🌍 Tourism Data Analytics & AI Dashboard

An end-to-end Data Science & Machine Learning project that analyzes tourism datasets, predicts hotel prices, performs sentiment analysis on tourist reviews, and provides an interactive AI-powered dashboard using Streamlit.

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🚀 Project Overview

Tourism generates massive amounts of data including destinations, hotels, reviews, and visitor preferences. This project transforms raw tourism data into actionable insights using:

• Data Cleaning & Processing
• Exploratory Data Analysis (EDA)
• Machine Learning Models
• Natural Language Processing (NLP)
• Interactive Web Dashboard

The system helps users explore tourism trends, analyze hotel pricing, evaluate tourist sentiments, and visualize monument datasets.

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🎯 Objectives

• Analyze tourism destinations data
• Predict hotel prices using Machine Learning
• Perform sentiment analysis on tourist reviews
• Build an interactive analytics dashboard
• Integrate tourism image datasets
• Demonstrate end-to-end Data Science workflow

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🧠 Key Features

✅ Tourism Destination Analytics ✅ Hotel Price Prediction Model ✅ Sentiment Analysis using NLP ✅ Interactive Streamlit Dashboard ✅ Monument Image Gallery ✅ Data Cleaning & Feature Engineering ✅ Visualization & Insights

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🏗️ Project Structure

Tourism-Data-Analysis/
│
├── data/
│   ├── raw/
│   │   ├── destinations.csv
│   │   ├── Hotels.csv
│   │   ├── Review_db.csv
│   │   └── Indian-monuments/
│   │        └── images/
│   │
│   └── processed/
│       ├── clean_destinations.csv
│       ├── clean_hotels.csv
│       └── clean_reviews.csv
│
├── notebooks/
│   ├── 01_data_cleaning.ipynb
│   ├── 02_eda_analysis.ipynb
│   └── 03_model_training.ipynb
│
├── models/
│   ├── price_model.pkl
│   └── sentiment_model.pkl
│
├── app.py
├── train_model.py
├── requirements.txt
└── README.md

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📊 Dataset Description

1️⃣ Destinations Dataset

• City
• State
• Tourist Type
• Establishment Year
• Google Rating
• Weekly Off
• Entrance Fee

2️⃣ Hotels Dataset

• Hotel Name
• Location
• Price
• Rating
• Facilities

3️⃣ Reviews Dataset

• Tourist Reviews
• Sentiment Labels

4️⃣ Monument Image Dataset

• Indian monuments categorized by folders
• Used for visual tourism exploration

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⚙️ Technologies Used

Category	Tools
Programming	Python
Data Analysis	Pandas, NumPy
Visualization	Matplotlib, Seaborn
Machine Learning	Scikit-learn
NLP	TF-IDF, Logistic Regression
Dashboard	Streamlit
Version Control	Git & GitHub

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🤖 Machine Learning Models

🏨 Hotel Price Prediction

• Algorithm: Linear Regression
• Input: Hotel Rating
• Output: Estimated Price

💬 Sentiment Analysis

• TF-IDF Vectorization

• Logistic Regression Classifier

• Classifies reviews as:

  • Positive
  • Negative

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🖥️ Dashboard Modules

📍 Destination Analysis

• Explore tourist destinations
• Visual insights & statistics

🏨 Hotel Analysis

• Price distribution
• Top luxury hotels

💬 Sentiment Analysis

• Predict tourist review sentiment

💰 Price Prediction

• ML-based hotel price estimator

🗺 Monument Gallery

• Select monument
• View categorized images

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📈 Results

• Generated tourism insights from raw data
• Built predictive ML models
• Developed interactive analytics platform
• Integrated NLP & Computer Vision-ready dataset

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🔮 Future Scope

• AI Trip Planner
• Budget Optimization System
• Tourism Recommendation Engine
• AI Travel Chatbot
• Real-time Travel Alerts
• Weather & Event Integration
• Personalized Travel Suggestions

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🧑‍💻 Author

Raj BTech Computer Science Engineering Student Aspiring Data Scientist & AI Developer

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⭐ Acknowledgement

This project was developed as part of academic learning to demonstrate practical applications