📌 Overview This project focuses on analyzing student exam performance across Math, Reading, and Writing scores. The dataset is explored to calculate average scores, identify patterns based on gender, and visualize insights using Python libraries.
🛠️ Tools & Libraries Used Python 🐍 Pandas → For data analysis and manipulation Matplotlib → For basic visualizations Seaborn → For advanced and attractive plots
📂 Project Workflow Importing Libraries & Dataset Data Cleaning & Preparation Calculating Average Scores Grouping by Gender Visualizing Performance Trends Bar plots Color palettes Insights & Conclusion
📊 Key Insights Calculated average performance for each subject. Compared scores gender-wise. Created colorful visualizations (using Matplotlib & Seaborn palettes). Highlighted differences and patterns in exam results.
🚀 Learning Outcomes Hands-on experience in data wrangling Improved skills in data visualization Better understanding of statistical analysis How to present insights clearly with graphs
📝 Conclusion This project demonstrates how raw exam data can be transformed into meaningful insights using Python. It’s a beginner-friendly analysis that can be extended to include correlation analysis, prediction models, and deeper statistical exploration in the future.