This project uses machine learning to classify mushrooms as either edible or poisonous based on their physical characteristics. The dataset used for this project is the Mushroom dataset, which contains various features of mushrooms, including their cap shape, cap surface, gill color, and more. The goal is to build a neural network that can accurately predict whether a mushroom is edible or poisonous.
To run this project, you need the following dependencies:
- Python 3.6+
- PyTorch
- pandas
- scikit-learn
You can install the required packages using:
pip install torch pandas scikit-learnThe dataset used is mushrooms.csv, which should be placed in the root directory of the project. Each row in the dataset represents a single mushroom, with various features and a class label indicating whether it is edible or poisonous.
The categorical features in the dataset are encoded using LabelEncoder from scikit-learn. The features (X) and labels (y) are then converted to PyTorch tensors.
The model used is a simple feedforward neural network with the following architecture:
- Input layer: Size equal to the number of features
- Hidden layer: 16 neurons with ReLU activation
- Output layer: 2 neurons (edible or poisonous) with softmax activation
The model is trained using the following settings:
- Loss function: CrossEntropyLoss
- Optimizer: Adam
- Learning rate: 0.001
- Number of epochs: 50
- Batch size: 32
During training, the model's weights are updated based on the loss computed between the predicted and actual labels. The training progress is printed out for each epoch.
After training, the model is evaluated on the test set to determine its accuracy. The accuracy is printed out as a percentage.