This synthetic dataset contains 4,362 rows and five columns, including both numerical and categorical data. It is designed for data cleaning, imputation, and analysis tasks, featuring structured missing values at varying percentages (63%, 4%, 47%, 31%, and 9%).

The dataset includes:
- Category (Categorical): Product category (A, B, C, D)
- Price (Numerical): Randomized product prices
- Rating (Numerical): Ratings between 1 to 5
- Stock (Categorical): Availability status (In Stock, Out of Stock)
- Discount (Numerical): Discount percentage

This dataset is ideal for practicing missing data handling, exploratory data analysis (EDA), and machine learning preprocessing.

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.

📊 Dataset Description – Handling Missing Data

This dataset contains 1,000 employee records across different departments and cities, designed for practicing data cleaning, preprocessing, and handling missing values in real-world scenarios.

🔹 Features (Columns)

• ID (Integer): Unique identifier for each employee.
• Age (Float): Age of the employee (some values are missing).
• Salary (Float): Annual salary of the employee in USD (some values are missing).
• Experience (Float): Total years of professional experience (some values are missing).
• Department (Categorical): Department of the employee (e.g., IT, Sales, Finance, Admin) – contains missing values.
• City (Categorical): Work location of the employee (e.g., London, Berlin, New York) – contains missing values.

🔹 Missing Data Information

• Columns Age, Salary, Experience, Department, and City contain around 100 missing values each.
• The dataset is ideal for testing different missing data handling techniques, such as:
  • Mean / Median / Mode imputation
  • Random sampling imputation
  • Forward / Backward filling
  • Predictive modeling approaches

🔹 Use Cases

• 🧹 Practice data cleaning & preprocessing for ML projects.
• 🔧 Explore imputation techniques for both numerical and categorical data.
• 🤖 Build predictive models while handling incomplete datasets.
• 🎓 Great for educational purposes, tutorials, and workshops on missing data handling.

In this Datasets i simply showed the handling of missing values in your data with help of python libraries such as NumPy and pandas. You can also see the use of Nan and Non values. Detecting, dropping and filling of null values.

Dataset

This dataset was created by Safacan Metin

Contents

Dataset

This dataset was created by Tuệ Nguyễn

Released under Apache 2.0

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Dataset

This dataset was created by Pankesh Patel

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Dataset

This dataset was created by Zaid Mohammed Ibrahim

Released under MIT

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Dataset

This dataset was created by Feroz Shinwari

Released under Apache 2.0

Contents

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.

Context

Dataset is final solution for dealing with missing values in the Spaceship Titanic competition. Kaggle Notebook: https://www.kaggle.com/sardorabdirayimov/best-way-of-dealing-with-missing-values-titanic-2/

Dataset for the paper "Identifying missing data handling methods with text mining".
It contains the type of missing data handling method used by a given paper.

Column description

id: ID of the article
origin: Source journal
pub_year: Publication year
discipline: Discipline category of the article based on origin
about_missing: Is the article about missing data handling? (0 - no, 1 - yes)
imputation: Was some kind of imputation technique used in the article? (0 - no, 1 - yes)
advanced: Was some kind of advanced imputation technique used in the article? (0 - no, 1 - yes)
deletion: Was some kind of deletion technique used in the article? (0 - no, 1 - yes)
text_tokens: Snipped out parts from the original articles

• This dataset is designed for learning how to identify missing data in Python.
• It focuses on techniques to detect null, NaN, and incomplete values.
• It includes examples of visualizing missing data patterns using Python libraries.
• Useful for beginners practicing data preprocessing and data cleaning.
• Helps users understand missing data handling methods for machine learning workflows.
• Supports practical exploration of datasets before model training.

Dataset

This dataset was created by Himanshu Kumar

Contents

Activity Title: "Fix the Gaps: Data Hospital Simulation" (This activity is created for students to practice techniques to handle missing data)

Description: Provide each team with a “broken patient record” dataset (incomplete entries with NaNs or blanks). Teams act as data doctors: • Diagnose the type of missingness (MCAR, MAR, MNAR) • Choose suitable imputation techniques (mean, median, KNN, regression) • Compare outcomes from different methods

Tools: Jupyter notebook / Pandas

Outcome: Group presentation on the impact of imputation and justification of the method used.

Dataset

This dataset was created by Deep Jani

Released under Data files © Original Authors

Contents

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.

Original dataset that is shared on Github can be found here. These are hands on practice datasets that were linked through the Coursera Guided Project Certificate Course for Handling Missing Values in R, a part of Coursera Project Network. The datasets links were shared by the original author and instructor of the course Arimoro Olayinka Imisioluwa.

Things you could do with this dataset: As a beginner in R, these datasets helped me to get a hang over making data clean and tidy and handling missing values(only numeric) using R. Good for anyone looking for a beginner to intermediate level understanding in these subjects.

Here are my notebooks as kernels using these datasets and using a few more preloaded datasets in R, as suggested by the instructor. TidY DatA Practice MissinG DatA HandlinG - NumeriC

The dataset contains sales records from a café. Initially, it was messy, with missing values represented as NaN, UNKNOWN, and ERROR. The following cleaning steps were applied: 1. Handling Missing Values Replaced missing values with appropriate statistics: i. Mode for categorical columns (Item, Payment Method, and Location). ii. Mean for numerical columns (Quantity). iii. Median for temporal data (Transaction Date).

2. Price Standardization Inconsistent values in the Price per Unit column were corrected by filling them with the appropriate consistent price from the dataset.

3. Data Type Conversion Converted all columns to their appropriate data types (e.g., datetime for transaction dates, numeric for quantities and prices, categorical for items, payment methods, and locations)

Dive into this specially curated dataset on credit card applications 📊.

An interesting approach to privacy has been taken in this dataset– every name and value has been creatively altered to ensure confidentiality 🔒.

What's inside?

A diverse collection of data that's sure to pique your interest. You'll encounter a range of continuous variables, giving you a glimpse into quantitative insights 📈.

Then, there are categorical variables – some with just a handful of options offering a neat, compact view, and others with a plethora of choices, adding layers of complexity and richness.

But here's where it gets even more intriguing – the dataset has been intentionally peppered with additional missing values 💡.

This isn't your average dataset; it's a playground for those who love a good data challenge.

The goal?

To equip you with real-world skills in handling and imputing missing data 🧩. You'll learn to navigate through these informational gaps, employing various imputation techniques to unveil the hidden stories within the data.

This dataset isn't just about understanding credit card applications 💳. It's a journey into the heart of data analysis and machine learning 🤖.

Whether you're a beginner eager to learn the ropes or an experienced data enthusiast looking to refine your skills, this dataset offers a unique opportunity. It challenges you to apply theoretical knowledge to practical scenarios, transforming abstract concepts into tangible skills.

So, if you're ready to test your mettle against real-world data puzzles, this is your chance. Unleash your analytical prowess, explore diverse imputation strategies, and uncover the secrets hidden in incomplete data. Welcome to a world where data tells a story, and you're the storyteller 🌐