titanic_dummies = pd.concat(dummies, axis=1) We have 8 columns transformed to columns where 1,2,3 represents passenger class. 2 of the features are floats, 5 are integers and 5 are objects.Below I have listed the features with a short description: survival: Survival PassengerId: Unique Id of a passenger. So given that, lets take a closer look at the people who survived this disaster and predict the likelihood of survival. But again this is not complete dataset. Following this I will test the new features using cross-validation to see if they made a difference. pclass refers to passenger class (1st, 2nd, 3rd), and is a proxy for socio-economic class. Introductory project of Udacity Machine Learning Nanodegree. Data Description. Heat map uses warm to cool color spectrum. I built this analysis with help from the Titanic Data Science Solutions kernel.  •  Some children travelled only with a nanny, therefore parch=0 for them. The titanic2 data frame has no missing data and includes records for The Titanic dataset after preprocessed contains twenty-two features and one label. Count for ‘Age’ column is 714, it means dataset has some missing values. The Titanic data set is said to be the starter for every aspiring data scientist. Now, over to trying out different basic predictive models on the training and testing data. Related Post. […] And by understanding we mean that we are going to extract any intuition we can get from this data and we are going to exercise on “Learning from disaster: Titanic” from kaggle. Mlle means ‘Mademoiselle’ which is the honorific used to describe someone who would be called ‘Miss’ in France, Mme means ‘Madame’ which is the honorific used to describe someone who would be called ‘Mrs’ in France, Lady, Countess, Dona are female honorifics of nobility, Don, Sir, Jonkheer are male honorifics of nobility, Capt refers to the Captain of the Titanic; Col, Major are military positions; Dr are doctors, Rev is a Reverend who all have special roles in society, Special — the captain, doctors and reverends who might have been called onto help during the disaster. Spouse = husband, wife (mistresses and fiancés were ignored). From above table I see that mean of survived column is 0.38, but since this is not complete dataset we cannot conclude on that. Margaret Edith 888 889 0 3 Johnston, Miss. sex Sex. As part this project I want to explore answers to following questions. I will use ‘pearson’ standard correlation coefficient for the calculation. parch: Number of Parents/Children Aboard. On April 15, 1912, during her maiden voyage, the Titanic sankafter colliding with an iceberg, killing 1502 out of 2224 passengers andcrew.In this Notebook I will do basic Exploratory Data Analysis on Titanicdataset using R & ggplot & attempt to answer few questions about TitanicTragedy based on dataset. Let’s see agewise distribution of the passenger aboard the Titanic. I was also inspired to do some visual analysis of the dataset from some other resources I came across. The trainin g-set has 891 examples and 11 features + the target variable (survived). From above correlation table we can see that Survival is inversly correlated to Pclass value. So it was that I sat down two years ago, after having taken an econometrics course in a university which introduced me to R, thinking to give the competition a shot. In our case since Class 1 has lower numerical value, it had better survival rate compared to other classes. After all, this comes with a pride of holding the sexiest job of this century. From above visualization we can see that class played important for Survival of Male and Female passengers. I will have to cleanup the data before I start exploring. More can be done on this data set. Titanic Dataset from Kaggle Kaggle Kernel of the above Notebook Github Code Notebook Viewer. We are going to make some predictions about this event. I will compute pairwise correlation of columns(excluding NA/null values) using pandas.DataFrame.corr method. Above visualization compares passengers who survived the tragedy and who did not, across three classes. Dataset was obtained from kaggle(https://www.kaggle.com/c/titanic/data). In this case, because the number of features is pretty low and the Sex feature dominating the survival rate, the decision tree classifier was more accurate than the random forest classifier. So for filling in the null values in columns with predicted values, let us calculate the median age for passengers in each passenger class and sex; and fill in the age values for the 177 rows based on their passenger class and sex. From above visualization, it is evident that Women had better survival chance. I see Class did play role in survival of the passengers. sibsp: Number of Siblings/Spouses Aboard. age Age. We can do this by grouping the dataframe with respect to Pclass, Survived and Sex. Pretty sad stuff but the data doesn’t lie or hide these things from us. That would be 7% of the people aboard. I haven’t made optimized my classifiers, I have not tested the data against a validation set after optimization. Lets pull a histogram of ‘Survived’ column. The variables that we had provided had the following descriptors to mean what they were: Trying to find out how much each feature contributes to the survival rate, I first tried to find out the expect survival for 4 features: Now, these percentages and numbers are likely to bore anyone. For the missing ‘Ages’ and ‘Embarked’ I will omit those rows when I use the data. There are some columns which are not required in my analysis so I will drop them. Now we convert the Pclass, Sex, Embarked to columns in pandas and drop them after conversion. This article is moderately rigorous and goes through each step I have taken to build this analysis of the titanic dataset. I also see that Class(Socio-Economic status) of the passengers had played a role in their survival.
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