Hello everyone, I going to discuss the machine learning process, what we do in each step, and introduce the first step of the process.
When building machine learning models, we follow a specific step-by-step process. This process has three main steps, so let’s take a look at each of them. The first one is data pre-processing.
Next, we move on to modeling, where we first build a model, then train it, and finally make predictions. The last step is evaluation, where we calculate performance metrics and decide whether our model is a good fit and if it works well with our data.
Let’s see what our first stage, data pre-processing, involves. From the word “processing,” we can understand that it means preparing raw data by cleaning and organizing it to make it suitable for building and training machine learning models.
So Why Do We Need to Pre-process The Data?
Data in the real world is often messy and corrupted with inconsistencies, noise, incomplete information, and missing values. It is aggregated from various sources using data mining and warehousing techniques. A common rule in machine learning is that the more data we have, the better models we can train.
Applying algorithms to noisy data won’t yield good results, as they will fail to identify patterns effectively. Duplicate or missing values may give an incorrect view of the overall statistics of the data. Outliers and inconsistent data points can disrupt the model’s learning, leading to false predictions.
The quality of our decisions depends on the quality of the data as well. Data pre-processing is essential for obtaining quality data, without which it would be a “garbage in, garbage out” scenario.
Now, let’s discuss the four main stages of data pre-processing: data cleaning, data integration, data transformation, and data reduction or dimensionality reduction. Let’s look at each of them carefully.
Data Cleaning
This involves cleaning the data by filling in missing values, smoothing noisy data, resolving inconsistencies, and removing outliers.
A few ways to address missing values include ignoring the tuples, which should be considered when the dataset is large and many missing values are present within a tuple or filling in the missing values manually, predicting them using regression methods, or using numerical methods like attribute mean, median, or mode.
Noisy Data
Noisy data can be addressed with techniques such as binning, where sorted data values are smoothed by dividing the data into equal-sized bins and dealing with each bin independently.
All data in a segment can be replaced by its mean, median, or boundary values. Clustering involves creating groups or clusters from data with similar values; values that don’t lie in a cluster can be treated as noisy data and removed.
Other machine learning techniques, like regression, can also be used; this technique is generally used for prediction and helps smooth noise by fitting all data points into a regression function. Linear regression equations are used if there is only one independent attribute, while polynomial equations are used otherwise.
Inconsistent Data
Inconsistent data can be removed using techniques like clustering, where you group together similar data points. The tuples that lie outside the cluster are outliers or inconsistent data, which can be easily removed.
Data Integration
Data integration is one of the data preprocessing steps used to merge data present in multiple sources into a larger data store, like a data warehouse.
Data integration is especially needed when we aim to solve real-world scenarios, such as detecting the presence of nodules from CT scan images. The only option is to integrate the images from multiple medical nodes to form a larger database.
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Data Transformation
Once data cleaning is done, we need to consolidate the quality data into alternate forms by changing the value structure or format using the data transformation strategies mentioned below:
– Smoothing: This process is used to remove noise from a dataset using algorithms, allowing for the highlighting of important features in the dataset. It helps predict patterns. The concept behind data smoothing is that it can identify simple changes to help predict different trends and patterns.
– Generalization: This involves converting lower-level or granular data into higher-level information using concept hierarchies. For example, we can transform primitive data like a city into higher-level information like a country.
– Normalization: This is one of the most important and widely used data transformation techniques. Numerical attributes are scaled up or down to fit within a specified range, constraining our data attributes to a particular container to develop a correlation among different data points.
Normalization can be done in multiple ways, such as min-max normalization, z-score normalization, or decimal scaling normalization.
– Attribute Construction or Selection: New properties of data are created from existing attributes. For example, in predicting diseases or survival chances, a date of birth attribute can be transformed into another property like “senior citizen” to check if the person is a senior citizen, which has a direct influence on the outcome.
– Aggregation: This method stores and presents data in a summary format. For example, sales data can be aggregated and transformed to show a monthly or yearly format.
– Discretization: This process converts continuous data into a set of intervals. Continuous attribute values are substituted by small interval labels, making the data easier to study and analyze.
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Data Reduction
The size of a dataset in a data warehouse can be too large to handle. One possible solution is to obtain a reduced representation of the dataset that is much smaller in volume but produces the same quality of analytical results. Various data reduction strategies include:
– Dimensionality Reduction: These techniques are used for feature extraction. The dimensionality of a dataset refers to the attributes or individual features of the data. This technique aims to reduce the number of redundant features considered in machine learning algorithms. Reduction can be done using techniques like principal component analysis, etc.
– Numerosity Reduction: Data can be represented as a model or equation, like a regression model, saving the burden of storing huge datasets by using a model instead.
– Data Cube Aggregation: This is a way of data reduction in which the gathered data is expressed in a summary form.
– Data Compression: By using encoding techniques, the size of data can be significantly reduced. Data compression can be either lossy or non-lossy. If the original data can be obtained after reconstruction from compressed data, it is referred to as lossless reduction.
Otherwise, it is referred to as lossy reduction, where you can’t obtain the original data after reconstruction from compressed data.
Discretization
Data discretization is used to define the attributes of continuous data into data with intervals. This is done because continuous features tend to have a smaller chance of correlation with the target variable, making it harder to interpret the results.
After discretizing a variable, groups corresponding to the target can be interpreted. For example, the attribute “age” can be discretized into bins like “below 18 years,” “18 to 44 years,” “44 to 60 years,” and “above 60 years.”
Conclusion
Data processing is a multifaceted entity that requires technical acumen, sector-specific knowledge, and critical thinking abilities. Its role in empowering organizations is significant, as it converts raw data into insightful, actionable intelligence, driving business growth and innovation.
In today’s digital age, data processing is indispensable for making informed decisions, optimizing operations, and uncovering valuable insights within vast volumes of data. Understanding the stages of data processing and its importance ensures that organizations can effectively harness the power of data to achieve their goals.
Thank you, and have a nice day.