PM2.5 Prediction Web App

Inspiration

What it does

Predicts PM2.5 based on some factors such as Year , Month , Day , Temperature , Pressure and so on

How we built it

ML - We used Random Forest Regressor for Building ml model where we did Hyper Parameter Tuning to improve the accuracy Website - We used Streamlit (python Library) to build the front end and deploy the models

Challenges we ran into

Building the Website and deployment . Also it was a bit difficult increasing the accuracy of ml model

Accomplishments that we're proud of

We completed the entire end to end web application including the deployment of Website in streamlit cloud

What we learned

Working on a full project in limited time span Team Work

What's next for PM2.5 Prediction Web App

In future we are planning to add Deep Learning models like Ann , LSTM for prediction

Built With

• machine-learning
• scikit-learn
• streamlit