I need python help. I do not not where to start and keep getting errors in my code. The dataset (CreditData.csv) classifies customers as "approved" or "not approved" (Yes or No) (i.e., target class).
The target class is in the 21st column and its name is "Approved".
Number of Attributes for Classification: 20 (7 numerical, 13 categorical).
The task must be developed using Python.
Tasks:
1 - Divide data into two datasets
• 80% as training data
• 20% as test data
 

2 - Build a classification model based on the training data to predict if a new customer is approved or not.
• You can use Regression or Decision Tree.

3 - Test the model on the test data and build the confusion matrix.

4 - Perform the following tasks:
• Using the confusion matrix, calculate the accuracy, precision, and recall 

 

Here is the attribute description for the dataset:
 

Attribute 1: (qualitative)
Status of existing checking account
•A11: balance = $0
•A12: balance ≤ $200K
•A13: balance > $200K
•A14: no checking account

Attribute 2: (numerical)
Duration of bank membership in month

Attribute 3: (qualitative)
Credit history
•A30: no credits taken/all credits paid back duly
•A31: all credits at this bank paid back duly
•A32: existing credits paid back duly till now
•A33: delay in paying off in the past
•A34: critical account/other credits existing (not at this bank)
 

Attribute 4: (qualitative)
Purpose of applying for a loan
•A40: car (new)
•A41: car (used)
•A42: furniture/equipment
•A43: radio/television
•A44: domestic appliances
•A45: repairs
•A46: education
•A47: vacation
•A48: retraining
•A49: business
•A410: others

Attribute 5: (numerical)
Credit amount

 

Attribute 6: (qualitative)
Savings account/bonds
•A61: value < $10K
•A62: $10K ≤ value < $50K
•A63: $50K ≤ value < $100K
•A64: value ≥ $100K
•A65: unknown/ no savings account

Attribute 7: (qualitative)
Present employment since
•A71: unemployed
•A72: employment period < 1 year
•A73: 1 ≤ employment period < 4 years
•A74: 4 ≤ employment period < 7 years
•A75: employment period ≥ 7 years
Attribute 8: (numerical)
Installment rate in percentage of disposable income

Attribute 9: (qualitative)
Personal status and sex
•A91: male and married/divorced/separated
•A92: female and married/divorced/separated
•A93: male and single
•A94: female and single

Attribute 10: (qualitative)
Other debtors / guarantors
•A101: none
•A102: co-applicant
•A103: guarantor

Attribute 11: (numerical)
Present residence since how many year ago

Attribute 12: (qualitative)
Property
•A121: real estate
•A122: if not A121: building society savings agreement/life insurance
•A123: if not A121/A122: car or other, not in attribute 6
•A124: unknown/no property

Attribute 13: (numerical)
Age in years

Attribute 14: (qualitative)
Other installment plans
•A141: bank
•A142: stores
•A143: none

Attribute 15: (qualitative)
Housing
•A151: rent
•A152: own
•A153: for free

Attribute 16: (numerical)
Number of existing credits at this bank

Attribute 17: (qualitative)
Job
•A171: unemployed/unskilled - non-resident
•A172: unskilled - resident
•A173: skilled employee/official
•A174: management/self-employed/highly qualified employee/officer

Attribute 18: (numerical)
Number of people being liable to provide maintenance for

Attribute 19: (qualitative)
Telephone
•A191: none
•A192: yes, registered under the customer's name

Attribute 20: (qualitative)
Foreign worker
•A201: yes
•A202: no