Student_performance_Analysis

Project Overview

This project analyzes students' exam performances based on various demographic, socio-economic, and educational factors. Using Exploratory Data Analysis (EDA),visualizations and statistical method we uncover insights about what influences academic outcomes.

The dataset contains scores in three subjects: Math, Reading, and Writing, along with attributes like gender, race/ethnicity, parental education, lunch type, and test preparation.

Project Workflow

Data Loading and Preprocessing:

Cleaning and preparing the dataset for analysis.

Exploratory Data Analysis (EDA):

Understanding data distributions, relationships between variables, and basic trend discovery.

Statistical Analysis:

Applying statistical methods like correlation analysis, mean/median/mode calculations, hypothesis testing, and group comparisons to validate observed patterns.

Data Visualization:

Using graphical met

Dataset Details

Column Name	Description
gender	Student's gender (male/female)
race/ethnicity	Group of the student (A, B, C, D, E)
parental level of education	Highest education level of parents
lunch	Standard or free/reduced lunch
test preparation course	Completed or not completed
math score	Math exam score (0-100)
reading score	Reading exam score (0-100)
writing score	Writing exam score (0-100)

Files in the Repository

File Name	Description
StudentPerformance.csv	Original dataset containing student data.
cleaned_student_data.csv	Dataset after cleaning .
DataCleaning.ipynb	data cleaning and preprocessing steps.
EDA_Matplotlib.ipynb	Exploratory Data Analysis (EDA) and visualizations using Matplotlib library.
EDA_Seaborn.ipynb	Exploratory Data Analysis (EDA) and visualizations using Seaborn library.
Statistics_Analysis_p2.ipynb	Basic statistical operations like mean, median, mode, standard deviation, etc.
Statistics_Analysis.ipynb	Deep statistical analysis including hypothesis testing and correlation study.
SQL_Insights.sql	SQL queries for deriving insights from the student performance data stored in SQL tables.
Database_schema.sql	SQL script to create database and tables for storing student performance data.
ER_Diagram.png	Entity-Relationship Diagram showing the database structure for the project.
README.md	Project documentation explaining the dataset, workflow, files, and insights.

How to Run the Project

1. Data Cleaning

Run DataCleaning.ipynb to clean the raw dataset (StudentsPerformance.csv) and save it as cleaned_student_data.csv.

2. Load Data into SQL (Optional - For SQL Analysis)

Create a database and table using Students_performance.sql. Import the cleaned_student_data.csv into your SQL database.

3. SQL Analysis

Run queries in SQL_Insights.sql to generate SQL-based insights, such as: Average scores based on gender or parental education Top-performing students Subject-wise performance analysis Group-based performance comparisons

4. Statistical Analysis

Run Statistics_Analysis_p2.ipynb and Statistics_Analysis.ipynb in Jupyter Notebook to: Calculate mean, median, mode, standard deviation Perform hypothesis testing and correlation studies

5. Exploratory Data Analysis (EDA)

Run EDA_Matplotlib.ipynb to explore the data using Matplotlib visualizations. Run EDA_Seaborn.ipynb to explore the data using Seaborn visualizations.

Insights from Analysis

1. Gender-Based Performance

• Female students generally performed better than male students in all subjects.

2. Parental Education and Performance

• Students with more educated parents tended to score higher, especially in writing and reading.

3. Subject-Wise Performance

• Writing scores were the highest on average, while Mathematics had the lowest average scores.

4. Distribution of Scores

• The distribution of scores showed most students scored in the middle range, with a few outliers in high and low scores.

5. Performance Correlations

• Strong positive correlation between reading and writing scores, while Mathematics had a weaker correlation with other subjects.

Project Submission

GitHub Repository: Student_performance_Analysis

Contributors

• Sangeetha Thangam
• Mahesh Mahato