Presentation about data science

1. Applications of Machine Learning
for Interdisciplinary Research
Dr. R. Balasundaram, M.E., Ph.D.,
Professor
Department of Mechanical Engineering,
SRM Institute of Science and Technology ,
Tiruchirappalli Campus – 621105
E-mail : balasundaram_rbs@yahoo.co.in 1

2. Topics to be covered
What is Data Science ?- some examples
DT Algorithm introduction and application
Numerical illustration of Decision Tree algorithm for
manufacturing dataset
Association Rule analysis
Other Areas of implementation
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3. Credit Card & Loan
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4. Grocery Shop
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5. US – Visa Processing
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6. Metrological Applications
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Direction of Michaung - Cyclone

7. Algorithms
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Source : Machine learning approaches for prediction of properties of
natural fiber composites : Apriori Algorithm
Australian Journal of Mechanical Engineering
https://www.tandfonline.com/doi/full/10.1080/14484846.2022.2030091

8. Example for Decision Tree
Sl. No. Gender Age Blood Pressure Drug
1 Male 20 Normal A
2 Female 43 Normal B
3 Male 37 High A
4 Male 33 Low B
5 Female 48 High A
6 Male 29 Normal A
7 Female 52 Normal B
8 Male 42 Low B
9 Male 61 Normal B
10 Female 30 Normal A
11 Female 26 Low B
12 Male 54 High A
Input variables : (predictor attributes) – Gender, Age, BP
Output Variables : (class attribute / target variable ) : Type of Drug
Source : Data mining : Theory and Practice by K.P. Soman
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9.  Highest value of entropy will act as “ root node “
 The rules from the tree are
i) If BP is High then prescribe Drug ‘A’ (accuracy – 3/3 ,100% Sl.No. 3, 5,12))
ii)If BP is low then Prescribe Drug ‘B’ etc.,( accu – 3/3, 100% 4,8,11)
III) if BP is normal and AGE <= 40 , Drug ‘A” (accu- 3/3 100% , 1,6,10)
Iv) if BP is normal and AGE >=40 Drug ‘B’ ( accur – 3/3, 100% 2,4,9)
The Overall Accuracy of the tree is 100 %
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10. Decision Tree Prediction of wear rate for zinc
oxide filled AA7075 matrix composites
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Taguchi and Decision Tree Approach Algorithm for Prediction of wear rate in ZnO filled
AA7075 Matrix Composite
Published : Surface Topography – Metrology and Properties , vol.9, 2021

11. Pin on disc
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12. Instance No.
Input variables (predictor attributes) Output variable (class attribute)
Reinforcement (wt.%) Applied Load(N)
Sliding Velocity
(m/s)
Sliding Distance (m) Wear Rate (mm3/m)
1 0 10 1 1000 0.00294
2 0 10 2 1500 0.00343
3 0 10 3 2000 0.00386
4 0 20 1 1500 0.00318
5 0 20 2 2000 0.00367
6 0 20 3 1000 0.00441
7 0 30 1 2000 0.00514
8 0 30 2 1000 0.00477
9 0 30 3 1500 0.00465
10 5 10 1 1500 0.00266
11 5 10 2 2000 0.00309
12 5 10 3 1000 0.00254
13 5 20 1 2000 0.00327
14 5 20 2 1000 0.00315
15 5 20 3 1500 0.00290
16 5 30 1 1000 0.00347
17 5 30 2 1500 0.00363
18 5 30 3 2000 0.00381
19 10 10 1 2000 0.00179
20 10 10 2 1000 0.00143
21 10 10 3 1500 0.00167
22 10 20 1 1000 0.00215
23 10 20 2 1500 0.00239
24 10 20 3 2000 0.00269
25 10 30 1 1500 0.00335
26 10 30 2 2000 0.00359
27 10 30 3 1000 0.00287
Wear rate of 27 Samples
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13. DT dataset :
Output must be categorical one
Yes /No
A, B, C ..
Low , Medium , High
Input parameters :
Either Categorical or Numerical
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14. Significance of DT
• It converts the low level data in to High level knowledge
• The Rules are easy to understand
• Need not depends experts
• It is type of classification algorithm to classify the given data
set
(For Decision Tree – ID3, C4.5, CART)
C4.5 is most powerful
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15. Association Rule (Market Basket)
Analysis
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16. Dataset
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It will not accept numerical input

17. Case study –
Quality tests of 15 engines
Authors used “Rapid Miner “ software
And paper is published in information science and applications
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18. Case study- process control
. The objectives of the research are to design and verify the data mining tools
to support production process control for decision making,. The authors used
CRISP – DM tool
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19. Data Mining concepts can be
implemented in
• Group Technology – clustering of components
• Analysis of engine performance
• Analysis of boiler – stream of data (similar to net
browsing)
• Analysis of sensors outputs – stream of data
• Analysis of solar panel performance
• Combination of two or more algorithms ( like DM +
Non traditional optimization)
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20. Algorithms
 Classification (DT)
 Regression ( NN, SVM)
 Association (Apriori, FP Growth)
 Clustering ( KNN)
Current trend:
 Hybrid of DM + Optimization
 Wavelets
 Image Processing
 Fractals
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21. Thank you
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Contact : balasundaram_rbs@yahoo.co.in