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This project is an oversight of INX Future Inc. employee performance and factors affecting the same. This analysis focuses on employee efficiency development areas that are to be identified and provide suitable recommendations ensuring improved employee performance, thus improving the service delivery and customer satisfaction.
A powerful and customizable Angular rating component that allows full and half ratings with support for read-only mode, dynamic scaling, and SVG customization. Perfect for use in reviews, feedback forms, and rating-based applications.
Code to rate tail risk (cat) using 100 years of public data. Using pandas, MC simulations, and classification/regression models, this hybrid quant/ml model suits various insurance risks (tail risk) with publicly available data. Vectorization of categorical features could improve loss ratios, outperforming GLMs. No actuaries needed!