Flick shoot: United kingdom

Experience the excitement of Flick Shoot in the United Kingdom! Step into the stadium and show off your free kick skills as you aim for the enemy's goal. Help your team secure victory and become a hero. With this Android game, you can create your own unique character by choosing their appearance and outfit. Take control of your player on the field and make precise shots at the goal. Practice and hone your skills in various single player modes. Challenge players from all over the world in quick matches or compete in an international tournament. Aim for the top of the leaderboards and claim your spot as the ultimate Flick Shoot champion! Game Features: - Stunning graphics - 6 exciting single player modes - Easy-to-use controls - Addictive gameplay<|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('housing.csv') # Exploring the dataset print(df.head()) # Checking for missing values print(df.isnull().sum()) # Visualizing the correlation between the features corr = df.corr() sns.heatmap(corr, annot=True) plt.show() # Splitting the dataset into training and testing sets X = df[['RM', 'LSTAT', 'PTRATIO']] y = df['MEDV'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Training the model model = LinearRegression() model.fit(X_train, y_train) # Making predictions on the testing set y_pred = model.predict(X_test) # Evaluating the model print('Mean squared error: %.2f' % mean_squared_error(y_test, y_pred)) print('Coefficient of determination: %.2f' % r2_score(y_test, y_pred)) # Plotting the predicted values against the actual values plt.scatter(y_test, y_pred) plt.xlabel('Actual values') plt.ylabel('Predicted values') plt.title('Actual vs Predicted values') plt.show()<|endoftext|>x = 5 y = 10 # Addition print(x + y) # Output: 15 # Subtraction print(x - y) # Output: -5 # Multiplication print(x * y) # Output: 50 # Division print(x / y) # Output: 0.5 # Modulus print(x % y) # Output: 5 # Exponentiation print(x ** y) # Output: 9765625<|endoftext|>x = 5 y = 10 # Addition