Yes, you can, but maybe, It's will be a imprecise solution. But, I'll try to help you to find a solution.
Number of attributes: first of all, a machine learning model requires good features about the person historic to make predictions about their future, use only two attributes could be not the sufficiente to do accurate predictions.
Size of training: another important aspect is the training size, for example, do you have a long historic for all employees? For example, do you have the individual historic, for each employee, for their last decade?
Modelling: An important aspect that you should think is about the modelling. How do you want to train the model? For example, do you will use the january's employee historic to predict the february? Or, do you will use the 2021 employee's historic to predict 2022, and use this model to predict 2023? Do you have any others features to feed the model? What explain the number of days and hours worked? For example, the hours worked could be explained by the day week, this means that, if you work in a restaurant (for example), you can work more in the weekends. So, it's important to model know the week day. The days count could be afect by the vacations/hollydays, so, include these informations in the data should be very important. After that, you must consider how you will split the data into training and test. Do you will use the historic of the last seven-days to predict the next one day? Or, you will use the last month historic to predict the next month?
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是的，你可以，但也许，这将是一个不精确的解决方案。但是，我会尽力帮助您找到解决方案。
属性数量：首先，机器学习模型需要有关历史人物的良好特征来预测他们的未来，仅使用两个属性可能不足以进行准确的预测。
培训规模：另一个重要方面是培训规模，例如，您对所有员工的培训历史悠久吗？例如，您是否有每位员工过去十年的个人历史记录？
建模：您应该考虑的一个重要方面是建模。您想如何训练模型？例如，您是否会使用一月份的员工历史来预测二月份？或者，您是否会使用 2021 年员工的历史来预测 2022 年，并使用此模型来预测 2023 年？您还有其他功能可以为模型提供支持吗？如何解释工作天数和工作小时数？例如，工作时间可以用星期来解释，这意味着，如果您在餐馆工作（例如），您可以在周末工作更多。因此，了解工作日的模型很重要。天数可能会受到假期/节假日的影响，因此，在数据中包含这些信息应该非常重要。之后，您必须考虑如何将数据拆分为训练和测试。您会使用过去 7 天的历史记录来预测下一天吗？或者，您将使用上个月的历史记录来预测下个月？
Yes, you can, but maybe, It's will be a imprecise solution. But, I'll try to help you to find a solution.
Number of attributes: first of all, a machine learning model requires good features about the person historic to make predictions about their future, use only two attributes could be not the sufficiente to do accurate predictions.
Size of training: another important aspect is the training size, for example, do you have a long historic for all employees? For example, do you have the individual historic, for each employee, for their last decade?
Modelling: An important aspect that you should think is about the modelling. How do you want to train the model? For example, do you will use the january's employee historic to predict the february? Or, do you will use the 2021 employee's historic to predict 2022, and use this model to predict 2023? Do you have any others features to feed the model? What explain the number of days and hours worked? For example, the hours worked could be explained by the day week, this means that, if you work in a restaurant (for example), you can work more in the weekends. So, it's important to model know the week day. The days count could be afect by the vacations/hollydays, so, include these informations in the data should be very important. After that, you must consider how you will split the data into training and test. Do you will use the historic of the last seven-days to predict the next one day? Or, you will use the last month historic to predict the next month?