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deep-learning-charity-funding-predictor's Introduction

Deep Learning - Charity Funding Predictor

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Tools, Languages, & Libraries Utilized
  • Python
  • Pandas
  • TensorFlow
  • Sklearn
  • VS Code
  • Jupyter Notebook

    • A binary classifier that is capable of predicting whether applicants will be successful if funded.

    Review the analysis here:

    https://github.com/rb25s13/deep-learning-charity-funding-predictor/blob/main/analysis.md

    Overview:

    The purpose of this analysis is to show what was done for the algorithm to achieve the results stated below. This is an attempt to create a binary classifier that can predict whethor or not the charity funding will be successful.

    Model w/o optimization:

    Model 1 w/ optimization:

    model w/ opt1 - 0.7383 - 3 layers: 80, 30, 10 - activation='relu' - epochs=1500 (less features)

    Model 2 w/ optimization:

    model w/ opt2 - 0.7328 - 3 layers: 80, 30, 10 - activation='selu' - epochs=500 (less features than opt1)

    Model 3 w/ optimization:

    model w/ opt3 - 0.7392 - 3 layers: 160, 60, 20 - activation='relu' - epochs=1500 (doubled nodes from opt1)

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