Honey rush: Run Teddy run

Honey Rush: Teddy's Escape - assist a comical teddy bear in gathering as much honey as he can while escaping from a swarm of bees chasing him. Dash forward and surpass any obstacles in your way. Lead the protagonist of this Android game through village streets, swipe through thick forests, and other settings. Shift from left to right to dodge different objects. Leap over rocks, barriers, and other hindrances. Make sure to not miss any honey pots along the way. Utilize powerful bonuses to boost your bear's speed. Unlock new characters as you progress. Game Highlights: Numerous vibrant missions Amusing teddy bear characters Incredible power-ups Easy controls<|endoftext|><|endoftext|># Language: Python 3 Notebook # Language: Python # -*- coding: utf-8 -*- # Importing necessary libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, r2_score # Loading the dataset df = pd.read_csv('https://raw.githubusercontent.com/AdiPersonalWorks/Random/master/student_scores%20-%20student_scores.csv') # Exploring the dataset print(df.head()) # Checking for null values print(df.isnull().sum()) # Visualizing the data sns.scatterplot(x='Hours', y='Scores', data=df) plt.title('Hours vs Scores') plt.show() # Preparing the data X = df.iloc[:, :-1].values y = df.iloc[:, 1].values # Splitting the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) # Training the model regressor = LinearRegression() regressor.fit(X_train, y_train) # Making predictions y_pred = regressor.predict(X_test) # Comparing actual vs predicted values df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred}) print(df) # Evaluating the model print('Mean Squared Error:', mean_squared_error(y_test, y_pred)) print('R2 Score:', r2_score(y_test, y_pred)) # Plotting the regression line line = regressor.coef_*X+regressor.intercept_ # Plotting for the test data plt.scatter(X, y) plt.plot(X, line) plt.show() # Predicting the score for 9.25 hours hours = 9.25 pred = regressor.predict([[hours]]) print('Number of Hours = {}'.format(hours)) print('Predicted Score = {}'.format(pred[0]))<|endoftext|>x = 5 y = 10 # Addition print(x + y) # Output: 15 # Subtraction print(x - y) #