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Michel Arents's Projects

live_sentiment_analyzer icon live_sentiment_analyzer

Developed a dash application to visualize the live sentiment on a topic on twitter searched by user dynamically.

load-complex-xml-to-sql-server icon load-complex-xml-to-sql-server

This generic SSIS script code loads any complex XML data into SQL Server Database. The table structure based on the XML structure is created dynamically and the data is loaded. There is an option of making this to work as Framework with Framework tables to store the File details and load the relationship between the tables in the XML File. The script loops through the XML files provided in the path and loads the data into the SQL Server database. All these values are configurable.

lvvd icon lvvd

Listed Volatility and Variance Derivatives (Wiley Finance)

machine-learning-from-scratch icon machine-learning-from-scratch

Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). Examples of Logistic Regression, Linear Regression, Decision Trees, K-means clustering, Sentiment Analysis, Recommender Systems, Neural Networks and Reinforcement Learning.

machinelearning icon machinelearning

A repo for all the relevant code notebooks and datasets used in my Machine Learning tutorial videos on YouTube

market-basket-optimization icon market-basket-optimization

Market Basket Analysis What is it? Market Basket Analysis is a modelling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items. For example, if you are in an English pub and you buy a pint of beer and don't buy a bar meal, you are more likely to buy crisps (US. chips) at the same time than somebody who didn't buy beer.

mascalogics-automatic-reddit-text-to-video-generator-and-youtube-uploader icon mascalogics-automatic-reddit-text-to-video-generator-and-youtube-uploader

You would have noticed alot of channel getting popular on YouTube just by uploading “Reddit to Text-To-Speech” YouTube Videos. So I decided to create a program that can automate the process of receiving, generating and uploading these videos to YouTube with as little intervention as possible. It took me one month to complete this project. I divided the project to 3 scripts. The idea was to minimize as much manual intervention as possible and automate all the trivial tasks. However the process cannot be 100% automated. For example comments with links in them cannot be kept as quality of the video will be comprised due to the TTS. Additionally while a comment might have a large number of votes it could potentially be offensive and not safe for a YouTube video and thus must be removed. The thumbnail, while partially generated, must be edited in order to create any kind of appeal to viewers to click on your video. The same goes for the title of the video which must be clickbait-y in order to receive any attention. I have attempted to streamline the manual process with the client program and it takes me approximately 30 minutes to create 6 videos (the max that can be uploaded within 24 hours with the YouTube Data API).

ml_board icon ml_board

a machine learning dashboard that displays hyperparameter settings alongside visualizations, and logs the scientist's thoughts throughout the training process

mlguidenotebooks icon mlguidenotebooks

Collection of notebooks I made to illustrate some machine learning concepts and models in this repo, most of the models in this repo are built once from scratch and once using built-in models from libraries like sklearn.

mlt icon mlt

Machine Learning Tools

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