.0, 6.0], [40.0, 99.0, 10.0], [41.0, 84.0, 7.0], [43.0, 83.0, 8.0], [42.0, 71.0, 9.0], [43.0, 76.0, 8.0], [41.0, 96.0, 6.0], [42.0, 95.0, 9.0], [41.0, 81.0, 8.0], [43.0, 94.0, 9.0], [40.0, 93.0, 8.0], [40.0, 80.0, 8.0], [40.0, 86.0, 9.0], [44.0, 88.0, 6.0], [43.0, 83.0, 9.0], [40.0, 96.0, 7.0], [40.0, 83.0, 7.0], [42.0, 93.0, 9.0], [45.0, 72.0, 10.0], [43.0, 85.0, 7.0], [42.0, 80.0, 6.0], [41.0, 97.0, 6.0], [44.0, 96.0, 9.0], [44.0, 71.0, 10.0], [40.0, 72.0, 10.0], [43.0, 85.0, 10.0], [43.0, 70.0, 8.0], [43.0, 89.0, 10.0], [43.0, 83.0, 6.0], [45.0, 72.0, 6.0], [40.0, 70.0, 9.0], [40.0, 73.0, 6.0], [44.0, 89.0, 9.0], [41.0, 97.0, 9.0], [44.0, 87.0, 9.0], [40.0, 82.0, 10.0], [43.0, 75.0, 9.0], [44.0, 88.0, 7.0], [42.0, 74.0, 10.0], [45.0, 78.0, 10.0], [42.0, 94.0, 6.0], [43.0, 88.0, 9.0], [42.0, 99.0, 6.0], [44.0, 86.0, 10.0], [41.0, 70.0, 8.0], [44.0, 89.0, 8.0], [44.0, 76.0, 7.0], [45.0, 93.0, 6.0], [43.0, 95.0, 6.0], [44.0, 76.0, 10.0], [40.0, 79.0, 7.0], [45.0, 87.0, 7.0], [45.0, 92.0, 7.0], [41.0, 100.0, 10.0], [42.0, 88.0, 8.0], [42.0, 77.0, 7.0], [42.0, 92.0, 8.0], [45.0, 80.0, 10.0], [45.0, 70.0, 9.0], [41.0, 84.0, 10.0], [43.0, 84.0, 8.0], [44.0, 99.0, 8.0], [44.0, 97.0, 7.0], [41.0, 84.0, 6.0], [44.0, 86.0, 10.0], [41.0, 100.0, 6.0], [41.0, 77.0, 6.0], [45.0, 95.0, 9.0], [45.0, 93.0, 6.0], [41.0, 92.0, 6.0], [40.0, 94.0, 9.0], [43.0, 97.0, 7.0], [44.0, 73.0, 9.0], [41.0, 83.0, 7.0], [40.0, 88.0, 6.0], [44.0, 85.0, 8.0], [41.0, 78.0, 7.0], [42.0, 77.0, 9.0], [40.0, 96.0, 10.0], [44.0, 96.0, 7.0], [40.0, 92.0, 8.0], [43.0, 72.0, 6.0], [45.0, 84.0, 8.0], [43.0, 73.0, 9.0], [42.0, 96.0, 8.0], [41.0, 84.0, 6.0], [41.0, 100.0, 8.0], [45.0, 82.0, 6.0], [41.0, 100.0, 10.0], [45.0, 79.0, 7.0], [44.0, 79.0, 7.0], [44.0, 99.0, 7.0], [42.0, 73.0, 9.0], [42.0, 71.0, 7.0], [44.0, 71.0, 10.0], [44.0, 86.0, 8.0], [41.0, 76.0, 10.0], [44.0, 93.0, 8.0], [42.0, 70.0, 6.0], [42.0, 100.0, 9.0], [45.0, 77.0, 7.0], [41.0, 74.0, 10.0], [44.0, 84.0, 8.0], [44.0, 87.0, 8.0], [40.0, 85.0, 8.0], [43.0, 89.0, 8.0], [40.0, 84.0, 6.0], [45.0, 78.0, 9.0], [44.0, 77.0, 6.0], [44.0, 80.0, 7.0], [40.0, 76.0, 8.0], [42.0, 90.0, 6.0], [41.0, 72.0, 10.0]]
Female dataset
Female_age = [20,25]
Female_wgt = [50,60]
Female_inc = [2,6]
Female_age = [random.randint(Female_age[0],Female_age[1]) for x in xrange(N)]
Female_wgt = [random.randint(Female_wgt[0],Female_wgt[1]) for x in xrange(N)]
Female_inc = [random.randint(Female_inc[0],Female_inc[1]) for x in xrange(N)]
