AIR POLLUTION FORECASTING AND PREDICTION

MODELS ✨

⚡️Models for Prediction:

• Random Forest - Random forests or random decision forests are an ensemble learning method for classification, regression.
• XGBoost - XGBoost is an open-source software library which provides a gradient boosting.
• Deep Learning - Multilayer Perceptron, Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning.
• CatBoost - CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box.

🌈Models For Forecasting:

• LSTM- A Deep Learning method to find Future values of AQI upto 7 days
• Prophet - a package developed by facebook

🔥Features:

• Temperature (°C)
• Wind Speed (Km/h)
• Pressure (Pa)
• NO2 (ppm)
• Rainfall (Cm)
• PM10 (μg/m3)
• PM2.5 (μg/m3)
• AQI

📦 Used Packages

1. caret
2. tidyverse
3. tidymodels
4. randomforest
5. xgboost
6. data.table
7. Hmisc
8. catboost
9. VIM
10. Shiny

Prediction Data 📝

Forecast Data 📝

Interface 🔮

🚀 Interface Using shiny: Shiny is an R package that makes it easy to build interactive web apps straight from R.it is used for showing the insight of the data and prediction.

Collaborators

Vishnu Unnikrishnan 💻 🎨

Sruthy K S 💻 🎨

Teslin Rose 💻 🎨

Vini 💻 🎨

Happy Coding ❤︎