mkumar262006 / -implementation-of-logistic-regression-using-gradient-descent

Implementation-of-Logistic-Regression-Using-Gradient-Descent

AIM:

To write a program to implement the the Logistic Regression Using Gradient Descent.

Equipments Required:

1. Hardware – PCs
2. Anaconda – Python 3.7 Installation / Jupyter notebook

Algorithm:

1. Initialize Parameters
2. Compute Predictions
3. Compute Loss
4. Compute Gradients
5. Update Parameters
6. Continue the Process
7. Evaluate and Predict the data

Program:

'''
Program to implement the the Logistic Regression Using Gradient Descent.
Developed by: MANOJ KUMAR S
RegisterNumber:  212223240082
'''
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

dataset = pd.read_csv("D:/introduction to ML/jupyter notebooks/Placement_Data.csv")
print(dataset)

#dropping the serial no and salary col

dataset = dataset.drop('sl_no',axis=1)
dataset = dataset.drop('salary',axis=1)
print(dataset)

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')
print(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

print(dataset)

X = dataset.iloc[:,:-1].values
Y = dataset.iloc[:,-1].values
#Initialize the model parameters
theta = np.random.randn(X.shape[1])
y = Y
#Define the sigmoid function:
def sigmoid(z):
    return 1 / (1+np.exp(-z))
# define the loss function:
def loss(theta,X,y):
    h = sigmoid(X.dot(theta))
    return -np.sum(y * np.log(h) + (1 - y) * np.log(1 - h))

#define the gradient descent algorithms
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

#train the model:
theta = gradient_descent(theta,X,y,alpha = 0.01,num_iterations = 1000)

# make predictions:
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)
print(y_pred)

Accuracy = np.mean(y_pred.flatten()==y)
print("Accuracy: ",Accuracy)

output:

dataset:

dataset.dtypes:

dataset after catcodes:

Accuracy:

y_predicted value:

Result:

Thus the program to implement the the Logistic Regression Using Gradient Descent is written and verified using python programming.