Learning more about ICOs

In this blog post, I would like to introduce the project we did at the 5th Unhackathon organized by Data Science Hong Kong. Our team’s project was to look at Initial Coin Offering (ICO) data extracted from ICObench to determine which ICOs are scams. Given we only had a few hours to work on it at the Unhackathon, we focused on data wrangling and visualization to learn more ICOs. After the Unhackathon, I had spent some time to conduct simple analysis of a few additional features. As the data was insufficient to determine which ICOs are scams, I played around with the data to see if there are any patterns on what makes an ICO profitable.

To summarize, I found that –

  • There are a lot of outliers in terms of the return on investment. It is hard to predict which ICOs will be profitable, at least based on the data we got.
  • ICObench provides a rating to each ICO, which is calculated based on ICObench’s algorithm and ratings from “Experts” (certain groups of ICO users). These 2 metrics have rather different rating standard and it seems that more weight is given to ICObench’s algorithm in determining the overall rating.
  • It seems that returns on investment are not strongly related to ratings based on our data. It is also affected by the huge outliers.

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Predicting the Survival of Titanic Passengers (Part 1)

This is a classic project for those who are starting out in machine learning aiming to predict which passengers will survive the Titanic shipwreck. I will give this project a try using the training and testing data obtained from Kaggle.

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