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CHEMICAL CONCENTRATION PREDICTION

Data Science in the Petrochemical Industry

Problem

  • Quality Control Process: Identification of chemical and compound concentration in a product
  • Cost of chemical analysis of individual chemical and compounds varies.
  • Measurement of some chemicals is expensive

Objective

  • Reduce cost of chemical analysis.

Solution (Automated AI and Machine Learning)

  • Build hundreds of predictive models.
  • Individual and ensembles.
  • Optimize models.

Result

An optimized model that predicts concentration of “expensive” chemicals based on the concentration of “cheap” chemicals. Accuracy ~ 94.2%

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PREDICTION OF ER PATIENT WAITING TIME

Data Science in the Healthcare Industry

Problem

  • Patients registering at ER desk in a hospital get frustrated from waiting times.
  • Frequent questioning of counter staff poses hurdles in ER work

Objective

  • Improve client experience while waiting

Solution

  • Process historic data of ER visits, patient history and resource utilization.

Automated AI and ML

  • Build hundreds of predictive models.
  • Individual and ensembles.
  • Optimize models.

Result

An optimized ensemble of models that predicts waiting time for a patient. Accuracy ~ 86%

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CUSTOMER SEGMENTATION

Data Science in Marketing

Problem

  • Company has over 10M customers.
  • Company has a large bag of products and services.
  • Company wants to conduct targeted advertising campaign to sell a specific product.

Objective

  • Identify top 10,000 potential customers.
  • Instruct the telemarketing department to promote a chosen product.

Solution

  • Analyze customer usage history.
  • Capitalize on demography data and survey results (provided by client).
  • Automated Data Mining
  • Identify association patterns.
  • Cluster search.

Result

Provide list of potential customers. Acceptance Rate ~ 72% (reported by client)

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VARIABLE TOLL

Data Science for Traffic Control

Problem

  • Narrow traffic corridors often experience congestion during peak hours. Especially on toll booths.

Objective

  • Distribute traffic evenly throughout the entire day by linking toll price with predicted traffic.
  • Optimize toll booth operations.

Solution

  • Analyze traffic and demographic data of travelers.
  • Automated AI and ML
  • Build hundreds of predictive models.
  • Individual and ensembles.
  • Optimize models.

Result

An optimized prediction model to predicts traffic at narrow corridors of a national highway. Theoretical Accuracy ~ 91.2%

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OPERATIONS OVERHEAD COST REDUCTION

Data Science for Operations Monitoring and Cost Reduction

Problem

  • Client has grid of sensing stations over a vast geographic area.
  • As the grid grows, so does the operation, monitoring and maintenance costs.

Objective

  • Reduce grid operation cost by eliminating grid points.

Solution

Automated AI and ML

  • Build hundreds of predictive models for each grid point.
  • Identify grid points for elimination.
  • Replace eliminated grid point readings with predictive model.

Result

Propose candidate grid points for replacement with prediction models. Cost reduction ~ 37%