the data set we used for this project is titanic data set which provide information about each persons like age,Sex,did he/she survived or not source

Survival:0 = didn't survive, 1 = survived
pclass:Ticket class 1 = 1st, 2 = 2nd, 3 = 3rd
sex:the gender of the passnger (male or female)
Age:Age of each passnger
sibsp: number of siblings / spouses aboard the Titanic
parch: number of parents / children aboard the Titanic
ticket:Ticket number
fare:Passenger fare
cabin:Cabin number
embarked:Port of Embarkation C = Cherbourg, Q = Queenstown, S = Southampton

further information taken from the source
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 traveled only with a nanny, therefore parch=0 for them.

the source of data provided us with three CSV file (train,test,gender_submission)

• we assigned the gender_submission to the test data to make the number of the column the same as the train set
• we merged the train set and test set(after adding the new column )
• We checked for NaN value in each column and after that, we decided to drop the cabin column because it contains the most NaN value
• we also dropped the raws with Age==NaN and Embarked=NaN

the Survived column we encoded

• 0-didn't survive
• 1-did survive

• we created a new column called the Family
  
• which is the sum of SibSp + Parch and it will indicate to the family size that was on the ship
  
• when we create the new column we will add 1 to give indicate to that passenger is only one from his family on this ship
  and if it 2 that means he and one of his family members are on the ship and the same goes from 3 or 4,...
  

• we can say that most survived gender is female
• we can say that most most gender that did not survive is male
• Furthermore, half of our data were men and did not survive with percentage =54%
• the most common port for male and female passenger to embark from was S-Southampton
  
• the most common port that people emparked from did survive and did not was S-Southampton
  
• half of the passenger were men that embarked from the S-Southampton port with 50% percentage from our data set
  
• the most gender of the passengers that didn't survive and embarked form S-Southampton port were male with a 43% percentage form all data set
  
• the most gender of the passengers that did survive and embarked form S-Southampton port was female with a 20% percentage form all data set
  
• the most family size that survived and didn't survive for both the female and male passenger was = 1 meaning by that the most affected people by survived and didn't survive rate was the solo passenger
  

we started the presentation with a plot to tell us what is the dominated gender in our data set, after that, we investigate what is the most survived and didn't survive gender in our data set. then we investigated what is the port that most passenger that embarked from it from and after that we investigate what is the common (age, family size, gender) for the passenger that emparked from that port