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- import numpy as np
- import seaborn as sns
- import matplotlib
- import sklearn.linear_model as li
- import pandas as pd
- import sklearn.model_selection as ms
- from keras.models import Sequential
- from keras.layers.core import Dense, Activation
- from keras.utils import np_utils
- sns.set(style="whitegrid", palette="muted")
- # Load the example iris dataset
- iris = sns.load_dataset("../dataset")
- data = iris.data
- column_names = iris.feature_names
- df = pd.DataFrame(iris.data, iris.feature_names)
- X = iris.values[:, 0:4]
- y = iris.values[:, 4]
- print(column_names)
- print(df)
- # "Melt" the dataset to "long-form" or "tidy" representation
- iris = pd.melt(iris, "species", var_name="measurement")
- g = sns.pairplot(iris)
- # Draw a categorical scatterplot to show each observation
- sns.swarmplot(x="measurement", y="value", hue="species", data=iris)
- regression = li.LogisticRegressionCV()
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