Space Mission ML Project - Part 2(Data Visualization)

Let's Visualize the Story, our Space Mission Dataset is trying to tell

In the first part of this blog have tried to separate the mission details of each organization.

Now we are going to visualize how the cost/price of the space missions of a specific organization have varied/changed over time.

Here we have used the Plotly visualization library to create a date v/s price for ISRO.

Now we'll see the frequency distribution of all mission costs. So we have created a distribution plot here using the matplotlib and seraborn library(python's seaborn library has been built on the roof of matplotlib).

Now we'll see counts of the status of the missions which means how many space missions are in each status worldwide.

Probably a concern is raising in your mind what if we get to see how many total missions have been launched by each space organization?
here we have created a bar plot.

To see the success or failure rate of the missions we can create a piechart. When we are creating a piechart with the Mission-status and count column(created using value-counts) using plotly the counts will default converted to percentages.