CST383 - Week 1

CST383 Week 1

New semester! This is the Intro to Data Science course.

    The reading started us off with the Iris dataset, and how to manipulate the data from it to predict future iris data (at a 97% success rate). This style of machine learning is supervised learning, where we train it on data that we know to be true. This allows us to measure the accuracy of the model in identifying irises.
    For unsupervised learning, we feed data into the algorithm but do not provide any known output data that we know to be true. An example would be to create a summary of reviews of a product. There is no known output data because the reviews must be fed into the algorithm first and compare it to existing reviews in the database, and find commonalities with them.
    Before creating a supervised model, the best practice is to look at your data (visually helps, like a scatter plot) and find commonalities with each feature of the samples. This can help identify inconsistencies to be accounted for in the model. 

    We also started learning about NumPy with the homework. I haven't really done too much in Python, so the refresher is great. NumPy is an efficient way to create array-like structures in Python, which doesn't have by default. We can do a number of operations on these arrays, like slicing and accessing ends quickly.
Isopod Update: I found a second pink pak chong! Very exciting! Maybe I can create a sort of model to predict the likelihood of a pink pak chong being born.

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