International Journal of Academic Engineering Research (IJAER) ISSN: 2643-9085 Vol. 3 Issue 9, September – 2019, Pages: 8-12 www.ijeais.org/ijaer 8 Predicting Titanic Survivors using Artificial Neural Network Alaa M. Barhoom, Ahmed J. Khalil, Bassem S. Abu-Nasser, Musleh M. Musleh, Samy S. Abu-Naser Department of Information Technology, Faculty of Engineering and Information Technology, Al-Azhar University, Gaza, Palestine Abstract – Although the Titanic disaster happened just over one hundred years ago, it still appeals researchers to understand why some passengers survived while others did not. With the use of a machine learning tool (JustNN) and the provided dataset we study which factors or classifications of passengers have a strong relationship with survival for passengers that took that trip on 15 th of April, 1912. The analysis seeks to identify characteristics of passengers cabin class, age, and point of departure – and that relationship to the chance of survival for the disaster. Furthermore, we developed a model for classifying passengers. The model was trained and tested and we found the accuracy to be more than 99.28%. Keywords-learning; titanic; classification; JustNN 1. INTRODUCTION The Titanic was a ship disaster that on its maiden voyage sunk in the northern Atlantic on 15 th of April, 1912, killing 1502 out of 2224 passengers and its crew [1]. While there exists conclusions regarding the cause of the sinking, the analysis of the data on what impacted the survival of passengers continues to this date [2]. The approach taken is utilize a publically available data set from a web site known as Kaggle[3]. We used the JustNN tool for analysis after data review and normalization. In Machine Learning, the data is mainly divided into two parts - Training and Testing. Training data is for training our model and testing data is to check how well our model performs [4-10]. The split ratio between the train and test data is usually around 70%–30%. Hence, here we have a total of 891 entries for training and 417 entries for testing[2]. 2. ARTIFICIAL NEURAL NETWORK Artificial Neural Networks (ANN) are the pieces of a computing system designed to simulate the way the human brain analyzes and processes information. They are the foundations of Artificial Intelligence (AI) and solve problems that would prove impossible or difficult by human or statistical standards. ANN have self-learning capabilities that enable them to produce better results as more data become available [11-17]. Artificial Neural Networks (ANN) are paving the way for life-changing applications to be developed for use in all sectors of the economy. AI platforms that are built on ANN are disrupting the traditional way of doing things. From translating web pages into other languages to having a virtual assistant order groceries online to conversing with chatbots to solve problems, AI platforms are simplifying transactions and making services accessible to all at negligible costs[18-28]. ANN are built like the human brain, with neuron nodes interconnected like a web. The human brain has hundreds of billions of cells called neurons. Each neuron is made up of a cell body that is responsible for processing information by carrying information towards (inputs) and away (outputs) from the brain. ANN has hundreds or thousands of artificial neurons called processing units, which are interconnected by nodes. These processing units are made up of input and output units. The input units receive various forms and structures of information based on an internal weighting system, and the neural network attempts to learn about the information presented to produce one output report. Just like humans need rules and guidelines to come up with a result or output, ANNs also use a set of learning rules called backpropagation, an abbreviation for backwards propagation of error, to perfect their output results[29-33]. ANN model initially goes through a training phase where it learns to recognize patterns in data, whether visually, aurally, or textually. During this supervised phase, the network compares its actual output produced with what it was meant to produce, i.e., the desired output. The difference between both outcomes is adjusted using backpropagation. This means that the network works backward going from the output unit to the input units to adjust the weight of its connections between the units until the difference between the actual and desired outcome produces the lowest possible error[34-40]. During the training and supervisory stage, the ANN model is taught what to look for and what its output should be, using Yes/No question types with binary numbers. For example, a bank that wants to detect credit card fraud on time may have four input units fed with these questions: (1) Is the transaction in a different country from the user's resident country? (2) Is the website the card is being used at affiliated with companies or countries on the bank's watch list? (3) Is International Journal of Academic Engineering Research (IJAER) ISSN: 2643-9085 Vol. 3 Issue 9, September – 2019, Pages: 8-12 www.ijeais.org/ijaer 9 the transaction amount larger than $2,000? (4) Is the name on the transaction bill the same as the name of the cardholder? The bank wants the "fraud detected" responses to be Yes Yes Yes No, which in binary format would be 1 1 1 0. If the network's actual output is 1 0 1 0, it adjusts its results until it delivers an output that coincides with 1 1 1 0. After training, the computer system can alert the bank of pending fraudulent transactions, saving the bank lots of money[41-50]. Artificial neural networks have been applied in all areas of operations. Email service providers use ANN to detect and delete spam from a user's inbox; asset managers use it to forecast the direction of a company's stock; Credit rating firms use it to improve their credit scoring methods; ecommerce platforms use it to personalize recommendations to their audience; chatbots are developed with ANN for natural language processing; deep learning algorithms use ANN to predict the likelihood of an event; and the list of ANN incorporation goes on across multiple sectors, industries and countries[51-61]. 