Introduction In my last post, I went over some of the highlights of the open data set of all Philadelphia Parking Violations. In this post, I’ll go through the steps to build a model to predict the amount of violations the city issues on a daily basis. I’ll walk you through cleaning and building the data set, selecting and creating the important features, and building predictive models using Random Forests and Linear Regression.

Introduction A few weeks ago, I stumbled across Dylan Purcell’s article on Philadelphia Parking Violations. This is a nice glimpse of the data, but I wanted to get a taste of it myself. I went and downloaded the entire data set of Parking Violations in Philadelphia from the OpenDataPhilly website and came up with a few questions after checking out the data:
How many tickets in the data set?

INTRO This is post is a continuation of my last post. There I pulled tweets from Twitter related to “Comcast email,” got rid of the junk, and removed the unnecessary/unwanted data.
Now that I have the tweets, I will further clean the text and subject it to two different analyses: emotion and polarity.
WHY DOES THIS MATTER Before I get started, I thought it might be a good idea to talk about WHY I am doing this (besides the fact that I learned a new skill and want to show it off and get feedback).

INTRO So… This post is my first foray into the R twitteR package. This post assumes that you have that package installed already in R. I show here how to get tweets from Twitter in preparation for doing some sentiment analysis. My next post will be the actual sentiment analysis.
For this example, I am grabbing tweets related to “Comcast email.” My goal of this exercise is to see how people are feeling about the product I support.

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