/*
Developed by: Easwari M
RegisterNumber: 212223240033
*/

import numpy as np

import pandas as pd

from sklearn.preprocessing import StandardScaler

def linear_regression(X1,y,learning_rate=0.01,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',header=None)
print(data.head())  

X=(data.iloc[1:, :-2].values)
print(X)
X1=X.astype(float)
scaler=StandardScaler()
y=(data.iloc[1:,-1].values).reshape(-1,1)
print(y)
X1_Scaled=scaler.fit_transform(X1)
Y1_Scaled= scaler.fit_transform(y)
print(X1_Scaled)
print(Y1_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(f"Predicted Value = {pre}")