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Flight Data Analysis

Developed by: Rehan Nasir, Aditya Shah, Aryan Chaudhary, Jhomar Gharbarran

Introduction

Air travel is a complex industry influenced by various factors. This project utilizes machine learning techniques to analyze flight data and identify key factors affecting flight prices.

Libraries

The code is written in Python and uses the following libraries:

  • Pandas
  • NumPy
  • Matplotlib
  • Seaborn
  • Scikit-learn

Dataset

The dataset used in this project is a modified dataset from Kaggle.

Code Overview

The code is organized into the following sections:

  1. Data Loading and Exploration: The dataset is loaded into a Pandas DataFrame, and exploratory data analysis (EDA) is performed to understand the distribution and relationships between different variables.

  2. Data Preprocessing: The dataset undergoes preprocessing steps such as handling missing values, encoding categorical variables, and scaling numerical features.

  3. Model Training: Three regression models - Linear Regression, Elastic Net, and Random Forest Regression - are trained on the preprocessed data to predict flight prices.

  4. Model Evaluation: The performance of each model is evaluated using metrics such as ( R^2 ) score, mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE).

  5. Results Visualization: Visualizations such as bar plots, line plots, and scatter plots are generated to visualize the relationships between different variables and the model predictions.