Recently, I was working on a project while using bagging tree-based algorithms for low-frequency time-series data. Originally, the data set contained only three variables for each series. However, given the nature of time-series data stored in the wide formar, I succeeded in engineeing 300 more features. The presentation was first introduced during an online session organized by A.I. Socratic Circles https://aisc.ai.science/
This article targets anyone with previous exposure to machine learning but with little to no knowledge of the recommendation systems. However, it is highly probable that anyone interested in this work interacts with a recommender system regularly. Anyone who listens to Spotify or watches movies on Netflix benefits from the rigorous algorithms (recommendation systems) developed by teams of data scientists and software engineers. The theoretical part of the article explains the fundamentals of various recommendation systems.
In this post, I will analyze major Crime Indicators in Toronto in years from 2014 to 2018. I obtained the publicly available data set from the Toronto Police Service. First, I will visually inspect the crime scene in the City. Specifically, I will use the Matplot library to demonstrate the composition of assaults. Additionally, I will mark the most criminal neighborhoods on the map while utilizing both the MarkerCluster and HeatMap as plugins of folium package.
In this post, I will explore and configure a few classification algorithms (supervised machine learniIn this post, I will explore and configure a few classification algorithms (supervised machine learning). I originally wanted to apply ML and fit some models on diabetes data. The diabetes dataset is one of the most well known data available to ML enthusiasts for their learning purposes. After I had started writing my script for the post, I found an article written by Lahiru Liyanapathirana more than an year ago.