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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]

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]