/Neural_Network_Charity_Analysis

Primary LanguageJupyter Notebook

Neural_Network_Charity_Analysis

Project Overview

Using Machine learning and neural networks to create a binary classifier cabpable of predicting whether applicants will be successful if funded by Alphabet Soup.

Resources

  • Applications\Software: Jupyter Notebook 6.1.4
  • Languages\Libraries: Python 3.8.5, pandas, scikit-learn, tensorflow
  • Data: Charity Data

Results

Data Preprocessing

1. Target Variable for the model

  • IS_SUCCESSFUL will be the target variable for this model

2. Feature Variables for the model

  • APPLICATION_TYPE, AFFILIATION, CLASSIFICATION, USE_CASE, ORGANIZATION, INCOME_AMT, SPECIAL_CONSIDERATIONS will be the features for this model

3. Variables to be removed

  • Non-beneficial variables of NAME and EIN will be removed from this model

Compiling, Training, and Evaluating the Model

Original Model

  • Hidden Layers: 2
  • Layer 1 Nodes/Activation Feature: 90/ReLU
  • Layer 2 Nodes/Activation Feature: 30/ReLU
  • Output Layer Activation Feature: Sigmoid
  • Epochs = 100

  • The original model was not able to achieve the target model 75% accuracy.

The following attempts were made to increase model performance:

1. Optimized Model - Attempt 1

  • Change activation features for hidden layers, decrease node count, decrease epochs:
    • Hidden Layers: 2
    • Layer 1 Nodes/Activation Feature: 40/Sigmoid
    • Layer 2 Nodes/Activation Feature: 20/Sigmoid
    • Output Layer Activation Feature: Sigmoid
    • Epochs = 10

2. Optimized Model - Attempt 2

  • Add hidden layer, decrease node count, decrease epochs:
    • Hidden Layers: 3
    • Layer 1 Nodes/Activation Feature: 40/ReLU
    • Layer 2 Nodes/Activation Feature: 20/ReLU
    • Layer 3 Nodes/Activation Feature: 10/ReLU
    • Output Layer Activation Feature: Sigmoid
    • Epochs = 15

3. Optimized Model - Attempt 3

  • Add hidden layer, decrease node count, decrease epochs:
    • Hidden Layers: 4
    • Layer 1 Nodes/Activation Feature: 100/ReLU
    • Layer 2 Nodes/Activation Feature: 80/ReLU
    • Layer 3 Nodes/Activation Feature: 60/ReLU
    • Layer 4 Nodes/Activation Feature: 10/ReLU
    • Output Layer Activation Feature: Sigmoid
    • Epochs = 20

Summary

  • The original model achieved 70% accuracy which does not meet the targeted accuracy of 75%
  • After 3 attempts to optimize the original model, the model was still unable to achieve the desired accuracy.
  • The closest result was 73% on Attempt 2, by reducing the epochs to 15, adding an additional third hidden layer, and reducing the node count for each hidden layer.

Recommendations to achieve the 75% accuracy for future models:

  • Adjust the source data to remove outliers or variables that are confusing the model.
  • Create additional bins for rare occurrences in columns.
  • Increase or decrease the value for each bin.
  • Remove additional unnecessary columns.