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Donor's Choose Classifier

This project focuses on predicting whether a Donor's Choose project is "exciting" which is the likelihood that it will get funded. It is part of a Kaggle Competition to help teachers get funding for their school projects.

Process:

  1. Data: Project data provided by Kaggle
  2. Exploration: Visualizations of projects overtime and location
  3. Modeling: Random Forrest, Logictic Regression
  4. Optimization: GridsearchCV
  5. Submission

The best model was Logistic Regression with an AUC score of .68

About

Trained a Random Forrest Classifier on more than 800,000 Donors' Choose Projects to calculate the likelihood of a teacher's project getting funded.

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