This post evaluates different approaches when facing a multi-output within a classification problem. A multi-output describes a combination of several output...
This post elaborates on the idea of using Generative Adversarial Networks to design new products, such as car rims. Every time a company is looking for a new...
This blog-post elaborates on the workings of Generative Adversial Networks (GANs). Particularly we are looking at the high-level mathematics and intuition of...
In this blog-post we discuss the concept of transfer-learning and show an implementation in Python, using tensorflow. Namely, we are using the pre-trained mo...
Explaining the concept of Dynamic-Time-Warping and testing it extensively on sound and tabular data.
Using a combination of regression, classification and smoother for our second take on the DengAI challenge.
The workings of the underlying prediction pipeline
Accuracy/ Recall/ Precision/ Confusion Matrix/ ROC Curve/ AUC
Why geographically correct maps show elections results inaccurately
This blogpost elaborates on how to implement a reinforcement algorithm, which not only masters the game “Snake”, it even outperforms any human in a game with...
Visualizing 30 years of economic data between East and West Germany
Using the STL Forecasting Method with an ARIMA model, which is parameterized through the Box-Jenkins Method.
One of the most powerful visualization tools for regional Panel data there is.
Polarization is increasing. How does this play out in the Senate?
Evidence for increasing polarization from the voting record of the United States Congress
Building and simulating different stock portfolio and assessing risk and return with Python and R Shiny Link to App
An intuitive and replicable explanation to some of the most important questions of portfolio building
Detecting the engine-brand from a Formula 1 car by its sound using Neural Networks
Applying OpenCV and Tesseract to do your math-homework
Scoring in the top one percent in the Richter’s Predictor: Modeling Earthquake Damage on DrivenData.
An intuitive explanation of how to deal with a multi-label classification problem using ImdB movie-data.
As an economist it is pretty frustrating to hear the news anchor saying that the cast of the new James Bond movies did it again. “The highest gross revenue e...
What we learned predicting real estate prices with data scraped from the web.
Applying our NLP feature vector to the Gradient Boosting model.
Analyzing and exploring real estate advertisement descriptions using NLP pre-processing and visualization techniques.
Machine Learning for predicting Spanish real estate prices.
Feature Creation for Real Estate Price Prediction This post elaborates on feature engineering for Spanish real estate price prediction.
Using unsupervised learning techniques to create features for supervised price prediction.
Preparing the data for feature extraction and modeling In the prior post, we outlined how we scraped real estate data for 42,367 properties in Spain. Before ...
Using BeautifulSoup and Python to scrape real estate data from the web
Machine learning is an extremely powerful tool, applicable to an astounding breadth of use cases. Today, almost any question imaginable can be the starting p...