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What Are Data Mining and Data Analytics?

Data mining is the process of discovering hidden patterns in data, where (a) Patterns refer to inherent relationships and/or dependencies in the data, and (b) Large-scale data is typically stored in a database environment. Data mining is also frequently referred to as knowledge discovery in database (KDD). Data analytics is the process of transforming raw data into knowledge and insight for making better decisions.

Related Fields

Data mining and analytics are closely related to the areas of databases, artificial intelligence (AI), statistics, and information retrieval. However, there are considerable differences between data mining and these fields.

• Databases — focuses on data storage and access technology, while data mining focuses on data analysis and knowledge discovery. Artificial Intelligence — there are overlaps between AI and data mining techniques, including those concerning machine learning. However, AI techniques are not necessarily data-oriented (e.g., expert systems).

• Statistics — statistical science assumes data is scarce; it focuses on numeric data and a parametric approach (e.g., “assume data follows normal distribution”). Conversely, data mining assumes data is abundant; it deals with various data types and focuses on efficient algorithms for large-scale data.

• Information retrieval (IR) — concerns finding materials (e.g., documents) of an unstructured nature (e.g., text) that satisfies an information need; it is closely related to text and web mining. A typical example of IR techniques is a search engine.

There are many business application of Data Mining: Database marketing , Credit evaluation , Fraud detection , Market basket analysis , Market segmentation , Web usage mining and personalization

Data Mining Tasks

Supervised Learning - Where there is a predefined attribute whose values are to be predicted:

• Classification

• Prediction (of numeric values)

Unsupervised Learning - Where there is no predefined attribute for prediction:

• Clustering (or Cluster Analysis)

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Classification

Classification is the process of assigning data records into one of several predefined groups, referred to as classes. Classification involves building a model, called a classifier, which can be a mathematical function, a set of rules, or other representations.

From the above table, if we want to use "Age", "Income", and "Year-of-Education" to predict "Favorite" then this task is Classification , since the outcomes are "YES" or "NO".

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More Examples of Classification:

• Fraud detection (true or false)

• Security trading decision (buy, sell, or hold)

• Medical diagnosis (presence or absence of a disease)

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Prediction

The prediction of numeric values helps us discover the relationship between one set of variables (called independent or input variables), and another set of variables (called dependent or output variables in data). Once these relationships are discovered, the past or current values of independent variables can be used to predict the future values of dependent variables.

Prediction vs. Classification:

Prediction - The values of the attribute to be predicted (dependent variable) are numeric

Classification - The values of the attribute to be predicted (class attribute) are categorical

From the above table, if we want to use "Age", "Income", and "Year-of-Education" to predict "Purchase-Amount" then this task is Prediction , since the outcomes are numeric values .

More Examples of Prediction:

• Sales volume / revenue prediction

• Stock price prediction association

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Clustering

Clustering is the process of grouping data records into a number of groups, called clusters, such that records within the same cluster are more similar than those belonging to different clusters. This process differs from classification in that clusters are formed as a result of analysis, instead of being predefined.

From the above table, if we want to use "Age" to group customers, then this task is Clustering. For example we can group customers into 3 groups: Young (Age between 20-30), Mid-Age (Age between 31-60), Senior (Age 60 above)

More Examples of Clustering:

• Market segmentation

• Grouping of library books by field

Data Mining Process

1. Problem Identification:

• Define the purpose of the data-mining project and nature of the problem (classification, prediction, clustering).

2. Data Preparation:

• Data collection - retrieving, merging, and/or dividing data

• Data cleaning - correcting errors, handling missing data, resolving inconsistencies

• Data reduction - sampling (in rows), feature (attribute) selection (in columns)

• Data transformation - standardizing data, reforming data, conversation between numeric and categorical data

3. Model Formulation and Pattern Exploration:

• Select appropriate data-mining techniques and tools, then use the selected techniques and tools to build models and explore the patterns/relationships hidden in the data

4. Verification and Modification:

• Test if the models built are valid; modify the models if necessary

• Compare different candidate models

5. Interpretation and Implementation:

• Interpret the results of data mining in an intuitive manner

• Implement (AKA deploy) the model into related applications

Data Mining Visualization Help

At Realcode4you you can get all Data mining programming Visualization Related Help, Here you can get all data mining  Visualization Assignment help, Data Mining Visualization Homework Help, Data mining Visualization Coursework Help which is related to any data mining techniques(clustering, classification, regression, etc.)

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Visualization techniques used to deliver insights in data using visual cues such as graphs, charts, maps, and many others. The popular data visualization tools that are available are Tableau, Plotly, R, Google Charts, Infogram, and Kibana.

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There are many types of  Graphs or Plot in which you can get Assignment Help, Project Help, Homework Help and coursework Help:

• Bar Plot

• Histogram

• Scatter Plot

• Line Plot

• Pie Chart

• Time Series Analysis

• Box Plot

• etc.

Hire Expert To Get Help In Time Series Analysis

Time Series in machine learning is used to see how an object behaves over a period of time. In machine learning there are many data mining techniques that are used to visualize the time series data.

At Realcode4you you can get all types of time series analysis help like: 

Univariate Analysis: Univariate analysis is the technique of comparing and analyzing the dependency of a single predictor and a response variable. The prefix "uni" means one, emphasizing the fact that the analysis only accounts for one variable's effect on a dependent variable. In simple way we can say analysis using single variable or visualization using single variable called univariate analysis. 

Multivariate Analysis: Multivariate analysis deals with the statistical analysis of data collected on more than one dependent variable. Multivariate techniques are popular because they help organizations to turn data into knowledge and thereby improve their decision making.

Forecasting: Time series forecasting occurs when you make scientific predictions based on historical time stamped data. It involves building models through historical analysis and using them to make observations and drive future strategic decision-making.

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The Classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. In Classification, a program learns from the given dataset or observations and then classifies new observation into a number of classes or groups.

There are many types of Classification Techniques:

• Decision Trees – These are organised in the form of sets of questions and answers in the tree structure.

• Naive Bayes Classifiers – A probabilistic machine learning model that is used for classification.

• K-NN Classifiers – Based on the similarity measures like distance, it classifies new cases.

• Support Vector Machines – It is a non-probabilistic binary linear classifier that builds a model to classify a case into one of the two categories.

If you need any help of the above ML Algorithms then hire expert to get instant help. Here you can get all supervised learning algorithms related help without any plagiarism issue.

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