Finding Donors for CharityML

In this project, I applied supervised learning techniques and an analytical mind on data collected for the U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause. I first explored the data to learn how the census data is recorded. Next, I applied a series of transformations and preprocessing techniques to manipulate the data into a workable format. Then I evaluated several supervised learners of my choice on the data, and considered which is best suited for the solution. Afterwards, I optimized the model I had selected and presented it as my solution to CharityML. Finally, I explored the chosen model and its predictions under the hood, to see just how well it’s performing when considering the data it’s given. predicted selling price to the statistics.

The main techniques used:

  • Decision Trees
  • Regression & Classification
  • Regressions
  • Kernel Methods & SVM
  • GaussianNB
  • Ensemble learning
  • RandomForestClassifier

You can see the code(iPython notebook) there.

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