Using Bitcoin Pricing Data to Create A Profitable Algorithmic Trading Strategy

Crypto currency has drastically increased its growth in recent years and Bitcoin(BTC)is a very popular type of currency among all the other types of crypto currencies which is been used in most of the sectors nowadays for trading, transactions, bookings etc. In this paper, we aim to predict the change in bitcoin prices by using machine learning techniques on data from Investing.com. We interpret the output and accuracy rate using various machine learning models. To see whether to buy or sell the bitcoin we created exploratory data analysis from a year of data set and predict the next 5 days change using machine learning models like logistic Regression, Logistic Regression with PCA (Principal Component Analysis) and Neural network

Case Study Analysis of Stock Market Using Algorithms

Data Science consists of data cleaning, arranging it in the proper manner, and then analyzing it to get the desired output. It is an interdisciplinary field of study that uses data for various research and reporting purposes to derive insights and meaning out of that data. In today's economy, it is very important to predict the stock prices and their ups and downs. As we are seeing a lot of people are getting closure towards the stock or the bitcoin-like crypto's. So there are techniques so that one can predict the appropriate result. There are many algorithms that are available to trace the movement of the market. Big data, Data mining, Data visualization all help to analyze the data and predict the outcome

Comprehensive evaluation and performance analysis of machine learning in heart disease prediction

Abstract Heart disease is a leading cause of mortality on a global scale. Accurately predicting cardiovascular disease poses a significant challenge within clinical data analysis. The present study introduces a prediction model that utilizes various combinations of information and employs multiple established classification approaches. The proposed technique combines the genetic algorithm (GA) and the recursive feature elimination method (RFEM) to select relevant features, thus enhancing the model’s robustness. Techniques like the under sampling clustering oversampling method (USCOM) address the issue of data imbalance, thereby improving the model’s predictive capabilities. The classification challenge employs a multilayer deep convolutional neural network (MLDCNN), trained using the adaptive elephant herd optimization method (AEHOM). The proposed machine learning-based heart disease prediction method (ML-HDPM) demonstrates outstanding performance across various crucial evaluation parameters, as indicated by its comprehensive assessment. During the training process, the ML-HDPM model exhibits a high level of performance, achieving an accuracy rate of 95.5% and a precision rate of 94.8%. The system’s sensitivity (recall) performs with a high accuracy rate of 96.2%, while the F-score highlights its well-balanced performance, measuring 91.5%. It is worth noting that the specificity of ML-HDPM is recorded at a remarkable 89.7%. The findings underscore the potential of ML-HDPM to transform the prediction of heart disease and aid healthcare practitioners in providing precise diagnoses, exerting a substantial influence on patient care outcomes

Luffa cylindrica and phytosterols bioconversion: from shake flask to jar bioreactor

International audienceAbstract Bioconversion of lipophilic compounds poorly soluble in water, such as sterols, required the use of chemicals and solubilizing agents. On the other hand, it was shown that immobilization of Mycobacterium species on the dried fruit of Luffa cylindrica (DFLC) allows a close interaction between immobilized cells and cholesterol particles and increases by then the product’s yield. In this work, the use of DFLC in a 5-l jar bioreactor with phytosterols mixture (1 g/l) as substrate was assessed without addition of any chemicals or solubilizing agents. DFLC increased by a factor of four the volumetric productivity of androstenones (0.08 g/l day). Products were accumulated in the aqueous medium while substrates remained on the fibers of DFLC. This observation lets envisage a green semi-continuous process of androstenone production. DFLC has no influence on cell growth, and is moreover natural, inexpensive, non-toxic, and mechanically strong