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About 10.5 percent of global adult population is living with diabetes. India has 77 million diabetic patients, it is the second highest in the world. Developing countries such as India face a huge burden of diabetes and its complications. Even children at the age of five are suffering from this disease. It is high time that people understand the gravity of the situation and make themselves fit to fight the disease than to suffer with it. If diabetes is not identified and treated in right time, it may lead to chronical health issues. In this paper a machine learning based prediction model is built to find the factors leading to complicated health issues such as cardio vascular disease. This model identifies the attributes that highly contribute to cardio vascular disease and compare various machine learning algorithms to predict cardio vascular disease among diabetic patients. It identifies the best algorithm from a set of supervised learning algorithms such as KNN, Decision Tree, Random Forest, Naïve Bayes and Gradient Boosting for prediction based on several performance metrics. The algorithms are compared based on the performance metrics such as accuracy, precision, recall, F1 score, time taken to train and time taken to test. We identified that Decision Tree with entropy as the split criterion achieved the highest accuracy. [ABSTRACT FROM AUTHOR]

Landslides are a significant issue in India due to the country's varied topography, heavy monsoon rains, and deforestation, which contribute to soil instability and increased landslide risk. These natural disasters can cause damage to infrastructure and loss of life. In light of the ongoing problem of landslides in India, this research paper aims to address the need for effective landslide prediction strategies. Through the findings of this research study, a novel approach has been presented for predicting landslide occurrences in India, which will aid in reducing the impact of these events on infrastructure, communities, and lives. The work that has been carried out using data and information based in India has shown to have a low accuracy level. As a result, the model created using this information is not deemed to be very reliable. This study focuses on predicting landslides in India using machine learning models such as (Xtreme Gradient Boost) XGboost, random forest, and AdaBoost. Previous research on landslide prediction in India had not been widely done or had not achieved acceptable accuracy levels. This study aims to address this gap in knowledge and improve the predictability of landslides in India. The research is based on a database of different areas in India and aims to increase awareness and save lives and resources by predicting landslides with 91% accuracy. [ABSTRACT FROM AUTHOR]

The business sector's finances have already begun to suffer as a result of decreased production and lockdowns, which have already begun to take their toll. In order to keep operations running smoothly, a significant public budget or stimulus is required to address issues such as interruptions in supply chains, difficulties in manufacturing, and paralyzed health systems. The banking and financial industry, whose prospects are related closely to those of the economy, is sure to face the brunt of the anticipated slowdown in economic development. The International Monetary Fund has reduced India's GDP growth prediction to 1.9 percent for 2020-21. There is a possibility of an increase in defaulted loans. "The downturn might potentially lead to job losses, which could put on on the retail lending books of financial institutions. The loss of revenue from tourism and the entertainment industries, amongst many other areas of business, has already damaged the economy. These and other such factors are all building up to put a burden on the economy of the whole world, which may potentially have ramifications for the next few years. The governments, central banks, and regulatory bodies in the Asia- Pacific region have each implemented their own unique set of countermeasures in response to COVID-19. These include the infusion of additional cash, loans directed at damaged companies and areas, and reductions in the policy interest rate. Additionally, it involves assistance for financial institutions in their provision of forbearance to economically viable people and companies that were negatively impacted by COVID-19. [ABSTRACT FROM AUTHOR]

We develop an enhanced accident occurrence prediction model which depends on the heterogeneous ensemble learning to tackle the topic of a accident period prediction in the early stages of the tragedy using millions of the traffic accident information's from the India. In order to start with, we concentrate on the early stages of development of accidents and choose few useful data from five categories: location, the traffic, climate, objects, and the time field. Further, we implement data cleansing, processing of outlier, and the missing value of processing to raise the quality of the data. Data mining methods can support in foreseeing the factors that are influential in concern to make severe damages. The research has significant factors that are closely connected through the severity of accidents on thruways are identified by Random Forest. Top elements influencing unintentional seriousness include temperature, distance, wind Chills, moisture, direction of wind and visibility. The main aim of this research work is to give a architecture to anticipate road crashes gathering data from the social media handles and the open access data, by implementing a ensembled Deep Learning Model. After which the result shows decent outcomes as a resort to the problem and fulfills the objective of prediction model based on algorithms and deep Learning models. [ABSTRACT FROM AUTHOR]