Edureka!, profile picture
Uploaded byEdureka!
2,572 views

Logistic Regression in R | Machine Learning Algorithms | Data Science Training | Edureka

The document provides an overview of logistic regression, a statistical method used for predicting binary outcomes based on independent variables. It discusses key concepts such as the differences between logistic and linear regression, the use of a logistic function to constrain outcomes between 0 and 1, and the process of implementing logistic regression with practical examples, including data analysis in R. Additionally, it covers logistic regression applications in various fields such as healthcare and marketing.

www.edureka.co/data-scienceEdureka’s Data Science Certification Training Logistic Regression
www.edureka.co/data-scienceEdureka’s Data Science Certification Training What Will You Learn Today? What is Regression? The 5 Questions asked in Data Science Logistic Regression – What and Why? How does Logistic Regression work? Demo In R: Diabetes Use Case 1 2 3 4 65 Logistic Regression – Use Cases
www.edureka.co/data-scienceEdureka’s Data Science Certification Training The 5 Questions Asked In Data Science
www.edureka.co/data-scienceEdureka’s Data Science Certification Training The 5 Questions Asked In Data Science In data science, basically we have 5 kind of problems. Classification Algorithm Anomaly Detection Algorithm Regression Algorithms Clustering Algorithms Reinforcement Learning Q1. Q2. Q4. Q3. Q5. Is this A or B? Is this weird? How much or how many? How is this organized? What should I do next?
www.edureka.co/data-scienceEdureka’s Data Science Certification Training The 5 Questions Asked In Data Science In data science, basically we have 5 kind of problems. Q1. Q2. Q4. Q3. Q5. Is this A or B? Is this weird? How is this organized? What should I do next? Classification Algorithm Anomaly Detection Algorithm Regression Algorithms Clustering Algorithms Reinforcement Learning How much or how many? Is this A or B?
www.edureka.co/data-scienceEdureka’s Data Science Certification Training What Is Regression?
www.edureka.co/data-scienceEdureka’s Data Science Certification Training What Is Regression? ➢ Regression analysis is a predictive modelling technique. ➢ It estimates the relationship between a dependent (target) and an independent variable (predictor). Input value = 7.00 Predicted outcome = 123.9 X-axis Y-axis
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Types Of Regression
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Types Of Regression Linear Regression • When there is a linear relationship between independent and dependent variables. • When the dependent variable is categorical (0/ 1, True/ False, Yes/ No, A/B/C) in nature. Logistic Regression Polynomial Regression • When the power of independent variable is more than 1. X Y X Y
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Types Of Regression Linear Regression • When there is a linear relationship between independent and dependent variables. • When the dependent variable is categorical (0/ 1, True/ False, Yes/ No, A/B/C) in nature. Logistic Regression Polynomial Regression • When the power of independent variable is more than 1. X Y X Y
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Why Logistic Regression?
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Why Logistic Regression? Whenever the outcome of the dependent variable (Y) is discrete, like 0 or 1, Yes or No, A, B or C, we use logistic regression.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Why can’t we use Linear Regression? Why Not Linear Regression? Whenever the outcome of the dependent variable (Y) is discrete, like 0 or 1, Yes or No, A, B or C, we use logistic regression.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Y-axis X-axis 0 1 Now since our value of Y will be between 0 and 1, the linear line has to be clipped at 0 and 1. Why Not Linear Regression?
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Y-axis X-axis 0 1 With this, our resulting curve cannot be formulated into a single formula. We needed a new way to solve this kind of problem. Hence, we came up with Logistic Regression! Why Not Linear Regression?
www.edureka.co/data-scienceEdureka’s Data Science Certification Training 0 0.2 0.4 0.6 0.8 1 1.2 0 1 2 3 4 5 6 7 8 9 10 LOGISTIC REGRESSION The S Curve Logistic Regression Curve
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Why Logistic Regression? Equation for a straight line Y = C + B1X1 + B2X2 + …. Range of Y is from – (infinity) to infinity
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Why Logistic Regression? Equation for a straight line Y = C + B1X1 + B2X2 + …. Range of Y is from – (infinity) to infinity Let’s try to reduce the Logistic Regression Equation from this equation Y = C + B1X1 + B2X2 + …. In Logistic Regression Y can only be between 0 and 1.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Why Logistic Regression? Equation for a straight line Y = C + B1X1 + B2X2 + …. Range of Y is from – (infinity) to infinity Let’s try to reduce the Logistic Regression Equation from this equation Y = C + B1X1 + B2X2 + …. In Logistic Equation Y can only be between 0 and 1. Now, to get the range of Y between 0 and infinity, let’s transform Y Y 1 − Y Y=0 | 0 Y=1 | infinity Now, we have the range between 0 and infinity
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Why Logistic Regression? Equation for a straight line Y = C + B1X1 + B2X2 + …. Range of Y is from – (infinity) to infinity Let’s try to reduce the Logistic Regression Equation from this equation Y = C + B1X1 + B2X2 + …. In Logistic Equation Y can only be between 0 and 1. Now, to get the range of Y between 0 and infinity, let’s transform Y Y 1 − Y Y=0 | 0 Y=1 | infinity Now, we have the range between 0 and infinity Let us transform it further, to get the range between –( infinity ) and infinity Y 1 − Y log 𝐘 𝟏 − 𝐘 log = C + B1X1 + B2X2 + ….
www.edureka.co/data-scienceEdureka’s Data Science Certification Training What Is Logistic Regression?
www.edureka.co/data-scienceEdureka’s Data Science Certification Training What Is Logistic Regression? Logistic regression, or logit regression, or logit model is a regression model where the dependent variable (DV) is categorical. DependentCategorical Variables that can have only fixed values such as A, B or C, Yes or No Y = f(X) i.e Y is dependent on X.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Therefore, whenever the outcome of the dependent variable (Y) is categorical, like 0 or 1, Yes or No, A, B or C, we use logistic regression. What Is Logistic Regression? 0.0 1.0
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Does Logistic Regression Work?
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Let us take an example to understand this: MODEL Selected 147, 120, 121, 128, 110, 119, 133 Not Selected 107, 89, 92, 106, 104, 114 How does Logistic Regression Work?
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Let us take an example to understand this: MODEL Selected 147, 120, 121, 128, 110, 119, 133 Not Selected 107, 89, 92, 106, 104, 114 How does Logistic Regression Work?
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Does Logistic Regression Work? Let’s take a sample dataset in R, which is called mtcars. Our aim is to predict whether a car will have a V-engine or a Straight engine based on our inputs. Mpg - Miles/US Gallon Cyl – Number of cylinders Disp – Number of cylinders Hp – Gross horsepower Drat – Rear axle ratio Wt – Weight (lb/1000) Qsec – 1/4 mile time Vs – V Engine Am – Transmission Type Gear – Number of forward gears Carb - Number of carburetors Key
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Does Logistic Regression Work? For now, let’s take disp and wt as our primary independent variables. Why? We’ll be discussing it in our next section. Mpg - Miles/US Gallon Cyl – Number of cylinders Disp – Number of cylinders Hp – Gross horsepower Drat – Rear axle ratio Wt – Weight (lb/1000) Qsec – 1/4 mile time Vs – V Engine Am – Transmission Type Gear – Number of forward gears Carb - Number of carburetors Key
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Does Logistic Regression Work? Since our aim is to know which engine will fit, the engine will either be V – type or not, i.e either 1 or 0. Therefore, our dependent variable is Y. Mpg - Miles/US Gallon Cyl – Number of cylinders Disp – Number of cylinders Hp – Gross horsepower Drat – Rear axle ratio Wt – Weight (lb/1000) Qsec – 1/4 mile time Vs – V Engine Am – Transmission Type Gear – Number of forward gears Carb - Number of carburetors Key
