Air-pollution prediction through feature selection and deep learning

Air-pollution prediction through feature selection and deep learning

Air pollution detection

Air pollution detection and monitoring is one the important area as these days air pollution is increasing through various parameters such as vehicles, industries etc. Air pollution prediction is made easier through artificial intelligence. This proposed work used feature selection algorithm for choosing important attributes from dataset.

The dataset from Central Air pollution board is used for this study. It has many attributes and the labels are considered as multi class classification as given below

  1. Residential
  2. Industrial
  3. RIRUO
  4. Sensitive

Regression algorithm and Neural Network algorithm is used for air pollution detection of above class type. Logistic regression gives best accuracy of around 63% for air pollution prediction.

Air Pollution Detection

The following link shows you the implementation process and methodology used. Source code used is Python and dataset downloaded from Central pollution board of India.

More Machine Learning projects are available, some listed below

Regional Detection of Traffic Congestion Using in a Large-Scale Surveillance System via Deep Learning

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