4.1 util.pyYou need to write three functions in util.py. When they are done correctly, linreg_mi.py willrun unchanged. The three functions are:read_excel data which reads in the excel file and returns X, Y, and labels. Y is a 1-dimentional numpy array containing the last column of the spreadsheet. X is a 2-dimensionalnumpy array that contains the data in the other columns and the first column is filled with1s. labels is a list of strings from the header in the spreadsheet.• format_prediction which takes B (the vector of coefficients) and labels that you createdin read_excel_data. Then it returns a string like this:predicted price = $32,362.85+ ($85.61 x sqft_hvac) + ($2.73 x sqft_yard) +($59,195.07 x bedrooms) + ($9,599.24 x bathrooms)($-17,421.84 x miles_to_school)}score that takes B, X, and Y and returns the R² score.When util.py is done, you should be able to run linreg_mi.py and linreg_scikit.py.4 12:21ShareBusiness card Sep 11, 2022Donelinreg_mi (3 of 6)import numpy as npimport pandas as pdimport sysimport util# Check command lineif len(sys.argv) != 2:print (f"{sys.argv[0]} ")exit (1)#Read in the argumentinfilename = sys.argv[1]# Read the spreadsheetX, Y, labels util.read_excel_data(infilename)n, d= X. shapeprint (f"Read {n} rows, {d-1} features from{infilename)",")# Find the coefficients for the linearregressionB = np.linalg.inv(X.T @ X) @ X.T @ Y# Pretty print themprint (util.format_prediction (B, labels))R2 = util.score (B, X, Y)print (f"R2 (R2: f}")Doneimport pandas as pdimport numpy as np.util (6 of 6)#Read in the excel file# Returns:#X: first column is 1s, the rest are from thespreadsheet#Y: The last column from the spreadsheet#labels: The list of headers for the columnsof X from the spreadsheetdef read_excel_data(infilename):## Your code herereturn X, Y, labelsMake it prettydef format prediction (B, labels):## Your code herereturn pred_stringModify# Return the R2 score for coefficients B#Given inputs X and outputs Ydef score (B, X, Y):## Your code herereturn R2EKText actions20Save contactMore

Python is the program languages that are used to create the applications. Python which refers to…

You need to write three functions in util.py. When they are done correctly, linreg_mi.py will un unchanged. The three functions are: • read_excel_data which reads in the excel file and returns X, Y, and labels. Y is a 1- dimentional numpy array containing the last column of the spreadsheet. X is a 2-dimensional numpy array that contains the data in the other columns and the first column is filled with 1s. labels is a list of strings from the header in the spreadsheet. format_prediction which takes B (the vector of coefficients) and labels that you created in read_excel_data. Then it returns a string like this: predicted price = $32,362.85+ ($85.61 x sqft_hvac) + ($2.73 x sqft_yard) + ($59,195.07 x bedrooms) + ($9,599.24 x bathro ($-17,421.84 x miles_to_school)} • score that takes B, X, and Y and returns the R² score.

You need to write three functions in util.py. When they are done correctly, linreg_mi.py will un unchanged. The three functions are: • read_excel_data which reads in the excel file and returns X, Y, and labels. Y is a 1- dimentional numpy array containing the last column of the spreadsheet. X is a 2-dimensional numpy array that contains the data in the other columns and the first column is filled with 1s. labels is a list of strings from the header in the spreadsheet. format_prediction which takes B (the vector of coefficients) and labels that you created in read_excel_data. Then it returns a string like this: predicted price = $32,362.85+ ($85.61 x sqft_hvac) + ($2.73 x sqft_yard) + ($59,195.07 x bedrooms) + ($9,599.24 x bathro ($-17,421.84 x miles_to_school)} • score that takes B, X, and Y and returns the R² score.

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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Concept explainers

SQL

SQL stands for Structured Query Language, is a form of communication that uses queries structured in a specific format to store, manage & retrieve data from a relational database.

Queries

A query is a type of computer programming language that is used to retrieve data from a database. Databases are useful in a variety of ways. They enable the retrieval of records or parts of records, as well as the performance of various calculations prior to displaying the results. A search query is one type of query that many people perform several times per day. A search query is executed every time you use a search engine to find something. When you press the Enter key, the keywords are sent to the search engine, where they are processed by an algorithm that retrieves related results from the search index. Your query's results are displayed on a search engine results page, or SER.

Question

4.1 util.py
You need to write three functions in util.py. When they are done correctly, linreg_mi.py will
run unchanged. The three functions are:
read_excel data which reads in the excel file and returns X, Y, and labels. Y is a 1-
dimentional numpy array containing the last column of the spreadsheet. X is a 2-dimensional
numpy array that contains the data in the other columns and the first column is filled with
1s. labels is a list of strings from the header in the spreadsheet.
• format_prediction which takes B (the vector of coefficients) and labels that you created
in read_excel_data. Then it returns a string like this:
predicted price = $32,362.85+ ($85.61 x sqft_hvac) + ($2.73 x sqft_yard) +
($59,195.07 x bedrooms) + ($9,599.24 x bathrooms)
($-17,421.84 x miles_to_school)}
score that takes B, X, and Y and returns the R² score.
When util.py is done, you should be able to run linreg_mi.py and linreg_scikit.py.
4

12:21
Share
Business card Sep 11, 2022
Done
linreg_mi (3 of 6)
import numpy as np
import pandas as pd
import sys
import util
# Check command line
if len(sys.argv) != 2:
print (f"{sys.argv[0]} <xlsx>")
exit (1)
#Read in the argument
infilename = sys.argv[1]
# Read the spreadsheet
X, Y, labels util.read_excel_data(infilename)
n, d= X. shape
print (f"Read {n} rows, {d-1} features from
{infilename)",")
# Find the coefficients for the linear
regression
B = np.linalg.inv(X.T @ X) @ X.T @ Y
# Pretty print them
print (util.format_prediction (B, labels))
R2 = util.score (B, X, Y)
print (f"R2 (R2: f}")
Done
import pandas as pd
import numpy as np.
util (6 of 6)
#Read in the excel file
# Returns:
#X: first column is 1s, the rest are from the
spreadsheet
#Y: The last column from the spreadsheet
#
labels: The list of headers for the columns
of X from the spreadsheet
def read_excel_data(infilename):
## Your code here
return X, Y, labels
Make it pretty
def format prediction (B, labels):
## Your code here
return pred_string
Modify
# Return the R2 score for coefficients B
#Given inputs X and outputs Y
def score (B, X, Y):
## Your code here
return R2
EK
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