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LR.py

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+#coding=gbk
+import numpy as np
+import pandas as pd
+from sklearn.metrics import accuracy_score
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LogisticRegression
+import seaborn as sns 
+from sklearn.model_selection import cross_val_score, train_test_split
+import warnings
+warnings.filterwarnings("ignore")
+
+train=pd.read_csv("E:\\python1\\123\\Data\\new_train.csv")
+test=pd.read_csv("E:\\python1\\123\\Data\\new_test.csv")
+gender=pd.read_csv("E:\\python1\\123\\Data\\gender_submission.csv")
+sur_train=train.Survived
+train.drop(['Survived'], axis=1, inplace =True)
+test.drop(['Survived'], axis=1, inplace =True)
+
+
+#相关性热力图
+dataset = train._append(test,sort=False)
+plt.figure(figsize=(14,12))
+sns.heatmap(dataset.corr(),annot = True)
+plt.show()
+
+
+#展示两个数据集前五个的内容
+# print(train.head())
+# print(test.head())
+
+
+
+LR=LogisticRegression()
+#train数据集前100个数据训练，剩下数据测试
+# LR.fit(train.iloc[0:-100,:],sur_train.iloc[0:-100])
+# print(accuracy_score(LR.predict(train.iloc[-100:,:]),sur_train[-100:].values.reshape(-1,1)))
+#0.82
+
+#train数据集训练，test数据集测试
+LR.fit(train,sur_train)
+prediction=LR.predict(test)
+print(accuracy_score(gender['Survived'], prediction))
+#0.9186602870813397