This project focuses on developing a deep learning model to predict the success of funding applications submitted to Alphabet Soup, a charitable organization. The goal is to create a binary classification model that efficiently classifies applications as either successful or unsuccessful based on various features.
AlphabetSoupCharity.ipynb
: Jupyter Notebook containing the code for data preprocessing, model development, and evaluation.AlphabetSoupCharityOpt.ipynb
: Jupyter Notebook containing the code for data preprocessing, model development, and evaluation of the optmized model.checkpoints/
: Directory to save model checkpoints during training.AlphabetSoupCharity.h5
: Saved model file in HDF5 format.
pandas
: Data manipulation and analysis.tensorflow
: Deep learning library for building and training neural networks.scikit-learn
: Tools for machine learning tasks.numpy
: Mathematical operations.
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Setup:
- Install the required dependencies.
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Data Preparation:
- Obtain the dataset from the provided link.
- Run the data preprocessing steps in
AlphabetSoupCharity.ipynb
.
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Model Development:
- Adjust model architecture and hyperparameters as needed.
- Train the model using the provided data.
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Model Evaluation:
- Evaluate the model performance on a separate test set.
- Save the trained model to
AlphabetSoupCharity.h5
.
- The model architecture, preprocessing steps, and results are documented in the Jupyter Notebook.