Student Performance - Exploratory Data Analysis (EDA)

A mini data analysis project to explore and visualize the factors influencing student exam performance in mathematics, reading, and writing.

📖 Project Overview

This project performs an Exploratory Data Analysis (EDA) on the "Students Performance in Exams" dataset from Kaggle. The goal is to apply foundational data analysis and statistics skills to uncover patterns, relationships, and insights within the data.

Key steps include:

• Data loading and cleaning (handling missing values, outliers).
• Computation of descriptive statistics.
• Creation of various visualizations (histograms, box plots, scatter plots, heatmaps).
• Summarizing key findings on how factors like gender, parental education, and test preparation correlate with academic scores.

📊 Dataset

Source: [Students Performance in Exams | Kaggle]

The dataset contains the following columns:

• gender: Student's gender (male/female)
• race/ethnicity: Student's ethnic background (grouped as A, B, C, D, E)
• parental level of education: Highest education level of parents
• lunch: Type of lunch (standard/free or reduced)
• test preparation course: Whether the student completed the test prep course
• math score, reading score, writing score: Exam scores (0-100)

🚀 Getting Started

Prerequisites

Before running the code, ensure you have the following installed:

• Python 3.8+
• pip (Python package manager)

Installation & Setup

1. Clone the repository

   git clone https://github.com/your-username/student-performance-eda.git
   cd student-performance-eda

2. Create a virtual environment (Recommended)

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required Python packages

   pip install -r requirements.txt

Running the Analysis

The project is structured into modular scripts for a clear workflow. Run the script to load and clean the raw data. This will generate a cleaned_student_performance.csv , descriptive_statistics_table.csv file.All visualizations will be saved in the /plots directory.

Alternatively, you can run the entire pipeline at once using the main controller:

python main.py
📁 Project Structure
text
student-performance-eda/
│
├── data/
│   ├── studentsperformance.csv       # Original dataset (add manually)
│   └── cleaned_student_performance.csv  # Generated cleaned data
│
├── plots/                            # Generated visualizations
│   ├── hist_math_score.png
│   ├── box_math_score_by_gender.png
│   └── comprehensive_association_heatmap.png
├── src.ipynb
├── requirements.txt
├── .gitignore
└── README.md
🔍 Key Insights Summary
Test Preparation: Completing the test preparation course is strongly associated with higher scores across all three subjects.

Gender Gap: Female students tend to outperform male students in Reading and Writing, while male students show greater variability in Math scores.

Socioeconomic Factors: Parental education level and lunch type (a potential proxy for socioeconomic status) show a positive correlation with student performance.

High Correlation: Scores in Math, Reading, and Writing are highly correlated. A student strong in one subject is likely strong in the others.

For a detailed report with all charts and findings, please refer to the generated plots and the comments within the Jupyter Notebook (analysis_notebook.ipynb), if provided.