Result Analysis (Class 12th CS Project)

A comprehensive Python application designed to analyze, manage, and visualize student examination results from Excel sheets. This project parses raw result data, organizes it, and provides tools for statistical analysis and graphical representation.

Idea Given By Mam

Features

• 📂 Excel Data Import: Seamless parsing of result sheets with automatic detection of subjects, marks, and student details. Supports fuzzy search to quickly find Excel files in the directory.
• 📊 Data Visualization: Generate insights using Bar Charts, Line Charts, and Scatter Plots to analyze class performance.
• 📈 Statistical Analysis: Automatically calculates percentages and totals. Groups students based on custom or default percentage thresholds.
• 💾 Organized Storage: Keeps processed data structured by Class and Exam name in a local user-data database (CSV format).
• 🖥️ Interactive Interface: Features a user-friendly CLI menu with support for keyboard navigation (using curses) and a compatibility mode for standard terminals.
• 🗑️ Data Management: Built-in tools to view and safely delete old class or exam records.

Requirements

Ensure you have Python installed. The project relies on the following external libraries:

• pandas (Data manipulation)
• numpy (Numerical operations)
• matplotlib (Plotting graphs)
• thefuzz (Fuzzy string matching for file search)
• openpyxl (Excel file reading backend)

Installation

You can install the package easily using pip:

pip install result-analysis

From Source

1. Clone this repository.

2. Navigate to the project directory.

3. Install using pip:

   pip install .
   

   For development (editable mode), use:

   pip install -e .
   

   Note: Windows users might need windows-curses for the full UI experience.

Usage

Once installed, you can run the application from anywhere using the main command or its shortcuts (rsa, rta):

result-analysis
# OR
rsa
# OR
rta

Download Samples

To download sample Excel files for testing:

rsa download samples

This will create a samples/ folder in your current working directory containing test data (IIIA.xlsx, etc.).

Running the App

Alternatively, you can run it as a Python module:

python -m main

1. Place your raw Result Excel files (.xlsx) in the directory where you are running the command.
2. Follow the on-screen menu to:
   • Upload: Select and process an Excel file.
   • Group: Create summary statistics based on percentage ranges.
   • View: Display result tables in the terminal.
   • Plot: Visualize the data.
   • Delete: Remove unwanted data.

Project Structure

• main.py: Entry point.
• data/: Data parsing and handling logic.
• ui/: User interface components.
• graphs/: Plotting and visualization logic.
• group/: Grouping logic.
• user-data/: Automatically generated directory where processed CSV files are stored, organized by Class and Exam.