Dataset research report
Student Performance research report
A reproducible data report with schema notes, generated chart evidence, suggested follow-up questions, and export-ready Helix queries.
Executive Summary
Student performance The Student performance dataset from Kaggle. Configuration Task Description encoding Encoding dictionary showing original values of encoded features. math Binary classification Has the student passed the math exam? writing Binary classification Has the student passed the writing exam? reading Binary classification Has the student passed the reading exam? Usage from datasets import load_dataset dataset =… See the full description on the dataset page: https://huggingface.co/datasets/mstz/student_performance.
Research Context
Student Performance: 500 rows by 8 columns. These exploratory charts are generated automatically from the data - open the dataset in Helix to ask your own questions.
Data Profile
Chart Evidence
These views are generated from the dataset profile. Each chart is paired with a Helix query so it can be opened, adjusted, and exported.
Total parental_level_of_education by is_male
Top is_male values ranked by summed parental_level_of_education.
Open and export this chartparental_level_of_education vs reading_score
parental_level_of_education vs reading_score, coloured by is_male.
Open and export this chartDistribution of parental_level_of_education
Histogram of parental_level_of_education values.
Open and export this chartCorrelation of numeric columns
Pearson correlation between numeric columns.
Open and export this chartparental_level_of_education by is_male
Spread of parental_level_of_education across is_male groups.
Open and export this chartFollow-Up Queries
- average parental_level_of_education by ethnicity
- top 10 ethnicity by total parental_level_of_education
- scatter parental_level_of_education vs reading_score coloured by ethnicity
- histogram of parental_level_of_education
- boxplot of parental_level_of_education by ethnicity
- correlation heatmap of all numeric columns
Preview Rows
| # | is_maleboolean | ethnicitytext | parental_level_of_educationinteger | has_standard_lunchboolean | has_completed_preparation_testboolean | reading_scoreinteger | writing_scoreinteger | has_passed_math_examinteger |
|---|---|---|---|---|---|---|---|---|
| 1 | False | group D | 2 | True | True | 70 | 78 | 0 |
| 2 | True | group D | 5 | True | False | 93 | 87 | 1 |
| 3 | False | group D | 2 | False | False | 76 | 77 | 0 |
| 4 | True | group B | 2 | False | False | 70 | 63 | 1 |
| 5 | False | group D | 5 | True | False | 85 | 86 | 1 |
| 6 | True | group C | 0 | True | False | 57 | 54 | 1 |
Data Dictionary
- is_male bool
- ethnicity categorical
- parental_level_of_education numeric
- has_standard_lunch bool
- has_completed_preparation_test bool
- reading_score numeric
- writing_score numeric
- has_passed_math_exam numeric
Method And Limits
- Load the catalog entry and preview rows from the processed dataset file.
- Infer numeric, categorical, time, and location fields from real columns.
- Generate a small set of defensive Plotly chart specifications from that profile.
- Expose each chart idea as a query link so the report can be rerun or exported in Helix.
This report is intentionally reproducible. It uses the local catalog metadata and generated chart specifications rather than claiming external conclusions beyond the dataset.