Python Para Analise De Dados - 3a Edicao Pdf Online

# Filter out irrelevant data data = data[data['engagement'] > 0] With her data cleaned and preprocessed, Ana moved on to exploratory data analysis (EDA) to understand the distribution of variables and relationships between them. She used histograms, scatter plots, and correlation matrices to gain insights.

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.

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.

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

# 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)

# 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. # Filter out irrelevant data data = data[data['engagement']

from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error

# 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.

# 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. Ana realized that data analysis is not just

# 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 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.

# Plot histograms for user demographics data.hist(bins=50, figsize=(20,15)) plt.show()

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