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Visualising Machine learning: Humanising the advanced Intelligence

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Deck presented by Ganes Kesari B at the NJ Data Science Meetup, on 24th Feb 2018

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Visualising Machine learning: Humanising the advanced Intelligence

  1. 1. B Ganes Kesari Head - Analytics & Design, Gramener Visualizing Machine Learning: Humanizing the Intelligence NJ DataScience Meetup | 24th Feb
  2. 2. 2 ADVANCED ANALYTICS... AI… SALVATION OR SORCERY?
  3. 3. 3 TELECOM CHURN “Churn of customers is a particularly severe problem in the telecom industry. The challenge is to identify the propensity of churn up to a month in advance, even before a customer moves out, so that proactive interventions can begin”
  4. 4. 4 OK WASTED Marketing cost $ 10 MISSED Acquisition cost $ 80 OK No churn Churn NochurnChurn Prediction Actual 8.3% 0.0% MISSED WASTED 6.61 COST PER CUST. 0.0% IMPROVEMENT Base MODELS
  5. 5. 5 Outgoing call (days ago) 0 0 - 4 15+5-14 1 RECHARGE AMT > $20 01 YN > 1 RECHARGE 0 N Y 3.2% 3.6% MISSED WASTED 4.0 COST PER CUST. 39% IMPROVEMENT Decision Tree MODELS
  6. 6. 60.6% 2.5% MISSED WASTED 2.21 COST PER CUST. 66% IMPROVEMENT SVM MODELS OK WASTED Marketing cost $10 MISSED Acquisition cost $25 OK No churn ChurnNochurnChurn PredictionActual
  7. 7. INCREASINGLY, OUR BEST MODELS ARE BLACK-BOX
  8. 8. PRICE FORECASTING FOR AN ASIAN AGRICULTURAL ENTERPRISE Problem Approach Outcome A Gramener Advanced Analytics Case Study A leading agricultural enterprise wanted price forecasts for their products in order to plan inventory release to optimise revenue. Incorrect timing was leading either to loss of revenue or unsold inventory. Gramener applied a suite of price forecasting models based on internal and external factors. The models were evaluated on multiple test datasets to select one that minimised median absolute deviation. The model was able to forecast the price to an accuracy of 88%. Within the first quarter of deploying the model, the revenue uplift attributable directly to pricing was +3.2%.
  9. 9. 9 A COMPARISON OF PRICE FORECAST ACCURACY OF PURE MODELS Product Moving Average Auto- regression Exponential Smoothing ARIMA Exponential Smoothing Over State Space Hybrid Model Neural Network Multi-Linear Regression Commodity 1 65.13 54.13 65.98 66.16 71.67 73.24 78.96 70.46 Commodity 2 66.89 56.66 66.74 68.12 74.41 74.65 89.15 73.87 Commodity 3 37.53 9.84 44.55 42.28 50.49 46.86 61.35 53.03 Commodity 4 37.16 4.92 50.22 43.50 52.19 53.40 68.63 53.15 Commodity 5 68.83 71.24 68.38 68.12 75.58 71.47 90.80 72.69 Commodity 6 69.41 69.60 69.24 70.16 77.55 75.75 80.41 75.09 Commodity 7 69.27 64.76 68.61 69.21 73.39 74.06 82.10 75.20 Commodity 8 64.54 52.50 63.93 64.41 68.31 70.82 79.70 70.78 Commodity 9 57.97 52.64 57.40 58.53 63.90 63.15 78.80 63.04 Commodity 10 53.61 55.90 54.54 56.47 59.78 58.63 90.28 61.96 Commodity 11 52.02 26.49 54.92 53.65 60.80 63.89 78.40 52.23 Commodity 12 45.83 28.50 53.59 49.43 56.09 53.63 85.34 48.33 Commodity 13 41.30 28.98 40.51 38.88 50.84 47.57 63.76 50.55 Commodity 14 41.14 17.41 41.51 38.05 45.95 48.69 71.55 44.10 Commodity 15 86.40 84.00 86.58 87.29 88.80 90.78 99.91 88.04 Commodity 16 85.76 83.83 85.66 85.59 85.30 88.43 91.76 78.59
  10. 10. WE NEED A WAY OF UNDERSTANDING BLACK-BOX MODELS
  11. 11. 11 UNDERSTANDING BLACK-BOX MODELS Visuals Abstracti on Interactivity We understand better through…
  12. 12. 1212 BEHAVIORAL CLUSTERING “Delivering targeting media content to different regions of the country could improve reach. The challenge is to identify the right clustering of regions that are similar, but may not be geographically contiguous, so that targeted interventions can begin”
  13. 13. 13 SEGMENTING INDIA’S DISTRICTS BASED ON BEHAVIOUR Previously, the client was treating contiguous regions as a homogenous entity, from a channel content perspective. To deliver targeted content, we divided India into 6 clusters based on their demographic behaviour:
