The other functions are fairly self-explanatory.Now let’s put the “text” column through this preprocessing pipeline. Micro-courses cover skills relevant to data scientists in a few hours each: Python, machine learning, data visualization, Pandas, feature engineering, deep learning, SQL, geospatial analysis, and so on. Using Google Cloud Platform services may incur charges to your Google Cloud Platform account if you exceed free tier allowances.Notebooks run in kernels, which are essentially Docker containers. This means that if word 1 appears once in document A but also once in the total corpus, while word 2 appears four times in document A but 16 in the total corpus, word 1 will have a tf-idf score of 1.0 while word 2 will only receive a score of 0.25. This means that ‘hello world’ becomes [‘hello’, ‘world’]. With this feature, we can analyze on which days people don’t show up more often.Let’s check whether there are null values in each column in this elegant way:Alternatively, if you want to check an individual column for the presence of null values, you can do it this way:We are lucky — there are no null values in our dataset.Analyzing existing techniques and approaches, I’ve come to the conclusion that the most popular strategies for dealing with missing data are:Once you’ve cleaned the data, it’s time to inspect it more profoundly.It’s clear that only 20.2% of patients didn’t show up while 79.8% were present on the appointment day.With this interactive plot, you can see that the middle quartile of the data (That means that 50% of patients are younger than 37 and the other 50% are older than 37.The range of age values from lower to upper quartile is called the Our data contains only one outlier — a patient with age Another insight this plot allows to get is that the data is clearly For this, we can use the same box plot but it’s grouped by “Presence” column.You can see that people don’t show up mostly on Tuesdays and Wednesdays.Possible techniques that can be applied to this data later:That’s it for now! You’ve finished exploring the dataset but you can continue revealing insights.Hopefully, this simple project will be helpful in grasping the The importance of feedback can’t be overestimated. “text” has 100% density.For more information about Exploratory Data Analysis for text data, I recommend Our preprocessing method consists of two stages: preparation and vectorization. While going through with such, I was introduced to the website of Kaggle approx a month ago. These steps are shown in my Gist for this article Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. You should never neglect data exploration — skipping this significant stage of any data science or machine learning project may lead to generating inaccurate models or wrong data analysis results. It is important to do this step after the preparation step because tokenization would include punctuation as separate tokens.The lemmatization step takes the tokens and breaks each down into its lemma. It’s not, however, a replacement for paid cloud data science services or for doing your analysis.These are in a variety of publication formats, including comma-separated values (CSV) for tabular data, JSON for tree-like data, SQLite databases, ZIP and 7z archives (often used for image datasets), and BigQuery Datasets, which are multi-terabyte SQL datasets hosted on Google’s servers.Scripts are files that execute everything as code sequentially. Make learning your daily ritual. Anthony Goldbloom (CEO) Ben Hamner (CTO) founded Kaggle in 2010, and Google acquired the company in 2017.Kaggle competitions have improved the state of the machine learning art in several areas.Despite being a free service, Kaggle can help address an increasing number of data challenges:In a Kaggle competition, you can compete for money or glory.