How I used a deep neural network to predict on blueberry crop yields

I look forward to every fortnightly Tuesday because Kaggle offers a competition that students of data science can enter so they can improve their machine learning skills. In the month of May 2023, Kaggle has sponsored a competition where the data scientist is asked to make predictions on wild blueberry crop yields.

In this competition I attempted to calculate how many neurons to place in the hidden layers of the deep neural network (DNN) that I would create. If the reader would like to know more about how to calculate the neurons in the hidden layers of a dnn then please refer to my earlier blog post:- https://medium.com/@tracyrenee61/how-do-you-calculate-the-correct-number-of-neurons-to-place-in-the-hidden-layers-of-a-neural-19cbaf68452c

I created the program used for Kaggle’s competition using their free online Jupyter notebook, which is stored in my account. The free online Jupyter notebook is a great platform because it enables me to work on a variety of datasets that I would not otherwise have access to.

After I created the program, I imported the libraries that I would need to execute it, namely:-

1. Numpy to create numpy arrays and perform numerical computations,
2. Pandas to create series and dataframes, as well as perform…