for i in xrange(N): Female.append([j*1. for j in [Female_age[i],Female_wgt[i],Female_inc[i]]]) Female_class = [0 for i in xrange(N)] ## 0 for class Female print Female [[24.0, 50.0, 3.0], [21.0, 56.0, 5.0], [25.0, 51.0, 3.0], [23.0, 53.0, 2.0], [23.0, 50.0, 4.0], [23.0, 55.0, 2.0], [22.0, 56.0, 5.0], [22.0, 56.0, 4.0], [23.0, 50.0, 4.0], [20.0, 57.0, 4.0], [21.0, 57.0, 2.0], [22.0, 60.0, 2.0], [20.0, 52.0, 2.0], [25.0, 50.0, 6.0], [25.0, 54.0, 6.0], [22.0, 58.0, 5.0], [23.0, 60.0, 5.0], [22.0, 53.0, 4.0], [21.0, 58.0, 2.0], [20.0, 57.0, 5.0], [24.0, 60.0, 4.0], [22.0, 59.0, 6.0], [20.0, 52.0, 4.0], [22.0, 57.0, 3.0], [20.0, 54.0, 4.0], [23.0, 56.0, 5.0], [21.0, 55.0, 6.0], [21.0, 53.0, 3.0], [23.0, 59.0, 6.0], [25.0, 57.0, 4.0], [23.0, 57.0, 4.0], [22.0, 55.0, 4.0], [20.0, 51.0, 2.0], [24.0, 56.0, 5.0], [23.0, 57.0, 4.0], [24.0, 55.0, 4.0], [22.0, 51.0, 4.0], [20.0, 58.0, 6.0], [23.0, 50.0, 5.0], [23.0, 52.0, 3.0], [23.0, 58.0, 5.0], [23.0, 57.0, 3.0], [21.0, 56.0, 3.0], [21.0, 54.0, 5.0], [20.0, 58.0, 3.0], [25.0, 60.0, 4.0], [23.0, 51.0, 5.0], [21.0, 57.0, 6.0], [25.0, 58.0, 2.0], [21.0, 51.0, 4.0], [24.0, 51.0, 6.0], [22.0, 54.0, 2.0], [21.0, 56.0, 6.0], [21.0, 60.0, 4.0], [23.0, 52.0, 4.0], [23.0, 57.0, 6.0], [22.0, 52.0, 2.0], [24.0, 56.0, 6.0], [21.0, 50.0, 2.0], [24.0, 51.0, 4.0], [25.0, 51.0, 3.0], [24.0, 50.0, 5.0], [24.0, 51.0, 4.0], [22.0, 50.0, 6.0], [23.0, 58.0, 5.0], [21.0, 58.0, 3.0], [20.0, 60.0, 3.0], [21.0, 56.0, 6.0], [24.0, 59.0, 6.0], [25.0, 54.0, 4.0], [25.0, 53.0, 4.0], [25.0, 53.0, 6.0], [20.0, 51.0, 2.0], [22.0, 59.0, 6.0], [23.0, 50.0, 5.0], [21.0, 53.0, 6.0], [22.0, 57.0, 6.0], [21.0, 59.0, 2.0], [22.0, 59.0, 3.0], [24.0, 50.0, 3.0], [22.0, 55.0, 2.0], [25.0, 57.0, 2.0], [25.0, 58.0, 5.0], [21.0, 60.0, 3.0], [25.0, 58.0, 3.0], [22.0, 50.0, 6.0], [20.0, 56.0, 4.0], [21.0, 54.0, 6.0], [24.0, 51.0, 3.0], [25.0, 55.0, 6.0], [21.0, 53.0, 4.0], [23.0, 52.0, 2.0], [23.0, 59.0, 2.0], [20.0, 59.0, 6.0], [23.0, 52.0, 6.0], [20.0, 60.0, 6.0], [20.0, 51.0, 5.0], [23.0, 54.0, 3.0], [21.0, 54.0, 4.0], [24.0, 52.0, 6.0], [20.0, 59.0, 3.0], [21.0, 55.0, 6.0], [22.0, 59.0, 3.0], [20.0, 58.0, 6.0], [20.0, 58.0, 2.0], [24.0, 55.0, 2.0], [21.0, 57.0, 6.0], [21.0, 55.0, 5.0], [21.0, 52.0, 4.0], [24.0, 52.0, 5.0], [20.0, 56.0, 4.0], [21.0, 56.0, 2.0], [22.0, 51.0, 2.0], [22.0, 59.0, 3.0], [25.0, 52.0, 3.0], [23.0, 59.0, 4.0], [22.0, 58.0, 6.0], [24.0, 55.0, 6.0], [25.0, 60.0, 5.0], [23.0, 51.0, 4.0], [23.0, 54.0, 3.0], [21.0, 51.0, 4.0], [22.0, 53.0, 5.0], [23.0, 50.0, 6.0], [25.0, 56.0, 3.0], [22.0, 56.0, 3.0], [23.0, 59.0, 4.0], [25.0, 52.0, 5.0], [24.0, 54.0, 4.0], [24.0, 58.0, 6.0], [21.0, 53.0, 6.0], [24.0, 58.0, 6.0], [22.0, 50.0, 3.0], [20.0, 56.0, 2.0], [21.0, 60.0, 6.0], [25.0, 50.0, 5.0], [22.0, 53.0, 3.0], [25.0, 51.0, 5.0], [25.0, 58.0, 5.0], [21.0, 60.0, 3.0], [25.0, 54.0, 6.0], [21.0, 53.0, 5.0], [23.0, 53.0, 4.0], [23.0, 55.0, 3.0], [21.0, 58.0, 5.0], [23.0, 52.0, 5.0], [23.0, 54.0, 