Flight Fare Analytics using Python

This project focuses on analyzing flight ticket prices to uncover insights into how various factors — such as airline, source city, travel class, and booking time — influence airfare.
Using Python’s data analysis and visualization tools, the project explores price patterns, builds visualizations, and derives meaningful insights that can help both customers and businesses understand flight pricing behavior.

Dataset

Source: Kaggle – Flight Price Prediction Dataset

Columns:

Column	Description
airline	Name of the airline
flight	Flight number/code
source_city	City of origin
departure_time	Departure time category (Morning, Evening, etc.)
stops	Number of stops
arrival_time	Arrival time category
destination_city	Destination city
class	Type of ticket (Economy / Business)
duration	Total duration of the flight (in hours)
days_left	Number of days left before departure
price	Ticket price (target variable)

Project Objectives

• Analyze how airline, class, and booking timing affect flight prices.
• Identify which airlines are most/least expensive.
• Study the impact of advance booking on flight fare.
• Compare Economy vs Business ticket pricing patterns.
• Visualize flight pricing trends using data-driven insights.

Tools & Libraries Used

• Python 3.10+
• Pandas – Data manipulation and analysis
• NumPy – Numerical computations
• Matplotlib & Seaborn – Visualization
• Jupyter Notebook – Interactive environment

Exploratory Data Analysis (EDA)

1. Average Flight Price per Airline

sns.barplot(x='airline', y='price', data=df, estimator='mean', errorbar=None)