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.
import pandas as pd import numpy as np import matplotlib.pyplot as plt
# Plot histograms for user demographics data.hist(bins=50, figsize=(20,15)) plt.show() Python Para Analise De Dados - 3a Edicao Pdf
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.
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. To further refine her analysis, Ana decided to
# 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.
# Handle missing values and convert data types data.fillna(data.mean(), inplace=True) data['age'] = pd.to_numeric(data['age'], errors='coerce') As a data enthusiast, she understood the importance
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.
# 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.