Heart disease has been the leading cause of a huge number of deaths in recent years. As a result, an accurate and feasible system is required to diagnose this disease early to provide better treatment. Advances in machine learning have the potential to enhance healthcare access. Given the importance of a crucial organ like the heart, medical professionals and physicians have made it a priority to forecast heart failure-related events in clinical practice, nevertheless, forecasting heart failure-related events in clinical practice has generally failed to achieve high accuracy. The objective here is to demonstrate how machine learning may be used to solve the problem. By analyzing hundreds of healthcare data and other semantics, machine learning algorithms can analyze related cases with diseases and health conditions. Here a demonstration of how to load the data, generate predictions through different models from patient data is shown. The metrics are then compared for a better understanding of their function and what impact can be inferred from them.

Classification of stars is essential to investigate the characteristics and behavior of stars. Performing classifications manually is error-prone and time-consuming. Machine learning provides a computerized solution to handle huge volumes of data with minimal human input. k-Nearest Neighbor (kNN) is one of the simplest supervised learning approaches in machine learning. This paper aims at studying and analyzing the performance of the kNN algorithm on the star dataset. In this paper, we have analyzed the accuracy of the kNN algorithm by considering various distance metrics and the range of k values. Minkowski, Euclidean, Manhattan, Chebyshev, Cosine, Jaccard, and Hamming distance were applied on kNN classifiers for different k values. It is observed that Cosine distance works better than the other distance metrics on star categorization.

A time series is an order of observations engaged serially in time. The prime objective of time series analysis is to build mathematical models that provide reasonable descriptions from training data. The goal of time series analysis is to forecast the forthcoming values of a series based on the history of the same series. Forecasting of stock markets is a thought-provoking problem because of the number of possible variables as well as volatile noise that may contribute to the prices of the stock. However, the capability to analyze stock market leanings could be vital to investors, traders and researchers, hence has been of continued interest. Plentiful arithmetical and machine learning practices have been discovered for stock analysis and forecasting/prediction. In this paper, we perform a comparative study on two very capable artificial neural network models i) Deep Neural Network (DNN) and ii) Long Short-Term Memory (LSTM) a type of recurrent neural network (RNN) in predicting the daily variance of NIFTYIT in BSE (Bombay Stock Exchange) and NSE (National Stock Exchange) markets. DNN was chosen due to its capability to handle complex data with substantial performance and better generalization without being saturated. LSTM model was decided, as it contains intermediary memory which can hold the historic patterns and occurrence of the next prediction depends on the values that preceded it. With both networks, measures were taken to reduce overfitting. Daily predictions of the NIFTYIT index were made to test the generalizability of the models. Both networks performed well at making daily predictions, and both generalized admirably to make daily predictions of the NiftyIT data. The LSTM-RNN outpaced the DNN in terms of forecasting and thus, grips more potential for making longer-term estimates.

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Early detection of mental health issues allows specialists to treat them more effectively and it improves patient’s quality of life. Mental health is about one’s psychological, emotional, and social well-being. It affects the way how one thinks, feels, and acts. Mental health is very important at every stage of life, from childhood and adolescence through adulthood. This study identified five machine learning techniques and assessed their accuracy in identifying mental health issues using several accuracy criteria. The five machine learning techniques are Logistic Regression, K-NN Classifier, Decision Tree Classifier, Random Forest, and Stacking. We have compared these techniques and implemented them and also obtained the most accurate one in Stacking technique based with an accuracy of prediction 81.75%.