I have below datasets for two years each holding about 10.000 records. Every week a new report is generated that shows the performance for the current or any previous month. Therefore a more recent weekly report would overwrite the monthly value of a previous report (for that specific month). I need to predict this year's performance based on the weekly data coming in.
I have two questions:
- How would I need to structure my dataset for machine learning? If I keep the above I would get about 150 features (3 per week) and I do not know whether there is even a ML algorithm that can handle the many n/a (all forthcoming weeks) for the year for which I need to run the prediction. I can also transform this into a new dataset by combining the three columns by week into three features (would result in long narrow table). But that would replicate each entity and output value about 50 times (once per week).
- Based on the answer to 1. what would be suitable training algorithms?