Supervised Machine Learning

Pragathi Technical Session
'Supervised Machine Learning'
By, Amal Mohanan

Pragathi - Tech & Socio Cultural Activity Group Livares Technologies

#livares #pragathi #technicalsession #technology #technicaldiscussion #machinelearning #supervisedmachinelearning

1. SUPERVISED MACHINE LEARNING Presented By Amal Mohanan Livares Technologies Pvt Ltd Tech&Socio-Cultural Group

2. Machine learning Continue… Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd

3. Types of Machine Learning Continue… Supervised Machine Learning Unsupervised Machine Learning Tech&Socio-Cultural Group Livares Technologies Pvt Ltd

4. What is Supervised Learning? Continue… Supervised learning is the types of machine learning in which machines are trained using well "labelled" training data, and on basis of that data, machines predict the output. In supervised learning, the training data provided to the machines work as the supervisor that teaches the machines to predict the output correctly. It applies the same concept as a student learns in the supervision of the teacher. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd

5. How Supervised Learning Works? Continue… Tech&Socio-Cultural Group Livares Technologies Pvt Ltd

6. What is Unsupervised Learning? Continue… Unsupervised learning is a type of machine learning in which models are trained using unlabeled dataset and are allowed to act on that data without any supervision The goal of unsupervised learning is to find the underlying structure of dataset, group that data according to similarities, and represent that dataset in a compressed format. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd

7. Working of Unsupervised Learning Continue… Tech&Socio-Cultural Group Livares Technologies Pvt Ltd

8. Steps Involved in Supervised Learning Continue… Determine the type of training dataset Collect/Gather the labelled training data. Split the training dataset into training dataset, test dataset, Determine the suitable algorithm for the model Execute the algorithm on the training dataset. Evaluate the accuracy of the model by providing the test set. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd

9. Types of Supervised Learning Algorithms Continue… Regression Analysis Regression analysis is a statistical method to model the relationship between a dependent (target) and independent (predictor) variables with one or more independent variables. Classification Algorithm The Classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd

10. Types Regression Analysis Continue… Linear Regression Linear relationship between dependent and independent variables Polynomial Regression Non linear relationship between dependent and independent variables Tech&Socio-Cultural Group Livares Technologies Pvt Ltd

11. Types Regression Analysis Continue… Tech&Socio-Cultural Group Livares Technologies Pvt Ltd

12. Linear Regression Continue… Linear regression algorithm shows a linear relationship between a dependent (y) and one or more independent (x) variables, hence called as linear regression. Since linear regression shows the linear relationship, which means it finds how the value of the dependent variable is changing according to the value of the independent variable Tech&Socio-Cultural Group Livares Technologies Pvt Ltd

13. Assumptions of Linear Regression Continue… Tech&Socio-Cultural Group Livares Technologies Pvt Ltd

14. Types of Linear Regression Analysis Continue… Simple Linear Regression Multiple Linear Regression Tech&Socio-Cultural Group Livares Technologies Pvt Ltd

15. Simple Linear Regression Continue… Simple Linear Regression is a type of Regression algorithms that models the relationship between a dependent variable and a single independent variable. The relationship shown by a Simple Linear Regression model is linear or a sloped straight line, hence it is called Simple Linear Regression. Equation y = b0 + b1x b0 = It is the intercept of the Regression line b1 = It is the slope of the regression line, which tells whether the line is increasing or decreasing. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd

16. Multiple Linear Regression Continue… Multiple Linear Regression is one of the important regression algorithms which models the linear relationship between a single dependent continuous variable and more than one independent variable. Equation y = b0 + b1x1 + b2x2 + b3x3 +….. b0,b1.b2,…. =   Coefficients of the model x 1 , x 2 , x 3 , x 4 ,...= Various Independent/feature variable Tech&Socio-Cultural Group Livares Technologies Pvt Ltd

17. Backward Elimination Continue… Backward elimination is a feature selection technique while building a machine learning model. It is used to remove those features that do not have a significant effect on the dependent variable or prediction of output.  There are various ways to build a model in Machine Learning, which are: All-in Backward Elimination Forward Selection Bidirectional Elimination Score Comparison Tech&Socio-Cultural Group Livares Technologies Pvt Ltd

18. Steps of Backward Elimination Continue… Step-1:  Firstly, We need to select a significance level to stay in the model. (SL=0.05) Step-2:  Fit the complete model with all possible predictors/independent variables Step-3:  Choose the predictor which has the highest P-value, such that. If P-value >SL, go to step 4. Else Finish, and Our model is ready. Step-4:  Remove that predictor. Step-5:  Rebuild and fit the model with the remaining variables. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd

19. Reference https://www.javatpoint.com/supervised-machine-learning Tech&Socio-Cultural Group Livares Technologies Pvt Ltd

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