The state-of-the-art in feature importance
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Machine learning interpretability is a topic of growing importance in this field. Interpret means to explain or to present in understandable terms. In the context of ML systems, interpretability is the ability to explain or to present in understandable terms to a human[Finale Doshi-Velez]. When investments are at stake, institutions prefer models which are explainable over models which might be giving relatively better accuracy. …
HANDS-ON TUTORIALS
Using object-oriented programming, not conditional programming.
How often have you had a great idea ML project idea that you think you would be able to show-off on your resume? You quickly collect data, analyze it and build models on it; all inside a Jupyter Notebook and then you face your worst nightmare— somebody asks you to deploy the model and show inferencing with a user-friendly interface. If you’re working alone, this can be really intimidating at times and why wouldn’t it be? You’re a data scientist and not a web developer! That’s where Streamlit comes in. …
Efficient and Quick Text Analysis Tool built using Streamlit including Text Summarization, POS Tagging and Named Entity Recognition.
Introduction to Text Analysis
Text analytics is the automated process of translating large volumes of unstructured text into quantitative data to uncover insights, trends, and patterns. Combined with data visualization tools, this technique enables companies to understand the story behind the numbers and make better decisions. It is an artificial intelligence (AI) technology that uses natural language processing (NLP) to transform the unstructured text in documents and databases into normalized, structured data suitable for analysis or to drive machine learning (ML) algorithms. [1]
Need to automate text analytics process
Anyone who has worked on an NLP problem knows that text analysis is the most important step before starting with any…
Risk Management
Implementing Semideviation, VaR and CVaR risk estimation strategies in Python
Risk management is the key to making smart investing decisions which lead to profitable outcomes. While doing technical analysis, investors often focus on the returns of an asset and do not focus a lot on the risk involved. This can often lead to unexpected outcomes. The most commonly used form of risk measure is volatility, often calculated using the standard deviation of the returns. New investors often rely on this method and if you’re one of them, I have some bad news. Volatility calculated using standard deviation takes into account both upside and downside risk measures and this leads to…
Investing
Implementing a stock market risk analysis strategy in Python
One of the key factors involved in asset and portfolio management is accurately assessing the risk involved in your investment. Researchers and hedge fund managers have come up with multiple approaches for this and in this article, I will talk about one of these approaches and teach you how to implement it in Python.
Introduction
Most of the risk management techniques deal with deviations which means that if the returns of the stock go too far away from the stock in either direction, it would be labelled as risky. While this approach makes sense to an extent, one can argue that…
Reddit Flair Prediction Series
Deploying the application on Heroku
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I am really proud of you! You have completed all the previous parts and they were really heavy. Congratulations on making it this far. You have collected your own data, modelled it, made predictions on it and you have also deployed the model using a web-application in Parts 1, 2 and 3. After all that hard work, I think your model deserves to be seen by the whole world. Heroku will help you with just that.
Background
Heroku is a platform as a service…
REDDIT FLAIR PREDICTION SERIES
Creating a web app and deploying the machine learning model
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Welcome to Part 3 of this series where I continue working on the Reddit Flair Detection Problem. In Part 1, I discussed the background of the problem and the data collection method and in Part 2, I built a Machine Learning Model to predict the corresponding flairs. It is highly recommended that you go through both of them before starting this one because I have shared insights and reasoning behind the data collection and the model building process. If you have completed part…
Reddit Flair Prediction Series
Text Analysis and Model Building
If you’re stuck behind a paywall, click here to get my friend link and view this article.
Welcome to Part 2 of this series where I continue working on the Reddit Flair Detection Problem. In Part 1, I discussed the background of the problem and the data collection method. It is highly recommended that you go through part 1 before starting this because I have shared insights and reasoning behind the data collection process. I have also described the various indicators that I have used in my project and model building. In case, you haven’t been…
An iterative approach to solving a prediction problem.
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Recently, I came across a problem. Based on the card combination that you get, you had to predict the hand you have in a game of poker. If the word ‘predict’ made your ears stand and you’re already thinking about which classification model you’ll be using then there’s a chance that you may have already lost. Here’s why…
Background
Before I start discussing the approach, here’s a quick poker refresher for those of you who are new to the game. Poker is probably…
Reddit Flair Prediction Series
Problem Definition and Data Collection
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Reddit is a very popular social media website with roughly 330 million active users and it produced huge amounts of User Generated Content which data scientists like me love to mine and analyse. I recently completed a project using Reddit data and I intend to talk about my experience as well as my process of solving the problem. This will help anyone who is looking for an end-to-end machine learning project. I will walk you through the process of collecting data, analyzing data…
