image_output_11.png image_output_24.png image_output_25.png image_output_17.png image_output_30.png image_output_14.png image_output_28.png image_output_23.png image_output_13.png image_output_5.png image_output_18.png image_output_21.png image_output_7.png image_output_31.png image_output_20.png image_output_4.png image_output_8.png image_output_16.png image_output_27.png image_output_6.png image_output_12.png image_output_22.png image_output_3.png image_output_29.png image_output_2.png image_output_1.png image_output_10.png image_output_15.png image_output_9.png image_output_19.png image_output_26.png
import matplotlib.pyplot as plt

import pandas as pd

import pandas as pd

import seaborn as sns

import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')

base = base.iloc[:, :32]

base.columns

len(base.columns)

a = list(base.columns)

cor_base = base[['diagnosis', 'radius_mean', 'texture_mean', 'area_mean']]
cor_base

cor_base = base[['diagnosis', 'radius_mean', 'texture_mean', 'area_mean']]
cor_base.head(5)