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  Data Stitching and cleansing

  Unifying data from multiple sources to deliver end-to-end supply chain analytics

  Cleaning data such as removing null values, identifying and treating data errors and anomalies

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  Feature Engineering and Model Building

  Engineer new variables for precise insights banking upon TransOrg’s experience in hospitality

  Build forecasts using machine learning models, time series approach or cloud-based forecasting

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  Model Review and Feedback

  Review and compare models using train test approach

  Build automations in outputs based on self–learning models

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  Solution integration

  Integrate TransOrg’s solution with your existing on-prem or on-cloud infrastructure using native models, data and process orchestration layers and augment it with necessary data science capabilities and insights

Want to learn more about TransOrg’s value proposition solution methodology and implementation approach?