What is data preparation for machine learning? – Netnut

Introduction

Machine learning is a branch of computer science and artificial intelligence (AI) that utilizes data and algorithms to mimic how humans think to optimize its accuracy. 

Therefore, data preparation for machine learning is a critical aspect that must be considered. However, before we proceed, let us briefly consider how machine learning works.

First, the machine learning algorithms require data, which could be labeled or unlabeled to make predictions. Then, an error function is introduced to determine the efficiency of the model’s predictions. Moving on, the machine model is further optimized by adding quality data until a threshold of accuracy has been established. 

Subsequently, a machine model’s accuracy (output) depends on the data (input) quality. Therefore, data preparation for machine learning becomes essential, and this guide will examine what you need to know about it.

Let us dive in!

What is Data Preparation for Machine Learning?

Data preparation for machine learning, also known as data processing, involves raw data transformation, which makes it easier for data scientists to run it through machine learning algorithms to identify patterns and make predictions. In simpler terms, data preparation for machine learning involves gathering, combining, cleaning, and transforming raw data for enhanced predictions in models.  

Data preparation for machine learning is significant because it leads to optimized and increased accuracy in algorithms. In addition, it becomes critical in identifying problems, which allows users and data scientists to save time and effort in building a model.

Furthermore, data preparation for machine learning is essential because raw data is often inconsistent, which could lead to inaccuracy. Therefore, data preparation ensures the quality of machine learning datasets before they are fed to the algorithm. 

Here are some benefits of data preparation for machine learning:

• It enhances the model’s capacity to make accurate and relevant decisions
• It may have a significant effect on the reduction of the project’s general cost
• Data preparation for machine learning minimizes the chances of errors as data issues are promptly identified
• It optimizes the model’s performance as its prediction becomes reliable

Prerequisites for Data Preparation

Data preparation for machine learning is essential because it determines the model’s performance. Here are some of the tasks to consider during data preparation for machine learning:

• Data cleaning: This task for data preparation for machine learning involves finding errors and correcting them
• Data selection: Regarding data preparation for machine learning, it is essential to choose the most relevant data for the model.
• Data transformation: This involves the conversion of raw data into a suitable format in the process of data preparation for machine learning.
• Feature engineering: This task includes deriving new variables from the original dataset
• Dimensionality reduction: Data preparation for machine learning includes the conversion of higher dimensions to lower dimensions without significantly altering the core of the information.

Common Challenges with Data Preparation for Machine Learning

Several issues could arise during data preparation for machine learning. They include:

Incomplete datasets

One of the common issues with data preparation for machine learning is missing data. For example, certain values may be written as question marks or NULL. This can be a problem because the machine model may not comprehend them.

Data format

Another issue with data preparation for machine learning is data format. The data scientists often recommend the most suitable format of data to feed the algorithm, usually based on its eventual function.

Non-standardized variable

Since machine learning data is obtained from multiple sources, there is a high chance of non-standardized variables. This could lead to confusion for the model. Therefore, data preparation for machine learning is critical to sort them and standardize them in a format that increases the accuracy of the model.

Complex processes like feature engineering

Feature engineering is a critical aspect of data preparation for machine learning. This technique is essential because it generates more content that could enhance the model’s learning and its output’s reliability. However, this may present a challenge, as it requires a professional to get it done.

Limited data attributes

A common issue with data preparation for machine learning is limited data attributes. This challenge often arises due to the increasing diversity of the dataset. Usually, combining data from various sources is straightforward if the variables are the same. However, data combination, which is necessary for data feature enhancement, becomes an issue if there is no easy way to merge the variables.

Steps in Data Preparation for Machine Learning 

Data preparation for machine learning is critical for training a machine model since the input significantly influences its performance. Data scientists spend about 80% of their time on data preparation for machine learning, which is more than the time they spend on other tasks, including creating data visualization, model training, and deploying models. 

Here are some critical steps in data preparation for machine learning 

Formulating business problem

Formulating the problem is one of the most critical steps in data preparation for machine learning. It is essential to understand the problem before you move on to suggest possible solutions. 

Therefore, before building a model, you must get in-depth information on what it aims to do. Subsequently, this step plays a significant role in data preparation for machine learning as it saves time and effort. In addition, a clear problem statement makes it easier to explain the machine-learning language to investors and the public. 

Data collection

After the initial brainstorming and formulation of the problem statement, data collection is the next step in data preparation for machine learning. Factors like data type, quality, and volume play significant roles in data collection.

Machine learning uses three types of data: Structured, semi-structured, and unstructured data. Structured data is organized in a specific format, such as a spreadsheet. Semi-structured data usually don’t follow a format and include JSON or XML datasets. However, it is not totally disorganized as it may contain some structural elements like tags, which makes the data easy to interpret. On the other hand, unstructured data involves audio, videos, and images that don’t follow standard data models.

The structure of data must be considered in data preparation for machine learning. For example, structured data may be easier to clean and organize. Meanwhile, cleaning and organizing unstructured data may require complex techniques such as natural language processing.

