Data preprocessing is the process of generating raw data for machine learning models. This is the first step in creating a machine-learning model. This is the most complex and time-consuming aspect of data science. Data preprocessing is required in machine learning algorithms to reduce its complexities.

Data in the real world can have many problems. It can miss some elements or pieces of information. While incomplete or missing data is completely useless, adjusting and refining the data to make it valuable is the primary objective of data preprocessing. 

Why Do We Need Data Preprocessing?

Data Preprocessing is an important step in the machine learning algorithm. Imagine a situation where you are working on an assignment at your college, and the lecturer does not provide the raw headings and the idea of the topic. In this case, it will be very difficult for you to complete that assignment because raw data is not presented well to you. The same is the case in Machine Learning. Suppose the Data preprocessing step is missing while implementing the machine learning algorithm. In that case, it will definitely affect your work at the end, when it will be the final stage of applying the available data set to your algorithm. 

While performing data preprocessing, it is important to ensure data accuracy so that it doesn't affect your machine learning algorithm at the final stage. 

Steps in Data Preprocessing

There are six steps of data preprocessing in machine learning 

Step 1: Import the Libraries 

The foremost step of data preprocessing in machine learning includes importing some libraries. A library is basically a set of functions that can be called and used in the algorithm. There are many libraries available in different programming languages. 

Step 2: Import the Loaded Data 

The next important step is to load the data which has to be used in the machine learning algorithm. This is the most important machine learning preprocessing step. Collected data is to be imported for further assessment.

Once the data is loaded, checking for noisy or missing content is important. 

Step 3: Check for Missing Values 

Assess the loaded data and check for missing values. If missing values have been found, there are particularly two ways to resolve this issue: 

  • Either Remove the entire row that contains a missing value. However, removing the entire row can generate a possibility of losing some important data. This approach is useful if the dataset is very large
  • Or Estimate the value by taking the mean, median or mode. 

Step 4: Arrange the Data

Machine learning modules cannot understand non-numeric data. It is important to arrange the data in a numerical form in order to prevent any problems at later stages. Converting all text values into numerical form is the solution to this problem. You can use the LabelEncoder() function to do this. 

Step 5: Do Scaling 

Scaling is a technique that can convert data values into shorter ranges. Rescaling and Standardization can be used for scaling the data. 

Step 6: Distribute Data into Training, Evaluation and Validation Sets

The final step is to distribute data in three different sets, namely 

  • Training 
  • Validation
  • Evaluation

The training set is to train the data 

The validation set is to validate the data 

The evaluation set is to evaluate the data 

Data Preprocessing Examples 

An example to explain data preprocessing is explained using the table below. Appropriate data preprocessing techniques in machine learning will be applied to solve the problem. 

Name

Age 

Gender

John

27

Male

George

26

Female

Olivia

25

Male

Jack

30

Male

Here in the table above, we can see that there are three variables, namely Name, Age and Gender. We can see that #2 and #3 have been assigned the wrong gender. 

We can use data cleaning here to remove the inappropriate data rows, as we know that this data is already corrupt. 

After data mining, the data table will look like: 

Name 

Age

Gender

John

27

Male

Jack 

30 

Male

Else, we can do manual data transformation, which will make the table look like this: 

Name

Age

Gender

John

27

Male

George

26

Male

Olivia

25

Female

Jack

30

Male

Once the issue is fixed, the next step is to perform data reduction by descending the age. 

Name

Age 

Gender

Jack

30

Male

John

27

Male

George

26

Male

Olivia

25

Female

Now, the issue is fixed, and the data set is complete and ready to be used for machine learning models and algorithms.

Best Practices

The best practices for data preprocessing in machine learning include:

Data Cleaning 

Data cleaning is important to detect any missing values or noisy data that can corrupt the entire data set. 

Categorize the Data 

It is important to categorize the data as machine learning algorithms can only handle numerical values. Categorizing the data will prevent problems at the later stages. 

Data Reduction 

Reduce the data and arrange it in a way that simplifies the objective behind running and processing the data. 

Integrating

Integrate the data set and prepare the raw material for processing in the machine learning algorithm.

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Conclusion

Data preprocessing is an important part of the data science algorithms, especially the machine learning models. When we present  raw data to the machine, the accuracy for better results increases. This increases the overall performance and efficiency of the machine learning model. 

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FAQs

1. What is data preprocessing in machine learning?

Data preprocessing is the process of presenting accurate raw data to the machine learning models. 

2. What are the major steps of data preprocessing?

The steps of data preprocessing include:

  • Collecting the data.
  • Checking for noisy or missing values.
  • Resolving the missing value issue.
  • Arranging the data.
  • Scaling and distributing the data into particular sets. 

3. What is an example of data preprocessing in machine learning? 

Data Reduction and Data Transformation are the best examples of data preprocessing in machine learning. 

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