Abstract: The term "air pollution" generally is the process of releasing pollutants into the air which can be harmful to the health of humans and the environment in general. It is one of the greatest challenges humanities has have ever had to face. It can cause harm to crop, animals, and forests, among others. To stop this from happening in the transport sector, it is necessary to identify air quality issues caused by pollution using machine learning techniques. Therefore, air quality assessment and prediction are now an important area of research. The objective is to explore methods based on machine learning to achieve forecasting the air quality of air using predictions that have the highest accuracy. Analysis of data by a supervised machine-learning technique (SMLT) to collect a variety of data points such as, variables identification, univariate analysis bi-variate and multi-variate analyses as well as missing value treatment and examine the data validation as well as data cleaning/preparing, and visualization will be carried out for the entire data set. The analysis we present gives an entire guideline for the analysis of the sensitivity of model parameters with respect to their performance in predicting levels of pollution in the air by accuracy calculations. The aim of this paper is to propose a machine-learning-based method for accurately predicting an accurate Air Quality Index value by predictions in the form of highest accuracy by the comparison of supervised classification machine learning algorithms. Furthermore, to evaluate and analyse the effectiveness of different machine learning algorithms based on the transportation traffic department data with an evaluation classification reports, to identify the confusion matrix, and then categorizing the data according to priority. the outcome shows the efficiency of the proposed machine-learning algorithm method can be evaluated with most accuracy, precision, recall as well as F1 Score.
Keywords: Air Pollution, Air Quality Index, Machine Learning Algorithms, Decision Tree, Support Vector Machine.
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DOI:
10.17148/IJARCCE.2022.115199
