Aim:

              To systematically compare and evaluate the performance of various regression techniques for Air Quality prediction in smart cities, aiming to identify the most effective model that can contribute to accurate and timely forecasting.

Description:

                In smart cities, air pollution has detrimental impacts on human physical health and the quality of living environment. Therefore, correctly predicting air quality plays an important effective action plan to mitigate air pollution and create healthier and more sustainable environments. Monitoring and predicting air pollution is crucial to empower individuals to make informed decisions that protect their health. This research presents a comprehensive comparative analysis focused on air quality prediction using three distinct regression techniques- Random Forest regression, linear regression, and Decision Tree regression. The main goal of this study is to discern the most effective model by considering a range of evaluation criteria, including Mean Absolute Error and R2 measures. Moreover, it considers the crucial aspects of minimizing prediction errors and enhancing computational efficiency by evaluating the regression models within two frameworks.

         The findings of this study underscore the superiority of the Decision Tree regression approach over the other models, demonstrating its exceptional accuracy with a high R2 score and a minimal error rate. Moreover, integrating cloud computing technology has resulted in substantial improvements in the execution time of these approaches. This technology enhancement significantly affects the overall efficiency of the air quality prediction process. By leveraging distributed computing resources, real-time air quality forecasting becomes feasible, enabling timely decision-making and proactive measures to address air pollution episodes effectively.

Proposed System:

       This study provides to create an effective prediction model using different regression of ML methods to predict Air Quality. First of all, the datasets are collected, and then the preprocessing is accomplished via the missing values imputation. Feature selection for supervised models using SelectKbest. This feature selection is techniques where we choose those features in our data that contribute most to the target variable. In other words we choose the best predictions for target variable. Then we are using Decision Tree, Logistic regression and Random Forest Algorithm for prediction accuracy. Decision Tree gives best results with respect to high accuracy. Compare to existing system our new system gives results are more Accuracy.