{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customer Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有一家商店积累了很多持有会员卡的客户的基本数据,如客户ID、年龄、性别、年收入和支出分数。支出分数是基于顾客行为和购买数据的得分。该商店想出售一些市场上的新产品,希望找到对新产品感兴趣的目标用户。\n",
    "\n",
    "机器学习对这项任务很有用。尤其是聚类,这一最重要的无监督学习问题,能够将相似的个体分类。\n",
    "\n",
    "这些类别称为簇。簇是数据集中点的集合。它们之间的这些点比属于其他簇的点更相似。\n",
    "\n",
    "基于距离的聚类将这些点分成若干个簇,这样簇内的距离应该很小,簇间的距离应该很大。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入需要的模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "针对这个问题我们可以使用 scikit-learn 中的 Kmeans and PCA方法。 先对特征数据使用PCA进行降维,再将用户使用Kmeans进行聚类分组。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Collecting plotly\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/15/90/918bccb0ca60dc6d126d921e2c67126d75949f5da777e6b18c51fb12603d/plotly-4.6.0-py2.py3-none-any.whl (7.1MB)\n",
      "Collecting retrying>=1.3.3 (from plotly)\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/44/ef/beae4b4ef80902f22e3af073397f079c96969c69b2c7d52a57ea9ae61c9d/retrying-1.3.3.tar.gz\n",
      "Requirement already satisfied: six in d:\\programdata\\anaconda3\\lib\\site-packages (from plotly) (1.12.0)\n",
      "Building wheels for collected packages: retrying\n",
      "  Building wheel for retrying (setup.py): started\n",
      "  Building wheel for retrying (setup.py): finished with status 'done'\n",
      "  Created wheel for retrying: filename=retrying-1.3.3-cp37-none-any.whl size=11435 sha256=b8e0e4d65579c351357af9ce89ba6deb6d652b35453fd03a9901b3b9be73fb4a\n",
      "  Stored in directory: C:\\Users\\Admin\\AppData\\Local\\pip\\Cache\\wheels\\e7\\3b\\76\\a56d52081b6a74f47272e950a374d9119b51d41311acf96c79\n",
      "Successfully built retrying\n",
      "Installing collected packages: retrying, plotly\n",
      "Successfully installed plotly-4.6.0 retrying-1.3.3\n"
     ]
    }
   ],
   "source": [
    "! pip install -i https://pypi.tuna.tsinghua.edu.cn/simple plotly\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "import plotly.graph_objs as go\n",
    "import plotly.io as pio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import extra_graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers = pd.read_csv(\"customers.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
      "0           1    Male   19                  15                      39\n",
      "1           2    Male   21                  15                      81\n",
      "2           3  Female   20                  16                       6\n",
      "3           4  Female   23                  16                      77\n",
      "4           5  Female   31                  17                      40\n"
     ]
    }
   ],
   "source": [
    "## TODO: 取出数据前5条进行观察\n",
    "print(customers.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 探索数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察数据缺失情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in each variable: \n",
      "CustomerID                0\n",
      "Gender                    0\n",
      "Age                       0\n",
      "Annual Income (k$)        0\n",
      "Spending Score (1-100)    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(f\"Missing values in each variable: \\n{customers.isnull().sum()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察数据是否有重复的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated rows: 0\n"
     ]
    }
   ],
   "source": [
    "print(f\"Duplicated rows: {customers.duplicated().sum()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看每个特征的数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable:                  Type: \n",
      "CustomerID                 int64\n",
      "Gender                    object\n",
      "Age                        int64\n",
      "Annual Income (k$)         int64\n",
