By the end of this project, you should have a working solution that meets the given problem statement. This mini-project will help you learn how to visualize data using different Python libraries like matplotlib and Seaborn. You will also perform data analysis based on a dataset.
This dataset contains information about Titanic passengers and their survival status. It provides insights into different factors that might have influenced survival rates, such as age, gender, class, and fare paid.
- survived: Indicates whether the passenger survivied (1) or not (0).
- pclass: Passengers class (1st, 2nd, 3rd).
- sex: Gender of the passenger (male and female)
- age: Age of the passengers in years.
- sibsp: Number of siblings/spouses aboard.
- parch: Number of parents/children aboard.
- fare: Fare paid for the ticket.
- embarked: Port of embarkation (C=Cherbourg, Q=Queentown, S= Southampton).
- class: The ticket class (First, Second, Third).
- who: Categorization of the passengers (man, woman, child).
- adult_male: Indicates if the passengers is an adult male (True/False).
- deck: The deck the passenger was assigned to.
- embark_town: Name of the town where the passengers embarked.
- alive: Survival status (yes/no).
- alone: Indicates whether the passenger was alone (True/False).
- What is the survival rate of passengers?
- What is the gender distribution of passengers?
- How does survival rate differ by class?
- What is the distribution of passenger ages?
- How many passengers embarked from each location?
- What is the average fare paid per class?
- How does gender affect survival rates?
- What is the correlation between fare and survival?
- Understand the problem - Take time to read and analyze the project description before coding.
- Write clean codes - Follow best coding practices, use meaningful variable names, and comment your codes.
- Test your solution - Run multiple test cases to ensure your program works correctly.
- Debugging is Key - If something doesn't work, use print statements or a swbugger to find issues.