code.py

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

dataset=pd.read_csv(r'C:\Users\ADMIN\Downloads\logit classification.csv')  

x= dataset.iloc[:,[2,3]].values
y = dataset.iloc[: , -1].values

from sklearn.model_selection import train_test_split
x_train , x_test , y_train , y_test=train_test_split(x,y,test_size=0.20,random_state=0)


from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x_train = sc.fit_transform(x_train)
x_test = sc.transform(x_test)


from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(x_train,y_train)

y_pred = classifier.predict(x_test)


from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test,y_pred)
cm

from sklearn.metrics import accuracy_score
ac = accuracy_score(y_test,y_pred)
ac

 
#----future predictions

dataset1 = pd.read_csv(r'C:\Users\ADMIN\Downloads\2.LOGISTIC REGRESSION CODE\2.LOGISTIC REGRESSION CODE\final1.csv')

d2=dataset1.copy()

dataset1 =dataset1.iloc[:,[3,4]].values



from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
M=sc.fit_transform(dataset1)


y_pred1 = pd.DataFrame()


d2['y_pred1']=classifier.predict(M)
d2.to_csv('final1.csv')



from sklearn.metrics import roc_auc_score,roc_curve
y_pred_prob = classifier.predict_proba(x_test)[:,1]

auc_score=roc_auc_score(y_test,y_pred_prob)
auc_score

fpr,tpr,thresholds = roc_curve(y_test,y_pred_prob)


plt.figure(figsize=(8,6))
plt.plot(fpr,tpr,label = f' logistic Regression (AUC = () auc_score:.2f)')
plt.plot([0,1],[0,1],'k--')
plt.xlabel('false')
plt.ylabel('true')
plt.title('roc')
plt.legend(loc='lower right')
plt.grid()
plt.show()