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Write python code to do the followings 1 Read the attached fie "Breast cancer_dataset.co and store all its columns fexcept classification) into a variable (X), and read column "classification" into a variable (Y). Note that if Classification-1 means patient is Healthy, and Classificetion=2 means patient has Breast cancer 2. Use the package below to train a logistic regression model to learn to predict whether a patient has breast cancer or not using the variables X and Y. from sklearn.inear odel iapert Logistictegression 3. Predict the class of a patient. Choose any patient from the input file "Breast cancer_dataset cw. 4. Compute error in whichever way you prefer. S. Use your model to show the feature/attribute that has the highest impact on Breast cancer. Print the name of the attribute Explain your findings in one line. The assignment is out of 5 marks. Each one of the above points weighs one grade. Any unnecessary (or extra) lines of code will deduct grades.

Write python code to do the followings 1 Read the attached fie "Breast cancer_dataset.co and store all its columns fexcept classification) into a variable (X), and read column "classification" into a variable (Y). Note that if Classification-1 means patient is Healthy, and Classificetion=2 means patient has Breast cancer 2. Use the package below to train a logistic regression model to learn to predict whether a patient has breast cancer or not using the variables X and Y. from sklearn.inear odel iapert Logistictegression 3. Predict the class of a patient. Choose any patient from the input file "Breast cancer_dataset cw. 4. Compute error in whichever way you prefer. S. Use your model to show the feature/attribute that has the highest impact on Breast cancer. Print the name of the attribute Explain your findings in one line. The assignment is out of 5 marks. Each one of the above points weighs one grade. Any unnecessary (or extra) lines of code will deduct grades.

Computer Networking: A Top-Down Approach (7th Edition)
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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Write python code to do the followings 1. Read the attached file "Breast_cancer_dataset.co and store all its columns (except classification) inta a variable (X), and read column "classilication" into a variable (V). Note that if Classification-1 means patient is Healthy, and Classification=2 means patient has Breast cancer 2. Use the package below to train a logistic regression model to learn to predict whether a patient has breast cancer or not using the variables X and Y. from sklearn.linear model iaport Logistietegression 3 Predict the class of a patient. Choose any patient from the input file "Breast cancer_dataset.cs, 4. Compute error in whichever way you prefer. S. Use your model to show the feature/attribute that has the highest impact on Breast cancer. Print the name of the attribute. Explain your findings in one line. The assignment is out of 5 marks. Each one of the above points weighs one grade. Any unnecessary (or extra) lines of code will deduct grades.
Transcribed Image Text:Write python code to do the followings 1. Read the attached file "Breast_cancer_dataset.co and store all its columns (except classification) inta a variable (X), and read column "classilication" into a variable (V). Note that if Classification-1 means patient is Healthy, and Classification=2 means patient has Breast cancer 2. Use the package below to train a logistic regression model to learn to predict whether a patient has breast cancer or not using the variables X and Y. from sklearn.linear model iaport Logistietegression 3 Predict the class of a patient. Choose any patient from the input file "Breast cancer_dataset.cs, 4. Compute error in whichever way you prefer. S. Use your model to show the feature/attribute that has the highest impact on Breast cancer. Print the name of the attribute. Explain your findings in one line. The assignment is out of 5 marks. Each one of the above points weighs one grade. Any unnecessary (or extra) lines of code will deduct grades.
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