Building a machine learning model to predict crop yields based on environmental data I have a dataset containing data on temperature, precipitation and soybean yields for a farm for 10 years (2005 - 2014). I would like to predict yields for 2015 based on this data.

Please note that the dataset has DAILY values for temperature and precipitation, but only 1 value per year for the yield, since harvesting of crop happens at end of growing season of crop.

I want to build a regression or some other machine learning based model to predict 2015 yields, based on a regression/some other model derived by studying the relation between yields and temperature and precipitation in previous years.

I am familiar with performing machine learning using scikit-learn. However, not sure how to represent this problem. The tricky part here is that temperature and precipitation are daily but yield is just 1 value per year.

How do I approach this?
For starters, you can predict the yield for the upcoming year based on the daily data for the previous year. You can estimate the model parameters by considering each year's worth of data as one "point", then validate the model using cross-validation. You can extend this model by considering more than the past year, but look back too far and you'll have trouble validating your model and overfit.