The non-profit foundation Alphabet Soup wants to create an algorithm to predict whether or not applicants for funding will be successful. Created a binary classifier that is capable of predicting whether applicants will be successful if funded by Alphabet Soup, based on a dataset containing more than 34,000 organizations that have received funding from Alphabet Soup over the years.
Used Pandas and Scikit-Learn’s StandardScaler() to preprocess the dataset. Cleaned data, and for columns with more than 10 unique values, determined the number of data points for each unique value. Selected a cutoff point to bin "rare" categorical variables together in a new value, Other. Used pd.get_dummies() to one-hot encode categorical variables.
Designed a neural network to create a binary classification model that can predict if an Alphabet Soup–funded organization will be successful based on features in this dataset.
- Created a neural network model by assigning the number of input features and nodes for each layer using Tensorflow Keras.
- Created the hidden layers and chose appropriate activation function.
- Created an output layer with an appropriate activation function.
- Compiled and trained the model.
- Created a callback that saves the model's weights every 5 epochs.
- Evaluated the model using the test data to determine the loss and accuracy.
- Saved and exported your results to an HDF5 file, and name it
AlphabetSoupCharity.h5.
Optimized model in order to achieve a target predictive accuracy higher than 75%.
See file 'CharityFund Model Optimization Report' for a summary of hypertunings, findings and results.