  14. 14. 14 VISUALIZING THE BEHAVIOURALLY SEGMENTED DISTRICTS The 6 clusters were created using the three composite indices based on the economic development lifecycle: • Education (literacy, higher education) that leads to... • Skilled jobs (in mfg or services) that leads to... • Purchasing power (higher income, asset ownership) Districts were divided (at the average cut-off) by: Offering targeted content to these clusters will reach a more homogenous demographic population. Poor Rural, uneducated agri workers. Young population with low income and asset ownership. Mostly in Bihar, Jharkhand, UP, MP. Breakout Rural, educated agri workers poised for skilled labour. Higher asset ownership. Parts of UP, Bihar, MP. Aspirant Regions with skilled labour pools but low purchasing power. Cusp of economic development. Mostly WB, Odisha, parts of UP Owner Regions with unskilled labour but high economic prosperity (landlords, etc.) Mostly AP, TN, parts of Karnataka, Gujarat Business Lower education but working in skilled jobs, and prosperous. Typical of business communities. Parts of Gujarat, TN, Urban UP, Punjab, etc Rich Urban educated population working in skilled jobs. All metros, large cities, parts of Kerala, TN Skilled Poorer Richer Unskilled Skilled Uneducated Educated Uneducated Educated Unskilled Purchasing power Skilled jobs Education Poor Breakout Aspirant Owner Business RichThe 6 clusters are
  15. 15. BEHAVIOURAL CLUSTERING
  16. 16. 16 UNDERSTANDING BLACK-BOX MODELS Visuals Abstracti on Interactivity We understand better through…
  17. 17. 17http://worrydream.com/LadderOfAbstraction/
  18. 18. 18 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? SECURITIES
  19. 19. 19 LET’S EXAMINE CURRENCY FORECASTS
  20. 20. 20 68% correlation between AUD & EUR Plot of 6 month daily AUD - EUR values Block of correlated currencies … clustered hierarchically
  21. 21. 21 UNDERSTANDING BLACK-BOX MODELS Visuals Abstracti on Interactivity We understand better through…
  22. 22. 22 UNDERSTANDING BLACK-BOX MODELS Visuals Abstracti on Interactivity We understand better through… 1. Model Internals
  23. 23. 23 ENERGY UTILITY NEURAL NETWORKS Inspired by biological networks, artificial neural networks are a network of interconnected nodes that make up a model, like humans & animals. Neural network processes information by passing it through layers: one input layer, 1 or more hidden layers, and an output layer.
  24. 24. 24 Visualizing Neural Networks playground.tensorflow.org
  25. 25. 25http://scs.ryerson.ca/~aharley/vis/conv/flat.html Handwriting recognition through Convolutional NN
  26. 26. 26 UNDERSTANDING BLACK-BOX MODELS Visuals Abstracti on Interactivity We understand better through… 1. Model Internals 2. Model Results
  27. 27. 27 ENERGY UTILITY RENT OR BUY? “A constant question with home decisions is in taking a call whether to rent or buy. The challenge is to consider a variety of factors that could have long-term implications and arriving at a sound financial decision, while also understanding what drives it”
  28. 28. 28https://www.nytimes.com/interactive/2014/upshot/buy-rent-calculator.html
  29. 29. 29 CARGO DELAY SIMULATION “A global cargo carrier is struggling to improve operations by better handling cargo at the airports. The challenge is to identify a combination of the most important factors that cause delays, and being able to simulate turnaround times for potential interventions”
  30. 30. 30 Shift Evening Morning Night Weekday Fri Mon Sat Sun Thu Tue Wed Product category FAH N70 RPP TDS ZDH Part shipment 20-40% 40-60% 60-80% <20% Full CARGO DELAY This visualisation measures the cargo price (average price per unit of capacity), and identifies which factors most influence the cargo price the most. It allows automatically detection of statistically significant flows and highlights only relevant ones to users. The system therefore analyses all possible patterns, but users only see the insights that matter. gramener.com/cargo/delay
  31. 31. IN SUMMARY… BLACK-BOX MODELS ARE INCREASINGLY ACCURATE ANALYTICAL MODELS NEED INTERPRETATION (EVEN MORE) AS PRACTITIONERS, OUR RESPONSIBILITY TO SIMPLIFY AS CONSUMERS, SELF- EDUCATE & DEMAND EXPLANATIONS ..AND TOOLS ARE LESS IMPORTANT THAN TECHNIQUE
  32. 32. 32
  33. 33. 33 THANK YOU! B Ganes Kesari @kesaritweets @kesari Talk slides on: slideshare.net/gramener INSIGHTS Extract meaning using automated patterns AI & MACHINE LEARNING SERVICES VISUAL NARRATIVES STORYTELLING Creative ThinkingCritical Reasoning SOFTWARE THROUGH SERVWARE: augmenting human intelligence with technology We bridge the Data Consumption Gap by leveraging technology to automate Analytics, Visuals and Narration Binding visuals together into a logical story GRAMENER IS A DATA SCIENCE COMPANY

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