Find the length of a string

 string = "hello world"

length = len(string)

print("The length of the string is", length)


Comments

Post a Comment

Popular posts from this blog

Iris Flowers Classification ML Project with Code

In this code, we first load the iris dataset using the load_iris() function from scikit-learn. We then split the data into training and testing sets using the train_test_split() function. Next, we scale the features using the StandardScaler() function from scikit-learn. We then train the KNN model with n_neighbors = 3 using the KNeighborsClassifier() function. After training the model, we predict the species of the iris flowers in the testing set using the predict() function. Finally, we evaluate the performance of the model using the accuracy_score() , precision_score() , recall_score() , and f1_score() functions from scikit-learn. Note that the values of the metrics may vary slightly depending on the random seed used for splitting the dataset.   # Import necessary libraries from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.preprocessing import StandardScaler from sklea...
Read more

Simple Python Program Code for Snake Game

 import pygame import time import random   pygame.init()   white = (255, 255, 255) yellow = (255, 255, 102) black = (0, 0, 0) red = (213, 50, 80) green = (0, 255, 0) blue = (50, 153, 213)   dis_width = 600 dis_height = 400   dis = pygame.display.set_mode((dis_width, dis_height)) pygame.display.set_caption('Snake Game by @ChatGPT')   clock = pygame.time.Clock()   snake_block = 10 snake_speed = 15   font_style = pygame.font.SysFont(None, 30)     def message(msg, color):     mesg = font_style.render(msg, True, color)     dis.blit(mesg, [dis_width / 6, dis_height / 3])     def gameLoop():       game_over = False     game_close = False       x1 = dis_width / 2     y1 = dis_height / 2       x1_change = 0            y1_change = 0       foodx = round(random.randrange(0, dis_width - snake_block) / 10.0) ...
Read more

Linear Regression Model for House Price Prediction in Python

S imple example of how to use scikit-learn library in Python to implement machine learning: Suppose we want to train a model to predict the price of a house based on its size (in square feet). We can use a linear regression model for this task. Here's the code: import numpy as np from sklearn.linear_model import LinearRegression # Generate some random data for training size = np.array([[100], [150], [200], [250], [300], [350], [400], [450], [500], [550]]) price = np.array([150000, 200000, 250000, 300000, 350000, 400000, 450000, 500000, 550000, 600000]) # Train the model model = LinearRegression() model.fit(size, price) # Predict the price of a new house with 400 square feet new_size = np.array([[400]]) predicted_price = model.predict(new_size) print(predicted_price) Output [358181.81818182] Explanation: First, we import the necessary libraries: numpy for numerical operations and LinearRegression from sklearn.linear_model for the linear regression model. We generate some rand...
Read more