[Python] Simple Linear Regression.ipynb

Kernel: Python 3

Simple Linear Regression

from IPython.display import Image

Image("img/01.png")

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• b0 is constant representing the base salary of anyone who come to profession and have no experience i.e. Experience = 0

• b1 is coefficient representing the slope. The more experience the more raise will be their in salary.

Here in the graph, the black line is Best Fitting Line

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Image("img/02.png")

Actual value vs Model value and Ordinary Least Square

Image("img/03.png")

Data Preprocessing

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
%matplotlib inline  

# Importing the dataset
dataset = pd.read_csv('Salary_Data.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 1].values

# Splitting the dataset into the Training set and Test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 42)

# Feature Scaling
"""from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
sc_y = StandardScaler()
y_train = sc_y.fit_transform(y_train)"""

X_train

array([[  4. ],
       [  1.1],
       [  2.2],
       [  5.1],
       [  2.9],
       [  4.1],
       [  4. ],
       [  7.9],
       [  1.3],
       [  1.5],
       [  9. ],
       [  2. ],
       [  7.1],
       [  9.5],
       [  5.9],
       [ 10.5],
       [  6.8],
       [  3.2],
       [  3.9],
       [  4.5],
       [  6. ],
       [  3. ]])

X_test

array([[  9.6],
       [  4.9],
       [  8.2],
       [  5.3],
       [  3.2],
       [  3.7],
       [ 10.3],
       [  8.7]])

y_train

array([  56957.,   39343.,   39891.,   66029.,   56642.,   57081.,
         55794.,  101302.,   46205.,   37731.,  105582.,   43525.,
         98273.,  116969.,   81363.,  121872.,   91738.,   54445.,
         63218.,   61111.,   93940.,   60150.])

y_test

array([ 112635.,   67938.,  113812.,   83088.,   64445.,   57189.,
        122391.,  109431.])

Fitting Simple Linear Regression to the Training Set

regressor = LinearRegression() # Object of LinearRegression class

regressor.fit(X_train, y_train) # Method for make the machine learn the correlation

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

Predicting the Test set result

y_pred = regressor.predict(X_test)

y_pred

array([ 115439.88180109,   71396.10622651,  102320.45928951,
         75144.51265839,   55465.37889103,   60150.88693088,
        121999.59305688,  107005.96732936])

Visualising the Training set results

• X = Years of Experience

• Y = Salary

plt.scatter(X_train, y_train, c = 'red')
plt.plot(X_train, regressor.predict(X_train), c = 'green')
plt.title('Salary vs Experience (Training Set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')

Text(0,0.5,'Salary')

Visualising the Test set results

plt.scatter(X_test, y_test, c = 'red')

# We don't need to change X_train by X_test in plot as our regressor is unique
plt.plot(X_train, regressor.predict(X_train), c = 'green')
plt.title('Salary vs Experience (Test Set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')

Text(0,0.5,'Salary')