Multinomial_Regression

 #!/usr/bin/env python

# coding: utf-8

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#import the necessary libraries

from sklearn import datasets

from sklearn.linear_model import LogisticRegression

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

from sklearn.metrics import accuracy_score

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#load the digit datasets

digits=datasets.load_digits()

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#defining feature matrix x and response vecoor y

X=digits.data

y=digits.target

print(X)

print(y)

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#split the test train data

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=1)

print(y_test)

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#standradize features using standardscaler

scaler=StandardScaler()

scaler.fit(X_train)

X_train_scaled=scaler.transform(X_train)

X_test_scaled=scaler.transform(X_test)

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#create the logistic regression model

model=LogisticRegression()

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#train the model on scaled training data

model.fit(X_train_scaled,y_train)

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#make predidction on the sclaed testing data

y_pred=model.predict(X_test_scaled)

print(y_pred)

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#evaluate model performance

acc=accuracy_score(y_test,y_pred)

print("Logistic Regression model accuarcy (in %):",acc*100)

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