Python Para Analise De Dados - 3a Edicao Pdf Guide

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.

# 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

# Handle missing values and convert data types data.fillna(data.mean(), inplace=True) data['age'] = pd.to_numeric(data['age'], errors='coerce') To further refine her analysis, Ana decided to

import pandas as pd import numpy as np import matplotlib.pyplot as plt These insights were crucial for informing the social

# Split the data into training and testing sets X = data.drop('engagement', axis=1) y = data['engagement'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

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.