They are the Pandas allows you to a have a high-level simple statistical description of the numerical features. We import the useful li… text-align: center; }
We selected : Let's check if the titles have been filled correctly.There is indeed a NaN value in the line 1305. Make learning your daily ritual. X-axis = Age ,Y-axis = Ticket_Fare ,Green dots = Survived, Red dots= DiedSmall green dots between x=0 & x=10 : Children who were survivedSmall red dots between x=10 & x=45: Adults who died (from a lower classes)Large green dots between x=20 & x=45 : Adults with larger ticket fares who are survived.I have combined the train and test data to apply the transformations on both. Let's get started.As in different data projects, we'll first start diving into the data and build up our first intuitions.We tweak the style of this notebook a little bit to have centered plots.Two datasets are available: a training set and a test set. When feature engineering is done, we usually tend to decrease the dimensionality by selecting the "right" number of features that capture the essential.In fact, feature selection comes with many benefits:Tree-based estimators can be used to compute feature importances, which in turn can be used to discard irrelevant features.Let's have a look at the importance of each feature.As you may notice, there is a great importance linked to Title_Mr, Age, Fare, and Sex. They do however come with some parameters to tweak in order to get an optimal model for the prediction task.To learn more about Random Forests, you can refer to this Now that the model is built by scanning several combinations of the hyperparameters, we can generate an output file to submit on Kaggle.I haven't personally uploaded a submission based on model blending but here's how you could do itTo have a good blending submission, the base models should be different and their correlations uncorrelated.In this article, we explored an interesting dataset brought to us by We went through the basic bricks of a data science pipeline:This post can be downloaded as a notebook if you ever want to test and play with it : Lots of articles have been written about this challenge, so obviously there is a room for improvement.I would be more than happy if you could find out a way to improve my solution. Women are more likely to survive.Let's now correlate the survival with the age variable.As we saw in the chart above and validate by the following:The age conditions the survival for male passengers:These violin plots confirm that one old code of conduct that sailors and captains follow in case of threatening situations: Let's now focus on the Fare ticket of each passenger and see how it could impact the survival. Title also can contribute in computing the age. Metric.
Put differently, passengers with more expensive tickets, and therefore a more important social status, seem to be rescued first.Ok this is nice. We tweak the style of this notebook a little bit to have centered plots. 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. In this section, we'll be doing four things.
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. As in different data projects, we'll first start diving into the data and build up our first intuitions.
Then do the predictions on test data and submit to Kaggle.Then for all the records with missing age, based on their Sex,Title and Pclass we assign the age. 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. Let's now see how the embarkation site affects the survival.It seems that the embarkation C have a wider range of fare tickets and therefore the passengers who pay the highest prices are those who survive.We also see this happening in embarkation S and less in embarkation Q.Let's now stop with data exploration and switch to the next part.In the previous part, we flirted with the data and spotted some interesting correlations.In this part, we'll see how to process and transform these variables in such a way the data becomes manageable by a machine learning algorithm.We'll also create, or "engineer" additional features that will be useful in building the model.We'll see along the way how to process text variables like the passenger names and integrate this information in our model.We will break our code in separate functions for more clarity.But first, let's define a print function that asserts whether or not a feature has been processed.
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