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
- Read the given dataset.
- Fitting the dataset into the training set and test set.
- Applying the feature scaling method.
- Fitting the logistic regression into the training set.
- Prediction of the test and result
- Making the confusion matrix
- Visualizing the training set results.
/*
Program to implement the the Logistic Regression Using Gradient Descent.
Developed by : Shrruthilaya G
RegisterNumber : 212221230097
*/
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
datasets = pd.read_csv("/content/Social_Network_Ads (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.fit_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(('red','green')))
plt.xlim(X1.min(), X2.max())
plt.ylim(X1.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.label('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.