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Does Logistic Regression Work? Before creating the model, we divide our dataset into training and testing. 80 % 20% Training Dataset Testing Dataset
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Does Logistic Regression Work? Training to create our model and testing to validate it. 80 % Create model from this Training Dataset
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How Does Logistic Regression Work? Once the model is created we get the following outputs, which are calculated using MLE*. 𝛽° 𝛽1 𝛽2 *Maximum Likelihood Estimation is a method of estimating the parameters.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Estimated Regression Equation Estimated Regression Equation: Here, β° = Constant Coefficient 𝛽1 = Coefficient of x1 𝛽2 = Coefficient of x2 𝑥1 = Independent variable 𝑥2 = Independent variable e = Euler’s Number P(Y) = Probability that Y equals 1 𝑒 𝛽 ° + 𝛽1 𝑥 1+ 𝛽2 𝑥 2 1 + 𝑒 𝛽 ° + 𝛽1 𝑥 1+ 𝛽2 𝑥 2 Logit (Y) = Y 1 − Y log =
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Substituting Values Let’s take a value from the test dataset How Does Logistic Regression Work? 0.9849 1.9849 = = 0.4962 β° = 1.83010 β1 = 1.09428 β2 = - 0.02529 e = 2.7183 X1 = 120.3 X2 = 2.140 = 0.4962Probability of ‘vs’ being ‘1’ We will assume the threshold to be 0.5 Hence our car will not have a VS engine and hence have a straight engine. Logit (Y)
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Substituting Values Let’s take a value from the test dataset How Does Logistic Regression Work? 0.9849 1.9849 = = 0.4962 β° = 1.83010 β1 = 1.09428 β2 = - 0.02529 e = 2.7183 X1 = 120.3 X2 = 2.140 = 0.4962Probability of ‘vs’ being ‘1’ We will assume the threshold to be 0.5 Hence our car will not have a VS engine and hence have a straight engine. Logit (Y)
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Logistic Regression Demo In R
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Logistic Regression Demo In R Key Npreg – number of pregnancies Glu – plasma glucose concentration Bp – diastolic blood pressure Skin – triceps skin fold thickness Bmi – body mass index Ped – diabetes pedigree function Age – age in years Type – 1 for yes and 0 for No for diabetic Our aim is to predict whether a patient is diabetic or not based on the following values.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Logistic Regression Demo In R First, we will read the data from our CSV file, by entering this command:
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Logistic Regression Demo In R First, we will read the data from our CSV file, by entering this command: Then, we will split our dataset into training and testing, with the ratio 8:2
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Logistic Regression Demo In R First, we will read the data from our CSV file, by entering this command: Then, we will split our dataset into training and testing, with the ratio 8:2 After that we’ll create our model using the training dataset
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Logistic Regression Demo In R The summary of the model will give this.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Logistic Regression Demo In R The summary of the model will give this. *** - 99.9% confident ** - 99% confident * - 95% confident . - 90% confident
www.edureka.co/data-scienceEdureka’s Data Science Certification Training • This is the summary model that we get after improving our model. • So the insignificant fields is skin Logistic Regression Demo In R Null deviance shows how well the response variable is predicted by a model that includes only the intercept (grand mean) Residual deviance shows how well the response variable is predicted with inclusion of independent variables.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Logistic Regression Demo In R We will the predict the values for the test dataset and then categorize them according to threshold which is 0.5
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Logistic Regression Demo In R We will the predict the values for the test dataset and then categorize them according to threshold which is 0.5 Create Confusion Matrix for the training dataset
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Logistic Regression Demo In R We will the predict the values for the test dataset and then categorize them according to threshold which is 0.5 And then finding the accuracy Create Confusion Matrix for the training dataset
www.edureka.co/data-scienceEdureka’s Data Science Certification Training How To Find The Threshold?
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Let us take a sample for our significant fields and see whether our patient is diabetic or not based on the model that we created. Store the predicted values for training dataset in ‘res’ variable Logistic Regression Demo In R
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Let us take a sample for our significant fields and see whether our patient is diabetic or not based on the model that we created. Store the predicted values for training dataset in ‘res’ variable Logistic Regression Demo In R Import the library for the ROCR package
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Let us take a sample for our significant fields and see whether our patient is diabetic or not based on the model that we created. Store the predicted values for training dataset in ‘res’ variable Logistic Regression Demo In R Import the library for the ROCR package Define the ‘ROCRPred’ and and ‘ROCRPerf’ variables
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Let us take a sample for our significant fields and see whether our patient is diabetic or not based on the model that we created. Store the predicted values for training dataset in ‘res’ variable Logistic Regression Demo In R Import the library for the ROCR package Define the ‘ROCRPred’ and and ‘ROCRPerf’ variables Plot the graph!
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Logistic Regression Demo In R
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Logistic Regression Demo In R Confusion Matrix for Test Dataset with 0.5 threshold Confusion Matrix for Test Dataset with 0.3 threshold Accuracy = 73.8% TrueNegatives = 9 Accuracy = 67.8% TrueNegatives = 5
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Logistic Regression Demo In R Confusion Matrix for Test Dataset with 0.5 threshold Confusion Matrix for Test Dataset with 0.3 threshold Accuracy = 73.8% TrueNegatives = 9 Accuracy = 67.8% TrueNegatives = 5 Confusion Matrix for Training Dataset with 0.3 threshold Accuracy = 79.4%
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Logistic Regression: Use Cases
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Logistic Regression – Use Case • Logistic Regression was used in conjugation with Geographic Information system in 2005, to predict the malaria breeding grounds in Africa. • Logistic regression was used to approximate areas where malaria patients would exist based on geographical inputs.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Logistic Regression – Use Case • Logit analysis is a statistical technique used by marketers to assess the scope of customer acceptance of a product, particularly a new product. • It attempts to determine the intensity or magnitude of customers' purchase intentions and translates that into a measure of actual buying behavior. • Many e-commerce websites assess this behavior using this model.
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Session In A Minute The 5 Questions In Data Science Use CasesLogistic Regression Working? Logistic Regression – What & Why? Demo What Is Regression?
www.edureka.co/data-scienceEdureka’s Data Science Certification Training Course Details Go to www.edureka.co/data-science Get Edureka Certified in Data Science Today! What our learners have to say about us! Shravan Reddy says- “I would like to recommend any one who wants to be a Data Scientist just one place: Edureka. Explanations are clean, clear, easy to understand. Their support team works very well.. I took the Data Science course and I'm going to take Machine Learning with Mahout and then Big Data and Hadoop”. Gnana Sekhar says - “Edureka Data science course provided me a very good mixture of theoretical and practical training. LMS pre recorded sessions and assignments were very good as there is a lot of information in them that will help me in my job. Edureka is my teaching GURU now...Thanks EDUREKA.” Balu Samaga says - “It was a great experience to undergo and get certified in the Data Science course from Edureka. Quality of the training materials, assignments, project, support and other infrastructures are a top notch.”
www.edureka.co/data-scienceEdureka’s Data Science Certification Training