2.0], [24.0, 51.0, 6.0], [25.0, 59.0, 2.0], [20.0, 51.0, 3.0], [25.0, 52.0, 5.0], [21.0, 55.0, 3.0], [21.0, 55.0, 2.0], [20.0, 60.0, 3.0], [21.0, 56.0, 5.0], [23.0, 56.0, 3.0], [25.0, 52.0, 2.0], [24.0, 60.0, 4.0], [24.0, 51.0, 5.0], [24.0, 56.0, 5.0], [23.0, 52.0, 5.0], [25.0, 56.0, 2.0], [25.0, 60.0, 4.0], [20.0, 53.0, 6.0], [25.0, 59.0, 2.0], [25.0, 52.0, 3.0], [24.0, 56.0, 4.0], [25.0, 51.0, 6.0], [21.0, 58.0, 6.0], [25.0, 50.0, 3.0], [23.0, 59.0, 3.0], [21.0, 57.0, 5.0], [25.0, 60.0, 2.0], [25.0, 53.0, 4.0], [25.0, 57.0, 5.0], [21.0, 53.0, 3.0], [23.0, 57.0, 3.0], [20.0, 56.0, 5.0], [25.0, 52.0, 4.0], [21.0, 55.0, 6.0], [24.0, 60.0, 5.0], [23.0, 56.0, 2.0], [20.0, 55.0, 2.0], [24.0, 58.0, 4.0], [23.0, 58.0, 2.0], [22.0, 58.0, 3.0], [25.0, 53.0, 5.0], [24.0, 58.0, 3.0], [20.0, 56.0, 3.0], [23.0, 50.0, 3.0], [25.0, 51.0, 2.0], [22.0, 53.0, 6.0], [22.0, 51.0, 3.0], [21.0, 53.0, 4.0], [23.0, 51.0, 2.0], [21.0, 60.0, 4.0], [20.0, 50.0, 5.0], [23.0, 53.0, 3.0], [21.0, 55.0, 6.0], [23.0, 52.0, 4.0], [20.0, 58.0, 3.0], [21.0, 57.0, 4.0], [21.0, 53.0, 2.0], [20.0, 53.0, 6.0], [25.0, 52.0, 2.0], [25.0, 57.0, 3.0], [22.0, 60.0, 6.0], [23.0, 59.0, 3.0], [22.0, 51.0, 3.0], [25.0, 59.0, 6.0], [20.0, 58.0, 3.0], [22.0, 59.0, 5.0], [23.0, 53.0, 5.0], [24.0, 58.0, 2.0], [21.0, 50.0, 6.0], [20.0, 52.0, 2.0], [25.0, 60.0, 3.0], [22.0, 60.0, 3.0], [21.0, 57.0, 3.0], [25.0, 59.0, 6.0], [24.0, 53.0, 3.0], [21.0, 58.0, 2.0], [21.0, 57.0, 4.0], [22.0, 52.0, 4.0], [20.0, 50.0, 4.0], [21.0, 54.0, 5.0], [25.0, 58.0, 6.0], [22.0, 53.0, 2.0], [21.0, 56.0, 6.0], [24.0, 59.0, 5.0], [21.0, 60.0, 4.0], [25.0, 60.0, 3.0], [20.0, 54.0, 4.0], [24.0, 57.0, 5.0], [24.0, 60.0, 6.0], [23.0, 55.0, 4.0], [22.0, 60.0, 6.0], [23.0, 60.0, 5.0], [24.0, 57.0, 5.0], [25.0, 59.0, 5.0], [22.0, 55.0, 5.0], [21.0, 59.0, 4.0], [25.0, 60.0, 5.0], [21.0, 51.0, 5.0], [25.0, 51.0, 2.0], [25.0, 54.0, 2.0], [23.0, 59.0, 5.0], [21.0, 59.0, 4.0], [21.0, 51.0, 5.0], [21.0, 57.0, 6.0], [20.0, 54.0, 5.0], [23.0, 56.0, 2.0], [22.0, 52.0, 2.0], [20.0, 59.0, 2.0], [21.0, 57.0, 4.0], [25.0, 57.0, 4.0], [20.0, 54.0, 3.0], [24.0, 58.0, 5.0], [24.0, 60.0, 4.0], [24.0, 50.0, 6.0], [21.0, 56.0, 4.0], [25.0, 55.0, 5.0], [23.0, 52.0, 4.0], [24.0, 51.0, 5.0], [21.0, 59.0, 3.0], [21.0, 60.0, 6.0], [22.0, 56.0, 4.0], [23.0, 58.0, 4.0], [25.0, 59.0, 5.0], [22.0, 56.0, 6.0], [24.0, 51.0, 3.0], [24.0, 51.0, 3.0], [23.0, 60.0, 5.0], [24.0, 60.0, 5.0], [25.0, 57.0, 3.0], [22.0, 50.0, 4.0], [24.0, 53.0, 2.0], [25.0, 58.0, 4.0], [22.0, 50.0, 3.0], [20.0, 57.0, 5.0], [20.0, 53.0, 5.0], [24.0, 59.0, 5.0], [23.0, 58.0, 6.0], [22.0, 57.0, 6.0], [24.0, 55.0, 3.0], [20.0, 51.0, 4.0], [23.0, 53.0, 3.0], [21.0, 58.0, 2.0], [23.0, 58.0, 6.0], [21.0, 58.0, 