      "Spending Score (1-100)     int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(f\"Variable:                  Type: \\n{customers.dtypes}\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 观察数据的统计和分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "获取数据的平均值、标准差、中值和方差,如果变量不是数值类型,就统计每个类的数量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "##TODO:对数值型变量平均值、标准差、中值和方差,如果变量不是数值类型,就统计每个类的数量\n",
    "def statistics(variable):\n",
    "    if variable.dtype == \"int64\" or variable.dtype == \"float64\":\n",
    "        return pd.DataFrame([variable.mean(),variable.std(),variable.median(),variable.var()],index=['Mean','Standard Deviation', 'Median','Variance'],columns=[variable.name]).T\n",
    "    else:\n",
    "        return pd.DataFrame(variable.value_counts()).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "##绘制柱形图\n",
    "def graph_histo(x):\n",
    "    # 针对数值型变量绘制图\n",
    "    if x.dtype == \"int64\" or x.dtype == \"float64\":\n",
    "        # 通过取最大值和最小值来选择箱的大小,并将减量除以10 \n",
    "        size_bins = 10\n",
    "        ##TODO: 通过获得列的名字获取title\n",
    "        title = x.name\n",
    "        #为每个图形随机分配颜色\n",
    "        color_kde = list(map(float, np.random.rand(3,)))\n",
    "        color_bar = list(map(float, np.random.rand(3,)))\n",
    "        \n",
    "        # 绘制displot\n",
    "        sns.distplot(x, bins=size_bins, kde_kws={\"lw\": 1.5, \"alpha\":0.8, \"color\":color_kde},\n",
    "                       hist_kws={\"linewidth\": 1.5, \"edgecolor\": \"grey\",\n",
    "                                \"alpha\": 0.4, \"color\":color_bar})\n",
    "        # 自定义记号和标签\n",
    "        plt.xticks(size=14)\n",
    "        plt.yticks(size=14);\n",
    "        plt.ylabel(\"Frequency\", size=16, labelpad=15);\n",
    "        # 自定义标题\n",
    "        plt.title(title, size=18)\n",
    "        # 自定义网格和坐标轴\n",
    "        plt.grid(False);\n",
    "        plt.gca().spines[\"top\"].set_visible(False);\n",
    "        plt.gca().spines[\"right\"].set_visible(False);\n",
    "        plt.gca().spines[\"bottom\"].set_visible(False);\n",
    "        plt.gca().spines[\"left\"].set_visible(False);   \n",
    "    else: #针对非数值类型变量绘制图\n",
    "        x = pd.DataFrame(x)\n",
    "        # 绘图       \n",
    "        sns.catplot(x=x.columns[0], kind=\"count\", palette=\"spring\", data=x)\n",
    "        # 自定义标题\n",
    "        title = x.columns[0]\n",
    "        plt.title(title, size=18)\n",
    "        # 自定义记号和标签\n",
    "        plt.xticks(size=14)\n",
    "        plt.yticks(size=14);\n",
    "        plt.xlabel(\"\")\n",
    "        plt.ylabel(\"Counts\", size=16, labelpad=15);        \n",
    "        # 自定义网格和坐标轴\n",
    "        plt.gca().spines[\"top\"].set_visible(False);\n",
    "        plt.gca().spines[\"right\"].set_visible(False);\n",
    "        plt.gca().spines[\"bottom\"].set_visible(False);\n",
    "        plt.gca().spines[\"left\"].set_visible(False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察 **Spending Score**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "spending = customers[\"Spending Score (1-100)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Median</th>\n",
       "      <th>Variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Spending Score (1-100)</td>\n",
       "      <td>50.2</td>\n",
       "      <td>25.823522</td>\n",
       "      <td>50.0</td>\n",
       "      <td>666.854271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Mean  Standard Deviation  Median    Variance\n",
       "Spending Score (1-100)  50.2           25.823522    50.0  666.854271"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##TODO:使用statistics()函数对spending数据进行统计 \n",
    "statistics(spending)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##TODO:使用graph_histo()函数绘制spending数据分布图\n",
