Optimization and Prediction of Bead Geometry

In order to support and supervise patients, the key detection and estimation of cancer type should establish a compulsion in the cancer research. Many research teams from the biomedical and bioinformatics fields have been advised to learn and evaluate the use of machine learning (ML) methods because of the relevance of classifying cancer patients into high or low risk clusters. To predict breast cancer, the logistic regression method and many classifiers have been proposed to generate profound predictions about breast cancer data in a new environment. This paper discusses the various approaches to data mining using classification to create deep predictions that can be applied to Breast Cancer data. In addition, by testing datasets on different classifiers, this analysis predicts the best model that delivers high efficiency. In this paper, the UCI machine learning repository has 699 instances with 11 attributes collected from the Breast cancer dataset. First, the data set is pre-processed, visualized and fed to different classifiers such as Logistic Regression, Support Vector Classifier, K-Nearest Neighbour, Decision Tree and Random Forest. 10-fold cross validation is implemented and testing is carried out in order to create and validate new models. Effective analysis shows that Logistic Regression generates the deep predictions of all classifiers and obtains the best model delivering strong and precise outcomes, followed by other methods: Support Vector Classifier, K-Nearest Neighbour, Decision Tree and Random Forest. Most models were less reliable compared to the approach of logistic regression.

With this product, my objective was to make cryptocurrency investments simple & profitable. This product operates via an easy to use website allowing customers to buy, sell, and invest in cryptocurrencies, and in stock markets, all in one single place. The vision was to make a product for the global financial system that is easily accessible to everyone. The costumer will first have to make investments in cryptocurrencies, then he/she can invest in share markets as per their needs. This product also has a built-in machine learning based stock prediction model which helps it’s users to decide which stock to invest in & when it is the right time to sell such stocks.