Data Preparation in Machine Learning

Nowadays, data has become one of the crucial thingsfor any technology and application. Similarly, data plays a vital role in machine learning projects. In Machine Learning,each projectrequires different data sets; hence data preparation may be considered the most critical step for an ML project.

Data preparation is the later stage of the ML lifecycle. Firstly, the data is collected from various sources, and later garbage data is cleaned and transformed into real-time machine learning projects to uncover insights or make predictions. Machine learning also helps to find patterns in data to make accurate predictions and construct the data sets and transform the data correctly. In this topic, "Data Preparation in Machine Learning," we will discuss various steps for data preparation in machine learning, data preparation steps, data pre-processing, data splitting, etc. So, let's start with a quick introduction to data preparation in Machine Learning.

What is Data Preparation?

Data preparation is defined as a gathering, combining, cleaning, and transforming raw data to make accurate predictions in Machine learning projects.

Data preparation is also known as data "pre-processing," "data wrangling," "data cleaning," "data pre-processing," and "feature engineering." It is the later stage of the machine learning lifecycle, which comes after data collection.

Data preparation is particular to data, the objectives of the projects, and the algorithms that will be used in data modeling techniques.

Prerequisites for Data Preparation

Everyone must explore a few essential tasks when working with data in the data preparation step. These are as follows:

• Data cleaning: This task includes the identification of errors and making corrections or improvements to those errors.
• Feature Selection: We need to identify the most important or relevant input data variables for the model.
• Data Transforms: Data transformation involves converting raw data into a well-suitable format for the model.
• Feature Engineering: Feature engineering involves deriving new variables from the available dataset.
• Dimensionality Reduction: The dimensionality reduction process involves converting higher dimensions into lower dimension features without changing the information.

Data Preparation in Machine Learning

Data Preparation is the process of cleaning and transforming raw data to make predictions accurately through using ML algorithms. Although data preparation is considered the most complicated stage in ML, it reduces process complexity later in real-time projects. Various issueshave been reported during the data preparation step in machine learning as follows:

• Missing data: Missing data or incomplete records is a prevalent issue found in most datasets. Instead of appropriate data, sometimes records contain empty cells, values (e.g., NULL or N/A), or a specific character, such as a question mark, etc.
• Outliers or Anomalies: ML algorithms are sensitive to the range and distribution of values when data comes from unknown sources. These values can spoil the entire machine learning training system and the performance of the model. Hence, it is essential to detect these outliers or anomalies through techniques such as visualization technique.
• Unstructured data format: Data comes from various sources and needs to be extracted into a different format. Hence, before deploying an ML project, always consult with domain experts or import data from known sources.
• Limited Features: Whenever data comes from a single source, it contains limited features, so it is necessary to import data from various sources for feature enrichment or build multiple features in datasets.
• Understanding feature engineering: Features engineering helps develop additional content in the ML models, increasing model performance and accuracy in predictions.

Why is Data Preparation important?

Each machine learning project requires a specific data format. To do so, datasets need to be prepared well before applying it to the projects. Sometimes, data in data sets have missing or incomplete information, which leads to less accurate or incorrect predictions. Further, sometimes data sets are clean but not adequately shaped, such as aggregated or pivoted, and some have less business context. Hence, after collecting data from various data sources, data preparation needs to transform raw data. Below are a few significant advantages of data preparation in machine learning as follows:

• It helps to provide reliable prediction outcomes in various analytics operations.
• It helps identify data issues or errors and significantly reduces the chances of errors.
• It increases decision-making capability.
• It reduces overall project cost (data management and analytic cost).
• It helps to remove duplicate content to make it worthwhile for different applications.
• It increases model performance.

