STATISTICAL AND MACHINE LEARNING MODELS FOR ESTIMATING CROP VARIABLES

TRAVELLING THERE MIGHT BE DELAY IN REPLYING

Hi, I have a big dataset of several years which consists of crop variables (for e.g A, B, C and D) each of these individual target variables needs to be predicted from a set of 30 independent variables.

I want to implement both statistical (for e.g huber regression, multiple linear regression, etc.. ) and machine learning models (random forest regression, KNN-regressor, XGBoost, etc) to predict the target variables. I will hire someone who has extensive knowledge in implementing machine learning codes in python environment. I will let you choose 4 best statistical models and 4 best machine learning models.

You will also be required to:
1. identify the best test split size for each of the target variables (is it 90:10, 80:20, 70:30, 60:40, 50:50?). For example you identified that 70:30 works best)
2. use this split size to train (70%) the model and test (30%)
3. perform cross-validation
4. hyperparameter tuning
5. minimize overfitting and other issues
6. Generate a table like table 4 in https://www.mdpi.com/2072-4292/11/7/740/htm showing the performance of all statistical and machine learning models using evaluation metrics (R2, RMSE, MAPE, MAE or other metrics)
7. A graph of predicted versus observed like graph of figure 6 in https://www.mdpi.com/2072-4292/11/7/740/htm
8. Finally a predictor importance figure like figure 7 in https://www.mdpi.com/2072-4292/11/7/740/htm

Deadline: 2 months from now (30 June 2023)

Useful reference articles are:
1. https://www.mdpi.com/2072-4292/11/7/740/htm
2. https://www.frontiersin.org/articles/10.3389/fpls.2021.683078/full

CONFIDENTIALITY SHOULD BE MAINTAINED AND DATASET SHOULD BE DELETED AFTER COMPLETION OF PROJECT NDA AND IP AGREEMENT WILL BE SIGNED

Thanks
Ryan