Are you a data mining researcher who spends up to 75% of your time ensuring data quality and then optimizing it for the development and implementation of predictive models?
And do you find lots of literature on data mining theory and principles but do you find no “how to” knowledge when it comes to practical advice?
What Is Data Preparation?
A large amount of sequential data is investigated in the first step of each research. Data preparation, counting, and value-based ordering, distribution visualization, and determination of subsequent duration, confirmation, and re-visualization are included at this level.
Machine learning allows us to find patterns in data — patterns that we then use to predict new data points. We have to build the data set and convert the data correctly to get those predictions right.
The basic idea is simple — we take data points, they are either not meaningful or have a significant signal, we build a prediction and measure how accurate the prediction is, and we compare those predicted and measured scores.
In fact, you probably already know how to make those comparisons. What you don’t know is how accurate those predictive scores actually are.
If the prediction is as accurate as we think it is, then we should see predictions for every possible combination of data points.
Data preparation is the act of transforming (or preprocessing) raw data (which may come from different sources of data) into a form that can be processed readily and accurately, for example for business purposes.
Data preparation is the first step in data analytics projects and can include several separate activities such as data loading or data ingestion, data integration, data washing, data increase and data delivery.
The issues to be addressed fall into two main categories:
systematic errors involving large numbers of data records, probably because they came from different sources;
individual errors affecting small amounts of data records, likely due to errors in the original data entry.
Steps to Constructing Your Dataset
To construct your dataset (and before doing data transformation), you should:
Gather raw data.
Identify the sources of the feature and the label.
Choose a sampling method.
These steps very much depend on how you framed your ML problem. Use the self-check to refresh your memory about framing issues, and test your data collection assumptions.
Data preparation is the process of cleaning and transforming raw data prior to processing and analysis.
Identify the original data. As you construct your dataset, you should focus on collecting the raw data.
The raw data will enable you to test your model, but it does not give you enough information to try to replicate the original data.
The raw data you will collect depends on how you have framed the problem.
Traditional methods for machine learning rely on either prediction or clustering to divide the space into parts that make intuitive sense. Thus, for a given population of traits, such as height or IQ, predictors may be used to break each dimension up into categories. In the case of humans, this is called latent variable analysis.
Such methods tend to be predictive and do well at assigning clusters of people and variables based on past data. This approach is useful in providing useful patterns
As a rough thumb rule, the model will train more examples than trainable parameters on at least one order of magnitude. Typically, simple models on big data sets beat complex models on small data sets. Google trained simple linear regression models on large data sets with great success; that’s why they use deep learning to do it (particularly using convolutional neural networks).
But the biggest controversy has been whether training models on large data sets lead to top models.
My conclusion is that it depends on the question. For example, in our recent machine learning paper, we noted that the accuracy of many simple linear regression models on large data sets would increase with more data, while the success of the hierarchical k-means model (its accuracy also increased with more data) would not.
Here is a key indicator used to evaluate data quality. As a rule, a value can best be compared to correlation. There are no perfect comparisons and there are always cases where the data may not relate to your business in any meaningful way.