To write a program to predict the profit of a city using the linear regression model with gradient descent.
- Hardware – PCs
- Anaconda – Python 3.7 Installation / Jupyter notebook
- Import the required library and read the dataframe.
- Write a function computeCost to generate the cost function.
- Perform iterations og gradient steps with learning rate.
- Plot the Cost function using Gradient Descent and generate the required graph.
Program to implement the linear regression using gradient descent.
Developed by: SYED MOKTHIYAR S.M
RegisterNumber: 212222230156
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
def linear_regression(X1,y,learning_rate = 0.1, num_iters = 1000):
X = np.c_[np.ones(len(X1)),X1]
theta = np.zeros(X.shape[1]).reshape(-1,1)
for _ in range(num_iters):
predictions = (X).dot(theta).reshape(-1,1)
errors=(predictions - y ).reshape(-1,1)
theta -= learning_rate*(1/len(X1))*X.T.dot(errors)
return theta
data=pd.read_csv("50_Startups.csv")
data.head()
X=(data.iloc[1:,:-2].values)
X1=X.astype(float)
scaler=StandardScaler()
y=(data.iloc[1:,-1].values).reshape(-1,1)
X1_Scaled=scaler.fit_transform(X1)
Y1_Scaled=scaler.fit_transform(y)
print(X)
print(X1_Scaled)
theta= linear_regression(X1_Scaled,Y1_Scaled)
new_data=np.array([165349.2,136897.8,471784.1]).reshape(-1,1)
new_Scaled=scaler.fit_transform(new_data)
prediction=np.dot(np.append(1,new_Scaled),theta)
prediction=prediction.reshape(-1,1)
pre=scaler.inverse_transform(prediction)
print(prediction)
print(f"Predicted value: {pre}")
Thus the program to implement the linear regression using gradient descent is written and verified using python programming.