5.0], [21.0, 59.0, 5.0], [23.0, 60.0, 6.0], [25.0, 54.0, 5.0], [22.0, 60.0, 6.0], [24.0, 57.0, 2.0], [23.0, 56.0, 6.0], [22.0, 51.0, 6.0], [25.0, 51.0, 4.0], [22.0, 53.0, 4.0], [21.0, 60.0, 5.0], [23.0, 60.0, 6.0], [20.0, 58.0, 2.0], [24.0, 52.0, 6.0], [22.0, 52.0, 6.0], [22.0, 58.0, 3.0], [21.0, 52.0, 2.0], [24.0, 56.0, 4.0], [20.0, 56.0, 3.0], [24.0, 52.0, 2.0], [22.0, 50.0, 6.0], [24.0, 57.0, 4.0], [23.0, 58.0, 3.0], [22.0, 53.0, 3.0], [25.0, 54.0, 5.0], [24.0, 54.0, 2.0], [21.0, 53.0, 4.0], [20.0, 55.0, 5.0], [24.0, 59.0, 2.0], [25.0, 60.0, 4.0], [25.0, 59.0, 4.0], [23.0, 52.0, 2.0], [25.0, 51.0, 5.0], [23.0, 60.0, 4.0], [24.0, 56.0, 2.0], [24.0, 52.0, 2.0], [25.0, 58.0, 5.0], [25.0, 50.0, 6.0], [24.0, 52.0, 4.0], [20.0, 50.0, 4.0], [25.0, 52.0, 5.0], [25.0, 60.0, 3.0], [20.0, 51.0, 4.0], [24.0, 54.0, 5.0], [21.0, 53.0, 5.0], [25.0, 54.0, 3.0], [23.0, 53.0, 5.0], [25.0, 59.0, 2.0], [20.0, 51.0, 2.0], [21.0, 52.0, 6.0], [21.0, 59.0, 6.0], [20.0, 50.0, 3.0], [23.0, 52.0, 2.0], [22.0, 53.0, 3.0], [22.0, 56.0, 2.0], [24.0, 52.0, 4.0], [20.0, 52.0, 6.0], [22.0, 50.0, 3.0], [22.0, 52.0, 2.0], [24.0, 50.0, 3.0], [20.0, 53.0, 4.0], [20.0, 52.0, 3.0], [24.0, 56.0, 4.0], [21.0, 57.0, 3.0], [20.0, 53.0, 5.0], [20.0, 56.0, 5.0], [20.0, 51.0, 2.0], [25.0, 58.0, 6.0], [20.0, 54.0, 3.0], [24.0, 54.0, 2.0], [23.0, 57.0, 3.0], [22.0, 52.0, 3.0], [21.0, 52.0, 6.0], [24.0, 55.0, 2.0], [20.0, 55.0, 4.0], [24.0, 52.0, 3.0], [21.0, 59.0, 3.0], [20.0, 51.0, 6.0], [25.0, 56.0, 2.0], [24.0, 59.0, 2.0], [23.0, 57.0, 2.0], [20.0, 51.0, 2.0], [21.0, 55.0, 3.0], [23.0, 56.0, 5.0], [22.0, 53.0, 2.0], [21.0, 58.0, 5.0], [25.0, 52.0, 2.0], [22.0, 59.0, 5.0], [23.0, 50.0, 3.0], [22.0, 54.0, 4.0], [23.0, 51.0, 3.0], [21.0, 50.0, 6.0], [20.0, 60.0, 6.0], [24.0, 57.0, 2.0], [23.0, 55.0, 4.0], [20.0, 55.0, 5.0], [20.0, 60.0, 3.0], [25.0, 54.0, 2.0], [20.0, 60.0, 5.0], [22.0, 51.0, 2.0], [21.0, 54.0, 3.0], [20.0, 55.0, 2.0], [21.0, 51.0, 5.0], [24.0, 50.0, 3.0], [23.0, 59.0, 4.0], [24.0, 53.0, 6.0], [24.0, 53.0, 3.0], [21.0, 58.0, 3.0], [24.0, 59.0, 2.0], [23.0, 50.0, 4.0], [23.0, 57.0, 4.0], [24.0, 56.0, 6.0], [24.0, 60.0, 3.0], [20.0, 52.0, 5.0], [25.0, 55.0, 4.0], [25.0, 57.0, 2.0], [24.0, 56.0, 6.0], [22.0, 58.0, 3.0], [23.0, 54.0, 5.0], [24.0, 58.0, 5.0], [21.0, 57.0, 2.0], [20.0, 58.0, 5.0], [21.0, 58.0, 2.0], [20.0, 59.0, 5.0], [25.0, 59.0, 5.0], [20.0, 59.0, 4.0], [20.0, 51.0, 3.0], [24.0, 56.0, 6.0], [22.0, 58.0, 5.0], [24.0, 59.0, 2.0], [25.0, 59.0, 4.0], [21.0, 60.0, 6.0], [25.0, 54.0, 5.0], [20.0, 60.0, 6.0], [22.0, 52.0, 6.0], [25.0, 56.0, 4.0], [22.0, 57.0, 5.0], [20.0, 55.0, 5.0], [21.0, 56.0, 3.0], [23.0, 54.0, 5.0], [25.0, 55.0, 4.0], [20.0, 58.0, 3.0], [24.0, 50.0, 3.0], [22.0, 57.0, 