flight-fare-prediction

Flight Fare Prediction MH

Table of Contents

• Overview
• Notebooks
• Tools and Libraries Used
• Models and Eval Metrics
• Future Scope

Overview

Predicting the ticket air-fare given all the necessary features.

Notebooks

• EDA and Model

Tools and Libraries Used

• Sklearn
• Viz libraries(Seaborn and Matplotlib)

Models and Test Metrics

Model	RMSE	MSE %	R-Squared
Lasso Regressor	3171.13	33	0.468
Ridge Regressor	3170.48	33	0.468
K Neighbors Regressor	2761.66	20	0.597
Decision Tree Regressor	1750.41	9	0.8381
Random Forest Regressor	1254.14	8	0.9168
XGBoost Regressor	1567.10	11.0	0.88

Random Forest Regressor & XGB Regressor are giving Maximum Accuracy as compare to other Regressor algorithm.

Future Scope

• This model could be deployed using flask or django
• Neural Nets could be used to train a more optimal regressor model
• High performing models could lay a foundation to help hypertune to provide opitimal metrics