When our team’s project scored first in the text subtask of this year’s CALL Shared Task challenge, one of the key components of our success was careful preparation and cleaning of data. Data cleaning and preparation is the most critical first step in any AI project. As evidence shows, most data scientists spend most of their time — up to 70% — on cleaning data.
In this blog post, we’ll guide you through these initial steps of data cleaning and preprocessing in Python, starting from importing the most popular libraries to actual encoding of features.
Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. //Wikipedia
Step 1. Loading the data set
Importing libraries
The absolutely first thing you need to do is to import libraries for data preprocessing. There are lots of libraries available, but the most popular and important Python libraries for working on data are Numpy, Matplotlib, and Pandas. Numpy is the library used for all mathematical things. Pandas is the best tool available for importing and managing datasets. ...
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