Steps in Data Preparation Process

Data preparation is one of the critical steps in the machine learning project building process, and it must be done in particular series of steps which includes different tasks. There are some essential steps of the data preparation process in machine learning suggested by different ML experts and professionals as follows:

1. Understand the problem: This is one of the essential steps of data preparation for a machine learning model in which we need to understand the actual problem and try to solve it. To build a better model, we must have detailed information on all issues, such as what to do and how to do it. It is also very much effective to retain clients without wasting much effort.
2. Data collection: Data collection is probably the most typical step in the data preparation process, where data scientistsneed to collect data from various potential sources. These data sources may be either within enterprise or third parties vendors. Data collection is beneficial to reduce and mitigate biasing in the ML model; hence before collecting data, always analyze it and also ensure that the data set was collected from diverse people, geographical areas, and perspectives.
   There are some common problems that can be addressed using data collection as follows:
   • It is helpful to determine the relevant attributes in the string for the .csv file format.
   • It is used to parse highly nested data structures files such as XML or JSON into tabular form.
   • It is significant in easier scanning and pattern detection in data sets.
   • Data collection is a practical step in machine learning to find relevant data from external repositories.
3. Profiling and Data Exploration: After analyzing and collecting data from various data sources, it's time to explore data such as trends, outliers, exceptions, incorrect, inconsistent, missing, or skewed information, etc. Although source data will provide all model findings, it does not contain unseen biases. Data exploration helps to determine problems such as collinearity, which means a situation when the Standardization of data sets and other data transformations are necessary.
4. Data Cleaning and Validation: Data cleaning and validation techniques help determine and solve inconsistencies, outliers, anomalies, incomplete data, etc. Clean data helps to find valuable patterns and information in data and ignoresirrelevant data in the datasets. It is very much essential to build high-quality models, and missing or incomplete data is one of the best examples of poor data. Since missing data always reduces prediction accuracy and performance of the model, data must be cleaned and validated through various imputation tools to fill incomplete fields with statistically relevant substitutes.
5. Data Formatting: After cleaning and validating data, the following approach is to ensure that the data is correctly formatted or not. If data is formatted incorrectly, it will help build a high-quality model.
   Since data comes from various sources or is sometimes updated manually, there are high chances of discrepancies in the data format. For example, if you have collected data from two sources, one source has updated the product's price to USD10.50, and the other has updated the same value to $10.50. Similarly, there may be anomalies in their spelling, abbreviation, etc. This type of data formation leads to incorrect predictions. To reduce these errors, you must format your data inconsistent manner by using some input formatting protocols.
6. Improve data quality: Quality is one of the essential parameters in building high-quality models. Quality data helps to reduce errors, missing data, extreme values, and outliers in the datasets. We can understand it with an example such, In one dataset, columns have First Name and Last NAME, and another dataset has Column named as a customer that combines First and Last Name. Then in such cases, intelligent ML algorithms must have the ability to match these columns and join the dataset for a singular view of the customer.
7. Feature engineering and selection:
   Feature engineering is defined as the study of selecting, manipulating, and transforming raw data into valuable features or most relevant variables in supervised machine learning.Feature engineering enables you to build an enhanced predictive model with accurate predictions.
   For example, data can be spitted into various parts to capture more specific information, such as analyzingmarketing performance by the day of the week, not only the month or year. In this situation, segregating the day as a separate categorical value from the data (e.g., "Mon; 07.12.2021") may provide the algorithm with more relevant information. There are various feature engineering techniques used in machine learning as follows:
   • Imputation: Feature imputation is the technique to fill incomplete fields in the datasets. It is essential because most machine learning models don't work when there are missing data in the dataset. Although, the missing values problem can be reduced by using techniques such as single value imputation, multiple value imputation, K-Nearest neighbor, deleting the row, etc.
   • Encoding: Feature encoding is defined as the method to convert string values into numeric form. This is important as all ML models require all values in numeric format. Feature encoding includes label encoding and One Hot Encoding (also known as get_dummies).
   
   Similarly, feature engineering also includes handling outliers, log transform, scaling, normalization, Standardization, etc.
8. Splitting data:
   After feature engineering and selection, the last step is to split your data into two different sets (training and evaluation sets). Further, always select non-overlapping subsets of your data for the training and evaluation sets to ensure proper testing.

Conclusion

Data preparation is one of the key players in developing high-quality machine learning models. Data preparation allows us to explore, clean, combine, and format data for sampling and deploying ML models. It is essential as most ML algorithms need data to be in numbers to reduce statistical noise and errors in the data, etc. In this topic, we have learned about data preparation, the importance of data preparation in building predictive modeling machine learning projects, etc.