6.0], [20.0, 59.0, 3.0], [23.0, 60.0, 3.0], [24.0, 53.0, 2.0], [24.0, 60.0, 5.0], [20.0, 51.0, 6.0], [22.0, 55.0, 6.0], [25.0, 50.0, 2.0], [23.0, 58.0, 3.0], [24.0, 50.0, 2.0], [23.0, 55.0, 3.0], [21.0, 58.0, 4.0], [25.0, 50.0, 5.0], [22.0, 50.0, 3.0], [22.0, 60.0, 5.0], [22.0, 51.0, 2.0], [25.0, 52.0, 6.0], [22.0, 58.0, 4.0], [22.0, 52.0, 3.0], [22.0, 55.0, 3.0], [22.0, 52.0, 6.0], [20.0, 57.0, 4.0], [20.0, 50.0, 6.0], [24.0, 53.0, 4.0], [20.0, 57.0, 2.0], [24.0, 55.0, 4.0], [24.0, 57.0, 3.0], [20.0, 57.0, 4.0], [24.0, 53.0, 6.0], [24.0, 60.0, 6.0], [23.0, 52.0, 3.0], [20.0, 57.0, 2.0], [20.0, 56.0, 6.0], [23.0, 51.0, 6.0], [25.0, 56.0, 6.0], [21.0, 57.0, 3.0], [20.0, 57.0, 4.0], [22.0, 57.0, 3.0], [23.0, 58.0, 2.0], [24.0, 57.0, 5.0], [21.0, 60.0, 4.0], [21.0, 55.0, 4.0], [20.0, 52.0, 6.0], [21.0, 53.0, 4.0], [24.0, 52.0, 6.0], [22.0, 57.0, 6.0], [20.0, 58.0, 6.0], [24.0, 58.0, 3.0], [22.0, 50.0, 5.0], [24.0, 57.0, 2.0], [21.0, 58.0, 4.0], [22.0, 53.0, 2.0], [25.0, 51.0, 2.0], [20.0, 50.0, 6.0], [23.0, 60.0, 6.0], [23.0, 60.0, 5.0], [25.0, 58.0, 6.0], [20.0, 54.0, 4.0], [21.0, 59.0, 3.0], [24.0, 56.0, 4.0], [22.0, 55.0, 2.0], [24.0, 58.0, 6.0], [24.0, 51.0, 2.0], [22.0, 57.0, 5.0], [23.0, 52.0, 5.0], [20.0, 54.0, 4.0], [20.0, 53.0, 3.0], [20.0, 55.0, 6.0], [23.0, 50.0, 3.0], [22.0, 55.0, 4.0], [25.0, 51.0, 5.0], [21.0, 59.0, 2.0], [22.0, 51.0, 2.0], [20.0, 52.0, 3.0], [25.0, 58.0, 4.0], [25.0, 50.0, 3.0], [21.0, 52.0, 2.0], [22.0, 52.0, 5.0], [24.0, 55.0, 2.0], [22.0, 53.0, 3.0], [20.0, 51.0, 2.0], [21.0, 54.0, 4.0], [22.0, 56.0, 6.0], [25.0, 60.0, 2.0], [21.0, 58.0, 3.0], [23.0, 51.0, 6.0], [24.0, 55.0, 2.0], [23.0, 50.0, 4.0], [21.0, 59.0, 4.0], [23.0, 50.0, 3.0], [23.0, 60.0, 5.0], [24.0, 51.0, 5.0], [22.0, 56.0, 4.0], [25.0, 52.0, 4.0], [23.0, 60.0, 3.0], [24.0, 51.0, 2.0], [24.0, 60.0, 6.0], [21.0, 59.0, 2.0], [21.0, 59.0, 3.0], [20.0, 60.0, 3.0], [24.0, 60.0, 2.0], [23.0, 52.0, 5.0], [22.0, 52.0, 3.0], [25.0, 54.0, 2.0], [25.0, 58.0, 5.0], [21.0, 60.0, 4.0], [25.0, 59.0, 5.0], [24.0, 56.0, 3.0], [20.0, 59.0, 2.0], [22.0, 56.0, 5.0], [23.0, 51.0, 3.0], [21.0, 54.0, 6.0], [24.0, 57.0, 5.0], [24.0, 54.0, 5.0], [25.0, 60.0, 2.0], [21.0, 55.0, 5.0], [25.0, 51.0, 6.0], [24.0, 59.0, 6.0], [22.0, 50.0, 2.0], [21.0, 58.0, 2.0], [22.0, 52.0, 3.0], [23.0, 55.0, 6.0], [22.0, 56.0, 5.0], [23.0, 55.0, 4.0], [22.0, 60.0, 4.0], [20.0, 59.0, 4.0], [20.0, 51.0, 6.0], [22.0, 54.0, 6.0], [21.0, 51.0, 2.0], [20.0, 56.0, 3.0], [24.0, 58.0, 4.0], [20.0, 55.0, 6.0], [23.0, 55.0, 4.0], [25.0, 53.0, 6.0], [25.0, 50.0, 6.0], [24.0, 56.0, 6.0], [24.0, 59.0, 2.0], [20.0, 53.0, 2.0], [24.0, 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[21.0, 50.0, 2.0], [23.0, 56.0, 5.0], [23.0, 59.0, 