#Case: Titanic.
This data analysis case was developed as result of the class Introduction to Data Science from the bootcamp CARREFOUR BANK DATA ENGINEER from Digital Innovation One.
In this project, a pack of data downloaded from Kaggle containing roleplay characteristics from Titanic Travelers (input), including the fact of whether they survived or not the incident (target) is separated according to 30/70 Test/Train ratio to be fed in the Scikit Learn Random Forest algorithm for machine learning.
After training, the algorithm tries to predict whether the 30%-sized testset survives or not. A HTML page presents the Table 1 containing the original data provided by Kaggle and Table 2 containing the treated 30% test set with the results provided from the original data and the results predicted by the model. Figure 1 presents the original data together with the predicted values in matplotlib fashion.