Making susanne
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The document outlines the development of SUSANNE, a web tool designed to analyze the susceptibility of economies to global recessions by evaluating economic variables and their effects on recession impacts between 2008 and 2010. It discusses the challenges of data collection, cleanup, and analysis, including regression and classification methods, to derive meaningful insights on economic patterns and susceptibility. The goal is to create a predictive model to help understand recession risks and present global economic comparisons through an intuitive interface.
![Aside: Some nifty R snippets
Populating Missing Values smartly:
d7.agg <- aggregate(d7.norm[,5:48], by
= list(Country.Name =
d7.norm$Country.Name, Time =
d7.norm$Time), FUN = function(x) {
if(anyNA(x)) { mean(na.omit(x)) } else
{ mean(x) } })
Class labelling:
Rec.Affect <- ifelse
(CompleteDataFinal$Drop.SD. >= 1, 1,
ifelse(CompleteDataFinal$Drop.SD. >= -
0.25, 0, -1)).
SVM (predict Recession Susceptibility)
16
> model <- svm(as.factor(Rec.Affect) ~
., data = CompleteDataFinal[,
c(6:46)])
> predictions <- predict(model,
CompleteDataFinal[,c(6:46)])
> table(pred = predictions, true =
Rec.Affect)
true
pred -1 0 1
-1 383 8 7
0 33 675 41
1 15 61 876
> t <- table(pred = predictions, true
= Rec.Affect)
> (t[1,1] + t[2,2] + t[3,3])/sum(t)
[1] 0.9213911](https://image.slidesharecdn.com/makingsusanne-141204123639-conversion-gate01/85/Making-susanne-16-320.jpg)
Making susanne
- 1. Making Susanne THE WORLD RECESSION SUSCEPTIBILITY ANALYSIS WEBTOOL Aditya Gupta (2011009) 1
- 2. Overview Objective and Motive Process Data Data Collection Data Cleanup Dependent Variable Analysis Regression, Classification Clustering and Results Further Work Presentation Drawbacks 2
- 3. Objective Key Questions To study what economic composition makes economies more susceptible to Global Recession By how much? How significantly? Can we predict recession impact? Studying similarity of economies How to quantify Recession impact Susceptibility Key Goals Empirically ascertain significance and impact of certain economic traits vis-à-vis expert opinions Develop a powerful model to predict recession susceptibility Present a global and intuitive view across parameters and their significance, i.e., Susanne. 3
- 4. Why? 4 Recessions Hurt 2008 – 2010 Unemployment and layoffs Austerity Bearish Markets Inflation Bank Runs
- 5. Data A BIG PROBLEM IN ITSELF 5
- 6. Data Collection 60+ economic variables linked to Recession Allegedly, as per sources like Economist, Forbes, World Bank, WWW Or as per our suspicion Preferable country-specific ratios (Normalized and Structural Information) 13 years, starting from 2000 to 2013 210 Countries 2500 rows of 60+ columns, each row is a country-year identified Source: World Bank (OECD National Account File): http://data.worldbank.org/ United Nations Comtrade Database, International Monetary Fund, Direction of Trade Database, Balance of Payments Database, and more. 6
- 7. Simple Enough? No, its blistering gunk. Only 86 out of ~2500 rows have complete data (no NA’s) Non-normalized values What now? 7
- 8. Data Cleanup Manually add values for nearly complete columns Compress and remove years 2008-2010 Observed Class Variables – not Causal Remove countries with almost no data (Afghanistan and 30 others) Down to 2100 rows Drop columns if: Significant, and have very little data available Year values for super-specific variables like “Merchandise Exports to Scandinavian Countries as % of exports” We surely don’t have this for most countries, especially those like Albania Insignificant Determined from Regressional Analysis (MLR) p-values 8
- 9. Still too many missing values What to do? Drop row (done) Weighted expansion of row (did not consider) Infer a value Average value for parameter for country over 13 years Result: Fixed 5000 out of 70K cells Still only 86 full rows Why? No value for property exists for a country at all… Solution: Global Average – the sacrilege! Awful. Pull towards the mean, misclassify. But no directional bias. Trade-off: Unlocks a world of data 9 For a specific country… Year V1 Pred V2 Pred 2001 15 15 15 15 2002 17 17 17 17 2003 15 15 19 19 2004 16 16 21 21 2005 ? 16 ? 18 Avg 16 18
