Alphabet Soup, a nonprofit foundation, is in need of developing a tool to identify applicants most likely to succeed in their endeavors when funded. Using machine learning and neural networks with the sklearn and Tensorflow libraries. This model will predict whether applicants have a high likelihood of success if they receive funding from Alphabet Soup using the given dataset (charity_data.csv) to train the model.
The purpose of this analysis is to use machine learning and neural networks to help make decisions on the best business venture.
Data Preprocessing
What variable(s) are the target(s) for your model?
- is_successful
What variable(s) are the features for your model?
- Classifications, Application Type, Ask Amount, Income Amount
What variable(s) should be removed from the input data because they are neither targets nor features?
- The EIN and Name isn't needed here
How many neurons, layers, and activation functions did you select for your neural network model, and why? A variation of 2-3 layers with 2-16 neurons with activation relu.
Were you able to achieve the target model performance? The model didn't achieve the optimum accuracy of 75+.
What steps did you take in your attempts to increase model performance? Increasing layers, number of units and epochs.
A different model, such as a Random Forest or Gradient Boosting classifier, could also be considered as an 4A85 alternative to this projecy deep learning model. In conclusion, a repeated shift in layers and units can achieve a higher optimization, but perhap other models could be explored.