Predictive data mining tasks stock market crash 1929 causes

Prediction definition in data mining

Different Data Mining Tasks a) Classification. Classification derives a model to determine the class of an object based on its attributes. A b) Prediction. Prediction task predicts the possible values of missing or future data. Prediction involves developing a c) Time – Series Analysis. Time. Descriptive mining tasks describe the characteristics of the data in a target data set. On the other hand, predictive mining tasks carry out the induction over the current and past data so that predictions can be made. In terms of accuracy, the descriptive technique is more precise and accurate as compared to predictive mining. The predictive analysis involves control over the situation along with responding to it Estimated Reading Time: 4 mins. Note − These primitives allow us to communicate in an interactive manner with the data mining system. Here is the list of Data Mining Task Primitives −. Set of task relevant data to be mined. Kind of knowledge to be mined. Background knowledge to be used in discovery process. Interestingness measures and thresholds for pattern evaluation. Representation for visualizing the discovered patterns. Set of task relevant data to be mined. 17/07/ · Classification is the most widely used data mining task in businesses. As a predictive analytics task, the objective of a classification model is to predict a target variable that is binary (e.g., a loan decision) or categorical (e.g., a customer type) when a set of input variables are given (e.g., credit score, income level, etc.).Name: CHUNK.

Novel coronavirus COVID or nCoV pandemic has neither clinically proven vaccine nor drugs; however, its patients are recovering with the aid of antibiotic medications, anti-viral drugs, and chloroquine as well as vitamin C supplementation. It is now evident that the world needs a speedy and quicker solution to contain and tackle the further spread of COVID across the world with the aid of non-clinical approaches such as data mining approaches, augmented intelligence and other artificial intelligence techniques so as to mitigate the huge burden on the healthcare system while providing the best possible means for patients‘ diagnosis and prognosis of the nCoV pandemic effectively.

The decision tree, support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor algorithms were applied directly on the dataset using python programming language to develop the models. The model predicted a minimum and maximum number of days for COVID patients to recover from the virus, the age group of patients who are of high risk not to recover from the COVID pandemic, those who are likely to recover and those who might be likely to recover quickly from COVID pandemic.

The results of the present study have shown that the model developed with decision tree data mining algorithm is more efficient to predict the possibility of recovery of the infected patients from COVID pandemic with the overall accuracy of Severe Acute Respiratory Syndrome Coronavirus two SARS-CoV-2 , the causative agent of novel coronavirus COVID or nCoV , has emerged in late which is believed to be originated from Hubei Province, China called Wuhan [ 16 , 25 ].

The major symptoms of SARS-CoV-2 include fever, cough, and shortness of breath which in many instances appeared to be similar to that flu [ 16 ]. COVID had since reached a decisive point and pandemic potential which claimed the lives of many people across the world and human-to-human transmission of COVID from infected individuals with mild symptoms have been reported [ 16 , 20 ].

According to Worldometers, COVID pandemic affects countries and territories around the world and 2 02 international conveyances with 6,, confirmed cases, 2,, recovered cases, and , deaths as of May 30th, , GMT [ 27 ]. However, there is no drug or vaccine clinically proven to treat COVID pandemic, therefore other non-clinical or non-medical therapeutic techniques are urgently needed to contain and prevent further outbreak of COVID pandemic such as data mining techniques, machine learning and expert system among other artificial intelligence techniques.

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Basic Data Mining tasks. Introduction to Data Mining Tasks The data mining tasks can be classified generally into two types based on what a specific task tries to achieve. Those two categories are descriptive tasks and predictive tasks. The descriptive data mining tasks characterize the general properties of data whereas predictive data mining tasks perform inference on the available data set to predict how a new data set will behave.

There are a number of data mining tasks such as classification, prediction, time-series analysis, association, clustering, summarization etc. All these tasks are either predictive data mining tasks or descriptive data mining tasks. A data mining system can execute one or more of the above specified tasks as part of data mining. Predictive data mining tasks come up with a model from the available data set that is helpful in predicting unknown or future values of another data set of interest.

A medical practitioner trying to diagnose a disease based on the medical test results of a patient can be considered as a predictive data mining task. Descriptive data mining tasks usually finds data describing patterns and comes up with new, significant information from the available data set.

predictive data mining tasks

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The descriptive and predictive data mining techniques are used in data mining to mine the types of patterns. The descriptive analysis is used to mine data and provide the latest information on past or recent events. On the other hand, the predictive analysis provides answers of the future queries that move across using historical data as the chief principle for decisions.

