Titanic_with_DecisionTree.m

%The version with Decision Tree method
%Prepared by Özlem Körpe
%Github: https://github.com/ozlemkorpe/Titanic-Machine-Learning-from-Disaster-MATLAB
%Note1: Please check out the data path before run, fix if necessary.
%Note1: Change the number of iterations for calculating average/general accuracy of prediction.

clear %clear workspace

%----------------Import Data
        titanic_train = readtable('titanic_train.csv');
        % titanic_test = readtable('titanic_test.csv');

%----------------Missing data check
        titanic_train_missing = sum(ismissing(titanic_train));

%----------------Handling Missing Values
        %For age column which has 6 missing values use mean value and replace with missing value
        mean_age = cast(mean(titanic_train.Age, 'omitnan'),'uint8') ; %Cast double to integer
        filled_age = fillmissing(titanic_train.Age, 'constant', mean_age);
        filled_data = titanic_train ;
        filled_data.Age = filled_age;
        %For Fare column which has 6 missing values use mean value and replace with missing value        
        mean_fare = cast(mean(titanic_train.Fare, 'omitnan'),'uint8') ; %Cast double to integer
        filled_fare = fillmissing(titanic_train.Fare, 'constant', mean_fare);
        filled_data.Fare = filled_fare;
        
%------ Dealing with Categorical Data without order relation
        filled_data = categorical_data_to_dummy_variables(filled_data, filled_data.Sex); %Seperate genders into different columns
        filled_data.Sex = [];
        
%----------------Check for Outliers
        %plot(filled_data.Age) %Age varies between 0-80 which can be accepted as normal
        %Remove rows which has age less than 1
        toDelete = filled_data.Age < 1;
        filled_data(toDelete,:) = [];
        %Remove rows which has age not integer
        toDelete2 = mod(filled_data.Age,1) ~= 0;
        filled_data(toDelete2,:) = [];

%----------------Feature Scaling with Normalization Method
        %New table for normalized data
        normalized_data = filled_data ;
        %Feature scaling for the Age
        normalized_age = (filled_data.Age - min(filled_data.Age)) / (max(filled_data.Age) - min(filled_data.Age));
        normalized_data.Age = normalized_age;
        %Feature scaling for the Fare
        normalized_fare = (filled_data.Fare - min(filled_data.Fare)) / (max(filled_data.Fare) - min(filled_data.Fare));
        normalized_data.Fare= normalized_fare;
        %Feature scaling for the SibSp
        normalized_sibsp = (filled_data.SibSp - min(filled_data.SibSp)) / (max(filled_data.SibSp) - min(filled_data.SibSp));
        normalized_data.SibSp = normalized_sibsp;
        %Feature scaling for the Parch
        normalized_parch = (filled_data.Parch - min(filled_data.Parch)) / (max(filled_data.Parch) - min(filled_data.Parch));
        normalized_data.Parch = normalized_parch;
        %Feature scaling fot the Pclass
        normalized_pclass = (filled_data.Pclass - min(filled_data.Pclass)) / (max(filled_data.Pclass) - min(filled_data.Pclass));
        normalized_data.Pclass = normalized_pclass;

%----------------Classification with Decision Tree
        classification_model = fitctree(normalized_data, 'Survived~Age+Fare+Parch+SibSp+female+male+Pclass','MaxNumSplits',37); %Classification Model

%----------------FOR LOOP FOR CALCULATING ACCURACY IN "a" NUMBER OF EXECUTION
general_accuracy = 0;
for a = 1:1
 %----------------Partitioning
cv = cvpartition(classification_model.NumObservations,'HoldOut', 0.03); %Built-in function for partitioning
cross_validated_model = crossval(classification_model, 'cvpartition', cv); %Use training set only to built model 

%----------------Prediction
Predictions = predict(cross_validated_model.Trained{1}, normalized_data(test(cv),1:end-1));

%----------------Analyzing the Result
%Confusion Matrix: / diagonal will give the false predictions, \ will
    %be the rigth predictions.
Results = confusionmat(cross_validated_model.Y(test(cv)),Predictions); 
right_results = Results(1,1) + Results(2,2);
wrong_results = Results(1,2) + Results(2,1);

truth_score = right_results /(right_results + wrong_results);

general_accuracy = general_accuracy + truth_score;
end

general_accuracy = general_accuracy / a; 
%Print general accuracy 
disp('General accuracy is:');
disp(general_accuracy);

%----------------Visualize the result
        view(cross_validated_model.Trained{1}, 'Mode', 'Graph');

%----------------Funcion tho handle unordered categorical data.
    function data = categorical_data_to_dummy_variables(data,variable)
unique_values = unique(variable);
for i=1:length(unique_values)
    dummy_variable(:,i) = double(ismember(variable,unique_values{i})) ;
end 
T = table;
[rows, col] = size(dummy_variable);
for i=1:col
    T1 = table(dummy_variable(:,i));
    T1.Properties.VariableNames = unique_values(i);
    T = [T T1];
end 
    data = [T data]; 
    end