<jupyter_start><jupyter_text>PROBLEM DISCRIPTION:    The dataset contains cases from a study that was conducted between 1958 and 1970     at the University of Chicago's Billings Hospital on the survival of patients     who had undergone surgery for breast cancer.<jupyter_code>#eda on heberman's dataset
#here no of data points are 305
#no of features are 4

#import required libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
#above are required libraries for data_analysis for this problem
#to read the dataset
heber=pd.read_csv('heberman.csv')
heber.set_axis(['age','year of treatment','nodes detected','servival_status '],axis=1,inplace = 'true')
print(heber)

print(heber.shape)     #to get the shape of dataset
print(heber.head);
print(heber.tail)
#here the data set contains 305 rows or data points and 4 coloums or features
#describe gives all required analysis of data set like mean,std,25% value etc..
heber.describe()
#2d scatter plot
#bivarient analysis
#i am not doing 3D scatter plot due to visualization problem
heber.plot(kind='scatter',x='nodes detected',y='age')
plt.show()
#plotting scatter plot to analyze datapoints of age vs nodes detected

sns.FacetGrid(heber,size=4).map(plt.scatter,'nodes detected','age').add_legend()
plt.show()
#plotting total data
heber.plot()
plt.show()
heber.hist()
plt.legend
plt.show()                #histograms for all features

sns.pairplot(heber,size=4)        #to know the best features pair plots are plotted 4c2 combinations are possible
plt.show()<jupyter_output><empty_output><jupyter_text>univarient analysis<jupyter_code>sns.FacetGrid(heber,size=5).map(sns.distplot, "nodes detected").add_legend();
plt.show();
plt.legend;
#in the above graph most of the points lies in between 0 to 5 in x-axis and 0.2 in y-axis as show in pdf
sns.FacetGrid(heber, size=5).map(sns.distplot, "age")
plt.show();
plt.legend;
#in the above plot the age group of people in between 30 to 60
sns.FacetGrid(heber, size=5).map(sns.distplot, "year of treatment")
plt.show();
plt.legend;
sns.FacetGrid(heber,size=5).map(sns.distplot,"nodes detected")
plt.show();
plt.legend;<jupyter_output><empty_output><jupyter_text>box plots and violin plots<jupyter_code>sns.boxplot(x='nodes detected',y='age', data=heber)
plt.show()
sns.boxplot(x='nodes detected',y='year of treatment', data=heber)
plt.show()
sns.violinplot(x='year of treatment', y='nodes detected', data=heber, size=6)
plt.show()
sns.violinplot(x="nodes detected", y="year of treatment", data=heber, size=2)
plt.show()<jupyter_output><empty_output><jupyter_text>conclution<jupyter_code>1:  the servival_status tells that 1 represents patient survived after the treatment about 5 years or longer
    and 2 represents patient died within 5 years of treatment
2:  most number of nodes detected at the year 64 and less number of nodes detected at the year 60
3:  when the age increases the number of deteceted nodes increases<jupyter_output><empty_output>
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