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  How do you handle missing data? What imputation techniques do you recommend? Missing data can be dealt with in a variety of ways. I believe the most common reaction is to ignore it. Choosing to make no decision, on the other hand, indicates that your statistical programme will make the decision for you. Your application will remove things in a listwise sequence most of the time. Depending on why and how much data is gone, listwise deletion may or may not be a good idea. Another common strategy among those who pay attention is imputation. Imputation is the process of substituting an estimate for missing values and analysing the entire data set as if the imputed values were the true observed values. And how would you choose that estimate? The following are some of the most prevalent methods: Mean imputation Calculate the mean of the observed values for that variable for all non-missing people. It has the advantage of maintaining the same mean and sample size, but it also has a slew...

  When we want to communicate with one another, language is crucial. Every human being uses many languages like Hindi, Tamil, Malayalam, English, and so on to convey their queries to others. This medium allows us to communicate our thoughts to others. One of the aspects of human intelligence is language. Natural Language Processing (NLP) is a branch of AI that strives to make the system capable of doing written and spoken human language. Translators between languages, text to speech or speech to text, chatbots, automatic (Q&A), automatic generation of image descriptions, generation of subtitles in videos, and classification of sentiments in sentences are just a few examples of practical applications. Learning about this topic can help you find solutions to your current and future problems. What is the purpose of NLP? Natural Language Processing widely used applications for., NLP is used in language translation apps like Google Translate and word processors like Microsoft Word ...

  Data science can help solve real-world problems by properly using relevant data. A data scientist can help businesses understand customer behaviour, forecast product performance based on data. That's why companies looking for data scientists prefer applicants who have earned a Data Science Certification from a reputed university. If you want your resume to stand out from the crowd while looking for a job, it must include some fresh data science initiatives. Here are fifteen data science initiatives that might help you build a strong online presence. 1. Sentiment Analysis What is this and how does it work? Customer’s attitude toward a product or service is assessed via sentiment analysis . Businesses use it to gauge client satisfaction.  Aim: To determine why sales are below expectations or why a product or service is not well received by the target market. Project description With the use of NLP, computational linguistics, text analysis, and biometrics, this data science re...