This project folder consists of 10 projects for Python Beginners. They include:
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Mad Libs
- It is a phrasal template word game. That you fill in blank spaces with appropriate word which can be an adjective, noun, etc. It is considered a word play game.
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Guess The Number (Human Player and Computer Player)
- This ia a game that defs two types of functions that guess a number correctly and prompts user to retry if the input is wrong(using loops). Giving clues whether the input (if wrong) is too low or too high.
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Rock-Paper-Scissors -This is a playful game, lets you play 1v1 against the computer letting you choose between rock, paper and scissors. Read more in Game Instructions.
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Hangman -This is is a word game, wher you get the chances to guess letters for the word hidden. Give it a try.
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TicTacToe
- This is a game of X's and O's. Its a two player with the aim to align one chosen value 3 times in a 3X3 grid before the other player.
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Unbeatable
- Just like normal TicTacToe but much difficult to beat the computer player.
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Binary Search
- This is an Implementation of binary search algorithm. Here we shall prove that binary search is faster than naive search. For Naive search: scan entire list and ask if its equal to the target. For Binary Search: we use divide and conquer. We leverage the fact that our list is sorted.
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Minesweeper
- Minesweeper is a single-player puzzle game. The objective is to clear a board with hidden mines/bombs without detonating any of them, with help from neighbouring mines in each field
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Soduko SOlver
- Here we use the conce[t of backtracking(retracing steps), to solve a soduko puzzle.
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Markov Chain Composer
- This is a stochastic model created by Andrey Markov, which outlines the probaility associated with a sequence of events ocurring based on the state in the previous state.
- It generally refers to scenrios when the time associated with an event occuring does not depend on how much time has already elapsed. these predictions are conditional on its current and is independent of past and future states. read more at: https://towardsdatascience.com/markov-chain-explained-210581d7a4a9