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• Flight-Price-Prediction
• Created a tool that estimates Flight Prices to help users look for best prices when booking flight tickets.
• Engineered features from the Departure Time, Date of Journey, to quantify the data and make it more understandable.
• Optimized multiple Regression models using GridsearchCV to reach the best model.
• Built a client facing API using flask
• Codes and Resources Used
• Python Version: 3.8.5
• Packages: pandas, numpy, sklearn, matplotlib, seaborn, flask, json, pickle
• For Web Framework Requirements: pip install -r requirements.txt
• Dataset: https://www.kaggle.com/nikhilmittal/flight-fare-prediction-mh
• Flask Productionization: https://towardsdatascience.com/productionize-a-machine-learning-model-with-flask-and-heroku-8201260503d
• Here are some ideas to get you started: