Dataset

This dataset was created by Mohanad Hazem Qabil

Contents

All data are prone to error and require data cleaning prior to analysis. An important example is longitudinal growth data, for which there are no universally agreed standard methods for identifying and removing implausible values and many existing methods have limitations that restrict their usage across different domains. A decision-making algorithm that modified or deleted growth measurements based on a combination of pre-defined cut-offs and logic rules was designed. Five data cleaning methods for growth were tested with and without the addition of the algorithm and applied to five different longitudinal growth datasets: four uncleaned canine weight or height datasets and one pre-cleaned human weight dataset with randomly simulated errors. Prior to the addition of the algorithm, data cleaning based on non-linear mixed effects models was the most effective in all datasets and had on average a minimum of 26.00% higher sensitivity and 0.12% higher specificity than other methods. Data cleaning methods using the algorithm had improved data preservation and were capable of correcting simulated errors according to the gold standard; returning a value to its original state prior to error simulation. The algorithm improved the performance of all data cleaning methods and increased the average sensitivity and specificity of the non-linear mixed effects model method by 7.68% and 0.42% respectively. Using non-linear mixed effects models combined with the algorithm to clean data allows individual growth trajectories to vary from the population by using repeated longitudinal measurements, identifies consecutive errors or those within the first data entry, avoids the requirement for a minimum number of data entries, preserves data where possible by correcting errors rather than deleting them and removes duplications intelligently. This algorithm is broadly applicable to data cleaning anthropometric data in different mammalian species and could be adapted for use in a range of other domains.

Dataset

This dataset was created by Ahmed Elsayed taha

Released under Apache 2.0

Contents

Data Cleaning or Data cleansing is to clean the data by imputing missing values, smoothing noisy data, and identifying or removing outliers. In general, the missing values are found due to collection error or data is corrupted.

Here some info in details :Feature Engineering - Handling Missing Value

Wine_Quality.csv dataset have the numerical missing data, and students_Performance.mv.csv dataset have Numerical and categorical missing data's.

The mean, standard deviation, preservation of data (PD), sensitivity and specificity of five data cleaning approaches with and without an algorithm (A) compared to uncleaned longitudinal growth measurements in CLOSER data with and without simulated duplications and 1% errors.

Sample data for exercises in Further Adventures in Data Cleaning.

Petroleum production data are usually stored in a format that makes it easy to determine the year and month production started, if there are any breaks, and when production ends. However, in some cases, you may want to compare production runs where the start of production for all wells starts at month one regardless of the year the wells started producing. This report describes the JAVA program the U.S. Geological Survey developed to examine water-to-oil and water-to-gas ratios in the form of month one, month two, and so on with the objective of estimating quantities of water and proppant used in low-permeability petroleum production. The text covers the data used by the program, the challenges with production data, the program logic for checking the quality of the production data, and the program logic for checking the completeness of the data.

The percentage of gold standard corrections of errors induced into CLOSER data with simulated duplications and 1% errors using the algorithmic data cleaning methods.

This dataset is designed specifically for beginners and intermediate learners to practice data cleaning techniques using Python and Pandas.

It includes 500 rows of simulated employee data with intentional errors such as:

Missing values in Age and Salary

Typos in email addresses (@gamil.com)

Inconsistent city name casing (e.g., lahore, Karachi)

Extra spaces in department names (e.g., " HR ")

✅ Skills You Can Practice:

Detecting and handling missing data

String cleaning and formatting

Removing duplicates

Validating email formats

Standardizing categorical data

You can use this dataset to build your own data cleaning notebook, or use it in interviews, assessments, and tutorials.

The percentage of alterations made to Dogslife, SAVSNET, Banfield and CLOSER data with simulated duplications and 1% simulated errors using the NLME-A data cleaning method.

