Code for Logistic Regression

# Logistic Regression


## Upload dataset

from google.colab import files

uploaded = files.upload()

for fn in uploaded.keys():
  print('User uploaded file "{name}" with length {length} bytes'.format(
      name=fn, length=len(uploaded[fn])))

## Importing Libraries

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

## Loading dataset

ds=pd.read_csv("fetal_health_logistic.csv")
print("Shape of dataset : ",ds.shape)
print("Dataset :-\n",ds.head())

## Count of each class in Target 

count=ds['fetal_health'].value_counts()
print("Count of each class in Target :-\n",count)

## Assigning X and y values

X=ds.iloc[:,:-1].values
print("X Shape is : ",X.shape)
print("X is :-\n",X);

y=ds.iloc[:,-1].values
print("y Shape is : ",y.shape)
print("y is :-\n",y)

## Splitting the dataset into Train set and Test set

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 50)

print("Shape of X_train is : ",X_train.shape)
print("X_train is :-\n",X_train)

print("Shape of y_train is : ",y_train.shape)
print("y_train is :-\n",y_train)

print("Shape of X_test is : ",X_test.shape)
print("X_test is :-\n",X_test)

print("Shape of y_test is : ",y_test.shape)
print("y_test is :-\n",y_test)

## Feature Scaling

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

print("Now X_train is :-\n",X_train)

print("Now X_test is :-\n",X_test)

## Training Logistic Regression model on the Train set


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

## Predicting the Test set results

y_pred = classifier.predict(X_test)
print("Predicted vs Actual on Test set")
print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))

## Making the Confusion Matrix

from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix is :-\n",cm)

## Graphical Confusion Matrix display

from sklearn.metrics import ConfusionMatrixDisplay
ConfusionMatrixDisplay(cm).plot()
plt.show()

## Accuracy of the Logistic Model

from sklearn.metrics import accuracy_score
#print("Accuracy : ",accuracy_score(y_test,y_pred))
print("Accuracy : ",accuracy_score(classifier.predict(X_test),y_test))