logistic_regression.py

# -*- coding: utf-8 -*-
"""Logistic Regression.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1rXBOC2pxC472zkAi5lqI4ARVQ8_2ZeTe
"""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

df=pd.read_csv("card.csv")

df.shape

"""https://www.kaggle.com/datasets/uciml/default-of-credit-card-clients-dataset

There are 25 variables:

ID: ID of each client

LIMIT_BAL: Amount of given credit in NT dollars (includes individual and family/supplementary credit

SEX: Gender (1=male, 2=female)

EDUCATION: (1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown)

MARRIAGE: Marital status (1=married, 2=single, 3=others)

AGE: Age in years

PAY_0: Repayment status in September, 2005 (-1=pay duly, 1=payment delay for one month, 2=payment delay for two months, … 8=payment delay for eight months, 9=payment delay for nine months and above)

PAY_2: Repayment status in August, 2005 (scale same as above)

PAY_3: Repayment status in July, 2005 (scale same as above)

PAY_4: Repayment status in June, 2005 (scale same as above)

PAY_5: Repayment status in May, 2005 (scale same as above)

PAY_6: Repayment status in April, 2005 (scale same as above)

BILL_AMT1: Amount of bill statement in September, 2005 (NT dollar)

BILL_AMT2: Amount of bill statement in August, 2005 (NT dollar)

BILL_AMT3: Amount of bill statement in July, 2005 (NT dollar)

BILL_AMT4: Amount of bill statement in June, 2005 (NT dollar)

BILL_AMT5: Amount of bill statement in May, 2005 (NT dollar)

BILL_AMT6: Amount of bill statement in April, 2005 (NT dollar)

PAY_AMT1: Amount of previous payment in September, 2005 (NT dollar)

PAY_AMT2: Amount of previous payment in August, 2005 (NT dollar)

PAY_AMT3: Amount of previous payment in July, 2005 (NT dollar)

PAY_AMT4: Amount of previous payment in June, 2005 (NT dollar)

PAY_AMT5: Amount of previous payment in May, 2005 (NT dollar)

PAY_AMT6: Amount of previous payment in April, 2005 (NT dollar)

default.payment.next.month: Default payment (1=yes, 0=no)





"""

df.head(3)

df=df.drop(["ID"],axis=1)

df.head(3)

y=df["default.payment.next.month"]
x=df.drop(["default.payment.next.month"],axis=1)

x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.77,random_state=6)

log=LogisticRegression()
model=log.fit(x_train,y_train)

model.score(x_test,y_test)

testperson=np.array(x.iloc[1907])

model.predict([testperson])

y.iloc[1907]

#Predict and result are same for 1907. person