Trips & Travel.Com – AI-Powered Travel Package Prediction

📌 Problem Statement

"Trips & Travel.Com" aims to expand its customer base by introducing a new offering of Wellness Tourism Packages. Traditionally, marketing costs were high because customers were contacted randomly without analyzing their data.

To improve efficiency, I used Machine Learning (AdaBoost Algorithm) to identify potential customers who are most likely to purchase travel packages.

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🚀 Features

• AI-powered prediction of customer interest in travel packages.
• Uses AdaBoost Algorithm for accurate classification.
• Interactive web-based UI for user convenience.

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🛠️ Tech Stack

• Frontend: HTML, CSS, JavaScript
• Backend/Model: Python (scikit-learn)
• Machine Learning: AdaBoost Classifier
• Deployment: GitHub / Render / Flask

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📊 Machine Learning Model

The system is trained using the AdaBoost Algorithm, which combines multiple weak learners to form a strong predictive model.

• Algorithm Used: AdaBoost (Adaptive Boosting)
• Target: Predict whether a customer will purchase a package
• Metric: ROC-AUC, Accuracy

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⚡ How to Run

1. Clone this repository

   git clone https://github.com/your-username/trips-travel-ai.git
   cd trips-travel-ai