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India Analytics and Big Data Summit 2015

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India Analytics and Big Data Summit 2015

  1. 1. India Analytics and 
 Big Data Summit 2015 Location : Mumbai Date : 3 Feb 2015 Name of the Speaker : Kanwal Prakash Singh, Data Scientist Company Name : Housing www.unicomlearning.com www.bigdatainnovation.org
  2. 2. www.bigdatainnovation.org www.unicomlearning.com ● Information and data ● Data - Raw Facts or Figures
 ● Information - Processed facts, sensible 
 ● Information is derived from data 
 ● examples
  3. 3. www.bigdatainnovation.org www.unicomlearning.com ● Why do we need data ? ● Some scenarios where scarcity of data led to dangerous consequences 
 ○ Earth is Flat ○ Columbus and America vs India ○ Prosperity will last forever then stock markets crashed
  4. 4. www.bigdatainnovation.org www.unicomlearning.com ● Take a guess , data collected per day - scale 
 ○ Housing ○ Linked In ○ Facebook ○ Zomato
  5. 5. www.bigdatainnovation.org www.unicomlearning.com ● It’s not the Data it’s the questions you seek form data ● Are you expecting the right questions from data ? ○ do you have adequate amount to test your hypothesis, ○ if so are you sure you are not making strong beliefs by overlooking on some bias in data ! ○ Correlation != causation
  6. 6. www.bigdatainnovation.org www.unicomlearning.com ● Analytics @ housing 
 ● What we capture, what we do with that - 
 ● Optimise Operations / data collection in-house / recommendations and understanding users / user bucketing 
 ● Forecasting, Price Estimates
 ● Heatmaps - Demand Supply , Price , CFI
  7. 7. www.bigdatainnovation.org www.unicomlearning.com
  8. 8. www.bigdatainnovation.org www.unicomlearning.com ● How can Data science be used for optimising operations ? ○ Flat Duplication ○ Listing Decay ○ Forecasting - Supply / Demand / Load ○ Route Optimisation 
 ● Problem formulation followed by solution through Statistical methods 
 ● Follow the curiosity and desire for perfection `
  9. 9. www.bigdatainnovation.org www.unicomlearning.com ● utilization (useful DC hours/total available DC hours) metric is not up to the mark. ● Why ? 
 ● Could have been a load issue (not enough listing requests hence DC sat idle) but that was not the case
  10. 10. www.bigdatainnovation.org www.unicomlearning.com ● In fact it appeared we were overloaded. 
 ● Again how ?
 ● Data Collectors were travelling a lot ( between two jobs)
  11. 11. www.bigdatainnovation.org www.unicomlearning.com ● Hence came the idea of Branching
 ● The aim was two-fold: ○ reduce the travel time per flat ○ develop capability to serve a request within 45 minutes 
 ● Done ? Awesome, problem identification done :)
  12. 12. www.bigdatainnovation.org www.unicomlearning.com ● Not Really Done !
 ● There was a vast scope of improvement in the Scheduling Algorithm
 ● So all in all two problems ○ Find New offices ( delocalisation) ○ Optimise the Scheduling algorithm
  13. 13. www.bigdatainnovation.org www.unicomlearning.com ● New Office Identification (Constrained Cost Optimisation)
 ● Expanding through setup of new Branches ● Estimation of branch locations 
 ● Costs and capacities of new branches
  14. 14. www.bigdatainnovation.org www.unicomlearning.com
  15. 15. www.bigdatainnovation.org www.unicomlearning.com
  16. 16. www.bigdatainnovation.org www.unicomlearning.com
  17. 17. www.bigdatainnovation.org www.unicomlearning.com
  18. 18. www.bigdatainnovation.org www.unicomlearning.com Penalty Additions
  19. 19. www.bigdatainnovation.org www.unicomlearning.com ● Bingo ! Nailed it 

  20. 20. www.bigdatainnovation.org www.unicomlearning.com
  21. 21. www.bigdatainnovation.org www.unicomlearning.com
  22. 22. www.bigdatainnovation.org www.unicomlearning.com ● Scheduling Algorithm for collection and distribution Systems
 ● Optimal allocation of timed tasks (Listing Requests) to the work force (Data Collectors) 
 ● Minimum cost maximum matching in a graph 

  23. 23. www.bigdatainnovation.org www.unicomlearning.com ● Hungarian Algorithm 
 ● Optimal Allocation of jobs to people - each person has some cost to perform a job 
 ● Minimum Cost Maximum Matching in a Bipartite Graph
 ○ Matching - Set of Edges, with no vertices repeated
  24. 24. www.bigdatainnovation.org www.unicomlearning.com
  25. 25. www.bigdatainnovation.org www.unicomlearning.com
  26. 26. www.bigdatainnovation.org www.unicomlearning.com
  27. 27. www.bigdatainnovation.org www.unicomlearning.com
  28. 28. www.bigdatainnovation.org www.unicomlearning.com
  29. 29. www.bigdatainnovation.org www.unicomlearning.com ● Nailed it now :D

  30. 30. www.bigdatainnovation.org www.unicomlearning.com ● Nailed it now :D
 ● 30 % operational cost reduced 
 ● The best part - solution is transferable
 ○ All Delivery and collection systems 
 ○ Any general Density Based Branching model 

  31. 31. www.bigdatainnovation.org www.unicomlearning.com ● Takeaways
 ○ Data is brahmastra 
 ○ A noob cant master brahmastra, so rise to the levels of Elite Warriors - (Mahabharata had several)
 ○ How ? ■ Mindset - Curious / Hardworking/ Focused ■ Read/ Learn - Blogs / Books / Courses / Peers ■ Apply - Personal Projects / Kaggle ■ Teach
  32. 32. www.bigdatainnovation.org www.unicomlearning.com ● Acknowledgements ○ Mr. Shanu Vivek, Operations BI, Housing ○ Mr. Vaibhav Krishan, Sr. Quant Analyst ○ Mr. Jaspreet Saluja, Co-Founder, Housing ○ Mr. Rishabh Gupta, Operations, Housing ○ Mr. Arpit Agarwal, Operations, Housing ○ Mr. Abhishek Anand, CTO, Housing ○ Mr. Nitin Sangwan, DSL, Housing 

  33. 33. www.unicomlearning.com www.bigdatainnovation.org Speaker Name: Kanwal Prakash Singh Email ID: kanwalprakashsingh@gmail.com India Analytics and Big Data Summit 2015 Organized by UNICOM Trainings & Seminars Pvt. Ltd.
 contact@unicomlearning.com THANK YOU

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