World University Rankings 2023 - Cleaned

Dataset Name: World University Rankings 2023 - Cleaned

Dataset Source

This dataset is a cleaned and preprocessed version of the "World University Rankings 2023" originally provided by Syed Ali Taqi on Kaggle. The original dataset included 13 features, covering information about universities worldwide.

Dataset Features

1. University Rank
2. Name of University
3. Location
4. No of Student
5. No of Student per Staff
6. International Student
7. Female : Male Ratio
8. Overall Score
9. Teaching Score
10. Research Score
11. Citations Score
12. Industry Income Score
13. International Outlook Score

Description

This cleaned version of the dataset has undergone rigorous preprocessing, including handling missing values and encoding categorical features, resulting in a dataset with enhanced usability and cleanliness. It now consists of 2,341 rows and 2,361 columns, providing valuable insights for data analysis, machine learning, and research in the field of higher education.

Original vs. Cleaned Version Comparison

Original Version

The original version of the "World University Rankings 2023" dataset was a comprehensive collection of data on 1,799 universities across 104 countries and regions. While it provided valuable insights into higher education worldwide, it presented some challenges due to missing values, inconsistencies, and a mix of data types.

Original Dataset Source:
World University Rankings 2023

Cleaned Version

In this cleaned version of the dataset, significant efforts have been made to enhance its quality and usability. The following improvements were made:

Handling Missing Values:

• All missing values, including NaN and Null values, have been meticulously addressed for every feature in the dataset.
• Specifically, missing values in the "Name of University" and "Location" columns have been replaced with meaningful placeholders: "Unknown University" and "Unknown Location," respectively.

Encoding and Transformation:

• One-hot encoding has been applied to the "Name of University" and "Location" columns, converting categorical data into a numerical format suitable for analysis and modeling.
• The "Female Ratio" and "Male Ratio" columns have been separated, allowing for more straightforward analysis of gender ratios.
• "OverAll Score" has been divided into "OverAll Score Min" and "OverAll Score Max" columns, providing insights into the range of scores.
• "International Student" values have been encoded as fractional values, making it easier to interpret and analyze.
• Several features, including "Female Ratio," "Male Ratio," "OverAll Score Min," "OverAll Score Max," "No of Student," and "International Student," have been encoded as numerical values, improving their compatibility with data analysis and modeling techniques.

These enhancements have transformed the dataset into a cleaned and well-structured resource for data analysis, machine learning, and research in the field of higher education. Researchers and data enthusiasts can now explore and gain valuable insights from this improved dataset with confidence.

Whether you are conducting exploratory data analysis, building predictive models, or conducting research, this cleaned version of the dataset provides a solid foundation for your analytical endeavors.

GitHub Repository

For more details on the data preprocessing steps and to access the cleaned dataset, you can visit the GitHub repository where the preprocessing was performed: GitHub Repository

If you find value in this "World University Rankings 2023 - Cleaned" dataset, please consider upvoting it on Kaggle to boost its visibility. Additionally, star our GitHub repository to show your support for the data preprocessing efforts. Your support is greatly appreciated!