Whenever we come across such articles, we instinctively feel that something doesn't feel right. There are so many posts out there that it is nearly impossible to sort out the right from the wrong. Fake news can be claimed in two ways: First, an argument against the facts. The former can only be accomplished with automated query systems and substantial searches into the internet. The latter is possible through a natural language processing pipeline followed by a machine learning pipeline.

Brain tumors are one of the most challenging diseases for clinical researchers, as it causes severe harm to patients. The brain is a central organ in the human body, and minor damage to this organ could affect the correct functioning of the human body. Brain tumors can lead to irreversible and dysfunctional damage to patients, including memory and vision loss. For these reasons, medical studies have, for a long time, focused on the study of the brain and its diseases, including brain tumors. Computer studies have contributed to medical research by offering machine learning algorithms to classify medical analysis records as brain tumors or normal clinical conditions.

The power of data science is not limited to solving technical or business issues. Its usage is not limited to data analytics to create new technologies, target ads to consumers, and maximize profits and sales in business. The concept of open science has led organizations to use data to handle social problems. It can offer a statistical and data-driven solution to hidden human behavior and cultural patterns. We will be using data from the San Francisco crime department to understand the relation between civilian-reported incidents of crime and police-reported incidents of crime. To store and readily access a large amount of data, we will be using GridDB as our database platform.

Customer churn is a key business concept that determines the number of customers that stop doing business with a specific company. The churn rate is then defined as the rate by which a company loses customers in a given time frame. For example, a churn rate of 15%/year means that a company loses 15% of its total customer base every year. Customer churn takes special importance in the telecommunication sector, given the increasing competition and appearance of new telecommunication companies. For this reason, the telecom industry expects high churn rates every year.

On the Internet, there are a lot of sources that provide enormous amounts of daily news. Further, the demand for information by users has been growing continuously, so it is important to classify the news in a way that lets users access the information they are interested in quickly and efficiently. Using this model, users would be able to identify news topics that go untracked, and/or make recommendations based on their prior interests. Thus, we aim to build models that take news headlines and short descriptions as inputs and produce news categories as outputs. The problem we will tackle is the classification of BBC News articles and their categories.

So, if you get to the website, there are also some tips that would help you detect a phishing website. Since the dataset downloaded from the UCI website is an ARFF file, there's a need to convert it into a CSV file so we can use it in our Python code. All these values determine what the result would be, the Result column also has 1 and -1 values which represent Phishing Website and Not a Phishing Website respectively. This stage is where we build a model to predict if a website is a phishing website or not We would use a Decision Trees Classifier. The dataset has many columns but only the last column represents the result of the predictions if the values in all other columns are true.

Every asset has a life cycle and thus requires frequent maintenance. However, we may not want to spend resources too soon as that is a waste and we cannot be too late as it is risky. Thus, "when" to repair is an important problem. Predictive maintenance is a way to predict or forecast the probability of breakdown of a fixed asset. Predictive maintenance is important for all kinds of businesses, from a large company predicting the breakdown of motors to a small businesses predicting the breakdown of printers.

Neural Networks have taken the world of machine learning and predictive modelling in the last 5 years. Neural network have the ability to learn complex relationships in data and have been shown to work for a variety of applications from finance to robotics. Inspired by the human brain, Neural Networks work on the principle of signal transmission from one neuron to the other. Neural networks comprise of mainly three types of node layers -- an input layer, one or more hidden layers, and an output layer. Each node is an artificial neuron which connects to another using a nonlinear function and has an associated weight and threshold.

In this project, we use GridDB to create a Machine Learning platform where Kafka is used to import stock market data from Alphavantage, a market data provider. Tensorflow and Keras train a model that is then stored in GridDB, and then finally uses LSTM prediction to find anomalies in daily intraday trading history. The last piece is that the data is visualized in Grafana and then we configure GridDB to send notifications via its REST Trigger function to Twilio's Sendgrid. The actual machine learning portion of this project was inspired by posts on Towards Data Science and Curiously. This model and the data flow is also applicable to many other datasets such as predictive maintenance or machine failure prediction or wherever you want to find anomalies in time series data.

In this tutorial, we will build a trivial linear regression model with the data stored in GridDB. We will begin with GridDB's python-connector to insert and access the data. Afterwards, we will see how to retrieve and convert the data using pandas and numpy. In the end, we will train and visualize our regression model using scikit-learn and matplotlib. The following tutorial is carried out on Ubuntu Operating system (v.