academic repository

school coursework, practical labs, and exploratory scripts written across CISCE Grade 9-10 (Robotics & Artificial Intelligence) and Grade 11-12 (ISC Artificial Intelligence).

external work and systems:

• Sentinel-X - robotics systems and firmware logic
• FAM - AI logic and exploration

repository layout

core coursework & lab assignments

• gradeprogram/ - asynchronous student grade calculation system (asyncio)
• 12ISC/ - Grade 12 ISC AI labs, statistical subplots, Seaborn/Matplotlib workflows, notebook PDF exports
• assignment1(ISC)/ - foundational Python labs, data plotting, notebook rendering scripts
• assignment2(ISC)/ - student dataset cleaning, CSV normalization, outlier handling
• assignment2/ - core geometry routines, unit conversions, salary calculation logic
• assignment3/ - algorithmic math (Pascal's triangle, prime sequence generators, Roman numeral converters)
• assignment4/ - data structure manipulation across lists, tuples, and strings
• assignment5/ - theoretical Python fundamentals and notebook implementations
• assignment6/ - NumPy matrix operations, Matplotlib visualization, functional programming
• patternproblems/ - interactive string and character pattern generation tool
• patternpractice/ - experimental pattern logic scripts
• prelimsICSE/ - ICSE preliminary examination practice notebooks
• first_term/ - first term exam preparation lab
• venn_diagrams.py - set logic visualization using Matplotlib

educational guides & question banks

• global-local-variables.md - scoping mechanics and symbol resolution in Python
• implicit-and-explicit.md - explicit return values versus default None returns
• icseSyllabusQuestions/ - targeted ICSE exam prep, Turtle graphics, file handling
• 11thISC/ - Grade 11 ISC AI material and Seaborn practice

setup & environments

this repository runs on Python 3.12+ and accelerated ML environments.

1. standard virtual environment: activate .venv for core lightweight Python scripts.
2. GPU environment: use the dedicated tf-gpu Anaconda environment (conda activate tf-gpu) for CUDA-accelerated TensorFlow and heavy deep learning / matrix workloads.
3. follow repository conventions in AGENTS.md.
4. use pnpm for any web-related dependencies or tooling.