Use Tensorflow to make predictions on Kaggle’s abalone dataset

I really enjoy trying my hand at Kaggle’s playground competitions, so when April’s competition came around, centering around the abalone dataset,I spent the morning working on it. The link to this playground competition can be found here:- https://www.kaggle.com/competitions/playground-series-s4e4

Truth be known, the abalone dataset, along with the solution to making a linear regression model of it can be found in the Tensorflow library documentation, here:- https://www.tensorflow.org/tutorials/load_data/csv

Even though I had seen the abalone dataset previously, I decided to give it a try to see if I could improve my prediction.

I created a program with Kaggle’s Jupyter Notebook and saved it in my Kaggle account.

Once the Jupyter Notebook was created, I imported the libraries that I would need to execute the program, being:-

1. Numpy to create numpy arrays and perform numerical computations,
2. Pandas to create dataframes and process data,
3. Os to go into the operating system of the computer and retrieve necessary files,
4. Sklearn to provide machine learning functionality,
5. Tensorflow to provide deep learning functionality,