LEARNING 6 FUNDAMENTAL STEPS TO ENHANCE QUALITY DATASETS


Introduction

The fact is, every dataset is flawed. This is what makes data preparation an important part of the process of machine learning. In short the term "data preparation" refers to a set of processes which help make your data more suitable to machine learning. In more general terms, preparation of data also involves establishing the proper data collection method. These processes consume the majority of the time used for machine learning. Sometimes, it can take months until the initial algorithm is created!

How do you collect Quality Datasets for machine learning if the information is not available?

If you're just to the market, the there's a tendency to be a lack of information However there are methods to transform that negative into an advantage. You can begin collecting Quality datasets in a proper method. Companies that began collecting data using the use of paper ledgers, and then moved to .xlsx or .csv files will probably be more difficult in preparing data than those that have an incredibly small, but eminently data set that is ML-friendly. If you have a clear idea of the issues that machine learning could be able to solve, you can create an automated data collection system prior to.

1. Speak up early about the issue.

When you are formulating your problem do some an exploration of data and think about the different groups of clustering, classification as well as regression and ranking which we discussed in our paper on the application to business to machine learning. In plain English the tasks are distinguished according to the following ways:

  • Classification: You want an algorithm that can be used to answer binary inquiries (cats or dogs bad or good goats or sheep you get the picture) or create an all-class classification (grass trees, trees, bushes as well as dogs, cats or birds, etc.) Additionally, you must have the answers that are correct identified to ensure that the algorithm can be taught from the answers. Read our guide on how to take on data labeling in your company.
  • clustering. You want an algorithm that can determine the classification rules and the amount of classes. The primary difference between classes is that you don't understand what the categories and the underlying principles of how they are divided. This, for instance, typically occurs when you have to divide your customers into segments and then tailor an approach for every segment based on the characteristics of each segment.
  • Regression. You want an algorithm that can give you a numeric value. In the case of, for instance, if are spending too much time trying to come up with the ideal value for the product as it is influenced by a variety of factors and variables, regression algorithms could help in estimating this price.

2. Data collection mechanisms Establishment

Instilling a data-driven culture within an organization could be one of the biggest challenges of the whole process. We have discussed this topic in our piece on machine learning strategies. If you intend to utilize machine learning to improve your predictive analytics first thing you need to take care of is to combat the fragmentation of data.

The majority of times, gathering data is the responsibility of an the data engineer or specialist who is in charge of creating data infrastructures. However, in the beginning you may need to engage an engineer in software who has some experience with databases.

3. Data should be formatted to ensure it is uniform

Data formatting can be known as the format of your file. This isn't too big of a problem when you transform a data set into the format that best fits your machine learning system the best.

It's about consistency in format of the records themselves. When you're consolidating data from multiple sources or your database is being manually updated by various people, it's important to make sure that all variables in an attribute are consistent written.



4. Complete data cleaning

As missing values can significantly affect prediction accuracy, you should consider this an issue to be addressed. When it comes to machine learning, approximate or assumed values are "more accurate" for algorithms rather than missing ones. Even when you don't know exactly what value you're looking for, techniques exist to "assume" the value that is missing , or even bypass the problem. What is the best way to clean information? The best method to use heavily depends on the data you have and the field you work in:

Substitute missing values by using Dummy value, e.g., n/a in categorical, or 0 in numerical values
Substitute the numerical values that are missing with the mean figures.
If you are looking for categorical values You can also employ the most commonly used items to complete the equation.

5. Data Rescaling

Data rescaling is a set of normalization methods that are aimed to improve the quality of data by reducing the dimensions and avoiding the scenario where some of the values outweigh other values. What is this referring to?

A simpler method can be decimal scale. It involves the process of scaling data by shifting one decimal place in either direction to achieve the same reasons.

6. Discretize data

Sometimes, you'll make better prediction if you convert numbers into categorical values. This is possible for instance, by splitting the entire range of values into several groups.

The datasets that are public come from companies and organizations who are willing to share. The data sets typically include information on general processes across many different sectors, such as healthcare records and historical weather records, transportation measurements, translation and text data collection, data on the use of hardware, etc. While these will not help to identify the data-related dependencies within your own business, they may provide an excellent understanding of the industry you work in and its particular niche, and sometimes the customer segments you serve.
Even if you haven't collected information for a long time take a look and start searching. There could be data sets you can immediately use.


CONCLUSION

Global Technology Solutions is an AI data collection Company that provides  AI Training Datasets for machine learning. GTS is the forerunner when it comes to artificial intelligence (AI) data collection. We are seasoned experts with recorded success in various forms of data collection, we have improved systems of image, language, video, and text data collection. The data we collect is used for Artificial intelligence development and Machine Learning. Because of our global reach, we have data on many languages spoken all over the world, we expertly utilize them. We solve problems faced by Artificial Intelligence companies, problems related to machine learning, and the bottleneck relating to datasets for machine learning. We provide these datasets seamlessly. We make your machine ,model ready with our premium datasets that are totally Human-Annotated. 

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