Airplane Accident Severity Prediction

Flying has been the go-to mode of travel for years now; it is time-saving, affordable, and extremely convenient. According to the FAA, 2,781,971 passengers fly every day in the US, as in June 2019. Passengers reckon that flying is very safe, considering strict inspections are conducted and security measures are taken to avoid and/or mitigate any mishappenings. However, there remain a few chances of unfortunate incidents.

tl;dr

Compared different ensemble learning models to find the best fit for the data
Created a flask application using HTML and Bootsrap to deploy the Machine Learning model

Technologies Used

Python
Pandas
Numpy
Matplotlib
Seaborn
Scikit Learn
Ensemble Learning
Flask
Bootstrap
HTML
CSS

Data

The dataset consists of certain parameters recorded during the incident⁠ such as cabin temperature, turbulence experienced, number of safety complaints prior to the accident, number of days since the last inspection was conducted before the incident, an estimation of the pilot’s control given the various factors at play, and the likes.

Pairplot of the data

Results

Classifier	With Safety Score (accuracy %)	Without Safety Score (accuracy %)
Decision Tree Classifier	94.54	53.73
Random Forest Classifier	89.92	66.60
Gradient Boosting Classifier	96.87	64.80
Adaptive Boosting Classifier	94.94	64.93
XGBoost Classifier	96.4	70.07

Model Deployment

Developed a simple web application using Flask and Bootstrap to deploy the Gradient Boosting Classifier