Flight Fare Prediction

• Tech Stack: Python, Data Analysis, Scikit Learn, RegEx, Pandas, Numpy, Matplotlib, Seaborn, Flask, HTML
• Github URL: Project Link

1. Conducted data wrangling and exploratory data analysis on flight fare data, and selected the best features to train a random forest regressor model for better accuracy and to reduce overfitting.

2. Calculated errors and model score, and used RandomizedSearchCV for hyperparameter tuning to further improve the model's performance.

3. Achieved an r2_score of 0.8222 and deployed the model as a web app using Flask for easy access and usage