Data science for finance Day 2

1. Location:
#BostonFintechWeek
Babson College
Boston Campus
Data Science for Finance Crash Course
Day 2
2018 Copyright QuantUniversity LLC.
Presented By:
Sri Krishnamurthy, CFA, CAP
Sri.krishnamurthy@qusandbox.com
www.analyticscertificate.com

2. 2
Slides & Materials will be available at:
https://researchhub.qusandbox.com

3. MODULE 1:
• Data Science in Finance
Orientation on the Credit risk case study
Lab 1:
Exploring Data sets to make sense in Python
MODULE 2:
• Machine Learning in 30 minutes!
Lab 2:Credit risk case study
Building your first model
Agenda

4. MODULE 3:
• Evaluating machine learning models: The metrics
Lab 3:Credit risk case study
Understanding and tuning your model
MODULE 4:
• Deployment of machine learning models and Prediction through AP
Lab 4:Credit risk case study
Deploying your model and predicting interest rates
Agenda

5. 5
Data pre-
processing &
EDA
Building a
Machine
Learning model
Evaluating
different
models and
model selection
Deploying your
model in
production
Recap
Day 1 Day 2 Day 3 Day 4

6. 6

7. 7
Types of algorithms
Machine
learning
Supervised
Learning
Prediction
Classification
Unsupervised
Learning
Clustering

8. 8
• Supervised Algorithms
▫ Given a set of variables 𝑥𝑖, predict the value of another variable 𝑦 in a
given data set such that
▫ If y is numeric => Prediction
▫ If y is categorical => Classification
Machine Learning
x1,x2,x3… Model F(X) y

9. 9
• Unsupervised Algorithms
▫ Given a dataset with variables 𝑥𝑖, build a model that captures the
similarities in different observations and assigns them to different
buckets => Clustering
Machine Learning
Obs1,
Obs2,Obs3
etc.
Model
Obs1- Class 1
Obs2- Class 2
Obs3- Class 1

10. 10
Supervised
Learning
algorithms
Parametric
models
Non-
Parametric
models
Supervised learning Algorithms - Prediction

11. 11
• Parametric models
▫ Assume some functional form
▫ Fit coefficients
• Examples : Linear Regression, Neural Networks
Supervised Learning models - Prediction
𝑌 = 𝛽0 + 𝛽1 𝑋1
Linear Regression Model Neural network Model

12. 12
• Non-Parametric models
▫ No functional form assumed
• Examples : Random Forest
Supervised Learning models
https://commons.wikimedia.org/wiki/File:Random_forest_diag
ram_complete.png

13. • Given estimates መ𝛽0, መ𝛽1, … , መ𝛽 𝑝We can make predictions using the
formula
ො𝑦 = መ𝛽0 + መ𝛽1 𝑥1 + መ𝛽2 𝑥2 + ⋯ + መ𝛽 𝑝 𝑥 𝑝
• The parameters are estimated using the least squares approach to
minimize the sum of squared errors
𝑅𝑆𝑆 = ෍
𝑖=1
𝑛
(𝑦𝑖 − ො𝑦𝑖)2
Multiple linear regression
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14. 14
• Parametric models
▫ Assume some functional form
▫ Fit coefficients
• Examples : Logistic Regression, Neural Networks
Supervised Learning models - Classification
Logistic Regression Model Neural network Model

15. 15
• Non-Parametric models
▫ No functional form assumed
• Examples : K-nearest neighbors, Decision Trees
Supervised Learning models
K-nearest neighbor Model Decision tree Model

16. 16
• Unsupervised Algorithms
▫ Given a dataset with variables 𝑥𝑖, build a model that captures the
similarities in different observations and assigns them to different
buckets => Clustering
Machine Learning
Obs1,
Obs2,Obs3
etc.
Model
Obs1- Class 1
Obs2- Class 2
Obs3- Class 1

17. K-means clustering
• These methods partition the data into k clusters by assigning each data point to
its closest cluster centroid by minimizing the within-cluster sum of squares
(WSS), which is:
෍
𝑘=1
𝐾
෍
𝑖∈𝑆 𝑘
෍
𝑗=1
𝑃
(𝑥𝑖𝑗 − 𝜇 𝑘𝑗)2
where 𝑆 𝑘 is the set of observations in the kth cluster and 𝜇 𝑘𝑗 is the mean of jth
variable of the cluster center of the kth cluster.
• Then, they select the top n points that are the farthest away from their nearest
cluster centers as outliers.
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18. 18
Anomaly Detection vs Unsupervised Learning

19. 19
Distance functions
• Euclidean distance:

20. 20
Distance functions
• Manhattan distance:
D =|𝑋𝐴- 𝑋 𝐵|+ |𝑌𝐴- 𝑌𝐵|

21. 21
Distance functions
• Correlation distance:

22. 22
Machine Learning Algorithms
Machine
Learning
Supervised
Prediction
Parametric
Linear
Regression
Neural
Networks
Non-
parametric
KNN Decision Trees
Classification
Parametric
Logistic
Regression
Neural
Networks
Non
Parametric
Decision Trees KNN
Unsupervised
algorithms
K-means
Associative
rule mining

23. 23

24. 24
Data pre-
processing &
EDA
Building a
Machine
Learning model
Evaluating
different
models and
model selection
Deploying your
model in
production
Recap
Day 1 Day 2 Day 3 Day 4

25. Thank you for attending Day 2!
Sri Krishnamurthy, CFA, CAP
Founder and CEO
QuantUniversity LLC.
srikrishnamurthy
www.QuantUniversity.com
Information, data and drawings embodied in this presentation are strictly a property of QuantUniversity LLC. and shall not be
distributed or used in any other publication without the prior written consent of QuantUniversity LLC.
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