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

import pandas as pd
import seaborn as sns   

df = sns.load_dataset('flights')  # Reading flights dataset from seaborn package

# Reshaping the DataFrame
df = pd.pivot_table(df, values = 'passengers', index = ['month'], columns = 'year')

# Plotting the heatmap
sns.heatmap(df, annot = True, fmt = '')

The above problem plots the data on a heatmap after reshaping it.

On __line 7__, the `pivot_table` function is used to reshape the dataset according to our requirements. The `passengers` are set as values and the `month` and `year` as row and column indexes, respectively, as we want to display the count of people on different months and years.

On __line 10__, the `heatmap` function of the seaborn is used to plot the reshaped dataset onto a heatmap. The `fmt` parameter keeps the numbers on the heatmap concise for a better view and understanding. It is basically a number formatting function. Try plotting the heatmap without it and observe how it affects the display.


# Regression plot solution

This lesson gives a detailed review of the solutions to the challenges in the previous lesson.

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

Solution: Visualizing Seaborn Datasets

Heatmap solution #