Dirty Cafe Sales Dataset

Overview

The Dirty Cafe Sales dataset contains 10,000 rows of synthetic data representing sales transactions in a cafe. This dataset is intentionally "dirty," with missing values, inconsistent data, and errors introduced to provide a realistic scenario for data cleaning and exploratory data analysis (EDA). It can be used to practice cleaning techniques, data wrangling, and feature engineering.

File Information

• File Name: dirty_cafe_sales.csv
• Number of Rows: 10,000
• Number of Columns: 8

Columns Description

Data Characteristics

1. Missing Values:

   • Some columns (e.g., Item, Payment Method, Location) may contain missing values represented as None or empty cells.

2. Invalid Values:

   • Some rows contain invalid entries like "ERROR" or "UNKNOWN" to simulate real-world data issues.

3. Price Consistency:

   • Prices for menu items are consistent but may have missing or incorrect values introduced.

Menu Items

The dataset includes the following menu items with their respective price ranges:

Use Cases

This dataset is suitable for: - Practicing data cleaning techniques such as handling missing values, removing duplicates, and correcting invalid entries. - Exploring EDA techniques like visualizations and summary statistics. - Performing feature engineering for machine learning workflows.

Cleaning Steps Suggestions

To clean this dataset, consider the following steps: 1. Handle Missing Values: - Fill missing numeric values with the median or mean. - Replace missing categorical values with the mode or "Unknown."

1. Handle Invalid Values:

   • Replace invalid entries like "ERROR" and "UNKNOWN" with NaN or appropriate values.

2. Date Consistency:

   • Ensure all dates are in a consistent format.
   • Fill missing dates with plausible values based on nearby records.

3. Feature Engineering:

   • Create new columns, such as Day of the Week or Transaction Month, for further analysis.

License

This dataset is released under the CC BY-SA 4.0 License. You are free to use, share, and adapt it, provided you give appropriate credit.

Feedback

If you have any questions or feedback, feel free to reach out through the dataset's discussion board on Kaggle.

Restaurant Menu DatasetWith approximately 45,000 menus dating from the 1840s to the present, The New York Public Library’s restaurant menu collection is one of the largest in the world. The menu data has been transcribed, dish by dish, into this dataset. For more information, please see http://menus.nypl.org/about.This dataset is not clean and contains many missing values, making it perfect to practice data cleaning tools and techniques.Dataset Variables:id: identifier for menuname: sponsor: who sponsored the meal (organizations, people, name of restaurant)event: categoryvenue: type of place (commercial, social, professional)place: where the meal took place (often a geographic location)physical_description: dimension and material description of the menuoccasion: occasion of the meal (holidays, anniversaries, daily)notes: notes by librarians about the original materialcall_number: call number of the menukeywords: language: date: date of the menulocation: organization or business who produced the menulocation_typecurrency: system of money the menu uses (dollars, etc)currency_symbol: symbol for the currency ($, etc)status: completeness of the menu transcription (transcribed, under review, etc)page_count: how many pages the menu hasdish_count: how many dishes the menu has

