A Machine Learning based solution for the competition 'Flu Shot Learning' hosted by DrivenData
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Updated
Nov 16, 2020 - Jupyter Notebook
A Machine Learning based solution for the competition 'Flu Shot Learning' hosted by DrivenData
The goal is to predict the last column, whether he/she donated blood in March 2007.
Machine Learning with a Heart
Classify Pumps into “functional”, “functional needs repair” or “Non-functional” categories based on a number of variables about what kind of pump is operating, when it was installed, and how it is managed, etc.
DrivenData Challenge
28th place solution for "Pover-T Tests: Predicting Poverty" challenge
👷♀️🚰 🇹🇿 DrivenData ML contest to classify water pumps in Tanzania
Segmentation of clouds using satellite imagery
Implemented a machine learning model to predict the likelihood of individuals receiving H1N1 and seasonal flu vaccinations and ranked 46 in the Driven Data out of 6500+ competitors.
Solution for DengAI Competition by DrivenData (CS4642 Data Mining and Information Retrieval, CS4622 Machine Learning - assignments)
Data files and code for the Driven Data project on predicting which water pumps need repair for about 60K water pumps in Tanzania. See the URL https://www.drivendata.org/competitions/7/pump-it-up-data-mining-the-water-table/
My notebook on the flu shot competition on drivendata.org
Blood donation has been around for a long time. The first successful recorded transfusion was between two dogs in 1665, and the first medical use of human blood in a transfusion occurred in 1818. Even today, donated blood remains a critical resource during emergencies. More info on : https://www.drivendata.org/competitions/2/warm-up-predict-bloo…
My explorations, visualizations, and models from the DrivenData.co DengAI competition
A data analysis as part of a challenge on drivendata.org which aims to find some correlations and predict how likely individuals are to receive their H1N1 and seasonal flu vaccines.
Estimating the extent of Giant Kelp Forests by segmenting Landsat imagery
This repository utilizes Machine Learning to solve the water crisis problem in Tanzania. This is one of the best use cases for machine learning to be used in the social causes helping people.
Exploratory data analysis and model preparation for DrivenData contest: PumpItUp!
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