Flight Price EDA

Overview

Performed Exploratory Data Analysis (EDA) on a flight price dataset to understand the factors affecting ticket prices and prepare the data for machine learning.

Tools Used

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

Steps Performed

• Data Cleaning
• Missing Value Handling
• Duplicate Removal
• Feature Engineering
• Univariate Analysis
• Bivariate Analysis
• Correlation Analysis
• One-Hot Encoding

Dataset

Flight Price Prediction Dataset (Kaggle)

Key Findings

• Ticket prices vary significantly across airlines.
• Flights with more stops generally cost more.
• Date and time features were extracted for better analysis.
• Dataset was prepared for machine learning.