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

# Users activity
As mentioned in the [previous](https://www.educative.io/courses/predictive-data-analysis-with-python/section-1-brand-analysis) lesson, the user can perform three actions that get recorded in the dataset.

* **view**: The user can view an item. 

* **cart**: The user can add the item to the cart. 

* **purchase**: The user can purchase the item. 

Analyzing the *view* and *purchasing* actions of the user across the different timelines in a month can provide very important information as to at what time most of the users visit the site. When such times are known, resources can be allocated according to that information to optimize performance. 

For example, if we know that a significant amount of users visit the site on *Sunday* just to view the products, resources from other components can be transferred to viewing components to enhance the user experience. Similarly, the same approach can be used on other components if we know at what times certain, user activity is preferred.

Let's apply this approach to our data and review what analysis can be drawn from it.

## Preprocessing

Before we move to extract information, some preprocessing needs to be done on our initial `DataFrame`. The time values are separated from the `event_time` column and are made into separate columns. The `day`, `week_day`, and `hour` are computed for each `event_time` value.





In this lesson, the customers activity data throughout the month will be analyzed.

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

Section 2: Customer Activity Analysis

Users activity