python预测算法代码

在Python中，有许多种类的预测算法可以用来解决不同类型的问题。根据提供的引用内容，我可以给出一个基于K最近邻算法的预测代码的例子。请注意，这只是一个例子，实际应用中需要根据具体情况进行相应的调整和修改。 ```python from sklearn.neighbors import KNeighborsClassifier from sklearn import metrics import numpy as np # 假设有一个数据集X和相应的目标变量y # 数据集标准化和划分训练集和测试集 X = np.asarray(dataset[['open', 'high', 'low', 'close', 'volume']]) y = np.asarray(dataset['Buy_Sell']) from sklearn import preprocessing X = preprocessing.StandardScaler().fit(X).transform(X) from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0) # 模型实例化，训练和预测 Ks = 10 mean_acc = np.zeros((Ks-1)) std_acc = np.zeros((Ks-1)) for n in range(1, Ks): neigh = KNeighborsClassifier(n_neighbors=n).fit(X_train, y_train) yhat = neigh.predict(X_test) mean_acc[n-1 = metrics.accuracy_score(y_test, yhat) std_acc[n-1 = np.std(yhat == y_test) / np.sqrt(yhat.shape<span class="em">1</span><span class="em">2</span><span class="em">3</span> #### 引用[.reference_title] - *1* [Python函数综合案例.md](https://download.csdn.net/download/qq_42431718/88241199)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"] - *2* *3* [基于Python实现五大常用分类算法(原理+代码)](https://blog.csdn.net/weixin_57712785/article/details/123085701)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"] [ .reference_list ]