🎓 Students Performance Analysis

📌 Problem Statement

The education board seeks to determine whether attending a test preparation course significantly impacts students’ exam performance, specifically in math scores. This study aims to evaluate if test preparation leads to measurable academic improvement, guiding decisions on curriculum enhancements and educational support programs.

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🎯 Objectives

• Analyze score distributions in Math, Reading, and Writing.
• Compare performance by gender, parental education, and test preparation status.
• Examine correlations between subjects.
• Conduct statistical tests and visual analysis to validate assumptions.
• Provide actionable insights for educational policy and curriculum planning.

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🗂️ Project Structure and Visualizations

• Students_Performance_Analysis.ipynb
  Complete analysis with visualizations, statistical evaluation, and insights.

• images/
  Repository of key visual outputs:

  1. Box Plot of Math Scores by Gender
     

  2. Box Plot of Math Scores by Test Preparation Status
     

  3. Correlation Heatmap of Scores
     

  4. Histogram and Q-Q Plot
     

  5. Math Score Distribution by Test Prep
     

  6. Pairplot of Scores by Test Preparation
     

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🔍 Key Insights & Outcomes

• Students who completed the test preparation course scored significantly higher in math than those who did not, indicating a positive impact of test preparation.
• There is a statistically significant difference in performance between the two student groups (Completed and None) based on test preparation status.
• Math and reading scores are not normally distributed, suggesting that student performance data may often be skewed or unevenly spread.
• The difference in math scores between groups (Completed and None) is not due to random chance, but reflects a real, measurable effect.
• The analysis supports the importance of preparation and study support programs in improving academic outcomes.
• Boys and girls may show slight differences in math scores, based on the gender-based box plots.
• Students who perform well in one subject (like reading) often do well in others (like writing or math), showing a strong link between skills.
• The visualizations like box plots helped us clearly see the score differences between groups.
• There may be underlying variability in how students perform across subjects, which is influenced by factors like preparation, effort, or access to resources.

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🛠️ Technologies & Tools

• Languages & Libraries: Python, Pandas, NumPy, Matplotlib, Seaborn, SciPy
• Environment: Jupyter Notebook

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💻 Setup & Installation Instructions

Follow these steps to set up the project locally and run the analysis:

1. Clone the Repository: Open a terminal or command prompt and run:

git clone https://github.com/indu-explores-data/Students-Performance-Analysis.git

2. Navigate to the Project Directory:

cd Students-Performance-Analysis

3. Create and Activate a Virtual Environment (Recommended):

python -m venv venv

Windows:

venv\Scripts\activate

Mac/Linux:

source venv/bin/activate

4. Install Required Libraries:

pip install pandas numpy matplotlib seaborn scipy jupyter

5. Launch Jupyter Notebook:

jupyter notebook

6. Open Students Performance Analysis.ipynb and run all cells to reproduce the analysis.

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🚀 Usage / How to Run

• Open Students Performance Analysis.ipynb in Jupyter Notebook.
• Run all cells sequentially to execute the analysis.
• Explore visualizations and statistical insights from the notebook.
• Review final findings and conclusions in the output cells.

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🔗 Connect with Me

Let’s connect on LinkedIn for project discussions or data-driven collaborations:

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🙌 Feedback & Support

If you found this project helpful, please ⭐ star the repository and share your thoughts. Suggestions and contributions are always welcome!