More Related Content

PDF
Logistic Regression in Python | Logistic Regression Example | Machine Learnin...
byEdureka!
 
PDF
Naive Bayes Classifier
byYiqun Hu
 
PDF
Machine Learning Course | Edureka
byEdureka!
 
PPTX
Support vector machines (svm)
bySharayu Patil
 
PDF
Linear Regression Algorithm | Linear Regression in Python | Machine Learning ...
byEdureka!
 
PDF
Feature selection
byDong Guo
 
PDF
L2. Evaluating Machine Learning Algorithms I
byMachine Learning Valencia
 
PDF
Logistic regression in Machine Learning
byKuppusamy P
 
Logistic Regression in Python | Logistic Regression Example | Machine Learnin...
byEdureka!
 
Naive Bayes Classifier
byYiqun Hu
 
Machine Learning Course | Edureka
byEdureka!
 
Support vector machines (svm)
bySharayu Patil
 
Linear Regression Algorithm | Linear Regression in Python | Machine Learning ...
byEdureka!
 
Feature selection
byDong Guo
 
L2. Evaluating Machine Learning Algorithms I
byMachine Learning Valencia
 
Logistic regression in Machine Learning
byKuppusamy P
 

What's hot

ODP
NAIVE BAYES CLASSIFIER
byKnoldus Inc.
 
ODP
Machine Learning with Decision trees
byKnoldus Inc.
 
PPTX
Naive bayes
byAshraf Uddin
 
PPTX
Linear Regression
byAbdullah al Mamun
 
PPTX
Feature selection
bydkpawar
 
PDF
Logistic regression
byVenkata Reddy Konasani
 
PDF
Feature Engineering in Machine Learning
byKnoldus Inc.
 
