The 2019–20 coronavirus pandemic is an ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2). The outbreak was identified in Wuhan, China, in December 2019.Among the standard models for COVID-19 global pandemic predictionBased on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak
This study used interpretable classification models to identify patterns of terrorist attacks, according to known characteristics derived from historical data. A For this purpose, we used the Global Terrorism Database (GTD) , which is an open-source database on terrorist attacks around the world from 1970 to 2016. It contains data on more than 170,000 domestic and international terrorist incidents, including dozens of features on location, tactics, perpetrators, targets, and outcomes of the events..
The dataset consists of training data, validation data, and testing data. The training data consists of 5,216 chest x-ray images with 3,875 images shown to have pneumonia and 1,341 images shown to be normal. The validation data is relatively small with only 16 images with 8 cases of pneumonia and 8 normal cases.
With our busy schedule, we prefer to read the summary of those articles before we decide to jump in for reading the entire article. Reading a summary helps us to identify the interest area, gives a brief context of the story. Summarization can be defined as the task of producing a concise and fluent summary while preserving key information and overall meaning.
complete deployment of the iris, breast cancer, and wine quality dataset for visualization using the KNN, SVM, and random forest algorithms from Machine Learning using STREAMLIT. In this application, you can compare the accuracy of the above three algorithms on the same dataset and can decide which is best and you can somewhat calculate the parameters also to see the change in accuracy while the parameters are changed.
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Using the Breast Cancer Wisconsin (Diagnostic) Database, we can create a classifier that can help diagnose patients and predict the likelihood of a breast cancer. ... In this exercise Logistic regression and Decission Tree and Random Forests is being implemented.
WineEnthusiasts use a points scale ranging from 1 to 100 to rate their wines (1 being the worst, 100 being the best).To an end user (i.e. wine shopper), the points are only as important as the information they convey.
Credit card fraud detection using machine learning techniques: A comparative analysis. ... Dataset of credit card transactions is sourced from European cardholders containing 284,807 transactions. A hybrid technique of under-sampling and oversampling is carried out on the skewed data.
Doing this brief simple examination of the McDonald’s menu will definitely help me be more mindful about the food the next time I choose to eat there. However in terms of of take-aways, there is nothing here really too surprising – we can see that McDonald’s food is, in general, very high in calories, fat, sugar and sodium. This is probably not a surprise for most, as many continue to eat it while being aware of these facts, myself included.
In order to build our OpenCV face recognition pipeline, we'll be applying deep learning in two key steps.To apply face detection, which detects the presence and location of a face in an image, but does not identify it.To extract the 128-d feature vectors (called “embeddings”) that quantify each face in an image.
Heart disease is one of the biggest causes of morbidity and mortality among the population of the world. Prediction of cardiovascular disease is regarded as one of the most important subjects in the section of clinical data analysis. The amount of data in the healthcare industry is huge. Data mining turns the large collection of raw healthcare data into information that can help to make informed decisions and predictions.
Predicting how the stock market will perform is one of the most difficult things to do. There are so many factors involved in the prediction – physical factors vs. physhological, rational and irrational behaviour, etc. All these aspects combine to make share prices volatile and very difficult to predict with a high degree of accuracy.
In any text document, there are particular terms that represent specific entities that are more informative and have a unique context. These entities are known as named entities , which more specifically refer to terms that represent real-world objects like people, places, organizations, and so on, which are often denoted by proper names. A naive approach could be to find these by looking at the noun phrases in text documents. Named entity recognition (NER) , also known as entity chunking/extraction , is a popular technique used in information extraction to identify and segment the named entities and classify or categorize them under various predefined classes.
Dataset was analyzed using Word2Vec algorithm, KMeans clustering, and tfidf weighting. Based on word embeddings trained for given dataset using gensim's Word2Vec. Main steps included detection of negative and positive clusters in word vectors space with use of sklearn's implementation of KMeans clustering algorithm, which were then used to transform every sentence into vector of replaced sentiment scores for a given words in a sentence. Second vector for given sentence was obtained through replacing all words in a sentence with their corresponding tfidf-scores. Final prediction was obtained as a dot product from these two vectors for each sentence - if their dot product was positive, the overall sentiment was predicted as positive, and if dot product was negative, overall sentiment was predicted as negative.