Flight Price Prediction System

I successfully developed a machine learning model for predicting flight ticket prices based on various features such as airline, departure time, arrival time, duration, and number of stops. The project involved extensive data preprocessing, exploratory data analysis, feature engineering, and model building using the Random Forest Regressor algorithm.

● Model Building: Developed a Random Forest Regressor model for accurate price prediction. Evaluated model performance using Rsquared score, MAE, MSE, RMSE, and MAPE.

● Hyperparameter Tuning: Conducted randomized hyperparameter tuning using RandomizedSearchCV to optimize model parameters (e.g., number of estimators, maximum depth).

● Exploratory Data Analysis (EDA): Utilized seaborn and matplotlib for visualizing relationships and outliers.