This Linear Regression in Machine Learning Presentation will help you understand the basics of Linear Regression algorithm - what is Linear Regression, why is it needed and how Simple Linear Regression works with solved examples, Linear regression analysis, applications of Linear Regression and Multiple Linear Regression model. At the end, we will implement a use case on profit estimation of companies using Linear Regression in Python. This Machine Learning presentation is ideal for beginners who want to understand Data Science algorithms as well as Machine Learning algorithms. Below topics are covered in this Linear Regression Machine Learning Tutorial: 1. Introduction to Machine Learning 2. Machine Learning Algorithms 3. Applications of Linear Regression 4. Understanding Linear Regression 5. Multiple Linear Regression 6. Use case - Profit estimation of companies What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning - - - - - -

2. Profit Estimation of a Company
Which companies
shall we invest?
Venture Capital firm
A Venture Capital firm is trying to understand which companies should they invest

3. Profit Estimation of a Company
Idea
Based on companies expenses
Predict the profit companies make
Decide companies to invest

4. Profit Estimation of a Company
Administration
Marketing
State
R&D
Based on
Expenditure and
Location
Company Calculate profit

5. Profit Estimation of a Company
For simplicity, lets consider a single variable (R&D) and find out which companies to invest in
R&D
Profit
R&D
Profit
Companies spending
more on R&D make
good profit, let’s
invest in them
Plotting profit based on R&D
expenditure
Prediction line to estimate profit

6. What’s in it for you?
Machine Learning Algorithms
Understanding Linear Regression
Introduction to Machine Learning
Applications of Linear Regression
Multiple Linear Regression
Use Case – Profit Estimation of Companies

7. Introduction to Machine Learning

8. Introduction to Machine Learning
Based on the amount of rainfall, how much would be the crop yield?
Crop Field Predict crop yieldBased on Rainfall

9. Independent and Dependent Variables
Independent variable Dependent variable
A variable whose value does not change
by the effect of other variables and is
used to manipulate the dependent
variable. It is often denoted as X.
A variable whose value change when
there is any manipulation in the values of
independent variables. It is often denoted
as Y.
Crop yield depends on the amount of
rainfall received
Rainfall – Independent variable Crop yield – Dependent variable
In our example:

10. Numerical and Categorical Values
Data
SalaryAge Height Gender
Dog’s
BreedColor
12345
167891
46920
12345
90984
Numerical Categorical
A
C
D
E
B

11. Machine Learning Algorithms
Machine Learning
Algorithms
Supervised
Unsupervised
Reinforcement

12. Machine Learning Algorithms
Machine Learning
Algorithms
Supervised
ClassificationRegression

13. Machine Learning Algorithms
Machine Learning
Algorithms
Supervised
Regression
Simple Linear
Regression
Polynomial Linear
Regression
Multiple Linear
Regression

14. Applications of Linear Regression

15. Applications of Linear Regression
Economic Growth
Used to determine the Economic Growth of a country or a state in the coming
quarter, can also be used to predict the GDP of a country

16. Applications of Linear Regression
Product price
Can be used to predict what would be the price of a product in the future

17. Applications of Linear Regression
Housing sales
To estimate the number of houses a builder would sell and at what price in the
coming months

18. Applications of Linear Regression
Score Prediction
To predict the number of runs a player would score in the coming matches based
on previous performance

19. Understanding Linear Regression

20. Understanding Linear Regression
Linear Regression is a statistical model used to predict the relationship between
independent and dependent variables.
Examine 2 factors
Which variables in
particular are
significant predictors
of the outcome
variables?
1
How significant is
the Regression line
to make predictions
with highest
possible accuracy
2

21. Regression Equation
The simplest form of a simple linear regression equation with one dependent and one independent variable is represented by:
y = m x + c*
y ---> Dependent Variable
x ---> Independent Variable
c ---> Coefficient of the line
y2 - y1
x2 – x1
m =m ---> Slope of the line
Y
X
c
m
y2
y1
x2x1

22. Prediction using the Regression line
Rainfall (X)
CropYield(Y)
Plotting the amount of Crop Yield based on
the amount of Rainfall

23. Prediction using the Regression line
Rainfall (X)
CropYield(Y)
Plotting the amount of Crop Yield based on
the amount of Rainfall
Rainfall (X)
CropYield(Y)

24. Prediction using the Regression line
The Red point on the Y axis is the amount of Crop
Yield you can expect for some amount of Rainfall
(X) represented by Green dot
Rainfall (X)
CropYield(Y)
Plotting the amount of Crop Yield based on
the amount of Rainfall
Rainfall (X)
CropYield(Y)
Regression Line

25. Intuition behind the Regression line
Lets consider a sample dataset with 5 rows and find out how to draw the regression
line
X Y
1 2
2 4
3 5
4 4
5 5
Independent
variable
Dependent variable
Plotting the data points

26. Intuition behind the Regression line
Calculate the mean of X and Y and plot the values
X Y
1 2
2 4
3 5
4 4
5 5
Independent
variable
Dependent variable
Plotting the mean of X and Y
Mean 3 4

27. Intuition behind the Regression line
Regression line should ideally pass through the mean of X and Y
X Y
1 2
2 4
3 5
4 4
5 5
Independent
variable
Dependent variable
Regression line
Mean 3 4
(3,4)