- 10. Dependent Variable Goal: Capture Recession Impact between 2008-10 Technical Definition: Absolute Growth Rate Problem: 15% to 1%, still not recession Average Growth Rate drop between 2008 – 2010? Problem: 1% drop for UK (at 0.5%) vs. AFG (at 15%) Percentage Drop in Growth Rate Problem: 0.1% to 3%, 3000% change! Solution? Drop.SD: Drop in Growth Rate in number of Standard Deviations over 2008 – 2010. Variance in Growth rate over last 20 years. Variance: Lot of manual data collection 10
- 11. Distribution and Discretization of Drop.SD Corresponded well with web information about countries that “avoided recession” and those “hit worst” 21% of the countries labelled as unsusceptible Less than -0.25 Drop in GDP during 2007-2010 Middle 36% labelled as relatively unaffected between -0.25 to 0.75 SD GDP Drop in Recession Highest 43% adversely affected >0.75 SD drop in GDP How good was this division? lm R-squared rose from 41.58% to 44.14% (no loss in predictive power, i.e. reasonable classification) Good split. Most countries were affected horribly. 11 Other Options • Equal Density Split • Maximise Classification Accuracy • Purely Contextual
- 12. Analysis AFTER WE FINALLY HAVE 2099 COMPLETE ROWS OF DATA 12
- 13. Classification: Can we predict Recession Susceptibility? Assumption (non trivial) Drop.SD correctly represents Recession Impact in 08-10 Recession Impact in 08-10 correctly represents Recession Susceptibility Can’t do better but guess a few things. SVM has 92% accuracy. Seems like it. Caveat: Bootstrap Analysis: Training Data = Test Data Workaround (can’t generate new countries or years): 5-Fold Cross Validation 13
- 14. Important Variables and their Impact Using Multilinear Regression (MLR), for each Economic Variable, we get degree and direction of impact on Drop.SD 14
- 15. Clustering Motivation: “Are we brute-engineering a predictor, or is there an actual underlying economic structural pattern of recession-susceptibility?” 70% accuracy (consistent clusters)with k-means, using multiple k values. You decide. 15
- 16. Aside: Some nifty R snippets Populating Missing Values smartly: d7.agg <- aggregate(d7.norm[,5:48], by = list(Country.Name = d7.norm$Country.Name, Time = d7.norm$Time), FUN = function(x) { if(anyNA(x)) { mean(na.omit(x)) } else { mean(x) } }) Class labelling: Rec.Affect <- ifelse (CompleteDataFinal$Drop.SD. >= 1, 1, ifelse(CompleteDataFinal$Drop.SD. >= - 0.25, 0, -1)). SVM (predict Recession Susceptibility) 16 > model <- svm(as.factor(Rec.Affect) ~ ., data = CompleteDataFinal[, c(6:46)]) > predictions <- predict(model, CompleteDataFinal[,c(6:46)]) > table(pred = predictions, true = Rec.Affect) true pred -1 0 1 -1 383 8 7 0 33 675 41 1 15 61 876 > t <- table(pred = predictions, true = Rec.Affect) > (t[1,1] + t[2,2] + t[3,3])/sum(t) [1] 0.9213911
- 17. Allowing similarity checks between Economies Overall Over Economic Categories Using a Semantic Web compliant Cardinality Checks and Ontology Classify Economic Variables into one or more of: Central Government Economic Structure Net Exports Banking Manufacturing GDP Discretize them into , and over: Value And Impact Using middle 80 percentile cut-offs Further Work - Accessibility 17
- 18. Presentation THE WEBTOOL, I.E, SUSANNE 18
- 19. A Knowledge Map (Ontology) 19
- 20. Ontology (2) 20 • About 3500 Nodes • And 7000 Edges
- 21. Backend 21
- 22. Frontend 22 Takeaway: Single Page Architecture Global State: (a) Country (a) Year (a) Property Multiple Interesting Views: Map Country Property And 2-way state integration and state- update thanks, angular
- 24. Search (2) 24
- 25. Search (3) 25
- 26. Search (4) 26
- 27. Search (5) 27
- 28. Search (6) 28
- 29. Search (7) – and the last one, breathe. 29