In the present day business landscape, data is at the core of decisions with every organization coming up with new ways to gather as much as they can from all sources. Having plenty of data at hand is important but what’s even more important is knowing how to use it well. Businesses are constantly on the lookout for ways to use data to solve everyday challenges through machine learning.
The most important aspect of machine learning is data because it sets the stage for algorithm training and eventual machine learning implementation. However, before using the data at hand for machine learning implementation, it is crucial that you ensure the data is consistent, accurate and clean. And this is where data preparation for machine learning comes in.
What is Data Preparation?
Data preparation essentially refers to the process used to transform noisy data from any given database into information that is ready for consumption.
In machine learning, the process involves a number of steps which fall into three broad categories:
- Data Selection
- Data Pre-processing
- Data Transformation
In order to execute every facet of these three categories effectively, here are the seven steps you need to undertake:
Step 1: Data Collection
This is certainly the most important step as it sets the foundation for your machine learning implementation project. The type of data you collect will obviously depend on the problem that you want your machine learning algorithms to solve.
To make this step a success, ensure that you pick the right data. Keep in mind that even though it is important to have lots of data, this is only a means to an end. What this means is that quantity is not as important as quality.
With this in mind, you might elect to create your own dataset so as to customize the project to your own organizational needs.
Step 2: Data Profiling
Next, you would need to assess the condition of the data you have collected. This is the time to analyze trends, identify outliers and find out whether there is any skewed or incorrect information.
It is important to make this step as thorough as possible because the source data you use is what will determine the insights your model presents. As such, you want to make sure that it does not have any unseen biases.
To illustrate, if your machine learning application is assessing customer behavior in a given country, using a limited sample could mean missing some regions. Therefore, take time to find any issue that could skew the findings in the long run.
Step 3: Data Formatting
At this point, consider how you plan to use the data and ensure that the type of formatting you apply fits your machine learning applications. For example, if you have collected data from diverse sources, you might find that the formatting inconsistent (e.g. $10 vs. USD10).
Similarly, it is important to standardize the values at hand, for example, the use of abbreviations vs. spelled out words. Having a consistent format for all data would make it possible to use the same input formatting protocol for the entire dataset.
Step 4: Data Cleaning
Data cleaning basically refers to identifying and addressing instances of missing data. Unless your dataset is completely perfect, which is highly unlikely, this step could take the longest. In this case, you need to identify the incomplete data instances and remove them. And in cases where the dataset includes sensitive information, possibly confidential, you need to remove or anonymize it.
Be smart in the way you handle different aspects of the information. For instance, if you are handling traveling data and a portion of it is missing the subjects’ nationalities, it could be tempting to replace it with a null value. However, nationality is a very important aspect with regards to traveling data. It would be best to delete the records entirely.
Step 5: Data Sampling
In certain cases, you could find that the available data is far more than what you need for your machine learning implementation project. Having more data might seem like a good thing but keep in mind that it would mean longer running times for your machine learning algorithms. It would also translate into higher memory and computational requirements.
If that is the case, it would be best to take a representative sample of the required data. However, it is important to exercise caution to ascertain that the sample selected is truly representative of the whole and does not skew the findings of your model.
Step 6: Feature Engineering
This step involves a delicate blend of art and science in transforming your raw dataset into the features that represent a pattern to your machine learning applications.
Decomposition is one of the processes you might want to undertake at this point. This refers to breaking down specific features into their constituent parts. It applies in cases where using the parts of a complex concept would be more useful to a machine learning model than simply presenting the whole.
Similarly, you might decide to aggregate features if they would make more meaning in their collective state rather than as individual parts. To illustrate, consider a dataset that contains a record of numerous customer login instances. Aggregating this into the total number of logins might make more sense.
Just like data cleaning, feature engineering could also take a significant amount of time. The end results might, however, be worthwhile as it will greatly impact the performance of your machine learning algorithms.
Step 7: Splitting Data
The final step is splitting the data at hand into two sets, training and evaluation. A majority of data scientists work with the 80/20 rule, 80% data for training and 20% for evaluation. Making a success of this step will mean putting some thought into splitting your data. It cannot be a random procedure.
First, ensure that the data subsets for the two roles do not overlap. Second, use tools that allow you to version and catalogue the original source of the data. You also want to keep track of the linkage between the original data source and the prepared data that you use for the machine learning applications.
Doing so will make it possible to trace the outcomes of any predictions back to the source data. In turn, that will allow you to optimize and refine the models as time goes.
Laying the Proper Foundation for a Reliable Machine Learning Model
Successful machine learning implementation in any organization requires proper training, testing and validation of the models prior to deployment. Data preparation for machine learning holds significant weight in this regard. Though it could take lots of time, proper data preparation will help clean and annotate the foundation for your machine learning project. With time, it will help to streamline your project and deliver the required business value.
The process can involve plenty of iterations but the results will be well worth your while. Doing it right will make the difference between mastering machine learning and failing to properly implement it. Take the above steps into consideration while preparing your dataset, always being on the lookout for a clearer way to represent a problem.
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