Python Para Analise De Dados - 3a Edicao Pdf Link

# Filter out irrelevant data data = data[data['engagement'] > 0] With her data cleaned and preprocessed, Ana moved on to exploratory data analysis (EDA) to understand the distribution of variables and relationships between them. She used histograms, scatter plots, and correlation matrices to gain insights.

# Calculate and display the correlation matrix corr = data.corr() plt.figure(figsize=(10,8)) sns.heatmap(corr, annot=True, cmap='coolwarm', square=True) plt.show() Ana's EDA revealed interesting patterns, such as a strong correlation between age and engagement frequency, and a preference for video content among younger users. These insights were crucial for informing the social media platform's content strategy. Python Para Analise De Dados - 3a Edicao Pdf

She began by importing the necessary libraries and loading the dataset into a Pandas DataFrame. # Filter out irrelevant data data = data[data['engagement']

# Train a random forest regressor model = RandomForestRegressor() model.fit(X_train, y_train) These insights were crucial for informing the social