🏠 Houseboat AI Recommendation System

An AI-powered houseboat discovery and recommendation platform built with Django, MySQL, Machine Learning, and a database-driven chatbot.

The system allows users to explore houseboats, search and filter available options, receive personalized recommendations, make bookings, manage wishlists, and interact with an AI-style chatbot for houseboat information.

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🚀 Project Overview

Houseboat AI Recommendation System is a full-stack web application designed to make houseboat discovery and selection easier through intelligent recommendations and data-driven search.

The application combines:

• Full-stack web development
• Machine Learning
• Database management
• AI-based recommendation
• Chatbot interaction
• Data analytics

The project uses a dataset containing 500 houseboat records with information such as location, price, bedrooms, capacity, facilities, ratings, bookings, and luxury status.

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✨ Key Features

👤 User Features

• User registration and login
• Browse houseboats
• Search houseboats
• Filter houseboats based on preferences
• View detailed houseboat information
• AI-powered houseboat recommendations
• Add houseboats to wishlist
• Book houseboats
• View booking history
• Contact/message functionality

🤖 AI & Machine Learning

• K-Nearest Neighbors (KNN) based recommendation system
• User-preference-based houseboat recommendations
• Machine-learning-driven similarity matching
• Database-driven chatbot for houseboat queries

💬 Chatbot

The chatbot can answer queries such as:

• "Show me luxury houseboats"
• "Show me houseboats in Alleppey under ₹10,000"
• "Which houseboat has the highest rating?"
• "Show me houseboats with AC"
• "Show me houseboats with Wi-Fi"

👨‍💼 Admin Features

• Admin dashboard
• Houseboat management
• User management
• Booking management
• Wishlist management
• Contact message management
• Dataset monitoring

📊 Data Analytics

A separate Power BI dashboard was created to analyze:

• Total houseboats
• Luxury vs non-luxury houseboats
• Average rating
• Houseboat distribution by location
• Average price by location
• Rating distribution
• Most-booked houseboats

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🧠 System Architecture

                    ┌──────────────────────┐
                    │       User           │
                    └──────────┬───────────┘
                               │
                               ▼
                    ┌──────────────────────┐
                    │  Django Web Interface│
                    │   HTML/CSS/Bootstrap  │
                    └──────────┬───────────┘
                               │
                ┌──────────────┼──────────────┐
                │              │              │
                ▼              ▼              ▼
          Search & Filter   Booking       Wishlist
                │              │              │
                └──────────────┼──────────────┘
                               │
                               ▼
                    ┌──────────────────────┐
                    │      MySQL Database  │
                    └──────────┬───────────┘
                               │
                ┌──────────────┴──────────────┐
                │                             │
                ▼                             ▼
       ┌─────────────────┐          ┌─────────────────┐
       │ Recommendation  │          │    Chatbot      │
       │   System (KNN)  │          │ Database Query  │
       └─────────────────┘          └─────────────────┘
                │                             │
                └──────────────┬──────────────┘
                               ▼
                    Personalized Results

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🛠️ Technologies Used

Frontend

• HTML5
• CSS3
• Bootstrap

Backend

• Python
• Django

Database

• MySQL

Machine Learning

• Scikit-learn
• K-Nearest Neighbors (KNN)
• Feature-based similarity

Data Processing

• Pandas
• NumPy
• Exploratory Data Analysis (EDA)

Analytics

• Microsoft Power BI

Development Tools

• Visual Studio Code
• MySQL Workbench
• Git
• GitHub
• Google Colab

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📂 Dataset

The project uses a 500-row professional houseboat dataset.

Important attributes include:

Feature	Description
HouseboatID	Unique houseboat identifier
HouseboatName	Name of the houseboat
Location	Houseboat location
PriceINR	Price in Indian Rupees
Bedrooms	Number of bedrooms
Capacity	Guest capacity
AC	Air-conditioning availability
Luxury	Luxury classification
Rating	Customer rating
ReviewCount	Number of reviews
BookingCount	Number of bookings
Food	Food availability
WiFi	Wi-Fi availability
Jacuzzi	Jacuzzi availability
Fishing	Fishing facility
Canoeing	Canoeing facility

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🤖 Recommendation System

The recommendation component uses K-Nearest Neighbors (KNN) to identify houseboats that are similar to the user's selected preferences.

Relevant features can include:

• Price
• Bedrooms
• Capacity
• Rating
• Location
• Facilities
• Luxury preference

The system compares the user's requirements with available houseboats and returns suitable recommendations.

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💬 Chatbot

The chatbot provides database-driven responses to natural-language-style houseboat queries.

Example:

User:
Show me luxury houseboats

System:
Returns houseboats where Luxury = Yes

Another example:

User:
Show me houseboats in Alleppey under 10000

System:
Filters houseboats based on location and price
and returns matching results.

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📊 Power BI Dashboard

The project also includes a Power BI analytics dashboard for exploring the houseboat dataset.

Dashboard insights include:

• Houseboat count
• Average rating
• Location-wise distribution
• Location-wise average price
• Luxury distribution
• Rating distribution
• Top 10 most-booked houseboats

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📸 Project Screenshots

🏠 Home Page

🤖 AI Recommendation

💬 Chatbot

📅 Booking

👨‍💼 Admin Dashboard

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⚙️ Installation & Setup

1. Clone the repository

git clone https://github.com/24ubc226/HouseboatAI.git

2. Navigate to the project

cd HouseboatAI

3. Create a virtual environment

python -m venv venv

4. Activate the virtual environment

Windows

venv\Scripts\activate

5. Install dependencies

pip install -r requirements.txt

6. Configure the database

Create a MySQL database and update the Django database configuration using environment variables.

7. Apply migrations

python manage.py migrate

8. Run the development server

python manage.py runserver

Open:

http://127.0.0.1:8000/

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🔐 Environment Variables

Sensitive information should be stored in environment variables rather than committed to GitHub.

Example:

SECRET_KEY=your-secret-key
DB_NAME=your-database-name
DB_USER=your-database-user
DB_PASSWORD=your-database-password
DB_HOST=localhost
DB_PORT=3306

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📁 Project Structure

HouseboatAI/
│
├── houseboat_ai/
│   ├── settings.py
│   ├── urls.py
│   └── ...
│
├── recommendation/
│   ├── models.py
│   ├── views.py
│   ├── forms.py
│   ├── urls.py
│   ├── templates/
│   └── ...
│
├── static/
│   ├── css/
│   ├── js/
│   └── images/
│
├── screenshots/
│
├── manage.py
├── requirements.txt
├── .gitignore
└── README.md

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🎯 Learning Outcomes

This project helped develop practical experience in:

• Full-stack web development
• Django application development
• MySQL database integration
• Machine Learning
• KNN recommendation systems
• Data preprocessing
• Exploratory Data Analysis
• Chatbot development
• Power BI data visualization
• Git and GitHub
• Debugging and deployment preparation

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🔮 Future Improvements

Possible future enhancements include:

• Cloud deployment
• Real-time booking availability
• Payment gateway integration
• Advanced NLP chatbot
• Vector database-based RAG
• Deep-learning recommendation models
• Personalized user profiles
• Mobile application
• Real-time notifications

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👨‍💻 Author

Bijil Biju

BCA (Honours) – Artificial Intelligence & Machine Learning

GitHub: https://github.com/24ubc226

LinkedIn: https://linkedin.com/in/bijil-biju-7a14b9320

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⭐ Project Purpose

This project was developed as a portfolio project to demonstrate practical skills in Artificial Intelligence, Machine Learning, Data Analytics, Web Development, and Database Management.