# Titanic-Machine-Learning-from-Disaster-MATLAB
The goal of this project is predicting the survival of passengers based on a set of data. Necessary data is retrieved from
Kaggle competition "Titanic: Machine Learning from Disaster".
## Basic Usage
* .m files should be opened with MATLAB.
* Datasets should be in the same path with the code, if not path of the datasets must be set manually or fixed in the code.
```
titanic_train = readtable('titanic_train.csv');
titanic_test = readtable('titanic_test.csv');
```
* Project consists a for loop for calculating the average accuracy for a number of iterations. Hence it will take longer time to perform calculations, it is initially set to zero. You can change it if you want to calculate the average/general accuracy score.
* For exporting .csv file the local file path must be changed in the code.
```
writetable(Resulttable, 'resultedtable.csv');
```
## Predictions_for_Kaggle
Contains training, optimization of the training model, and performing prediction for partitioned training set's test data and kaggle's test data. Exports .csv file for Kaggle submission. Submission has 0.78947 score in Kaggle competition.
## Titanic_with_DecisionTree
In this file prediction and testing is only done by using training data to calculate average accuracy. It has about 0.80380 average score.
![Kaggle result](https://github.com/ozlemkorpe/Titanic-Machine-Learning-from-Disaster-MATLAB/blob/master/images/treegraph.jpg)
## Results
![Kaggle result](https://github.com/ozlemkorpe/Titanic-Machine-Learning-from-Disaster-MATLAB/blob/master/images/kaggle.png)
## Authors
* **Özlem Körpe** - *Initial work* - [ozlemkorpe](https://github.com/ozlemkorpe)
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matlab精度检验代码-Titanic-Machine-Learning-from-Disaster-MATLAB:该项目的目...
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csv:3个
m:2个
md:1个
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2021-05-22
02:52:22
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matlab精度检验代码泰坦尼克号机器学习灾难MATLAB 该项目的目标是根据一组数据预测乘客的生存。 从Kaggle竞赛“泰坦尼克号:灾难中的机器学习”中检索必要的数据。 基本用法 .m文件应使用MATLAB打开。 数据集应与代码位于同一路径,否则,数据集的路径必须手动设置或在代码中固定。 titanic_train = readtable('titanic_train.csv'); titanic_test = readtable('titanic_test.csv'); Project包含一个for循环,用于计算多次迭代的平均精度。 因此,将需要更长的时间来执行计算,初始设置为零。 如果要计算平均/一般准确性分数,则可以更改它。 要导出.csv文件,必须在代码中更改本地文件路径。 writetable(Resulttable, 'resultedtable.csv'); Predictions_for_Kaggle 包含训练,训练模型的优化以及对分区的训练集的测试数据和kaggle的测试数据进行预测。 导出.csv文件以提交Kaggle。 参赛作品在Kaggle比赛中的得分为0.
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Titanic-Machine-Learning-from-Disaster-MATLAB-master.zip (8个子文件)
Titanic-Machine-Learning-from-Disaster-MATLAB-master
titanic_test.csv 28KB
images
treegraph.jpg 144KB
kaggle.png 366KB
titanic_train.csv 59KB
resultedtable.csv 3KB
Titanic_with_DecisionTree.m 5KB
Predictions_For_Kaggle.m 7KB
README.md 2KB
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