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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 V U
Vishnu Unnikrishnan

💻 🎨
Sruthy K S
Sruthy K S

💻 🎨
Teslin Rose
Teslin Rose

💻 🎨
Vini
Vini

💻 🎨

Postwoman.io

Happy Coding ❤︎