***THIS MUST BE DONE IN PYTHON*** *If you could add #Notes so i can understand the process that would be great* # Q1 (1p

[answerhappygod]

***THIS MUST BE DONE IN PYTHON***
*If you could add #Notes so i can understand the process that
would be great*
# Q1 (1pt):
# Given the following data associated with customer credit
card
# Convert the lists to a list of tuples
data_columns =
["CLIENTNUM","Attrition_Flag","Customer_Age","Gender","Dependent_count","Education_

Level","Marital_Status","Income_Category","Card_Category","Months_on_book","Total_R

elationship_Count","Months_Inactive_12_mon","Contacts_Count_12_mon","Credit_Limit",

"Total_Revolving_Bal","Avg_Open_To_Buy","Total_Amt_Chng_Q4_Q1","Total_Trans_Amt","T

otal_Trans_Ct","Total_Ct_Chng_Q4_Q1","Avg_Utilization_Ratio"]
credit_card_data = [
["768805383","Existing Customer","45","M","3","High
School","Married","$60K -
$80K","Blue","39","5","1","3","12691","777","11914","1.335","1144","42","1.625","0.

061"],
["818770008","Existing
Customer","49","F","5","Graduate","Single","Less than
$40K","Blue","44","6","1","2","8256","864","7392","1.541","1291","33","3.714","0.10

5"],
["713982108","Existing
Customer","51","M","3","Graduate","Married","$80K -
$120K","Blue","36","4","1","0","3418","0","3418","2.594","1887","20","2.333","0"],

["769911858","Existing Customer","40","F","4","High
School","Unknown","Less than
$40K","Blue","34","3","4","1","3313","2517","796","1.405","1171","20","2.333","0.76

"],
["709106358","Existing
Customer","40","M","3","Uneducated","Married","$60K -
$80K","Blue","21","5","1","0","4716","0","4716","2.175","816","28","2.5","0"],

["713061558","Existing
Customer","44","M","2","Graduate","Married","$40K -
$60K","Blue","36","3","1","2","4010","1247","2763","1.376","1088","24","0.846","0.3

11"],
["810347208","Existing
Customer","51","M","4","Unknown","Married","$120K
+","Gold","46","6","1","3","34516","2264","32252","1.975","1330","31","0.722","0.06

6"],
["818906208","Existing Customer","32","M","0","High
School","Unknown","$60K -
$80K","Silver","27","2","2","2","29081","1396","27685","2.204","1538","36","0.714",

"0.048"]
]
# Remember tuple() can convert a list container into a tuple
container
list_of_tuples = []
# Q2 (2.5 pts):
# Create a dictionary of CLIENTNUM (as key) with the highest
Avg_Utilization_Ratio
(last column of data)
# Use python to iterate through the dataset, and output the highest
value
credit_util_dict = {}
for c in credit_card_data:
pass