43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
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import pandas
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from pandas.plotting import scatter_matrix
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import matplotlib.pyplot as plt
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from sklearn import model_selection
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from sklearn.metrics import classification_report
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from sklearn.metrics import confusion_matrix
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from sklearn.metrics import accuracy_score
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from sklearn.linear_model import LogisticRegression
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
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from sklearn.naive_bayes import GaussianNB
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from sklearn.svm import SVC
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# Load dataset
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#url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
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names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
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#dataset = pandas.read_csv(url, names=names)
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dataset = pandas.read_csv("./data/iris.data", names=names)
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print("shape")
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print(dataset.shape)
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print("head")
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# head
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print(dataset.head(20))
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print("descriptions")
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print(dataset.describe())
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# class distribution
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print(dataset.groupby('class').size())
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# box and whisker plots
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dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)
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plt.show()
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# scatter plot matrix
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scatter_matrix(dataset)
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plt.show()
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