Data cleaning is one of the most important but time-consuming tasks for data scientists. The data cleaning task consists of two major steps: (1) error detection and (2) error correction. The goal of error detection is to identify wrong data values. The goal of error correction is to fix these wrong values. Data cleaning is a challenging task due to the trade-off among correctness, completeness, and automation. In fact, detecting/correcting all data errors accurately without any user involvement is not possible for every dataset. We propose a novel data cleaning approach that detects/corrects data errors with a novel two-step task formulation. The intuition is that, by collecting a set of base error detectors/correctors that can independently mark/fix data errors, we can learn to combine them into a final set of data errors/corrections using a few informative user labels. First, each base error detector/corrector generates an initial set of potential data errors/corrections. Then, the approach ensembles the output of these base error detectors/correctors into one final set of data errors/corrections in a semi-supervised manner. In fact, the approach iteratively asks the user to annotate a tuple, i.e., marking/fixing a few data errors. The approach learns to generalize the user-provided error detection/correction examples to the rest of the dataset, accordingly. Our novel two-step formulation of the error detection/correction task has four benefits. First, the approach is configuration free and does not need any user-provided rules or parameters. In fact, the approach considers the base error detectors/correctors as black-box algorithms that are not necessarily correct or complete. Second, the approach is effective in the error detection/correction task as its first and second steps maximize recall and precision, respectively. Third, the approach also minimizes human involvement as it samples the most informative tuples of the dataset for user labeling. Fourth, the task formulation of our approach allows us to leverage previous data cleaning efforts to optimize the current data cleaning task. We design an end-to-end data cleaning pipeline according to this approach that takes a dirty dataset as input and outputs a cleaned dataset. Our pipeline leverages user feedback, a set of data cleaning algorithms, and a set of previously cleaned datasets, if available. Internally, our pipeline consists of an error detection system (named Raha), an error correction system (named Baran), and a transfer learning engine. As our extensive experiments show, our data cleaning systems are effective and efficient, and involve the user minimally. Raha and Baran significantly outperform existing data cleaning approaches in terms of effectiveness and human involvement on multiple well-known datasets.

Netflix is a popular streaming service that offers a vast catalog of movies, TV shows, and original contents. This dataset is a cleaned version of the original version which can be found here. The data consist of contents added to Netflix from 2008 to 2021. The oldest content is as old as 1925 and the newest as 2021. This dataset will be cleaned with PostgreSQL and visualized with Tableau. The purpose of this dataset is to test my data cleaning and visualization skills. The cleaned data can be found below and the Tableau dashboard can be found here .

Data Cleaning

We are going to: 1. Treat the Nulls 2. Treat the duplicates 3. Populate missing rows 4. Drop unneeded columns 5. Split columns Extra steps and more explanation on the process will be explained through the code comments

--View dataset

SELECT * 
FROM netflix;

--The show_id column is the unique id for the dataset, therefore we are going to check for duplicates
                                  
SELECT show_id, COUNT(*)                                                                                      
FROM netflix 
GROUP BY show_id                                                                                              
ORDER BY show_id DESC;

--No duplicates

--Check null values across columns

SELECT COUNT(*) FILTER (WHERE show_id IS NULL) AS showid_nulls,
    COUNT(*) FILTER (WHERE type IS NULL) AS type_nulls,
    COUNT(*) FILTER (WHERE title IS NULL) AS title_nulls,
    COUNT(*) FILTER (WHERE director IS NULL) AS director_nulls,
    COUNT(*) FILTER (WHERE movie_cast IS NULL) AS movie_cast_nulls,
    COUNT(*) FILTER (WHERE country IS NULL) AS country_nulls,
    COUNT(*) FILTER (WHERE date_added IS NULL) AS date_addes_nulls,
    COUNT(*) FILTER (WHERE release_year IS NULL) AS release_year_nulls,
    COUNT(*) FILTER (WHERE rating IS NULL) AS rating_nulls,
    COUNT(*) FILTER (WHERE duration IS NULL) AS duration_nulls,
    COUNT(*) FILTER (WHERE listed_in IS NULL) AS listed_in_nulls,
    COUNT(*) FILTER (WHERE description IS NULL) AS description_nulls
FROM netflix;

We can see that there are NULLS. 
director_nulls = 2634
movie_cast_nulls = 825
country_nulls = 831
date_added_nulls = 10
rating_nulls = 4
duration_nulls = 3 

The director column nulls is about 30% of the whole column, therefore I will not delete them. I will rather find another column to populate it. To populate the director column, we want to find out if there is relationship between movie_cast column and director column

-- Below, we find out if some directors are likely to work with particular cast

WITH cte AS
(
SELECT title, CONCAT(director, '---', movie_cast) AS director_cast 
FROM netflix
)

SELECT director_cast, COUNT(*) AS count
FROM cte
GROUP BY director_cast
HAVING COUNT(*) > 1
ORDER BY COUNT(*) DESC;

