Predictive Analytics Date: 25-06-2020 FDP Advanced Computing Techniques LBRCE College

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2. Predictive analytics
Few Case Studies
Dr. Rajesh Kulkarni
rajeshkulkarni@sigce.edu.in
SIGCE, Navi Mumbai

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5. Anatomy of Analytics
•Analytics 1.0—the era of “business
intelligence.”
DWH and BI
•Analytics 2.0—the era of big data
linkedin data products, Hadoop, nosql, cloud,
machine learning methods
•Analytics 3.0—the era of data-enriched
offerings
intelligent offerings, IOT, Big data, analytics

6. Intelligent Offerings
• intelligent- fleet management, vehicle-
charging infrastructures, energy management,
security video analysis
•asset and operations optimization services
•GE- Predix (a platform for building “industrial
internet” applications) and Predictivity (a series
of 24 asset or operations optimization
applications that run on the Predix platform
across industries).

7. intelligent offerings
• UPS- ORION (On-Road Integrated
Optimization and Navigation)
•Google, Amazon, and others have prospered
not by giving customers information but by
giving them shortcuts to decisions and actions

8. Analytics
• Exploratory Data Analytics
•Confirmatory data Analytics
•Qualitative Data Analytics
•Predictive Data Analytics

9. Predictive Analytics
• a way to predict the future using data from the
past
•statistical techniques from data mining,
predictive modelling, and machine learning
•global market projected to reach approximately
$10.95 billion by 2022
•Rapidminer, weka, R

10. Predictive Analytics- Why?
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13. Take a look at these pics…

14. Take a look at these pics…

15. Nashik farm- Field visits
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16. National Agripreneur Award
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17. Ignisnova Robotics- Problem Statement
•Farmers- Challenges
•Right Price at right time- desired
•Have to sell to local traders
•Corporates- buy from big traders
•Yield and other farming activities- uncertain
•Analytics could help bring certainty
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18. Agriforetell- Suite
•Collect data- satellite, drone, field visits
•Analyze trend on ground
•Crop health monitoring
•Crop Performance- Accurate harvesting
•Yield Prediction- Yield statistics- solves price
fluctuation
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19. Agriforetell- Suite
•Drone based precision agriculture- Pesticides,
irrigation
•Forecasting of disease-
- no lead time
- symptoms are not visible
- Crop damage 35% to 40%
•Suite tells 7 days before disease
•Prediction ( disease + weather + Satellite
Data): prepare farmers to take prior measures
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20. Agriforetell- Suite- Predictive Analytics
•At least 500 research papers+ 2000 acres farm
field visits+ Pitching
•Drone + High Precision camera + Satellite data
+ ML ( ANN + Statistics)
•Paid satellite Data sources- frequency/price
•Free satellite data sources – processing
charges
•Resolution- 5 cm to 500 meters
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21. Agriforetell- Suite- Predictive Analytics
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• technologies - AI, big data, remote sensing
• software - tensorflow, big data frameworks
• data visualization software's, python programming
language
• tools - hardware for data collection (drones, custom
camera, sensors)
• High end GPUs for training of algorithms

22. Agriforetell- Suite- Complete Digitized Ecosystem
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23. Predictive Analytics- Few Case Studies
•Rainfall Prediction- weather prediction, water
resources planning, nature hazards prediction
•Machine Learning Algorithms/ WEKA
•Evolutionary Algorithms- discrete/continuous
variables/ categorical data

24. Case Study 1: Rainfall Prediction
•A SVR-ANN Combined Model Based on
Ensemble EMD for Rainfall Prediction
• Conventional Rainfall Prediction
Techniques: Numerical Prediction
Model, Bayesian
• Recent Rainfall Prediction Techniques:
Machine Learning, GA, SA, CPSO
• Rainfall Prediction Techniques being discussed
today: SVR, ANN, EEMD

25. Design/ Experimental Work
25Constraint Based Mining

26. Design/ Experimental Work: EMD Algorithm
26Constraint Based Mining

27. Design/ Experimental Work: BP-ANN
27Constraint Based Mining

28. E-SVR-ANN Algorithm
Input: data: the rainfall time series; noise: the added white noise; n: the number of
IMFs; iter: the iteration, iter [50, 200] ; x: the input vector; yt: the observerd data; y:
the predicted data.
Output: O: the predicted result.
Procedure:
IMFs <- eemd(data, noise, iter);
for i=1; i<=n do
lag <- Pacf(IMF i );
IMF i <- Normalization(IMF i );
If IMF 1
[yt , x] <- SVR_format(IMF 1 );
SVR_ parameters <- SVR_gridresearch( yt,x); //finding the values of cost
(c), variable gamma (g), and the error tolerance
Model 1 <- SVR_train(yt,x);
y <- SVR_predict(x, Model 1 );
Else
[train_label, train_data] <- ANN_format(IMF 1 ); //using lag
ANN_ parameters <- ANN_traverse( yt , x);
Model i <- ANN_train(yt , x);
y <- ANN_predict( Model i );
End if
O <- Summation of y

29. Case Study 2: Machine Learning
•ML- 3 types- Supervised, Unsupervised,
Reinforcement
• Supervised- Class variable, prediction
• Prediction- number/regression,
string/classification
•Rows and columns- instances

30. Case Study 2: Machine Learning
•ML- 3 types- Supervised, Unsupervised,
Reinforcement
• Supervised- Class variable, prediction
• Prediction- number/regression,
string/classification
• Rainfall Prediction Techniques being
discussed today: SVR, ANN, EEMD

31. WEKA- Library of Machine Learning Algorithms
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32. WEKA…
•Explorer/ Knowledgeflow/ Work- Mining Algo
• WEKA extension- .arff, can open .csv
files
• WEKA-src: source code in JAVA-
projects
• 3 types of iris flowers/ 4 attributes + class
and distinguish which type of flower it is

33. WEKA…
•Classification should be the last item in the
data set
• Sepal length and width cannot be
used to distinguish iris flower
• Petal length and width can be used to
distinguish and useful for algorithm
• Algorithms- identify useful attributes: why?
Confusion/redundant attributes

34. Datasets
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36. WEKA…
•Dataset has 150 rows, algorithm trains for
those and model is built
• Preprocessing- called as filters in
WEKA
• Class value is unknown/ ML algorithm
should identify that
• Structure of model dpends on algorithm

37. WEKA...
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49. WEKA… can you

50. 6/24/2020 IP Adventure LLP 50
Thank you for your
attention

51. References
• https://hbr.org/2013/12/analytics-30
• https://emerj.com/ai-sector-overviews/predictive-analytics-5-examples-of-industry-
applications/
• Yu Xiang, Ling Gou, Lihua He, Shoulu Xia, Wenyong Wang, “A SVR–ANN combined model based
on ensemble EMD for rainfall prediction”,Applied Soft Computing,
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