5.0], [20.0, 50.0, 4.0], [20.0, 53.0, 5.0], [22.0, 57.0, 3.0], [22.0, 58.0, 2.0], [21.0, 60.0, 2.0], [20.0, 53.0, 2.0], [24.0, 53.0, 6.0], [21.0, 56.0, 2.0], [25.0, 52.0, 3.0], [21.0, 51.0, 5.0], [24.0, 53.0, 2.0], [22.0, 53.0, 5.0], [25.0, 58.0, 6.0], [20.0, 58.0, 3.0], [25.0, 59.0, 4.0], [20.0, 58.0, 2.0], [23.0, 60.0, 4.0], [20.0, 52.0, 6.0], [20.0, 52.0, 3.0], [20.0, 54.0, 4.0], [24.0, 56.0, 2.0], [25.0, 52.0, 5.0], [21.0, 57.0, 4.0], [20.0, 52.0, 6.0], [21.0, 51.0, 4.0], [24.0, 60.0, 4.0], [24.0, 59.0, 4.0], [20.0, 60.0, 3.0], [20.0, 60.0, 5.0], [24.0, 59.0, 6.0], [23.0, 53.0, 6.0], [22.0, 51.0, 3.0], [21.0, 57.0, 3.0], [20.0, 57.0, 4.0], [25.0, 58.0, 5.0], [20.0, 57.0, 5.0], [25.0, 58.0, 6.0], [23.0, 57.0, 3.0], [23.0, 55.0, 2.0], [23.0, 52.0, 2.0], [25.0, 59.0, 5.0], [23.0, 50.0, 2.0], [20.0, 50.0, 2.0], [25.0, 58.0, 3.0], [24.0, 59.0, 5.0], [21.0, 52.0, 6.0], [24.0, 52.0, 2.0], [23.0, 57.0, 6.0], [23.0, 60.0, 5.0], [21.0, 60.0, 6.0], [23.0, 55.0, 3.0], [24.0, 56.0, 4.0], [23.0, 53.0, 5.0], [24.0, 52.0, 3.0], [21.0, 56.0, 3.0], [23.0, 52.0, 5.0], [24.0, 54.0, 2.0], [21.0, 54.0, 6.0], [24.0, 52.0, 5.0], [22.0, 52.0, 5.0], [20.0, 59.0, 4.0], [23.0, 52.0, 3.0], [22.0, 56.0, 4.0], [21.0, 58.0, 2.0], [23.0, 60.0, 3.0], [25.0, 60.0, 6.0], [24.0, 58.0, 4.0], [23.0, 52.0, 6.0], [25.0, 52.0, 3.0], [22.0, 58.0, 2.0], [21.0, 50.0, 6.0], [20.0, 57.0, 4.0], [24.0, 55.0, 5.0], [24.0, 53.0, 5.0], [20.0, 53.0, 4.0], [20.0, 56.0, 3.0], [24.0, 54.0, 3.0], [23.0, 53.0, 2.0], [25.0, 52.0, 2.0], [22.0, 58.0, 4.0], [22.0, 54.0, 6.0], [24.0, 57.0, 3.0], [25.0, 60.0, 6.0], [25.0, 60.0, 5.0], [25.0, 60.0, 5.0], [23.0, 52.0, 5.0], [25.0, 56.0, 6.0], [22.0, 52.0, 5.0], [23.0, 53.0, 3.0], [23.0, 53.0, 4.0], [25.0, 56.0, 5.0], [21.0, 60.0, 6.0], [23.0, 50.0, 5.0], [22.0, 53.0, 6.0], [22.0, 50.0, 4.0], [24.0, 52.0, 3.0], [24.0, 55.0, 2.0], [22.0, 54.0, 4.0], [21.0, 54.0, 4.0], [24.0, 57.0, 3.0], [24.0, 54.0, 3.0], [25.0, 58.0, 3.0], [24.0, 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[21.0, 57.0, 5.0], [23.0, 52.0, 2.0], [23.0, 52.0, 4.0], [21.0, 57.0, 5.0], [25.0, 51.0, 3.0], [24.0, 56.0, 3.0], [22.0, 55.0, 2.0], [25.0, 50.0, 5.0], [24.0, 54.0, 2.0], [22.0, 51.0, 2.0], [22.0, 56.0, 2.0], [24.0, 51.0, 2.0], [24.0, 57.0, 4.0], [20.0, 51.0, 4.0], [25.0, 53.0, 3.0], [21.0, 56.0, 5.0], [20.0, 54.0, 5.0], [21.0, 53.0, 4.0], [23.0, 59.0, 6.0], [22.0, 52.0, 2.0], [20.0, 55.0, 5.0], [21.0, 52.0, 4.0], [23.0, 56.0, 2.0], [22.0, 50.0, 2.0], [20.0, 52.0, 2.0], [23.0, 53.0, 5.0], [24.0, 53.0, 5.0], [25.0, 60.0, 4.0], [23.0, 52.0, 3.0], [23.0, 50.0, 4.0], [20.0, 59.0, 3.0], [20.0, 59.0, 4.0], [23.0, 59.0, 2.0], [25.0, 59.0, 5.0], [24.0, 58.0, 2.0], [23.0, 60.0, 6.0], [21.0, 58.0, 5.0], [24.0, 