With this, we can now populate NULL rows in directors 
using their record with movie_cast 

UPDATE netflix 
SET director = 'Alastair Fothergill'
WHERE movie_cast = 'David Attenborough'
AND director IS NULL ;

--Repeat this step to populate the rest of the director nulls
--Populate the rest of the NULL in director as "Not Given"

UPDATE netflix 
SET director = 'Not Given'
WHERE director IS NULL;

--When I was doing this, I found a less complex and faster way to populate a column which I will use next

Just like the director column, I will not delete the nulls in country. Since the country column is related to director and movie, we are going to populate the country column with the director column

--Populate the country using the director column

SELECT COALESCE(nt.country,nt2.country) 
FROM netflix AS nt
JOIN netflix AS nt2 
ON nt.director = nt2.director 
AND nt.show_id <> nt2.show_id
WHERE nt.country IS NULL;
UPDATE netflix
SET country = nt2.country
FROM netflix AS nt2
WHERE netflix.director = nt2.director and netflix.show_id <> nt2.show_id 
AND netflix.country IS NULL;


--To confirm if there are still directors linked to country that refuse to update

SELECT director, country, date_added
FROM netflix
WHERE country IS NULL;

--Populate the rest of the NULL in director as "Not Given"

UPDATE netflix 
SET country = 'Not Given'
WHERE country IS NULL;

The date_added rows nulls is just 10 out of over 8000 rows, deleting them cannot affect our analysis or visualization

--Show date_added nulls

SELECT show_id, date_added
FROM netflix_clean
WHERE date_added IS NULL;

--DELETE nulls

DELETE F...

Description of the data entries, individuals, data entries per individual, mean and standard deviation of the longitudinal height or weight measurements in Dogslife, SAVSNET, Banfield and CLOSER data with and without simulated duplications and 1% errors before and after removal of duplicated measurement records.

Abstract

The Palestinian society's access to information and communication technology tools is one of the main inputs to achieve social development and economic change to the status of Palestinian society; on the basis of its impact on the revolution of information and communications technology that has become a feature of this era. Therefore, and within the scope of the efforts exerted by the Palestinian Central Bureau of Statistics in providing official Palestinian statistics on various areas of life for the Palestinian community, PCBS implemented the household survey for information and communications technology for the year 2019. The main objective of this report is to present the trends of accessing and using information and communication technology by households and individuals in Palestine, and enriching the information and communications technology database with indicators that meet national needs and are in line with international recommendations.

Geographic coverage

Palestine, West Bank, Gaza strip

Analysis unit

Household, Individual

Universe

All Palestinian households and individuals (10 years and above) whose usual place of residence in 2019 was in the state of Palestine.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of master sample which were enumerated in the 2017 census. Each enumeration area consists of buildings and housing units with an average of about 150 households. These enumeration areas are used as primary sampling units (PSUs) in the first stage of the sampling selection.

Sample size The estimated sample size is 8,040 households.

Sample Design The sample is three stages stratified cluster (pps) sample. The design comprised three stages: Stage (1): Selection a stratified sample of 536 enumeration areas with (pps) method. Stage (2): Selection a stratified random sample of 15 households from each enumeration area selected in the first stage. Stage (3): Selection one person of the (10 years and above) age group in a random method by using KISH TABLES.

Sample Strata The population was divided by: 1- Governorate (16 governorates, where Jerusalem was considered as two statistical areas) 2- Type of Locality (urban, rural, refugee camps).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaire The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on Individuals (10 years and over) about computer use, access to the Internet and possession of a mobile phone.

Cleaning operations

Programming Consistency Check The data collection program was designed in accordance with the questionnaire's design and its skips. The program was examined more than once before the conducting of the training course by the project management where the notes and modifications were reflected on the program by the Data Processing Department after ensuring that it was free of errors before going to the field.

