💡 New Idea: Add Airfare Predictor Project Using LSTMs — Decoding the Skies #79
Description
💡 New Idea: Add Airfare Predictor Project — Decoding the Skies
Hello everyone 👋,
I'd love to contribute a project titled “Decoding the Skies: An Airfare Predictor” — a machine learning-based airfare forecasting tool designed to help travelers across India predict flight prices using LSTM and other models.
✈️ Problem Statement
Flight prices in India fluctuate unpredictably. As someone who frequently travels between my hometown and Jaipur for college, I've experienced firsthand how hard it is to book flights at the right time. This project aims to bring clarity and transparency to airfare pricing.
📊 Key Highlights
- Data Source: Historical flight price data from Goibibo
- Preprocessing:
- One-hot encoding for airlines and cities
- Extracted date features (day, month) to capture seasonal trends
- Standardized flight durations
- Models Used:
- Random Forest / XGBoost for baseline regression
- LSTM-based deep learning model for temporal sequence modeling
- Evaluation Metrics: RMSE and MAE
🧠 Why It Matters
This tool empowers travelers with price insights, helping them plan trips smarter and potentially save money. Future enhancements could include real-time data integration and consideration of seasonal events (e.g., festivals, holidays) to improve prediction accuracy.
📁 Code Repositories
This is also my first open-source contribution, and I’m excited to be part of the community! 🙌
Please let me know how I can best align this with the repo’s structure. I’m happy to create a pull request and make any necessary changes based on your guidance.
Thank you very much!
Label: new idea