wyattowalsh

def ols(X, y, fit_intercept=True):
    """Ordinary Least Squares (OLS) Regression model with intercept term.
    Fits an OLS regression model using the closed-form OLS estimator equation.
    Intercept term is included via design matrix augmentation.

    Params:
        X - NumPy matrix, size (N, p), of numerical predictors
        y - NumPy array, length N, of numerical response
        fit_intercept - Boolean indicating whether to include an intercept term

wyattowalsh

def ridge(X, y, l2):
    """Ridge Regression model with intercept term.
    L2 penalty and intercept term included via design matrix augmentation.
    This augmentation allows for the OLS estimator to be used for fitting.

    Params:
        X - NumPy matrix, size (N, p), of numerical predictors
        y - NumPy array, length N, of numerical response
        l2 - L2 penalty tuning parameter (positive scalar) 

wyattowalsh

def lasso(X, y, l1, tol=1e-6, path_length=100, return_path=False):
    """The Lasso Regression model with intercept term.
    Intercept term included via design matrix augmentation.
    Pathwise coordinate descent with co-variance updates is applied.
    Path from max value of the L1 tuning parameter to input tuning parameter value.
    Features must be standardized (centered and scaled to unit variance)

    Params:
        X - NumPy matrix, size (N, p), of standardized numerical predictors
        y - NumPy array, length N, of numerical response

wyattowalsh

def elastic_net(X, y, l, alpha, tol=1e-4, path_length=100, return_path=False):
    """The Elastic Net Regression model with intercept term.
    Intercept term included via design matrix augmentation.
    Pathwise coordinate descent with co-variance updates is applied.
    Path from max value of the L1 tuning parameter to input tuning parameter value.
    Features must be standardized (centered and scaled to unit variance)

    Params:
        X - NumPy matrix, size (N, p), of standardized numerical predictors
        y - NumPy array, length N, of numerical response

wyattowalsh

def ols_numpy(X, y, fit_intercept=True):
    """
    Fits an OLS regression model using the closed-form OLS estimator equation.
    Intercept term is included via design matrix augmentation.
    Params:
        X - NumPy matrix, size (N, p), of numerical predictors
        y - NumPy array, length N, of numerical response
        fit_intercept - Boolean indicating whether to include an intercept term
    Returns:
        NumPy array, length p + 1, of fitted model coefficients

wyattowalsh

def ridge(X, y, l2):
    """Ridge Regression model with intercept term.
    L2 penalty and intercept term included via design matrix augmentation.
    This augmentation allows for the OLS estimator to be used for fitting.
    Params:
        X - NumPy matrix, size (N, p), of numerical predictors
        y - NumPy array, length N, of numerical response
        l2 - L2 penalty tuning parameter (positive scalar) 
    Returns:
        NumPy array, length p + 1, of fitted model coefficients

wyattowalsh

Project Context

There are certainly many topics that are good for a data scientist to know and practice! I've tried my best so far to research and develop what I would consider an outline of the different topics broken up into the major sections of interest and practice. This is by no means a comprehensive list, but is ever developing and so far contains most topics. The goal with this is to present the reader with a solid index of the different topics with easily accessible content of the topic's context, overview, and more specific details.

In the Jupyter Book framework (and as specified in the _toc.yml project file) this strategy breaks down into parts, each containing chapters of which can single pages or collections of pages, where each page can be either a single page or collection of pages. [[Read more about Jupyter Book structure here](https://jupyterbook.org/c

wyattowalsh

# action to update my kaggle basketball dataset (see more here https://www.kaggle.com/wyattowalsh/basketball)
name: Update Kaggle Basketball Dataset - Daily

# Controls when the action will run. 
on:
  schedule:
  - cron: "15 3 * * *" # update every night at 3:15am
  # Allows you to run this workflow manually from the Actions tab
  workflow_dispatch:

wyattowalsh

Machine Learning for NBA Game Attendance Prediction

This project seeks to provide a tool to accurately predict the attendance of NBA games in order to better inform the business decisions of different stakeholders across the organization. Predicting game attendance is crucial to making optimized managerial decisions such as planning necessary staffing needs or procuring the proper level of supplies (janitorial, food services, etc). The project is currently being worked on in its second version, version_2. In version 1, an entire machine learning pipeline is established throughout a host of modules ranging from web scraping for data collection to neural-network regression modeling for prediction. These efforts resulted in a high accuracy model with mean absolute error values for attendance around 800 people. However, improvements in data sources and modeling paradigms for improved accuracy are being sought in a few ways in the upcoming version. Click the link below to view the analysis and modeling versio

wyattowalsh

#!/bin/zsh

echo "Installing xcode ..."
xcode-select --install

# Check for Homebrew,
# Install if we don't have it
if test ! $(which brew); then
  echo "Installing homebrew..."
  /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"