application_vnd.jupyter.stderr_output_1.png
import matplotlib.pyplot as plt

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn as sns

data = pd.read_csv('../input/data.csv')

import seaborn as sns
from pandas.plotting import scatter_matrix
import matplotlib.cm as cm
import matplotlib.pyplot as plt
data['diagnosis'] = data['diagnosis'].map({'M': 1, 'B': 0})
y = pd.DataFrame(data=data['diagnosis'])
list = ['Unnamed: 32', 'id', 'diagnosis']
x = data.drop(list, axis=1)

#correlation map
f,ax = plt.subplots(figsize=(18, 18))
sns.heatmap(x.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)

rem = ['perimeter_mean', 'radius_mean', 'compactness_mean', 'concave points_mean', 'radius_se', 'perimeter_se', 'radius_worst', 'perimeter_worst', 'compactness_worst', 'concave points_worst', 'compactness_se', 'concave points_se', 'texture_worst', 'area_worst']
data_new = x.drop(rem, axis=1)
f, ax = plt.subplots(figsize=(14, 14))
sns.heatmap(data_new.corr(), annot=True, linewidths=0.5, fmt='.1f', ax=ax)