最近在阅读一些AI项目，写入markdown，持续更新，算是之后也能回想起做法

项目 https://github.com/calssion/Fun_AI

 

Titanic: Machine Learning from Disaster(泰坦尼克号：从灾难中机器学习)

kaggle：Titanic(比赛地址) https://www.kaggle.com/c/titanic#evaluation

tutorial(教程)：https://www.kaggle.com/acsrikar279/titanic-higher-score-using-kneighborsclassifier

after trying so many models and others' kernal， I find a good score is using KNN， you may see some score on the leaderboard > 0.9, but I believe that their models are already overfitting to the test giving by kaggle。
(尝试过不同的模型和kernal之后，我发现能取得很好的评分的一种模型是采用KNN，或许你会看到排行榜上一些评分>0.9，但我认为他们的模型已经对于kaggle给出的test过拟合了)

Goal(目标)

It is your job to predict if a passenger survived the sinking of the Titanic or not.(你的任务预测一位乘客在泰坦尼克沉船事故中是否能够生还)
For each PassengerId in the test set, you must predict a 0 or 1 value for the Survived variable.(对于每个在测试集的乘客ID，你需要预测生还变量0或1)

Metric(评价)

Your score is the percentage of passengers you correctly predict. This is known simply as "accuracy”.(你的得分是对于乘客的正确预测，即准确率)

KNeighborsClassifier(K近邻分类算法)

data preprocessing(数据预处理)

data['Title'] = data['Name']
for name_string in data['Name']:
  data['Title'] = data['Name'].str.extract('([A-Za-z]+).', expand=True)
data['Title'].value_counts()

the columns 'name' has various kinds，we need to combine some kinds with few nums(名字的列有很多不同的种类，我们需要合并一些数量较少的种类)

and add a new column ‘familysize’(添加一个家庭成员数量)

data['Family_Size'] = data['Parch'] + data['SibSp']

impute the missing values(填充缺失值)

LabelEncoder ‘ticket’、‘fare’、‘AgeBin’、‘sex’ and do some trick(数值化列并处理)

now find the best parameters of KNN(现在寻找KNN的最佳参数)

n_neighbors = [6,7,8,9,10,11,12,14,16,18,20,22]
algorithm = ['auto']
weights = ['uniform', 'distance']
leaf_size = list(range(1,50,5))
hyperparams = {'algorithm': algorithm,   'weights': weights,
  'leaf_size': leaf_size,
  'n_neighbors': n_neighbors}
gd=
  GridSearchCV(
   estimator = KNeighborsClassifier(),
   param_grid = hyperparams,
   verbose=True,
   cv=10, scoring = "roc_auc") gd.fit(X, y)

get the score of 0.83253