This Analysis is about creating a neural network model that is able to predict whether applicants will be successful if funded by Alphabet Soup. The dataset includes more than 34,000 organizations that have received funding from Alphabet Soup over the years.
variable(s) that are considered the target(s) for your model?
The variable we are targeting in this module is the IS_SUCCESSFUL column.
variable(s) that are considered to be the features for your model .?
The features that we are using are every column except the ones that we will drop.
What variable(s) are neither targets nor features were removed .?
The first features we drop are the 'EIN' & 'NAME' because we expect both features to have little to do with our outcome.
How many neurons, layers, and activation functions did you select for your neural network model
This model is made with input features & two hidden layers. The first hidden layer has 20 neurons, the second has 12, the third has 10, the fourth has 8, the fifth has 8, there is also an output layer. The activation function of hidden layers or output layers is changed from "relu" to "tanh" & the output layer is "sigmoid".
Was the model able to achieve the target model performance?
Was the model able to achieve the target model performance?
Although the target for the model was to be 75% or above, I was not able to reach the target.
What steps were taken to try and increase model performance?
The steps I took were adding hidden layers, changing the activation type, I have tried changing the number of epochs but I put it back to 100 and changing the number of neurons in each layer to try and make the model more accurate but unfortunately I could not achieve 75% or above
The accuracy ended up being 70.17 % - we started with a data set and tried to predict whether or not the project would be successful I did not get to the accuracy of 75% I think The best way to increase the accuracy of your model is to have more data. If we have solid data added to this model, the accuracy of this model will be much more concrete.