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📊 Student Analytics System v2.1 — Beast Mode

MADE BY USSU | github.com/issu321

You enter the data. The system does EVERYTHING else.

Fully automated student performance analytics with ML predictions, animated charts, smart insights, and professional PDF reports. Built for Kali Linux with Python 3.13+ and match/case architecture.


🚀 Quick Start

# 1. Clone/download the project
cd student_analytics_v2

# 2. Install dependencies
./install.sh

# 3. Run the beast
./run.sh

# Or with your own data file
./run.sh data/your_students.csv

📁 Project Structure

student_analytics_v2/
├── src/
│   ├── main.py              # Entry point & menu
│   └── analytics_engine.py  # Core engine (ML, charts, insights)
├── data/                    # Put your CSV/Excel/JSON here
├── output/                  # Generated charts & reports
├── assets/                  # Static assets
├── requirements.txt         # Python 3.13+ compatible dependencies
├── install.sh              # One-command installer
├── run.sh                  # Launch script
└── README.md               # This file

🎮 What You Enter vs What System Does

You Enter System Does Automatically
X-axis data (comma-separated) Auto-detects scale, labels, formatting
Y-axis data (comma-separated) Auto-detects scale, labels, formatting
Names for Y-axis Auto-labels every chart
X/Y labels (optional) Smart defaults if skipped
Chart title (optional) Auto-generates from labels
Extra columns (optional) Auto-includes in correlation & radar

Everything else is fully automated:

  • ✅ Color gradients based on performance
  • ✅ Trend lines with R² scores
  • ✅ Bubble sizes = performance magnitude
  • ✅ Outlier detection & alerts
  • ✅ Feature importance ranking
  • ✅ Animated GIF generation
  • ✅ Multi-format export (CSV/JSON/Excel/PDF)

🧠 Features

7 Chart Types

Option Chart Auto-Features
A Individual Bars Gradient colors, value labels, ranking
B Advanced Scatter Bubble size = performance, trend line, annotations
C Distribution Histogram + box plot with mean/median lines
D Radar Chart Top 5 performers spider chart
E Correlation Heatmap Auto-detected numeric correlations
F 6-Panel Dashboard All charts combined in one mega view
G Smart Insights AI-generated outliers, variability, top/bottom alerts

ML & Predictions

Option Feature
H Interactive Predictor — Enter X value, get predicted Y
I Animated Race — GIF showing progressive ranking reveal
J PDF Report — Professional multi-page PDF with all charts
K Export All — CSV + JSON + Excel + Metadata in one shot
L Regenerate All — Run A-F in sequence automatically
S Load Sample Data — USSU built-in demo dataset

🖥️ Menu System (Advanced Match/Case)

╔══════════════════════════════════════════════════════════════════════╗
║                    BEAST MODE ANALYTICS MENU                          ║
║                    MADE BY USSU | github.com/issu321                  ║
╠══════════════════════════════════════════════════════════════════════╣
║  [A] Individual Bars        → Ranked horizontal bar chart             ║
║  [B] Advanced Scatter       → Bubble scatter + trend line             ║
║  [C] Distribution           → Histogram + box plot                      ║
║  [D] Radar Chart            → Spider chart for top performers         ║
║  [E] Correlation Heatmap    → Auto-detected numeric correlations      ║
║  [F] Full 6-Panel Dashboard → Everything combined                     ║
║  [G] Smart Insights         → AI-generated analysis                   ║
║  [H] Interactive Predict    → Enter X, get predicted Y                  ║
║  [I] Animated Race          → GIF animation of rankings               ║
║  [J] PDF Report             → Professional multi-page PDF             ║
║  [K] Export All Formats     → CSV + JSON + Excel + Metadata           ║
║  [L] Regenerate All Charts  → Run A-F in sequence                     ║
║  [M] Load New Data          → Enter new dataset                       ║
║  [S] Load Sample Data       → USSU built-in demo dataset               ║
║  [Q] Quit                   → Exit beast mode                           ║
╚══════════════════════════════════════════════════════════════════════╝

🎨 Theme

50-30-20 Color Rule Applied:

  • 50% #0B0F19 — Deep space background
  • 30% #151B2B — Charcoal panels
  • 20% #00D4AA — Cyber teal accent

Dynamic gradients auto-generated for every visualization.


📊 Sample Data Entry

Enter X-AXIS data (comma-separated): 85, 92, 78, 96, 88
Enter Y-AXIS data (comma-separated): 82, 94, 75, 98, 86
Enter NAMES (comma-separated): Alice, Bob, Charlie, Diana, Eve
Enter X-AXIS label [default: X Axis]: Study Hours
Enter Y-AXIS label [default: Y Axis]: Final Score
Enter chart title [default: Analysis]: Class 10A Performance

Or press S in the menu to load the built-in USSU sample dataset instantly!


📦 Dependencies (Python 3.13+ Compatible)

# Core (required)
numpy>=2.1.0
pandas>=2.2.0
matplotlib>=3.9.0

# ML (recommended)
scikit-learn>=1.5.0

# Export
openpyxl>=3.1.0

# UI enhancement
rich>=13.0.0

# Animation
pillow>=10.4.0

Install all at once:

pip3 install -r requirements.txt

🔧 Requirements

  • Python 3.10+ (tested on 3.13.12) — for match/case statements
  • Kali Linux / Debian / Ubuntu / Any Linux
  • Terminal with Unicode support (for box-drawing characters)

🐍 Python Match/Case Architecture

match choice:
    case 'A' | '1' | 'a':
        engine.plot_individual_bars()
    case 'B' | '2' | 'b':
        engine.plot_scatter_advanced()
    case 'S' | '14' | 's':
        return "SAMPLE_DATA"
    case 'Q' | 'QUIT' | 'EXIT' | 'q':
        sys.exit(0)
    case _:
        print("Invalid choice")

📤 Output Files

All generated files saved to output/:

File Description
01_individual_performance.png Ranked bar chart
02_scatter_analysis.png Scatter + trend line
03_distribution.png Histogram + box plot
04_radar_chart.png Top 5 spider chart
05_correlation.png Correlation matrix
06_full_dashboard.png 6-panel mega view
07_animated_race.gif Animated ranking
report.pdf Professional PDF
data_*.csv/json/xlsx Exported data
metadata_*.json Analysis metadata

🎯 Use Cases

  • Teachers: Analyze class performance automatically
  • Students: Track personal progress with predictions
  • Researchers: Quick statistical analysis without coding
  • Data Analysts: Rapid visualization pipeline

🛠️ Troubleshooting

Issue Solution
Python 3.10+ required sudo apt install python3.13 python3.13-pip
Module not found Run ./install.sh again
Charts not showing Check DISPLAY env var, use export DISPLAY=:1
Permission denied chmod +x install.sh run.sh
Rich not installed Optional — program works without it
scikit-learn fails pip3 install scikit-learn>=1.5.0

📜 License

MIT License — Free for educational and commercial use.


MADE BY USSU | github.com/issu321

Built with 🔥 for Kali Linux warriors | Python 3.13+ Ready

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