Create README.md

README.md

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+---
+language:
+- en
+- es
+tags:
+- machine-learning
+- random-forest
+- fuel-consumption
+- tabular-regression
+license: apache-2.0
+datasets:
+- your_dataset_name
+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],  # Truck_ID
+    '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")