3. METHODOLOGY 3.1 Data Sets The data we used for our study was provided on the Kaggle website [3]. We were given 891 passenger samples for our training set and their associated labels of whether or not the passenger survived. For each passenger, we were given his/her passenger class, name, sex, age, number of siblings/spouses aboard, number of parents/children aboard, ticket number, fare, cabin embarked, and port of embarkation (as shown in Table 1). For the test data, we had 418 samples in the same format. The dataset is not complete, meaning that for several samples, one or many of fields were not available and marked empty (especially in the latter fields – age, fare, cabin, and port). However, all sample points contained at least information about gender and passenger class. Table 1: Description of each attribute in our dataset Feature type Description PassengerId int Id Survived int Survival (0=No; 1=Yes) Pclass int Passenger Class Name object Name Sex object Sex Age float Age SibSp int Number of Siblings/Spouses Aboard Parch int Number of Parents/Children Aboard Ticket object Ticket Number Fare float Passenger Fare Cabin object Cabin number Embarked object (C=Cherbourg; Q=Queenstown; S=Southampton) 3.2 Feature Engineering Feature engineering is measuring the impact of each feature on the output. But the more important thing is that it is not just about using existing features, it is about creating new ones that can make a significant improvement in our output. Andrew Ng said, ―Coming up with features is difficult, timeconsuming, requires expert knowledge. Applied machine learning is basically feature engineering.‖ We will go through each feature we are using so that we can understand how to use existing features and how to create new ones[1,2]. 3.3 Passenger Class It is obvious that the class of passenger is directly proportional to survival rate. If the importance of a person is more than others, they'll get out of the disaster first. And our data tells the same story. 63% of people survived from Class 1. Therefore, this feature is definitely impactful. Data in Pclass column is complete hence no need to manipulate it. 3.4 Sex Sex is again important and directly proportional to survival rate. Female and children were saved first during this tragedy. We can see that 74% of all females were saved and only 18% of all males were saved. Again, this will impact our outcome. 3.5 Family Size Next two columns are SibSp and Parch, which are not directly related to whether a person has survived or not. That is where the idea of creating a new feature came in. For each row/passenger, we will determine his/her family size by adding SibSp + Parch + 1(himself/herself). Family size differs from a minimum of 1 to a maximum of 11, where the family size of 4 having the highest survival rate of 72%. It seems to have a good effect on our prediction but let's go further and categorizes people to check whether they are alone in this ship or not. And after looking through it too, it seems to have a considerable impact on our output. 3.6 Embarked From which place a passenger embarked has something to do with survival (not always). So, let's take a look. In this column, there are plenty of Not Available (NAs). To deal with it, we are going to replace NAs with  S' because it is the most occurred value. 3.7 Fare There are missing data in this column as well. We cannot deal with every feature in the same way. To fix the issue here, we are going to take the median value of the entire column. When you cut with qcut, the bins will be chosen so that you International Journal of Academic Engineering Research (IJAER) ISSN: 2643-9085 Vol. 3 Issue 9, September – 2019, Pages: 8-12 www.ijeais.org/ijaer 10 have the same number of records in each bin (equal parts). Looking through the output, it is considerable. 3.8 Age Age has some missing values. We will fill it with random numbers between (average age minus average standard deviation) and (average age plus average standard deviation). After that, we will group it in the set of 5. It has a good impact as well. 3.9 Name This one is a little tricky. From the name, we have to retrieve the title associated with that name, i.e. Mr or Captain. First, we get the title from the name and store them in a new list called title. After that, we cleaned the list by narrowing down to common titles. 3.10 Mapping After cleaning our features, they are now ready to use. However; there is one more step before we feed our data to JustNN tool. The thing about ML algorithms is that they only take numerical values and not strings. So, we have to map our data to numerical values and convert the columns to the integer data type. 3.11 ANN Model Architecture The resulted architecture ANN model for Titanic survivors is shown in Figure 1. It consists of one impute layer, two hidden layers, and one output layer. Figure1: ANN Model Architecture 3.12 ANN Model Validation Our ANN model was able to predict Titanic survivors with 99.28% accuracy, with about 0.005 errors as seen in Figure 2. Furthermore, The Model showed that the most effective factors in Titanic survivors are Sex, Pclass, and Cabin. More details are shown in Figure 3. Figure 2: ANN Model training and validation Figure 3: Effective of input valuables to Titanic Survivors 4. 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