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Air quality prediction and analysis using 4 different machine learning techniques such as Linear Regression, Classification, Principal Component Analysis and Gaussian Mixture Models

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Air-Quality-Prediction-using-Machine-Learning-Algorithms

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General info

The objective of this project is to apply four main techniques which are considered as the pillars of machine learning, namely - Regression, Classication, Dimensionality Reduction and Density Estimation. These methods will be applied to a dataset that includes data from different states of India. It consists of parameters like SO2, NO2, RSPM, SPM, PM2.5, and other meteorological parameters useful for air quality prediction.

Technologies

Project is created with:

  • Software: Google Colab, Jupyter Notebook
  • Programming Language: Python

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Air quality prediction and analysis using 4 different machine learning techniques such as Linear Regression, Classification, Principal Component Analysis and Gaussian Mixture Models

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