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Dec 8th, 2016
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  1. import numpy as np
  2. import seaborn as sns
  3. import matplotlib
  4. import sklearn.linear_model as li
  5. import pandas as pd
  6. import sklearn.model_selection as ms
  7. from keras.models import Sequential
  8. from keras.layers.core import Dense, Activation
  9. from keras.utils import np_utils
  10.  
  11.  
  12. sns.set(style="whitegrid", palette="muted")
  13.  
  14. # Load the example iris dataset
  15. iris = sns.load_dataset("../dataset")
  16. data = iris.data
  17. column_names = iris.feature_names
  18. df = pd.DataFrame(iris.data, iris.feature_names)
  19.  
  20. X = iris.values[:, 0:4]
  21. y = iris.values[:, 4]
  22.  
  23. print(column_names)
  24. print(df)
  25.  
  26. # "Melt" the dataset to "long-form" or "tidy" representation
  27. iris = pd.melt(iris, "species", var_name="measurement")
  28.  
  29.  
  30. g = sns.pairplot(iris)
  31.  
  32.  
  33. # Draw a categorical scatterplot to show each observation
  34. sns.swarmplot(x="measurement", y="value", hue="species", data=iris)
  35.  
  36.  
  37.  
  38. regression = li.LogisticRegressionCV()
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