Data Preprocessing: The Secret Recipe for Machine Learning Success

Data Preprocessing: The Secret Recipe for Machine Learning Success
Image by ShotPot/ Canva

Do you like cooking? I do, especially when preparing a meal for someone. Possibly because the influence of my mum. Coming from an Asian family, we're not particularly good at expressing our emotions to people we care about. My mum would express her love through the meal she prepared for our family. Mum would carefully wash, chop, and measure the ingredients before she cooks to ensure a quality meal nourishing our family.

Why am I telling you this, you may wonder. Just like how the quality of a meal depends heavily on the ingredients and their preparation, machine learning relies heavily on the quality of the data preprocessing, which is like the preparation phase of your ingredients before you start cooking.

In both cooking and machine learning, the success of the outcome relies on the attention given to preparation. So in this blog, let's explore:

  1. What is Data Preprocessing?
  2. Why is Data Preprocessing Important?
  3. What are the Common Tasks Involved in Data Preprocessing?

What is Data Preprocessing?

Data preprocessing is the process of preparing raw data for analysis by cleaning, transforming, and manipulating the data to ensure that it is in a format suitable for machine learning algorithms. Raw data is rarely in a state that is immediately suitable for analysis, and preprocessing is necessary to transform it into a format that is easily analyzed by algorithms.

Why is Data Preprocessing Important?

Data preprocessing is critical in machine learning for several reasons. First, it ensures that the data is in a suitable format for analysis. Raw data may contain errors, inconsistencies, missing data, or other issues that must be addressed before applying machine learning algorithms. Preprocessing the data helps to ensure that the algorithms receive high-quality data, which results in more accurate and reliable models.

Second, data preprocessing helps to reduce the complexity of the data. Large datasets can be difficult to manage, and preprocessing can help reduce the dataset's size by removing irrelevant features, scaling the data, and encoding categorical variables. This helps to improve the performance of machine learning algorithms, which can be slow and resource-intensive when working with large datasets.

Third, data preprocessing helps improve machine learning algorithms' accuracy and performance. By removing noise, handling missing values, and transforming the data, preprocessing can help to identify patterns and relationships within the data that might not otherwise be apparent. This, in turn, leads to more accurate and reliable models that can make better predictions.

What are the Common Tasks Involved in Data Preprocessing?

Data preprocessing involves several tasks. The following are some of the common tasks in preprocessing; it's not an exhaustive list. I've divided the list into three categories:

  1. Data Cleaning
  2. Data Tansformation
  3. Feature Engineering

Data Cleaning

Data cleaning involves identifying and correcting errors, inconsistencies, and missing data in the dataset. This is a critical step in data preprocessing as machine learning algorithms are sensitive to errors in the data. Common tasks in data cleaning include:

Removing duplicates: duplicate data can skew the results of machine learning algorithms. Removing duplicates ensures the data is accurate and representative of the real world.

Handling missing values: missing data can affect the accuracy of machine learning models. There are a few strategies to minimize the negative impact of missing data. One approach is to use the mode value, i.e., the most frequently occurring values. A second approach is to approximate the missing values using the median value, i.e., value(s) in the middle of the dataset. As a last resort, rows with missing values can be removed altogether. However, there is obviously a downside to this: we have less data to analyze and potentially less comprehensive insight.

Handling Outliers: outliers are data points significantly different from the rest of the data. Measurement errors, data entry errors, or legit real-world occurrences can cause them. Outliers can be handled by removing, transforming, or treating them as separate classes.

Data Transformation

Data transformation refers to the process of converting data from one format, structure, or representation to another so that it is more suitable for analysis. This includes tasks such as scaling and normalization, dealing with categorical variables, and condensing data.

