Projects

Decision Tree Tuning
Hyperparameter Tuning and Pruning in Decision Trees Introduction In this lab, I demonstrate the impact of tree pruning and hyperparameter tuning on the predictive performance of a decision tree classifier.
GridsearchCV - Example
GridSearchCV - Example Introduction This post explores how to use scikit-learn’s GridSearchCV class to exhaustively search through every combination of hyperparameters until we find optimal values for a given model.
Logistic Regression Example: Predicting Forest Cover Type
Logistic Regression - Predicting Forest Cover Type Introduction In this project example, I walk through a complete machine learning workflow of logistic regression, including data preparation, modeling (including hyperparameter tuning), and final model evaluation.
Nonparametric Machine Learning Model Example
Nonparametric ML Models - Cumulative Lab Introduction This demonstrates my applications of two nonparametric models — k-nearest neighbors and decision trees — to the forest cover dataset. Here I will be using an adapted version of the forest cover dataset from the UCI Machine Learning Repository.
Machine Learning Theory and Fundamentals

Machine Learning Theory and Fundamentals

This lab works through an end-to-end machine learning workflow, focusing on the fundamental concepts of machine learning theory and processes. The main emphasis is on modeling theory (not EDA or preprocessing).
A-B Testing Lab

Website A/B Testing - Lab

Introduction

This lab demonstrates my knowledge related to A/B testing and analysis including data explorating and processing skills.
Hypothesis Testing Codethrough
Linear Regression Analysis

Linear Regression Analysis

This project demonstrates a full linear regression analysis and reports the findings of a final model, including both predictive model performance metrics and interpretation of fitted model parameters.
Preprocessing Data with Scikit-Learn
Using Exploratory Data Analysis to Generate Insights for a Business Stakeholder

Overview

This project analyzes current trends in the movie industry to establish a vision for Microsoft’s new movie studio, Microsoft Tenné. Descriptive analysis of data from IMDb, The Movie Database, and Box Office Mojo provides insight into the genre of movies over the past five years (2013-2018) that have the highest audience ratings and highest gross earnings. Microsoft Tenné can use this analysis to guide their decision-making process in determining what genre of movie to produce.