diff --git a/notebook_必备数学基础/.ipynb_checkpoints/案例：汽车价格预测任务-checkpoint.ipynb b/notebook_必备数学基础/.ipynb_checkpoints/案例：汽车价格预测任务-checkpoint.ipynb
new file mode 100644
index 0000000..dcbbd35
--- /dev/null
+++ b/notebook_必备数学基础/.ipynb_checkpoints/案例：汽车价格预测任务-checkpoint.ipynb
@@ -0,0 +1,1211 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**数据集简介**\n",
+    "<br>\n",
+    "主要包括3类指标:\n",
+    "<br>\n",
+    "<ul>\n",
+    "    <li>汽车的各种特性\n",
+    "    <li>保险风险评级:(-3,-2,-1,0,1,2,3)\n",
+    "    <li>每辆保险车辆年平均相对损失支付.\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**类别属性**\n",
+    "<ul>\n",
+    "    <li>make:汽车的商标(爽迪,宝马。。。)\n",
+    "    <li>fuel-type:汽油还是天然气\n",
+    "    <li>aspiration:涡轮\n",
+    "    <li>num- of-doors:两门还是四门\n",
+    "    <li>body-style:硬顶车、轿车、菰背车、散篷车\n",
+    "    <li>drive- wheels:驱动轮\n",
+    "    <li>engine-location:发动机位置\n",
+    "    <li>engine-type:发动机类型\n",
+    "    <li>num- of-cylinders:几个气缸\n",
+    "    <li>fuel- system:燃油系统\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**连续指标**\n",
+    "<ul>\n",
+    "<li>bore: continuous from 2.54 to 3.94.\n",
+    "<li>stroke: continuous from 2.07 to 4.17.\n",
+    "<li>compression-ratio: continuous from 7 to 23.\n",
+    "<li>horsepower: continuous from 48 to 288.\n",
+    "<li>peak-rpm: continuous from 4150 to 6600.\n",
+    "<li>city-mpg: continuous from 13 to 49.\n",
+    "<li>highway-mpg: continuous from 16 to 54.\n",
+    "<li>price: continuous from 5118 to 45400.\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration',\n",
+       "       'num-of-doors', 'body-style', 'drive-wheels', 'engine-location',\n",
+       "       'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type',\n",
+       "       'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke',\n",
+       "       'compress-ratio', 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg',\n",
+       "       'price', 'output'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# loading packages\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from pandas import datetime\n",
+    "\n",
+    "#data visualization and missing values\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns #基于Matplotlib，更高级的\n",
+    "import missingno as msno  #提供了一个灵活且易于使用的缺失数据可视化和实用程序的小工具集\n",
+    "%matplotlib inline\n",
+    "\n",
+    "#stats\n",
+    "from statsmodels.distributions.empirical_distribution import ECDF #对许多不同统计模型估计的类和函数，可以进行统计测试和统计数据的探索。\n",
+    "from sklearn.metrics import mean_squared_error,r2_score #常用的机器学习方法，包括回归(Regression)、降维(Dimensionality Reduction)、分类(Classfication)、聚类(Clustering)等方法\n",
+    "\n",
+    "#mechine learning \n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.linear_model import Lasso,LassoCV\n",
+    "from sklearn.model_selection import train_test_split,cross_val_score\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "seed = 123  # 随机种子，使随机策略结果一致，不指定则每次随机结果不一样\n",
+    "\n",
+    "#import data\n",
+    "data = pd.read_csv('auto_data.csv', na_values='?')\n",
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "symboling              int64\n",
+       "normalized-losses      int64\n",
+       "make                  object\n",
+       "fuel-type             object\n",
+       "aspiration            object\n",
+       "num-of-doors          object\n",
+       "body-style            object\n",
+       "drive-wheels          object\n",
+       "engine-location       object\n",
+       "wheel-base           float64\n",
+       "length               float64\n",
+       "width                float64\n",
+       "height               float64\n",
+       "curb-weight            int64\n",
+       "engine-type           object\n",
+       "num-of-cylinders      object\n",
+       "engine-size            int64\n",
+       "fuel-system           object\n",
+       "bore                 float64\n",
+       "stroke               float64\n",
+       "compress-ratio       float64\n",
+       "horsepower             int64\n",
+       "peak-rpm               int64\n",
+       "city-mpg               int64\n",
+       "highway-mpg            int64\n",
+       "price                  int64\n",
+       "output                object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "In total: (205, 27)\n"
+     ]
+    },
+    {
+     "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>symboling</th>\n",
+       "      <th>normalized-losses</th>\n",
+       "      <th>make</th>\n",
