A newbie evaluation on trying out Machine Learning Models for Classification and Data Augmentation to Support better results using Scikit-learn and XGBoost.

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The risk of False Negatives when predicting hazards…

We are immensely grateful to Nabanita Roy for pointing out this very interesting dataset, her previous work formed the base for which we were able to build on, you can have a look at her work here (Part I and Part II)!

Luciana Azubuike was our fantastic mentor in this #WaiLEARN Project and we hope you get to learn as much as we did in this article.

Based on the data exploration and performance analysis carried out by Nabanita, the performance of the previous models were very low for the recall metric having just a 0.06 score for the best…

CathL

Product Manager with keen interest in Tech, AI, Deep Machine Learning , Human Rights, Environment and Progressive and Disruptive ideas in general!

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