Flight Delay Predict Chatbot

A tool for developers aimed at optimizing passenger experience for airlines through data integration on air traffic, flights, and passenger needs. This project is highly relevant in the aviation industry, addressing crucial aspects of operational efficiency and passenger satisfaction.

Table of Contents

• Project Description
• Installation
• Usage
• Features
• Data
• Model

Project Description

The Flight Delay Predict Chatbot is designed to predict flight delays based on historical flight data. It integrates various factors that influence flight delays, including:

• Flight number
• Airline
• Departure and arrival airports
• Scheduled departure and arrival times
• Actual departure and arrival times
• Weather conditions
• Day of the week
• Seasonal factors
• Holidays
• Historical delays for the same route
• Political factors

This project has significant potential to improve operational efficiency and passenger satisfaction, leading to potential cost savings and increased customer loyalty for airlines.

Installation

To get started with the Flight Delay Predict Chatbot, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/flight-delay-predict-chatbot.git
   

2. Navigate to the project directory: cd flight-delay-predict-chatbot

3.Install the necessary dependencies pip install -r requirements.txt

Usage

To use the chatbot for predicting flight delays, follow these steps:

1. Prepare your dataset with the required fields.

2. Run the main application: python app.py

3. Interact with the chatbot through the provided interface. Example: User: What is the delay prediction for flight ABC123 on July 4th? Bot: The predicted delay for flight ABC123 on July 4th is 15 minutes due to expected weather conditions.

Features

Predict flight delays using historical flight data. Integrate multiple factors influencing flight delays. Provide real-time delay predictions through a chatbot interface.

Data

The model uses a variety of data sources, including but not limited to:

• Flight schedules
• Weather data
• Holiday schedules
• Political events

Model

The project utilizes machine learning techniques provided by libraries such as Pandas, NumPy, and Scikit-learn (sklearn) to train a predictive model. The model is trained on both historical and synthetic data to forecast future delays with a high degree of accuracy.