45 lines
1.1 KiB
Python
45 lines
1.1 KiB
Python
# Self Organizing Map
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# Importing the libraries
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import numpy as np
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import pandas as pd
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# Importing the dataset
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dataset = pd.read_csv('Credit_Card_Applications.csv')
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X = dataset.iloc[:, :-1].values
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y = dataset.iloc[:, -1].values
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# Feature Scaling
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from sklearn.preprocessing import MinMaxScaler
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sc = MinMaxScaler(feature_range = (0, 1))
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X = sc.fit_transform(X)
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# Training the SOM
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from minisom import MiniSom
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som = MiniSom(x = 10, y = 10, input_len = 15, sigma = 1.0, learning_rate = 0.5)
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som.random_weights_init(X)
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som.train_random(data = X, num_iteration = 100)
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# Visualizing the results
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from pylab import bone, pcolor, colorbar, plot, show
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bone()
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pcolor(som.distance_map().T)
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colorbar()
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markers = ['o', 's']
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colors = ['r', 'g']
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for i, x in enumerate(X):
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w = som.winner(x)
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plot(w[0] + 0.5,
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w[1] + 0.5,
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markers[y[i]],
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markeredgecolor = colors[y[i]],
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markerfacecolor = 'None',
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markersize = 10,
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markeredgewidth = 2)
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show()
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# Finding the frauds
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mappings = som.win_map(X)
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frauds = np.concatenate((mappings[(8,1)], mappings[(6,8)]), axis = 0)
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frauds = sc.inverse_transform(frauds)
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