Flight_Delay_Prediction

Flight scheduling has been a problem since the dawn of air travel and is something that airline companies wish to tackle. For an airport to be able to schedule the flights such that they reach on time, they must be able to tell if the flight will arrive on time or not. A flightis said to be delayed if the flight either takes off or arrives later than the scheduled time. This Project predicts whether if the flight will arrive delayed or not, after the flight’s departure, and if the flight is classifiedas arriving late, then the arrival delay in minutes is predicted.

Notebooks

1. Data-Preprocessing, processing the flight data

2. Data-Preprocessing, processing the weather data

3. Data-Preprocessing, merging the flight data and weather data

4. Classification- Predicting if the flight will be delayed or not

5. Regression- Predicting the number of minutes by which a flight will be delayed

6. Using results from a Classifier for Regression (Pipelining)

7. Regression for Ranges- Predicting the number of Minutes by which the flight will be delayed for various ranges (Regression Testing)

8. Project Report

Installation Requirements

1. jupyter
2. pandas
3. numpy
4. matplotlib
5. seaborn
6. sklearn
7. xgboost
8. imblearn
9. SciPy
10. scikit-learn

Use the following code to install the required packages

pip install -r requirements.txt

Classification models and algorithms used:

1. Logistic regression
2. Random Forest
3. Extra trees
4. Decision Trees
5. Gradient Descent
6. XGBoost

Regressors used in the Prediction file

1. Linear Regressor
2. Extra Trees Regressor
3. XGBoost Regressor
4. Random Forest Regressor