Python Para Analise De Dados - 3a Edicao Pdf -

# 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.

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

import pandas as pd import numpy as np import matplotlib.pyplot as plt Python Para Analise De Dados - 3a Edicao Pdf

# 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.

# 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. # Evaluate the model y_pred = model

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

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. She used histograms, scatter plots, and correlation matrices

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.