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Prediction of air quality is a topic of great interest in air quality research due to direct association with health effect. The prediction provides pre-information to the overall population of the area about the status of pollution on which they can take precautionary measures and can protect their health. The problem arises when the level of SO2, NO2 and residual suspended particulate matters in the air increases than that of theirs restricted level. In this paper, the Prophet Algorithm, open source software, is applied to predict the trend of air pollution in the city of Mumbai, Maharashtra. The Prophet is machine learning algorithm to forecast and also to predict time series data. It is based on additive model where non-linear trends are fit with yearly and weekly seasonality. The graphical results are generated after using this algorithm which shows the trending pattern of the pollutants in the air of Mumbai.

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In this paper more than one approaches are evaluated to optimise machine learning models for diabetes disease diagnosis. The main goal is to sort the medical data computation and choose the most suitable parameters to construct a faster and more perfect model using feature selection. Reducing the number of features to construct a model could direct to more useful machine learning algorithms which helps the doctors to focus on what are the most significant assessment to take into story. Feature selection is one of the process in machine learning which choose a subset of topical features namely variables for construction of models. In this research paper we use three feature selection techniques like Recursive Feature Elimination (RFE), Genetic Algorithm (GA) and Burota Package. After using feature selection at the end we use Decision Tree to predict the diagnosis Diabetes using a dataset named Pima Indian Diabetes Dataset and verify the performance of result model.

Our goal is to explore consistent water quality systems, the dry season, and the water quality standard structure. In fact, river water will be destroyed by a variety of hazards such as capital sewage, agricultural waste and current waste, making it unusable for anthropological practice. Employees are expected to add web water testing and submit it to the evaluation office and then conduct assessments at different water boundaries, which is an exaggerated and long-term relationship. The light display shows the distribution of information between the central lights. Information is transmitted to the cloud via a mobile screen to reduce energy consumption. In addition, these assessments can be used to configure IoT infrastructure so that a decision can be made whenever there is a dramatic change in the assessment of any water quality toilets. The project is proposed for natural water observations under a network of remote sensors. It consists of three parts: information bulbs, a basic information center and a remote authentication focus. Valid multi-temperature sensor sensors must be aware of the bulbs to meet the requirements of the multi-water display and access to rest areas. Water is one of the basic compounds that affects the environment inside and out. In this way, the water control center is important to see water quality in large areas such as lake, stream, and hydroponics. Looking at the level of pollution, water surveys are becoming more widespread. Guidelines for identifying water quality essentially exacerbate the deterioration of water quality. To think about this substance, we need a web control center for water quality integration.