diff --git "a/2 \350\200\201\345\271\264\344\272\272\347\232\204\350\277\234\347\250\213\346\231\272\350\203\275\350\257\212\347\226\227/\350\200\201\344\272\272\345\277\203\350\204\217\347\227\205\350\257\212\346\226\255-\345\274\240\351\271\217\346\266\233/README.md" "b/2 \350\200\201\345\271\264\344\272\272\347\232\204\350\277\234\347\250\213\346\231\272\350\203\275\350\257\212\347\226\227/\350\200\201\344\272\272\345\277\203\350\204\217\347\227\205\350\257\212\346\226\255-\345\274\240\351\271\217\346\266\233/README.md"
new file mode 100644
index 00000000..6d60d0bc
--- /dev/null
+++ "b/2 \350\200\201\345\271\264\344\272\272\347\232\204\350\277\234\347\250\213\346\231\272\350\203\275\350\257\212\347\226\227/\350\200\201\344\272\272\345\277\203\350\204\217\347\227\205\350\257\212\346\226\255-\345\274\240\351\271\217\346\266\233/README.md"	
@@ -0,0 +1,13 @@
+## 项目介绍
+
+通过aws的sagemaker建立模型，模型的应用场景是预测老人是否患有心脏病，同时使用aws进行模型的部署
+
+## 团队介绍
+
+张鹏涛，来自明略科技，从事nlp以及推荐领域，对机器学习以及深度学习的应用，有着丰富的经验，联系方式： 772215156@qq.com
+
+## 使用到的aws技术
+
+1， sagemaker    
+2， aws的存储功能
+
diff --git "a/2 \350\200\201\345\271\264\344\272\272\347\232\204\350\277\234\347\250\213\346\231\272\350\203\275\350\257\212\347\226\227/\350\200\201\344\272\272\345\277\203\350\204\217\347\227\205\350\257\212\346\226\255-\345\274\240\351\271\217\346\266\233/data/data_raw.csv" "b/2 \350\200\201\345\271\264\344\272\272\347\232\204\350\277\234\347\250\213\346\231\272\350\203\275\350\257\212\347\226\227/\350\200\201\344\272\272\345\277\203\350\204\217\347\227\205\350\257\212\346\226\255-\345\274\240\351\271\217\346\266\233/data/data_raw.csv"
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index 00000000..2f5cdb4f
--- /dev/null
+++ "b/2 \350\200\201\345\271\264\344\272\272\347\232\204\350\277\234\347\250\213\346\231\272\350\203\275\350\257\212\347\226\227/\350\200\201\344\272\272\345\277\203\350\204\217\347\227\205\350\257\212\346\226\255-\345\274\240\351\271\217\346\266\233/data/data_raw.csv"	
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diff --git "a/2 \350\200\201\345\271\264\344\272\272\347\232\204\350\277\234\347\250\213\346\231\272\350\203\275\350\257\212\347\226\227/\350\200\201\344\272\272\345\277\203\350\204\217\347\227\205\350\257\212\346\226\255-\345\274\240\351\271\217\346\266\233/src/__pycache__/utils.cpython-36.pyc" "b/2 \350\200\201\345\271\264\344\272\272\347\232\204\350\277\234\347\250\213\346\231\272\350\203\275\350\257\212\347\226\227/\350\200\201\344\272\272\345\277\203\350\204\217\347\227\205\350\257\212\346\226\255-\345\274\240\351\271\217\346\266\233/src/__pycache__/utils.cpython-36.pyc"
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diff --git "a/2 \350\200\201\345\271\264\344\272\272\347\232\204\350\277\234\347\250\213\346\231\272\350\203\275\350\257\212\347\226\227/\350\200\201\344\272\272\345\277\203\350\204\217\347\227\205\350\257\212\346\226\255-\345\274\240\351\271\217\346\266\233/src/main.py" "b/2 \350\200\201\345\271\264\344\272\272\347\232\204\350\277\234\347\250\213\346\231\272\350\203\275\350\257\212\347\226\227/\350\200\201\344\272\272\345\277\203\350\204\217\347\227\205\350\257\212\346\226\255-\345\274\240\351\271\217\346\266\233/src/main.py"
new file mode 100644
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+++ "b/2 \350\200\201\345\271\264\344\272\272\347\232\204\350\277\234\347\250\213\346\231\272\350\203\275\350\257\212\347\226\227/\350\200\201\344\272\272\345\277\203\350\204\217\347\227\205\350\257\212\346\226\255-\345\274\240\351\271\217\346\266\233/src/main.py"	
@@ -0,0 +1,74 @@
+from sklearn.model_selection import train_test_split
+import sagemaker
+from sagemaker import get_execution_role
+from sagemaker.amazon.amazon_estimator import get_image_uri
+from sagemaker.predictor import csv_serializer
