Python Para Analise De Dados - 3a Edicao Pdf -

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.

# Train a random forest regressor model = RandomForestRegressor() model.fit(X_train, y_train) Python Para Analise De Dados - 3a Edicao Pdf

Ana's first project involved analyzing a dataset of user engagement on a popular social media platform. The dataset included user demographics, the type of content they engaged with, and the frequency of their engagement. Ana's goal was to identify patterns in user behavior that could help the platform improve its content recommendation algorithm. 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. # Train a random forest regressor model =

She began by importing the necessary libraries and loading the dataset into a Pandas DataFrame.

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