To write a program to implement the the Logistic Regression Using Gradient Descent.
- Hardware โ PCs
- Anaconda โ Python 3.7 Installation / Jupyter notebook
1.Import Libraries: Import the necessary libraries - pandas, numpy, and matplotlib.pyplot.
2.Load Dataset: Load the dataset using pd.read_csv.
3.Remove irrelevant columns (sl_no, salary).
4.Convert categorical variables to numerical using cat.codes.
5.Separate features (X) and target variable (Y).
6.Define Sigmoid Function: Define the sigmoid function.
7.Define Loss Function: Define the loss function for logistic regression.
8.Define Gradient Descent Function: Implement the gradient descent algorithm to optimize the parameters.
9.Training Model: Initialize theta with random values, then perform gradient descent to minimize the loss and obtain the optimal parameters.
10.Define Prediction Function: Implement a function to predict the output based on the learned parameters.
11.Evaluate Accuracy: Calculate the accuracy of the model on the training data.
12.Predict placement status for a new student with given feature values (xnew).
13.Print Results: Print the predictions and the actual values (Y) for comparison.
Program to implement the the Logistic Regression Using Gradient Descent.
Developed by: Dhivyapriya.R
RegisterNumber: 212222230032
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
dataset=pd.read_csv('Placement_Data.csv')
dataset
dataset=dataset.drop('sl_no',axis=1)
dataset=dataset.drop('salary',axis=1)
dataset["gender"]=dataset["gender"].astype('category')
dataset["ssc_b"]=dataset["ssc_b"].astype('category')
dataset["hsc_b"]=dataset["hsc_b"].astype('category')
dataset["degree_t"]=dataset["degree_t"].astype('category')
dataset["workex"]=dataset["workex"].astype('category')
dataset["specialisation"]=dataset["specialisation"].astype('category')
dataset["status"]=dataset["status"].astype('category')
dataset["hsc_s"]=dataset["hsc_s"].astype('category')
dataset.dtypes
dataset["gender"]=dataset["gender"].cat.codes
dataset["ssc_b"]=dataset["ssc_b"].cat.codes
dataset["hsc_b"]=dataset["hsc_b"].cat.codes
dataset["degree_t"]=dataset["degree_t"].cat.codes
dataset["workex"]=dataset["workex"].cat.codes
dataset["specialisation"]=dataset["specialisation"].cat.codes
dataset["status"]=dataset["status"].cat.codes
dataset["hsc_s"]=dataset["hsc_s"].cat.codes
dataset
X=dataset.iloc[:,:-1].values
y=dataset.iloc[:,-1].values
y
theta=np.random.randn(X.shape[1])
Y=y
def sigmoid(z):
return 1/(1+np.exp(-z))
def loss(theta,X,y):
h=sigmoid(X.dot(theta))
return -np.sum(y*np.log(h)+(1-y)*np.log(1-h))
def gradient_descent(theta,X,y,alpha,num_iterations):
m=len(y)
for i in range(num_iterations):
h=sigmoid(X.dot(theta))
gradient=X.T.dot(h-y)/m
theta-=alpha*gradient
return theta
theta=gradient_descent(theta,X,y,alpha=0.01,num_iterations=1000)
def predict(theta,X):
h=sigmoid(X.dot(theta))
y_pred=np.where(h>=0.5,1,0)
return y_pred
y_pred=predict(theta,X)
accuracy=np.mean(y_pred.flatten()==y)
print("Accuracy:",accuracy)
print(y_pred)
print(Y)
xnew=np.array([[0,87,0,95,0,2,78,2,0,0,1,0]])
y_prednew=predict(theta,xnew)
print(y_prednew)
xnew=np.array([0,0,0,0,0,2,8,2,0,0,1,0])
y_prednew=predict(theta,xnew)
print(y_prednew)
Thus the program to implement the the Logistic Regression Using Gradient Descent is written and verified using python programming.