The forest and the pump! [mlw2]

The random forest algorithm is the topic of the second assignment of Machine Learning for Data Analysis by Wesleyan University on Coursera. This assignment extends the previous one because besides from using random forest instead of decision trees I included more variables than the previous assignment. In this analysis I included also Continue reading “The forest and the pump! [mlw2]”

The forest and the pump! [mlw2]

Pump it up with a decision tree [mlw1]

This post is about the first assignment of Machine Learning for Data Analysis by Wesleyan University on Coursera. In the past month I have tried to mine the dataset of the pumpItUp challenge on DrivenData. The challenge requires Continue reading “Pump it up with a decision tree [mlw1]”

Pump it up with a decision tree [mlw1]

Making up for univariate [DAI IVb]

This post is an extension of this one, which was (supposed to be) the final post of the coursera course ‘data analysis and interpretation’. This current post extends or complements the previous one because in that assignment I forgot to include univariate graphs in my plot. Since I only had a bivariate graph, the other reviewers failed my assignment. I was quite disappointed by their reaction, but I understood their motives. If univariate graphs get points and the absence thereof does not, I was righteously failed. Therefore, in this post I try to fix my previous mistake including three univariate graphs. The conclusion one can gather from these graphs remains unchanged and one should Continue reading “Making up for univariate [DAI IVb]”

Making up for univariate [DAI IVb]

Citations Network

This post describes the visualisation of a social network I made for a Coursera course on Data Visualisation. For this specific assignment I opted for gathering data on my own rather than using the datasets provided by the course instructor. I wanted to gather the data myself to try to visualise ‘real’ data. With real data I mean data that I try to scrape from the web and visualise. Basically with ‘real’ data I mean what other people call dirty data (i.e. data that is not been processed or polished before use). The question was also whether I could Continue reading “Citations Network”

Citations Network

Do men cheat more than women? [DAI IV]

First of all let me make clear that this post is about identifying cheaters who fills in questionnaires with fictitious answers. This post does not describe how to determine whether your (or your friend’s) lover is cheating on you (or your friend’s). Cheater identification will not work with the method I will describe below unless, Continue reading “Do men cheat more than women? [DAI IV]”

Do men cheat more than women? [DAI IV]

Cheat Hunt [DAI III]

This post title is inspired by the title of a movie, witch hunt, I did not see, but I do like the sound of the title. I decided to change the dataset I am exploring for the data management and visualization course (if you need an introduction check this previous post). I decided to change dataset because it is not interesting to do the assignments with an already clean dataset. In fact, this week assignment requires pure data management, which is 1) identification and removal of missing values 2) computation of new variables etc. Since my dataset is already clean and only has three variables, I have nothing to do for the assignment. In the previous assignment I already came up with a new variable, and I was not capable to invent something new. But then I got a fantastic idea.

Continue reading “Cheat Hunt [DAI III]”

Cheat Hunt [DAI III]

Loading data and frequencies [DAI -II]

Second assignment for the Coursera Data Management and Visualization ‘challenge’ (here is the introduction). I rename the data management and visualization course to challenge since it has been a bit challenging to keep up with the weekly deadlines (and this is only the second week). But I am happy I am still close to the deadline when submitting the assignments. The goal of this second assignment is to load the data set and explore it by means of some descriptive statistics. Below I adapted the sample text for this assignment.
Continue reading “Loading data and frequencies [DAI -II]”

Loading data and frequencies [DAI -II]

Color-coded parallel coordinates in R

Parallel coordinates can be very helpful in understanding relationships among more than two variables. The first time I encountered parallel coordinates I did not understand their potential, until I saw Alberto Cairo’s slopegraph. In that slopegraph Cairo color-coded the Continue reading “Color-coded parallel coordinates in R”

Color-coded parallel coordinates in R

Data Analysis and Interpretation I

To keep digging into data and analysis I enrolled into a Coursera specialization about Data Analysis and Interpretation by the Wesleyan University. Since part of the course requires to 1) write blog posts about analysis performed during the course and 2) that the submissions have deadline, I thought it was a good way to keep to a deadline and analyze some old data that I have collected and never looked at. Below is a short description of the data set I would like to analyze. Continue reading “Data Analysis and Interpretation I”

Data Analysis and Interpretation I