Another post starts with you beautiful people !
Today we will work on a famous dataset Titanic Dataset taken from kaggle.
This dataset gives information about the details of the passengers aboard the Titanic and a column on survival of the passengers. Those who survived are represented as “1” while those who did not survive are represented as “0”.
The columns in the dataset are as below-
PassengerId: Passenger Identity
Survived: Whether passenger survived or not
Pclass: Class of ticket
Name: Name of passenger
Sex: Sex of passenger (Male or Female)
Age: Age of passenger
SibSp: Number of sibling and/or spouse travelling with passenger
Parch: Number of parent and/or children travelling with passenger
Ticket: Ticket number
Fare: Price of ticket
Cabin: Cabin number
Let's starts some hands on-
Let's generates descriptive statistics-
Result:
Note: if you are seeing error- ImportError: No module named 'seaborn' then it mean you need to install the seaborn library using command- pip install seaborn in the command prompt.
Result:
Let's find out the children in the dataset-
Let's count the person individually-
Now plot Male, Female, Child in Pclass-
Result:
People Who Survived and Who Didn't:
How many Male and Female survived :
Result-More females survive than males.
Let's compute pairwise correlation of columns, excluding NA/null values:-
Result:
See with the help of above visualization how you can easily transform a dataset into a story telling.
Try in your notebook and share your thoughts in comment.
Today we will work on a famous dataset Titanic Dataset taken from kaggle.
This dataset gives information about the details of the passengers aboard the Titanic and a column on survival of the passengers. Those who survived are represented as “1” while those who did not survive are represented as “0”.
The columns in the dataset are as below-
PassengerId: Passenger Identity
Survived: Whether passenger survived or not
Pclass: Class of ticket
Name: Name of passenger
Sex: Sex of passenger (Male or Female)
Age: Age of passenger
SibSp: Number of sibling and/or spouse travelling with passenger
Parch: Number of parent and/or children travelling with passenger
Ticket: Ticket number
Fare: Price of ticket
Cabin: Cabin number
Let's starts some hands on-
Let's generates descriptive statistics-
Result:
Note: if you are seeing error- ImportError: No module named 'seaborn' then it mean you need to install the seaborn library using command- pip install seaborn in the command prompt.
Result:
Let's find out the children in the dataset-
Let's count the person individually-
Now plot Male, Female, Child in Pclass-
Result:
How many Male and Female survived :
Result-More females survive than males.
Let's compute pairwise correlation of columns, excluding NA/null values:-
See with the help of above visualization how you can easily transform a dataset into a story telling.
Try in your notebook and share your thoughts in comment.
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