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Data Modeling Concepts in Data Science

To predict something useful from the datasets, we need to implement machine learning algorithms.

Since, there are many types of algorithms like SVM Algorithm in Python, Bayes, Regression, etc.

We will be using four algorithms-

  • Dimensionality Reduction

It is a very important algorithm as it is unsupervised i.e. it can implement raw data to structured data. It is used to reduce the number of random variables to improve accuracy. It tries to find a subset of the original variable.

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It is also useful in converting the data from a higher dimension to a lower dimension.
By using this algorithm, it is easy to visualize the data which will be in lower dimensions.

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  • Clustering

This algorithm is used to categorize a group of variables into similar types. Clustering is useful in data visualization as well. Clustering is very much useful when there is some unique value available in a dataset. This algorithm is very much popular in today’s world as it is an unsupervised algorithm and it is known as the best algorithm for raw datasets.

It is considered a statistical approach as well as a machine learning algorithm.
However, this algorithm is not popular for predictive results. It implements a statistical model when there is a relationship between the dependent and independent variable, it gives the most accurate result.

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The dependent variable is categorical in this case. When the outcome is ‘0’ or ‘1’, it indicates success/failure. This model is used to find the probability of binary output based on the predictor variable.
It is used to analyze the risk factor for a particular case like fraud detection, etc.

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  • Classification

Classification is considered under supervised learning. It is often used for sentimental analysis or finding polarity. It is used to classify objects.
For example- If any mail is received, then this algorithm helps to classify whether the received mail is spam or not.
It is also used to categorize objects like names of persons in the same category who live in the same area.

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