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Finding_Donors_for_CharityML. Project Overview:

First project for Introduction to ML with TensorFlow Nanodegree Program at Udacity

Optimized several different supervised learners to predict highest donation yield (3.7x fscore (0.75 vs 0.2 naive predictor) +15% accuracy (0.869 vs 0.752 naive predictor).

  • Business Understanding
  • Data Understanding: Explored data collected from the 1994 US Census with 45222 observations and 13 variables + target. This dataset is a modified version of the dataset published in the paper "Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid", by Ron Kohavi. You may find this paper online, with the original dataset hosted on UCI.
  • Data Preparation: Normalized numerical features, transformed skewed continuous features plus one-hot encoded categorical variables.
  • Data Modeling: Compared and Optimized different ensemble methods using GridCV.
  • Results Evaluation: Discussed effects of feature selection.

Code and Resources Used

  • Python Version: 3.8.5
  • Packages: pandas, numpy, sklearn, matplotlib, seaborn

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