To write a program to implement the the Logistic Regression Using Gradient Descent.
- Hardware โ PCs
- Anaconda โ Python 3.7 Installation / Moodle-Code Runner
- Use the standard libraries in python for finding linear regression.
- Set variables for assigning dataset values.
- Import linear regression from sklearn.
- Predict the values of array.
- Calculate the accuracy, confusion and classification report by importing the required modules from sklearn.
- Obtain the graph.
/*
Program to implement the the Logistic Regression Using Gradient Descent.
Developed by: ASHMI.S
RegisterNumber: 212221040021
*/
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
datasets = pd.read_csv('/content/Social_Network_Ads (1) (1).csv')
X = datasets.iloc[:,[2,3]].values
Y = datasets.iloc[:,4].values
from sklearn.model_selection import train_test_split
X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size = 0.25, random_state = 0)
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_X
X_Train = sc_X.fit_transform(X_Train)
X_Test = sc_X.transform(X_Test)
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 0)
classifier.fit(X_Train, Y_Train)
Y_Pred = classifier.predict(X_Test)
Y_Pred
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(Y_Test, Y_Pred)
cm
from sklearn import metrics
accuracy = metrics.accuracy_score(Y_Test, Y_Pred)
accuracy
recall_sensitivity = metrics.recall_score(Y_Test, Y_Pred, pos_label=1)
recall_specificity = metrics.recall_score(Y_Test, Y_Pred, pos_label=0)
recall_sensitivity, recall_specificity
from matplotlib.colors import ListedColormap
X_Set, Y_Set = X_Train, Y_Train
X1, X2 = np.meshgrid(np.arange(start = X_Set[:,0].min() -1, stop = X_Set[:, 0].max() +1, step = 0.01),
np.arange(start = X_Set[:,1].min() -1, stop = X_Set[:,1].max() +1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('yellow','pink')))
plt.xlim(X1.min(), X2.max())
plt.ylim(X2.min(), X2.max())
for i,j,in enumerate(np.unique(Y_Set)):
plt.scatter(X_Set[Y_Set == j,0],X_Set[Y_Set == j,1], c = ListedColormap(('red','green'))(i),label = j)
plt.title('Logistic Regression(Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
Thus the program to implement the the Logistic Regression Using Gradient Descent is written and verified using python programming.