TrafficTelligence

🚦 TrafficTelligence

A machine learning-powered Flask web application that predicts real-time traffic volume based on weather and time-based parameters. Built to help urban planners, developers, and curious minds estimate traffic flow with intelligent, data-driven insights.

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🌐 Live Demo

Coming soon at: https://avalaprabhash.github.io/TrafficTelligence

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📊 Features

• Predicts traffic volume based on inputs like temperature, rain, snow, weather, holidays, and time
• Trained using a Random Forest Regressor for robust performance
• Clean HTML form interface and optional routing to traffic condition pages
• Modular Flask app architecture
• Preprocessing tools and model artifacts stored with pickle

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🧠 Machine Learning Workflow

• Cleaned and preprocessed historical traffic volume dataset
• Handled missing values via imputation
• Encoded categorical features (holiday, weather) using LabelEncoder
• Applied StandardScaler for feature normalization
• Trained and evaluated multiple models
• Saved the best-performing model (RandomForestRegressor) and preprocessing tools

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🛠️ Tech Stack

• Frontend: HTML5, Jinja2 Templates
• Backend: Python, Flask
• ML: scikit-learn, XGBoost, Pandas, NumPy
• Visualization: Matplotlib, Seaborn (for EDA)
• Deployment: GitHub Pages (planned), Localhost

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📁 Directory Structure

TrafficTelligence/ │ ├── app.py ├── scale.pkl ├── encoder.pkl ├── imputer.pkl ├── model.pkl ├── requirements.txt ├── traffic volume.csv ├── traffic volume.ipynb └── templates/ ├── index.html ├── result.html ├── chance.html └── noChance.html

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▶️ Getting Started

1. Install dependencies ```bash pip install -r requirements.txt

python app.py

🚀 Roadmap [ ] Add prediction-based redirection to chance/noChance pages

[ ] Integrate live weather API

[ ] Deploy with custom domain: traffiq.ai

[ ] Add charts and historical comparisons in UI

🙋‍♂️ Author PRABHAS Avala 🧠 Passionate about machine learning, web development, and building things that work.