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CST383 - Week 7

CST383 - Week 7 The final week of this course! This week, I learned about how to create logistic regression models, as well as understanding overfitting and how to reduce the effects of it. We also learned how to encode categorical variables. One-hot encoding is what I've used for a number of homework problems. It does not create new columns unlike using dummy variables, and encodes two-value nominal variables with 1 or 0. One example is "sex", where a one-hot encoded version would be recreating the column as "sex_female" with 1 == female and 0 == male. For ordinal variables, replace the variables with increasing order, like "spicy". This can be convert the possible values of "spicy" into dummy variables "spicy_none", "spicy_mild", "spicy_hot", and "spicy_extra". We can encode as many columns as needed, as long as the number of rows >= the number of columns squared. Logistic regression models are cre...
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CST383 - Week 6

CST383 Week 6 This week, we are learning about how to optimize machine learning algorithms. Hyperparameters are values that are not able to be learned by the algorithm, so must be set before the training of data begins. To find the best values for hyperparameters, tuning tests on the existing data needs to be ran. With hyperparameter tuning, we can use three strategies: grid search, random search, or weighted KNN. Grid search goes through every combination in a data set to find the best value for the hyperparameters. Random search quickly calculates a best value from the minimum amount of tests, but can be inaccurate. Weighted KNN makes inverse distance of points matter more than the mode of the nearest # neighbors. The steps for hyperparameter tuning best practices is:  Test/train split Cross validation with training data Train best model on full training set Compute test score with test set For supervised learning, classification predicts a category while regression predicts a qu...
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CST383 - Week 5

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CST383 - Week 5 This week we learned about how to prepare data for intro machine learning techniques. This week was rather difficult for me to understand how to properly create KNN classifiers, so I went online to find some supplementary material to help me. One resource that made it a lot easier for me to understand was this video:  The homework was good practice for me to work with, but I'll have to practice more to get the hang of creating classifiers! 
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CST383 - Week 4

CST383 - Week 4      This week, we worked more on plotting data, this time creating more complicated plots involving two or more variables. We used pd.crosstab() to create grouped bar plots for two variables, while for three or more we use sns.barplot() .       I had some trouble understanding why some of my plots are not plotting at all (loading infinitely) while others are able to plot. I tried a few ways to plot them, including normalizing the data, using different aggregations like sum() , and limiting the amount of data that was plotted using top 10 values [:10] . I will have to review the homework once it is graded to understand how best to work through these problems.     I also learned more about P notation for joint probability: use "and", or a ",", or a "^". Such as, what is the probability for a and b: P(a and b), or P(a, b), or P(a ^ b). For OR, use "or" or a "v".       For conditional probabilities like "for ...
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CST383 - Week 3

CST383 - Week 3      This week we learned more about how to work in Colab/Jupyter notebook. I personally used Jupyter because I thought self-hosted material is more appropriate for my use case. I think this notebook style of coding is very useful, because I can test only a single block of code at a time rather than the full file.      We started to learn how to code Pyplot visualizations of data, which is very nice. I am a visual learner so these exercises were very helpful for me to understand these concepts better. I didn't have much difficulty understanding these concepts to complete the homework, though I know I will need to read further into graphing two continuous variables in the coming weeks. A few things stood out to me to remember: Use a semicolon ( ; ) after the last plotting statement or use plt.show() at the end of the code block to prevent matplotlib from automatically adding text before a graph. Use plt.xlabel() or plt.ylabel() to label th...
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CST383 - Week 2

CST383 Week 2 This week we learned about Pandas, a data analysis Python library that makes working with data tables much easier. It can make use of NumPy if needed, specifically for functions like mean().  I have had some trouble with remembering Pandas syntax. I know to use df['x'] to obtain a Series named 'x', then df[df[x]] to obtain the whole Dataframe named 'x'. Once we got to aggregation, I was also unsure when to use 'groupby' and when to use 'aggregate'. One more thing that was difficult for me to get right was how to use conditions correctly in selecting data. One of the most helpful articles to understand this better was the  user guide to Pandas. Along with this, I also found a nice cheat sheet on the Pandas home page for remembering specific syntax. Here is the link to it. I think I would need a lot more specific examples in the lecture to understand the syntax better and when to apply them.
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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 i...
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