My code and plots for #tidytuesday, the weekly data visualization challenge. Sometimes in R, sometimes in Python, and sometimes in Excel.

This is the final project in the codecademy Data Science Career Path, using Python, EDA, Data Visualization, Data Cleaning and Prepping, Linear Regression Machine Learning.

Codecademy Machine Learning portfolio project using ML to predict whether someone has pets using OkCupid dataset.

Cumulative project for codecademy's classification module, classifying tweets.

Using census data with a random forest, we will try to predict whether or not a person makes more than $50,000.

Using decision trees to try to predict the continent of flags based on several features.

Using an SVM (Support Vector Machine) trained using a baseball dataset to find the decision boundary of the strike zone.

Using Naive Bayes on several different datasets. By reporting the accuracy of the classifier, we can find which datasets are harder to distinguish.

Using probabilities to determine if a student answered a question correctly, what is the probability that she really knows the material?

Using Logistic Regression model that predicts which passengers survived the sinking of the Titanic, based on features like age and class.

Using several Python libraries to make a K-Nearest Neighbor classifier that is trained to predict whether a patient has breast cancer.

Using linear regression to predict the outcome for a tennis player based on their playing habits.

Using linear regression to predict yelp reviews.

Using linear regression to investigate the decline and how the trends of the past predict the future for the honeybees.

Using gensim to analyze the inaugural addresses of the presidents of the United States of America, as collected by the Natural Language Toolkit, using word embeddings.

Using term frequency-inverse document frequency (tf-idf) to analyze an article’s content.

Using scikit-learn’s bag-of-words and Naive Bayes classifier to determine who the mystery writer of a postcard is.

Using matplotlib and seaborn to interpret data from the National Parks Service about endangered species in different parks.

Using matplotlib and seaborn to try and identify the relationship between the GDP and life expectancy of six countries.

Using seaborn to perform exploratory data analysis airline pricing data.

Using seaborn to create some of the visualizations for a stock profile of Netflix.

Using seaborn to explore information about loans awarded by the non-profit Kiva.

Visualizing World Cup Data With Seaborn

Four codecademy projects using Matplotlib to investigate twitch data, food delivery service data, and roller coaster data.

A neat little project with a rotating graph.

Using matplotlib to gain insight into Sublime Limes' customers and their sales habits.

Three projects in the codecademy data science career path.

Using numpy to help a food delivery service figure out the amount of data they’ll need to make meaningful decisions.

Using binom_test, ttest_1samp, ttest_ind, f_oneway, pairwise_tukeyhsd, chi2_contingency to investigate some data from a sample of patients who were evaluated for heart disease.

Using pearsonr and chi2_contingency from scipy stats to analyze data from the NBA (National Basketball Association) and explore possible associations.