- Appendix 2 Replace Missing Age Cells With The Mean Age Of All Passengers On The Titanic Hide Full Full Mutate Ag 1 (36.43 KiB) Viewed 82 times
1:37 Still 45% k Notebook 1.pdf Notebook 1: Titanic-Machine learning from disaster The writers target is to make it easier for folks getting into machine learning and data science from scratch. He explains in details how one can become a good data scientist or very proficient in machine learning through a number of steps. The author insists that data Science is a field which has embraced and made full use of open source platforms. While data analysis can be conducted in a number of languages, using the right tools can make or break projects, Python and R are the two most commonly used tools in this notebook Whichever language a person chooses, Jupiter Notebook and RStudio makes our life much easier. They allow us to visualize data while manipulating it. He further states that Machine Learning has been democratized by online courses or MOOCs from Coursera, EdX and others, where we learn from amazing professors at world class universities. It is also highlighted that most data scientist don't have a stabilized base and therefore Data scientists, according to interviews and expert estimates, spend 50 percent to 80 percent of their time mired in the mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets. In real sense, Data science is more than just building machine learning models. It's also about explaining the models and using them to drive data-driven decisions. the journey from analysis to data-driven outcomes, data visualization plays a very important role of presenting data in a powerful and credible way, For python and R. Matplotib library or ggplot in R are highlighted to offer complete 2D graphic support with very high flexibility to create high quality data visualizations The authors states that machine learning requires a lot of practice day in day out. He provides some suggestions such to help practice. The Scikit-learn library in Python or the caret, e1071 libraries in R provide a range of supervised and unsupervised learning algorithms via a consistent interface. These let you implement an algorithm without worrying about the inner workings or nitty-gritty details. Simultaneously, understand the inner workings of one algorithm after another. Starting with "Hello World!' of Machine Learning, Linear Regression then move to Logistic Regression, Decision Trees to Support Vector Machines. This will require you to brush up your statistics and linear algebra. While Machine Learning as a field was established long back, the recent hype and media attention is primarily due to Machine Learning applications in Al fields like Computer Vision, Speech Recognition, Language Processing. Many of these have been pioneered by the tech giants like Google, Facebook, Microsoft. These recent advances can be credited to the progress made in cheap computation, the availability of large scale data, and the development of novel Deep Learning architectures Quantitative methods On 14 April 1912, the RMS Titanic struck a large iceberg and took approximately 1,500 of its passengers and crew below the icy depths of the Atlantic Ocean. Considered one of the worst peacetime disasters at sea, this tragic event led to the creation of numerous safety regulations and policies to prevent such a catastrophe from happening again. Some critics, however, argue that circumstances other than luck resulted in a disproportionate number of deaths. The analysis conducted is aimed at exploring factors that influenced a person's likelihood to survive. The R software was used for statistical analysis. The datasets used had several missing data and as a result data cleaning was required for the purposes of data integrity and to prevent any errors in the obtained results. In data science the degree of accuracy in the results of data manipulation is very useful since they are used in decision making. There were a lot of missing 'Age' values (177 data points). We can normalize the 'Age' feature by creating an array that contains random numbers, which are computed based on the mean age value in regards to the standard deviation and is null. The missing age values were replaced by the mean value of that group 1 Percent missing data by feature 0 60-
cm Leal St Regesston, Decision Trees to Support Vector Machines. This will require you to brush up your statistics and linear algebra. While Machine Learning as a field was established long back, the recent hype and media attention is primarily due to Machine Learning applications in Al fields like Computer Vision, Speech Recognition, Language Processing. Many of these have been pioneered by the tech giants like Google, Facebook, Microsoft. These recent advances can be credited to the progress made in cheap computation, the availability of large scale data, and the development of novel Deep Learning architectures Quantitative methods On 14 April 1912, the RMS Titanic struck a large iceberg and took approximately 1,500 of its passengers and crew below the icy depths of the Atlantic Ocean. Considered one of the worst peacetime disasters at sea, this tragic event led to the creation of numerous safety regulations and policies to prevent such a catastrophe from happening again. Some critics, however, argue that circumstances other than luck resulted in a disproportionate number of deaths. The analysis conducted is aimed at exploring factors that influenced a person's likelihood to survive. The R software was used for statistical analysis, The datasets used had several missing data and as a result data cleaning was required for the purposes of data integrity and to prevent any errors in the obtained results. In data science the degree of accuracy in the results of data manipulation is very useful since they are used in decision making. There were a lot of missing 'Age' values (177 data points). We can normalize the 'Age' feature by creating an array that contains random numbers, which are computed based on the mean age value in regards to the standard deviation and is null. The missing age values were replaced by the mean value of that group 1 Percent missing data by feature 40- 20 Figure 1: Percentage of missing data per field About 70 % of the datasets is composed of numeric data types from age, cabin number, passenger ID fure as well as binary data types such as gender/sex or Passenger/crew and survive or didn't survive. The independent variable, Survived, is labeled as a Bernoulli trial where a passenger or crew member surviving is encoded with the value of 1. Among observations in the train set, approximately 38% of passengers and crew survived. Correlation Plot-The correlation plot package is a graphical display of a correlation matrix, confidence interval. It also contains some algorithms to do matrix reordering. In addition, corrplot is good at details, including choosing color, text labels, color labels, layout among others. Correlation measures between numeric features suggest redundant information such as Fare with Pclass. This relationship, however, may be distorted due to passengers who boarded as a family where Fare represents the sum of a family's total cost. 1 Pass 03 06 -0.33 Age 02 -0.55
or didn't survive. The independent variable, Survived, is labeled as a Bernoulli trial where a passenger or crew member surviving is encoded with the value of 1. Among observations in the train set, approximately 38% of passengers and crew survived. Correlation Plot-The correlation plot package is a graphical display of a correlation matrix, confidence interval. It also contains some algorithms to do matrix reordering. In addition, corrplot is good at details, including choosing color, text labels, color labels, layout among others.Correlation measures between numeric features suggest redundant information such as Fare with Pclass. This relationship, however, may be distorted due to passengers who boarded as a family where Fare represents the sum of a family's total cost, Pass -0.30 Aga 04 02 -0.55 Far 0 02 Family -0.4 0.6 0.44 0.82 kat 0.8 Figure 2: Correlation plot Economic class is established to be the most important predictor of whether one survived or didn't.Economic status (Pclass) played an important role regarding the potential survival of the Titanic passengers. First class passengers had a much higher chance of survival than passengers in the 3rd class. . FF Passenger Class Distribution - Survived Passengers 07 06 05 2 04
Media Passenger Class Distribution - Survived Passengers 06 0.5 04 03 02 0.1 00 2 Pclass Figure 3: Graph for survived passengers per class We note that: 63% of the 1st class passengers survived the Titanic wreck 48% of the 2nd class passenger survived Only 24% of the 3rd class passengers survived Correlation Matrix and Heat map- correlation matrix is simply a table which displays the correlation. The measure is best used in variables that demonstrate a linear relationship between each other. The fit of the data can be visually represented in a scatterplot. coefficients for different variables.