Logistic Regression In Data Science

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Data ScienceInject Intelligence Into

Business decisions Using

Regression

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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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Data Science Applications: Wine Recommendation

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Data Science Applications: Predict Accidents

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Cross Industry standard Process for data mining ( CRISP – DM )

Stages of Analytics / Data Mining

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Components data science??

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Components data science

R Programming Language

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Data Science: Demand Supply Gap

Big Data Analyst

Big Data Architect

Big Data Engineer

Big Data Research Analyst

Big Data Visualizer

Data Scientist

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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

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Hadoop and R together

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Machine LearningWe 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

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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

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• Common Machine Learning Algorithms

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Logistic Regression

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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,

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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.

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Sigmoid Function for Logistic Regression

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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.

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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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Module 1

» Introduction to Data Science

Module 2

» Basic Data Manipulation using R

Module 3

» Machine Learning Techniques using R Part -1

- Clustering

- TF-IDF and Cosine Similarity

- Association Rule Mining

Module 4

» Machine Learning Techniques using R Part -2

- Supervised and Unsupervised Learning

- Decision Tree Classifier

Course Topics

Module 5

» Machine Learning Techniques using R Part -3

- Random Forest Classifier

- Naïve Bayer’s Classifier

Module 6

» Introduction to Hadoop Architecture

Module 7

» Integrating R with Hadoop

Module 8

» Mahout Introduction and Algorithm Implementation

Module 9

» Additional Mahout Algorithms and Parallel Processing in R

Module 10

» Project

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Questions?Enroll for the Complete Course at : www.edureka.in/data_science

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Please Don’t forget to fill in the survey report

Class Recording and Presentation will be available in 24 hours at:http://www.edureka.in/blog/application-of-clustering-in-data-science-using-real-life-examples/