metadata

license: mit
language:
  - en
metrics:
  - accuracy
library_name: sklearn
tags:
  - Traffic
  - ML
  - Random-forest
  - Classification
  - Scikit-learn

Traffic Prediction Model

Model Description

This model is a Random Forest Classifier trained to predict traffic conditions based on various input features.
It helps estimate traffic congestion levels using structured data such as time of day, weather, and historical patterns.

Training Details

Algorithm: Random Forest Classifier
Dataset: Custom traffic dataset
Preprocessing: Label encoding for categorical variables
Framework: scikit-learn

How to Use

To use this model, install the required libraries and download the model from Hugging Face.

To load and use the model:

import joblib
from huggingface_hub import hf_hub_download

# Download model
model_path = hf_hub_download(repo_id="AhaseesAI/traffic-prediction", filename="traffic_classifier.pkl")
encoder_path = hf_hub_download(repo_id="AhaseesAI/traffic-prediction", filename="target_encoder.pkl")

# Load model
model = joblib.load(model_path)
target_encoder = joblib.load(encoder_path)

# Example prediction
sample_data = [[value1, value2, value3, ...]]  # Replace with actual feature values
prediction = model.predict(sample_data)

# Convert prediction to original label
predicted_label = target_encoder.inverse_transform(prediction)
print(f"Predicted Traffic Status: {predicted_label[0]}")