11/4/2022 0 Comments Basic dataThat means that pin ends (hinges)Īre meaningless, supports do not have rotation degrees of freedomĭefined and results consists of axial forces only. The idea behind this feature is: A complex task requiresĪ complex tool, but a simple task can get by a simple tool.Ĭarrying axial forces only. The functions and options that are not appropriate (are not possible practically)įor the particular type are hidden and do not add to the complexity of Simplification in the operation of the program for simpler types of structures. The sight of the model from the direction of X and Z axes respectively in the case of 2D frame oriented in plane XZ, the button for setting Of the functions and options of the program may be disabled or hidden Here, you can choose the type (or we can say "dimension") On first floor, check-in desks on second floor)ĭate of the last project modification, or variant A (underground parking, restaurants Is complex and consists of several partial sub-projectsĮ.g. This group of items allows the user to enter some statistical data about #Basic data download#I have incorporated these new features in a new data cleaning cleantitanic2.py script, which you can download here.The basic data of a project describe the project and define some of Here we divide the fare by the number of family members traveling together, I’m not exactly sure what this represents, but it’s easy enough to add in.ĭf=df/(df+1) This is an interaction term, since age and class are both numbers we can just multiply them. Perhaps people traveling alone did better? Or on the other hand perhaps if you had a family, you might have risked your life looking for them, or even giving up a space up to them in a lifeboat. Reading on the forums at Kaggle, some people have considered the size of a person’s family, the sum of their ‘SibSp’ and ‘Parch’ attributes. In a model like linear regression this should be unnecessary, but for a decision tree may find it hard to model such relationships. One thing you can do to create new features is linear combinations of features. The letter refers to the deck, and so we’re going to extract these just like the titles.Ĭabin_list = ĭf=df.map(lambda x: substrings_in_string(x, cabin_list)) This is going be very similar, we have a ‘Cabin’ column not doing much, only 1st class passengers have cabins, the rest are ‘Unknown’. Alice Leader, and she and her husband were physicians in New York city. Also interesting is that I was tempted to just send ‘Dr’ -> ‘Mr’, but decided to check first, and there was indeed a female doctor aboard! It seems 1912 was further ahead of its time than Doctor Who!Ĭurious, I looked her up: her name was Dr. You may be interested to know that ‘Jonkheer’ is a male honorific for Dutch nobility. If title in :ĭf=df.apply(replace_titles, axis=1) #replacing all titles with mr, mrs, miss, master Now that I have them, I recombine them to the four categories.ĭf=df.map(lambda x: substrings_in_string(x, title_list)) #Basic data full#Using this iteratively I was able to get a full list of titles. If string.find(big_string, substring) != -1: Thankfully the library ‘strings’ has just what we need.ĭef substrings_in_string(big_string, substrings): To do this we’ll need a function that searches for substrings. There are quite a few titles going around, but I want to reduce them all to Mrs, Miss, Mr and Master. These new features come from reading the Kaggle forums and also this helpful blog post.įirst up the Name column is currently not being used, but we can at least extract the title from the name. Following this I will test the new features using cross-validation to see if they made a difference. Today we are going to add a couple of features to the Titanic data set that I have discussed extensively, this will involve changing my data cleaning script.
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