analyzes auto-generated employee salary data for 2024, which was scheduled to drop on Airtable. The analysis includes exploratory data analysis and predictive modeling using machine learning techniques to understand salary trends and disparities across different departments and years of experience. The XGBoost model performed reasonably well, explaining about 46.7% of the variance in the salary data, with an average error of around 125,109 units. This analysis aims to provide insights for informed decision-making regarding salary adjustments and career paths.

هذا المشروع يحتوي علي كل خطوات الاساسيه باستخدام البرو باي بتاريخ 1/13/2025 اعداد فريق تنظيم مصر باش مهندس محمد الرشيد ربى حسن والاء عمر

When it comes to creating interactive visualizations in R there are several options available but if you are working with time series data, the dygraphs package offers some advantages.

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