Flight-Price-Prediction-Datascience-Project

Flight Price Prediction is a machine learning-based project that predicts the cost of a flight based on the user's desired travel details. The project uses a variety of features, such as the departure airport, destination airport, date of travel, and number of stops, to train a model that can predict the future price of a flight.

Problem Statement:

Flight ticket prices can be unpredictable, and it can be difficult to know what to expect when booking a flight. This project aims to predict the price of a flight based on a variety of factors, such as the departure airport, destination airport, date of travel, and number of stops.

Solution Approach:

The project uses a machine learning model to predict the price of a flight. The model is trained on a dataset of historical flight prices, and it uses the features in the dataset to predict the price of a flight.

Observations:

The project found that the following factors are most important in predicting the price of a flight:

• The departure airport
• The destination airport
• The date of travel
• The number of stops
• The airline
• The time of day

Insights:

The project provides insights into the factors that influence the price of a flight. This information can be used by travelers to save money on their flights. For example, travelers can choose to fly from a different airport, or they can choose to travel during the off-season.

Findings:

The project found that the machine learning model was able to predict the price of a flight with a high degree of accuracy. The model was able to predict the price of a flight within 10% of the actual price.The project also found that the model was able to generalize well to new data. The model was able to predict the price of flights that were not in the training dataset.