![]() ![]() What is binning? How does it help in data visualization and analysis?īinning is a data pre-processing technique used to group data into bins. What’s the difference between categorical data and continuous data?Ĭategorical data is data that can be divided into distinct groups, such as “male” and “female.” Continuous data is data that can be divided into any number of groups, such as “height” or “weight.” 7. There are a number of different methods that can be used for imputation, but the most common is probably the mean imputation method, which replaces missing values with the mean of the non-missing values in the data set. This can be done by using a technique called imputation, which is a process of replacing missing values with estimated values. ![]() Yes, it is possible to detect missing values from a data set without actually going through each row manually. Is it possible to detect missing values from a data set without actually going through each row manually? If yes, then how? Normalizing data helps to avoid this problem. This is important because some machine learning algorithms will weight data points differently if they are on different scales. Normalizing data is important because it ensures that all of the data is on the same scale. Why do we need to normalize data before training a model? Outliers can be dealt with by either removing them from the dataset or by transforming them so that they are more in line with the rest of the data. Missing values can be filled in using a variety of methods, such as mean imputation or k-nearest neighbors. Incorrect data types can be fixed by using the correct data type conversion functions. ![]() Some common issues that arise when cleaning data with Python include incorrect data types, missing values, and outliers. When using Python to clean a dataset, what are some of the common issues that arise and how do you deal with them? Data cleansing can help to remove these errors and ensure that the models are learning from high-quality data. If there are errors or inconsistencies in the training data, then the models may learn from these and produce inaccurate results. Can you explain why data cleansing is important for machine learning models?ĭata cleansing is important for machine learning models because it can help to improve the accuracy of the models. This can be done in Python using the pandas library. What is data cleaning? How can it be done in python?ĭata cleaning is the process of identifying and cleaning up inaccuracies and inconsistencies in data. Here are 20 commonly asked Data Cleaning interview questions and answers to prepare you for your interview: 1. Data Cleaning Interview Questions and Answers In this article, we discuss some common data cleaning interview questions and how you can answer them. It is a crucial step in any data analysis, and employers will often want to test your knowledge of data cleaning during the interview process. Data Cleaning is the process of identifying and correcting inaccuracies and inconsistencies in data. ![]()
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