# Rice Classification Model

## Overview

This repository contains an XGBoost-based model trained to classify rice grains using the `mltrev23/Rice-classification` dataset. The model is designed to predict the type of rice grain based on various geometric and morphological features. XGBoost (eXtreme Gradient Boosting) is a powerful, efficient, and scalable machine learning algorithm that excels at handling structured data.

## Model Details

### Algorithm

- **XGBoost**: A gradient boosting framework that uses tree-based models. XGBoost is known for its performance and speed, making it a popular choice for structured/tabular data classification tasks.

### Training Data

- **Dataset**: The model is trained on the `mltrev23/Rice-classification` dataset.
  - **Features**: The dataset includes the following features: `Area`, `MajorAxisLength`, `MinorAxisLength`, `Eccentricity`, `ConvexArea`, `EquivDiameter`, `Extent`, `Perimeter`, `Roundness`, and `AspectRation`.
  - **Target**: The target variable is `Class`, a binary label indicating the type of rice grain.

### Model Performance

- **Accuracy**: [Insert accuracy metric]
- **Precision**: [Insert precision metric]
- **Recall**: [Insert recall metric]
- **F1-Score**: [Insert F1-score]

(Replace the placeholders with actual values after evaluating the model on your test data.)

## Requirements

To run the model, you'll need the following Python libraries:

```bash
pip install xgboost
pip install pandas
pip install numpy
pip install scikit-learn
```

## Usage

### Loading the Model

You can load the trained model using the following code snippet:

```python
import xgboost as xgb

# Load the trained model
model = xgb.Booster()
model.load_model('rice_classification_xgboost.model')
```

### Making Predictions

To make predictions with the model, use the following code:

```python
import pandas as pd

# Example input data (replace with your actual data)
data = pd.DataFrame({
    'Area': [4537, 2872],
    'MajorAxisLength': [92.23, 74.69],
    'MinorAxisLength': [64.01, 51.40],
    'Eccentricity': [0.72, 0.73],
    'ConvexArea': [4677, 3015],
    'EquivDiameter': [76.00, 60.47],
    'Extent': [0.66, 0.71],
    'Perimeter': [273.08, 208.32],
    'Roundness': [0.76, 0.83],
    'AspectRation': [1.44, 1.45]
})

# Convert DataFrame to DMatrix for XGBoost
dtest = xgb.DMatrix(data)

# Predict class
predictions = model.predict(dtest)
```

### Evaluation

You can evaluate the model's performance on a test dataset using standard metrics like accuracy, precision, recall, and F1-score:

```python
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# Assuming you have ground truth labels and predictions
y_true = [1, 0]  # Replace with your actual labels
y_pred = predictions.round()  # XGBoost predictions may need to be rounded

print("Accuracy:", accuracy_score(y_true, y_pred))
print("Precision:", precision_score(y_true, y_pred))
print("Recall:", recall_score(y_true, y_pred))
print("F1 Score:", f1_score(y_true, y_pred))
```

## Model Interpretability

For understanding feature importance in the XGBoost model:

```python
import matplotlib.pyplot as plt

# Plot feature importance
xgb.plot_importance(model)
plt.show()
```

## References

If you use this model in your research, please cite the dataset and the following reference for XGBoost:

- **Dataset**: `mltrev23/Rice-classification`
- **XGBoost**: Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794).