10 Python Mini Projects for Beginners

Guys, once you've got the basics of Python down, it’s time to build stuff!

Here are 10 mini project ideas that are fun, practical, and boost your confidence!

1. Number Guessing Game 🎯
The computer picks a number, and the user keeps guessing until they get it right.
Perfect to practice loops, conditionals, and user input.

2. Calculator App ➕➖✖️➗
Build a simple calculator that takes two numbers and performs addition, subtraction, multiplication, or division.

3. To-Do List (Console Version) ✅
Let users add, view, and delete tasks. Great to practice lists and file handling if you want to save tasks.

4. Password Generator 🔐
Create random passwords using letters, numbers, and symbols. Use the random and string modules.

5. Dice Rolling Simulator 🎲
Simulate rolling a die. Add cool features like rolling multiple dice or counting the frequency.

6. Rock Paper Scissors Game ✊✋✌️
Let the user play against the computer. Introduces randomness and conditional logic.

7. Quiz App ❓
Create a multiple-choice quiz that gives a score at the end. Store questions and answers using dictionaries.

8. Countdown Timer ⏱️
User inputs minutes or seconds, and the timer counts down to zero. Helps practice time.sleep().

9. Tip Calculator 🍽️
Calculate how much each person should pay including tip. Useful for string formatting and arithmetic.

10. Weather App (Using API) ☁️☀️🌧️
Use a public weather API to fetch real-time weather for a city. Great to explore APIs and the requests library.


For all resources and cheat sheets, check out my Telegram channel: https://t.me/pythonproz

Hope it helps :)

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Top 10 machine Learning algorithms for beginners 👇👇

1. Linear Regression: A simple algorithm used for predicting a continuous value based on one or more input features.

2. Logistic Regression: Used for binary classification problems, where the output is a binary value (0 or 1).

3. Decision Trees: A versatile algorithm that can be used for both classification and regression tasks, based on a tree-like structure of decisions.

4. Random Forest: An ensemble learning method that combines multiple decision trees to improve the accuracy and robustness of the model.

5. Support Vector Machines (SVM): Used for both classification and regression tasks, with the goal of finding the hyperplane that best separates the classes.

6. K-Nearest Neighbors (KNN): A simple algorithm that classifies a new data point based on the majority class of its k nearest neighbors in the feature space.

7. Naive Bayes: A probabilistic algorithm based on Bayes' theorem that is commonly used for text classification and spam filtering.

8. K-Means Clustering: An unsupervised learning algorithm used for clustering data points into k distinct groups based on similarity.

9. Principal Component Analysis (PCA): A dimensionality reduction technique used to reduce the number of features in a dataset while preserving the most important information.

10. Gradient Boosting Machines (GBM): An ensemble learning method that builds a series of weak learners to create a strong predictive model through iterative optimization.

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