Abstract
Flight delays are a significant issue in the aviation industry, causing financial losses and customer dissatisfaction. Accurate prediction of flight delays can help airlines, passengers, and airport authorities better manage their schedules and resources. This project aims to develop a machine learning-based system to predict flight delays using Python. The model leverages historical flight data, including features such as departure time, arrival time, origin, destination, airline, and weather conditions.
Data preprocessing techniques like handling missing values, encoding categorical variables, and feature scaling are applied to prepare the dataset. Various machine learning algorithms including Logistic Regression, Random Forest, and Gradient Boosting are evaluated for performance. The final model is selected based on accuracy, precision, recall, and F1-score metrics. Results demonstrate that machine learning techniques can effectively predict the likelihood of flight delays, aiding in proactive decision-making and enhancing passenger experience.
Existing System
The current flight delay prediction systems in use by airlines and airports are often based on statistical analysis, rule-based heuristics, or basic real-time monitoring tools. These systems primarily rely on historical delay patterns, scheduled times, and weather data. However, many of these systems lack advanced predictive capabilities and may not integrate machine learning models that can learn complex relationships between multiple features. As a result, the accuracy of delay predictions remains limited, particularly in dynamic or unforeseen circumstances.
Proposed System
The proposed system uses machine learning techniques implemented in Python to predict flight delays more accurately and efficiently than traditional methods. It collects and processes large volumes of historical flight data, including flight schedules, weather conditions, airline information, and airport traffic. The system applies data preprocessing techniques such as missing value imputation, categorical encoding, and feature scaling to prepare the dataset for training.
Hardware Requirements
- Processor : Intel Core i3 (or above)
- System Type : 64-bit Operating System
- Storage : 500 GB HDD / SSD
- RAM : 4 GB (minimum)
Software Requirements
- Operating System : Windows 10 (or above)
- Software : Anaconda Navigator, Python IDE
- Libraries: Pandas, NumPy, Matplotlib , Keras
- Programming Language : Python
Architecture Diagram
Project Modules
- Day-of-Week Delay Analysis
- Flight Distance Impact Analysis
- Taxi-Out Time Distribution
- Carrier Delay Analysis and Reasons
- Carrier Cancellations and Root Cause Analysis
Components of Project Report
- Abstract
- Table of Contents
- List of Tables
- List of Figures
- Chapters
- Introduction
- Literature review
- Problem definition and requirement analysis
- Design and Implementation
- Testing and deployment
- Future enhancements
- Summary
- References
Project Report Pages : 80
Can be used in : Python
Delivery Time : Within 2 hours.
Support / Query : Call +91-7449000533
Email [email protected]
