Introduction: Air Travel has gained popularity among individuals, due to its time-saving benefits for both long-distance and local trips. The ability to quickly reach one's destination has made flying an attractive option for individuals with hectic schedules or limited time. However, along with the convenience, travelers are also keenly aware of the expenses associated with air travel and make careful plans to ensure their trips fit their budget constraints.

The cost factor cannot be ignored despite the convenience and efficiency of flying. Airfare can vary significantly based on several factors, such as the distance between the source and destination, the chosen airline, the travel time, and other variables. To ensure well-informed decisions regarding air travel, individuals must carefully consider these factors and plan accordingly.

While we can make rough estimates of the factors that influence flight costs, I am determined to gain a more comprehensive understanding by delving into the analysis and visual representation of various parameters. I hope to uncover valuable insights about the interrelationships among these factors and their impact on flight prices. Analyzing the data will enable me to identify patterns, trends, and correlations that can further enhance my understanding of the pricing dynamics of air travel. Problem Statement: My objective is to predict the price of travel for a trip considering information like distance between source and destination, Airlines, number of stops, duration, departure_hour, and day_of_travel.

Data Sources: This dataset is taken from Kaggle. There is already a different dataset for training and testing.

Training dataset: https://www.kaggle.com/datasets/absin7/airlines-fare-prediction?select=Data_Train.xlsx It has a total of 10683 rows and 11 columns