What is data preprocessing and how much is required in data science?
In the real world, data is often incomplete, random, and inconsistent. On the other hand, new data is constantly being generated and the number of different sources of data is also increasing. As a result, the risk of inconsistent or incorrect data collection is also increasing.
Post Index:
1. What is data preprocessing?
2. Why is data preprocessing necessary?
3. Properties of standard completed data
But in data science, it is only through high quality data that it is possible to make accurate models and make accurate predictions about the future. So the data has to be processed for the highest possible quality. This step of data processing is called data preprocessing and it is one of the most necessary steps in data science, machine learning and building artificial intelligence or AI complete technology framework.
What is data preprocessing?
Data preprocessing is the process of converting initially received data into an understandable format. Initially available data usually contains inconsistent formats and human errors. And it can be incomplete. Data preprocessing solves such problems and makes data sets more complete and efficient for data analysis.
This is an important necessary process for success in data mining and machine learning. This makes it possible to find the required information from the dataset more quickly. It also affects the performance of the machine learning model.
Simply put, data preprocessing is the process of converting data into a form that computers can easily work with. This makes data analysis easier and also increases the accuracy and speed of machine learning algorithms.
Why is data preprocessing necessary?
We know that a database is a collection of numerous data points. Data points are often called observations, data samples, events and records. Each sample has different characteristics of different types. Data preprocessing is essential for effectively modeling with these features.
Numerous problems can occur during data collection. You may need to combine data from a variety of data sources. Which may result in inconsistent data formats. For example, combining data from multiple datasets can create two different values for men in the gender field, ‘Man’ and ‘Male’. Similarly, if you combine data from 8 different datasets, the fields in 6 of them may not be present in the other 2 datasets.
Pre-processing actually simplifies the task of interpreting and using data. In order to create an accurate model, inconsistencies in the data are eliminated through this process. In addition, preprocessing eliminates human errors or bugs caused by human errors or bugs in the data. In short, the means of data preprocessing actually make the database more complete and more accurate.
Properties of standard completed data
There is nothing more important for machine learning algorithms than quality data. The performance and accuracy of the algorithm depends on how relevant and consistent the data is.
Let's get to know the features of good quality data.
• Accuracy: Accuracy means checking whether the information is correct. Expired data, typing mistakes or exaggerated descriptions of any information can ruin the accuracy of a dataset.
• Consistency: There is no inconsistency in the data. Inconsistent data can give you different answers to the same question.
• Completeness: Datasets often have incomplete fields or empty fields. Data scientists can analyze data accurately only if they can complete the missing information in those fields or data. Because, as a result, they have a complete picture of the situation created according to the data.
• Efficacy: A dataset is considered effective only when the data samples are in the correct format. Because, ineffective datasets are difficult to organize and analyze.
• Timeliness: Data should be collected as soon as an event occurs. The longer the delay, the less accurate and useless each dataset will become. Because, it does not represent the current reality. Therefore, the timeliness and relevance of the information is an important feature in maintaining the quality of the data.
Incomplete datasets can also lead to unintended consequences, such as bias in various cases, unfair advantage or disadvantage to a particular group of people. Incomplete or inconsistent data can also have a negative impact on data mining projects. The data preprocessing process is mainly used to solve such problems.