Use the sacramento.csv file( I cant upload it its a excel file) to complete the following assignment.a file, sacramento.py, that loads the .csv file and runs a logistic The regression should predict whether or not a house has 1 or more than one bathroom based on beds, sqft, and price, in that order. You will need to create a new variable from baths, and it should make it such that those observations of 1 bath correspond to a value of 0, and those with more than 1 bath correspond to a 1. Make sure to add a constant using sm.add_constant(X) Your file should print the results in this way: print(mod.params.round(2))print(mod.pvalues.round(2))print('The smallest p-value is for sqft')

Answered: Use the sacramento.csv file( I cant…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Use the sacramento.csv file( I cant upload it its a excel file) to complete the following assignment.a file, sacramento.py, that loads the .csv file and runs a logistic The regression should predict whether or not a house has 1 or more than one bathroom based on beds, sqft, and price, in that order.

You will need to create a new variable from baths, and it should make it such that those observations of 1 bath correspond to a value of 0, and those with more than 1 bath correspond to a 1.

Make sure to add a constant using sm.add_constant(X)

Your file should print the results in this way:

print(mod.params.round(2))

print(mod.pvalues.round(2))

print('The smallest p-value is for sqft')

Expert Solution

Step 1

Simple regression towards the mean

K-NN is not the only kind of regression; another quite useful and probably the most common kind of regression is called simple regression towards the mean. simple regression towards the mean is comparable to K-NN regression, where the target/response variable is quantitative. However, it differs quite differently in that coaching knowledge is used to predict the price of a replacement observation. instead of looking at the K-nearest neighbors and averaging their values for a prediction, by simple regression towards the mean all the squared measures of knowledge of the coaching points usually do not provide a range of best work, so the path is used to "find" the predicted value.

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