Data mining tasks can be descriptive, predictive and prescriptive. Here we are just discussing the two of them descriptive and prescriptive. In simple words, descriptive implicates discovering the interesting patterns or association relating the data whereas predictive involves the prediction and classification of the behaviour of the model founded on the current and past data.

Basis for comparison Descriptive Mining Predictive Mining Basic It identifies, what happened in the past by analyzing stored data It describes, what can happen in the future with the help past data analysis. Require Data aggregation and data mining Statistics and forecasting methods Preciseness Provides accurate data Produces results does not ensure accuracy. Predictive modelling, forecasting, simulation and alerts. Descriptive mining is generally used to produce correlation, cross tabulation, frequency etcetera.

These techniques are determined to find the regularities in the data and to reveal patterns. The other application of descriptive analysis is to discover the captivating subgroups in the major part of the data. Descriptive analytics focuses on the summarization and conversion of the data into meaningful information for reporting and monitoring.

Clustering, summarization, association are the techniques categorized under descriptive mining.

predictive data mining tasks

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To browse Academia. Log In with Facebook Log In with Google Sign Up with Apple. Remember me on this computer. Enter the email address you signed up with and we’ll email you a reset link. Need an account? Click here to sign up. Download Free PDF. IJCSMC Journal. Download PDF Download Full PDF Package This paper. A short summary of this paper. Meghana Deshmukh et al, International Journal of Computer Science and Mobile Computing, Vol. Akarte Asst.

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There are two forms of data analysis that can be used for extracting models describing important classes or to predict future data trends. Classification models predict categorical class labels; and prediction models predict continuous valued functions. For example, we can build a classification model to categorize bank loan applications as either safe or risky, or a prediction model to predict the expenditures in dollars of potential customers on computer equipment given their income and occupation.

A bank loan officer wants to analyze the data in order to know which customer loan applicant are risky or which are safe. A marketing manager at a company needs to analyze a customer with a given profile, who will buy a new computer. In both of the above examples, a model or classifier is constructed to predict the categorical labels.

These labels are risky or safe for loan application data and yes or no for marketing data. Suppose the marketing manager needs to predict how much a given customer will spend during a sale at his company. In this example we are bothered to predict a numeric value. Therefore the data analysis task is an example of numeric prediction. In this case, a model or a predictor will be constructed that predicts a continuous-valued-function or ordered value.

predictive data mining tasks

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Businesses in every sector are looking toward two fundamental processes, data mining and predictive analytics, to combine and prepare disparate datasets, uncover new insights, and make game-changing decisions. While data mining and predictive analytics represent different ways to extract information, both processes play a critical role in helping organizations make sense of their ever-expanding datasets and create measurable value. The best way to understand the difference between data mining and predictive analytics is that each term represents one half of a two-step process.

Where data mining plays a passive role, predictive analytics aims to drive action. Data mining uses software to capture, clean, and transform data, and uncovers patterns and relationships between disparate datasets. Predictive analytics uses the information that surfaced during the data mining process, to predict future outcomes, model different scenarios, and identify the best strategy for any given situation.

While the idea of gathering information and using those insights to develop a strategic plan is as old as time, predictive analytics and data mining allow this process to happen at speed and scale. It plays an essential role in uncovering the value in dark data and supports advanced technologies like AI, machine learning, and Natural Language Processing NLP.

Data mining also acts as a precursor to effective predictive analytics. Data mining allows businesses to learn more about their audiences, past trends, and current conditions.

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Data mining is the process of going through the system databases and finding relevant data to analyze. For example, the predictive data mining process may use algorithm-based tools to go through a customer database to look at past transactions in order to support theories about possible future volumes of Estimated Reading Time: 1 min. Following are the examples of cases where the data analysis task is Prediction − Suppose the marketing manager needs to predict how much a given customer will spend during a sale at his company. In this example we are bothered to predict a numeric value.

In this Data Mining MCQ , we will cover these topics such as data mining, techniques for data mining, techniques data mining, what is data mining, define data mining, definition data mining, data mining and analysis, process of data mining, data analysis and mining, data mining techniques, software data mine, data mining processes, data mining in research, concept of data mining and so on.

Continuous attribute b. Ordinal attribute c. Numeric attribute d. Nominal attribute Show Answer Feedback The correct answer is: Nominal attribute. Question 2 Which of the following activities is NOT a data mining task? Select one: a. Predicting the future stock price of a company using historical records b. Monitoring and predicting failures in a hydropower plant c. Extracting the frequencies of a sound wave d. Monitoring the heart rate of a patient for abnormalities Show Answer Feedback The correct answer is: Extracting the frequencies of a sound wave.

Question 3 Data Visualization in mining cannot be done using Select one: a. Photos b. Graphs c.

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