Create a deep learning classification model to predict whether applicants will be successful if funded by Alphabet Soup.
- The target variable is the 'IS_SUCCESSFUL' column.
- Feature variables
Features Unique Values APPLICATION_TYPE 17 AFFILIATION 6 CLASSIFICATION 71 USE_CASE 5 ORGANIZATION 4 STATUS 2 INCOME_AMT 9 SPECIAL_CONSIDERATIONS 2 ASK_AMT 8747 - The 'EIN'and 'NAME' are neither target nor features and should be removed from the input data.
- I have used 3 layers with 84, 69 and 41 neurons on each layer because that is what the Optimizer returned. For the activations, I used a combination of relu and tanh as recommended by the optimizer as well.
- I couldn’t achieve the 75% target. I had tried different number of layers, neurons, and activations, but not able to get to the target.
- Steps to increase the model performance:
- The model can predict whether applicants will be successful if funded by Alphabet Soup with an accuracy of 72%.
- Because of the small amount of data, a Random Forest may have a better prediction rate.
- Eliminating more features may help improve the model.