Logistic Regression In Data Science
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The document provides an overview of data science, covering topics such as data mining, machine learning, and the demand for data scientists. It outlines the stages of data mining, explains logistic regression, and discusses various machine learning algorithms. Additionally, it promotes a course that includes modules on data manipulation using R, clustering techniques, and integration with Hadoop.
![Slide 15 www.edureka.in/data-science
Logistic Regression
After some calculations we can get : p(X) /1−p(X) = eA+BX
The quantity p(X)/[1−p(X)] is called the odds, and can take on values between 0 and ∞.
Values of the odds close to 0 and ∞ indicate very low and very high probabilities resp.
Finally we get: log (p(X)/1-p(X)) = A + BX which is called the log-odds or logit
Logistic Regression is linear in x.](https://image.slidesharecdn.com/dslogisticreg-150620105320-lva1-app6891/85/Logistic-Regression-In-Data-Science-15-320.jpg)
Logistic Regression In Data Science
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- 2. www.edureka.in/data-scienceSlide 2 www.edureka.co/r-for-analyticsSlide 2 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Objectives What is data mining Stages of data mining?? What is R What is data science?? What is needed of data scientist?? Roles and Responsibilities of a Data Scientist. Logistic Regression At the end of this session, you will be able to
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- 5. www.edureka.in/data-scienceSlide 5Slide 5Slide 5 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Cross Industry standard Process for data mining ( CRISP – DM ) Stages of Analytics / Data Mining
- 6. www.edureka.in/data-scienceSlide 6Slide 6Slide 6 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Components data science??
- 7. www.edureka.in/data-scienceSlide 7Slide 7Slide 7 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Components data science R Programming Language
- 8. Slide 8 www.edureka.in/data-science Data Science: Demand Supply Gap Big Data Analyst Big Data Architect Big Data Engineer Big Data Research Analyst Big Data Visualizer Data Scientist 50 43 44 31 23 18 50 57 56 69 77 82 Filled job vs unfilled jobs in big data Filled Unfilled Vacancy/Filled(%) Gartner Says Big Data Creates Big Jobs: 4.4 Million IT Jobs Globally to Support Big Data By 2015http://www.gartner.com/newsroom/id/2207915
- 9. Slide 9 www.edureka.in/data-science Hadoop and R together
- 10. Slide 10 www.edureka.in/data-science Machine Learning We have so many algorithms for data mining which can be used to build systems that can read past data and can generate a system that can accommodate any future data and derive useful insight from it Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data
- 11. Slide 11 www.edureka.in/data-science Types of Learning Supervised Learning Unsupervised Learning 1. Uses a known dataset to make predictions. 2. The training dataset includes input data and response values. 3. From it, the supervised learning algorithm builds a model to make predictions of the response values for a new dataset. 1. Draw inferences from datasets consisting of input data without labeled responses. 2. Used for exploratory data analysis to find hidden patterns or grouping in data 3. The most common unsupervised learning method is cluster analysis. Machine Learning
- 12. Slide 12 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions • Common Machine Learning Algorithms
- 13. Slide 13 www.edureka.in/data-science Logistic Regression
- 14. Slide 14 www.edureka.in/data-science Logistic Regression In statistics, logistic regression, or logit regression, or logit model is a direct probability model Rather than modeling this response Y directly, logistic regression models the probability that Y belongs to a particular category In logistic regression, we use the logistic function,
- 15. Slide 15 www.edureka.in/data-science Logistic Regression After some calculations we can get : p(X) /1−p(X) = eA+BX The quantity p(X)/[1−p(X)] is called the odds, and can take on values between 0 and ∞. Values of the odds close to 0 and ∞ indicate very low and very high probabilities resp. Finally we get: log (p(X)/1-p(X)) = A + BX which is called the log-odds or logit Logistic Regression is linear in x.
- 16. Slide 16 www.edureka.in/data-science Sigmoid Function for Logistic Regression
- 18. Slide 18 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Maximum Likelihood Estimation (MLE) → MLE is a statistical method for estimating the coefficients of a model. → The likelihood function (L) measures the probability of observing the particular set of dependent variable values (p1, p2, ..., pn) that occur in the sample: L = Prob (p1* p2* * * pn) → The higher the L, the higher the probability of observing the ps in the sample.
- 19. Slide 19 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Maximum Likelihood Estimation (MLE) → MLE involves finding the coefficients (, ) that makes the log of the likelihood function (LL < 0) as large as possible → Or, finds the coefficients that make -2 times the log of the likelihood function (-2LL) as small as possible → The maximum likelihood estimates solve the following condition: {Y - p(Y=1)}Xi = 0 summed over all observations, i = 1,…,n
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