Flight Price Prediction

Overview

The Flight Price Prediction project aims to predict flight prices based on various features such as departure and arrival dates, source and destination cities, number of stops, and airline carriers. This application utilizes machine learning algorithms to provide accurate predictions, helping travelers make informed decisions.

Table of Contents

• Technologies Used
• Features
• Data Description
• Model
• Installation
• Usage
• License

Technologies Used

• Programming Language: Python
• Framework: Flask
• Machine Learning Libraries: Scikit-learn, Pandas, NumPy
• Frontend: HTML, Bootstrap
• Database: SQLite (optional)
• Version Control: Git
• Hosting: GitHub (for code) and any cloud platform for deployment

Features

• Predict flight prices based on user inputs.
• User-friendly interface for inputting flight details.
• Supports multiple source and destination options.
• Responsive design with Bootstrap.

Data Description

The dataset used for training the model includes the following features:

• Departure Date: Date and time of departure.
• Arrival Date: Date and time of arrival.
• Source: The city of departure (e.g., Delhi, Kolkata).
• Destination: The city of arrival (e.g., Cochin, New Delhi).
• No. of Stops: Number of stops during the flight (0 for non-stop).
• Airlines: The airline operating the flight (e.g., Jet Airways, IndiGo).

Model

The project utilizes machine learning techniques, including:

• Regression Models: Linear Regression or Random Forest Regression for price prediction.
• Data Preprocessing: Handling missing values, encoding categorical variables, and feature scaling.

Installation

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/flight-price-prediction.git
   cd flight-price-prediction