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Forecasting Demands in Python Using Various Multi-label classification Models

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Predictive-Demand-Model-Capgemini-Tech-Challenge-Data Science

InterstellarX Inc (IXI) is a company engaged in providing Strategy, Technology, Data sciences and Creative Design services to large global clients.

The core business entails revenue generation by providing the right resources to clients, across the world, for specific projects, based on service requirements.

Problem Statement

One of the most critical business compulsions as well as challenges for IXI, is to manage their People Supply Chain – by mapping the Demand for resources (A resource is tagged by metadata such as primary skillset, location, qualification and experience level etc See attached Demand.xls) to the Net Supply of resources (from various Internal and External hiring channels, keeping attrition in view).

IXI maintains, and periodically replenishes a Bench (Inventory) of resources depending upon forecasted demand for the next few quarters. There is a steep cost constraint associated with maintaining this bench (On average $685 per person per month) and hence the company needs to ensure demand forecast is as accurate as possible, so Supply can be planned accordingly. (Maintaining a bench implies absorbing the cost of a resource who is not billable, and hence is an unproductive cost to the company till he/she is allocated to a billable project)

You need to develop a machine-learning based predictive demand model.

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Forecasting Demands in Python Using Various Multi-label classification Models

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