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TITANIC DATASET

The data includes demographic and travel information for 1,309 Titanic passengers to predict their survival. The whole Titanic dataset is accessible in multiple forms from the Department of Biostatistics at Vanderbilt University School of Medicine (http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic3.csv). The go-to resource for information about the Titanic is the website for Encyclopaedia Titanica (https://www.encyclopedia-titanica.org/). It includes a complete list of the passengers and crew and all the relevant information on the Titanic's facts, history, and data. Additionally, the Titanic dataset is the focus of the inaugural competition on Kaggle.com (https://www.kaggle.com/c/titanic; needs creating a Kaggle account). Additionally, a CSV version is available in the GitHub repository at https://github.com/alexperrier/packt-aml/blob/master/ch4.


Download Dataset:

https://drive.google.com/file/d/1mYc9-t_snfQUSkEm_hVXBmLscz7pl40S/view?usp=drive_link ( with Null Values)

https://drive.google.com/file/d/1Z0csVhm0udDw1y3rUQhxxn-whWnmSDmF/view?usp=sharing (training set)

Data Dictionary

VariableDefinitionKey
survivalSurvival0 = No, 1 = Yes
pclassTicket class1 = 1st, 2 = 2nd, 3 = 3rd
sexSex
AgeAge in years
sibsp# of siblings / spouses aboard the Titanic
parch# of parents / children aboard the Titanic
ticketTicket number
farePassenger fare
cabinCabin number
embarkedPort of EmbarkationC = Cherbourg, Q = Queenstown, S = Southampton

Variable Notes

pclass: A proxy for socio-economic status (SES)
1st = Upper
2nd = Middle
3rd = Lower

age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5

sibsp: The dataset defines family relations in this way...
Sibling = brother, sister, stepbrother, stepsister
Spouse = husband, wife (mistresses and fiancés were ignored)

parch: The dataset defines family relations in this way...
Parent = mother, father
Child = daughter, son, stepdaughter, stepson
Some children travelled only with a nanny, therefore parch=0 for them.


Questions:

1. Identify the male and female count in the dataset and draw a graph using Excel and Python
2. Identify the male and female court based on age and draw a graph using Excel and Python
3. Identify the P-class types in that how many males and females count draw a graph using Excel and Python
4. Identify the P-class types in that how many males and females count according to survivals draw a graph using Excel and Python
+++++++++++++++++++++++++++++++***********+++++++++++++++++++++++++++++++++++++++++++


1. $         df['Sex'].value_counts()








USING SEABORN

# Group passengers by Sex and count the occurrences

sex_counts = df['Sex'].value_counts().reset_index()

sex_counts.columns = ['Sex', 'Count']


# Define a color palette

colors = sns.color_palette("pastel")


# Plotting the data with color palette

plt.figure(figsize=(6, 4))

ax = sns.barplot(x='Sex', y='Count', data=sex_counts, palette=colors)

plt.title('Count of Males and Females')

plt.xlabel('Sex')

plt.ylabel('Count')

 

# Annotate count numbers on the bars

for p in ax.patches:

    ax.annotate(str(int(p.get_height())), (p.get_x() + p.get_width() / 2., p.get_height()),ha='center', va='center', xytext=(0, 10), textcoords='offset points')


# Show the plot

plt.show()

OUTPUT:


+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

USING MATPLOT

# Group passengers by Sex and count the occurrences

sex_counts = df['Sex'].value_counts().reset_index()

sex_counts.columns = ['Sex', 'Count']


# Create a bar graph using matplotlib

plt.figure(figsize=(6, 4))

plt.bar(sex_counts['Sex'], sex_counts['Count'], color=['blue', 'pink'])

plt.title('Count of Males and Females')

plt.xlabel('Sex')

plt.ylabel('Count')


# Annotate count numbers on the bars

for i, count in enumerate(sex_counts['Count']):

    plt.text(i, count + 10, str(count), ha='center', va='bottom')


# Show the plot

plt.show()


OUTPUT:

 

++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

# Group passengers by Pclass, Sex, and Survival, then count the occurrences

pclass_sex_survived_counts = df.groupby(['Pclass', 'Sex', 'Survived']).size().reset_index(name='Count')

# Pivot the data to have Pclass as columns, Sex as rows, and Survival as values

pclass_sex_survived_pivot = pclass_sex_survived_counts.pivot_table(index=['Sex', 'Survived'], columns='Pclass', values='Count', fill_value=0)

# Define a color palette

colors = sns.color_palette("Set1")

# Plotting the data with color palette

ax = pclass_sex_survived_pivot.plot(kind='bar', stacked=True, color=colors)

plt.title('Survival Counts in Each Pclass by Gender')

plt.xlabel('(Gender, Survival)')

plt.ylabel('Count')

plt.xticks(rotation=0)

plt.legend(title='Pclass')


# Show the plot

plt.show()

OUTPUT:


















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