PPTX
Support vector machine
byzekeLabs Technologies
 
PDF
Data Science - Part V - Decision Trees & Random Forests
byDerek Kane
 
PDF
Classification Based Machine Learning Algorithms
byMd. Main Uddin Rony
 
PPTX
Ml2 train test-splits_validation_linear_regression
byankit_ppt
 
PPTX
Data Analysis: Evaluation Metrics for Supervised Learning Models of Machine L...
byMd. Main Uddin Rony
 
PDF
Performance Metrics for Machine Learning Algorithms
byKush Kulshrestha
 
PDF
Module 4: Model Selection and Evaluation
bySara Hooker
 
PPT
Chapter - 6 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
byerror007
 
PDF
Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorith...
byEdureka!
 
PDF
Cross-validation Tutorial: What, how and which?
byPradeep Redddy Raamana
 
PPTX
Wrapper feature selection method
byAmir Razmjou
 
PPTX
Decision Tree Learning
byMd. Ariful Hoque
 
PPT
Decision tree
byAmi_Surati
 
NAIVE BAYES CLASSIFIER
byKnoldus Inc.
 
Machine Learning with Decision trees
byKnoldus Inc.
 
Naive bayes
byAshraf Uddin
 
Linear Regression
byAbdullah al Mamun
 
Feature selection
bydkpawar
 
Logistic regression
byVenkata Reddy Konasani
 
Feature Engineering in Machine Learning
byKnoldus Inc.
 
Support vector machine
byzekeLabs Technologies
 
Data Science - Part V - Decision Trees & Random Forests
byDerek Kane
 
Classification Based Machine Learning Algorithms
byMd. Main Uddin Rony
 
Ml2 train test-splits_validation_linear_regression
byankit_ppt
 
Data Analysis: Evaluation Metrics for Supervised Learning Models of Machine L...
byMd. Main Uddin Rony
 
Performance Metrics for Machine Learning Algorithms
byKush Kulshrestha
 
Module 4: Model Selection and Evaluation
bySara Hooker
 
Chapter - 6 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
byerror007
 
Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorith...
byEdureka!
 
Cross-validation Tutorial: What, how and which?
byPradeep Redddy Raamana
 
Wrapper feature selection method
byAmir Razmjou
 
Decision Tree Learning
byMd. Ariful Hoque
 
Decision tree
byAmi_Surati
 

Similar to Logistic Regression in R | Machine Learning Algorithms | Data Science Training | Edureka

PDF
Linear Regression vs Logistic Regression | Edureka
byEdureka!
 
PPTX
Linear Regression Algorithm | Linear Regression in R | Data Science Training ...
byEdureka!
 
PDF
Fast Distributed Online Classification
byDataWorks Summit/Hadoop Summit
 
PPTX
CHAPTER 11 LOGISTIC REGRESSION.pptx
byUmaDeviAnanth
 
PDF
Fast Distributed Online Classification
byPrasad Chalasani
 
PPTX
Lecture 3.1_ Logistic Regression.pptx
byajondaree
 
PPTX
Lecture 3.1_ Logistic Regression powerpoint
byzahidwadiwale
 
PDF
Kaggle KDD Cup Report
byChamila Wijayarathna
 
PDF
Logistic regression, machine learning algorithms
byLearnbay Datascience
 
PPTX
Logistic Regression | Logistic Regression In Python | Machine Learning Algori...
bySimplilearn
 
PPTX
ml ppts, large lanfgguaf emodel learning curve
bysachincoder0
 
PPTX
Machine learning Algorithms with a Sagemaker demo
byHridyesh Bisht
 
PDF
L1 intro2 supervised_learning
byYogendra Singh
 
PPTX
CST413 KTU S7 CSE Machine Learning Supervised Learning Classification Algorit...
byresming1
 
PDF
Unit 2_Logistic Regression_Types_Regularization.pdf
byKanchanPatil34
 
PDF
MLT_KCS055 (Unit-2 Notes).pdfNNNNNNNNNNNNNNNN
byRAtna29
 
PDF
Machine Learning deep learning artificial
byAlaaShorbaji1
 
PPTX
Machine_Learning.pptx
byVickyKumar131533
 
DOCX
Essentials of machine learning algorithms
byArunangsu Sahu
 
PDF
7. logistics regression using spss
byDr Nisha Arora
 
Linear Regression vs Logistic Regression | Edureka
byEdureka!
 
Linear Regression Algorithm | Linear Regression in R | Data Science Training ...
byEdureka!
 
Fast Distributed Online Classification
byDataWorks Summit/Hadoop Summit
 
CHAPTER 11 LOGISTIC REGRESSION.pptx
byUmaDeviAnanth
 
Fast Distributed Online Classification
byPrasad Chalasani
 
Lecture 3.1_ Logistic Regression.pptx
byajondaree
 
Lecture 3.1_ Logistic Regression powerpoint
byzahidwadiwale
 
Kaggle KDD Cup Report
byChamila Wijayarathna
 
Logistic regression, machine learning algorithms
byLearnbay Datascience
 
Logistic Regression | Logistic Regression In Python | Machine Learning Algori...
bySimplilearn
 
ml ppts, large lanfgguaf emodel learning curve
bysachincoder0
 
Machine learning Algorithms with a Sagemaker demo
byHridyesh Bisht
 
L1 intro2 supervised_learning
byYogendra Singh
 
CST413 KTU S7 CSE Machine Learning Supervised Learning Classification Algorit...
byresming1
 
Unit 2_Logistic Regression_Types_Regularization.pdf
byKanchanPatil34
 
MLT_KCS055 (Unit-2 Notes).pdfNNNNNNNNNNNNNNNN
byRAtna29
 
Machine Learning deep learning artificial
byAlaaShorbaji1
 
Machine_Learning.pptx
byVickyKumar131533
 
Essentials of machine learning algorithms
byArunangsu Sahu
 
7. logistics regression using spss
byDr Nisha Arora
 

More from Edureka!

