We prepared a heart disease prediction system to predict whether the patient is likely to be diagnosed with a heart disease or not using the medical history of the patient. The research paper mainly focuses on which patient is more likely to have a heart disease based on various medical attributes. it should be performed precisely and efficiently. The observations are expected to help in formulating or reviewing relevant policies, in order to ensure on-time project delivery.ĭay by day the cases of heart diseases are increasing at a rapid rate and it's very Important and concerning to predict any such diseases beforehand. On the other hand, the most influential cause of delay in material supply was found to be poor materials procurement and inventory management system, which has other underlying reasons such as late identification of the type of materials needed. The most importantcausefor shortage of materials relates to the origin or availability of construction materials. The study identified six causes of shortageof materials and nine causes of delay in materials supply in Brunei. The study was conducted through fifteen semi-structured interviews of contractors and materials suppliers in Brunei. As such, this paper summarises the outcomes of a study that targeted identifying causes of shortage and delay in materials supply in Brunei Darussalam. However, the relevant underlying reasons vary from country to country. Shortage and delay in materials supply is argued to be one of the most important factors that lead to delay in construction project delivery globally. Then, we will perform feature selection methods, to experiment and choose the best fit features to obtain the highest precision, according to confusion matrix results. This process will result in feature extraction and vectorization we propose using Python scikit-learn library to perform tokenization and feature extraction of text data, because this library contains useful tools like Count Vectorizer and Tiff Vectorizer. This paper makes an analysis of the research related to fake news detection and explores the traditional machine learning models to choose the best, in order to create a model of a product with supervised machine learning algorithm, that can classify fake news as true or false, by using tools like python scikit-learn, NLP for textual analysis. A lot of research is already focused on detecting it. The fake news on social media and various other media is wide spreading and is a matter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts.

Dfind science and engineering