🧠 Pandas Student Performance Analysis 📋 Project Overview
This project analyzes student exam performance using Pandas, NumPy, and Matplotlib. It demonstrates how data can be cleaned, transformed, analyzed, and visualized to extract key insights about student outcomes such as grades, pass/fail ratio, and preparation effectiveness.
🧹 Step 1–2: Data Cleaning & Preparation
Loaded dataset using Pandas
Checked for missing values and duplicates
Renamed inconsistent column names for clarity
Converted categorical columns (Gender, Ethnicity, etc.) to category type
⚙️ Step 3: Feature Engineering
Created new columns:
Total_Score → Sum of all subjects
Average_Score → Mean of scores per student
Result → Pass/Fail classification (based on average ≥ 33)
Grades → Assigned grade letters (A, B, C, D, E, F) using conditional logic
Used NumPy operations for efficient calculations
🔍 Step 4: Exploratory Data Analysis (EDA)
Average Score by Gender
Average Score by Ethnicity
Effect of Test Preparation on performance
Correlation analysis between numerical features
📊 Step 5: Visualization Dashboard
Created an interactive data visualization dashboard using Matplotlib and Seaborn:
Visualization Purpose Histogram Distribution of average scores Barplots Comparison by Gender, Ethnicity, and Test Prep Boxplot Spread of scores by Gender Heatmap Correlation between numerical variables Countplot Pass/Fail distribution
💾 Step 6: Export Cleaned Data
Exported the final cleaned dataset for reuse or ML modeling:
df.to_csv('StudentsPerformance_Cleaned.csv', index=False)
File saved as → StudentsPerformance_Cleaned.csv
📈 Insight Highlights:
Students who completed Test Preparation scored significantly higher.
Female students slightly outperformed males on average.
Group E ethnicity performed best overall.
Strong positive correlation between Math, Reading, and Writing scores.