Data Science
9 posts
- I have Claude write up my portfolio every morningI know nothing about finance but I do have a portfolio. For twenty days I had forecasts written and scored. Mostly I learned a word: baseline.
Data Science Notes: introduction to machine learningData, datasets, and why a dataset gets split in two. The first post in the series looks at the basic concepts of machine learning.
Data Science Notes 8: Support vector regressionNotes I took while studying support vector regression: the epsilon margin, why it is called a support vector, and how it compares with polynomial regression.
Data Science Notes 7: Polynomial regressionOnce a variable takes a power above 1 the line becomes a curve. Comparing linear and polynomial regression on the same dataset.
Data Science Notes 6: Multiple linear regressionWorking with more than one independent variable: dummy variables, the P value and backward elimination.
Data Science Notes 5: Linear regressionThe idea of regression, simple linear regression and the least squares method, ending with a small scikit-learn example.
Data Science Notes 4: Feature scaling, normalisation, standardisationMaking the variables in a dataset comparable: normalisation and standardisation, and when to use which.
Data Science Notes 3: Data preprocessingFilling missing values, turning categorical attributes into numbers, and splitting a dataset into training and test sets.
Data Science Notes 2: Machine learning and PythonThe Python libraries we will use in this series: NumPy, Matplotlib, Pandas and scikit-learn, and why Python at all.