Logistic Regression In Data Science

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Data Science
Inject Intelligence Into
Business decisions Using

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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

Components data science??

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
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

Hadoop and R together

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

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

• Common Machine Learning Algorithms

Logistic Regression

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,

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.

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.

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
- Supervised and Unsupervised Learning
- Decision Tree Classifier
Course Topics
 Module 5
- 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?
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Logistic Regression In Data Science

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