This document provides an agenda for a meetup on data science topics. The meetup will be held once a month, with the next one on June 14th. It aims to provide the best networking and learning platform in Bangalore for areas like data science, big data, machine learning. The agenda includes introductions, an overview of the model building lifecycle, data exploration and feature engineering techniques, and modeling techniques like logistic regression, decision trees, random forests, and SVM. Teams will be formed to predict whether bids are from humans or robots using these techniques. Resources for implementing the techniques in Python and R are also provided.

2. About Analytics Vidhya

3. Thanks Tlabs & Venturesity for being a lovely
host…

5. First things first:
• Meetup frequency – Once every month
• Next meetup – 14th June 2015
• Aim to provide best networking and learning platform in
Bangalore
• Areas of Interest – Data Science, Big Data, Machine Learning,

6. Meet Your Volunteers
Kunal
Data Science Evangelist,
(Growth) Hacker, Blogger,
Husband, Father
Tavish
Blogger, Problem solver,
Data scientist

7. Agenda
• Introduction
• Model building – life cycle
• Data Exploration and Feature Engineering methods
• Talk about modelling Techniques like
• Logistic Regression
• Decision Tree
• Random Forest
• SVM
• Predict Man vs. Machine

8. Introduction
• Name
• Experience in Data Science
• Current Company
• Are you proficient with (SAS/ R/ Python)?

9. Team creation
• Look for diversity in experience
• Hopefully common toolset, but complementary can also work
• Competing against each other

10. Team Formation

11. A few ground rules for today
• This is not a tutorial – you are expected to solve this problem yourself
• We are here to help you, organize your thoughts and to make sure
you are going in the right direction.
• Good question to ask:
• While trying Logistic regression in R, I am facing following error?
• Bad question to ask:
• Help me understand what is Logistic Regression!

13. • Register on DataHack.io & discuss.analyticsvidhya.com
• Datahack.io – Website for hackathon memberships
• Discuss.analyticsvidhya.com – Discussions for the day & always
• One login for each participant
• Password would be mailed upon registration
• Registration on Kaggle.com

14. Model building – life cycle

15. Problem of the day : FB challenge – Human or Robot

16. OR
Question in detail

17. Datasets you will have access to
Training Testing
Bids
Submission
Unique at Bid ID
Unique at Bidder ID
Unique at Bidder ID

18. Hypothesis generation
• In your groups, list down all possible variables, which might influence the
chances of survival of a passenger
• Download the dataset from Kaggle
• Next, look at the dataset and see which variables are available
Make sure you always do this in this order

19. Data Exploration & Feature Engineering
• Import data set
• Variable identification
• Univariate, Bivariate and Multivariate analysis
• Identify and Treat missing and outlier values
• Create new variables or transform existing variables

20. Datasets details
Test and Train

21. Are you a newbie, refer to these guides:
• Import data set (SAS, Python, R)
• Variable identification (Methods, SAS, Python, R)
• Univariate, Bivariate and Multivariate analysis (Methods, SAS, Python, R)
• Identify and Treat missing and outlier values (Missing, Outlier, SAS, Python, R1,
R2)
• Create new variables or transform existing variables (Methods, SAS, Python, R1)

22. Practice
Explore the data set and share your inferences with the group

23. Break

24. Modelling Techniques – Logistic Regression
• Logistic regression is a form of regression analysis in which the outcome variable is binary or
dichotomous
• Used when the focus on whether or not an event occurred, rather than when it occurred
• Here, Instead of modelling the outcome, Y, directly, the method models the log odds(Y) using the
logistic function
• Analysis of variance (ANOVA) and logistic regression all are special cases of General Linear Model
(GLM)
• The probability of success falls between 0 and 1 for all possible values of X

25. Linear & Logistic Regression
20 30 40 50 60 70
Age
0.0
0.2
0.4
0.6
0.8
1.0
CHDProbability(p)
Predictor (x)
0 20 40 60 80
Age (yrs.)
0
20
40
60
80
100
LengthofStay(days)
Predictor(X)
Y
Y=aX+b

26. Logit Transformation

27. Logit is Directly related to Odds
• The logistic model can be written as:
• This implies that the odds for success can be expressed as:
• This relationship is the key to interpreting the coefficients in a logistic regression model !!
Xo
e
P
P 1
1
 



