linear

# -*- coding: utf-8 -*-
"""ML LAB 2.1.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/13d-BD_VbSiSPyERDAWfMtxeAtNRLdhgn

# ML EXP 2.1 -- SHRUTIKA PANDEY 102095004

# LINEAR REGRESSION
"""

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

dataset = pd.read_csv('sub.csv')
x = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values
print(X)
print(y)
plt.plot(x,y)

data.head()

data.tail()

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 1/3, random_state = 0)

from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, y_train)

x_pred= lr.predict(X_train)
y_pred = lr.predict(X_test)
print(y_pred)

#TRAINING DATA PLOT AND THE PREDCTED LINEAR
plt.scatter(X_train, y_train, color = 'red')
plt.plot(X_train, lr.predict(X_train), color = 'blue')
plt.title('Lower living standard vs houses bought')
plt.xlabel('Lower living standard of people')
plt.ylabel('Prices of houses bought(in $1000)')
plt.show()

#TESTING DATA PLOT AND THE PREDCTED LINEAR
plt.scatter(X_test, y_test, color = 'green')
plt.plot(X_train, lr.predict(X_train), color = 'blue')
plt.title('Lower living standard vs houses bought')
plt.xlabel('Lower living standard of people')
plt.ylabel('Prices of houses bought(in $1000)')
plt.show()