Data Preprocessing

Data preprocessing is the art of cleaning, transforming, organizing your data, and getting your data ready for analysis. This data can come in various forms. It could be spreadsheets, survey responses, sensor readings, or even text documents.

Because raw data often has issues. Issues like missing values, outliers, and inconsistencies.

Imagine if you were conducting a customer satisfaction survey. Some respondents might skip the questions or provide contradictory responses (rating a product highly but providing negative comments about it). Issues like these can create gaps and inconsistencies in the data.

Raw data can be:

Once we've preprocessed the data and it's cleaner, we move on to data transformation. This step involves converting the data into a more suitable format or structure for analysis. Data transformation can involve a range of operations, such as scaling, normalizing, and encoding so that the data is ready for analysis.

Let us explain this in detail.

In some cases, the range of values in the dataset can vary widely. For instance, income figures can be substantially larger when dealing with a dataset that includes income and age. Scaling ensures that each feature contributes proportionally to the analysis by bringing them to a similar scale, often between 0 and 1.

Most machine learning algorithms work with numerical data and encoding of categorical data is a way to convert words or labels into numbers. It's like giving each category a unique code or number.

For instance, let’s say our cars dataset has cars of "red," "green," and "blue" colors. Encoding would assign each color a number, such as "Red" becoming 1, "Blue" becoming 2, and "Green" becoming 3. This numeric representation allows algorithms to use this information in calculations and analysis.

Principal Component Analysis, or PCA, identifies the most vital details in data and creates a condensed version that captures its essence. Imagine we have a variety of fruits, each described by several features like color, size, taste, and weight. We want to understand what makes each fruit unique. PCA sorts through all these features and identifies the main directions (components) in which the fruits vary the most. It might find, for example, that color or size vary significantly across different fruits, whereas taste or weight might show less variation.

The first step in PCA is to find the most distinguishing feature combination – the one that shows the biggest differences between the fruits. This could be a mix of size and color. Then, PCA searches for the next most distinguishing set of features that provide new, different information. This might be a blend of weight & taste, offering insights that the combination of size & color doesn't.

PCA then combines these informative features into what we call "principal components.” These are new, simplified features that summarize the original data. The first principal component could be a balance of size and color that together tell us a lot about each fruit. The second principal component could be another combination, like weight and taste, giving us another perspective. These components help us understand the essence of the fruits without needing to consider every single feature in isolation.

Sometimes, the information that we need is not present in our data directly. So, we create new, more informative features from the ones we already have to improve our predictions.

Imagine we have data on people's height and weight, but we want to know if they're underweight, normal weight, or overweight. To do this, we can create a new feature called 'Weight Category' based on their Body Mass Index (BMI). If their BMI is low, they're underweight; if it's in the middle, they're normal weight; and if it's high, they're overweight. This 'Weight Category' feature simplifies understanding and using the data for weight classification.

Once we have the data ready we can feed it into machine learning algorithms. We will discuss some of these algorithms in future posts.