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Tejas Ekawade's Projects

data icon data

Data and code behind the stories and interactives at FiveThirtyEight

donors-choose-application-screening-kaggle icon donors-choose-application-screening-kaggle

Kaggle problem where we had to predict whether or not a DonorsChoose.org project proposal submitted by a teacher will be approved, using the text of project descriptions as well as additional metadata about the project, teacher, and school. Got an accuracy of 0.7748 with the uploaded Jupyter notebook.

home-credit-default-risk icon home-credit-default-risk

Kaggle problem to predict whether an applicant is likely to repay or default the loan based on applicant's data, previous loan history within and outside the comapny. Achieved a ROC-AUC score of 0.788 in the competition

time-series-and-equities icon time-series-and-equities

This repository involves analyzing real-world equities data using Python libraries and statistical methods including time-series techniques.

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