28. Modelling Techniques – Decision Tree
• Decision tree is a type of supervised learning algorithm
• It works for both categorical and continuous input and output variables
• It is a classification technique that split the population or sample into two or more homogeneous
sets (or sub-populations) based on most significant splitter / differentiator in input variables

29. Decision Tree - Example

30. Types of Decision Tree
• Binary Variable Decision Tree: Decision Tree which has binary target variable then it called as
Binary Variable Decision Tree. Example:- In above scenario of student problem, where the target
variable was “Student will play cricket or not” i.e. YES or NO.
• Continuous Variable Decision Tree: Decision Tree has continuous target variable then it is called
as Continuous Variable Decision Tree.

31. Decision Tree - Terminology

32. Decision Tree – Advantages/ Disadvantages
Advantages:
• Easy to understand
• Useful in data exploration
• Less Data Cleaning required
• Data type is not a constraint
Disadvantages:
• Overfit
• Not fit for continuous variables
• Not Sensitive to Skewed distributions

33. Modelling Techniques – Random Forest
• “Random Forest“ is an algorithm to perform very intensive calculations.
• Random forest is like a bootstrapping algorithm with Decision tree (CART) model.
• Random forest gives much more accurate predictions when compared to simple CART/CHAID or
regression models in many scenarios.
• It captures the variance of several input variables at the same time and enables high number of
observations to participate in the prediction.
• A different subset of the training data and subset of variables are selected for each tree
• Remaining training data are used to estimate error and variable importance

34. Random Forest – Advantages/ Disadvantages
Advantages:
• No need for pruning trees
• Accuracy and variable importance generated automatically
• Not very sensitive to outliers in training data
• Easy to set parameters
Disadvantages:
• Over fitting is not a problem
• It is black box, rules behind model building can not be explained

35. Modelling Techniques – SVM
35
• It is a classification technique.
• Support Vectors are simply the co-ordinates of individual
observation
• Support Vector Machine is a frontier which best segregates the one
class from other
• Solving SVMs is a quadratic programming problem
• Seen by many as the most successful current text classification
method
Sec. 15.1
Support vectors

36. Case Study
36
Sec. 15.1
We have a population of 50%-50% Males and
Females. Here, we want to create some set of rules
which will guide us the gender class for rest of the
population.
The blue circles in the plot represent females and
green squares represents male.
 Males in our population have a higher average
height.
 Females in our population have longer scalp
hairs.

37. Case Study – How to find right SVM
37
Sec. 15.1
Here we have three possible frontiers. Decide which
one is best.
Methods:
• Find the minimum distance of the frontier from
closest support vector (this can belong to any
class).
• Choose the frontier with the maximum distance
from the closest support vector. In this case, it is
black frontier with 15 unit distance.

38. Predict Robots out of all the bidders
Perform prediction for robot prediction

39. Python Resources:
Python:
• http://www.bigdataexaminer.com/dealing-with-unbalanced-classes-svm-random-forests-and-
decision-trees-in-python/
• http://nbviewer.ipython.org/github/justmarkham/gadsdc1/blob/master/logistic_assignment/kevi
n_logistic_sklearn.ipynb
• http://scikit-learn.org/stable/modules/svm.html
• http://scikit-learn.org/stable/modules/tree.html
• http://blog.yhathq.com/posts/random-forests-in-python.html

40. R Resources:
R:
• http://www.ats.ucla.edu/stat/r/dae/logit.htm
• http://www.cookbook-r.com/Statistical_analysis/Logistic_regression/
• http://www.rdatamining.com/examples/decision-tree
• http://www.statmethods.net/advstats/cart.html
• http://www.cair.org/conferences/cair2013/pres/58_Headstrom.pdf
• http://blog.yhathq.com/posts/comparing-random-forests-in-python-and-r.html
• http://www.louisaslett.com/Courses/Data_Mining/ST4003-Lab7-Introduction_to_Support_Vector_Machines.pdf
• http://thinktostart.com/build-a-spam-filter-with-r/
• http://cbio.ensmp.fr/~jvert/svn/tutorials/practical/svmbasic/svmbasic_notes.pdf

41. Thanks