- Analysis of the PISA 2012 dataset, detailing student performance in math, science, and reading across 68 countries.
- Aimed to uncover relationships between study habits, parental presence, and academic performance.
- Applied programming and analysis skills to derive insights from complex data.
- Language: Python
- Libraries:
numpypandasmatplotlib.pyplotseaborn
- Platform: Jupyter Notebook
- Study Time: Majority of students studied between 100 and 500 minutes per week, correlating with higher assessment scores.
- Parental Influence: Presence of parents at home positively impacts students' scores.
- Behavioral Insights: Absence of internet access and parental presence linked to more troublesome behavior among students.
- Utilized visualizations to showcase how study time impacts scores in math and science.
- Highlighted the effect of self-identification as a quick learner on total scores.
- Analyzed the impact of parental presence on student achievement and behavior through data-driven insights.
- Increased font size in plots for better readability.
This project not only explores the PISA 2012 dataset using Python and various libraries but also showcases the application of my programming and analysis skills to process and interpret complex datasets. Through Jupyter Notebook, I conducted a detailed analysis, emphasizing the role of study habits, parental influence, and access to resources on academic outcomes.