The dataset has about 34,000+ rows, each containing review text, username, product name, rating, and other information for each product. Here, I have taken a dataset containing reviews of various products manufactured by Amazon, like Kindle, Fire TV, Echo, etc. I’m a big fan of Amazon’s product lineup. The first step in any machine learning project is understanding the problem.
I would venture to say it’s a critical cog in your NLP project – a stage you simply cannot afford to skip.Įxploratory Data Analysis is the process of exploring data, generating insights, testing hypotheses, checking assumptions and revealing underlying hidden patterns in the data.
As a data scientist and an NLP enthusiast, it’s important to analyze all this text data to help your organization make data-driven decisions.Īnd exploratory data analysis ties all of this together. Trust me, you will appreciate the EDA stage the more you work on text data.Īnd there’s no shortage of text data, is there? We have data being generated from tweets, digital media platforms, blogs, and a whole host of other sources. We need to perform investigative and detective analysis of our data to see if we can unearth any insights. I found this to be true even for text data in Natural Language Processing (NLP) projects. I discovered, through personal experience and the advice of my mentors, the importance of spending time exploring and understanding my data. In my early days in this field, I couldn’t wait to dive into machine learning algorithms but that often left my end result hanging in the balance. I can say this with the benefit of hindsight having personally gone through this situation plenty of times. We need to plan our approach in a structured manner and the exploratory data analytics (EDA) stage plays a huge part in that. We can’t simply skip to the model building stage after gathering the data. There are no shortcuts in a machine learning project lifecycle. The Importance of Exploratory Data Analysis (EDA)
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