取D:\第二问 的前600条记录作为训练数据，后90条记录作为测试数据，利用支持向量机、逻辑回归、神经网络三种不同的模型依次做出预测，并计算其预测精度。将预测精度通过柱状图表示出来，其中横轴为模型名称（支持向量机、逻辑回归、神经网络），纵轴为其对应的预测精度。我需要最后的完整代码

为了完成这个任务，我们将使用Python编程语言和一些常用的机器学习库，如sklearn（用于支持向量机和逻辑回归）、tensorflow或keras（用于神经网络）。以下是实现此任务的一个基本步骤和代码示例：

首先，确保已安装所需的库：

pip install pandas numpy sklearn matplotlib tensorflow keras

然后，编写Python代码：

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt

# 读取数据
data = pd.read_csv('D:/第二问.csv')

# 提取特征和目标变量
X = data.iloc[:,:-1]  # 假设最后一列是目标变量
y = data.iloc[:,-1]

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)

# 支持向量机
svm = SVC()
svm.fit(X_train, y_train)
svm_pred = svm.predict(X_test)
svm_accuracy = accuracy_score(y_test, svm_pred)
print(f"Support Vector Machine Accuracy: {svm_accuracy}")

# 逻辑回归
log_reg = LogisticRegression()
log_reg.fit(X_train, y_train)
log_reg_pred = log_reg.predict(X_test)
log_reg_accuracy = accuracy_score(y_test, log_reg_pred)
print(f"Logistic Regression Accuracy: {log_reg_accuracy}")

# 神经网络 (这里使用简单的MLPClassifier)
nn = MLPClassifier(hidden_layer_sizes=(100,), activation='relu', solver='adam')
nn.fit(X_train, y_train)
nn_pred = nn.predict(X_test)
nn_accuracy = accuracy_score(y_test, nn_pred)
print(f"Neural Network (MLP) Accuracy: {nn_accuracy}")

# 绘制柱状图
models = ['SVM', 'Logistic Regression', 'Neural Network']
accuracies = [svm_accuracy, log_reg_accuracy, nn_accuracy]
plt.bar(models, accuracies)
plt.xlabel('Model')
plt.ylabel('Accuracy')
plt.title('Prediction Accuracies for Different Models')
plt.show()