Air Quality Prediction Based on Machine Learning | Python Final Year IEEE Project 2023

Air Quality Prediction Based on Machine Learning | Python Final Year IEEE Project 2023.
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📌Project Title: Air Quality Prediction Based on Machine Learning
💡Implementation Code: Python.
🚀Algorithm / Model Used: Random Forest Classifier.
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.3000/
IEEE Base paper Abstract:
In recent years, due to the vigorous development of industrialization, environmental protection measures cannot be effectively guaranteed. Increasingly serious environmental problems have gradually become the primary problem affecting the quality of national life. Therefore, we need to establish a relatively accurate air quality prediction model to understand the possible air pollution process in advance. According to the prediction results of the model, it is of great significance to establish and take corresponding control measures to reduce air pollution. This paper makes full use of data mining methods such as mutual information theory, neural networks, and intelligent optimization algorithm. We use the basic data of long-term air quality prediction of open monitoring points as training set and test set. Firstly, the SOM neural network model is used for unsupervised clustering of relevant pollutant data to analyze the correlation between various monitored pollutants. Aiming at the problems of a large amount of data and long calculation time of the algorithm, combined with the clustering results, and NSGA-II optimized neural network is proposed to predict the future pollution situation. The experimental results show that the prediction accuracy of pollutants can reach more than 90%.
REFERENCE:
Chenghao Shi, Yu Wang, Ying Wan, Shuguang Wu, “Air Quality Prediction Based on Machine Learning”, 2022 International Conference on Machine Learning and Knowledge Engineering (MLKE), IEEE Conference, 2022.
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