Data preparation for machine learning also relies on the volume of data. Usually, a large dataset is often preferred as this reduces bias. However, data preparation for machine learning is essential to prevent overstuffing. In contrast, a smaller dataset may not contain the required and relevant data to train the machine model. 

Subsequently, the quality of data collected is another essential aspect. This is because using biased data can have a significant impact, especially if the model is employed in healthcare, finance, or recruitment. 

Here are some of the sources of data that can be used for machine learning:

• Internal sources: One of the reliable places to collect data is your organization’s data warehouse. This data could include reviews from their social media platforms, sales transactions, customer interactions, etc.
• External sources: Another way to gather data for machine learning is external sources, including search engines, government databases, academic databases, and data-sharing communities like the UCI Machine Learning Repository.
• Surveys: A survey is used to collect data from a target audience at a specific time. A brand could use it to gather specific data from customers or the general public.
• Web scraping: You can use web scraping tools to collect data from multiple sources. Remember to use proxies to access geo-restricted content to increase the expanse of data collected.

Data cleaning

The next step in data preparation for machine learning is cleaning, which involves sorting and finding errors or inconsistencies in the dataset. Cleaning is essential in data preparation for machine learning as it significantly affects data quality. Therefore, these activities ensure the data is complete, accurate, and reliable.

Here are some of the processes involved in cleaning data as part of data preparation for machine learning:

Missing data

Incomplete data is one of the challenges of data collection, regardless of the application. However, you can handle missing data with the following techniques:

• Deletion, which involves removing the rows or columns with the missing values
• Imputation, which involves filling in the missing values with predicted data
• Interpolation, which involves deriving the missing values from the surrounding data

Duplicates

Removing duplicates is another critical step in data preparation for machine learning. Duplicates can skew machine model predictive capabilities. In addition, they take up extra storage space, which often increases the processing times, especially for large datasets. Therefore, data scientists may use duplicate identification techniques such as record linkage, exact matching, hashing, or fuzzy matching to remove duplicates. 

Incorrect data

Another aspect of data preparation for machine learning is handling incorrect data. Some techniques applicable here include completely removing the incorrect data or data transformation, which includes changing the data so it meets the standard.

Handling outliers

After collecting data, you may discover outliers- these are data that are significantly different from the rest of the dataset. Most times, they occur due to data entry or measurement errors or represent unusual observations. Subsequently, in data preparation for machine learning, outliers can be sorted by removing them, treating them as a different data class, or transforming them.

Imbalanced data

Imbalanced data is another aspect of data preparation for machine learning. It can result in a biased model that gives priority to the class with a higher amount of data. Subsequently, data scientists can handle imbalanced data through resampling, synthetic data generation, cost-sensitive learning, and ensemble learning.

Irrelevant data

Another aspect of data preparation for machine learning is handling irrelevant data. Irrelevant data describes data that is not useful to solving the problem, as stated in the problem statement. Therefore, removing irrelevant data can optimize the model’s accuracy. In addition, irrelevant data can be handled through correlation analysis or principal component analysis.

Data transformation

Data transformation is another crucial aspect of data preparation for machine learning. It involves the conversion of raw data into a format that is suitable for the machine model. This stage of data preparation for machine learning ensures enhanced predictive performance and accuracy of the algorithm. 

Here are some techniques used for data transformation in data preparation for machine learning:

Encoding

In data preparation for machine learning, encoding involves transforming categorical data into a numerical format. There are several encoding methods, such as label encoding, one-hot encoding, and ordinal encoding. Machine learning algorithms can only understand numerical data, which makes it necessary to encode categorical data.

Normalization

Normalization is a technique that can be applied in data preparation for machine learning. It involves changing the distribution of a given dataset. 

Log transformation

Another technique for data preparation for machine learning is log transformation. This involves the application of a logarithmic function to the values of a variable. Log transformation is often required in data preparation for machine learning when the dataset has a large range of values or is highly skewed. 

Discretization

Discretization is a technique in data preparation for machine learning that involves converting continuous variables, like weight, time, or temperature, into discrete variables. In data preparation for machine learning, discretization is necessary to reduce the complexity of the problem so the algorithm can process the data better. 

Dimensionality reduction

In data preparation for machine learning, dimensionality reduction describes limiting the number of variables in a dataset and maintaining only the data required to solve the problem. Some of the approaches to dimensionality reduction include principal component analysis, t-distributed stochastic neighbor embedding, and linear discriminant analysis.

Scaling

Scaling, in data preparation for machine learning, is a technique that changes the range of a dataset. When you collect data, different features use different units of measurement. As a result, the algorithm may give preference to the feature with the larger value, which affects the model’s performance. 

However, as a method of data preparation for machine learning, scaling converts all data points to fit a specified range, which makes a comparison of different variables more accurate.

Feature engineering

Feature engineering is a unique technique in data preparation for machine learning. It involves selecting, converting, and creating features in a dataset. Feature engineering utilizes a combination of computational, mathematical, and statistical techniques to create features that reflect the most relevant information in the data. 

This technique in data preparation for machine learning demands testing and evaluating different techniques to find the most suitable approach to solving a problem.