60.0, 4.0], [24.0, 52.0, 2.0], [24.0, 50.0, 5.0], [21.0, 51.0, 3.0], [25.0, 54.0, 5.0], [22.0, 56.0, 3.0], [23.0, 53.0, 6.0], [22.0, 51.0, 2.0], [25.0, 58.0, 3.0], [20.0, 53.0, 6.0], [21.0, 50.0, 4.0], [20.0, 60.0, 2.0], [20.0, 53.0, 5.0], [25.0, 51.0, 4.0], [24.0, 54.0, 6.0], [25.0, 56.0, 3.0], [21.0, 55.0, 5.0], [20.0, 51.0, 5.0], [25.0, 51.0, 2.0], [23.0, 58.0, 6.0], [24.0, 52.0, 4.0], [21.0, 60.0, 3.0], [20.0, 53.0, 5.0], [25.0, 57.0, 2.0], [20.0, 60.0, 4.0], [24.0, 51.0, 5.0], [21.0, 52.0, 2.0], [24.0, 54.0, 4.0], [24.0, 57.0, 4.0], [25.0, 54.0, 6.0], [20.0, 58.0, 3.0], [24.0, 58.0, 4.0], [23.0, 50.0, 6.0], [21.0, 50.0, 5.0], [21.0, 52.0, 6.0], [23.0, 59.0, 6.0], [22.0, 60.0, 4.0], [25.0, 58.0, 2.0], [25.0, 60.0, 5.0], [22.0, 55.0, 4.0], [20.0, 56.0, 6.0], [24.0, 50.0, 5.0], [21.0, 50.0, 4.0], [21.0, 60.0, 6.0], [20.0, 51.0, 3.0], [23.0, 56.0, 4.0], [24.0, 53.0, 2.0], [23.0, 51.0, 5.0], [22.0, 55.0, 2.0], [22.0, 52.0, 4.0], [20.0, 57.0, 6.0], [22.0, 53.0, 6.0], [23.0, 51.0, 6.0], [24.0, 50.0, 5.0], [21.0, 53.0, 3.0], [22.0, 60.0, 3.0], [25.0, 55.0, 6.0], [22.0, 57.0, 2.0], [20.0, 52.0, 2.0], [24.0, 53.0, 3.0], [20.0, 53.0, 2.0], [20.0, 55.0, 6.0], [20.0, 56.0, 4.0], [22.0, 60.0, 5.0], [20.0, 53.0, 6.0], [24.0, 54.0, 6.0], [24.0, 50.0, 2.0], [23.0, 51.0, 5.0], [22.0, 57.0, 6.0], [22.0, 56.0, 5.0], [22.0, 60.0, 2.0], [20.0, 59.0, 2.0], [21.0, 54.0, 6.0], [22.0, 55.0, 4.0], [24.0, 50.0, 5.0], [20.0, 59.0, 4.0], [23.0, 59.0, 4.0], [22.0, 53.0, 2.0], [22.0, 53.0, 6.0], [21.0, 59.0, 3.0], [20.0, 59.0, 4.0], [23.0, 50.0, 2.0], [20.0, 52.0, 3.0], [21.0, 52.0, 6.0], [23.0, 51.0, 3.0], [20.0, 58.0, 5.0], [22.0, 56.0, 4.0], [20.0, 56.0, 5.0], [20.0, 51.0, 5.0], [22.0, 52.0, 4.0], [24.0, 54.0, 4.0], [22.0, 56.0, 3.0], [24.0, 53.0, 2.0], [22.0, 58.0, 4.0], [25.0, 55.0, 4.0], [23.0, 50.0, 3.0], [20.0, 51.0, 5.0], [23.0, 58.0, 5.0], [25.0, 59.0, 2.0], [20.0, 55.0, 2.0], [21.0, 60.0, 2.0], [20.0, 56.0, 6.0], [20.0, 58.0, 3.0], [21.0, 54.0, 5.0], [22.0, 59.0, 4.0], [25.0, 60.0, 3.0], [23.0, 54.0, 4.0], [22.0, 53.0, 6.0], [20.0, 58.0, 3.0], [22.0, 55.0, 4.0], [21.0, 59.0, 6.0], [21.0, 52.0, 3.0], [25.0, 57.0, 3.0], [25.0, 59.0, 6.0], [23.0, 51.0, 4.0], [22.0, 60.0, 6.0], [25.0, 55.0, 3.0], [20.0, 50.0, 2.0], [21.0, 53.0, 5.0], [20.0, 57.0, 5.0], [22.0, 50.0, 4.0], [20.0, 51.0, 4.0], [21.0, 55.0, 3.0], [21.0, 51.0, 3.0], [21.0, 50.0, 6.0], [20.0, 51.0, 2.0], [22.0, 51.0, 6.0], [23.0, 59.0, 5.0], [23.0, 50.0, 5.0], [20.0, 57.0, 2.0], [24.0, 60.0, 5.0], [23.0, 56.0, 6.0], [20.0, 53.0, 2.0], [24.0, 58.0, 4.0], [20.0, 55.0, 2.0], [22.0, 57.0, 6.0], [22.0, 50.0, 5.0], [20.0, 54.0, 3.0], [23.0, 54.0, 6.0], [23.0, 54.0, 5.0], [25.0, 51.0, 2.0], [25.0, 55.0, 5.0], [20.0, 59.0, 3.0], [25.0, 