import pandas as pd import numpy as np import matplotlib.pyplot as plt
She began by importing the necessary libraries and loading the dataset into a Pandas DataFrame. Python Para Analise De Dados - 3a Edicao Pdf
# 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. import pandas as pd import numpy as np import matplotlib
Her first challenge was learning the right tools for the job. Ana knew that Python was a popular choice among data analysts and scientists due to its simplicity and the powerful libraries available for data manipulation and analysis. She started by familiarizing herself with Pandas, NumPy, and Matplotlib, which are fundamental libraries for data analysis in Python. Her first challenge was learning the right tools for the job
# Train a random forest regressor model = RandomForestRegressor() model.fit(X_train, y_train)