Gain insights into performing predictive data analysis using Python. Learn about tools like NumPy, Pandas, Matplotlib, and Seaborn, and applying techniques to real-world financial and advertisement projects.

seaborn.tar.gz

seaborn

In this course, you will learn how to perform predictive data analysis using Python. The ideal audience is those who want to start their careers as data analysts. The main goal of this course is to show you how to use statistics to draw useful insights from data which can help in predicting future behavior or patterns.

Beyond that, you’ll learn all the tools of the trade that data scientists use everyday including: NumPy, Pandas, Matplotlib, and Seaborn. You’ll learn not only how to extract meaningful insights from data, but you’ll also learn how to create stunning visualizations that you can use for reports.

Various datasets of real-world scenarios are used in each lesson to get you accustomed to handling any type of data. At the end of the course, you will work on two real-world projects that demonstrate how data analysis techniques are being used in the financial and advertisement sector to generate revenue.

Predictive Data Analysis with Python

# Selenium

Selenium is a browser automation tool. It is mostly used for automated testing purposes but in this course, this tool will be used to scrape information from websites.

While scraping information, all the information we need is never simply on a single page. Some buttons need to be clicked to traverse through the website to get to the required data, and then it is scraped and stored. Detailed info on selenium can be found [here](https://www.selenium.dev/).

## How it works

Selenium opens a browser and goes to the initial link given to it. Then, the user has the power to make it do anything as if they are controlling the browser behavior themselves. It locates specific elements in the *HTML DOM* structure and applies the action we want on that element. Selenium selects the elements on a web page through the following keywords:

* `id`: The id element of *HTML*

* `class`: The class element of *HTML*

* `css_selector`: Elements that affect the **CSS** of a webpage; all elements that let you change the `CSS` of a page can be used here. Some examples are `a` tag, `p` tag, etc

* `x_path`: Completes the path to certain elements in a webpage like the following one:
  `//[@id="something"]/div/div/div[2]/div[1]/div/div/div/div/div/div[4]/div/div[1]/div[1]/div/div[2]/div/div/div[2]/div[1]`. More on `x_path` and how to obtain it can be found [here](https://www.guru99.com/xpath-selenium.html).

After selecting an element, it gets the complete structure of all the elements that are inside the selected element. The user can then choose from those nested elements as well. We'll mostly be using the `x_path` selector for locating elements.

## Setup

To successfully run selenium with python, two things are needed: a *driver* that opens the browser of our choice and the selenium package for python.

* Every browser has a different driver that works with selenium to operate it. Our code will use *Google Chrome*, so we'll use the *chromedriver*. It can be downloaded from [here](https://chromedriver.chromium.org/downloads).

* The selenium package can be installed by the following command:

     ```shell
     pip install selenium
     ```

The following example demonstrates how selenium works.

In this lesson, a scraping and automation tool named selenium is discussed.

Getting Started

Numpy for Python

Pandas for Python

Statistics for Data Analysis

Data Wrangling

Visualizing the Data

Data Scraping

Project #1

Project #2

Conclusion

Predictive Data Analysis Exam

Exploring Selenium

Selenium

How it works