Introduction Overview The Data EDA Label (Mountainbike Category) Categorical Variables Continuous Variables ~Normally Distributed Variables: Skewed Variables: Multi-Modal Distributed Variables: Average bikes by flip-chip setting Methodology Variation Amongst Featureset 1. Correlation 2. Principal Component Analysis (PCA) Clustering K-Means Gaussian Mixture Model (GMM) GMM - 3 Clusters GMM - 6 Clusters Multi-class SVM Conclusions Findings Opportunities for Improved Analysis Introduction Overview For this post, I worked with Mike Czerwinski to determine whether the specifications of mountain bikes (MTB) are enough to differentiate between the different types of mountain bike categories.

Introduction Overview The Data EDA Label (Mountainbike Category) Categorical Variables Continuous Variables ~Normally Distributed Variables: Skewed Variables: Multi-Modal Distributed Variables: Average bikes by flip-chip setting Methodology Variation Amongst Featureset 1. Correlation 2. Principal Component Analysis (PCA) Clustering K-Means Gaussian Mixture Model (GMM) GMM - 3 Clusters GMM - 6 Clusters Multi-class SVM Conclusions Findings Opportunities for Improved Analysis Introduction Overview For this post, I worked with Mike Czerwinski to determine whether the specifications of mountain bikes (MTB) are enough to differentiate between the different types of mountain bike categories.

Introduction Overview The Data EDA Label (Mountainbike Category) Categorical Variables Continuous Variables ~Normally Distributed Variables: Skewed Variables: Multi-Modal Distributed Variables: Average bikes by flip-chip setting Methodology Variation Amongst Featureset 1. Correlation 2. Principal Component Analysis (PCA) Clustering K-Means Gaussian Mixture Model (GMM) GMM - 3 Clusters GMM - 6 Clusters Multi-class SVM Conclusions Findings Opportunities for Improved Analysis Introduction Overview For this post, I worked with Mike Czerwinski to determine whether the specifications of mountain bikes (MTB) are enough to differentiate between the different types of mountain bike categories.

The purpose of this script is to load and clean all of the various .csv files containing polling place data into R. The data, which is available for download here, is structured as follows: - Each state (32 in total) has its own folder - Within each state (folder), there are a variable number of CSV files, one for each year that polling place data is available

George Floyd. Eric Garner. Tamir Rice. Those names, among thousands of others, are emblematic of fatal police violence in the United States. The protest movement that has spread like wildfire across the U.S. has brought police brutality to the forefront of everyone’s minds. Following the deluge of information from news stations and social media, I can’t help but wonder, what does the data say? Are these new trends or longstanding realities?

The purpose of this script is to load and clean all of the various .csv files containing polling place data into R. The data, which is available for download here, is structured as follows: - Each state (32 in total) has its own folder - Within each state (folder), there are a variable number of CSV files, one for each year that polling place data is available

When Alexander Hamilton, John Jay, and James Madison came together in support of the ratification of the Constitution, they created what has become one of the most celebrated series of political texts in history. The Federalist Papers, a series of 85 essays, helped push New Yorkers towards ratification and laid some of the strongest arguments in favor of a strong Federal Government. In part one of our analysis, we perform an Exploratory Data Analysis (EDA) using modern Natural Language Processing (NLP) techniques to to better understand these essays.

When Alexander Hamilton, John Jay, and James Madison came together in support of the ratification of the Constitution, they created what has become one of the most celebrated series of political texts in history. The Federalist Papers, a series of 85 essays, helped push New Yorkers towards ratification and laid some of the strongest arguments in favor of a strong Federal Government. In part one of our analysis, we perform an Exploratory Data Analysis (EDA) using modern Natural Language Processing (NLP) techniques to to better understand these essays.

In a democracy, the polling booth is much more than a location where an unwitting citizen fills in a bubble on a piece of paper in a make-shift booth. It is a symbol of the promise of democracy, a connector between the citizen and the government. It is the shrine that, if protected and respected, powers a democracy. This article analyzes that symbol of American democracy by taking an analytical look at a dataset of U.

It all started in Montana. In 1916, Jeannette Rankin, a peace activist and a strong advocate for women’s suffrage, broke down centuries-long barriers by becoming the first woman elected to Congress. Since then, 366 women have served in U.S. Congress, and thousands more in various elected and executive offices at the state-level. In this post, we will analyze the Women in Politics dataset published by the Eagleton Institute of Politics’ Center for American Women and Politics.

George Floyd. Eric Garner. Tamir Rice. Those names, among thousands of others, are emblematic of fatal police violence in the United States. The protest movement that has spread like wildfire across the U.S. has brought police brutality to the forefront of everyone’s minds. Following the deluge of information from news stations and social media, I can’t help but wonder, what does the data say? Are these new trends or longstanding realities?

It all started in Montana. In 1916, Jeannette Rankin, a peace activist and a strong advocate for women’s suffrage, broke down centuries-long barriers by becoming the first woman elected to Congress. Since then, 366 women have served in U.S. Congress, and thousands more in various elected and executive offices at the state-level. In this post, we will analyze the Women in Politics dataset published by the Eagleton Institute of Politics’ Center for American Women and Politics.

George Floyd. Eric Garner. Tamir Rice. Those names, among thousands of others, are emblematic of fatal police violence in the United States. The protest movement that has spread like wildfire across the U.S. has brought police brutality to the forefront of everyone’s minds. Following the deluge of information from news stations and social media, I can’t help but wonder, what does the data say? Are these new trends or longstanding realities?

Introduction Overview The Data EDA Label (Mountainbike Category) Categorical Variables Continuous Variables ~Normally Distributed Variables: Skewed Variables: Multi-Modal Distributed Variables: Average bikes by flip-chip setting Methodology Variation Amongst Featureset 1. Correlation 2. Principal Component Analysis (PCA) Clustering K-Means Gaussian Mixture Model (GMM) GMM - 3 Clusters GMM - 6 Clusters Multi-class SVM Conclusions Findings Opportunities for Improved Analysis Introduction Overview For this post, I worked with Mike Czerwinski to determine whether the specifications of mountain bikes (MTB) are enough to differentiate between the different types of mountain bike categories.

In a democracy, the polling booth is much more than a location where an unwitting citizen fills in a bubble on a piece of paper in a make-shift booth. It is a symbol of the promise of democracy, a connector between the citizen and the government. It is the shrine that, if protected and respected, powers a democracy. This article analyzes that symbol of American democracy by taking an analytical look at a dataset of U.

It all started in Montana. In 1916, Jeannette Rankin, a peace activist and a strong advocate for women’s suffrage, broke down centuries-long barriers by becoming the first woman elected to Congress. Since then, 366 women have served in U.S. Congress, and thousands more in various elected and executive offices at the state-level. In this post, we will analyze the Women in Politics dataset published by the Eagleton Institute of Politics’ Center for American Women and Politics.

George Floyd. Eric Garner. Tamir Rice. Those names, among thousands of others, are emblematic of fatal police violence in the United States. The protest movement that has spread like wildfire across the U.S. has brought police brutality to the forefront of everyone’s minds. Following the deluge of information from news stations and social media, I can’t help but wonder, what does the data say? Are these new trends or longstanding realities?