Specifically, the Parch, SibSp, and the engineered variable Alone are not statistically significant even though there is a much higher proportion of people who were alone surviving.
This function encodes the values of Pclass (1,2,3) using a dummy encoding.Let's first see how the different ticket prefixes we have in our datasetThis part includes creating new variables based on the size of the family (the size is by the way, another variable we create).This creation of new variables is done under a realistic assumption: Large families are grouped together, hence they are more likely to get rescued than people traveling alone.In this part, we use our knowledge of the passengers based on the features we created and then build a statistical model.
Process Age: As we have seen earlier Age variable has 177 missing values, which is a huge number out of 891. We'll see if we'll use the reduced or the full version of the train set.As mentioned in the beginning of the Modeling part, we will be using a Random Forest model. We selected : Let's check if the titles have been filled correctly.There is indeed a NaN value in the line 1305. This post is the opportunity to share my solution with you.To make this tutorial more "academic" so that anyone could benefit, I will first start with an exploratory data analysis (EDA) then I'll follow with feature engineering and finally present the predictive model I set up.Throughout this jupyter notebook, I will be using Python at each level of the pipeline. Using the alpha = 0.05, p-values should be less than 0.05 to reject the null hypothesis. We import the useful li… display: table-cell; This number is quite large. Overview. In that case, we might introduce an additional information about the social status by simply parsing the name and extracting the title and converting to a binary variable.Let's first see what the different titles are in the train set This function parses the names and extract the titles. Submission File Format 4. X-axis : Fare, Y-axis: No of Passengers.Below is a single chart which shows the age and fare correlation with survival. They are the Pandas allows you to a have a high-level simple statistical description of the numerical features. Once this is done I separated the test and train data, train the model with the test data, validate this with the validation set (small subset of training data), Evaluate and tune the parameters. One trick when starting a machine learning problem is to append the training set to the test set together.We'll engineer new features using the train set to prevent information leakage. In this section, we'll be doing four things. """# extracting and then removing the targets from the training data # merging train data and test data for future feature engineering# we'll also remove the PassengerID since this is not an informative feature# set(['Sir', 'Major', 'the Countess', 'Don', 'Mlle', 'Capt', 'Dr', 'Lady', 'Rev', 'Mrs', 'Jonkheer', 'Master', 'Ms', 'Mr', 'Mme', 'Miss', 'Col'])# a function that fills the missing values of the Age variable# there's one missing fare value - replacing it with the mean.# two missing embarked values - filling them with the most frequent one in the train set(S)# set(['A', 'C', 'B', 'E', 'D', 'G', 'F', 'U', 'T'])# a function that extracts each prefix of the ticket, returns 'XXX' if no prefix (i.e the ticket is a digit)# introducing a new feature : the size of families (including the passenger)# introducing other features based on the family size# turn run_gs to True if you want to run the gridsearch again. Below are the features provided in the Test dataset.From the below table we can see that out of 891 observations in the test dataset only 714 records have the Age populated .i.e around 177 values are missing. text-align: center; Below is the graphical representation of the same.Finally to train the model again with one last time on entire training data I have only included the features whose importance is more than 0.01. That is passengers with expensive tickets (could be more important social status) are seem to be rescued on priority. Women on the other hand survived more than men, comparatively well on all the age groups.When we plot the ticket fare of passengers who are survived/dead, we can see that the passengers with cheaper ticket fares are more likely to die.
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