The objective of this project is to accurately predict the prices of flight tickets. This prediction can benefit both airlines and passengers by providing insights into ticket pricing trends.
This project follows a supervised learning approach, specifically regression, as historical flight data with labeled ticket prices is available for training the model.
Flight_ID
: Identifier for each flight.Airline
: The airline associated with the flight (categorical).Departure_City
: The city where the flight departs from.- ...
- Python
- SQL/MongoDB
- Machine Learning (Sklearn)
- Mathematics (Numpy)
- Visualization (Plotly)
- Statistics
Make sure you have the necessary packages and their versions installed.
Retrieve the flight ticket price dataset from the source.
Perform exploratory data analysis using Pandas to understand the dataset:
- Display the first few rows.
- ...
Create various visualizations to understand data patterns, relationships, and trends.
- Split the data into training, validation, and test sets to prevent data leakage.
- Handle missing values and outliers.
- ...
Encode categorical features using techniques like one-hot encoding or label encoding. Scale numerical features using standard scaling.
Choose an appropriate machine learning model. In this case, XGBoost is selected for regression.
Train the selected model using the training dataset. Evaluate the model's performance using metrics such as Root Mean Squared Error (RMSE).
The model's RMSE score is 12.5, indicating the accuracy of flight ticket price predictions.
This README provides an overview of the project, including its purpose, approach, tech stack, and the steps involved in the end-to-end process. It also serves as a guide for anyone interested in understanding and replicating the project.