Using Streamlit Dashboard to Predict Infrastructure Needs in Africa

May 25th, 2021

Author: Rehab Emam   African governments are using significant portions of public budgets to finance infrastructure, but that infrastructure often responds to past or current needs, not future needs based on expected changes related to climate change, migration, ur

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4 Steps of Using Latent Dirichlet Allocation for Topic Modeling in NLP

April 12th, 2021

  Topic Modeling is a technique that you probably have heard of many times if you are into Natural Language Processing (NLP). Topic Modeling in NLP commonly used for document clustering, not only for text analysis but also in search and recommendation engines. This

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Uncovering Infrastructural Needs Using Topic Modelling and NLP

April 6th, 2021

In the Omdena-ACET challenge, we turned to online information sources, such as social media, newspapers, scientific articles, and websites of institutions involved with infrastructure. Each source provides an abundance of information that’s not possible to analyse man

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NLP Pipeline: Understanding Land Ownership in Kenya through Network Analysis

March 17th, 2021

An end-to-end NLP pipeline from collecting and preparing more than 32.000 notices, legal entities, and court documents to build a web-based dashboard displaying land ownership in Kenya. The purpose of this project is to boost Kenya’s efforts to restore degraded land i

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NLP Data Preparation: From Regex to Word Cloud Packages and Data Visualization

March 11th, 2021

“REGEX‘’ and “Word Clouds” for Natural Language Processing (NLP) data preparation? `Yesss! Regex, short for “the regular expression”, is not an old technique to find and extract text data. It is still one of the basic techniques used in scrapin

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How to Webscrape 700,000 PDFs for Natural Language Processing in 14 Hours to Help the Planet

March 6th, 2021

In order to identify financial incentives for forest and landscape restoration in LATAM, we needed to webscrape policy documents from government pages combining more than 1,300 keywords for each of the 108 targetted states. We did this 61x times faster (than using a lap

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