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 ❤︎