Learn Best Practices of Data Preprocessing in Machine Learning

In the realm of machine learning, nothing can beat the significance of Data. However, what we get the most is raw data and the processing of the same is a very hectic task. In reality, cultivating a masterpiece of a machine-learning model is hidden in the art of raw data transformation.

In this article, we will grasp the role of data preprocessing in the machine learning landscape.

What is data preprocessing in machine learning?

Most real-world datasets are messy, inconsistent, and incomplete, leading to machine learning being incapable of deciphering them. Machine learning uses binary numbers, such as 0 and 1, to analyze the input.

Data preprocessing is a technique in machine learning to convert raw data into usable formats for constructing machine learning models. In one word, data processing techniques in machine learning ensure the maximum possible optimal functionality of the model.

Why do we require data preprocessing techniques in machine learning?

A machine learning algorithm needs high-quality data to process accurate outcomes. However, most of the time raw datasets consist of disputes like noisy data, redundant data, missing data value, outliers, and high dimensionality. Data processing helps resolve these issues to deliver enhanced ML models.

Below is a list of advantages of preprocessing in machine learning.

• Improved project accuracy.
• Enhanced efficacy of ML models.
• The clarity in business modeling.
• Elimination of duplications
• Early detection and filtering of data issues.

The step involves data preprocessing in machine learning:-

1. Gathering the dataset

This is the first move in data preprocessing to build a machine-learning model. Datasets usually vary according to their use cases. For, eg: Business data differ from medical datasets. This stage of preprocessing involves gathering data from sources(business databases, surveys, online databases, etc) and arranging it into proper format.

2. Importing libraries

This is an important data preprocessing technique in the machine learning step to perform a specific task. Python language is the easiest solution to importing gathered databases into the libraries.

Panda: It is an open-source and user-friendly Python library that is used to import and manage the dataset.

Numpy: It helps to insert mathematical operations in code. It will build large multidimensional arrays and matrics in code.

Matpotlib: This library is used to present a graphical representation of data. It aids us in planning any type of chart. That improves publication quality figures in hard copy formats.

3. Handling missing data

Many ML algorithms fail to deal to data with missing values. So clearing out missing value issues is the most significant step for accurate data analysis. This helps in building robust models. You might end up with a faulty solution If you fail to implement it.

Here the two important strategies are as follows:

Deleting the specific row/Column: This step deals with a null value. You can delete the particular row or column that consists null value, 75% missing value row. However, it will not give accurate output because you might end up with a loss of data.

Calculating the mean: This step yields better output than the previous one. In this process, you can miss value by calculating the mean of the column. You can substitute the better value. Mostly the numerical data such as age, salary, and year are calculated in this method.

4. Encoding categorical data

Categorical data refers to a type of qualitative data that can be grouped into particular categories. The machine learning model works on numbers. In this process, you will convert data into numerical values by using one-hot encoding and ordinal encoding. As a result, you can improve the prediction of the model.

5. Handling outlier

Outlier refers to the data point that deviates from the original position which will lead to inaccurate prediction. Outlier issues arise due to data corruption. In this process, you can remove, transfer, or replace the deviating data point with the technique of Z-score or IQR(interquartile range).

6. Model evaluation

In this process, you can do a model evaluation through training and testing the dataset that provides model accuracy and generalization.

7. Feature scaling

This process is necessary when the featured database has different ranges, measurements, and magnitudes. Feature scaling adjusts the value on the same scale. It ensures uniformity in the distribution of elements in the model which will lead to avoiding domination of larger values. As a result model performance and convergence speed will improve.

Best practices of data pre-processing in machine learning

• One of the first steps in data preprocessing is to know your data.
• By simply looking at your data set, you can get an intuitive sense of important data.
• You can conduct tests to check the quality of data based on the missing values, duplicates, and outliers.
• Use tools and techniques to visualize the dataset and it gives you a clear representation of data in class distribution.
• Delete any fields that you consider to be unnecessary for modeling, or that have a close relationship with additional attributes.
• Dimensional reduction is an important step to reduce the complicated model. You can use the feature selection technique to adjust the dimensions of the dataset.
• Use feature engineering techniques to check the trait that affects the most in modeling.

End notes,

Data preprocessing is a foundation step for building the ML model. This step-by-step guidance gives you a better understanding of the path of data into the algorithm model. By using these steps data scientists can improve the quality of data for accurate prediction in machine learning.

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