Data visualisation using Javascript

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Talk at jsFoo Chennai, Sat 18 Feb 2012

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  • Good evening. My name is Anand, and you can find more about me by googling for “S Anand”. My site is the first hit.I’ll be talking about recent trends in technology, and how you can leverage them.
  • Data visualisation using Javascript

    1. 1. S ANAND DATA SCIENTIST GRAMENER.COMDATA VISUALISATION IN JAVASCRIPT
    2. 2. WHY VISUALISE?Consider the sales report shown 2010 Bangalore Delhi Hyderabad Mumbaialongside Month Price Sales Price Sales Price Sales Price SalesIt shows performance of 4 Jan 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58branches with average price and Feb 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76sales across 4 cities Mar 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 Apr 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84Each of the branches change May 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47prices every month with a Jun 14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04corresponding change in the Jul 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25sales value Aug 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50Basic analytics of these Sep 12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56numbers reveal a consistent Oct 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91performance across 4 branches. Nov 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89 Average 9.0 7.50 9.0 7.50 9.0 7.50 9.0 7.50Further, these sales figures have Variance 10.0 3.75 10.0 3.75 10.0 3.75 10.0 3.75a consistent Correlation andLinear regression across all cities
    3. 3. WHY VISUALISE?The four cities are completelydifferent in behaviour and needdifferent strategies for growth.Bangalore sales has generallyincreased with price.Hyderabad has a nearly perfectincrease in sales with price,except for one aberration.Delhi, however, shows a declinein sales as price is increasedbeyond a certain point.Mumbai sales fluctuated despitea constant price, except for 1month.
    4. 4. DETECTING FRAUD “ We know meter readings are incorrect, for various reasons. We don’t, however, have the concrete proof we need to start the process of meter readingENERGY UTILITY automation. Part of our problem is the volume of data that needs to be analysed. The other is the inexperience in tools or analyses to identify such patterns.
    5. 5. This plot shows the frequency of all meter readings from Why would Apr-2010 to Mar-2011. An unusually large number ofthese happen? readings are aligned with the tariff slab boundaries.This clearly shows Apr-10 May-10 Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10 Jan-11 Feb-11 Mar-11collusion of some form 217 219 200 200 200 200 200 200 200 350 200 200with the customers. 250 200 200 200 201 200 200 200 250 200 200 150 250 150 150 200 200 200 200 200 200 200 200 150This happens with specific 150 200 200 200 200 200 200 200 200 200 200 50customers, not randomly. 200 200 200 150 180 150 50 100 50 70 100 100Here are such customers’ 100 100 100 100 100 100 100 100 100 100 110 100 100 150 123 123 50 100 50 100 100 100 100 100meter readings. 0 111 100 100 100 100 100 100 100 100 50 50 0 100 27 100 50 100 100 100 100 100 70 100If we define the “extent of 1 1 1 100 99 50 100 100 100 100 100 100fraud” as the percentageexcess of the 100 unitmeter reading, Section Apr-10 May-10 Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10 Jan-11 Feb-11 Mar-11the value varies Section 1 70% 97% 136% 65% 110% 116% 121% 107% 114% 88% 74% 109%considerably Section 2 66% 92% New section 66% 87% 70% 64% is … and 63% 50% 58% 38% 41% 54% manager arrives transferred50% outacross sections, Section 3 90% 46% 47% 43% 28% 31% 32% 19% 38% 8% 34% Section 4 44% 24% 36% 39% 21% 18% 24% 49% 56% 44% 31% 14%and time Section 5 4% 63% -27% 20% 41% 82% 26% 34% 43% 2% 37% 15% Section 6 18% 23% 30% 21% 28% 33% 39% 41% 39% 18% 0% 33%… with some Section 7 36% 51% 33% 33% 27% 35% 10% 39% 12% 5% 15% 14%explainable Section 8 22% 21% 28% 12% 24% 27% 10% 31% 13% 11% 22% 17%anamolies. Section 9 19% 35% 14% 9% 16% 32% 37% 12% 9% 5% -3% 11%
    6. 6. SECURITIES FINDING PATTERNS Which securities move together? How should I diversify? What should I sell to reduce risk? What’s a reliable predictor of a security?
    7. 7. 68% correlation between AUD & EURPlot of 6 month daily AUD - EUR values … that move counter-cyclically to indices Block of correlated currencies … clustered hierarchically
    8. 8. LET’S MAKE A FEW
    9. 9. http://s-anand.net

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