Flight Fare Predictions

Flight Fare Predictive Analysis utilizes historical airfare data and various influencing factors to forecast future ticket prices, aiding travelers in making cost-effective booking decisions. The process begins with data collection and preprocessing, where information such as flight dates, airlines, departure and arrival locations, class, and demand is gathered, cleaned, and transformed for analysis. Exploratory Data Analysis (EDA) follows, using visualizations like histograms and scatter plots to identify trends and correlations between variables such as booking time, distance, and seasonal fluctuations. Various regression models, including Linear Regression, Polynomial Regression, Decision Trees, Random Forests, and advanced ensemble techniques like XGBoost, are employed to capture both linear and non-linear relationships in fare pricing. Python libraries such as Pandas and NumPy handle data manipulation, while Matplotlib and Seaborn aid in visualization. Scikit-learn is used for model implementation and evaluation, employing performance metrics like RMSE and R² score to fine-tune predictions. Advanced techniques such as hyperparameter tuning enhance model accuracy, and deployment through Flask or FastAPI allows real-time predictions. By leveraging these methods, predictive analysis provides valuable insights into price trends, enabling travelers to make informed decisions and optimize their flight bookings.