Rice Binary Classification using PyTorch ANN

Overview

This project performs binary classification of rice samples using an Artificial Neural Network (ANN) implemented with PyTorch.
It represents a complete end-to-end machine learning pipeline, covering data preprocessing, feature scaling, model training, evaluation, and prediction.

The objective is to accurately classify rice samples into two classes based on their extracted features.

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Dataset

The dataset consists of tabular rice feature data used for binary classification.

Feature	Description
Feature 1	Rice grain characteristic
Feature 2	Rice grain characteristic
Feature 3	Rice grain characteristic
...	...
Feature N	Rice grain characteristic
Label	0 = Class A, 1 = Class B

The dataset is preprocessed and normalized before training the neural network.

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Project Workflow

1. Data loading
2. Feature preprocessing
3. Feature scaling using StandardScaler
4. Train-test split
5. ANN model definition using PyTorch
6. Model training
7. Model evaluation
8. Prediction and performance analysis

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Artificial Neural Network Model

The project uses a fully connected Artificial Neural Network (ANN) with:

• Linear (Dense) layers
• ReLU activation function
• Sigmoid activation for binary output

The model is trained using:

• Binary Cross Entropy Loss
• Adam Optimizer

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Model Performance

The model is evaluated using:

• Accuracy
• Confusion Matrix
• Classification Report (Precision, Recall, F1-Score)

The trained model demonstrates effective performance on unseen test data.

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🛠️ Technologies Used

• Python
• PyTorch
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Jupyter Notebook

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How to Run the Project

1️⃣ Clone the repository

git clone https://github.com/KavinKohli/rice-binary-classification-pytorch.git
cd rice-binary-classification-ann-pytorch