Understanding what will be the Burn Rate for the employee working in an organization based on the current pandemic situation where work from home is a boon and a bane. How are employees' Burn Rate affected based on various conditions provided?
當前在家工作大流行的情況下，欲了解在組織中工作員工的工作疲勞率是多少。根據提供的各種條件，以AI與機器學習的現有module來推估員工的疲勞率如何受到影響
將數據進行資料前處理(KNNImputer、TargetEncoding、Feature Selection)後，套入近期熱門之機器學習演算法建立多種模型，並使用 Optuna 預設之 TPESampler 進行超參數優化。(除了 DeepLearning 模型是以 Bayesian Optimization 優化超參數)
- XGBRegressor 程式檔
- LGBMRegressor 程式檔
- CatBoostRegressor 程式檔
- StackingRegressor(XGB+LGBM+CAT+MLP作輸出層) 程式檔
- RandomForestRegressor 程式檔
- Tensorflow 框架建立之深度學習模型 程式檔
- Employee ID: The unique ID allocated for each employee
- Date of Joining: The date-time when the employee has joined the organization (example: 2008-12-30)
- Gender: The gender of the employee (Male/Female)
- Company Type: The type of company where the employee is working (Service/Product)
- WFH Setup Available: Is the work from home facility available for the employee (Yes/No)
- Designation: The designation of the employee of work in the organization (In the range of [0.0, 5.0] where bigger is higher designation)
- Resource Allocation: The amount of resource allocated to the employee to work, ie. number of working hours (In the range of [1.0, 10.0] where higher means more resource)
- Mental Fatigue Score: The level of fatigue mentally the employee is facing (In the range of [0.0, 10.0] where 0.0 means no fatigue and 10.0 means completely fatigue)
- Burn Rate: The value we need to predict for each employee telling the rate of Bur out while working (In the range of [0.0, 1.0] where the higher the value is more is the burnout)