    "graph_histo(spending)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后,观察变量**Age**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "age = customers[\"Age\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Median</th>\n",
       "      <th>Variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Age</td>\n",
       "      <td>38.85</td>\n",
       "      <td>13.969007</td>\n",
       "      <td>36.0</td>\n",
       "      <td>195.133166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Mean  Standard Deviation  Median    Variance\n",
       "Age  38.85           13.969007    36.0  195.133166"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##TODO:使用statistics()函数对age数据进行统计 \n",
    "statistics(age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##TODO:使用graph_histo()函数绘制age数据分布图\n",
    "graph_histo(age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后,统计 **Annual Income** 变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "income = customers[\"Annual Income (k$)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Median</th>\n",
       "      <th>Variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Annual Income (k$)</td>\n",
       "      <td>60.56</td>\n",
       "      <td>26.264721</td>\n",
       "      <td>61.5</td>\n",
       "      <td>689.835578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Mean  Standard Deviation  Median    Variance\n",
       "Annual Income (k$)  60.56           26.264721    61.5  689.835578"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##TODO:使用statistics()函数对income数据进行统计 \n",
    "statistics(income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##TODO:使用graph_histo()函数绘制income数据分布图\n",
    "graph_histo(income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender = customers[\"Gender\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Gender</td>\n",
       "      <td>112</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Female  Male\n",
       "Gender     112    88"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##TODO:使用statistics()函数对gender数据进行统计 \n",
    "statistics(gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##TODO:使用graph_histo()函数绘制gender数据分布图\n",
    "graph_histo(gender)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 变量间的相关性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用“pairplot”函数,分析数值变量间的关系。为了观察性别之间有没有差别,设置“hue”参数,将女性或客户的点涂不同颜色。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning:\n",
      "\n",
      "Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504.625x432 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(customers, x_vars = [\"Age\", \"Annual Income (k$)\", \"Spending Score (1-100)\"], \n",
    "               y_vars = [\"Age\", \"Annual Income (k$)\", \"Spending Score (1-100)\"], \n",
    "               hue = \"Gender\", \n",
    "               kind= \"scatter\",\n",
    "               palette = \"YlGnBu\",\n",
    "               height = 2,\n",
    "               plot_kws={\"s\": 35, \"alpha\": 0.8});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 研究变量之间的描述性统计、分布和相关性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于聚类问题可以使用K-means方法,使用该方法需要满足以下算法假设\n",
    "K-means 假设:\n",
    "\n",
    "- **团簇的形状**: 分布的方差是球形的,这意味着团簇是球形的。为了实现这一点,所有变量都应该是正态分布的,并且具有相同的方差。\n",
    "- **团簇的大小**: 所有的团簇需要有相同数量的观测样本.\n",
    "- **变量间的关系**: 变量间几乎没有相关性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在本数据集中,变量是正态分布的,差异非常接近,只有年龄的方差比其他变量低。针对年龄找到一个适当的转变来解决这个问题,可以应用对数或Box-Cox变换修正非正态分布变量。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据降维"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在验证了可以应用k-均值之后,可以应用主成分分析(PCA)来发现哪些维度的特征方差最大。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 主成分分析(Principal Component Analysis,PCA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先,将分类变量转换为两个二进制变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers[\"Male\"] = customers.Gender.apply(lambda x: 0 if x == \"Male\" else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers[\"Female\"] = customers.Gender.apply(lambda x: 0 if x == \"Female\" else 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后,从数据集中选择所有有用的列,其中客户ID不是一个有用的特征可以忽略,另外性别特征会将数据分为两类,它不应该出现在最终的数据集中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = customers.iloc[:, 2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Annual Income (k$)  Spending Score (1-100)  Male  Female\n",
