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--- |
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datasets: |
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- name: UniGame Dataset |
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task_categories: |
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- text-classification |
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license: mit |
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tags: |
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- machine learning |
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- data science |
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- mental health |
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pretty_name: UniGame |
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size_categories: |
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- n<1K |
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--- |
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# UniGame Dataset |
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This dataset explores the relationship between gaming habits and academic performance among students. It includes various attributes such as age, educational level, CGPA, gaming habits, and other related factors. |
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## Dataset Details |
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### Dataset Description |
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This dataset aims to investigate how gaming affects the academic performance of students. It includes information on the respondents' demographics, gaming habits, and academic results. |
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- **Curated by:** Hossain et. al |
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- **Language(s):** English |
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- **License:** MIT |
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## Uses |
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### Direct Use |
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This dataset can be used for various purposes, including but not limited to: |
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- Analyzing the impact of gaming on academic performance. |
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- Studying the correlation between gaming habits and lifestyle factors. |
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- Developing machine learning models to predict academic performance based on gaming habits and other related factors. |
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### Out-of-Scope Use |
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This dataset should not be used for malicious purposes or any application that the dataset is not suitable for. |
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## Dataset Structure |
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### Files |
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The dataset consists of two files: |
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- `train.csv`: The training set. |
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- `test.csv`: The testing set. |
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### Columns |
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The dataset contains the following columns: |
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1. **What is your age?**: The age of the respondent. |
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2. **Current educational position?**: The educational level of the respondent. |
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3. **Gender?**: The gender of the respondent. |
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4. **Your current CGPA?**: The current CGPA of the respondent. |
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5. **Your Higher Secondary School(H. SC) or A level or equivalent result?**: The higher secondary school result of the respondent. |
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6. **At what age you had started playing games?**: The age at which the respondent started playing games. |
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7. **Do you play games on mobile or pc?**: The platform on which the respondent plays games. |
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8. **When you go to sleep?**: The time the respondent goes to sleep. |
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9. **Do you attend your morning class regularly?**: Whether the respondent attends morning classes regularly. |
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10. **The average time you spend playing games?**: The average time the respondent spends playing games. |
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11. **Do you play paid or non-paid games?**: Whether the respondent plays paid or non-paid games. |
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12. **How many time you spend with family and friend?**: The time the respondent spends with family and friends. |
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13. **How you fill when you can not play game in whole day?**: The respondent's feeling when they cannot play games for a whole day. |
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14. **How you fill to complete game level?**: The respondent's feeling when they complete a game level. |
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15. **If you didn't finish games last level what is your feeling?**: The respondent's feeling if they didn't finish the last level of a game. |
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16. **Do you fill Fatigue?**: Whether the respondent feels fatigue. |
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17. **Do you play games for stress relief?**: Whether the respondent plays games for stress relief. |
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18. **Are you wearing glasses?**: Whether the respondent wears glasses. |
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## Dataset Creation |
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### Curation Rationale |
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The dataset was created to understand the impact of gaming on students' academic performance and lifestyle. It aims to provide insights that can help educators and policymakers make informed decisions. |
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### Source Data |
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#### Data Collection and Processing |
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The data was collected through a structured questionnaire filled out by students. The responses were then processed to ensure consistency and accuracy. |
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#### Who are the source data producers? |
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The source data was produced by students who participated in the survey. Their responses were anonymized to protect their privacy. |
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### Annotations |
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#### Annotation process |
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No additional annotations were made to the dataset beyond the initial data collection. |
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#### Who are the annotators? |
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The respondents themselves provided the data. |
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#### Personal and Sensitive Information |
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The dataset contains information that might be considered personal, such as age, gender, and academic results. All data was anonymized to ensure the privacy of the respondents. |
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## Bias, Risks, and Limitations |
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Users should be aware of potential biases in the dataset, as it may not represent all student populations equally. The dataset should be used with caution, considering its limitations. |
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### Recommendations |
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Users should consider the biases, risks, and limitations of the dataset. It is recommended to use the dataset in conjunction with other data sources to ensure robust analysis. |
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## Usage |
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To load and use this dataset, you can use the following code snippets in Python: |
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### Loading the dataset with Hugging Face Datasets library |
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```python |
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from datasets import load_dataset |
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# Load the dataset |
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dataset = load_dataset("ismail31415/uniGame") |
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# Access the training and testing sets |
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train_df = dataset['train'] |
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test_df = dataset['test'] |
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