Commando: Adventure shooting

"Join the Commando in an Adventure Shooting game as you guide the courageous hero through challenging levels filled with obstacles and dangers. Prepare for an exhilarating and perilous journey through dense jungles and other locations in this Android game. Assist the hero in overcoming all obstacles along the way. Leap onto platforms and avoid pits. Utilize powerful weapons to defeat hordes of enemies. Dodge enemy attacks and turrets. Enhance the hero's abilities by purchasing upgrades. Game Highlights: - Easy controls - Wide selection of weapons - Various bonuses and power-ups - Numerous enemies to battle against"<|endoftext|><|endoftext|> // This is a single line comment /* This is a multi-line comment */<|endoftext|> The term "sustainability" refers to the ability to maintain or support something over time without depleting resources or causing harm to the environment. It is often used in the context of environmental sustainability, which focuses on preserving natural resources and ecosystems for future generations. However, sustainability can also refer to social and economic sustainability, which involves creating systems and practices that promote social equity and economic stability. Overall, sustainability aims to find a balance between meeting current needs and ensuring the ability to meet future needs.<|endoftext|><|endoftext|># Language: Python 3 Notebook # Importing necessary libraries import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns # Reading the dataset df = pd.read_csv('heart.csv') # Exploring the dataset df.head() # Checking for missing values df.isnull().sum() # Checking for duplicate rows df.duplicated().sum() # Checking the data types of columns df.dtypes # Converting the target variable to categorical df['target'] = df['target'].astype('category') # Checking the distribution of target variable df['target'].value_counts() # Visualizing the distribution of target variable sns.countplot(x='target', data=df) plt.show() # Checking the correlation between variables corr = df.corr() corr # Visualizing the correlation matrix sns.heatmap(corr, annot=True) plt.show() # Splitting the dataset into features and target variable X = df.drop('target', axis=1) y = df['target'] # Splitting the dataset into train and test sets from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Scaling the features from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # Training a logistic regression model from sklearn.linear_model import LogisticRegression lr = LogisticRegression() lr.fit(X_train, y_train) # Making predictions on test set y_pred = lr.predict(X_test) # Evaluating the model from sklearn.metrics