Introduction
The Student Performance Exploratory Data Analysis (EDA) project aims to uncover valuable insights into the factors influencing student outcomes. By analyzing academic performance data, this project helps identify trends, patterns, and key drivers that can support educators and institutions in making informed decisions to enhance learning experiences.
Exploring Insights: EDA on Student Performance
In my efforts to analyze and uncover meaningful insights from data, I conducted an Exploratory Data Analysis (EDA) on Student Performance, which focused on understanding how various factors influence students' test scores. This project showcases my ability to transform raw data into actionable insights by employing systematic analysis and visualization techniques.
Project Link : Github
Objective and Dataset
The primary goal of this project was to identify patterns and correlations between students’ performance and variables such as gender, ethnicity, parental education level, lunch type, and test preparation. The dataset, sourced from Kaggle, consisted of 1,000 records across 8 columns, representing both numerical and categorical features like math, reading, and writing scores.
Technical Approach
Leveraging Python and its versatile libraries, I performed a comprehensive EDA. Key tools included Pandas for data manipulation, NumPy for numerical analysis, Matplotlib and Seaborn for data visualization. The process began with thorough data checks for missing values, duplicates, and statistical summaries to ensure data integrity
Key Insights and Observations
Performance by Gender: Female students demonstrated higher average scores compared to their male counterparts, particularly in reading and writing.
Impact of Lunch Type: Students who had a standard lunch outperformed those on free/reduced lunch plans, highlighting the potential influence of nutrition on academic outcomes.
Parental Education Level: While parental education level showed limited direct impact on female students' performance, male students whose parents had higher education (associate's or master’s degrees) tended to perform better.
Ethnic Group Patterns: Ethnic groups displayed varied performance levels; for instance, students from Groups C and D consistently performed better, while Groups A and B showed lower average scores.
Value and Applications
This analysis provides valuable insights for educators, policymakers, and institutions to understand the socio-economic and educational factors influencing student outcomes. By identifying these patterns, stakeholders can devise targeted interventions to improve academic performance.
Visualizing the Story
Using heatmaps, histograms, and KDE plots, I brought the data to life, highlighting key trends and correlations. This visual storytelling approach not only aids in better comprehension but also ensures the insights are actionable.
This EDA project underlines my ability to approach complex datasets methodically, extract meaningful insights, and present them effectively to guide decision-making.
