3- Data Pre-processing In Data Sciences

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Data scientists apply different machine learning models on data to get meaningful insights from the data using data science. But each machine learning and data sciences model has its requirements. It is necessary to change the data according to the needs of machine learning and data science models. The data gathered from the business firms, companies, and warehouses are not in a suitable format. That is why it is always necessary to change the real-world data into a proper structure before applying a machine learning model to data. Earn yourself a promising career in data science by enrolling in the Data Science Classes in Pune offered by 360DigiTMG.

What is meant by Data Preprocessing:

It is the method to transform real-world data into a structured and understandable format to avoid any inconvenience while applying the machine learning models to the data. In this process, errors are removed from the raw data to make it suitable for machine learning and data science algorithms. Earn yourself a promising career in data science by enrolling in the data science online course offered by 360DigiTMG.

Why It is Necessary To Perform Data Preprocessing :

As discussed earlier, data gathered from real-world business firms and warehouses are not in the appropriate format to apply any data science or machine learning model to the data. The data can be in the wrong format, and the values can be missing in the data. It is always necessary to solve all these problems before applying any data science model. Otherwise, the accuracy of the results will be much affected by these problems with the data. After transforming the data into the correct format, the data science model can understand the data more accurately and efficiently. It is always better to apply data preprocessing steps on the data before applying a data sciences model. Don’t delay your career growth, kickstart your career by enrolling in this Data Science Training Institute In Chennai.

Data Preprocessing Techniques:

There are several data preprocessing techniques that are used to transform data into the appropriate and correct format. Here we will discuss some of the data preprocessing techniques:

Cleaning The Data:

In this process, the data scientists clean the data gathered from the enterprises, business firms, and warehouses by applying some data cleaning methods. First of all, data scientists remove the noise from the data. The data scientists check for the outlier values in different attributes of the data. In the next step, all the outliers are removed from the data. There can be human compile errors in the data. For example, some dataset attributes can have the same values in the dataset due to typing errors. Also, there can be some missing values in the data set, which can cause a disturbance in the accuracy of the model. Also, Looking forward to becoming a Data Scientist? Check out the data science training in bangalore and get certified today.

How To Deal With the Missing Values in the Dataset:

There can be missing values in the data that can cause several problems during the model’s training. Moreover, the accuracy of the model is disturbed in most cases. So, it is essential to solving the problem of missing values in the data set. There are different ways to deal with missing values. One way is to delete the missing values from the data set if they are more than 50 percent. The other way is to fill them manually by using the statistical formulas. In most cases, the average method is used to fill up the missing values. For more articles related to data sciences, please keep visiting our platform.

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