AirML

Using machine learning to predict the aircraft life and other parameters.

The project is divided into 3 parts.

1. Training

The dataset used here is the Turbofan engine degradation simulation dataset by NASA.

It contains data from 249 engines with 21 sensor readings and 3 operational settings.

A look at the raw data:

After data cleaning, labeling and feature engineering the data is used to feed into Machine Learning algorithms.

The cleaned data:

The algorithms Random Forest, XGBoost and Neural Network were tested and the best one to perform was Random Forest for predictions.

2. Frontend

The frontend was built using Angular 4.

First install the dependencies using npm install and then run the angular server use the following command ng serve.

3. Backend

The backend is built using Django.

Install the dependencies using pip install -r requirements.txt and then run the server using python manage.py runserver.

Now, goto http://localhost:4200 to see the application running.

You can see the working of the application below

To download the models and data directories contact me at [email protected]

Developed by: Adesh Gautam, Kunal Sharma, Shreya Gupta