Question

Create a jupyter notebook called titanic.ipynb. Use this Notebook to create an in-depth EDA on the Titanic dataset provided in this task. Your EDA should contain descriptions of your EDA and appropriate visualisations. Use the following guiding questions for your EDA: - What is the most important factor in determining survival of the Titanic incident? - In the movie, the upper-class passengers were given preference on lifeboats. Does this show in the data? - "Women and children first". Was this the case? - Add one other observation that you have noted in the dataset.

Answer 1

The Titanic dataset's EDA in a Jupyter notebook should focus on analyzing survival factors, class-based survival rates, adherence to the 'Women and children first' policy using visualizations and statistics, and include an additional unique observation from the data.

Step-by-step explanation:

The task is to create a Jupyter notebook titled titanic.ipynb to conduct an Exploratory Data Analysis (EDA) on the Titanic dataset. An EDA is crucial for understanding the intricacies of a dataset by uncovering patterns, anomalies, and correlations through descriptive statistics and visualizations.

In answering the guiding questions:

1. The most important factor in determining survival during the Titanic incident may be hypothesized from the data, often involving passenger class, gender, and age.
2. Analyze the data to ascertain if upper-class passengers had a higher survival rate as compared to other classes, which would indicate preferential access to lifeboats.
3. By examining the survival rates among different genders and age groups, the adage "Women and children first" can be validated from the dataset.

Additionally, a personal observation drawn from the dataset should be included, which might reveal insights into less-discussed factors affecting survival, such as embarkation points or fare prices.

Collaborative exercises involving sports data can be analogous to this task, where data visualization and statistical tools are employed to draw conclusions and fit probability distributions. Similarly, class discussions about observations and interpretations help deepen the understanding of statistical analysis.