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Logistic Regression.py
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Logistic Regression.py
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import pandas as pd
import seaborn as sb
import numpy as np
from sklearn.linear_model import LogisticRegression
data=pd.read_csv("D:\\Data Science\\Assignment files\\Logistic Regression\\creditcard.csv")
data1=pd.read_csv("D:\\Data Science\\Assignment files\\Logistic Regression\\creditcard.csv")
data.head()
data.columns
def owner_num(x):
if x=="yes":
return 1
if x=="no":
return 0
data["owner_num"]=data["owner"].apply(owner_num)
def selfemp_num(x):
if x=="no":
return 1
if x=="yes":
return 0
data["selfemp_num"]=data["selfemp"].apply(selfemp_num)
data=data.drop(["card","owner","selfemp"],axis=1)
data=data.drop(data.columns[0],axis=1)
from sklearn.preprocessing import StandardScaler
scaler=StandardScaler()
scaler.fit(data)
scaled_feat=scaler.transform(data)
df_feat=pd.DataFrame(scaled_feat,columns=data.columns[:])
from sklearn.model_selection import train_test_split
X_train,X_test,Y_train,Y_test=train_test_split(df_feat,data1["card"],test_size=0.3)
model=LogisticRegression()
model.fit(X_train,Y_train)
pred=model.predict(X_test)
pd.Series(pred).value_counts()
np.mean(Y_test==pred) #Accuracy=92.29
#Using Kfold cross validation#
X=df_feat.iloc[:,[0,1,2,3,4,5,6,7,8,9,10]]
Y=data1.iloc[:,[1]]
from sklearn.model_selection import cross_val_score
score=cross_val_score(model,X,Y,cv=5)
score
score.mean() #accuracy=95.37