text_plain_output_1.png image_output_1.png
import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

import seaborn as sns

data = pd.read_csv('../input/breast-cancer/breast-cancer - breast-cancer.csv')

C = data['diagnosis'].value_counts()

corr = data.corr()
top_feature = corr.index[abs(corr['diagnosis']) > 0.5]
Important_Data = data[top_feature.values]
top_corr = data[top_feature].corr()

radius = data[['radius_mean', 'radius_se', 'radius_worst', 'diagnosis']]
sns.pairplot(radius, hue='diagnosis', palette='husl', markers=['o', 's'])
texture = data[['texture_mean', 'texture_se', 'texture_worst', 'diagnosis']]
sns.pairplot(texture, hue='diagnosis', palette='Blues_d')
perimeter = data[['perimeter_mean', 'perimeter_se', 'perimeter_worst', 'diagnosis']]
sns.pairplot(perimeter, hue='diagnosis')
area = data[['area_mean', 'area_se', 'area_worst', 'diagnosis']]
sns.pairplot(area, hue='diagnosis')
smoothness = data[['smoothness_mean', 'smoothness_se', 'smoothness_worst', 'diagnosis']]
sns.pairplot(smoothness, hue='diagnosis')
compactness = data[['compactness_mean', 'compactness_se', 'compactness_worst', 'diagnosis']]
sns.pairplot(compactness, hue='diagnosis')
concavity = data[['concavity_mean', 'concavity_se', 'concavity_worst', 'diagnosis']]
sns.pairplot(concavity, hue='diagnosis')
concave_points = data[['concave points_mean', 'concave points_se', 'concave points_worst', 'diagnosis']]
sns.pairplot(concave_points, hue='diagnosis')
symmetry = data[['symmetry_mean', 'symmetry_se', 'symmetry_worst', 'diagnosis']]
sns.pairplot(symmetry, hue='diagnosis')
fractal_dimension = data[['fractal_dimension_mean', 'fractal_dimension_se', 'fractal_dimension_worst', 'diagnosis']]
sns.pairplot(fractal_dimension, hue='diagnosis')