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2020-us-election-twitter-sentiment-and-qanon

decision-trees-and-random-forests

exploring_tfs_firefighter_casualties

facial-recognition-system

Created a real facial recognition system in Python that can recognizes individuals in photos.

facial-recogniton-system

Coded a facial recognition system in Python! It can recognize anybodies face in their photo. Once a face is recognized, you can also apply digital makeup to the face (like a Snapchat filter) or find your lookalike.

fuzzy-names

An algorithm for finding people in different databases using fuzzy name matching

fuzzywuzzy-matching

Python Script for matching names based on their nicknames or fuzzy logic

github-slideshow

A robot powered training repository :robot:

handwriting-recognition-in-keras

Built a Keras model that performs OCR Handwriting Recognition

hmni

📛 Fuzzy Name Matching with Machine Learning

housing-values-in-the-suburbs-of-boston

Data description The Boston data frame has 506 rows and 14 columns. This data frame contains the following columns: crim per capita crime rate by town. zn proportion of residential land zoned for lots over 25,000 sq.ft. indus proportion of non-retail business acres per town. chas Charles River dummy variable (= 1 if tract bounds river; 0 otherwise). nox nitrogen oxides concentration (parts per 10 million). rm average number of rooms per dwelling. age proportion of owner-occupied units built prior to 1940. dis weighted mean of distances to five Boston employment centres. rad index of accessibility to radial highways. tax full-value property-tax rate per \$10,000. ptratio pupil-teacher ratio by town. black 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town. lstat lower status of the population (percent). medv median value of owner-occupied homes in \$1000s. Source Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

interdisciplinary_offenders

introduction-to-statistical-learning-labs

Documenting ISLR's Labs - Educational Purposes Only

machine-learning-books-with-python

Chapter by Chapter notes, exercises and code for a variety of machine learning books using Python

mario-kart-characters-shiny-app

Created a two-page shiny application using Mario Kart 8 Character Data < https://www.kaggle.com/barelydedicated/mariokart8>. The application consists of an introduction page and a visualization page. The visualization page includes an interactive vertical bar chart where the user can select which variable they would like to compare to each Mario Kart character.

movie-reviews-sentiment-analysis

A recurrent neural network in Keras that can "read" reviews and deduce whether the author liked the movie or not based on that text

nicknames-and-fuzzy-matching

non-linear-statistical-analysis-

ontario_covid-19_case_counts_february_2021

opening-ontario-restaurants-for-indoor-dining-and-contact-traced-covid-19-cases

pca-k-means-and-hierarchical-clustering

political-deepfakes-fx

Replication codebase for research article on behavioral fx of political deepfakes (Barari, Lucas, and Munger 2021).

political-deepfakes-usa

predict-political-party-based-on-votes

Fun little exercise where I used a public dataset on how US Congressmen vote on 17 different issues in the year 1984.

python-feature-engineering-cookbook

Python Feature Engineering Cookbook, published by Packt

queen-slayer

sales-of-child-car-seats

school-of-cities-student-moveto-project

This paper aims to examine the relationship between commute time as a factor in students’ postsecondary school selection and the ranking of the postsecondary institution they chose to attend, using data from the StudentMoveTO’s Fall 2019 dataset. Our hypothesis is that students’ self-reported perception that commute time impacted their school choice will be associated with attending a lower-ranked postsecondary school. Linear regression analysis was performed to determine if a student’s postsecondary school ranking was dependent on if their school choice was influenced by commute time, while controlling for network commute distance, gender, age, living situation, and family income level. Based on the results of this analysis, students that chose their postsecondary school based on commute time were more likely to attend a lower ranking school. However, our results found that family income level was also a significant predicting variable. Additionally, we found that students who were older men living with a host family or at a friend’s house were more likely to attend a lower ranking school.

sms-spam-detection-

ML Spam Detection system that uses a Random Forest Classifier and a Gradient Boosting Classifier to detect spam SMS messages.

statistical-learning-cookbooks