+       "      <th>fuel-type</th>\n",
+       "      <th>aspiration</th>\n",
+       "      <th>num-of-doors</th>\n",
+       "      <th>body-style</th>\n",
+       "      <th>drive-wheels</th>\n",
+       "      <th>engine-location</th>\n",
+       "      <th>wheel-base</th>\n",
+       "      <th>length</th>\n",
+       "      <th>width</th>\n",
+       "      <th>height</th>\n",
+       "      <th>curb-weight</th>\n",
+       "      <th>engine-type</th>\n",
+       "      <th>num-of-cylinders</th>\n",
+       "      <th>engine-size</th>\n",
+       "      <th>fuel-system</th>\n",
+       "      <th>bore</th>\n",
+       "      <th>stroke</th>\n",
+       "      <th>compress-ratio</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>peak-rpm</th>\n",
+       "      <th>city-mpg</th>\n",
+       "      <th>highway-mpg</th>\n",
+       "      <th>price</th>\n",
+       "      <th>output</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>164</td>\n",
+       "      <td>alfa-romero</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>two</td>\n",
+       "      <td>convertible</td>\n",
+       "      <td>rwd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>88.6</td>\n",
+       "      <td>168.8</td>\n",
+       "      <td>64.1</td>\n",
+       "      <td>48.8</td>\n",
+       "      <td>2548</td>\n",
+       "      <td>dohc</td>\n",
+       "      <td>four</td>\n",
+       "      <td>130</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>3.47</td>\n",
+       "      <td>2.68</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>111</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>21</td>\n",
+       "      <td>27</td>\n",
+       "      <td>13495</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>164</td>\n",
+       "      <td>alfa-romero</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>two</td>\n",
+       "      <td>convertible</td>\n",
+       "      <td>rwd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>88.6</td>\n",
+       "      <td>168.8</td>\n",
+       "      <td>64.1</td>\n",
+       "      <td>48.8</td>\n",
+       "      <td>2548</td>\n",
+       "      <td>dohc</td>\n",
+       "      <td>four</td>\n",
+       "      <td>130</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>3.47</td>\n",
+       "      <td>2.68</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>111</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>21</td>\n",
+       "      <td>27</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>164</td>\n",
+       "      <td>alfa-romero</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>two</td>\n",
+       "      <td>hatchback</td>\n",
+       "      <td>rwd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>94.5</td>\n",
+       "      <td>171.2</td>\n",
+       "      <td>65.5</td>\n",
+       "      <td>52.4</td>\n",
+       "      <td>2823</td>\n",
+       "      <td>ohcv</td>\n",
+       "      <td>six</td>\n",
+       "      <td>152</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>2.68</td>\n",
+       "      <td>3.47</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>154</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>19</td>\n",
+       "      <td>26</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>164</td>\n",
+       "      <td>audi</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>four</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>fwd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>99.8</td>\n",
+       "      <td>176.6</td>\n",
+       "      <td>66.2</td>\n",
+       "      <td>54.3</td>\n",
+       "      <td>2337</td>\n",
+       "      <td>ohc</td>\n",
+       "      <td>four</td>\n",
+       "      <td>109</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>102</td>\n",
+       "      <td>5500</td>\n",
+       "      <td>24</td>\n",
+       "      <td>30</td>\n",
+       "      <td>13950</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>164</td>\n",
+       "      <td>audi</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>four</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>4wd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>99.4</td>\n",
+       "      <td>176.6</td>\n",
+       "      <td>66.4</td>\n",
+       "      <td>54.3</td>\n",
+       "      <td>2824</td>\n",
+       "      <td>ohc</td>\n",
+       "      <td>five</td>\n",
+       "      <td>136</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>115</td>\n",
