<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/">
  <channel>
    <title>DEV Community: Ganesh Basani</title>
    <description>The latest articles on DEV Community by Ganesh Basani (@ganesh_basani_eb85cee3d24).</description>
    <link>https://dev.to/ganesh_basani_eb85cee3d24</link>
    <image>
      <url>https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4066469%2F475ace4c-fbdf-413b-9230-9aa66d10e5f9.png</url>
      <title>DEV Community: Ganesh Basani</title>
      <link>https://dev.to/ganesh_basani_eb85cee3d24</link>
    </image>
    <atom:link rel="self" type="application/rss+xml" href="https://dev.to/feed/ganesh_basani_eb85cee3d24"/>
    <language>en</language>
    <item>
      <title>Building an AI-Powered Ticket Routing System with Python and Machine Learning</title>
      <dc:creator>Ganesh Basani</dc:creator>
      <pubDate>Thu, 06 Aug 2026 20:43:36 +0000</pubDate>
      <link>https://dev.to/ganesh_basani_eb85cee3d24/building-an-ai-powered-ticket-routing-system-with-python-and-machine-learning-1gkp</link>
      <guid>https://dev.to/ganesh_basani_eb85cee3d24/building-an-ai-powered-ticket-routing-system-with-python-and-machine-learning-1gkp</guid>
      <description>&lt;ol&gt;
&lt;li&gt;Introduction&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Customer support teams receive hundreds or even thousands of tickets every day. Manually assigning these tickets to the correct department can be slow and inefficient.&lt;/p&gt;

&lt;p&gt;To solve this problem, I developed an AI-powered ticket routing system using Python and Machine Learning. The system automatically predicts the appropriate category for incoming support tickets and identifies tickets that are likely to breach their Service Level Agreement (SLA).&lt;/p&gt;

&lt;p&gt;This project helped me strengthen my skills in Python, Machine Learning, Flask, REST APIs, and data preprocessing.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Technologies Used&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;• Python&lt;br&gt;
• Flask&lt;br&gt;
• Scikit-learn&lt;br&gt;
• Pandas&lt;br&gt;
• NumPy&lt;br&gt;
• HTML&lt;br&gt;
• CSS&lt;br&gt;
• REST APIs&lt;br&gt;
• Git &amp;amp; GitHub&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Project Features&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;• Automatic ticket categorization&lt;br&gt;
• SLA breach prediction&lt;br&gt;
• Machine learning-based classification&lt;br&gt;
• REST API integration&lt;br&gt;
• User-friendly interface&lt;br&gt;
• Data preprocessing pipeline&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Project Workflow&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Collect customer support ticket data.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Clean and preprocess the dataset.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Convert text into machine-readable features.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Train a machine learning model.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Predict ticket categories.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Estimate SLA breach probability.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Display results through a Flask web application.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Challenges Faced&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;During development, I encountered several challenges:&lt;/p&gt;

&lt;p&gt;• Handling missing values&lt;br&gt;
• Cleaning textual data&lt;br&gt;
• Feature engineering&lt;br&gt;
• Selecting the appropriate machine learning model&lt;br&gt;
• Improving prediction accuracy&lt;/p&gt;

&lt;p&gt;Overcoming these challenges improved my understanding of practical machine learning workflows and data processing techniques.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Future Improvements&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Some planned enhancements include:&lt;/p&gt;

&lt;p&gt;• Large Language Model (LLM) integration&lt;br&gt;
• Real-time prediction API&lt;br&gt;
• Cloud deployment&lt;br&gt;
• Multi-language support&lt;br&gt;
• Improved prediction accuracy using advanced machine learning models&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Conclusion&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;This project demonstrates how Machine Learning can automate repetitive business tasks and improve operational efficiency.&lt;/p&gt;

&lt;p&gt;Developing this application enhanced my understanding of Python programming, data preprocessing, machine learning, REST APIs, and software development best practices.&lt;/p&gt;

&lt;p&gt;I look forward to building more AI-powered applications and continuing my learning journey.&lt;/p&gt;

&lt;p&gt;Thank you for reading.&lt;/p&gt;

&lt;p&gt;Author:&lt;br&gt;
Ganesh Basani&lt;/p&gt;

</description>
      <category>webdev</category>
      <category>programming</category>
      <category>javascript</category>
    </item>
  </channel>
</rss>
