Data Preparation Techniques and Its Importance in Machine Learning. “Data are just summaries of thousands of stories, tell a few of those stories to help make the data meaningful.”
Data refers to examples of cases from the domain that characterize the problem you want to solve and the choice of data is dependent on the objective you want to satisfy. Here are some commonly used websites where open-source data is available for each topic so that you can build your own Machine Learning application and contribute to global success.
Kaggle — An organised platform, where each learner will learn to spend time. You will love how futuristic these datasets are, and with the help of kernels, you can process all in the platform without even downloading the data.
UCI Machine Learning Repository — It maintains a huge amount of diversified datasets as a service to the machine learning community
Data.gov — You can download data from multiple Indian government ministries. Data can range from government budgets to school performance scores.
CMU Libraries — High-quality dataset from various domains an initiative by Carnegie Mellon University
Google Dataset Search — This dataset search lets you find datasets wherever they’re hosted, whether it’s a publisher’s site, a digital library, or an author’s web page.
After collecting the data, the first task is to transform the data to meet the requirements of individual machine learning algorithms. The most challenging part of each machine learning project is how to prepare the one thing that is unique to the project i.e. The data used for modelling.
Data preparation is the transformation of raw data into the form that is more suitable for modelling because “the quality of data is more important than using complicated algorithms”. And to transform the raw data into more informative and self-explanatory you need to perform the same type of data preparation task for any modelling problem.
In this article, we will walk you through how to apply Data Preparation techniques using the Car Price Prediction Dataset as an example
Data Preparation tasks are :
This is one of the hardest steps, as most of the real-world data may have incorrect values in the form of misleading observation, wrong entry of data or rows may store incorrect values and many more but to clean data in order to create reliable dataset you need to have domain expertise which helps you to identify and observe abnormalities within attributes.
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