Week 3: Programming
Ishan B -
Last week, my research focused on gaining a better understanding of regressions and how they work. This helped narrow my choices of which models to test to linear, polynomial, ridge, and lasso regression. After deciding which type of models to test, the next step in my research is to set up the programming interface. For this project, I will be using Google Colab alongside the pandas and numpy libraries. As for the models, Initially, I am planning to test the open-source regression models from Scikit-learn, and in the upcoming weeks once that is completed, if time permits, I will also test the open-source regression models from other libraries such as Keras, Tensorflow, and PyTorch.
Libraries
Numpy is a python library which allows users to create multi-dimensional arrays and matrices, while also allowing for the capability to perform mathematical operations on them. Numpy is an essential library for programmers planning to perform data science operations in python. Numpy is most useful for numerical data.
Pandas is a python library that has similar functions to Numpy, however it excels in tabular data and performing operations such as data cleaning, and filling missing values.
Scikit-learn, Keras, Tensorflow, and PyTorch are all python libraries that give access to different types of machine learning models such as regressions, random forest, and classification models. Each library generates its models differently, so each model may provide slightly different results when trained on the same dataset, which is why I am planning to implement all of them separately and compare them to analyze which one gave the best results for my use case.
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