##  Artificial Intelligence

#  Predictive Maintenance for Improved Router Performance

 [ Back ](https://digica.com/index.php?Itemid=930)

##  Overview

Poor router performance can lead to disruptions and dissatisfaction for users. To tackle this, Digica partnered with a company to **predict router performance issues and prevent them using machine learning methods.**

##  Objectives

- ![](https://digica.com/images/23/svg/circle.svg)

    **Indicate reboot probability** using an **XGBoost algorithm.**
- ![](https://digica.com/images/23/svg/circle.svg)

    Identify **detailed reasons** for reboots and suggest **preventative steps**.
- ![](https://digica.com/images/23/svg/circle.svg)

    **Cluster devices** to train specialized models for more accurate results.

 ![Predictive Maintenance for Improved Router Performance](https://digica.com/templates/yootheme/cache/98/Predictive%20Maintenance-98a43605.jpeg)

##  Results

- ![](https://digica.com/images/23/svg/circle.svg)

    Created an **ML model** achieving >**80% precision** and >**85% accuracy**.
- ![](https://digica.com/images/23/svg/circle.svg)

    Predicted reboots 12 hours in **advance,** giving operators time to take preventive action.
- ![](https://digica.com/images/23/svg/circle.svg)

    The model was **deployed in production**, with big data tools used to handle **10M+ devices per hour.**
- ![](https://digica.com/images/23/svg/circle.svg)

    **Real-time predictions and insights** were made available via **Tableau dashboards**.
- ![](https://digica.com/images/23/svg/circle.svg)

    Cloud infrastructure was critical in handling the large-scale data involved:

    - One day of raw data: **~100GB**
    - Total dataset size: **16 TB**
    - Number of features: **>500** (Data was collected from each device every few minutes)

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