The goal of this analysis is to create a binary classification model using a neural network that can predict the success of organizations funded by Alphabet Soup. This report covers the preprocessing of the dataset, designing, training, and evaluating a neural network model. Additionally, it includes model optimization techniques to achieve higher accuracy.
- Target Variable: IS_SUCCESSFUL
- Features for the Model: All columns except 'EIN' and 'NAME'
- Removed Variables: 'EIN' and 'NAME' columns as they are not relevant to the analysis.
- Number of Unique Values:
- APPLICATION_TYPE: Handled with 'Other' for categories having more than 10 unique values.
- CLASSIFICATION: Binning rare categories as 'Other' with more than 1 occurrence.
- Model Architecture:
- 2 Hidden Layers with 8 and 5 neurons respectively, using the 'relu' activation function.
- Output Layer with 'sigmoid' activation.
- Target Performance: The initial model achieved an accuracy of approximately 61.1%.
- Steps for Performance Improvement:
- Adjusted hidden layer nodes to 32.
- Changed activation function to ELU.
- Increased epochs to 100.
The initial model's accuracy was 61.1%, falling short of the 75% target. Multiple attempts were made to enhance performance by modifying the neural network architecture, activation functions, and training duration. However, the target accuracy was not achieved.
A different approach using more complex models like a Convolutional Neural Network (CNN) or a Recurrent Neural Network (RNN) might better address the classification problem. A CNN is suitable for image data analysis, and an RNN is effective for sequential data. Utilizing these models might capture nuanced patterns within the dataset, potentially improving the accuracy for this specific problem.
In conclusion, despite applying various optimization techniques to the neural network model, the target performance of over 75% accuracy was not achieved. To tackle this classification problem effectively, employing more sophisticated neural network architectures specifically tailored for different data structures, such as CNNs or RNNs, could potentially yield better results.