1. Update Dataset Path:

   • Replace data_path with the path to your new CSV file (local or URL). data_path = 'path/to/your/new_dataset.csv'
   • Ensure the CSV has features in all columns except the last, which should contain labels.

2. Adjust Model Input and Output:

   • Update n_inputs to match the number of features in the new dataset.

     n_inputs = dataset.X.shape[1]  # Automatically set to number of features
     

   • Modify the output layer in MLP to match the number of classes. For example, for 5 classes:

     self.hidden3 = Linear(8, 5)  # Change 3 to 5
     

3. Tune Training Parameters:

   • Adjust batch_size, learning rate (lr), or number of epochs based on dataset size and complexity.

     train_dl = DataLoader(train, batch_size=64, shuffle=True)  # Example: larger batch size
     optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9)  # Example: smaller learning rate
     for epoch in range(100):  # Example: fewer epochs
     

4. Add Device Support (Optional):

   • To support GPU, add device handling:

     device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
     model = model.to(device)
     inputs, targets = inputs.to(device), targets.to(device)
     row = row.to(device)  # For single sample prediction