PDF
What to learn during the 21 days Lockdown | Edureka
byEdureka!
 
PDF
Top 10 Dying Programming Languages in 2020 | Edureka
byEdureka!
 
PDF
Top 5 Trending Business Intelligence Tools | Edureka
byEdureka!
 
PDF
Tableau Tutorial for Data Science | Edureka
byEdureka!
 
PDF
Python Programming Tutorial | Edureka
byEdureka!
 
PDF
Top 5 PMP Certifications | Edureka
byEdureka!
 
PDF
Top Maven Interview Questions in 2020 | Edureka
byEdureka!
 
PDF
Linux Mint Tutorial | Edureka
byEdureka!
 
PDF
How to Deploy Java Web App in AWS| Edureka
byEdureka!
 
PDF
Importance of Digital Marketing | Edureka
byEdureka!
 
PDF
RPA in 2020 | Edureka
byEdureka!
 
PDF
Email Notifications in Jenkins | Edureka
byEdureka!
 
PDF
EA Algorithm in Machine Learning | Edureka
byEdureka!
 
PDF
Cognitive AI Tutorial | Edureka
byEdureka!
 
PDF
AWS Cloud Practitioner Tutorial | Edureka
byEdureka!
 
PDF
Blue Prism Top Interview Questions | Edureka
byEdureka!
 
PDF
Big Data on AWS Tutorial | Edureka
byEdureka!
 
PDF
A star algorithm | A* Algorithm in Artificial Intelligence | Edureka
byEdureka!
 
PDF
Kubernetes Installation on Ubuntu | Edureka
byEdureka!
 
PDF
Introduction to DevOps | Edureka
byEdureka!
 
What to learn during the 21 days Lockdown | Edureka
byEdureka!
 
Top 10 Dying Programming Languages in 2020 | Edureka
byEdureka!
 
Top 5 Trending Business Intelligence Tools | Edureka
byEdureka!
 
Tableau Tutorial for Data Science | Edureka
byEdureka!
 
Python Programming Tutorial | Edureka
byEdureka!
 
Top 5 PMP Certifications | Edureka
byEdureka!
 
Top Maven Interview Questions in 2020 | Edureka
byEdureka!
 
Linux Mint Tutorial | Edureka
byEdureka!
 
How to Deploy Java Web App in AWS| Edureka
byEdureka!
 
Importance of Digital Marketing | Edureka
byEdureka!
 
RPA in 2020 | Edureka
byEdureka!
 
Email Notifications in Jenkins | Edureka
byEdureka!
 
EA Algorithm in Machine Learning | Edureka
byEdureka!
 
Cognitive AI Tutorial | Edureka
byEdureka!
 
AWS Cloud Practitioner Tutorial | Edureka
byEdureka!
 
Blue Prism Top Interview Questions | Edureka
byEdureka!
 
Big Data on AWS Tutorial | Edureka
byEdureka!
 
A star algorithm | A* Algorithm in Artificial Intelligence | Edureka
byEdureka!
 
Kubernetes Installation on Ubuntu | Edureka
byEdureka!
 
Introduction to DevOps | Edureka
byEdureka!
 

Recently uploaded

PDF
Session 4 - Specialized AI Associate Series: UiPath Document Understanding O...
byDianaGray10
 
PDF
Optimizing a Global Training Strategy: An LKQ Corporation Case Study
byRustici Software
 
PDF
Cloud Infrastructure Security: Navigating Threats & Overcoming Challenges
byTeleglobal International
 
PPTX
Cloud Metrics: The Cost-Value Equation (All Things Open 2025)
byEric D. Schabell
 
PDF
What's New? ThousandEyes Features and Highlights: October 2025
byThousandEyes
 
PDF
Fairness and Bias in AI Ethics and Explainability
byShiwani Gupta
 
PDF
Cassandra vs. ScyllaDB: Evolutionary Differences
byScyllaDB
 
PDF
2025 October Patch Tuesday
byIvanti
 
PDF
Atlantix is an AI-first venture studio, building companies with AI at the core
byadmin625551
 
PDF
From Infrastructure to Insight: Technical Pathways to Value in Europe’s Compu...
byMindtrek
 
PDF
Planetek Italia Corporate Profile brochure
byPlanetek Italia Srl
 
PPTX
sap 2016-09-14_PP_MRPType_VB_Blogged.pptx
byAmira Jemi
 
PDF
Master Deck: GraphSummit Bengaluru (Oct 7)
byNeo4j
 
PDF
“Introduction to Depth Sensing: Technologies, Trade-offs and Applications,” a...
byEdge AI and Vision Alliance
 
PPTX
WUG-DMV: Workfront Wizards: Tips & Tricks Exchange (Sept 2025)
byWUG-DC MARYLAND VIRGINIA
 