       "0   19                  15                      39     0       1\n",
       "1   21                  15                      81     0       1\n",
       "2   20                  16                       6     1       0\n",
       "3   23                  16                      77     1       0\n",
       "4   31                  17                      40     1       0"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将PCA应用在选择的特征上进行拟合\n",
    "pca = PCA(n_components=2).fit(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在拟合过程中,模型从数据中学习一些量:组成成分和解释方差。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.88980385e-01  5.88604475e-01  7.86022241e-01  3.32880772e-04\n",
      "  -3.32880772e-04]\n",
      " [ 1.30957602e-01  8.08400899e-01 -5.73875514e-01 -1.57927017e-03\n",
      "   1.57927017e-03]]\n"
     ]
    }
   ],
   "source": [
    "print(pca.components_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[700.26450987 684.33354753]\n"
     ]
    }
   ],
   "source": [
    "print(pca.explained_variance_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "组成成分定义向量的方向,解释方差定义向量的平方长度。向量表示数据的主轴,向量的长度表示该轴在描述数据分布时的重要性,每个数据点在主轴上的投影是数据的主要组成部分。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用PCA拟合变换样本\n",
    "pca_2d = pca.transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以用biplot的散点图来直观展示,每一个点都由主成分的得分来表示,有助于理解数据的降维。还可以发现主成分和原始变量之间的关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "extra_graphs.biplot(pca_2d[:,0:2], np.transpose(pca.components_[0:2, :]), labels=X.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以观察到年收入和支出得分两个最重要的组成成分。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-means 聚类 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了对数据进行聚类,需要确定如何判断两个数据点是否相似。接近度是指物体之间存在的相似性或相异性。可以选择两个点是否相似。所以如果这个值很大,这些点是非常相似的。或者选择确定它们是否不同。如果值很小,则点是相似的。这就是我们所说的“距离”。聚类算法可以使用多种距离:曼哈顿距离、Minkowski距离、欧氏距离等。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "${\\sqrt{\\sum_{i=1}^n (x_i-y_i)^2}}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K-means通常使用欧氏距离来确定两点的相似(或不同)程度。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先,我们需要确定要使用的聚类簇的数量。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们将考虑簇内总变化(或簇内总平方和(WSS))。目标是最小化WSS。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Elbow方法查看WSS总量如何随集群数量而变化。为此,我们将计算一系列不同k值的k均值,然后计算总WSS。我们绘制了WSS与集群数量的曲线最后,我们确定了情节的转折点。这一点被认为是适当数量的聚类簇。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eOYeSm1+PuxQRkXopbFqwjI459Pze/qx4aSbrPl8SdzkiItuksGnhen5vf9LaZ7HgFh3diEjqUti0cJl57ehx3iiWPfcp5bOXxV2OiEidFDatQK/xB5KWmc6Cv/w77lJEROqksGkFsgo70P2ckZQ++REbSlbGXY6IyDcobFqJXj84CMxYePsbcZciIvINCptWIrtXZ7qdMYIlj3zApsWr4y5HRKQGhU0rUnTpIXhFFQsmvBV3KSIiNShsWpGcvvkUnjKMJQ++x+YV6+IuR0RkC4VNK1N02aFUbahg0V1vx12KiMgWCptWpt2gQrqOGcqie6dSsao87nJERACFTavU+0eHUrlmI4vunRp3KSIigMKmVWq/R0/yjxvCgr/8mw1zV8RdjoiIwqa16v+bMVhmOsU/exqvqoq7HBFp4xQ2rVR2r870u3o0q6fOY/G978Zdjoi0cQqbVqzbmXvT5chBzPufSZTPWR53OSLShilsWjEzY9ffjcWy0vnq5/9Qd5qIxEZh08pl9+xE/2uOZ/XUeSy6R6PTRCQezRY2ZtbHzCab2Uwzm2FmV4T2fDObZGazwt+80G5mdrOZFZvZx2a2T8K2xoXlZ5nZuIT2fc3sk7DOzWZm9e2jrSg8YwR5R+3G1ze+TPlsdaeJSPNrziObCuBn7j4E2B+41MyGAlcBr7j7IOCV8BzgeGBQeIwHbocoOICrgf2AUcDVCeFxe1i2er3RoX1b+2gTzIwBN56MZaVT/HONThOR5tdsYePui9z9gzC9BpgJFAFjgfvDYvcDp4TpscADHnkH6GJmPYHjgEnuvsLdy4BJwOgwr5O7v+3uDjxQa1t17aPNyO7Zif7XHs+ad79Wd5qINLtYztmYWT9gb2Aq0N3dF0EUSEC3sFgRMD9htZLQVl97SR3t1LOPNqXwdHWniUg8mj1szKwD8CTwY3ev78YrVkeb70B7Y2obb2bTzGxaaWlpY1ZtEdSdJiJxadawMbNMoqB5yN2fCs1LQhcY4e/S0F4C9ElYvTewcDvtvetor28fNbj7BHcf6e4jCwsLd+xFpria3WnvxF2OiLQRzTkazYC7gZnu/qeEWc8C1SPKxgHPJLSfF0al7Q+sCl1gLwHHmlleGBhwLPBSmLfGzPYP+zqv1rbq2kebtLU77RV1p4lIs2jOI5uDgHOBI81seniMAW4EjjGzWcAx4TnA88BsoBi4E/ghgLuvAH4DvBce14U2gEuAu8I6XwEvhPZt7aNNin7seTKWHa6dVqnuNBFJLosGbkltI0eO9GnTpsVdRlItfWI6xT9+in5Xj6bXRQfGXY6ItA51nT/fuSMbMxtoZjk7sw2JT+Fpe5F3dPXotGVxlyMirViDw8bMflv9a/1wHmUS8CWwyMz2S1aBkjxmxq43nozlZFD8U3WniUjyNObI5hzgizB9PDCC6EoAD9DGz4G0ZFk9OtH/2jGsmTZfo9NEJGkaEzbd2fqjyTHAY+7+LnAL0Q80pYUqPG0v8o7ZXd1pIpI0jQmb5UDfMH0s8GqYzmAbJ4SkZajuTkvLyVR3mogkRWPC5kng4XCuJh94MbSPIBpqLC1YVveO9L8udKfd/Xbc5YhIK9OYsPkpcDPwGXCMu68L7T0JV2SWlq3g28Oj7rTfvUL5V+pOE5Gmo9/ZbENb+J1NXTYtWcP0I28ld2ABez51IZau++uJSKPs3O9szOywxCHOZna+mb1hZneEi2tKK7ClO+39+Sy8S91pItI0GvO19f+AHgBmtjtwB/AxcADwh6YvTeJS8O3h5B07mPm/V3eaiDSNxoTNrsAnYfo0ohuY/RC4CDipqQuT+JgZu/7PSRqdJiJNpjFh40B6mD6KraPRFgNdm7IoiV9W9470/03oTrvzrbjLEZEWrjFh8x7w32Z2LnAIW6+o3I8ocKSVKTh1OPnHDebrP7zK+uLWdzM5EWk+jQmbHxNdKeBW4AZ3/yq0nwHoq28rZGYM+J+TSM9Vd5qI7JwGh427f+ruw9y9s7tfmzDr58D5TV6ZpISsbh3p/5sTWPtBibrTRGSHNWbo8x5mNryOWbsBg5quJEk1BacMI/+4IepOE5Ed1phutAnAnnW0Dw3zpJWKutNOVHeaiOywxoTNcODdOtrfA4Y1TTmSqmp0p01Qd5qINE5jwqYS6FxHex666nObsKU77X9fZf0sdaeJSMM1JmxeA35lZtW/tcHMMoBfAa83dWGSetSdJiI7qjFhcyXR72uKzexBM3sQmAUcDPy/ZBQnqSerW0f6X38Caz8sYeEdb8Zdjoi0EI0Z+vw50Xmbh4nuZ9MVeAjYy91nJqc8SUUFY4eRP3oIX/9xsrrTRKRBGjP0+SVgHDARONndx7j7f7n7wqRVJylpy48922VR/JOn8IrKuEsSkRTXmG60acCJwBRgpZm9ZGa/MLMDEs/jSNuQVdiBAdefwNrpCzQ6TUS2qzHdaL9y94OBLsCpREOeTyAaOFCWnPIklXU9eU/yjx+q7jQR2a4duQ1jJ6LzNYVAN6Ih0e83ZVHSMpgZA357orrTRGS7GnPO5i9m9hkwG/gBsAgYD3Rx9yOSVJ+kuKzCDgy4IepOW3CHutNEpG4ZjVj2EqAUuJHo9gLvu7snpSppUbqetCf5E2cw/4+vkn/M7rTbrVvcJYlIimlMN9puRD/g3B14GlhhZs+Z2U/NbJ/trWxm95jZUjP7NKHtGjNbYGbTw2NMwrxfmFmxmX1hZscltI8ObcVmdlVCe38zm2pms8zsUTPLCu3Z4XlxmN+vEa9ZGsDMGHDDiaS3z6b4J0+rO01EvqExAwSK3f0ud/+uu/cBDgSWAb8jGiywPfcBo+tov8ndR4TH8wBmNhQ4C9gjrHObmaWHUW9/AY4nugDo2WFZQh03ufsgogELF4b2C4Eydx8I3BSWkya2pTvtowUs+Kt+7CkiNTXmnE2amY0ysyvN7AVgKnAO0eCA329vfXd/HVjRwN2NBR5x943uPgcoBkaFR7G7z3b3TcAjwFgzM+BI4Imw/v3AKQnbuj9MPwEcFZaXJtb1pD3JHzOU+X+azPovlsZdjoikkMZ0o60E/k007Pkj4Ewgz933d/df7EQNl5nZx6GbLS+0FQHzE5YpCW3bau8KrHT3ilrtNbYV5q8Ky3+DmY03s2lmNq20VEN5G2vL6LQO2cz6qUanichWjQmbM4H8EC5XufuL7r5uJ/d/O7ArMIJodNsfQ3tdRx6+A+31beubje4T3H2ku48sLCysr27ZhqyCDgy44UTWfbRQ3WkiskVjztk0RbjU3uYSd6909yrgTqJuMoiOTPokLNobWFhP+zKgS7gKdWJ7jW2F+Z1peHee7ICCk/ak6wl7MP9Pk1n3+ZK4yxGRFLAjP+psMmbWM+HpqUD1SLVngbPCSLL+RLedfpdoIMKgMPIsi2gQwbNhCPZk4PSw/jjgmYRtjQvTpwOvash28vW/4QTSO2RT/DONThORZgwbM/s78Dawu5mVmNmFwO/N7BMz+xg4AvgJgLvPAB4DPgNeBC4NR0AVwGXAS8BM4LGwLES3QPipmRUTnZO5O7TfDXQN7T8FtgyXluTJKujAgN9G3Wlzrn5B974RaeNMX/LrNnLkSJ82bVrcZbR4c659gUV3vk3e0bux261nkN4hO+6SRCS56hztG2s3mrR+/a8+nv43nEjZ5GI+OeUuNpSsjLskEYmBwkaSrue4UQx98LtsXLiKT068gzXvfx13SSLSzBQ20iy6HDqQYc9cRHr7bD498z5Kn/oo7pJEpBkpbKTZtBtUyLCJ4+k4oohZP3qSr//wCl6lgQMibYHCRppVZl47hv59HN2+sw8lf36NLy95nMryTXGXJSJJ1phbDIg0ibSsDHb937HkDipg3g2T2FBSxpC7/4OsHp3iLk1EkkRHNhILM6PoBwcz+O6zKZ+1jI9PnMDaTxZuf0URaZEUNhKr/GMHM+wf34c049Nv383y5z+LuyQRSQKFjcSu/dAeDJ84nnaDu/PF+EcoufV19GNjkdZFYSMpIatbR/Z47AIKxg7j6xtfpvgnT1O1sWL7K4pIi6ABApIy0nMzGXTr6eQOLGD+HyezYd4KBt91Npld28ddmojsJB3ZSEoxM/r85Ah2u+0M1n28kI9PvEN3/RRpBRQ2kpIKTh7GHk98j6qNFXwy9k7KXv0y7pJEZCcobCRlddy7N8MnXkxO3zxmnv8QC+96WwMHRFoohY2ktOxendnzqQvJP2Ywc695gdm/nEjVZt2MTaSlUdhIyktvn83ud36Hoh8ezJIH32PmuQ9SsbI87rJEpBEUNtIiWFoafX95LAP/dCqrp87j45MnUD57edxliUgDKWykRel25t7s8cg4KsrK+eSkCax6c3bcJYlIAyhspMXptF8/hk8cT2a3Dnx2zgMseUi37xZJdQobaZFy+uYz7JmL6HzQAL668lnmXPciXql744ikKoWNtFgZnXIYcv859LhgPxZNeIvPL3yYyrUb4y5LROqgsJEWzTLSGfCbE+h/w4mUTS7mk1PuYkPJyrjLEpFaFDbSKvQcN4qhD36XjQtX8cmJd7B62tdxlyQiCRQ20mp0OXQgw569iPT22cw4815Kn/oo7pJEJFDYSKvSbmAhwyaOp+M+fZj1oyf5+g+v4FUaOCASN4WNtDqZee0Y+vB5dPvOPpT8+TW+vORxKss3xV2WSJum+9lIq5SWlcGu/zuW3EEFzLthEhvml7H7X79Dzi55cZcm0iY125GNmd1jZkvN7NOEtnwzm2Rms8LfvNBuZnazmRWb2cdmtk/COuPC8rPMbFxC+75m9klY52Yzs/r2Ia2fmVH0g4MZfM/ZlH+1jOlH3sqC2/6tC3mKxKA5u9HuA0bXarsKeMXdBwGvhOcAxwODwmM8cDtEwQFcDewHjAKuTgiP28Oy1euN3s4+pI3IP2Ywe0++nC6H7cq8307i4zF/Zc378+MuS6RNabawcffXgRW1mscC94fp+4FTEtof8Mg7QBcz6wkcB0xy9xXuXgZMAkaHeZ3c/W2PbnjyQK1t1bUPaUOye3Vm8N3/we53nR1dV+2Uu/jql89RsXpD3KWJtAlxDxDo7u6LAMLfbqG9CEj86lkS2uprL6mjvb59SBvUdfQQ9p5yOT2/tx9L/jaNDw+/mWUTP9VN2USSLO6w2Raro813oL1xOzUbb2bTzGxaaWlpY1eXFiK9Qzb9rx3D8OfGk9W9I1/+4DFmjvsbG+aXxV2aSKsVd9gsCV1ghL9LQ3sJ0Cdhud7Awu20966jvb59fIO7T3D3ke4+srCwcIdflLQMHfYqYvhz4+l3zWhWvzOP6UfcyoLb39AAApEkiDtsngWqR5SNA55JaD8vjErbH1gVusBeAo41s7wwMOBY4KUwb42Z7R9GoZ1Xa1t17UMEy0in1/cPZO/Jl9H5kAHMu+FfGkAgkgTWXH3VZvZ34HCgAFhCNKrsH8BjwC7A18AZ7r4iBMatRCPK1gMXuPu0sJ3vAb8Mm73B3e8N7SOJRrzlAi8Al7u7m1nXuvaxvXpHjhzp06bpPiltibuz4sWZzPnv59m0ZA09zvsWu1x5NBmdcuIuTaQlqeu0RvOFTUujsGm7KtZsYP4fXmXRvVPJ7NaB/teOoesJQwk/3RKR+tX5DyXubjSRlJPRMYf+141h2HPjySrswJc/eJTPz39IAwhEdoLCRmQbOo4oYvjE8fS7ejSr3p4bXYHgrxpAILIjFDYi9bCMdHpdFAYQHDyAedf/i49PuIM1H2gAgUhjKGxEGiC7qAuD7/kPdp9wFhXL1/HJ2LuY/auJugKBSAMpbEQayMzoOmYoI6ZcTo/zR7H4gff48IhbWDZxhq5AILIdChuRRsromMOA35wQDSAoaK8BBCINoLAR2UEdRxQx/J8X0+/Xx7HqrTlbBhB4hQYQiNSmsBHZCZaRTq/xBzFi8uV0Pqg/867/Fx+N0QACkdoUNiJNIKd3Fwbfe44GEIhsg8JGpInUGEAwLhpAMP2IW1j+Tw0gEFHYiDSxjI45DLj+BIY9exEZXdvzxcWP8vkFD7GhZGXcpYnERmEjkiQd9+7NXs9fTN//Po5Vb85h+hG3sOD2N6hYo641aXt0Ic5t0IU4pSltKFnJnF9NpJhmltkAAA+uSURBVOyVL0nLySR/9BAKT9+LLofsiqXrO5+0Krrqc2MobKSpuTtrPyhh6ZPTWf7Mp1SsKieze0cKTxlO4ekjaD+ke9wlijQFhU1jKGwkmao2VlD28hcsffIjVr76JV5RRfs9elB4+ggKThlOVmGHuEsU2VEKm8ZQ2Ehz2bx8Hcue+YSlT05n3UcLIT2NvMMHUnj6CPKP2Z20nMy4SxRpDIVNYyhsJA7rv1xK6ZMfUfrkR2xavJr0TjkUnLgHhaePoOO3dtEN3KQlUNg0hsJG4uSVVax6ew6lT3zE8uc/o2r9JrL75lH47b3odtoIcvrlx12iyLYobBpDYSOponLdRpa/MJPSJ6az6s054E7Hb+1C4WkjKDhpDzI658ZdokgihU1jKGwkFW1cuIrSp6JutvJZpVh2BvnHDo6GUR86kLTM9LhLFFHYNIbCRlKZu7Pu44UsfWI6y575hIoV68ksaE9B9TDqPXro/I7ERWHTGAobaSmqNlWwckoxS5+YTtnLX+CbKmm3ezcKTx9B4anDyerRKe4SpW1R2DSGwkZaos1l61k+cQalT0xnzfvzIc3ocsgACk8bQf7oIaS3y4q7RGn9FDaNobCRlq589nJKn5xO6ZMfsbFkJWnts+h6wh50O20vOh3QD0vTZXIkKRQ2jaGwkdbCq6pY/e7XlD4+neX/nEHl2o1kdG1Pu927kTuwgHa7FpI7sIDcgQVk9eykEJKdpbBpDIWNtEaV5ZtY8dLnrHytmPLiZZR/tYzKhBu8peVmkjugK7kDC8ndtSCEUCE5/fNJz1UXnDSIwqYxFDbSFrg7m5eto7y4lPKvlkUBVFxK+ezlbJy/Eqo/H8zILupcI4CqpzMLO2jkmySq83+GjOauQkRSh5mRVdiBrMIOdD6gf415leWb2TBneQih0iiIZi9j9cPzqCrfvGW59I7ZIXhCd1yYzumbR1qWPmIkkhJHNmY2F1gDVAIV7j7SzPKBR4F+wFzgTHcvs+gr1J+BMcB64Hx3/yBsZxzwX2Gz17v7/aF9X+A+IBd4HrjCt/PCdWQjUjevqmLT4jVbA+irZVuOijYtXr11wfQ0cnbJqxFA1dOZee3iewGSbKnbjRbCZqS7L0to+z2wwt1vNLOrgDx3v9LMxgCXE4XNfsCf3X2/EE7TgJGAA+8D+4aAehe4AniHKGxudvcX6qtJYSPSeJVrN27tjvuqlPKvlkehNGc5vqlyy3IZ+e2i4BlQQE7/ruT270pO/67k9MsnPVdXuW7hWlw32ljg8DB9PzAFuDK0PxCOTN4xsy5m1jMsO8ndVwCY2SRgtJlNATq5+9uh/QHgFKDesBGRxkvvkE2HvYrosFdRjXavrGLj/JWs/6qUDSGM1heXUvbyF2xetq7Gslm9OpPbPz8Kn/5dyR0QgmgXdcu1ZKnyX86Bf5mZA3e4+wSgu7svAnD3RWbWLSxbBMxPWLcktNXXXlJH+zeY2XhgPMAuu+yys69JRAJLTyOnX350teqjdq8xr2L1BjbMXUH57GVsmLOcDXNWUD5nOcsnzqBiZfnWBdOM7N5dQhAVhKOhEEp9umAZui5cKkuVsDnI3ReGQJlkZp/Xs2xdh2i+A+3fbIxCbgJE3Wj1lywiTSGjUw4dhveiw/Be35i3uWw9G2Yvp3zO8iiI5kZBtOb96VSu3bhlOctII3uXvK3dcf27RqE0oIDsXvrtUCpIibBx94Xh71IzexoYBSwxs57hqKYnsDQsXgL0SVi9N7AwtB9eq31KaO9dx/IikuIy89qRuW87Ou7bp0Z79ZDtDXO2BlH57Ojvqrfm1BgtZ9kZ5PQNQdQvoVuuf1eyenTUsO1mEnvYmFl7IM3d14TpY4HrgGeBccCN4e8zYZVngcvM7BGiAQKrQiC9BPzWzPLCcscCv3D3FWa2xsz2B6YC5wG3NNfrE5Gmlzhku9OovjXmuTubFq+pEUAb5kahVDalGN9YsWXZtNxMcvrlk9WzE5l57cjokktGXjsy8tqR2SWXjLyaz9PaZymcdlDsYQN0B54O/wEzgIfd/UUzew94zMwuBL4GzgjLP080Eq2YaOjzBQAhVH4DvBeWu656sABwCVuHPr+ABgeItFpmRnbPTmT37ETnA2v+dsgrq9i4cFU4IloRzhEtZ9PSNZTPKqWirLxG99w3tp2ZHgVQl9wQTu22PM/Ia5cQWLkJgdWOtOxU+KiNV0oMfU5FGvos0jZVbaqgYmV59Chbz+ayrdMVZeupWFketSU+X1le44iptrR2WWTk5W4Jn4wQSpnVR05bwip3a4B1ymmpgx5a3NBnEZFml5aVQVa3jmR169jgddydqvLNCQG1PkzXfr6eirJy1i9avKWNqm1/4U/vnBOFUpeELr5w5JRZ66iq+mgrvVN2Sg6IUNiIiOwkMyO9XRbp7bLI7tW5wet5VRWVazZSUVZeM5BWltcMrJVRSG2Yu4LNK9dTuWrDtjeaZmR0zt3anbelyy8hrOpoS++YndTzUQobEZGYWFpaFAydc6PfIDWQV1ZRsSqhq69WKCWG1ebStQ06H0V62pauvQE3nkzn/fvt/AtMoLAREWlhLD2NzPz2ZOa3b9R6VZsro5DaRjBVH1VldMpp8poVNiIibURaZjpZBR3IKujQ/Ptu9j2KiEibo7AREZGkU9iIiEjSKWxERCTpFDYiIpJ0ChsREUk6hY2IiCSdwkZERJJOV33eBjMrBebFXcdOKgCWxV1ECtH7sZXei5r0ftS0M+/HMncfXbtRYdOKmdk0dx8Zdx2pQu/HVnovatL7UVMy3g91o4mISNIpbEREJOkUNq3bhLgLSDF6P7bSe1GT3o+amvz90DkbERFJOh3ZiIhI0ilsREQk6RQ2rZCZ9TGzyWY208xmmNkVcdcUNzNLN7MPzWxi3LXEzcy6mNkTZvZ5+H/kgLhriouZ/ST8G/nUzP5uZk1/i8oUZmb3mNlSM/s0oS3fzCaZ2azwN68p9qWwaZ0qgJ+5+xBgf+BSMxsac01xuwKYGXcRKeLPwIvuPhjYizb6vphZEfAjYKS77wmkA2fFW1Wzuw+o/QPMq4BX3H0Q8Ep4vtMUNq2Quy9y9w/C9BqiD5OieKuKj5n1Bk4A7oq7lriZWSfgUOBuAHff5O4r460qVhlArpllAO2AhTHX06zc/XVgRa3mscD9Yfp+4JSm2JfCppUzs37A3sDUeCuJ1f8B/wlUxV1IChgAlAL3hm7Fu8ysfdxFxcHdFwD/C3wNLAJWufu/4q0qJXR390UQfXEFujXFRhU2rZiZdQCeBH7s7qvjricOZnYisNTd34+7lhSRAewD3O7uewPraKJukpYmnIsYC/QHegHtzey78VbVeilsWikzyyQKmofc/am464nRQcDJZjYXeAQ40sz+Fm9JsSoBSty9+kj3CaLwaYuOBua4e6m7bwaeAg6MuaZUsMTMegKEv0ubYqMKm1bIzIyoT36mu/8p7nri5O6/cPfe7t6P6OTvq+7eZr+9uvtiYL6Z7R6ajgI+i7GkOH0N7G9m7cK/maNoo4MlankWGBemxwHPNMVGM5piI5JyDgLOBT4xs+mh7Zfu/nyMNUnquBx4yMyygNnABTHXEwt3n2pmTwAfEI3g/JA2dtkaM/s7cDhQYGYlwNXAjcBjZnYhUSCf0ST70uVqREQk2dSNJiIiSaewERGRpFPYiIhI0ilsREQk6RQ2IiKSdAobkZ1kZveZ2fUx7dvM7F4zKzOzdxu57lwzOzpZtYkkUthIqxM+RJckXvPLzL5vZlNiLCtZDgaOAXq7+6jm3rmZXdPGr8ggDaSwkdYqg+i2Ai2KmaU3cpW+wFx3X5eMepItXG1Z2gCFjbRWfwB+bmZdas8ws35m5okfdGY2xcy+H6bPN7M3zewmM1tpZrPN7MDQPj/cbGpcrc0WhBtNrTGz18ysb8K2B4d5K8zsCzM7M2HefWZ2u5k9b2brgCPqqLeXmT0b1i82s4tC+4VEt004wMzWmtm1db0RZnZRuEnaGjP7zMy+cS202l2BZnZ4+EV59fMrzWxB2MYXZnaUmY0Gfgl8J+z/o7BsZzO728wWhXWurw7RWu/tCuAaMxsY3rNVZrbMzB6t63VIy6ZvFdJaTQOmAD8H/msH1t+P6IO8K3At0UU8nwMGAocBT5rZk+6+Nix/DtE9c6YCvwceAg4OXXmTgF8DxwPDgX+Z2Qx3nxHW/Q9gDHAikFVHLX8HZhBdmXgwMMnMZrv73WZWCXzf3Q+u60WY2RnANUT3JJkG7ApsbswbEa6jdhnwLXdfGG5bke7uX5nZb4GBta43dz+whOi9ag9MBOYDd4T5+xG9n92ATOAe4F9EQZsFjGxMfdIy6MhGWrNfA5ebWeEOrDvH3e9190rgUaAPcJ27bwz3PNlE9GFa7Z/u/rq7bwR+RXS00YcoQOaGbVWEm9o9CZyesO4z7v6mu1e5+4bEIsI2DgaudPcN7j6dKATPbeDr+D7we3d/zyPF7j6vke9FJZANDDWzTHef6+5f1bWgmXUnCtUfu/s6d18K3ETNO2AudPdbwvtRThR+fYFe4TW+0cj6pAVQ2Eir5e6fEn2r3pH7tSxJmC4P26vd1iHh+fyE/a4luvthL6IP0f1Cd9xKM1tJdBTUo65169ALWBHuuFptHg2/82ofoM5gaCh3LwZ+THSEtNTMHjGzXttYvC/R0cqihNd7BzVvwFX79f4nYMC7ZjbDzL63M/VKalLYSGt3NXARNT+cq0+mt0toS/zw3xF9qicsumldPtEthucDr7l7l4RHB3e/JGHd+q6GuxDIN7OOCW27AAsaWNd8oq6z7VlHPe+Huz8cuur6EtX7u+pZdexvI1CQ8Ho7ufseiZurte3F7n6Ru/cCLgZuM7PEo0ZpBRQ20qqFb+WPAj9KaCsl+rD+rpmlh2/SDflArs8YMzs4XLb/N8BUd59PdGS1m5mda2aZ4fEtMxvSwPrnA28B/2NmOWY2HLiQ6JxQQ9xFNFBi3/CbnIGJgxcSTA+vId/MehAdyQDRORszO9LMsoENREd1lWH2EqCfmaWFehcRnX/5o5l1MrM0M9vVzA7bVoFmdoaZ9Q5Py4jCqHJby0vLpLCRtuA6ohPViS4C/h+wHNiD6AN9ZzxMdBS1AtiXqKuM0P11LNE5i4XAYqKjguxGbPtsoF9Y/2nganef1JAV3f1x4IZQ3xrgH0RHXbU9CHwEzCUKi8QRYdlE9zhZFurvRjQKDeDx8He5mX0Qps8jOtH/GVF4PAH0rKfMbwFTzWwt0Y27rnD3OQ15fdJy6H42IiKSdDqyERGRpFPYiIhI0ilsREQk6RQ2IiKSdAobERFJOoWNiIgkncJGRESSTmEjIiJJ9/8BRSgNgT20TD4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wcss = []\n",
    "for i in range(1,11):\n",
    "    km = KMeans(n_clusters=i,init='k-means++', max_iter=300, n_init=10, random_state=0)\n",
    "    km.fit(X)\n",
    "    wcss.append(km.inertia_)\n",
    "plt.plot(range(1,11),wcss, c=\"#c51b7d\")\n",
    "plt.gca().spines[\"top\"].set_visible(False)\n",
    "plt.gca().spines[\"right\"].set_visible(False)\n",
    "plt.title('Elbow Method', size=14)\n",
    "plt.xlabel('Number of clusters', size=12)\n",
    "plt.ylabel('wcss', size=14)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "k-means聚类是如何工作的?\n",
    "\n",
    "其主要思想是选择k个中心,每个中心一个。有几种方法可以初始化这些中心。我们可以随机进行,通过我们认为是中心的某些点,或者以一种聪明的方式放置它们(例如,尽可能远离彼此)。\n",
    "\n",
    "然后,我们计算每个点和簇中心之间的欧氏距离。我们将这些点分配到距离最小的簇中心。\n",
    "\n",
    "之后,我们重新计算新的集群中心。我们选择位于每个簇中间的点作为新的中心\n",
    "\n",
    "我们重新开始,计算距离,分配到簇,计算新的中心。我们什么时候停?当中心不再移动时。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kmeans 算法\n",
    "# n_clusters: 簇的数量,本例中为5\n",
    "# init: k-means++. 初始化\n",
