Flight Fare Prediction System:

AIM-

To predict the cost of flights for the users.This compares prices depending on the date of the flight, time, destination, duration and stops. It also considers flights of all major airlines.

Methods Used-

• Statistics
• Data Collection
• Data Preprocessing
• Data Visualization
• ML Techniques
• Hyperparameter Tuning
• Evaluation of Models
• Deployment

Dataset used:

https://www.kaggle.com/nikhilmittal/flight-fare-prediction-mh

Tech Stack Used:

• Tools-Jupyter Notebook, VS Code
• Frontend Web development HTML,CSS,Bootstrap
• Language- Python
• Libraries- Numpy,Pandas, Flask,scikit-learn,Scipy,Matplotlib, Seaborn

Screenshots

Metrics

• MAE: 1165.606162629916
• MSE: 4062650.6911608884
• RMSE: 2015.6018186042818

Accuracy

• 81.17 % [Random Forest Algorithm]