1. Abstract

Airline ticket prices are highly dynamic and often fluctuate due to multiple factors such as airline company, travel class, departure time, destination, and demand. This unpredictability makes it difficult for travelers to determine the best time to book tickets at the lowest price.

This project focuses on developing a Flight Fare Prediction System using Machine Learning and Automated Machine Learning (AutoML) techniques. The system analyzes historical flight data containing attributes such as airline name, source, destination, journey date, departure time, arrival time, duration, and class type.

Initially, traditional machine learning algorithms are used to build prediction models. These models analyze the relationship between different features and ticket prices. Later, AutoML techniques using the AutoSklearn library are applied to automate model selection, hyperparameter tuning, and performance optimization.

The developed model predicts the expected price of a flight ticket based on the input parameters provided by the user. This system can help travelers make better decisions while booking tickets and can assist airline companies in analyzing pricing trends.

2. Objectives

The main objectives of this project are:

1. To analyze historical flight fare data.
2. To understand the factors affecting airline ticket prices.
3. To implement machine learning algorithms for flight price prediction.
4. To perform data preprocessing and feature engineering techniques.
5. To apply AutoML techniques using AutoSklearn to automate model building.
6. To compare traditional machine learning models with AutoML models.
7. To build a system that can accurately predict flight ticket prices.

 3. Existing System

Currently, airline ticket pricing is determined using complex airline pricing systems and demand-based algorithms. However, travelers often rely on:

1. Travel websites
2. Manual price comparison
3. Historical trends
4. Guesswork

These methods do not provide accurate predictions of future ticket prices.

Limitations of Existing System

1. Flight prices are highly unpredictable.
2. Travelers cannot easily determine the best time to purchase tickets.
3. Manual analysis of ticket prices is difficult and time-consuming.
4. Traditional systems do not provide predictive insights for users.
5. Requires extensive market analysis to estimate price trends.

 4. Proposed System

The proposed system uses Machine Learning and Automated Machine Learning (AutoML) to predict flight ticket prices.

The system works as follows:

1. A dataset containing historical flight ticket prices is used.
2. Data preprocessing and feature engineering are performed.
3. Multiple machine learning algorithms are trained to predict ticket prices.
4. AutoML using AutoSklearn is applied to automatically select the best model and optimize its parameters.
5. The final model predicts the flight fare based on input attributes such as airline, source, destination, class, and journey time.

This automated approach improves prediction accuracy and reduces the manual effort involved in building machine learning models.

5. Implementation Procedure

The implementation of the project includes the following steps:

Step 1: Data Collection

A dataset containing flight ticket prices between different cities and airlines is collected.

Step 2: Data Analysis

Exploratory Data Analysis (EDA) is performed to understand relationships between variables such as airline, journey date, duration, and ticket price.

Step 3: Data Preprocessing

Data preprocessing techniques include:

1. Handling missing values
2. Label encoding
3. One-hot encoding
4. Feature scaling

Step 4: Feature Engineering

New features are created from existing attributes such as:

1. Journey day and month
2. Departure hour
3. Arrival hour
4. Duration of flight

Step 5: Model Building

Different machine learning algorithms are applied, such as:

1. Linear Regression
2. Random Forest Regression
3. Decision Tree Regression
4. Gradient Boosting

Step 6: Hyperparameter Tuning

Model performance is improved using hyperparameter tuning techniques.

Step 7: AutoML Implementation

The AutoSklearn library is used to automatically perform:

1. Algorithm selection
2. Hyperparameter optimization
3. Model ensemble creation

Step 8: Model Evaluation

The models are evaluated using metrics such as:

1. Mean Absolute Error (MAE)
2. Mean Squared Error (MSE)
3. R² Score

Step 9: Prediction

The trained model predicts flight ticket prices based on user input parameters.

6. Software Requirements

The software required for this project includes:

1. Python – Programming language
2. Jupyter Notebook / Google Colab – Development environment
3. AutoSklearn – Automated Machine Learning library
4. Scikit-learn – Machine learning library
5. Pandas – Data manipulation and analysis
6. NumPy – Numerical computing
7. Matplotlib / Seaborn – Data visualization

7. Hardware Requirements

Minimum hardware requirements include:

1. Processor: Intel i3 / Intel i5 or higher
2. RAM: 8 GB minimum
3. Storage: 256 GB or higher
4. Laptop or Desktop computer
5. Internet connection

 8. Advantages of the Project

1. Helps travelers estimate flight ticket prices in advance.
2. Reduces uncertainty in flight ticket booking decisions.
3. Uses machine learning to analyze complex pricing patterns.
4. AutoML automatically selects the best machine learning model.
5. Reduces manual effort in model building and tuning.
6. Improves prediction accuracy with automated optimization.
7. Can be extended for airline pricing analytics and travel platforms.