PDF
AI assisted software — Cathedral or Bazaar?.pdf
byMindtrek
 
PDF
Using Copilot with Microsoft Office Apps
byDeb Walther
 
PPTX
Top Trends in Kubernetes Security: TalosCon 2025
byCheryl Hung
 
PDF
How computers tell who they are using TPM2.pdf
byMindtrek
 
PDF
Digital Sovereignty has to start with Open Source.pdf
byMindtrek
 
Session 4 - Specialized AI Associate Series: UiPath Document Understanding O...
byDianaGray10
 
Optimizing a Global Training Strategy: An LKQ Corporation Case Study
byRustici Software
 
Cloud Infrastructure Security: Navigating Threats & Overcoming Challenges
byTeleglobal International
 
Cloud Metrics: The Cost-Value Equation (All Things Open 2025)
byEric D. Schabell
 
What's New? ThousandEyes Features and Highlights: October 2025
byThousandEyes
 
Fairness and Bias in AI Ethics and Explainability
byShiwani Gupta
 
Cassandra vs. ScyllaDB: Evolutionary Differences
byScyllaDB
 
2025 October Patch Tuesday
byIvanti
 
Atlantix is an AI-first venture studio, building companies with AI at the core
byadmin625551
 
From Infrastructure to Insight: Technical Pathways to Value in Europe’s Compu...
byMindtrek
 
Planetek Italia Corporate Profile brochure
byPlanetek Italia Srl
 
sap 2016-09-14_PP_MRPType_VB_Blogged.pptx
byAmira Jemi
 
Master Deck: GraphSummit Bengaluru (Oct 7)
byNeo4j
 
“Introduction to Depth Sensing: Technologies, Trade-offs and Applications,” a...
byEdge AI and Vision Alliance
 
WUG-DMV: Workfront Wizards: Tips & Tricks Exchange (Sept 2025)
byWUG-DC MARYLAND VIRGINIA
 
AI assisted software — Cathedral or Bazaar?.pdf
byMindtrek
 
Using Copilot with Microsoft Office Apps
byDeb Walther
 
Top Trends in Kubernetes Security: TalosCon 2025
byCheryl Hung
 
How computers tell who they are using TPM2.pdf
byMindtrek
 
Digital Sovereignty has to start with Open Source.pdf
byMindtrek
 

Logistic Regression in R | Machine Learning Algorithms | Data Science Training | Edureka

