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10 Datasets For Data Cleaning Practice For Beginners

10 Datasets For Data Cleaning Practice For Beginners

Ambika Choudhury

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In order to create quality data analytics solutions, it is very crucial to wrangle the data. The process includes identifying and removing inaccurate and irrelevant data, dealing with the missing data, removing the duplicate data, etc. Thus, eliminating the major inconsistencies and making the data more efficient to work with.

In this article, we list down 10 datasets for beginners, which can be used for data cleaning practice or data preprocessing. 



(The list is in alphabetical order)

1| Common Crawl Corpus

Common Crawl is a corpus of web crawl data composed of over 25 billion web pages. For all crawls since 2013, the data has been stored in the WARC file format and also contains metadata (WAT) and text data (WET) extracts. The dataset can be used in natural language processing (NLP) projects. 

Get the data here.

2| Google Books Ngrams

Google Books Ngrams is a dataset containing Google Books n-gram corpora. N-grams are fixed size tuples of items. In this dataset, the items are words extracted from the Google Books corpus. The size of the dataset is 2.2 TB.

Get the data here.


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3| Hourly Weather Surface – Brazil (Southeast region)

The Hourly Weather Surface – Brazil (Southeast region) covers hourly weather data from 122 weather stations of the southeast region (Brazil).The size of the dataset is 2 GB, and there are 17 climate parameters (continuous values) from 122 weather stations. The contents of the dataset include instant air temperature, relative humidity of the air, instant dew point, solar radiation, among others. 

Get the data here.

4| Hotel Booking Demand

The Hotel Booking demand dataset contains booking information for a city hotel and a resort hotel. It includes information such as booking time, length of stay, number of adults, children/babies, number of available parking spaces, among other things. This dataset is ideal for anyone looking to practice their exploratory data analysis (EDA) or get started in building predictive models. 

Get the data here.

5| Iris Species 

The Iris Species is the Iris Plant Database, which contains three classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other two, and the latter are not linearly separable from each other. The columns of this dataset include Id, Sepallength, PetalLength, etc. 

Get the data here.

6| New York City Airbnb Open Data

The New York City Airbnb Open Data is a public dataset and a part of Airbnb. It includes all needed information to find out more about hosts, geographical availability, necessary metrics to make predictions and draw conclusions. This dataset describes the listing activity and metrics in NYC, NY, for 2019.

Get the data here.

7| Slogan Dataset

The Slogan dataset can be used to analyse slogans of various organisations. It includes a list of slogans in the form of company_name, company_slogan. The data has been acquired from slogan-list.com, which contains more than 1000 pairs of “company, slogan” spread across 10+ categories.

See Also

Get the data here.

8| Taxi Trajectory Data

The Taxi Trajectory dataset provides a complete year (from 01/07/2013 to 30/06/2014) of the trajectories for all the 442 taxis running in the city of Porto, Portugal. Each ride has been categorised into three sub-categories which are taxi central based, stand-based and non-taxi central based. Each data sample corresponds to one completed trip and contains a total of nine features.

Get the data here.

9| Temperature Readings: IoT Devices

The Temperature Readings: IoT Devices dataset contains the temperature readings from IoT devices installed outside and inside of an anonymous room. The size of the data is 7 MB, and it has 5 columns with 97605 rows. The dataset can be used for time-series analysis project.

Get the data here.

The Trending YouTube Video Statistics is a daily record with daily statistics for trending Youtube videos which were collected using YouTube API. It includes several months (and counting) of data on daily trending YouTube videos, with up to 200 listed trending videos per day. Each region’s data is in a separate file. Data includes the video title, channel title, publish time, tags, views, likes and dislikes, description, and comment count.

Get the data here.

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