NEED HELP WITH THE THIRD PART ONLY. I ALREADY TRIED IT, BUT MY CODE DOESNT MATCH THE QUESTION, THIS IS LIKE MY 5th TIME TRYING THIS. KEEPS GETTING REJECTED. Create a dataframe variable 'a' with this dataset. This dataframe should have all the 569 instances,30 features and the class of 569 instances as 0 (Malignant) or 1 (Benign). The column that containsthe classes should be labeled as 'typeofcancer'. Show the output of the following input:In [13]:▸a.shape[Hints: the outputs should be same as below.Out [13] (569, 31)(b) Now create a dataframe variable 'df' by slicing dataframe 'a'. The new datafraeme 'df' shouldhave all the instances, their labels but with the following three features: mean radius, meanperimeter and mean area. [Hints: use .iloc method to extract necessary columns from 'a'](i) Show the first two rows.[Hint: The output should be same as below.Out [16]:800(ii) Show the rows with indexes 17, 18, 19, 20, 21.60140mean radius mean perim17.9920.57(iii) Suppose we want to explore the possibility of developing a machine learning model that candiagnose a new patient's cancer condition as benign or malignant from the features in df.20As a first step, you want to do some graphical analysis. Write the code to generate the followingfigure (Figure 1). Show screenshot of the code (input) and the figure (output) from your work. Youare free to choose your favorite data marker and color in your figure.10CO(M)25● c1(B)20OD07151050150100mean perimeter122.8132.9mean area typeofcancer1001.01326.0CO(Malignant)c1(Benign)0015 20mean radius2510CO(M)c1(B)5001000 1500 2000 2500mean areaFigure 1: From left of right: histogram of 'mean radius' data for each class, scatter plot of 'meanradius' versus 'mean perimeter', scatter plot of 'mean radius' versus 'mean area'.(iv) Briefly describe what each of the subplots in Figure 1 reveal about the data. In [11]:import numpy as npimport pandas as pdfrom pandas import Series, DataFrameimport matplotlib.pyplot as pltimport sklearn.from sklearn.datasets import load_breast cancerbreast cancer -load_breast cancer (return_X_y True, as frame True)a breast cancer [0]bbreast cancer [1]a['classes - bIn [12] cancer sklearn.datasets.load_breast cancer ()Out [15]:a pd. DataFrame (cancer ['data'],columns-cancer.feature_names)a['typeofcancer'] cancer['target']In [13]: print (a.shape)#creaing a dataframe df(569, 31).In [14]: df=a.iloc[:, [0,2,3,-1]]In [15]: df.head (2)01171819mean radius mean perimeter mean area typeofcancerIn [16]: df.iloc [[17,18,19,20,21],:]Out [16]:202117.9920.57mean radius mean perimeter mean area typeofcancer16.130108.10798.819.81013.54013.0809.504122.8132.9357212plt.tight_layout()In [18]: asorted.typeofcancer.value_counts()Out [18] 10Name: typeofcancer, dtype: int64100130.007587.4685.6360.34In [17]: asorted = a.sort_values ('typeofcancer',ignore_index=True)asorted;50-251001.01326.0axsl.set_xlabel('mean radius')axsl.set_ylabel('frequney')1260.0566.3In [19]: f0, fl asorted.typeofcancer.value_counts()fo, flOut [19]: (357, 212)520.0273.9In [20]: fig, axs plt.subplots (figsize (10,2.5))(0,0))axs1 plt.subplot2grid (shape= (1, 3), locplt.subplot2grid (shape= (1, 3), locaxs2(0,1))axs3 plt.subplot2grid (shape= (1, 3), loc= (0,2))1015axs1.hist (a.iloc [0:f0, 0], edgecolor='r', fe='none', label='c0')axs1.hist (a.iloc [f0:f0+f1l, 0], edgecolor='b', fe='none', label='c1')00axs1.legend()axs2.scatter (a.iloc [0:f0, 2], a.iloc [0:f0, 0], label='c0(M)')axs2.scatter (a.iloc [f0:f0+f1, 2], a.iloc [f0:f0+f1, 0], label='c1(B)')axs2.set_xlabel('mean perimeter')axs2.set_ylabel('mean radius')axs2.legend()axs3.scatter (a.iloc [0:f0, 3), a.iloc [0:f0, 0], label='c0(M)')axs3.scatter (a.iloc [f0:f0+f1, 3], a.iloc [f0:f0+f1, 0], label='cl(B)')axs3.set_xlabel('mean area')axs3.set_ylabel('mean radius')axs3.legend()2000Out [20]: 1c0(M)25 ● cl(B)20wor151050100mean perimetermean radius1251150..2510● CO(M)cl(B)500 1000 1500 2000 2500mean area

Answered: Image /qna-images/answer/c682192f-2588-4516-9a7c-197af28aa004.jpg

Create a dataframe variable 'a' with this dataset. This dataframe should have all the 569 instances, 30 features and the class of 569 instances as 0 (Malignant) or 1 (Benign). The column that contains the classes should be labeled as 'typeofcancer'. Show the output of the following input: In [13]: ▸a.shape

Create a dataframe variable 'a' with this dataset. This dataframe should have all the 569 instances, 30 features and the class of 569 instances as 0 (Malignant) or 1 (Benign). The column that contains the classes should be labeled as 'typeofcancer'. Show the output of the following input: In [13]: ▸a.shape

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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