🚀 Student Result Analysis System

🔥 Build a complete Data Analysis Project using Python, Pandas & Streamlit in just 45 minutes (One Shot)!

📌 Part of: Super Sunday Project Series 🚀
👉 New project every Sunday
👉 Learn by building real-world projects

Watch Video : https://youtu.be/v97xEEqj1MY?si=9_WpIZG6AlgS96li

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⭐ Support & Follow

If this project helps you:

⭐ Star this repository
👤 Follow me on GitHub for more projects
📺 Watch full video:

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🎥 Project Preview

*Welcome Page *After Upload Raw Data *Student Result *Topper

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💡 Why This Project?

If you understand this project, you can:

✔ Build your own data analysis apps
✔ Understand Pandas practically
✔ Work with real datasets
✔ Create portfolio-ready projects

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⚡ Features

• 📊 Upload CSV dataset
• 🏆 Topper analysis (Top N students)
• 🔍 Search student records
• 📈 Subject-wise performance analysis
• 📌 Pivot table insights
• ✅ Pass/Fail classification

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🧠 Concepts Used

• Pandas DataFrame
• Data Filtering & Aggregation
• GroupBy & Pivot Table
• Statistical Analysis (mean, etc.)
• Streamlit UI

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🛠️ Tech Stack

• Python 🐍
• Pandas 📊
• Streamlit 🌐

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📂 Dataset Format

Make sure your CSV file has:

• Name (Student Name)
• Subject (Subject Name)
• Marks (Numerical Score)

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⚙️ Installation & Setup

1. Clone the repository:

git clone https://github.com/your-username/Student_Result_Analysis.git
cd Student_Result_Analysis

2. Install dependencies:

pip install streamlit pandas streamlit-option-menu

3. Run the application:

streamlit run your_filename.py

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💡 How to Use

1. Launch the app and use the Sidebar to upload your student CSV file.
2. Navigate through the 📌 Menu to select different analysis modes.
3. For the Pass/Fail section, use the slider to adjust the threshold dynamically.
4. In the Topper section, input the number of top-performing students you wish to display.

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🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page if you want to contribute.

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