Titanic dataset Study guides, Revision notes & Summaries

Hands-On Exercise 6-1: Outlier Detection with Titanic dataset In this Hands-on exercise, you will learn. • How to use quantiles to detect the outliers in data (the Titanic Training dataset) Related DM Book Chapters/Sections: • Section 2.2.2 Measuring the Dispersion of Data: Range, Quartiles, Variance, Standard Deviation, and Interquartile Range Related Hands-on Exercises: • Exercise 1-2 Apache Spark and Basic Statistics Finish the assignments shown below. Submit a word document (...

Hands-On Experiment 4-2: Classification with Titanic dataset 2.2.1 (20pts) Assignment 1: Index the Gender values We have learned how to index values using StringIndexer in previous hands-on exercises • Write codes for indexing the gender values 1. Import a Class 2. Define an indexer – Input column: Gender – Output column: IndexedGender 3. Train and transform • Take a screenshot of running your codes and outputs using the show (5) function 3 Building a Model 3.1 Training and T...

You are expected to be able to program in R prior to taking this class. Use the titanic dataset from CRAN and perform EDA on various columns. Without using any modeling algorithms, and only using basic methods such as frequency distribution, describe the most important predictors of survival of Titanic passengers, e.g. were males or females more likely to survive, were young and rich females more likely to survive than old poor males etc?

Follow the instructions in the book to create a new project. Give it a name and an author. Execute the 3 code snippets from the preface to bring all the packages into this new project then create a new R Markdown document as instructed by the book. Load the tidyverse and socviz libaries. In order to execute code in an R Markdown object, the code must be enclosed by: ```{r}.....``` Use R studio to complete the following: 1. create a vector of the names of the members of your immediate family ...

The document covers topics related to data manipulation, real-world data sources, exploratory data analysis (EDA), Python programming with Pandas, ethics in data science, and various techniques for analyzing and visualizing data. It includes examples, explanations, and references to specific tools and libraries, such as Pandas, Seaborn, and Python, to illustrate key concepts in data science. The content is organized into sections, each focusing on different aspects of data science, such as wo...