diff --git a/车辆12分类数据集/第11组--张朋飞/案例代码/horse_analysis.py b/车辆12分类数据集/第11组--张朋飞/案例代码/horse_analysis.py
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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn import preprocessing
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score,confusion_matrix,roc_curve,roc_auc_score
+from sklearn.ensemble import RandomForestClassifier,BaggingClassifier,AdaBoostClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.svm import SVC
+from sklearn.linear_model import LogisticRegression
+from sklearn.neighbors import KNeighborsClassifier
+# 中文正常显示
+plt.rcParams['font.sans-serif'] = ['SimHei']
+plt.rcParams['axes.unicode_minus'] = False
+df = pd.read_csv("data_horse.csv",encoding="gbk")
+df.head()
+## 可视化列的缺失值、唯一值、类型
+df.corr()
+# 可视化变量y与其他的相关性曲线
+df.corr()['y'].sort_values(ascending=False)
+# 剔除相关性绝对值小于0.1的变量
+df = df[df.columns[df.corr()['y'].abs()>0.1]]
+df.head()
+df.isnull().sum()
+#可视化变量
+import warnings
+warnings.filterwarnings("ignore")
+for i in df.columns:
+    plt.figure(figsize=(10,5))
+    plt.title(i)
+    # 设置字体旋转角度
+    plt.xticks(rotation=90)
+    sns.countplot(df[i])
+    plt.show()
+
+# 我们认为这些变量中的0值都是不合理的，所以我们将变量中的0值都替换为np.nan
+df[df.columns[:-1]] = df[df.columns[:-1]].replace(0,np.nan)
+df.isnull().sum()
+# 用众数填充缺失值
+# 观察发现直肠温度列是正太分布的，因此可以用均值填充缺失值，其他使用中位数
+df['直肠温度'] = df['直肠温度'].fillna(df['直肠温度'].mean())
+df[['脉搏','红细胞体积','总蛋白值']]  = df[['脉搏','红细胞体积','总蛋白值']].fillna(df[['脉搏','红细胞体积','总蛋白值']].median())
+df.isnull().sum()
+## 删除缺失值
+df = df.dropna()
+# 再查看一下变量的分布
+for i in df.columns:
+    plt.figure(figsize=(10,5))
+    plt.title(i)
+    # 设置字体旋转角度
+    plt.xticks(rotation=90)
+    sns.countplot(df[i])
+    plt.show()
+
+# 由于变量的取值范围不一样，因此需要对数据进行标准化
+import time
+X_train,X_test,y_train,y_test = train_test_split(df[df.columns[:-1]],df['y'],test_size=0.3,random_state=0)
+X_train = StandardScaler().fit_transform(X_train)
+X_test = StandardScaler().fit_transform(X_test)
+
+model_result = {}
+
+models = [RandomForestClassifier(),BaggingClassifier(),AdaBoostClassifier(),GaussianNB(),LogisticRegression(),DecisionTreeClassifier(),SVC(),KNeighborsClassifier()]
+for model in models:
+    try:
+        model_name = str(model).split('(')[0]
+        start = time.time()
+        model.fit(X_train,y_train)
+        y_pred = model.predict(X_test)
+        end = time.time()
+        # 存储准确率和混淆矩阵
+        model_result[model_name] = [accuracy_score(y_test,y_pred),confusion_matrix(y_test,y_pred),end-start]
+    except Exception as e:
+        print(model_name,e)
+
+## 使用df保存模型的结果
+df_result = pd.DataFrame(model_result).T
+df_result.columns = ['accuracy_score','confusion_matrix','time']
+df_result.sort_values(by='accuracy_score',ascending=False)
+
+#第1步：导入算法
+from sklearn.linear_model import LogisticRegression
+#第2步：创建模型：逻辑回归（logistic regression）
+model = LogisticRegression()
+#第3步：训练模型
+model.fit( X_train , y_train )
+#查看模型的正确率
+model.score(X_test , y_test )
+
+clf = SVC(kernel='rbf', C=1.0, random_state=0)
+clf.fit(X_train, y_train)
+y_test_pred = clf.predict(X_test)
+print('Accuracy: %.2f' % accuracy_score(y_test, y_test_pred))
+
+cm = confusion_matrix(y_test, y_test_pred)
+sns.heatmap(cm, annot=True, fmt='d')
+plt.xlabel('Predicted Label')
+plt.ylabel('True Label')
+plt.title('Confusion Matrix')
+plt.show()
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