This code builds and trains a basic neural network for binary classification on the Stroke Prediction dataset. The dataset is preprocessed, features are normalized, and the model is trained using a custom-built neural network. The training loss is visualized, and the accuracy on the test set is reported as a measure of the model's performance.
- Python 3.x
- NumPy
- Pandas
- Scikit-learn
- Matplotlib
Ensure you have the required dependencies installed using:
pip install numpy pandas scikit-learn matplotlib-
Download the Stroke Prediction dataset (replace 'stroke_data.csv' with the actual file path).
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Run the script to preprocess the data, train the neural network, and evaluate its performance.
python stroke_prediction_nn.py-
Data Loading and Preprocessing:
- The Stroke Prediction dataset is loaded, and missing values are handled.
- Categorical variables are one-hot encoded, and features and labels are extracted.
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Neural Network Definition:
- A custom neural network class is defined with methods for forward pass, backward pass, and training.
- The network has an input layer, a hidden layer, and an output layer.
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Training:
- The neural network is instantiated and trained using the training data.
- Training loss is printed, and a plot of the training loss is displayed.
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Testing and Evaluation:
- The trained model is tested on the test set, and predictions are compared to true labels.
- The accuracy of the model is calculated and printed.
hidden_size: Number of neurons in the hidden layer (8).epochs: Number of training epochs (1000).learning_rate: Learning rate for updating weights and biases (0.01).
- Adjust the hyperparameters to experiment with the model's performance.
- Modify the neural network architecture, learning rate, or other parameters as needed.
This code is licensed under the MIT License.
Feel free to customize and use this code for your binary classification tasks. If you find it helpful, consider providing attribution to the original source.