This repo is the code base for InnSight.

The app will be deployed at http://localhost:5173/

1. Clone the repository (main branch) for HTTP: git clone https://github.com/Niwant/VA_Project.git for SSH: git clone git@github.com:Niwant/VA_Project.git
2. cd innsight_ui
3. npm install
4. npm run dev

Open the Deployment URL to explore the app.

Note: While exploring the app, please use checkin and checkout dates that are not <today's> date. Additionally please dont leave the minimum price empty, as these are some of the code quality tests we have implemented to ensure smooth running of the code.

These visualizations help users find the best hotels and understand the hotel landscape in a given location.

• Interactive Hotel Map 📍

  • Plot all hotels on a geo map with color-coded markers (green for high ratings, red for low ratings).
  • Clicking on a marker shows hotel details, reviews, and pricing.

• Hotel Heatmap 🔥

  • A heatmap showing the density of highly-rated hotels in a city or area.

• Top 10 Best-Rated Hotels 🏆

  • A bar chart showing the highest-rated hotels in a selected area.

• Histogram of Hotel Ratings 📊

  • Shows the distribution of hotel review scores in a region.

• Sentiment Analysis (Word Cloud & Bar Chart) 💬

  • Word clouds for common words in positive & negative reviews.
  • A bar chart of the most frequent complaints & praises.

• Pie Chart of Reviewer Nationalities 🌍

  • Helps users see which hotels are popular among international travelers.

• Average Price vs. Review Score Scatter Plot 💰

  • Helps users find the best value-for-money hotels.

These visualizations help hotel owners analyze their competitors and explore potential new locations.

• Competitor Pricing & Rating Comparison 📊

  • A scatter plot of hotel prices vs. review scores to compare competitors.

• Bar Chart of Total Reviews per Competitor 📈

  • Shows which hotels receive the most reviews (indicating popularity).

• Customer Complaint Analysis 🚨

  • A bar chart of common negative review words for competitors.

• Best Areas for New Hotels (Heatmap & Cluster Analysis) 🏨

  • Identify underserved areas where good hotels are missing.
  • Use clustering techniques to highlight potential locations for a new hotel.

• Hotel Demand Trend Over Time (Line Chart) ⏳

  • Show the growth of hotel reviews in a city over time to identify rising demand.

• Comparison of Nearby Hotel Ratings (Box Plot) 📦

  • Compare hotel ratings in different parts of the city.

• Frontend: Display these visualizations in an interactive web dashboard (React + D3.js / Chart.js).
• Backend: Use Python (Flask/Django) with Folium, Plotly, Matplotlib, and NLP (for sentiment analysis).
• Data Sources: Combine hotel reviews with real-time pricing from APIs (e.g., Google Hotels, Google Events, Google Places).