52.0, 6.0], [20.0, 58.0, 6.0], [25.0, 57.0, 4.0], [20.0, 57.0, 4.0], [24.0, 59.0, 6.0], [25.0, 59.0, 3.0], [23.0, 54.0, 5.0], [22.0, 57.0, 3.0], [20.0, 52.0, 5.0], [21.0, 55.0, 2.0], [22.0, 51.0, 4.0], [21.0, 58.0, 4.0], [24.0, 50.0, 5.0], [21.0, 52.0, 6.0], [25.0, 59.0, 4.0], [25.0, 56.0, 2.0], [23.0, 60.0, 6.0], [25.0, 57.0, 2.0], [24.0, 51.0, 4.0], [25.0, 50.0, 2.0], [23.0, 51.0, 6.0], [21.0, 57.0, 5.0], [22.0, 52.0, 4.0], [24.0, 52.0, 3.0], [20.0, 60.0, 6.0], [25.0, 52.0, 4.0], [24.0, 53.0, 2.0], [22.0, 52.0, 6.0], [20.0, 51.0, 6.0], [25.0, 53.0, 4.0], [21.0, 50.0, 5.0], [21.0, 51.0, 3.0], [24.0, 51.0, 5.0], [24.0, 60.0, 3.0], [20.0, 57.0, 5.0], [21.0, 52.0, 3.0], [22.0, 55.0, 3.0], [25.0, 52.0, 2.0], [22.0, 59.0, 3.0]]
Now merge both Male and female in one array as X.
X = Male + Female X[10]
[44.0, 87.0, 9.0]
Create an array of the class label for each data set.
y = Male_class+Female_class print y
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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0, 0]
Call MLPClassifier and pass X and y to train model.
clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(100, 20), random_state= 1) #print(clf) clf.fit(X, y)
MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08, hidden_layer_sizes=(100, 20), learning_rate='constant', learning_rate_init=0.001, max_iter=200, momentum=0.9, nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False, warm_start=False)
def predict(x):
result = clf.predict(x)
print result,
if result[0]:return “Male”
return “Female”
Test model for two diffrent data.
Male[ age = 60 year; Weight = 80 Kg ; Income = 8 k]
Female[ age = 25 year; Weight = 50 Kg ; Income = 5 k]
print predict([[60.0, 80.0, 8.0]]) print predict([[25.0, 50.0, 5.0]])
[1] Male [0] Female
In this example we generate random data but, one can train the model using their own data. Just read data and store in the list of list or use numpy array.
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I am Nilesh Kumar, a graduate student at the Department of Biology, UAB under the mentorship of Dr. Shahid Mukhtar. I joined UAB in Spring 2018 and working on Network Biology. My research interests are Network modeling, Mathematical modeling, Game theory, Artificial Intelligence and their application in Systems Biology.
I graduated with master’s degree “Master of Technology, Information Technology (Specialization in Bioinformatics)” in 2015 from Indian Institute of Information Technology Allahabad, India with GATE scholarship. My Master’s thesis was entitled “Mirtron Prediction through machine learning approach”. I worked as a research fellow at The International Centre for Genetic Engineering and Biotechnology, New Delhi for two years.
