data science projects @ runnr

Data Science TalkAMOL SAHASRABUDHEANKIT JAIN

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WHY THIS PRESENTATION

DEMOCRATIZE BRAINSTORMING

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• Why and What of Data Science

• Data Science @Roadrunnr• Multi Drop Logic• Demand Prediction• Future Work

Agenda

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What is Data Science ?

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Data is Everywhere

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HOUSE OF CARDS

MAKING SENSE OF DATA

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HOUSE OF CARDS

WHO USES DATA SCIENCE

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HOUSE OF CARDS

WHO IS A DATA SCIENTIST

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HOUSE OF CARDS

WHAT DO THEY DO

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HOUSE OF CARDS

WHAT DO THEY DO

IDEA EXPERIMENT VALIDATE SPEC DEPLOY

MODELING

SKILLS : Math, Statistics, Programming and Domain Knowledge

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HOUSE OF CARDSDATA SCIENCE@ROADRUNNR

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Use data as a raw input to develop products to:

• Minimize Estimated Time of Arrival (ETA)

• Improve Reliability

at minimal Costs

VALUES

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Multi Drop Clubbing Logic• Improve the multi drop grouping logic to:

• Reduce number of touchpoints/order (5-15%)• Reduce distance travelled per touch point• Increase driver satisfaction

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Existing Multi Drop Logic

HUB

Red Cluster can be done away with in this scenario

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Ideal Clubbing

HUB

DB should be able to cover points on his way

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How to Achieve Ideal Clubbing?

D1 D2

HUB

D1 < D2

Join two points which have minimum distance between them

• Joining two points

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Joining Two Groups

• Join on the basis of shortest distances and not center distances to achieve ideal clubbing

HUB

C1

C2s2 s1

• S1, S2 shortest distances• C1, C2 center distances

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Delhi West Hub Orders (Existing Clubbing)

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Delhi West Hub Orders (New Clubbing)

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Algorithm Limitations• Sub optimal solution due to accuracy vs complexity trade off• Performance limited by accuracy of Lat,Long of drop points• Not optimized for size and weight of shipments• Routing not included as a part of this version (V2)• Considers only bike as a carrier (V2)

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Demand Prediction

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Demand Prediction (Impact)30% stock-outs during peak hours70% demand during peak hoursSupply PlanningDriver PlacementSurge Pricing

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Demand Prediction(Actual Demand)

Koramangala

High Variance in hourly demand

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Demand Prediction(Method)

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Demand Prediction(Actual and Predicted)

Koramangala

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Demand Prediction(drill down)

Koramangala 8PM

Predictability in number of orders in this hour

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Demand Prediction(Model Failure)

Electronic City

Demand is too erratic to be captured by modeling

Bad predictions

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Demand Prediction(Future Work)Category specific demandPrediction for smaller clusters rather than localitiesPrediction over smaller intervalsStock-out incorporation

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Data Science Future ProjectsSurge pricingWait time prediction for food orders (deployment pending)Carrier Selection (Extension of multi drop logic)Driver Fraud detectionProduct size estimation using image

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THANK YOU