Python Para Analise De Dados - 3a Edicao Pdf -

# Handle missing values and convert data types data.fillna(data.mean(), inplace=True) data['age'] = pd.to_numeric(data['age'], errors='coerce')

# Load the dataset data = pd.read_csv('social_media_engagement.csv') The dataset was massive, with millions of rows, and Ana needed to clean and preprocess it before analysis. She handled missing values, converted data types where necessary, and filtered out irrelevant data. Python Para Analise De Dados - 3a Edicao Pdf

# Split the data into training and testing sets X = data.drop('engagement', axis=1) y = data['engagement'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Handle missing values and convert data types data