Iceberg, Right Ahead!

The project found that a passenger's class and sex had an impact on survival rates. The "Iceberg, Right Ahead" project aimed to examine whether sex and a passenger's class on the boat impacted their ability to survive on the ship. According to the data set, the ship had 892 passengers where the average age of female passengers was 28 years old and the average male age was 31. Although age was not a factor the projected considered that would impact survival rates, it was still important to recognize in order to gain a full understanding of the ship's demographic make up.

Data Collection Process

• Step 1: Develop a question or find a topic to explore
• Step 2: Collected my data from a .txt file that I converted into a .csv for easy transfer to Jupyter Notebook
• Step 3: Cleaned data by removing unwanted variables ( Cabin, Fare, Parch, Embarked, PassengerId, Ticket, SibSp, Name)

Data Analysis Snapshot

Analyzing the data included removing and grouping (.groupby) because I was comparing multiple variables against one. However, after removing the unwanted variables from my data set it was easier than just solely not using those when analyzing my data.

Project Limitations

The most difficult component of the project was wanting to further "break down" certain variables. For example, I wanted to look look at specifcally how many females and males survived the shipwreck, but I was unable to figure out how to perform that action. Also, in the future I would like to build my website out, but I was not able to fully build it out for this project due to time constraints.

So, What Did You Learn?

• I learned how to use W3 for a lot of my needs when it came to using CSS
• I learned how to use a combination of different tools at one time including Terminal, Jupyter Notebook, Github, and VSCode to bring everything together.
• The entire process was a lot of trial and error, which showed me the amount of time it actually takes to create a product.
• Instead of using Datawrapper and the other data visualization websites, I opted for ggplot because I wanted to challenge myself to create a graph "by hand" instead of having it automated.

Before Starting Your Analysis Be Sure To:

• Import pandas
• Import csv
• pip install jupyter notebook
• pip install matlockplot https://matplotlib.org/stable/

Questions? Comments? kesa.white@proton.me