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Supervised Learning with scikit-learn

Run the hidden code cell below to import the data used in this course.

1 hidden cell

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# Add your code snippets here
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
cols = ["fLength", "fWidth", "fSize", "fConc", "fConc1", "fAsym", "fM3Long", "fM3Trans", "fAlpha", "fDist", "class"]
df = pd.read_csv('magic04.data', names=cols)
df.head()
df['class'] = (df['class']  == 'g').astype(int)
df.head()
for label in cols[:-1]:
  plt.hist(df[df["class"]==1][label], color='blue', label='gamma', alpha=0.7, density=True)
  plt.hist(df[df["class"]==0][label], color='red', label='hadron', alpha=0.7, density=True)
  plt.title(label)
  plt.ylabel("Probability")
  plt.xlabel(label)
  plt.legend()
  plt.show()