calyxish/Yango-Traffic-Speed-Prediction

Yango Traffic Speed Prediction is a machine learning application designed to forecast traffic speed trends based on historical data in Accra. Built with Python, it employs modular design principles to ensure efficiency, scalability, and reproducibility. # Yango Traffic Speed Prediction ## 1. Description Yango Traffic Speed Prediction HACKATHON is a machine learning project designed to optimize urban mobility in Accra by predicting average traffic speeds every 15 minutes. This model helps Yango improve route planning, provide accurate estimated times of arrival (ETA), and offer an enhanced experience for both users and partner drivers. ### Situation: Urban traffic congestion in Accra is a significant challenge, causing delays and inefficiencies for both commuters and service providers. Efficiently predicting average traffic speeds can mitigate these challenges, enabling more reliable and quicker routes for users. ### Task: The goal of this project is to build a machine learning model that predicts average traffic speeds on major roads in Accra, leveraging traffic pattern data collected by Yango in September 2024. These predictions must capture variations at different times of the day to ensure accuracy and reliability. ### Action: A comprehensive data science pipeline was developed, covering data preprocessing, exploratory analysis, and machine learning modeling. By analyzing traffic trends and implementing advanced prediction algorithms, the project aims to deliver robust traffic speed forecasts every 15 minutes. ### Result: The resulting machine learning model empowers Yango to optimize route planning, reduce travel times, and provide accurate ETAs, directly impacting users’ daily schedules and improving their overall experience. ## 3. Why the Name "Yango Traffic Speed Prediction"? This project is named after Yango to reflect its core mission: enhancing mobility by leveraging data science to provide actionable traffic insights. ## 3. Table of Contents - Project Preview - Installation - Dependencies - Usage - Contributing - License - Contact Information - Acknowledgements ## 4. Project Preview ## 5. Installation 1. Clone the repository: ```sh markdown HTTPS git clone github.com …