Michael Rundell SOTON数据分析
Data mining and algorithms
Data mining is the process of discovering predictive information from the analysis of large databases. For a data scientist, data mining can be a vague and daunting task – it requires a diverse set of skills and knowledge of many data mining techniques to take raw data and successfully get insights from it. You’ll want to understand , and that can help you with data mining at scale.
This guide will provide an example-filled introduction to data mining using Python, one of the most widely used data mining tools – from cleaning and data organization to applying machine learning algorithms. First, let’s get a better understanding of data mining and how it is accomplished.
A data mining definition
The desired outcome from data mining is to create a model from a given dataset that can have its insights generalized to similar datasets. A real-world example of a successful data mining application can be seen in automatic fraud detection from banks and credit institutions.
Your bank likely has a policy to alert you if they detect any suspicious activity on your account – such as repeated ATM withdrawals or large purchases in a state outside of your registered residence. How does this relate to data mining? Data scientists created this system by applying algorithms to classify and predict whether a transaction is fraudulent by comparing it against a historical pattern of fraudulent and non-fraudulent charges. The model “knows” that if you live in San Diego, California, it’s highly likely that the thousand dollar purchases charged to a scarcely populated Russian province were not legitimate.
That is just one of a number of the powerful applications of data mining. Other applications of data mining include genomic sequencing, social network analysis, or crime imaging – but the most common use case is for analyzing aspects of the consumer life cycle. Companies use data mining to discover consumer preferences, classify different consumers based on their purchasing activity, and determine what makes for a well-paying customer – information that can have profound effects on improving revenue streams and cutting costs.
If you’re struggling to get good datasets to to begin your analysis,
What are some data mining techniques?
There are multiple ways to build predictive models from datasets, and a data scientist should understand the concepts behind these techniques, as well as how to use code to produce similar models and visualizations. These techniques include:
An example of a scatterplot with a fitted linear regression model.
– Identifying what category an object belongs to. An example is classifying email as spam or legitimate, or looking at a person’s credit score and approving or denying a loan request.
– Finding natural groupings of data objects based upon the known characteristics of that data. An example could be seen in marketing, where analysis can reveal customer groupings with unique behavior – which could be applied in business strategy decisions.