28. Intuition behind the Regression line
Drawing the equation of the Regression line
_
_ _
X Y (X ) (Y ) (X Y)
1 2 1 4 2
2 4 4 16 8
3 5 9 25 15
4 4 16 16 16
5 5 25 25 25
= 66
Linear equation is represented as Y = m X + c
=m =
*
Y = m X + c
= 0.6 3 + 2.2
= 4
*
2 2
= 55 = 86
*
= 15 = 20
c=
*
((n (X Y))-( (X) (Y))***
((n (X ))-( (X) )*
2 2
((5 66)-(15 20))* *
((5 55))-(225)*
=0.6
(( (Y) (X ))-( (X) (X Y)*
2
* *
((n (X ))-( (X) )*
2 2 = 2.2

29. Intuition behind the Regression line
Lets find out the predicted values of Y for corresponding values of X using the linear
equation where m=0.6 and c=2.2
Here the blue points represent the actual Y
values and the brown points represent the
predicted Y values. The distance between the
actual and predicted values are known as
residuals or errors. The best fit line should
have the least sum of squares of these errors
also known as
e square.
(3,4)
Y
Y=0.6 1+2.2=2.8
Y=0.6 2+2.2=3.4
Y=0.6 3+2.2=4
Y=0.6 4+2.2=4.6
Y=0.6 5+2.2=5.2
pred
*
*
*
*
*

30. Intuition behind the Regression line
Lets find out the predicted values of Y for corresponding values of X using the linear
equation where m=0.6 and c=2.2
(3,4)
_ _
X Y Y (Y-Y ) (Y-Y )
1 2 2.8 -0.8 0.64
2 4 3.4 0.6 0.36
3 5 4 1 1
4 4 4.6 -0.6 0.36
5 5 5.2 -0.2 0.04
= 2.4
pred pred pred
2
The sum of squared errors for this regression line is 2.4. We check this
error for each line and conclude the best fit line having the least e square
value.

31. Finding the Best fit line
Minimizing the Distance: There are lots of ways to minimize the distance between the line and the data points like Sum of
Squared errors, Sum of Absolute errors, Root Mean Square error etc.
We keep moving this line through the
data points to make sure the Best fit
line has the least square distance
between the data points and the
regression line

32. Multiple Linear Regression

33. Multiple Linear Regression
Simple Linear
Regression
Multiple Linear
Regression
Y = m x + c*
2 *
Y = m x + m x + m x + ………. + m x + c*1 1 * 2 3 3*2 n n*
Independent variables (IDV’s)
Dependent variable (DV) Coefficient
nm1, m2, m3…..m
Slopes

34. Implementation of Linear Regression

35. Use case implementation of Linear Regression
Let’s understand how
Multiple Linear
Regression works by
implementing it in Python

36. Use case implementation of Linear Regression
1000 Companies
Profit
Expenditure
Based on
Predict

37. Use case implementation of Linear Regression
Predicting Profit of 1000 companies based on the attributes mentioned in the figure:
Profit
Estimation

38. Use case implementation of Linear Regression
Predicting Profit of 1000 companies based on the attributes mentioned in the figure:
R&D Spend
1
Profit
Estimation

39. Use case implementation of Linear Regression
Predicting Profit of 1000 companies based on the attributes mentioned in the figure:
R&D Spend
1
Administration
2
Profit
Estimation

40. Use case implementation of Linear Regression
Predicting Profit of 1000 companies based on the attributes mentioned in the figure:
R&D Spend
1
Marketing Spend
3
Administration
2
Profit
Estimation

41. Use case implementation of Linear Regression
Predicting Profit of 1000 companies based on the attributes mentioned in the figure:
R&D Spend
1
State
4
Marketing Spend
3
Administration
2
Profit
Estimation

42. Use case implementation of Linear Regression
Predicting Profit of 1000 companies based on the attributes mentioned in the figure:
R&D Spend
1
State
4
Marketing Spend
3
Administration
2
ProfitProfit
Profit
Estimation

43. Use case implementation of Linear Regression
Predicting Profit of 1000 companies based on the attributes mentioned in the figure:
R&D Spend
1
State
4
Marketing Spend
3
Administration
2
ProfitProfit
Profit
Estimation
Predict Profit

44. Use case implementation of Linear Regression
1. Import the libraries:

45. Use case implementation of Linear Regression
2. Load the Dataset and extract independent and dependent variables:

46. Use case implementation of Linear Regression
3. Data Visualization:

47. Use case implementation of Linear Regression
4. Encoding Categorical Data:
5. Avoiding Dummy Variable Trap:

48. Use case implementation of Linear Regression
6. Splitting the data into Train and Test set:
7. Fitting Multiple Linear Regression Model to Training set:

49. Use case implementation of Linear Regression
8. Predicting the Test set results:

50. Use case implementation of Linear Regression
9. Calculating the Coefficients and Intercepts:

51. Use case implementation of Linear Regression
10. Evaluating the model:
R squared value of 0.91 proves the model is a good model

52. Use case summary
We successfully trained our model with
certain predictors and estimated the
profit of companies using linear
regression

53. Key Takeaways