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. B Ganes Kesari Head - Analytics & Design, Gramener Visualizing Machine Learning: Humanizing the Intelligence NJ DataScience Meetup | 24th Feb
- 2. 2 ADVANCED ANALYTICS... AI… SALVATION OR SORCERY?
- 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 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 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. 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. INCREASINGLY, OUR BEST MODELS ARE BLACK-BOX
- 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 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. WE NEED A WAY OF UNDERSTANDING BLACK-BOX MODELS
- 11. 11 UNDERSTANDING BLACK-BOX MODELS Visuals Abstracti on Interactivity We understand better through…
- 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 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 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
- 16. 16 UNDERSTANDING BLACK-BOX MODELS Visuals Abstracti on Interactivity We understand better through…
- 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 LET’S EXAMINE CURRENCY FORECASTS
- 20. 20 68% correlation between AUD & EUR Plot of 6 month daily AUD - EUR values Block of correlated currencies … clustered hierarchically
- 21. 21 UNDERSTANDING BLACK-BOX MODELS Visuals Abstracti on Interactivity We understand better through…
- 22. 22 UNDERSTANDING BLACK-BOX MODELS Visuals Abstracti on Interactivity We understand better through… 1. Model Internals
- 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.
- 25. 25http://scs.ryerson.ca/~aharley/vis/conv/flat.html Handwriting recognition through Convolutional NN
- 26. 26 UNDERSTANDING BLACK-BOX MODELS Visuals Abstracti on Interactivity We understand better through… 1. Model Internals 2. Model Results
- 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”
- 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 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. 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
- 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
Editor's Notes
- A decision tree is a visual representation of choices, consequences, probabilities and opportunities. They are visual representations of the average outcome. Applying the same fundamental to predict the churn handling we were able to calculate the cost per customer and improvements which were done. On an average, whether an outgoing call was made from the phone. In case of a viable answer, we were able to fix 3 buckets of 0-4 days, 5 to 14 and more than 15 days. If the call has not been made for more than 15 days, there will no recharge voucher applied and the customer may likely leave the network. In cases where the call has been done within 15 days and the simultaneous recharge has been done only once, what has been the recharge amount. If amount is greater than 50, the loop starts from beginning and we establish that the consumer is engaged and spending will not be huge. Earlier, the telecom operator for whom the design has been done was spending more. The decision tree helped them to save 62% of their costs with only 3.2% of cases on an overall basis.
- We were working with the wealth management team of a European bank. They said, “We have a problem. When telling our customers what transactions to make, we base our advice on two very simple principles. First, if you have two securities that behave similarly, you should consolidate. For example, there is no benefit in holding shares of two oil companies. When the price of one rises, the other invariably rises too. So it’s practically like holding the same company’s stock.” “On the other hand, having consolidated, make sure you have a good hedge. For example, if you hold oil companies, buy a bit of gold. When oil companies drop, gold typically rises. Gold is a reasonably good hedge against oil companies.” He said, “This is the basis of the bulk of the advice we give clients. But in order to arrive at this advice, our analysts have to go through 150 reports, which is humanly impossible. We know they don’t actually do that. We sometimes pass these reports on to our clients. They clearly never read these. As a result, our transaction volumes are not as high as we would like to be, mainly because people do not understand why they need to make a trade.”
- So, what we did was put a variant of this visual together. On the right, you have a series of currencies like the Australian dollar, the Euro, the British pound, etc; some commodities like silver and gold; and some stock indices like Sensex, FTSE, and S&P. The cells here have a number inside that indicates the pairwise correlation between a pair of securities. For example, the number 68 on the top left indicates a 68% correlation between the Australian dollar and the Euro. To the left of the Euro and just below the dollar (diagonally opposite to the 68), there’s a scatter plot that shows the daily prices of both these currencies. Each dot is one day’s data. The x-axis shows the Australian dollar value. The y-axis shows the Euro value. This helps identify what the pattern of movements of any two currencies is. From this, you can easily see visually that the Australian dollar and the Euro both tend to move together. Or, where there are strong correlations like the FTSE & S&P, the pattern is almost a straight line. In some cases there are negative correlations. For instance, if you take the Sensex against the Japanese Yen, the correlation is -79%. The cells are coloured based on their correlation values. Greens indicate strong positive correlation. Reds indicate strong negative correlation. These are also grouped hierarchically. On the left, we have a series of lines indicating clusters. The most similar securities are grouped together. So FTSE and S&P with a 98% correlation are very close. The ones that are less correlated are kept further away based on a tree-structure. This leads to clustering of securities. For example, there is a green block in the center which has SGD, JPY, XAU, CHF and CNY. All of these are fairly well correlated. When any one currency in this block goes up, all the others go up as well. When any one goes down, all others go down as well. Similarly, you have another block to its top left: S&P, FTSE, Sensex and to a certain extent, the Pakistani Rupee. These move together as a block as well. But when this block goes up, all the currencies in the other block go down, as indicated by the red negative correlations between these two blocks. This can be used very easily for decision making. For example, one client who was trading with Singapore and Japan looked at the strong correlation and decided to consolidate their holdings in Japanese Yen. They then moved up and down this column to find a good hedge. FTSE looked like a good hedge – it was the most negatively correlated with JPY at that time -- and they decided to place a third of their portfolio in FTSE. A sheet like this improves people’s understanding of relatively complex data, and results in significantly increased trade volumes.
- As Analytics Practitioners, please take up the responsibility of enabling consumption of data, interpreting analytical techniques & humanizing intelligence. Think Visualization, Abstraction & User Interactivity! As Analytics Consumers, please familiarize yourself with this technology, make informed choices and demand explanations, where its not provided