    "# max_iter: 一轮中k-means算法迭代计算的最大数量\n",
    "# n_init: 使用不同质心种子运行k-means算法的时间数 \n",
    "# random_state: 用于确定质心初始化的生成随机数\n",
    "kmeans = KMeans(n_clusters=5, init='k-means++', max_iter=10, n_init=10, random_state=0)\n",
    "\n",
    "## TODO: 使用kmeans.fit_predict()函数对X进行拟合\n",
    "y_means = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在,让我们看看我们的集群是什么样子的:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8, 6))\n",
    "\n",
    "plt.scatter(pca_2d[:, 0], pca_2d[:, 1],\n",
    "            c=y_means, \n",
    "            edgecolor=\"none\", \n",
    "            cmap=plt.cm.get_cmap(\"Spectral_r\", 5),\n",
    "            alpha=0.5)\n",
    "        \n",
    "plt.gca().spines[\"top\"].set_visible(False)\n",
    "plt.gca().spines[\"right\"].set_visible(False)\n",
    "plt.gca().spines[\"bottom\"].set_visible(False)\n",
    "plt.gca().spines[\"left\"].set_visible(False)\n",
    "\n",
    "plt.xticks(size=12)\n",
    "plt.yticks(size=12)\n",
    "\n",
    "plt.xlabel(\"Component 1\", size = 14, labelpad=10)\n",
    "plt.ylabel(\"Component 2\", size = 14, labelpad=10)\n",
    "\n",
    "plt.title('Dominios agrupados en 5 clusters', size=16)\n",
    "\n",
    "\n",
    "plt.colorbar(ticks=[0, 1, 2, 3, 4]);\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "centroids = pd.DataFrame(kmeans.cluster_centers_, columns = [\"Age\", \"Annual Income\", \"Spending\", \"Male\", \"Female\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "centroids.index_name = \"ClusterID\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "centroids[\"ClusterID\"] = centroids.index\n",
    "centroids = centroids.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income</th>\n",
       "      <th>Spending</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>ClusterID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>45.217391</td>\n",
       "      <td>26.304348</td>\n",
       "      <td>20.913043</td>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>32.692308</td>\n",
       "      <td>86.538462</td>\n",
       "      <td>82.128205</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>43.088608</td>\n",
       "      <td>55.291139</td>\n",
       "      <td>49.569620</td>\n",
       "      <td>0.582278</td>\n",
       "      <td>0.417722</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>40.666667</td>\n",
       "      <td>87.750000</td>\n",
       "      <td>17.583333</td>\n",
       "      <td>0.472222</td>\n",
       "      <td>0.527778</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>25.521739</td>\n",
       "      <td>26.304348</td>\n",
       "      <td>78.565217</td>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Age  Annual Income   Spending      Male    Female  ClusterID\n",
       "0  45.217391      26.304348  20.913043  0.608696  0.391304          0\n",
       "1  32.692308      86.538462  82.128205  0.538462  0.461538          1\n",
       "2  43.088608      55.291139  49.569620  0.582278  0.417722          2\n",
       "3  40.666667      87.750000  17.583333  0.472222  0.527778          3\n",
       "4  25.521739      26.304348  78.565217  0.608696  0.391304          4"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最重要的特征似乎是年度收入和支出得分\n",
    "\n",
    "我们有收入低但支出在同一范围内的人;收入高,花费大的人;收入中等但支出水平相同的客户 ;还有收入很高但消费最多的客户;最后,那些收入很少却花费很多的人。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "想象一下明天我们有一个新成员。我们想知道那个人属于哪一部分。我们可以预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The new customer belongs to segment 2\n"
     ]
    }
   ],
   "source": [
    "X_new = np.array([[43, 76, 56, 0, 1]]) \n",
    "##TODO:使用kmeans.predict()函数对X_new进行预测 \n",
    "new_customer = kmeans.predict(X_new)\n",
    "print(f\"The new customer belongs to segment {new_customer[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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