+       "      <td>5500</td>\n",
+       "      <td>18</td>\n",
+       "      <td>22</td>\n",
+       "      <td>17450</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   symboling  normalized-losses         make fuel-type aspiration  ... peak-rpm city-mpg highway-mpg  price  output\n",
+       "0          3                164  alfa-romero       gas        std  ...     5000       21          27  13495      no\n",
+       "1          3                164  alfa-romero       gas        std  ...     5000       21          27  16500      no\n",
+       "2          1                164  alfa-romero       gas        std  ...     5000       19          26  16500      no\n",
+       "3          2                164         audi       gas        std  ...     5500       24          30  13950      no\n",
+       "4          2                164         audi       gas        std  ...     5500       18          22  17450      no\n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('In total:', data.shape)  # 205条数据，26个特征，NaN表示为缺失值\n",
+    "data.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>symboling</th>\n",
+       "      <th>normalized-losses</th>\n",
+       "      <th>wheel-base</th>\n",
+       "      <th>length</th>\n",
+       "      <th>width</th>\n",
+       "      <th>height</th>\n",
+       "      <th>curb-weight</th>\n",
+       "      <th>engine-size</th>\n",
+       "      <th>bore</th>\n",
+       "      <th>stroke</th>\n",
+       "      <th>compress-ratio</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>peak-rpm</th>\n",
+       "      <th>city-mpg</th>\n",
+       "      <th>highway-mpg</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.834146</td>\n",
+       "      <td>128.965854</td>\n",
+       "      <td>98.756585</td>\n",
+       "      <td>174.049268</td>\n",
+       "      <td>65.907805</td>\n",
+       "      <td>53.724878</td>\n",
+       "      <td>2555.565854</td>\n",
+       "      <td>126.907317</td>\n",
+       "      <td>3.324878</td>\n",
+       "      <td>3.253366</td>\n",
+       "      <td>10.142537</td>\n",
+       "      <td>106.048780</td>\n",
+       "      <td>5131.463415</td>\n",
+       "      <td>25.219512</td>\n",
+       "      <td>30.751220</td>\n",
+       "      <td>13293.331707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.245307</td>\n",
+       "      <td>39.421600</td>\n",
+       "      <td>6.021776</td>\n",
+       "      <td>12.337289</td>\n",
+       "      <td>2.145204</td>\n",
+       "      <td>2.443522</td>\n",
+       "      <td>520.680204</td>\n",
+       "      <td>41.642693</td>\n",
+       "      <td>0.273049</td>\n",
+       "      <td>0.313937</td>\n",
+       "      <td>3.972040</td>\n",
+       "      <td>43.468803</td>\n",
+       "      <td>480.933330</td>\n",
+       "      <td>6.542142</td>\n",
+       "      <td>6.886443</td>\n",
+       "      <td>8115.709527</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-2.000000</td>\n",
+       "      <td>65.000000</td>\n",
+       "      <td>86.600000</td>\n",
+       "      <td>141.100000</td>\n",
+       "      <td>60.300000</td>\n",
+       "      <td>47.800000</td>\n",
+       "      <td>1488.000000</td>\n",
+       "      <td>61.000000</td>\n",
+       "      <td>2.540000</td>\n",
+       "      <td>2.070000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>48.000000</td>\n",
+       "      <td>4150.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>5000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>95.000000</td>\n",
+       "      <td>94.500000</td>\n",
+       "      <td>166.300000</td>\n",
+       "      <td>64.100000</td>\n",
+       "      <td>52.000000</td>\n",
+       "      <td>2145.000000</td>\n",
+       "      <td>97.000000</td>\n",
+       "      <td>3.130000</td>\n",
+       "      <td>3.110000</td>\n",
+       "      <td>8.600000</td>\n",
+       "      <td>70.000000</td>\n",
+       "      <td>4800.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>7775.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>122.000000</td>\n",
+       "      <td>97.000000</td>\n",
+       "      <td>173.200000</td>\n",
+       "      <td>65.500000</td>\n",
+       "      <td>54.100000</td>\n",
+       "      <td>2414.000000</td>\n",
+       "      <td>120.000000</td>\n",
+       "      <td>3.310000</td>\n",
+       "      <td>3.290000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>95.000000</td>\n",
