Day2_Simple_Linear_Regression.md

Simple Linear Regression

Step 1: Data Preprocessing

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

dataset = pd.read_csv('studentscores.csv')
X = dataset.iloc[ : ,   : 1 ].values
Y = dataset.iloc[ : , 1 ].values

from sklearn.cross_validation import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, random_state = 0) 

Step 2: Fitting Simple Linear Regression Model to the training set

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor = regressor.fit(X_train, Y_train)

Step 3: Predecting the Result

Y_pred = regressor.predict(X_test)

Step 4: Visualization

Visualising the Training results

plt.scatter(X_train , Y_train, color = 'red')
plt.plot(X_train , regressor.predict(X_train), color ='blue')

Visualizing the test results

plt.scatter(X_test , Y_test, color = 'red')
plt.plot(X_test , regressor.predict(X_test), color ='blue')