Applied Data Science with Python and Jupyter
Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications. Key Features Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts such as SVM, KN...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Packt Publishing
2018.
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| Edición: | 1st edition |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631851206719 |
Tabla de Contenidos:
- Intro
- Preface
- Jupyter Fundamentals
- Introduction
- Basic Functionality and Features
- What is a Jupyter Notebook and Why is it Useful?
- Navigating the Platform
- Exercise 1: Introducing Jupyter Notebooks
- Jupyter Features
- Exercise 2: Implementing Jupyter's Most Useful Features
- Converting a Jupyter Notebook to a Python Script
- Python Libraries
- Exercise 3: Importing the External Libraries and Setting Up the Plotting Environment
- Our First Analysis - The Boston Housing Dataset
- Loading the Data into Jupyter Using a Pandas DataFrame
- Exercise 4: Loading the Boston Housing Dataset
- Data Exploration
- Exercise 5: Analyzing the Boston Housing Dataset
- Introduction to Predictive Analytics with Jupyter Notebooks
- Exercise 6: Applying Linear Models With Seaborn and Scikit-learn
- Activity 1: Building a Third-Order Polynomial Model
- Using Categorical Features for Segmentation Analysis
- Exercise 7: Creating Categorical Fields From Continuous Variables and Make Segmented Visualizations
- Summary
- Data Cleaning and Advanced Machine Learning
- Introduction
- Preparing to Train a Predictive Model
- Determining a Plan for Predictive Analytics
- Exercise 8: Explore Data Preprocessing Tools and Methods
- Activity 2: Preparing to Train a Predictive Model for the Employee-Retention Problem
- Training Classification Models
- Introduction to Classification Algorithms
- Exercise 9: Training Two-Feature Classification Models With Scikit-learn
- The plot_decision_regions Function
- Exercise 10: Training K-nearest Neighbors for Our Model
- Exercise 11: Training a Random Forest
- Assessing Models With K-fold Cross-Validation and Validation Curves
- Exercise 12: Using K-fold Cross Validation and Validation Curves in Python With Scikit-learn
- Dimensionality Reduction Techniques.
- Exercise 13: Training a Predictive Model for the Employee Retention Problem
- Summary
- Web Scraping and Interactive Visualizations
- Introduction
- Scraping Web Page Data
- Introduction to HTTP Requests
- Making HTTP Requests in the Jupyter Notebook
- Exercise 14: Handling HTTP Requests With Python in a Jupyter Notebook
- Parsing HTML in the Jupyter Notebook
- Exercise 15: Parsing HTML With Python in a Jupyter Notebook
- Activity 3: Web Scraping With Jupyter Notebooks
- Interactive Visualizations
- Building a DataFrame to Store and Organize Data
- Exercise 16: Building and Merging Pandas DataFrames
- Introduction to Bokeh
- Exercise 17: Introduction to Interactive Visualization With Bokeh
- Activity 4: Exploring Data with Interactive Visualizations
- Summary
- Appendix A
- Index
- _GoBack
- Activity_A:_Web_Scraping_with_Jupyter_No
- _bookmark13.
