https://github.com/pranavgautam29/flight-price-prediction

The Flight Price Prediction project uses machine learning to forecast flight ticket prices based on historical data. Hosted on Streamlit Community Cloud and deployed via Streamlit, this application allows users to input flight details such as departure and arrival airports, travel dates, and class to receive accurate price predictions.
https://github.com/pranavgautam29/flight-price-prediction

machine-learning prediction-model regression scikit-learn statistical-machine-learning streamlit

Last synced: 3 days ago
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The Flight Price Prediction project uses machine learning to forecast flight ticket prices based on historical data. Hosted on Streamlit Community Cloud and deployed via Streamlit, this application allows users to input flight details such as departure and arrival airports, travel dates, and class to receive accurate price predictions.

• Host: GitHub
• URL: https://github.com/pranavgautam29/flight-price-prediction
• Owner: pranavgautam29
• Created: 2024-09-11T18:47:06.000Z (5 months ago)
• Default Branch: master
• Last Pushed: 2024-09-13T11:00:07.000Z (4 months ago)
• Last Synced: 2024-11-21T16:14:29.180Z (2 months ago)
• Topics: machine-learning, prediction-model, regression, scikit-learn, statistical-machine-learning, streamlit
• Language: Jupyter Notebook
• Homepage: https://cloudfare.streamlit.app/
• Size: 2.82 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Cloud Fare 

This app is currently running on: [cloudfare.streamlit.app](https://cloudfare.streamlit.app/)

Cloud Fare is a machine learning application that uses statistical machine learning models to predict flight prices.

This project leverages historical flight prices data of major Indian airlines in order to predict future flight prices.

Hosted and deployed on streamlit cloud, providing a user-friendly interface for real time predictions.

# Technologies Used

* Python: Primary language for data preprocessing and model training

* Jupyter Notebooks: Used for exploratory data analysis

* Scikit-learn/XGBoost: For implementing statistical machine learning models

* Pandas/Numpy: For data analysis and preprocessing

* Streamlit: Hosting and Deployment

# Installation

If you want to run this project locally, follow these steps

```bash

git clone https://github.com/pranavgautam29/flight-price-prediction.git # clone this repository

cd flight-price-prediction

```

Set-up a virtual environment

```bash

python -m venv venv

source venv/bin/activate  # On Windows use `venv\Scripts\activate`

```

Install Dependencies

```bash

pip install -r requirements.txt

```

Run the streamlit application

```bash

streamlit run app/app.py

```

# Usage and Deployment

Access the live version of this application here [cloudfare.streamlit.app](https://cloudfare.streamlit.app/). Input your flight details and get the approximate prediction for fare by clocking on Predict Price button.

This app is hosted on an open source python framework called [Streamlit](https://streamlit.io/) and deployed on a free deployment service [Streamlit Cloud](https://streamlit.io/cloud)