diff --git a/Three_iris.py b/Three_iris.py
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+++ b/Three_iris.py
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+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.naive_bayes import GaussianNB
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.preprocessing import StandardScaler
+from sklearn.neighbors import KNeighborsClassifier
+import numpy as np
+
+
+def bayes_iris(x):
+    """
+    朴素贝叶斯对鸢尾花种类进行预测
+    :param x: 预测数据
+    :return:
+    """
+
+    #1）获取数据集
+    iris = load_iris()
+    # print("查看特征值的名字：\n", iris.feature_names)
+    # print("查看特征值：\n", iris.data, iris.data.shape)
+
+    #2）划分数据集
+    x_train,x_test,y_train,y_test = train_test_split(iris.data,iris.target,random_state=22)
+
+    #3）建立模型
+    estimator= GaussianNB();
+
+    #4）模型训练
+    estimator.fit(x_train,y_train)
+
+    #5）模型评估
+    #对比真实值：
+    y_predict = estimator.predict(x_test)
+    # print("y_predict：\n",y_predict)
+    # print("直接对比真实值：\n",y_predict==y_test)
+
+    #计算准确率
+    score = estimator.score(x_test,y_test)
+    print("贝叶斯模型准确率为：\n",score)
+
+    #7）做出预测
+    #[0]表示setosa，[1]表示versicolor，[2]表示virginica
+    X_new = np.array(x)
+    prediction = estimator.predict(X_new)
+    print("贝叶斯模型预测的目标类别是：{}".format(prediction))
+    print("贝叶斯模型预测的目标类别花名是：{}".format(iris['target_names'][prediction]))
+    print()
+
+    return None
+
+def decisiontree_iris(x):
+    """
+    决策树对鸢尾花种类进行预测
+    :param x: 预测数据
+    :return:
+    """
+
+    # 1) 获取数据集
+    iris = load_iris()
+
+    # 2）划分数据集
+    x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=22,test_size=0.3)
+
+    # 3） 决策树预估器
+    estimator = DecisionTreeClassifier(criterion="entropy")
+    estimator.fit(x_train, y_train)
+
+    # 4） 模型评估
+    # 1、直接对比真实值和预测值
+    y_predict = estimator.predict(x_test)
+    # print("y_predict:\n", y_predict)
+    # print("比较:\n", y_test == y_predict)
+
+    # 2、计算准确率
+    score = estimator.score(x_test, y_test)
+    print("决策树模型准确度为:\n", score)
+
+    # 5) 做出预测
+    X_new = np.array(x)
+    prediction = estimator.predict(X_new)
+    print("决策树模型预测目标类别是:{}".format(prediction))
+    print("决策树模型预测目标花名是:", iris["target_names"][prediction])
+    print()
+
+    return None
+
+def knn_iris(x):
+    """
+    KNN近邻对鸢尾花种类进行预测
+    :param x:测试数据
+    :return:
+    """
+    # 1) 获取数据
+    iris = load_iris()
+
+    # 2）划分数据集
+    x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=22)
+
+    # 3）特征工程：标准化
+    transfer = StandardScaler()
+    x_train = transfer.fit_transform(x_train)
+    x_test = transfer.transform(x_test)
+
+    # 4) KNN算法预估器
+    estimator = KNeighborsClassifier(n_neighbors=3)
+    estimator.fit(x_train, y_train)
+
+    # 5）模型估计
+    # 方法1：直接比对真实值和预测值
+    y_predict = estimator.predict(x_test)
+    # print("y_predict:\n", y_predict)
+    # print("直接比对真实值和预测值:\n", y_test ==y_predict)
+
+    #方法2：计算准确值
+    score = estimator.score(x_test, y_test)
+    print("KNN模型准确值为：\n", score)
+
+    # 6) 做出预测
+    X_new = np.array(x)
+    prediction = estimator.predict(X_new)
+    print("KNN模型预测目标类别是:{}".format(prediction))
+    print("KNN模型预测目标花名是:", iris["target_names"][prediction])
+    print()
+
+    return None
+
+def input_feature():
+    """
+    顺序输入花萼长度、宽度，花瓣长度、宽度
+    :return:测试值二维数组
+    """
+    feature = [0] * 4
+    name = ['Sepal Length：','Sepal Width：','Petal Length：','Petal Width：']
+    predict = []
+    count = 0
+    for i in range(4):
+        try:
+            feature[i] = float(input(name[i]))
+        except ValueError:
+            print("请输入数值")
+            return None
+        else:
+            count += 1
+    predict.append(feature)
+    return predict
+
+if __name__ == '__main__':
+    X_new = input_feature()
+    if X_new != None:
+        decisiontree_iris(X_new)
+        knn_iris(X_new)
+        bayes_iris(X_new)
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