Vehicle Management System Using Python and AI Chatbot

Vehicle Management System Using Python and AI Chatbot

The Vehicle Management System Using Python and AI Chatbot is a web-based application developed using Django and SQLite to manage vehicle information, documents, insurance, maintenance, fuel records, expenses, and service requests. It includes separate access for administrators, fleet managers, and vehicle owners, along with a machine-learning chatbot that answers supported questions using project data.

The application provides dashboards, reports, CSV exports, notifications, and vehicle-related record management through a custom dashboard. The project uses Scikit-learn and NLTK for its AI chatbot functionality.

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Project Overview

Project Detail	Description
Project Name	Vehicle Management System Using Python and AI Chatbot
Programming Language	Python, HTML, CSS, JavaScript
Framework	Django
Database	SQLite
AI and Machine Learning	Scikit-learn and NLTK
Project Type	Web-Based Management System

What Are the Main Features of This Vehicle Management System?

The system includes the following features implemented in the project files.

• Role-Based Access: Separate permissions for administrators, fleet managers, and vehicle owners.
• Vehicle Management: Store vehicle registration numbers, VINs, make, model, year, category, fuel type, transmission, status, odometer readings, purchase details, and notes.
• Vehicle Categories: Organize vehicles by category.
• Ownership Records: Maintain vehicle ownership and transfer records.
• Vehicle Documents: Manage vehicle documents, document numbers, expiry dates, and uploaded files.
• Registration Management: Store registration numbers, RTO office details, registration dates, and expiry dates.
• Insurance Management: Record insurance providers, policy numbers, coverage types, premiums, and policy validity.
• Maintenance Scheduling: Track maintenance tasks, due dates, odometer targets, priority, status, and overdue maintenance.
• Service History: Record service dates, service types, garages, odometer readings, descriptions, and costs.
• Fuel Logs: Maintain fuel quantity, rate, total cost, date, station, and odometer readings.
• Expense Tracking: Record vehicle-related expenses by category, description, amount, and date.
• Service Requests: Submit and manage service requests with preferred dates and request statuses.
• Dashboards and KPIs: View role-specific dashboard summaries and vehicle-related information.
• Reports and CSV Exports: Access reports and export supported records.
• Alerts and Notifications: Generate notifications for relevant expiry and overdue items, and manage notification status.
• Custom Administration Panel: Manage users, system settings, and chatbot training intents without using Django’s built-in admin site.
• Profile and Account Management: Access account profile details and password-change functionality.

How Does the AI Chatbot Work?

The project includes a machine-learning chatbot that classifies user questions and retrieves supported answers from the application’s database. It uses NLTK for text preprocessing and Scikit-learn for intent classification.

• Text Processing: Uses NLTK RegexpTokenizer and PorterStemmer.
• Feature Extraction: Uses Scikit-learn’s TfidfVectorizer with one-word and two-word features.
• Intent Classification: Uses LogisticRegression to predict the intent of a question.
• Database-Based Answers: Uses chatbot handlers to retrieve relevant vehicle and management records.
• Fallback Responses: Returns fallback suggestions when confidence or vocabulary coverage is insufficient.
• Chat History: Stores messages, replies, predicted intents, confidence scores, and optional helpfulness feedback.
• Chatbot Analytics: Displays usage charts, fallback rate, model metrics, unanswered questions, and a classifier test area.
• Training Management: Supports chatbot training using stored intents or the JSON training dataset.

Example supported questions include “Which insurance expires soon?”, “Fuel cost this month”, “Any overdue maintenance?”, “Last service date”, and “Total expenses this year”. The answers depend on the available records and the user’s permissions.

What Are the User Roles?

Role	Available Access
Administrator	Manage users, system settings, chatbot intents, fleet records, reports, notifications, and chatbot analytics.
Fleet Manager	Manage fleet records, view dashboards and reports, export supported data, send notifications, and access chatbot analytics.
Vehicle Owner	Access a personal dashboard and permitted records associated with their vehicles, submit service requests, and view notifications.

Access restrictions are applied according to the user’s role. Vehicle owners’ data queries are scoped to their permitted records, while staff roles can access wider fleet information according to their permissions.

Which Technologies Are Used?

• Python: Main programming language.
• Django: Web framework, authentication, views, and application logic.
• SQLite: Database for application records.
• HTML and CSS: Page structure and styling.
• JavaScript: Interface interactions, charts, and chatbot functionality.
• Scikit-learn: Machine-learning intent classification.
• NLTK: Text tokenization and stemming.
• Joblib: Saving and loading the trained chatbot model.
• Chart.js: Dashboard charts, loaded through a CDN.

How to Download

The complete package is available so you can run, study, and submit it with confidence. It includes:

• Full Source Code
• Project Report
• Synopsis
• PPT Presentation

Screenshot

How to Install and Run the Project in VS Code

Follow these steps to run the project locally. Python 3.10 or newer is required.

Step 1: Extract and Open the Project

Extract the ZIP file and open the vehicle_management folder in Visual Studio Code. Open a new terminal from the Terminal menu.

Step 2: Create a Virtual Environment

python -m venv venv
venv\Scripts\Activate.ps1

If PowerShell blocks activation, run Set-ExecutionPolicy -Scope Process Bypass in the current terminal and activate the environment again.

Step 3: Install the Required Packages

pip install -r requirements.txt

Step 4: Prepare the Database

python manage.py makemigrations accounts fleet chatbot
python manage.py migrate

Step 5: Add Demo Data

python manage.py seed_demo

This command creates or updates the demonstration records and trains the chatbot unless training is skipped.

Step 6: Start the Server

python manage.py runserver

Open http://127.0.0.1:8000/ in your browser. The interface loads Google Fonts, Bootstrap Icons, and Chart.js from CDNs, so an internet connection is needed for those browser assets.

How to Log In to the Demo Application

After running the demo-data command, use one of the supplied demo accounts.

Role	Username	Password
Administrator	admin	Admin@12345
Fleet Manager	manager	Manager@12345
Vehicle Owner	user	User@12345

These credentials are intended for the local demonstration environment. Change demo passwords and configure secure production settings before deploying the application.

How Can You Test the Project?

The project includes tests for authentication, role permissions, owner-level data restrictions, CRUD validation, workflows, alerts, demo-data seeding, dashboards, and chatbot behavior. Run the test suite from the project directory:

python manage.py test

Frequently Asked Questions

1. Which framework is used in the Vehicle Management System?

The project uses Django, a Python web framework, for its application logic, views, authentication, and management panels.

2. Which database does the project use?

The project uses SQLite to store vehicle details, insurance policies, maintenance records, fuel logs, expenses, service requests, and other application data.

3. How does the AI chatbot process questions?

It uses NLTK for tokenization and stemming, TF-IDF for text features, and Scikit-learn LogisticRegression for intent classification. Supported handlers retrieve relevant database information.

4. Does the project provide different user roles?

Yes. It provides Administrator, Fleet Manager, and Vehicle Owner roles with different permissions and access to records.

5. How can I run the project locally?

Install the requirements, prepare the database, run python manage.py seed_demo, and start the server using python manage.py runserver.

Conclusion

The Vehicle Management System Using Python and AI Chatbot combines vehicle record management with maintenance scheduling, insurance and registration tracking, fuel and expense records, service requests, dashboards, reports, and a machine-learning chatbot. Its role-based access system separates administrator, fleet manager, and vehicle owner permissions, while its Django and SQLite foundation supports local setup and testing.

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