Nowadays, there is a famous saying ‘Data is the new oil’. Data is one of the primary reasons for the popularity of machine learning. Today we are generating a lot of data every day, and the machine learning models use this data for learning and prediction. Machine learning models are nothing without data. Data plays a major role in building an accurate, efficient machine learning model. The better the data, the better will be the model. In this article, we learn about data in machine learning in-depth. So, let’s begin with a famous quote.
“Learning from data is virtually universally useful. Master it and you’ll be welcomed anywhere”~ John Elder
What is Data?
Data is a collection of information or facts or that used to gain knowledge. It can be in various forms like text or numbers written or printed on paper or electronic data stored in the computer or the class of thoughts or facts in someone’s mind. So, we can say that data is present everywhere.
There are two main types of data which are:
1. Qualitative data
It means the data of categorical type. For example- the color of the shirt, the shape of the pizza. It has two types: Nominal and Ordinal.
2. Quantitative data
It means the data of numeric type. For example- Joe’s salary, number of days in a month, the height of a person. It also has two types: Discrete and Continuous.
Below is a chart to describe the types of data for better understanding.
Datasets for Machine Learning
Data is considered the core of ML. Machine learning models learn from data, without data Machine learning is no use. To provide data to the machine learning model we need to create datasets for the Machine learning model.
Datasets are a collection of data. A dataset stores several entries for a particular type of observation with n number of columns. For example- if we have a dataset of movies, it contains columns- movie_name, movie_cast and movie_rating then it will have several entries/rows containing different movies.
CSV files are the most common dataset format for Machine learning purposes.
For Machine learning, data collection or dataset generation is an important task. We can create a dataset in two ways: Collecting data on our own through surveys or downloading the most appropriate dataset from the internet. Collecting data through surveys is a very time taking task and it is suggested only when you need some particular type of data that might not be available on the internet. In most cases, the dataset we need is available on the internet.
Below is a list of some famous websites for downloading datasets.
You can refer to these websites for any kind of dataset.
Dealing with Data in Machine Learning
The dataset we have can’t be fed to the Machine learning model as this data is in raw form and needs to be processed to make it understandable for the Machine learning model. Also, we need to analyze and visualize the data for getting insights from data.
Below are the steps that are needed to be performed on the dataset before feeding it to the model.
The real-world data mostly contains noise, has null values, and might not be in a suitable format. So, we can’t train our model with this data. To deal with this problem, we need to pre-process data. Data preprocessing is a technique to prepare the raw data such that a Machine learning model can learn from it.
It is a technique to gain important information from data by manipulating, transforming, and visualizing the data. The goal is to seek out patterns in data. There are many reasons to perform data analysis as it helps in selecting the right algorithm and techniques.
It is a method to present data in graphical format. It makes data easily understandable as the data is in summarized form. Even a large amount of data can be easily understood just by looking at a graph or plot. In Python, we use matplotlib and seaborn for data visualization.
Splitting the dataset is one of the major parts of building a Machine learning model. In Machine learning problems, we split the dataset into two parts - the Training dataset and the test dataset.
The difference between training data and test data is that training data is used for training the model and test data is used by the model for predicting the values of unseen data.
Splitting data into training and testing data in Machine learning is the last step of data preparation.
Data is a vital part of Machine learning. In the article we learned about data, datasets for Machine learning, handling data in Machine learning, splitting data into training and testing data in Machine learning and the difference between training data and test data. We understood the importance of data in Machine learning.
We hope you gain an understanding of data in machine learning. Do reach out to us for queries on our AI-dedicated discussion forum and obtain your query resolved within a half-hour.
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