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https://github.com/mehraaaaa24/airlines-data-analysis


https://github.com/mehraaaaa24/airlines-data-analysis

data-analysis-python graph jupyter-notebook matplotlib pandas python seaborn sqlite3 tables visualization

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README 

          
Airlines Database Analysis Project



Our company operates a diverse fleet of aircraft ranging from small business jets to medium-sized machines. We have been providing high-quality air transportation services to our clients for several years, and our primary focus is to ensure a safe, comfortable, and convenient journey for our passengers. However, we are currently facing challenges due to several factors such as stricter environmental regulations, higher flight taxes, increased interest rates, rising fuel prices, and a tight labor market resulting in increased labor costs. As a result, the company's profitability is under pressure, and they are seeking ways to address this issue. To tackle this challenge, they are looking to conduct an analysis of their database to find ways to increase their occupancy rate, which can help boost the average profit earned per seat. I



BASIC ANALYSIS - 

The basic analysis of data provides insights into the number of planes with more than 100 seats, how the number of tickets booked and total amount earned changed over time, and the average fare for each aircraft with different fare conditions. These findings will be useful in developing strategies to increase occupancy rates and optimize pricing for each aircraft.



This project aims to analyze an airlines database containing information about tickets, bookings, customers, and other relevant data. Python programming language is utilized along with various libraries such as sqlite3, pandas, seaborn, matplotlib, and warnings to connect to the database, manipulate the data, and perform in-depth analysis.



Project Overview

The dataset used in this project provides comprehensive information about various aspects of airline operations. It includes details about tickets, bookings, customers, flights, and potentially more, making it a rich source for analysis.



Tools and Libraries Used

Python: The primary programming language used for scripting and analysis.

SQLite3: Used for connecting to the database and executing SQL queries.

Pandas: Leveraged for data manipulation, transformation, and analysis.

Seaborn: Employed for statistical data visualization to gain insights.

Matplotlib: Utilized for creating visualizations and graphs.

Warnings: Utilized to handle or suppress warnings if encountered during analysis.



Project Goals

Data Retrieval: Connect to the airlines database using Python and SQLite3.

Data Analysis: Perform exploratory data analysis to understand the characteristics and patterns within the dataset.

Visualization: Create meaningful visualizations using Seaborn and Matplotlib to illustrate insights obtained from the data.

Insights Generation: Derive actionable insights and conclusions from the analysis to inform decision-making processes.



CONCLUSION - 

To summarize, analyzing revenue data such as total revenue per year, average revenue per ticket, and average occupancy per aircraft is critical for airlines seeking to maximize profitability. Airlines can find areas for improvement and modify their pricing and route plans as a result of assessing these indicators. A greater occupancy rate is one important feature that can enhance profitability since it allows airlines to maximize revenue while minimizing costs associated with vacant seats. The airline

should revise the price for each aircraft as the lower price and high price is also the factor that people are not buying tickets from those aircrafts. They should decide the reasonable price according to the condition and facility of the aircraft and it should not be very cheap or high. Furthermore, boosting occupancy rates should not come at the price of consumer happiness or safety. Airlines must strike a balance between the necessity for profit and the significance of delivering high-quality service and upholding safety regulations. Airlines may achieve long-term success in a highly competitive business by adopting a data-driven strategy to revenue analysis and optimisation.