Python Para Analise De Dados - 3a Edicao Pdf 〈1080p〉

# Train a random forest regressor model = RandomForestRegressor() model.fit(X_train, y_train)

Ana had always been fascinated by the amount of data generated every day. As a data enthusiast, she understood the importance of extracting insights from this data to make informed decisions. Her journey into data analysis began when she decided to pursue a career in data science. With a strong foundation in statistics and a bit of programming knowledge, Ana was ready to dive into the world of data analysis. Python Para Analise De Dados - 3a Edicao Pdf

from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error # Train a random forest regressor model =

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

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.