锦中融合门户系统

        import nltk
        from nltk.chat.util import Chat, reflections

        # 定义对话规则
        pairs = [
            ["你好", "您好！有什么可以帮助您的吗？"],
            ["我想办理业务", "请问您需要办理什么类型的业务？"],
            ["怎么申请身份证？", "您可以通过我们的网站提交申请，或者前往服务大厅办理。"],
            ["谢谢", "不客气！如有其他问题，请随时联系。"]
        ]

        # 创建聊天机器人
        chatbot = Chat(pairs, reflections)

        # 用户输入
        user_input = input("请输入您的问题：")

        # 获取回复
        response = chatbot.respond(user_input)
        print("系统回复：", response)
        

        import pandas as pd
        from sklearn.neighbors import NearestNeighbors

        # 示例数据：用户-业务评分表
        data = {
            'user_id': [1, 1, 2, 2, 3, 3],
            'business_id': [101, 102, 101, 103, 102, 103],
            'rating': [5, 3, 4, 2, 3, 4]
        }

        df = pd.DataFrame(data)

        # 构建用户-业务评分矩阵
        user_business_matrix = df.pivot_table(index='user_id', columns='business_id', values='rating')

        # 使用KNN算法进行推荐
        model = NearestNeighbors(n_neighbors=2, metric='cosine')
        model.fit(user_business_matrix)
        distances, indices = model.kneighbors(user_business_matrix, n_neighbors=2)

        # 推荐相似用户感兴趣的业务
        def recommend_business(user_id):
            similar_users = indices[user_id - 1][1:]
            recommended_businesses = []
            for idx in similar_users:
                user = user_business_matrix.iloc[idx]
                recommended_businesses.extend(user[user > 3].index.tolist())
            return list(set(recommended_businesses))

        # 测试推荐
        print("推荐业务：", recommend_business(1))
        

        import matplotlib.pyplot as plt
        import numpy as np

        # 示例数据：每日访问量
        days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
        visits = [120, 150, 180, 200, 190, 160, 130]

        # 绘制柱状图
        plt.figure(figsize=(10, 5))
        plt.bar(days, visits, color='skyblue')
        plt.title('每日访问量统计')
        plt.xlabel('日期')
        plt.ylabel('访问人数')
        plt.show()