  • 1.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Logistic Regression
  • 2.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training What Will You Learn Today? What is Regression? The 5 Questions asked in Data Science Logistic Regression – What and Why? How does Logistic Regression work? Demo In R: Diabetes Use Case 1 2 3 4 65 Logistic Regression – Use Cases
  • 3.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training The 5 Questions Asked In Data Science
  • 4.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training The 5 Questions Asked In Data Science In data science, basically we have 5 kind of problems. Classification Algorithm Anomaly Detection Algorithm Regression Algorithms Clustering Algorithms Reinforcement Learning Q1. Q2. Q4. Q3. Q5. Is this A or B? Is this weird? How much or how many? How is this organized? What should I do next?
  • 5.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training The 5 Questions Asked In Data Science In data science, basically we have 5 kind of problems. Q1. Q2. Q4. Q3. Q5. Is this A or B? Is this weird? How is this organized? What should I do next? Classification Algorithm Anomaly Detection Algorithm Regression Algorithms Clustering Algorithms Reinforcement Learning How much or how many? Is this A or B?
  • 6.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training What Is Regression?
  • 7.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training What Is Regression? ➢ Regression analysis is a predictive modelling technique. ➢ It estimates the relationship between a dependent (target) and an independent variable (predictor). Input value = 7.00 Predicted outcome = 123.9 X-axis Y-axis
  • 8.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Types Of Regression
  • 9.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Types Of Regression Linear Regression • When there is a linear relationship between independent and dependent variables. • When the dependent variable is categorical (0/ 1, True/ False, Yes/ No, A/B/C) in nature. Logistic Regression Polynomial Regression • When the power of independent variable is more than 1. X Y X Y
  • 10.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Types Of Regression Linear Regression • When there is a linear relationship between independent and dependent variables. • When the dependent variable is categorical (0/ 1, True/ False, Yes/ No, A/B/C) in nature. Logistic Regression Polynomial Regression • When the power of independent variable is more than 1. X Y X Y
  • 11.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Why Logistic Regression?
  • 12.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Why Logistic Regression? Whenever the outcome of the dependent variable (Y) is discrete, like 0 or 1, Yes or No, A, B or C, we use logistic regression.
  • 13.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Why can’t we use Linear Regression? Why Not Linear Regression? Whenever the outcome of the dependent variable (Y) is discrete, like 0 or 1, Yes or No, A, B or C, we use logistic regression.
  • 14.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Y-axis X-axis 0 1 Now since our value of Y will be between 0 and 1, the linear line has to be clipped at 0 and 1. Why Not Linear Regression?
  • 15.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Y-axis X-axis 0 1 With this, our resulting curve cannot be formulated into a single formula. We needed a new way to solve this kind of problem. Hence, we came up with Logistic Regression! Why Not Linear Regression?
  • 16.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training 0 0.2 0.4 0.6 0.8 1 1.2 0 1 2 3 4 5 6 7 8 9 10 LOGISTIC REGRESSION The S Curve Logistic Regression Curve
  • 17.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Why Logistic Regression? Equation for a straight line Y = C + B1X1 + B2X2 + …. Range of Y is from – (infinity) to infinity
  • 18.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Why Logistic Regression? Equation for a straight line Y = C + B1X1 + B2X2 + …. Range of Y is from – (infinity) to infinity Let’s try to reduce the Logistic Regression Equation from this equation Y = C + B1X1 + B2X2 + …. In Logistic Regression Y can only be between 0 and 1.
  • 19.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Why Logistic Regression? Equation for a straight line Y = C + B1X1 + B2X2 + …. Range of Y is from – (infinity) to infinity Let’s try to reduce the Logistic Regression Equation from this equation Y = C + B1X1 + B2X2 + …. In Logistic Equation Y can only be between 0 and 1. Now, to get the range of Y between 0 and infinity, let’s transform Y Y 1 − Y Y=0 | 0 Y=1 | infinity Now, we have the range between 0 and infinity
  • 20.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Why Logistic Regression? Equation for a straight line Y = C + B1X1 + B2X2 + …. Range of Y is from – (infinity) to infinity Let’s try to reduce the Logistic Regression Equation from this equation Y = C + B1X1 + B2X2 + …. In Logistic Equation Y can only be between 0 and 1. Now, to get the range of Y between 0 and infinity, let’s transform Y Y 1 − Y Y=0 | 0 Y=1 | infinity Now, we have the range between 0 and infinity Let us transform it further, to get the range between –( infinity ) and infinity Y 1 − Y log 𝐘 𝟏 − 𝐘 log = C + B1X1 + B2X2 + ….
  • 21.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training What Is Logistic Regression?
  • 22.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training What Is Logistic Regression? Logistic regression, or logit regression, or logit model is a regression model where the dependent variable (DV) is categorical. DependentCategorical Variables that can have only fixed values such as A, B or C, Yes or No Y = f(X) i.e Y is dependent on X.
  • 23.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Therefore, whenever the outcome of the dependent variable (Y) is categorical, like 0 or 1, Yes or No, A, B or C, we use logistic regression. What Is Logistic Regression? 0.0 1.0
  • 24.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training How Does Logistic Regression Work?
  • 25.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Let us take an example to understand this: MODEL Selected 147, 120, 121, 128, 110, 119, 133 Not Selected 107, 89, 92, 106, 104, 114 How does Logistic Regression Work?
  • 26.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Let us take an example to understand this: MODEL Selected 147, 120, 121, 128, 110, 119, 133 Not Selected 107, 89, 92, 106, 104, 114 How does Logistic Regression Work?
  • 27.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training How Does Logistic Regression Work? Let’s take a sample dataset in R, which is called mtcars. Our aim is to predict whether a car will have a V-engine or a Straight engine based on our inputs. Mpg - Miles/US Gallon Cyl – Number of cylinders Disp – Number of cylinders Hp – Gross horsepower Drat – Rear axle ratio Wt – Weight (lb/1000) Qsec – 1/4 mile time Vs – V Engine Am – Transmission Type Gear – Number of forward gears Carb - Number of carburetors Key
  • 28.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training How Does Logistic Regression Work? For now, let’s take disp and wt as our primary independent variables. Why? We’ll be discussing it in our next section. Mpg - Miles/US Gallon Cyl – Number of cylinders Disp – Number of cylinders Hp – Gross horsepower Drat – Rear axle ratio Wt – Weight (lb/1000) Qsec – 1/4 mile time Vs – V Engine Am – Transmission Type Gear – Number of forward gears Carb - Number of carburetors Key
  • 29.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training How Does Logistic Regression Work? Since our aim is to know which engine will fit, the engine will either be V – type or not, i.e either 1 or 0. Therefore, our dependent variable is Y. Mpg - Miles/US Gallon Cyl – Number of cylinders Disp – Number of cylinders Hp – Gross horsepower Drat – Rear axle ratio Wt – Weight (lb/1000) Qsec – 1/4 mile time Vs – V Engine Am – Transmission Type Gear – Number of forward gears Carb - Number of carburetors Key
  • 30.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training How Does Logistic Regression Work? Before creating the model, we divide our dataset into training and testing. 80 % 20% Training Dataset Testing Dataset
  • 31.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training How Does Logistic Regression Work? Training to create our model and testing to validate it. 80 % Create model from this Training Dataset
