# 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)
And so, Ana's story became a testament to the power of Python in data analysis, a tool that has democratized access to data insights and continues to shape various industries.
# Train a random forest regressor model = RandomForestRegressor() model.fit(X_train, y_train) Python Para Analise De Dados - 3a Edicao Pdf
To further refine her analysis, Ana decided to build a simple predictive model using scikit-learn, a machine learning library for Python. She aimed to predict user engagement based on demographics and content preferences.
Her journey into data analysis with Python had been enlightening. Ana realized that data analysis is not just about processing data but about extracting meaningful insights that can drive decisions. She continued to explore more advanced techniques and libraries in Python, always looking for better ways to analyze and interpret data. # Split the data into training and testing sets X = data
from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error
# 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. Her journey into data analysis with Python had
# Evaluate the model y_pred = model.predict(X_test) mse = mean_squared_error(y_test, y_pred) print(f'Mean Squared Error: {mse}') Ana's model provided a reasonably accurate prediction of user engagement, which could be used to tailor content recommendations.