Kaggle Archives | Ultraviolet Analytics

20 May, 2016

Investigating missing data with missingno

Dave2023-01-16T18:56:08-08:00May 20th, 2016|0 Comments

I recently came across a new python package for visualizing missing elements of a data set. The aptly named "missingno" is super useful when you're taking your first look at a new data set and trying to get a feel for what you're working with. Having a sense of the completeness of the data can help inform decisions about how to best handle missing values. In this post, we'll take a quick look at the small and simple Shelter Animal Outcomes data set from one of the current Kaggle competitions. Matrix visualization The first visualization is the "matrix" display. [...]

10 May, 2016

Text Pre-processing Basics with Pandas

Dave2017-01-30T13:47:13-08:00May 10th, 2016|4 Comments

In this post, we'll take a look at the data provided in Kaggle's Home Depot Product Search Relevance challenge to demonstrate some techniques that may be helpful in getting started with feature generation for text data. Dealing with text data is considerably different than numerical data, so there are a few basic approaches that are an excellent place to start. As always, before we start creating features we'll need to clean and massage the data! In the Home Depot challenge, we have a few files which provide attributes and descriptions of each of the products on their website. The [...]

16 Dec, 2014

Kaggle Titanic Competition Part XI – Summary

Dave2023-01-16T21:22:27-08:00December 16th, 2014|6 Comments

This series was probably too long! I can't even remember the beginning, but once I started I figured I may as well be thorough. Hopefully it will provide some assistance to people getting started with scikit-learn and could use a little guidance on the basics. All the code is up on Github with instructions for running it locally, if anyone tries it out and has any issues running it on their machine please let me know! I'll update the README with whatever steps are missing. Thoughts: It can be tricky figuring out useful ways to transform string features, but [...]

16 Dec, 2014

Kaggle Titanic Competition Part X – ROC Curves and AUC

Dave2023-01-16T21:22:03-08:00December 16th, 2014|0 Comments

In the last post, we looked at how to generate and interpret learning curves to validate how well our model is performing. Today we'll take a look at another popular diagnostic used to figure out how well our model is performing. The Receiver Operating Characteristic (ROC curve) is a chart that illustrates how the true positive rate and false positive rate of a binary classifier vary as the discrimination threshold changes. Did that make any sense? Probably not, hopefully it will by the time we're finished. An important thing to keep in mind is that ROC is all about [...]

12 Dec, 2014

Kaggle Titanic Competition Part IX – Bias, Variance, and Learning Curves

Dave2023-01-17T16:19:42-08:00December 12th, 2014|0 Comments

In the previous post, we took at how we can search for the best set of hyperparameters to provide to our model. Our measure of "best" in this case is to minimize the cross validated error. We can be reasonably confident that we're doing about as well as we can with the features we've provided and the model we've chosen. But before we can run off and use this model on totally new data with any confidence, we would like to do a little validation to get an idea of how the model will do out in the wild. [...]

3 Dec, 2014

Kaggle Titanic Competition Part VIII – Hyperparameter Optimization

Dave2023-01-16T21:20:38-08:00December 3rd, 2014|0 Comments

In the last post, we generated our first Random Forest model with mostly default parameters so that we could get an idea of how important the features are. From that we can further reduce the dimensionality of our data set by throwing out some arbitrary amount of the weakest features. We could continue experimenting with the threshold with which to remove "weak" features, or even go back and experiment with the correlation and PCA thresholds as well to modify how many parameters we end up with... but we'll move forward with what we've got. Now that we've got our [...]