Build interactive visualizations to deliver insights on geospatial data using Python A. Introduction A.1. Motivation As data analysts, sooner or later we might “play” with data related to geographical location on earth, commonly known as geospatial data (the term is used interchangeably with spatial data) in which its absolute location is defined by two…

Build an end-to-end deep learning model to classify real-world images using TensorFlow, Docker, AWS Lambda and API Gateway — A. Introduction A.1. Background & Motivation In the data science life cycle, deployment is the stage where we can finally put our AI model into practice. Hence, after we build and evaluate the model, we need to deploy it as a solution that helps businesses solve real-world problems. Only from doing this, we can gain feedback…

Create a clustering model for segmenting Toronto neighborhoods to find the best locations for starting a business — A. Introduction A. 1. Background and Business Problem Toronto is Canada’s largest city with a population of more than 2,7 million and a density of 4,334.4 people per square kilometer. The city is renowned as one of the most multicultural cities globally due to its large population of immigrants from all over the globe. …

Develop a profitable wind energy demand forecasting model for energy traders based on deep learning. — Motivation Creating a steady supply of energy is always vital as our modern society genuinely depends on this. That is why predictable sources of energy like fossil fuels, or nuclear power, is still favorable. However, the risk of energy shortfall will continually exist, driving us to use finance — to predict…