In this paper, we formulate the problem of predicting a geolocation from free text as a sequence-to-sequence problem. Using this formulation, we obtain a geocoding model by training a T5 encoder-decoder transformer model using free text as an input and geolocation as an output. The geocoding model was trained on geo-tagged wikidump data with adaptive cell partitioning for the geolocation representation. All of the code including Rest-based application, dataset and model checkpoints used in this work are publicly available.

When dealing with large datasets for machine learning, have you ever come across an address column that looks like this? Location data can be very messy and difficult to process. It is difficult to encode addresses, since they are of very high cardinality. If you try to encode a column like this with a technique like one-hot encoding, it will lead to high dimensionality, and your machine learning model might not perform well. The easiest way to overcome this problem is to geocode these columns. Geocoding is the process of converting addresses into geographical coordinates.

Recently, I was attending a predicting house prices hackathon. That was the first time I was dealing with a dataset having geographic coordinates - latitude and longitude. While working on this hackathon I have understood about Geocoding, Reverse Geocoding, and finding the distance between two coordinates. In this article, you are going to learn these three techniques. We will make use of geopy and reverse_geocoder libraries in this article.

This article illustrates how Geocoding uncovers the untapped value within generally overlooked insurance categories, such as Life and Annuity, and how it can help address modern-day business challenges remarked by Orszag. While Geocoding in Big Data is gaining prominence within Property and Casualty (P&C), we believe the real opportunity lies in the actuarial adoption of AI framework capable of processing consumable inputs that weren't visible in the erstwhile "Ease of Geocoding" era. Establishing this premise for Life and Annuity, we then pivot towards crafting a general purpose Geo-inclusive architecture that can help actuaries of all disciplines apply Machine Learning to solve new generation of business problems, such as, dwindling subscribers or risk-attributed challenges, such as, Adverse Selection. Nearly all of the data in the insurance business has a location attribute, e.g. However, many insurance companies have not fully utilized this component besides billing and mailing purposes.