wasimmadha/flight-fare

Flight Fare Prediction

This project aims to solve the problem of predicting the predicting the Flight Fare price, using Sklearn's supervised machine learning techniques. It is a classification problem and predictions are carried out on dataset, Several regression techniques have been studied, including XGboost and Random forests of decision trees.

💿 Installing

1. Environment setup.

conda create --prefix venv python=3.9 -y

conda activate venv/

2. Install Requirements and setup

pip install -r requirements.txt

5. Run Application

Flask

🔧 Built with

• Flask
• Python 3.9
• Machine learning
• 🏦 Industrial Use Cases

Models Used

• Linear Regression

• Lasso Regression

• Ridge Regression

• K-Neighbors Regressor

• Decision Tree

• Random Forest Regressor

• XGBRegressor

• CatBoosting Regressor

• AdaBoost Regressor

• GridSearchCV is used for Hyperparameter Optimization in the pipeline.

• Any modification has to be done in Inside Config.yaml which can be done in route /update_model_config

flightFare is the main package folder which contains

Artifact : Stores all artifacts created from running the application

Components : Contains all components of Machine Learning Project

• DataIngestion
• DataValidation
• DataTransformations
• ModelTrainer
• ModelEvaluation
• ModelPusher

Frontend to show Artifact, Experiment, Model Training, Saved Models, logs which can be accessed from the developer

Routes for An API:

/predict

• Predict Route for User to predict

Example Input For Prediction: "Airline" : "Jet Airways",
"Date_of_Journey" : "9/10/2019",
"Source" : "Banglore",
"Destination" : "Cochin",
"Dep_Time" : "20:25",
"Arrival_Time" : "04:25",
"Duration" : "19h",
"Total_Stops" : 1,

Custom Logger and Exceptions are used in the Project for better debugging purposes.

Conclusion

• This Project can be used in real-life by Users to predict the Flight Price