Using PC-tablet devices reduced data processing stages, and fieldworkers collected data and sent it directly to server, and project management withdraw the data at any time.

In order to work in parallel with Jerusalem (J1), a data entry program was developed using the same technology and using the same database used for PC-tablet devices.

Data Cleaning After the completion of data entry and audit phase, data is cleaned by conducting internal tests for the outlier answers and comprehensive audit rules through using SPSS program to extract and modify errors and discrepancies to prepare clean and accurate data ready for tabulation and publishing.

Tabulation After finalizing checking and cleaning data from any errors. Tables extracted according to prepared list of tables.

Response rate

The response rate in the West Bank reached 77.6% while in the Gaza Strip it reached 92.7%.

Sampling error estimates

Sampling Errors Data of this survey affected by sampling errors due to use of the sample and not a complete enumeration. Therefore, certain differences are expected in comparison with the real values obtained through censuses. Variance were calculated for the most important indicators, There is no problem to disseminate results at the national level and at the level of the West Bank and Gaza Strip.

Non-Sampling Errors Non-Sampling errors are possible at all stages of the project, during data collection or processing. These are referred to non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, as well as practical and theoretical training during the training course.

The implementation of the survey encountered non-response where the case (household was not present at home) during the fieldwork visit become the high percentage of the non response cases. The total non-response rate reached 17.5%. The refusal percentage reached 2.9% which is relatively low percentage compared to the household surveys conducted by PCBS, and the reason is the questionnaire survey is clear.

Yield Data Cleaning Software Market Outlook

According to our latest research, the global Yield Data Cleaning Software market size in 2024 stands at USD 1.14 billion, with a robust compound annual growth rate (CAGR) of 13.2% expected from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 3.42 billion. This remarkable market expansion is being driven by the increasing adoption of precision agriculture technologies, the proliferation of big data analytics in farming, and the rising need for accurate, real-time agricultural data to optimize yields and resource efficiency.

One of the primary growth factors fueling the Yield Data Cleaning Software market is the rapid digital transformation within the agriculture sector. The integration of advanced sensors, IoT devices, and GPS-enabled machinery has led to an exponential increase in the volume of raw agricultural data generated on farms. However, this data often contains inconsistencies, errors, and redundancies due to equipment malfunctions, environmental factors, and human error. Yield Data Cleaning Software plays a critical role by automating the cleansing, validation, and normalization of such datasets, ensuring that only high-quality, actionable information is used for decision-making. As a result, farmers and agribusinesses can make more informed choices, leading to improved crop yields, efficient resource allocation, and reduced operational costs.

Another significant driver is the growing emphasis on sustainable agriculture and environmental stewardship. Governments and regulatory bodies across the globe are increasingly mandating the adoption of data-driven practices to minimize the environmental impact of farming activities. Yield Data Cleaning Software enables stakeholders to monitor and analyze field performance accurately, track input usage, and comply with sustainability standards. Moreover, the software’s ability to integrate seamlessly with farm management platforms and analytics tools enhances its value proposition. This trend is further bolstered by the rising demand for traceability and transparency in the food supply chain, compelling agribusinesses to invest in robust data management solutions.

The market is also witnessing substantial investments from technology providers, venture capitalists, and agricultural equipment manufacturers. Strategic partnerships and collaborations are becoming commonplace, with companies seeking to enhance their product offerings and expand their geographical footprint. The increasing awareness among farmers about the benefits of data accuracy and the availability of user-friendly, customizable software solutions are further accelerating market growth. Additionally, ongoing advancements in artificial intelligence (AI) and machine learning (ML) are enabling more sophisticated data cleaning algorithms, which can handle larger datasets and deliver deeper insights, thereby expanding the market’s potential applications.

