Clean Data Gives You the Best Machine Learning Model
“I think you can have a ridiculously enormous and complex data set, but if you have the right tools and methodology, then it’s not a problem.” — Aaron Koblin,

Today we take a look at the characteristics of data and the need to have a clean dataset when working on any project.

Introduction.

Did you know that data is never clean? Data is messy, to understand data a you need to clean it. Clean data is equal to a good and useful ML model. An ML model is dependent on data.

Data Scientists are required to sort out the data to eliminate the errors in untidy data. This process will determine the results you gain from the data.

Data Cleaning

Data is always messy and it requires to be cleaned or sorted out. To perform analysis on the data you will need to clean your data to have viable results from the data.

Data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a database.

According to Wikipedia Data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a recordset or a database and refers to identifying incomplete, incorrect, inaccurate, or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data”

Characteristics of quality/clean Data

Everyone who works with data knows the need for clean data. In my experience, the best gift to give a data scientist or data engineer is clean data. This is never the case data is always messy.

Validity

Data Validity is the precise and exact results acquired from the data collected. Data validity leads to proper and correct conclusions to be drawn from the sample that are generalizable to the entire population.

Having a valid data set means you have to avoid Insufficient data, too much variation in data, Wrong sample selection, and the inaccurate measurement method is taken for analysis.

Accuracy:

Data Accuracy refers to whether the data values stored for an object are the correct values. Values in a dataset need to be consistent and in an ambiguous form.

Consistency:

As data moves across a network and between various applications on a computer there is a tendency of losing values or quality during the process. Therefore the process of maintaining the uniformity of the data as it moves is data consistency.

Relevance and Timing:

The reason to collect data should justify the effort required, which also means it has to be collected at the right moment in time. Data collected too soon or too late could misrepresent a situation and drive inaccurate decisions.

Completeness:

It is an indication of whether or not all the data necessary to meet the current and future business information demand are available in the data resource. If a data set lacks missing values it is then considered a complete data set.

Uniqueness:

The level of detail at which data is collected is important because confusion and inaccurate decisions can otherwise occur. Summarizing and manipulating data leads to a different meaning than the data implied at a lower level.

Characteristics of untidy data:

Structural errors.

They arise due to poor data management. These are the errors that arise during the transfer, invalid entries, inconsistent punctuation, typos, and mislabeled classes are the most common problems. It also includes data that makes no sense at all, data registered from before sales started.

Missing values

Missing values are common in almost all data sets. Working with incomplete data leads to wrong conclusions. You will find null values in a data set. If you decide to work with such data, be sure to get incorrect values.

Repetitive data/ Duplicate Values.

Duplicate data is any record that inadvertently shares data with another record in the database. Duplication is a headache to anyone working with a data set. This leads to wrong conclusions and miscalculations that cost organizations or people. Removing duplicates in a data set is another important step when it comes to data cleaning

clean that data

Benefits of a clean data.

Clean data is equal to a useful machine learning model. Machine learning is dependent on data and not just any data but the right data. Machine learning models are used to extract patterns from raw data this enables a model to solve a problem. The quality of the output is dependent on the kind of data fed to a model.

Clean data improves the quality of production in an organization. Organizations that have clean and up-to-date data are able to make decisions from the data this ensures the quality of work done by the staff. Improves decision-making process. Clean data will give you insights that are not biased. This leads to a better decision making process. Clean data ensures efficiency in all acquisition processes. Improves business activities. Clean data and the right analytics lead an enterprise or organization to the launch of products to the market at the right time and for the right consumers.

Summary

In conclusion, the nature of the data will determine the output. This is similar to the case of garbage in garbage out. If you want an ML model that gives you the best result check the kind of data that you feed into it. Feed an ML with garbage data the output is similar to the input.

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@africadataschool
['Africa Data School']
['Machine Learning', 'Data Analytics', 'Data Cleaning', 'Technical Analysis', 'Data Science']