+from utils import read_data
+from sklearn.metrics import accuracy_score
+import os
+import pandas as pd
+import sys
+sys.path.append("./")
+
+data_file = "../data/data_raw.csv"
+data_columns = ["ATTR" + str(i) for i in range(1, 14)]
+data_columns.append("Label")
+
+X, Y = read_data(data_file=data_file, data_columns=data_columns)
+
+X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.1, random_state=42)
+
+pd.concat([X_train, y_train], axis=1).to_csv("../data/train.csv", index=False)
+pd.concat([X_test, y_test], axis=1).to_csv("../data/test.csv", index=False)
+
+# 将原始的数据以及处理好的数据集存储到s3上面
+session = sagemaker.Session()
+data_dir = "../data/"
+prefix = 'sentiment-web-app'
+
+test_location = session.upload_data(os.path.join(data_dir, 'test.csv'), key_prefix=prefix)
+train_location = session.upload_data(os.path.join(data_dir, 'train.csv'), key_prefix=prefix)
+
+# 使用sagemaker来进行模型的训练
+role = get_execution_role()
+
+container = get_image_uri(session.boto_region_name, 'xgboost')
+
+xgb = sagemaker.estimator.Estimator(container,
+                                    role,
+                                    train_instance_count=1,
+                                    train_instance_type='ml.m4.xlarge',
+                                    output_path='s3://{}/{}/output'.format(session.default_bucket(), prefix),
+                                    sagemaker_session=session)
+
+# 指定xgb需要的参数
+xgb.set_hyperparameters(max_depth=5,
+                        eta=0.2,
+                        gamma=4,
+                        min_child_weight=6,
+                        subsample=0.8,
+                        silent=0,
+                        objective='binary:logistic',
+                        early_stopping_rounds=10,
+                        num_round=500)
+# 从s3中读取数据
+s3_input_train = sagemaker.s3_input(s3_data=train_location, content_type='csv')
+s3_input_test = sagemaker.s3_input(s3_data=test_location, content_type='csv')
+
+# 模型训练
+xgb.fit({'train': s3_input_train, 'test': s3_input_test})
+
+# 模型测试
+xgb_transformer = xgb.transformer(instance_count=1, instance_type='ml.m4.xlarge')
+
+xgb_transformer.transform(test_location, content_type='text/csv', split_type='Line')
+
+xgb_transformer.wait()
+
+predictions = pd.read_csv(os.path.join(data_dir, 'test.csv.out'), header=None)
+predictions = [round(num) for num in predictions.squeeze().values]
+
+accuracy_score(y_test, predictions)
+
+# 模型的部署
+xgb_predictor = xgb.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')
diff --git "a/2 \350\200\201\345\271\264\344\272\272\347\232\204\350\277\234\347\250\213\346\231\272\350\203\275\350\257\212\347\226\227/\350\200\201\344\272\272\345\277\203\350\204\217\347\227\205\350\257\212\346\226\255-\345\274\240\351\271\217\346\266\233/src/utils.py" "b/2 \350\200\201\345\271\264\344\272\272\347\232\204\350\277\234\347\250\213\346\231\272\350\203\275\350\257\212\347\226\227/\350\200\201\344\272\272\345\277\203\350\204\217\347\227\205\350\257\212\346\226\255-\345\274\240\351\271\217\346\266\233/src/utils.py"
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--- /dev/null
+++ "b/2 \350\200\201\345\271\264\344\272\272\347\232\204\350\277\234\347\250\213\346\231\272\350\203\275\350\257\212\347\226\227/\350\200\201\344\272\272\345\277\203\350\204\217\347\227\205\350\257\212\346\226\255-\345\274\240\351\271\217\346\266\233/src/utils.py"	
@@ -0,0 +1,19 @@
+import pandas as pd
+import numpy as np
+
+
+def read_data(data_file, data_columns=None):
+    """
+    :param data_file:  要读出的数据
+    :param data_columns: 要读取的数据特征名称
+    :return:  经过处理后的特征值以及标签值
+    """
+    data = pd.read_csv(data_file)
+    data.columns = data_columns
+
+    label = data.iloc[:, -1]
+    feat = data.iloc[:, :-1]
+    # 对数据进行缺失值的填充
+    feat = feat.fillna(0)
+
+    return feat, label