README.md

---
language:
- en
- es
tags:
- machine-learning
- random-forest
- fuel-consumption
- tabular-regression
license: apache-2.0
metrics:
- mean_absolute_error
- r2_score
model_name: Fuel Burn Prediction Model
model_description: >
  This model predicts fuel consumption in kilograms based on truck ID,
  kilometers driven, and fuel consumption in liters using a
  RandomForestRegressor model.
widget:
- input:
  - type: text
    label: Truck ID
    example: Truck_ID
  - type: number
    label: Kms Driven
    example: 100000
  - type: number
    label: Litros (Fuel Consumed)
    example: 150
  output:
  - type: number
    label: Predicted Fuel Burn (kg)
    score: 125.25
base_model: RandomForestRegressor
library_name: scikit-learn
---

# Fuel Burn Prediction Model

## Model Overview
This is a **RandomForestRegressor** model designed to predict **fuel burn** in **kilograms** based on three key features:
- **Truck ID**: Identifier of the truck (e.g., `Truck_ID`).
- **Kms Driven**: The number of kilometers the truck has driven.
- **Litros (Fuel Consumed)**: The amount of fuel consumed in liters.

The model is trained using historical data of trucks, which includes fuel consumption and distances driven. It predicts fuel consumption (in kilograms) given the truck's specific parameters.

## Model Specifications
- **Algorithm**: Random Forest Regressor
- **Input Features**:
  - Truck ID (Categorical, one-hot encoded)
  - Kilometers driven (Continuous)
  - Fuel consumption in liters (Continuous)
- **Output**:
  - Predicted fuel burn (in kilograms)

### Model Performance
- **R-squared (R²)**: 0.9996 on the test set.
- **Mean Absolute Error (MAE)**: 0.1513.
- **Mean Squared Error (MSE)**: Low, showing strong model performance.

These metrics indicate that the model performs exceptionally well on the test set and can generalize to unseen data with high accuracy.

## Usage

You can load this model using `joblib` and use it to predict fuel consumption for new truck data.

### Example Usage:

```python
import joblib
import pandas as pd

# Load the model
model = joblib.load('fuel_burn_model.pkl')

# Example input data
input_data = pd.DataFrame({
    'Truck_ID': [0],  # Use the numerical representation of the Truck ID(1,2,3)
    'Kms': [100000],  # Kilometers driven
    'Litros': [150]   # Fuel consumption in liters
})

# Predict fuel burn in kilograms
predicted_fuel_burn = model.predict(input_data)
print(f"Predicted fuel burn: {predicted_fuel_burn[0]:.2f} kg")