Regionally, North America continues to dominate the Yield Data Cleaning Software market, supported by its advanced agricultural infrastructure, high rate of technology adoption, and significant investments in agri-tech startups. Europe follows closely, driven by stringent environmental regulations and a strong focus on sustainable farming practices. The Asia Pacific region is emerging as a high-growth market, fueled by the rapid modernization of agriculture, government initiatives to boost food security, and increasing awareness among farmers about the benefits of digital solutions. Latin America and the Middle East & Africa are also showing promising growth trajectories, albeit from a smaller base, as they gradually embrace precision agriculture technologies.

Component Analysis

The Yield Data Cleaning Software market is bifurcated by component into Software and Services. The software segment currently accounts for the largest share of the market, underpinned by the increasing adoption of integrated farm management solutions and the demand for user-friendly platforms that can seamlessly process vast amounts of agricultural data. Modern yield data cleaning software solutions are equipped with advanced algorithms capable of detecting and rectifying data anomalies, thus ensuring the integrity and reliability of yield datasets. As the complexity of agricultural operations grows, the need for scalable, customizable software that can adapt to

Overview and Contents This replication package was assembled in January of 2025. The code in this repository generates the 13 figures and content of the 3 tables for the paper “All Forecasters Are Not the Same: Systematic Patterns in Predictive Performance”. It also generates the 2 figures and content of the 5 tables in the appendix to this paper. The main contents of the repository are the following: Code/: folder of scripts to prepare and clean data as well as generate tables and figures. Functions/: folder of subroutines for use with MATLAB scripts. Data/: data folder. Raw/: ECB SPF forecast data, realizations of target variables, and start and end bins for density forecasts. Intermediate/: Data used at intermediate steps in the cleaning process. These datasets are generated with x01_Raw_Data_Shell.do, x02a_Individual_Uncertainty_GDP.do, x02b_Individual_Uncertainty_HICP.do, x02c_Individual_Uncertainty_Urate.do, x03_Pull_Data.do, x04_Data_Clean_And_Merge, and x05_Drop_Low_Counts.do in the Code/ folder. Ready/: Data used to conduct regressions, statistical tests, and generate figures. Output/: folder of results. Figures/: .jpg files for each figure used in the paper and its appendix. HL Results/: Results from applying the Hounyo and Lahiri (2023) testing procedure for equal predictive performance to ECB SPF forecast data. This folder contains the material for Tables 1A-4A. Regressions/: Regression results, as well as material for Tables 3 and 5A. Simulations/: Results from simulation exercise as well as the datasets used to create Figures 9-12. Statistical Tests/: Results displayed in Tables 1 and 2. The repository also contains the manuscript, appendix, and this read-me file.DisclaimerThis replication package was produced by the authors and is not an official product of the Federal Reserve Bank of Cleveland. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by the Federal Reserve Bank of Cleveland or the Federal Reserve System.

The method involves sucking gas mixture from a crude oil tank (10), and supplying inert gas into the tank. The energy content of the sucked gas mixture is determined. The gas mixture is supplied into a compressor (38) for compressing the gas mixture, when the energy content of the gas mixture exceeds a limitation value. The gas mixture is supplied into a load unit (40), when the energy content of the gas mixture is below the limitation value. Independent claims are also included for the following: (1) a device for suction of a gas mixture from a tank (2) a cleaning vehicle for cleaning a tank.

Dirty Retail Store Sales Dataset

Overview

The Dirty Retail Store Sales dataset contains 12,575 rows of synthetic data representing sales transactions from a retail store. The dataset includes eight product categories with 25 items per category, each having static prices. It is designed to simulate real-world sales data, including intentional "dirtiness" such as missing or inconsistent values. This dataset is suitable for practicing data cleaning, exploratory data analysis (EDA), and feature engineering.

File Information

• File Name: retail_store_sales.csv
• Number of Rows: 12,575
• Number of Columns: 11

Columns Description

Categories and Items

The dataset includes the following categories, each containing 25 items with corresponding codes, names, and static prices:

Electric Household Essentials

Furniture