algorithmic-thinking

Lillith is a beginner-friendly Python repository containing 31 fundamental programs with comments and 20-30 problem statements with solutions. It provides a structured approach for learning Python step by step.

0/1 Knapsack problem implementation using greedy algorithms for stock portfolio optimization (MIT 6.100B)

Beginner Python projects for coding practice and learning.

A curated collection of Python problem-solving exercises covering strings, numbers, arrays, typecasting, and common algorithmic challenges. Each problem includes clean, readable, and interview-ready solutions with explanations, aimed at helping learners strengthen their Python skills and prepare for coding interviews.

My very first coding project in high school! Armed with basic Python knowledge, my two best friends and I embarked on an adventurous dream to create this RPG. Writing the first 1000 lines of code felt incredible, and we couldn't be prouder of our achievement!

Tic-Tac-Toe game with an unbeatable AI using the Minimax algorithm and Pygame

Basic virtual therapist chatbot designed to engage in conversation and provide a supportive environment for users.

🧩 An AI solver for the classic 8-Puzzle game using AI search strategies like A* Search. Designed to make concepts like heuristics, pathfinding, and search trees easy to understand, while showcasing thoughtful software design and algorithmic thinking. Great for anyone curious about how AI plans ahead and solves problems, one move at a time.

A simple Python project that adds a duration to a given start time in 12-hour format (AM/PM), with optional support for the day of the week. It handles time rollovers, day changes, and large hour additions.

A Python program that formats arithmetic problems vertically with proper spacing and optional solutions. Built as part of freeCodeCamp’s Scientific Computing with Python.

All LeetCode problems solved in Python with clean code, explanations, time–space complexity, and structured directory indexing. Updated for 2026

A curated archive of algorithmic problems, broken down by strategy. It maps out how strong solutions are formed by highlighting key insights and extracting reusable heuristics. This project is designed to train better thinkers, smarter agents, and future-ready engineers.

Competitive programming problem designed to expose failure modes in frontier LLMs. Features a 3-layer trap invalidating greedy, O(N²) DP, and prefix-sum optimizations. Correct O(N log V) solution via monotonic stack + DP. Built for Turing's Python TopCoder Problemsetter Assessment.

Learned basic Python programming: Program design, Algorithmic thinking, Recursion, Object-oriented programming, Interpreters, Compilers, and Data representation

Global Alignment for Genomic Sequences