This project aims to use a deep-learning neural network with TensorFlow library to analyze and classify the success of charitable donations from Alphabet Soup.
- The columns 'EIN' and 'NAME' were dropped.
The target variable was the column 'IS_SUCCESSFUL'; this feature is the one that is going to be predicted and determine the success rate of the data.
- The categorical variables were encoded with 'pd.get_dummies()'.
While the preprocessing, the columns 'APPLICATION_TYPE', 'AFFILIATION', 'CLASSIFICATION', 'USE_CASE', 'ORGANIZATION', 'STATUS', 'INCOME_AMT', 'SPECIAL_CONSIDERATIONS', 'ASK_AMT' are considered as feature values.
- Compiling, Training, and Evaluating the Model
- Loss: 0.5653
- Accuracy: 0.7291
- Recall: 0.7826
- Graphics
The attempt at compiling a neural network consisted of layers of 80 neurons and the second one of 30; both layers had relu activation functions. In addition, the output layer had a sigmoid activation function. The assembled model had the Adam optimization algorithm, and the fitting model had a validation split of 0.15 and 100 epochs.
The results for this model were:
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- Compiling, Training, and Evaluating the Model
- Loss: 0.5760
- Accuracy: 0.7318
- Recall: 0.8021
- Graphics
The attempt at compiling a neural network consisted of layers of 1450 neurons and the second one of 80; both layers had relu activation functions. In addition, the output layer had a sigmoid activation function. The assembled model had the Adam optimization algorithm, and the fitting model had a validation split of 0.15 and 100 epochs.
The results for this model were:
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- Compiling, Training, and Evaluating the Model
- Loss: 0.5613
- Accuracy: 0.7255
- Recall: 0.7732
- Graphics
The attempt at compiling a neural network consisted of layers of 3 neurons, the second one of 15 and the third one of 3; all layers had relu activation functions. In addition, the output layer had a sigmoid activation function. The assembled model had the Adam optimization algorithm, and the fittin model had a validation split of 0.15 and 100 epochs.
The results for this model were:
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Any model was unable to perform a 75% accuracy rate. The graphic of the first and second models shows a region where the training error and validation error diverge, with the training error staying low and the validation error increasing due to overfitting the data and learning too much from the training examples. The validation error remains high because our model cannot generalize from the training data to new data.
The third model had a loss of 56%, an accuracy of 73%, and a sensitivity of 77%. This model had a lower loss and sensitivity than the others, but its loss graphic shows a good fit with a training and validation loss that decreases to the point of stability with a minimal gap between the two final loss values.