A step-by-step guide to data science classification

Data science starts with data, which can range from a simple array of a few numeric observations to a complex matrix of millions of observations with thousands of variables. Data science utilizes certain specialized computational methods in order to discover meaningful and useful structures within a dataset.

Data science problems can be broadly categorized into supervised or unsupervised learning models. Supervised or directed data science tries to infer a function or relationship based on labeled training data and uses this function to map new unlabeled data.

Supervised techniques predict the value of the output variables based on a set of input variables. To do this, a model is developed from a training dataset where the values of input and output are previously known.

The model generalizes the relationship between the input and output variables and uses it to predict for a dataset where only input variables are known.

The output variable that is being predicted is also called a class label or target variable. Supervised data science needs a sufficient number of labeled records to learn the model from the data.

Unsupervised or undirected data science uncovers hidden patterns in unlabeled data. In unsupervised data science, there are no output variables to predict.     

The objective of this class of data science techniques is to find patterns in data based on the relationship between data points themselves. An application can employ both supervised and unsupervised learners.

Data science problems can also be classified into tasks such as classification, regression, association analysis, clustering, anomaly detection, recommendation engines, feature selection, time series forecasting, deep learning, and text mining. Data science problems can be broadly categorized into supervised or unsupervised learning models.

Supervised or directed data science tries to infer a function or relationship based on labeled training data and uses this function to map new unlabeled data. Supervised techniques predict the value of the output variables based on a set of input variables.

To do this, a model is developed from a training dataset where the values of input and output are previously known. The model generalizes the relationship between the input and output variables and uses it to predict for a dataset where only input variables are known.

The output variable that is being predicted is also called a class label or target variable. Supervised data science needs a sufficient number of labeled records to learn the model from the data.

Unsupervised or undirected data science uncovers hidden patterns in unlabeled data. In unsupervised data science, there are no output variables to predict.

The objective of this class of data science techniques is to find patterns in data based on the relationship between data points themselves. An application can employ both supervised and unsupervised learners.

Data science problems can also be classified into tasks such as classification, regression, association analysis, clustering, anomaly detection, recommendation engines, feature selection, time series forecasting, deep learning, and text mining. 


 An overview is presented in this chapter and an in-depth discussion of the concepts and step-by-step implementations of many important techniques.
Classification and regression techniques predict a target variable based on input variables. The prediction is based on a generalized model built from a previously known dataset.

In regression tasks, the output variable is numeric (e.g., the mortgage interest rate on a loan). 


Classification tasks predict output variables, which are categorical or polynomial (e.g., the yes or no decision to approve a loan). Deep learning is a more sophisticated artificial neural network that is increasingly used for classification and regression problems.

Clustering is the process of identifying the natural groupings in a dataset.
 For example, clustering helps find natural clusters in customer datasets, which can be used for market segmentation. Since this is unsupervised data science, it is up to the end-user to investigate why these clusters are formed in the data and generalize the uniqueness of each cluster.

In retail analytics, it is common to identify pairs of items that are purchased together, so that specific item can be bundled or placed next to each other. 


This task is called market basket analysis or association analysis, which is commonly used in cross-selling. Recommendation engines are the systems that recommend items to the users based on individual user preference.

Anomaly or outlier detection identifies the data points that are significantly different from other data points in a dataset. Credit card transaction fraud detection is one of the most prolific applications of anomaly detection.[data science course](onlineitguru.com/data-sc…ourse.html Make an Inquiry about this news)


 Time series forecasting is the process of predicting the future value of a variable (e.g., temperature) based on past historical values that may exhibit a trend and seasonality. Text mining is a data science application where the input data is text, which can be in the form of documents, messages, emails, or web pages.

To aid the data science on text data, the text files are first converted into document vectors where each unique word is an attribute. Once the text file is converted to document vectors, standard data science tasks such as classification, clustering, etc., can be applied.

Feature selection is a process in which attributes in a dataset are reduced to a few attributes that matter. A complete data science application can contain elements of both supervised and unsupervised techniques (Tan et al., 2005).

Unsupervised techniques provide an increased understanding of the dataset and hence, are sometimes called descriptive data science.


 As an example of how both unsupervised and supervised data science can be combined in an application, consider the following scenario. In marketing analytics, clustering can be used to find natural clusters in customer records.

Each customer is assigned a cluster label at the end of the clustering process. A labeled customer dataset can now be used to develop a model that assigns a cluster label for any new customer record with a supervised classification technique.

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