Component Failure Analysis of Bombardier’s Railway: The project's goal is to build a Machine Learning system capable of identifying which critical component will break down soon and assist in the decision-making of the maintenance of components.

Effectively predicted and reduced the cost of sudden failure & achieve safe transportation using ML algorithms like regression, Decision tree, Random forest etc

Deployed on Django which shows the frontend page where all the reading have to fill and after submittin it will give you which component will fail

Predictive Maintenance analysis: Build Multivariate time series forecasting model which helps to reduce the production breakdown and suggest preventive maintenance schedule of plant machinery.

In this project we got the 10 days data from client which is IOT sensor based real time data for different plant machineries anddata was stored in Influxdb.

In this we performed data cleaning, exploratory analysis and Feature selection for plant maintenance. So, data consist of 70-80% nan values.

After dealing with nan values, we apply forecasting. For this we have to check whether data is stationary or not. If not then we have to make it stationary.

Afterwards we predict the future readings for next 48 hrs and deployed it on flask which gives us breakdown point on basis of the bottles readings for particular machines.

Effectively predicted and reduced the cost of sudden failure & achieve safe transportation using ML algorithms and deployed on Django

Used Content based recommendation system model on books data which helps to the end users that are most appropriate and helps people to find their specialized on-demand books