Data splitting

Data splitting is the next step in data preparation for machine learning. This process involves dividing the collected data into subsets. These subsets usually include the training, validation, and testing data subsets. Data splitting is a crucial step in data preparation for machine learning because it allows you to assess the performance of a model based on new data. Without splitting data, there is a high chance that the model will perform poorly when new data is introduced. This usually happens because the model memorized the data instead of learning patterns.

A training data set is used to teach the machine model to recognize patterns and relationships between input and target variables. Regarding data preparation for machine learning, the training data is often the largest as it plays a significant role in the performance of the model.

On the other hand, the validation dataset is used to evaluate the performance of a model during training. In simpler words, it helps to fine-tune the algorithm by adjusting parameters like the learning rate or number of hidden. The validation dataset is significant in data preparation for machine learning because it prevents overfitting training data.

Meanwhile, the testing dataset is utilized to evaluate the trained model’s performance. Its purpose in data preparation for machine learning is to assess the model’s accuracy on new data. Subsequently, the testing dataset is only employed when the model has been trained and fine-tuned based on the validation and training dataset. 

With regard to data preparation for machine learning, there are various approaches to data splitting. These techniques are influenced by the properties of the dataset and the problem being solved. They include:

Random sampling

Random sampling can be applied to data preparation for machine learning. In this approach, the data is split randomly and is often applied to large datasets representative of the population being modeled. 

Stratified sampling

Stratified sampling is another technique of splitting in data preparation for machine learning that involves data division based on labels, followed by random sampling. This approach of data splitting is often applied to imbalanced datasets.  

Cross-validation

Cross-validation is used in data preparation for machine learning when the data is divided into multiple subsets. These subsets are often used to train and evaluate the performance of the model. In addition, cross-validation offers a more accurate estimation of the model’s performance.

Time-based sampling

Time-based sampling is used when data collected up to a certain period is used as the training dataset, and those collected after the period are used as the testing dataset. This approach in data preparation for machine learning is mostly employed when data is collected over a long period.

The Role of NetNut Proxies in Data Preparation for Machine Learning

The core process in data preparation for machine learning is data collection. Web data scraping is one of the most efficient ways to obtain a large amount of data. However, many websites are wary of bots, which could lead to IP bans and bring your data collection to an abrupt stop. 

NetNut proxy servers aim to protect your IP address. Subsequently, when you use these proxies to access a page, the website sees the proxy’s IP address instead of your actual address. 

Therefore, rotating proxies are ideal for optimized privacy and data accessibility. You can also use datacenter proxies to reduce bias, so you can gather data from various sources to promote data diversity. Subsequently, this is important in data preparation for machine learning as it increases the model’s predictive accuracy.

Conclusion

This guide has explored data preparation for machine learning, including its key benefits. Raw data is often obtained from various sources and comes in different formats. Therefore, data preparation for machine learning becomes necessary to ensure the algorithm can process the data and make accurate predictions from them. 

In addition, we examined the six basic steps associated with data preparation for machine learning. They include formulating problem statements, data collection, data cleaning, data transformation, and data splitting.

Finally, we mentioned the significance of using proxy servers for data collection. Without a doubt, data collection is an integral aspect of data preparation for machine learning. Therefore, the use of a reliable proxy server ensures the protection of your digital footprint as well as access to unlimited data.

Feel free to contact us if you have any questions about choosing the right proxy server solution. 

Frequently Asked Questions

What are the dangers of inadequate data preparation for machine learning?

The quality of data preparation for machine learning determines its reliability and performance. Poor data preparation for machine learning can cause the following:

• Biased models: Inadequate data preparation for machine learning can generate bias, which can lead to ethical concerns. Models used for hiring or lending imitate these biases and perpetuate unfair practices. For example, a hiring model trained with biased data may be from certain demographics. As a result, the model will only be favorable to candidates from that demographic. 

• Compromised accuracy: Another danger of inappropriate data preparation for machine learning is compromised accuracy. Since machine learning models are highly dependent on data patterns. Poor data preparation for machine learning results in off-track predictions, which compromises accuracy and increases cost. 

• Compounding errors: Poor data preparation for machine learning can lead to compounding errors in interconnected systems where outputs from one are transferred into another. Subsequently, it can lead to large-scale inaccuracies, especially in complex supply chains.

What techniques can compensate for insufficient data during data preparation for machine learning?

Sometimes, the data collected is not as large as we require. However, you can use these techniques to compensate for lack of data:

• Data augmentation: This approach transforms data through scaling, rotating, or translating with the aim of generating more data from existing datasets.
• Collaborative data sharing: It involves collaboration with other organizations to gather and share data
• Transfer learning: This technique uses pre-trained ML algorithms as a starting point for training the new model 
• Active learning: It involves the selection of the most informative data sample for labeling by a data scientist

What is the purpose of data cleaning in data preparation for machine learning?

Data cleaning in data preparation for machine learning is necessary to identify inconsistencies, anomalies, missing data, and outliers. These will reduce the data quality, so they must be promptly resolved before training the model with the data. Subsequently, poor data quality will significantly reduce the prediction accuracy and performance of the model.