Hello everyone, long time no see! A few months ago, you may have come across my blog where I discussed using Linear Regression to build a model to predict the price of a used car. If you missed it, you can find it here.

To catch you all up, I used Linear Regression to build a model that predicted the price of a used car based off its year, mileage, fuel efficiency and if it was a luxury vehicle. It was cool, but having no prior experience with car purchasing or car ownership, I could only test my model against…

Last week we did a brief overview of ProPublica’s NYPD files, which detailed citizen complaints against the NYPD. I’ve compiled a few more graphs for people to peruse through.

As before, I am displaying simple graphs of the data I’ve gone through. I am drawing no conclusions nor making any statement of opinion. If you wish to see some of them in more detail, I will make the available in my github repo at the end of the page.

To start, I”m going to show you an annotated timeline of the complaints data. This is similar to my last one…

ProPublica recently released The NYPD Files, a database of records listing complaints filed against members of the NYPD from 1999–2020. This was a highly controversial move, as NYC Police, Firefighters, and Corrections Officers Unions are currently suing the state to keep these records secret. In any case, ProPublica has made this data public for their own reasons, and as a data enthusiast, I thought reviewing this data and analyzing it would serve both myself and the public some good. You can read more about it here, and access the database here.

The following are a few simple graphs I’ve created…

Hello everyone, I hope you’re all well. Today we’re going to be talking about normal distributions, their purpose, and what they mean. Distributions in general are an important component to statistics, and their analysis is frequently utilized in Data Science. It is also frequently used in areas such as finance, engineering, and medicine. Basically, any field that uses numbers, taps into statistics, and uses distributions to a certain extent.

Let’s start from the beginning then. What’s a normal distribution?

That’s a normal distribution, whoooo so fancy right? It is also known as a Gaussian distribution or a bell curve. In…

Hello everyone, today we’re going to be going over some basic Pandas work. As I mentioned in a previous blog, Pandas has become an essential part of any Data Scientist or Data Analysts toolkit. It is a relatively low memory, multifunctional tool that allows us to view, analyze and manipulate data in the Python environment. It also plugs in easily to many current linear regression and machine learning libraries so its got that going for it as well!

Throughout your work as a Data Scientist or Data Analyst, you will find yourself doing what’s known as “janitorial” work. In fact…

Hello everyone, a few months ago, I described my early experiences working with Python and how difficult I found it. That was only the FIRST week of my Data Science journey at Flatiron School, and it left me intensely despondent about my future in the Data Science field. I stuck it out, and what really turned it around for me starting SQL the following week.

To me SQL made sense, I didn’t have to wrap my mind around interpreting data structures and building loops to access subsequent layers or addressing index locations. …

Let’s talk about Bayes’ Theorem. Bayes’ Theorem is a simple probability formula that is both versatile and powerful. It has been hailed as the hot new thing in Machine Learning and Data Science until Neural Networks came along and played the Kim Kardashian to Bayes’ Paris Hilton. What’s particularly interesting about Bayes’ Theorem compared to other Machine Learning techniques is that the formula itself is almost 200 years old, whereas other forms of machine learning generally have a, let’s say younger vintage.

Probability can be a confusing field to wander through, and you have to have the mental capacity to…

In my last post, I talked about the Confusion Matrix and its importance in helping us determine the accuracy of our models. This week we’ll take a look at the basic structure of Confusion Matrices and how they help us determine the accuracy of our models.

Let’s Start From the Beginning…

As I mentioned in my last post, Confusion Matrixes are credited to Karl Pearson back in 1904, although for him it was the contingency table. They have been popular for statistical classification and machine learning and enable us to visually represent the performance of our classifier. A confusion matrix is a type of contingency…