Scaling and normalization: scaling and normalization are techniques used to standardize data so that it is on a common scale. This is important when working with features with different scales, as machine learning algorithms may treat them differently. Scaling and normalization help to ensure that each feature is given equal importance when making predictions.
For example, let's say we have a data set that includes two features: age and salary. Age ranges from 18 to 65, while salary ranges from $30,000 to $200,000. The salary feature has a much larger scale than the age feature, which may have a larger impact on the machine learning model's predictions. We can use scaling and normalization techniques to ensure that both features are given equal importance and that the machine learning algorithm makes fair and unbiased predictions. I won't go into the details of the scaling techniques, but here are three common ones if you want to look further into this: min-max scaling, Z-score normalization, and log transformation.

Encoding categorical variables: Categorical variables are variables that can take on a limited number of values, such as "red" or "blue" for a colour variable, or "small", "medium", or "large" for a size variable. These variables can be nominal (without any inherent order) or ordinal (with an inherent order). However, machine learning algorithms require numerical data, so categorical variables need to be converted to numerical data.

One popular technique for encoding categorical variables is one-hot encoding. This technique involves creating a new binary variable for each category in the original variable. Each binary variable represents whether the observation belongs to that category or not. For example, if we have a size variable with categories "small", "medium", and "large", we would create three new binary variables: "size_small", "size_medium", and "size_large". If an observation belongs to the "small" category, the "size_small" variable would be 1, and the other two variables would be 0. If an observation belongs to the "medium" category, the "size_medium" variable would be 1 and the other two variables would be 0. And so on.

Another technique for encoding categorical variables is label encoding. This technique involves assigning a numerical value to each category in the original variable. For example, we might assign "small" to 1, "medium" to 2, and "large" to 3. However, label encoding can introduce a bias into the analysis because it implies an order to the categories that may not actually exist. For example, if we were to label encode a colour variable with categories "red", "blue", and "green", assigning "red" a value of 1 and "blue" a value of 2 might imply that "blue" is somehow "better" or "more important" than "red", when in fact the two colours are simply different.

Data condensing: data condensing involves reducing the amount of data by summarizing or aggregating it; this is useful in cases where the data has many features or variables, making analysis difficult or computationally expensive.
For example, let's say we have a data set with information about customer purchases, including the date of purchase, the item purchased, and the purchase amount. If we want to analyze the total amount of sales for each month, we would need to condense the data by aggregating the purchase amounts for each month. This involves transforming the original data from individual purchase transactions to a summary of sales by month.

Another example of data condensing is reducing the dimensionality (variables) of the data. It's making the data simpler by taking away some of the parts that aren't important. Again, there are techniques that will help you identify which are important such as principal component analysis (PCA).

Something to note: Data condensing can help to simplify the analysis and reduce the computational complexity of machine learning algorithms; however, it can also lead to loss of information and may not always be appropriate for all types of data.

Feature Engineering

Feature engineering is another task that's done as part of preprocessing. It's when we get to flex our creative muscle to come up with new features (variables) based on our data. This can help improve the performance of machine learning models by providing them with more relevant information.

One common task in feature engineering is creating interaction terms between variables. Interaction terms are made by multiplying two or more variables together. For example, in a dataset that includes a person's age and income, we might create an interaction term by multiplying the age and income together. This can help to capture relationships between variables that might not be apparent in the raw data. For instance, the interaction between age and income might be useful in predicting whether a person is likely to buy a house or not.

Another task in feature engineering is combining variables in a meaningful way. This involves combining two or more variables to create a new feature that is more informative than the original variables alone. For example, in a dataset that includes a person's height and weight, we might combine the two variables to create a new feature called "body mass index" (BMI). This new feature can be more informative than the height and weight variables alone, as it takes into account the relationship between the two variables.

Finally, we can create polynomial features by raising a piece of information to a power. This can help us capture more complicated relationships between different pieces of information. For example, if we're trying to predict how well someone will do on a test, we might square their age to see if that helps us make a better prediction.

That's it for now. Just like the care that goes into preparing a delicious meal, data preprocessing plays a huge role in making machine learning models work well. By cleaning, transforming, and engineering the data, we set the stage for more accurate and reliable results. So, remember, the secret ingredient to successful machine learning is putting effort into data preprocessing—just like the love my mum pours into her home-cooked meals. 💕