https://github.com/samia35-2973/world-university-ranking-2023-prediction
This repository is about creating models for predicting world university rankings 2023. The World University Rankings 2023 dataset include 1,799 universities across 104 countries and regions, making them the largest and most diverse university rankings to date. A clean dataset is generated through data preprocessing.
https://github.com/samia35-2973/world-university-ranking-2023-prediction
data-cleaning data-preprocessing data-visualization decision-trees machine-learning machine-learning-algorithms model-training prediction world-university-rankings world-university-rankings-2023
Last synced: 8 months ago
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This repository is about creating models for predicting world university rankings 2023. The World University Rankings 2023 dataset include 1,799 universities across 104 countries and regions, making them the largest and most diverse university rankings to date. A clean dataset is generated through data preprocessing.
- Host: GitHub
- URL: https://github.com/samia35-2973/world-university-ranking-2023-prediction
- Owner: Samia35-2973
- License: mit
- Created: 2023-09-09T21:55:26.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-09-19T23:03:55.000Z (about 2 years ago)
- Last Synced: 2024-01-28T03:08:35.369Z (almost 2 years ago)
- Topics: data-cleaning, data-preprocessing, data-visualization, decision-trees, machine-learning, machine-learning-algorithms, model-training, prediction, world-university-rankings, world-university-rankings-2023
- Language: Jupyter Notebook
- Homepage: https://www.kaggle.com/datasets/samiatisha/world-university-rankings-2023-clean-dataset
- Size: 475 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# world-university-ranking-2023-prediction
## Overview
This repository is about creating models for predicting world university rankings 2023. The World University Rankings 2023 dataset include 1,799 universities across 104 countries and regions, making them the largest and most diverse university rankings to date. A clean dataset is generated through data preprocessing.
## Original Dataset
[World University Rankings 2023](https://www.kaggle.com/datasets/alitaqi000/world-university-rankings-2023)
## Cleaned Dataset
The Original data is preprocessed with proper encoding, handling null and NaN values. The code is in Decision Tree File. The cleaned Dataset is updated on kaggle for more information.
[World University Rankings 2023 - Cleaned](https://www.kaggle.com/datasets/samiatisha/world-university-rankings-2023-clean-dataset)
## Models Used
1. Decision Tree(Entropy)
2. Random Forest(Entropy)