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Flight Fare Predictor

Build Status

Introduction

Flight ticket costs, as we all know, may be difficult to predict. As a result, we attempted to forecast the pricing for future trip dates. To do so, we used web scraped data from my journey to make the forecast.

Steps done-

    1. EDA for extracting the possible features, trends in data and detecting outliers.
    1. Feature Extraction
    1. Feature Selection using FFS and Ensemble Learning
    1. Test
    1. Prediction

Algorithms for Model Building Used

Algorithm Obtained R2 Score
Linear Regression 0.21
Support Vector Regressor 0.10]
XGBoost (Tree based) 0.48
Random Forest 0.61
For getting more insights- view the PPT