Flight Ticket Price Prediction using SGD Model

This project leverages machine learning techniques to predict flight ticket prices based on various features such as departure and arrival times, airlines, origin and destination cities, and other relevant factors. The model used for the prediction is the Stochastic Gradient Descent (SGD) algorithm.

Project Overview

Flight ticket prices can vary significantly depending on numerous factors, such as time of booking, flight duration, airline, and more. In this project, we aim to predict flight ticket prices using an SGD model, which is an optimization method for linear regression. The dataset used for training and testing contains historical flight data, including both structured and categorical features.

Table of Contents

• Getting Started
• Prerequisites
• Installation
• Data
• Model
• Training
• Evaluation
• Deployment
• License

Getting Started

To get started with this project, you can clone the repository and install the necessary dependencies. Follow the instructions below to set up the environment and run the project on your local machine.

Cloning the Repository

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

Prerequisites

Before running the project, ensure you have the following tools and libraries installed:

Python 3.x pip (Python package manager)

Installation

Follow the steps below to set up the environment and install the necessary dependencies for this project.

Step 1: Clone the Repository

First, clone the repository to your local machine by running the following command:

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

Replace yourusername with your GitHub username or the appropriate username of the repository.

Step 2: Navigate to the Project Directory

After cloning the repository, navigate into the project directory:

cd flight-ticket-price-prediction

Step 3: Install the Required Dependencies

Data

The dataset used in this project contains historical flight data including features such as:

• Airline: The airline operating the flight.
• Origin: The origin airport.
• Destination: The destination airport.
• Departure Time: The scheduled departure time of the flight.
• Arrival Time: The scheduled arrival time.
• Duration: The duration of the flight.
• Price: The flight ticket price (target variable).

You can find the dataset in the data folder or you may need to download it from a public source

Model

This project uses Stochastic Gradient Descent (SGD) as the optimization method for linear regression. The model is trained to predict the price of a flight ticket based on the features provided in the dataset.

Model Summary:

• Model: Stochastic Gradient Descent Regressor
• Loss function: Mean Squared Error (MSE)
• Evaluation metric: Mean Absolute Error (MAE)

Training

Once the dependencies are installed and the data is ready, you can train the model by running the project1.ipynb .

This will:

1- Load and preprocess the dataset. 2- Train the SGD model using the training data. 3- Save the trained model as a .pkl file.

Evaluation

To evaluate the performance of the trained model, the evaluation is conducted within the project1.ipynb Jupyter notebook. This notebook will:

1. Load the saved model.
2. Apply the model to the test dataset.
3. Calculate key performance metrics, such as Mean Absolute Error (MAE) and R-squared.

To run the evaluation:

1. Open the project1.ipynb notebook in a Jupyter environment.
2. Run the cells to load the model, evaluate it on the test data, and display the performance metrics.

jupyter notebook project1.ipynb

Deployment

For a more interactive experience, you can use the Streamlit web application that allows you to input flight data and instantly get predictions for flight ticket prices.

Steps to Run the Streamlit App:

1. Install Streamlit
   If you haven't already installed Streamlit, run the following command to install it:

   pip install streamlit

2. Navigate to the Project Directory
   Once Streamlit is installed, navigate to the project directory where the Streamlit app is located.

   cd flight-ticket-price-prediction

3. Run the Streamlit App
   Start the Streamlit app with the following command:

   streamlit run deploy.py

4. Interact with the Web App
   This will launch a local web app in your browser where you can:

   • Upload a CSV file with the input data containing flight information.
   • View the predicted flight ticket prices directly on the web interface.

The Streamlit app provides an easy and interactive way to interact with the model, making it suitable for both technical and non-technical users.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• The dataset used in this project is publicly available and may be sourced from various open data platforms.
• The Stochastic Gradient Descent (SGD) algorithm is implemented using the scikit-learn library.

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Customization Tips:

• Repository link: Replace https://github.com/yourusername/flight-ticket-price-prediction.git with the actual link to your GitHub repository.

This README.md is designed to give potential users and collaborators clear instructions on setting up, using, and contributing to the project.