ANSWER MUST BE IN PYTHON3. METHOD HEADER BELOW.

#!/bin/python3

import math

import os

import random

import re

import sys

import numpy as np

import pandas as pd

from sklearn.linear_model import LinearRegression

def read_dataframe():

    # Return a pandas dataframe from stdin

    return pd.read_csv(sys.stdin)

def read_matrix():

    # Return column names and data as a matrix

    df = read_dataframe()

    return list(df.columns), df.values

def read_list():

    # Return column names and data as a list of lists

    # Each element in the list corresponds to columns in the data

    col_names, val = read_matrix()

    return col_names, val.T.tolist()

#

# Complete the 'main' function below.

#

# The function is expected to return a STRING.

#

def main():

    # Write your code here

if __name__ == '__main__':

    fptr = open(os.environ['OUTPUT_PATH'], 'w')

    result = main()

    fptr.write(result + '\n')

    fptr.close()

Transcribed Image Text:Question 3 is a bonus question and will not be scored. The parameters and data set remain the same from Question 2. Please finish Questions 1 and 2 before attempting Question 3. You are given the daily temperature reading of P towns and the daily temperature reading of New York City (NYC) for N days. For the given data, write a function that returns a set of five towns that are jointly as predictive as possible of the daily temperate of NYC. Note that the exact solution to this problem requires an exhaustive search that can be computationally challenging. We expect you to write an approximate solution that is as fast, or maybe faster, than your solution to Question 2. Function Description Complete the function main() to read the data from stdin and answer the question. The input data is in CSV format with a header row indicating the town names; use this header rather than making assumptions about column order. Depending on the language you choose we may provide boilerplate code for reading the data - you are welcome to use this if you wish. You are also welcome to import packages (such as sklearn in Python), as long as they are available in the HackerRank environment. The function main should return a string with the name of the five towns. For example, a valid output is "Town1,Town2,Town3,Town4,Town5". We will use the name of the towns you provide to compute the R² for each test case. Constraints • N>5 and 4 < P < 60 ▼ Input Format For Custom Testing The first line contains a comma separated list of strings corresponding to the name of the towns. Each subsequent line contains comma separated values corresponding to the temperature of each town. Example: Town1, Town2, Town3, Town4, Town5, Town6, NYC 34,32,53,5,10,29,40 86,91,24,10,12,50,45 56,78,90,23,23, 45, 23 23,92,45,44, 55, 67,34 12,99,23,34,64,56,35 22,102,11, 23, 45, 65,43
Transcribed Image Text:1v#!/bin/python3 OUолашин 2 3 4 5 6 7 8 9 10 11 12 N 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 import math import os import random import re import sys import numpy as np import pandas as pd from sklearn.linear_model import Linear Regression def read_dataframe(): def read_matrix(): # Return column names and data as a matrix df = read_dataframe() return list (df.columns), df.values # Return a pandas dataframe from stdin return pd. read_csv (sys.stdin) def read_list(): # Return column names and data as a list of lists # Each element in the list corresponds to columns in the data col_names, val = read_matrix() return col_names, val.T.tolist() | # # Complete the 'main' function below. # # The function is expected to return a STRING. # 32 33 34 35 ww 36 if __name_ def main(): # Write your code here __main__': ...

Question

ANSWER MUST BE IN PYTHON3. METHOD HEADER BELOW. #!/bin/python3import mathimport osimport randomimport reimport sysimport numpy as npimport pandas as pdfrom sklearn.linear_model import LinearRegressiondef read_dataframe():    # Return a pandas dataframe from stdin    return pd.read_csv(sys.stdin)def read_matrix():    # Return column names and data as a matrix    df = read_dataframe()    return list(df.columns), df.valuesdef read_list():    # Return column names and data as a list of lists    # Each element in the list corresponds to columns in the data    col_names, val = read_matrix()    return col_names, val.T.tolist()## Complete the 'main' function below.## The function is expected to return a STRING.#def main():    # Write your code hereif __name__ == '__main__':    fptr = open(os.environ['OUTPUT_PATH'], 'w')    result = main()    fptr.write(result + '
')    fptr.close() Question 3 is a bonus question and will not be scored. The parameters and data set remain the same from Question 2. Pleasefinish Questions 1 and 2 before attempting Question 3.You are given the daily temperature reading of P towns and the daily temperature reading of New York City (NYC) for N days.For the given data, write a function that returns a set of five towns that are jointly as predictive as possible of thedaily temperate of NYC. Note that the exact solution to this problem requires an exhaustive search that can be computationallychallenging. We expect you to write an approximate solution that is as fast, or maybe faster, than your solution to Question 2.Function DescriptionComplete the function main() to read the data from stdin and answer the question. The input data is in CSV format with aheader row indicating the town names; use this header rather than making assumptions about column order. Depending onthe language you choose we may provide boilerplate code for reading the data - you are welcome to use this if you wish. Youare also welcome to import packages (such as sklearn in Python), as long as they are available in the HackerRank environment.The function main should return a string with the name of the five towns. For example, a valid output is&#34;Town1,Town2,Town3,Town4,Town5&#34;. We will use the name of the towns you provide to compute the R² for each test case.Constraints• N>5 and 4 < P < 60▼ Input Format For Custom TestingThe first line contains a comma separated list of strings corresponding to the name of the towns.Each subsequent line contains comma separated values corresponding to the temperature of each town.Example:Town1, Town2, Town3, Town4, Town5, Town6, NYC34,32,53,5,10,29,4086,91,24,10,12,50,4556,78,90,23,23, 45, 2323,92,45,44, 55, 67,3412,99,23,34,64,56,3522,102,11, 23, 45, 65,43 1v#!/bin/python3OUолашин23456789101112N13141516171819202122232425262728293031import mathimport osimport randomimport reimport sysimport numpy as npimport pandas as pdfrom sklearn.linear_model import Linear Regressiondef read_dataframe():def read_matrix():# Return column names and data as a matrixdf = read_dataframe()return list (df.columns), df.values# Return a pandas dataframe from stdinreturn pd. read_csv (sys.stdin)def read_list():# Return column names and data as a list of lists# Each element in the list corresponds to columns in the datacol_names, val = read_matrix()return col_names, val.T.tolist()|## Complete the 'main' function below.## The function is expected to return a STRING.#32333435ww36 if __name_def main():# Write your code here__main__': ...

Accepted Answer

Code :
#!/bin/python3
import mathimport osimport randomimport reimport sysimport numpy as npimport…