  • 32.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training How Does Logistic Regression Work? Once the model is created we get the following outputs, which are calculated using MLE*. 𝛽° 𝛽1 𝛽2 *Maximum Likelihood Estimation is a method of estimating the parameters.
  • 33.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Estimated Regression Equation Estimated Regression Equation: Here, β° = Constant Coefficient 𝛽1 = Coefficient of x1 𝛽2 = Coefficient of x2 𝑥1 = Independent variable 𝑥2 = Independent variable e = Euler’s Number P(Y) = Probability that Y equals 1 𝑒 𝛽 ° + 𝛽1 𝑥 1+ 𝛽2 𝑥 2 1 + 𝑒 𝛽 ° + 𝛽1 𝑥 1+ 𝛽2 𝑥 2 Logit (Y) = Y 1 − Y log =
  • 34.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Substituting Values Let’s take a value from the test dataset How Does Logistic Regression Work? 0.9849 1.9849 = = 0.4962 β° = 1.83010 β1 = 1.09428 β2 = - 0.02529 e = 2.7183 X1 = 120.3 X2 = 2.140 = 0.4962Probability of ‘vs’ being ‘1’ We will assume the threshold to be 0.5 Hence our car will not have a VS engine and hence have a straight engine. Logit (Y)
  • 35.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Substituting Values Let’s take a value from the test dataset How Does Logistic Regression Work? 0.9849 1.9849 = = 0.4962 β° = 1.83010 β1 = 1.09428 β2 = - 0.02529 e = 2.7183 X1 = 120.3 X2 = 2.140 = 0.4962Probability of ‘vs’ being ‘1’ We will assume the threshold to be 0.5 Hence our car will not have a VS engine and hence have a straight engine. Logit (Y)
  • 36.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Logistic Regression Demo In R
  • 37.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Logistic Regression Demo In R Key Npreg – number of pregnancies Glu – plasma glucose concentration Bp – diastolic blood pressure Skin – triceps skin fold thickness Bmi – body mass index Ped – diabetes pedigree function Age – age in years Type – 1 for yes and 0 for No for diabetic Our aim is to predict whether a patient is diabetic or not based on the following values.
  • 38.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Logistic Regression Demo In R First, we will read the data from our CSV file, by entering this command:
  • 39.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Logistic Regression Demo In R First, we will read the data from our CSV file, by entering this command: Then, we will split our dataset into training and testing, with the ratio 8:2
  • 40.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Logistic Regression Demo In R First, we will read the data from our CSV file, by entering this command: Then, we will split our dataset into training and testing, with the ratio 8:2 After that we’ll create our model using the training dataset
  • 41.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Logistic Regression Demo In R The summary of the model will give this.
  • 42.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Logistic Regression Demo In R The summary of the model will give this. *** - 99.9% confident ** - 99% confident * - 95% confident . - 90% confident
  • 43.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training • This is the summary model that we get after improving our model. • So the insignificant fields is skin Logistic Regression Demo In R Null deviance shows how well the response variable is predicted by a model that includes only the intercept (grand mean) Residual deviance shows how well the response variable is predicted with inclusion of independent variables.
  • 44.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Logistic Regression Demo In R We will the predict the values for the test dataset and then categorize them according to threshold which is 0.5
  • 45.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Logistic Regression Demo In R We will the predict the values for the test dataset and then categorize them according to threshold which is 0.5 Create Confusion Matrix for the training dataset
  • 46.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Logistic Regression Demo In R We will the predict the values for the test dataset and then categorize them according to threshold which is 0.5 And then finding the accuracy Create Confusion Matrix for the training dataset
  • 47.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training How To Find The Threshold?
  • 48.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Let us take a sample for our significant fields and see whether our patient is diabetic or not based on the model that we created. Store the predicted values for training dataset in ‘res’ variable Logistic Regression Demo In R
  • 49.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Let us take a sample for our significant fields and see whether our patient is diabetic or not based on the model that we created. Store the predicted values for training dataset in ‘res’ variable Logistic Regression Demo In R Import the library for the ROCR package
  • 50.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Let us take a sample for our significant fields and see whether our patient is diabetic or not based on the model that we created. Store the predicted values for training dataset in ‘res’ variable Logistic Regression Demo In R Import the library for the ROCR package Define the ‘ROCRPred’ and and ‘ROCRPerf’ variables
  • 51.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Let us take a sample for our significant fields and see whether our patient is diabetic or not based on the model that we created. Store the predicted values for training dataset in ‘res’ variable Logistic Regression Demo In R Import the library for the ROCR package Define the ‘ROCRPred’ and and ‘ROCRPerf’ variables Plot the graph!
  • 52.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Logistic Regression Demo In R
  • 53.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Logistic Regression Demo In R Confusion Matrix for Test Dataset with 0.5 threshold Confusion Matrix for Test Dataset with 0.3 threshold Accuracy = 73.8% TrueNegatives = 9 Accuracy = 67.8% TrueNegatives = 5
  • 54.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Logistic Regression Demo In R Confusion Matrix for Test Dataset with 0.5 threshold Confusion Matrix for Test Dataset with 0.3 threshold Accuracy = 73.8% TrueNegatives = 9 Accuracy = 67.8% TrueNegatives = 5 Confusion Matrix for Training Dataset with 0.3 threshold Accuracy = 79.4%
  • 55.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Logistic Regression: Use Cases
  • 56.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Logistic Regression – Use Case • Logistic Regression was used in conjugation with Geographic Information system in 2005, to predict the malaria breeding grounds in Africa. • Logistic regression was used to approximate areas where malaria patients would exist based on geographical inputs.
  • 57.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Logistic Regression – Use Case • Logit analysis is a statistical technique used by marketers to assess the scope of customer acceptance of a product, particularly a new product. • It attempts to determine the intensity or magnitude of customers' purchase intentions and translates that into a measure of actual buying behavior. • Many e-commerce websites assess this behavior using this model.
  • 58.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Session In A Minute The 5 Questions In Data Science Use CasesLogistic Regression Working? Logistic Regression – What & Why? Demo What Is Regression?
  • 59.
    www.edureka.co/data-scienceEdureka’s Data ScienceCertification Training Course Details Go to www.edureka.co/data-science Get Edureka Certified in Data Science Today! What our learners have to say about us! Shravan Reddy says- “I would like to recommend any one who wants to be a Data Scientist just one place: Edureka. Explanations are clean, clear, easy to understand. Their support team works very well.. I took the Data Science course and I'm going to take Machine Learning with Mahout and then Big Data and Hadoop”. Gnana Sekhar says - “Edureka Data science course provided me a very good mixture of theoretical and practical training. LMS pre recorded sessions and assignments were very good as there is a lot of information in them that will help me in my job. Edureka is my teaching GURU now...Thanks EDUREKA.” Balu Samaga says - “It was a great experience to undergo and get certified in the Data Science course from Edureka. Quality of the training materials, assignments, project, support and other infrastructures are a top notch.”
  • 60.