You may have been aware that data is the most important component for any machine learning activity. We start with data that is being fed to an algorithm that extracts patterns and important information from the data and puts it all in a model. As a result, data is the starting point for machine learning. in this blog, we tell you about six important steps of composing data for Machine Learning.
We must take various steps to prepare the data for modeling before feeding it into machine learning algorithms. Six Important Steps of composing data for Machine Learning are as follows:
Web Scrapping
The first step is to gather information. You can locate it in one of two ways – you can either acquire pre-web scraped or organized data or you can web scrape your own data. There’s a slim possibility that you’ll always be able to locate someone who will simply serve your data on a platter.
The majority of the time, you will have to put in the effort to get data for the problem. Scraping data from several websites is known as web scraping. Text, photos, links, and tables can all be used to store data.
Putting Together A Single DataFrame
Because you usually end up with numerous scraped files after scraping the data, the next step is to merge the data into a single data frame. The benefit of integrating the data into a single file is that we can analyze it faster and save time.
You can try to do it manually using Excel, however, one of the most effective and fastest ways is to use pandas, a Python tool. Concat and merge are two pandas functions that can be used to combine two data frames into one in various scenarios.
Managing Missing Values
We now have a single CSV file that we can put into our notebook after completing the second step. The following step is to pre-process the data. The handling of missing values is the first step in the pre-processing phase. We may have a lot of missing values in our data, and we’ll have to deal with them in order to feed it into a machine learning model.
Using Categorical Data
Categorical data is a type of variable that has labels instead of numeric values. These are commonly referred to as nominal. Male, First, New York, and so on. Every beginner to machine learning has the same question – why do we need to spend so much time on feature engineering when machine learning is so powerful?
The solution is straightforward – every machine learning algorithm is based on a mathematical idea, and as you may know, arithmetic only works with numerical input.
Distribution
The most common action that data scientists will perform is to examine the data distribution. It is critical to understand the data distribution in order to proceed with the next phases. There are six basic methods of data dissemination – uniform distribution, binomial distribution, normal distribution, poisson distribution, and exponential distribution are all examples of distributions.
Every data distribution is unique and necessitates a unique preprocessing method. Normal Distribution is the optimum distribution. Otherwise, based on the situation, every other distribution must be turned into a normal distribution.
Scaling and Transformation of Features
A function that transforms features from one representation to another is known as feature transformation. Feature scaling is a method of turning all of a feature’s values into a single range. For instance, 0 to 1. When you have two columns with different units — for example, one column in kilometers and the other in meters or centimeters — it’s critical to think about feature transformation.
When you have two columns with distinct ranges, such as age columns ranging from 1–100 and income columns ranging from 10000–50000, feature scaling is necessary. In these situations, the column with the higher values has a greater impact on the outcome.
Leave a Reply