+       "      <td>5200.000000</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>10295.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>102.400000</td>\n",
+       "      <td>183.100000</td>\n",
+       "      <td>66.900000</td>\n",
+       "      <td>55.500000</td>\n",
+       "      <td>2935.000000</td>\n",
+       "      <td>141.000000</td>\n",
+       "      <td>3.580000</td>\n",
+       "      <td>3.410000</td>\n",
+       "      <td>9.400000</td>\n",
+       "      <td>120.000000</td>\n",
+       "      <td>5500.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>34.000000</td>\n",
+       "      <td>16503.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>256.000000</td>\n",
+       "      <td>120.900000</td>\n",
+       "      <td>208.100000</td>\n",
+       "      <td>72.300000</td>\n",
+       "      <td>59.800000</td>\n",
+       "      <td>4066.000000</td>\n",
+       "      <td>326.000000</td>\n",
+       "      <td>3.940000</td>\n",
+       "      <td>4.170000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>288.000000</td>\n",
+       "      <td>6600.000000</td>\n",
+       "      <td>49.000000</td>\n",
+       "      <td>54.000000</td>\n",
+       "      <td>45400.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        symboling  normalized-losses  wheel-base      length  ...     peak-rpm    city-mpg  highway-mpg         price\n",
+       "count  205.000000         205.000000  205.000000  205.000000  ...   205.000000  205.000000   205.000000    205.000000\n",
+       "mean     0.834146         128.965854   98.756585  174.049268  ...  5131.463415   25.219512    30.751220  13293.331707\n",
+       "std      1.245307          39.421600    6.021776   12.337289  ...   480.933330    6.542142     6.886443   8115.709527\n",
+       "min     -2.000000          65.000000   86.600000  141.100000  ...  4150.000000   13.000000    16.000000   5000.000000\n",
+       "25%      0.000000          95.000000   94.500000  166.300000  ...  4800.000000   19.000000    25.000000   7775.000000\n",
+       "50%      1.000000         122.000000   97.000000  173.200000  ...  5200.000000   24.000000    30.000000  10295.000000\n",
+       "75%      2.000000         158.000000  102.400000  183.100000  ...  5500.000000   30.000000    34.000000  16503.000000\n",
+       "max      3.000000         256.000000  120.900000  208.100000  ...  6600.000000   49.000000    54.000000  45400.000000\n",
+       "\n",
+       "[8 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()  #查看数据描述，count统计、mean均值、std标准差、min最小值、50%中位数、max最大值"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**缺失值处理**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x20cfd85d7f0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1800x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(style='ticks')  #指定风格\n",
+    "msno.matrix(data)  #查看缺少情况"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里是没缺失值的，你可以手动删掉一些数据造成缺少的情况"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>symboling</th>\n",
+       "      <th>normalized-losses</th>\n",
+       "      <th>make</th>\n",
+       "      <th>fuel-type</th>\n",
+       "      <th>aspiration</th>\n",
+       "      <th>num-of-doors</th>\n",
+       "      <th>body-style</th>\n",
+       "      <th>drive-wheels</th>\n",
+       "      <th>engine-location</th>\n",
+       "      <th>wheel-base</th>\n",
+       "      <th>length</th>\n",
+       "      <th>width</th>\n",
+       "      <th>height</th>\n",
+       "      <th>curb-weight</th>\n",
+       "      <th>engine-type</th>\n",
+       "      <th>num-of-cylinders</th>\n",
+       "      <th>engine-size</th>\n",
+       "      <th>fuel-system</th>\n",
+       "      <th>bore</th>\n",
+       "      <th>stroke</th>\n",
+       "      <th>compress-ratio</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>peak-rpm</th>\n",
+       "      <th>city-mpg</th>\n",
+       "      <th>highway-mpg</th>\n",
+       "      <th>price</th>\n",
+       "      <th>output</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [symboling, normalized-losses, make, fuel-type, aspiration, num-of-doors, body-style, drive-wheels, engine-location, wheel-base, length, width, height, curb-weight, engine-type, num-of-cylinders, engine-size, fuel-system, bore, stroke, compress-ratio, horsepower, peak-rpm, city-mpg, highway-mpg, price, output]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#缺失值填充（也可以删掉，但是我们数据不多删掉就更少了）\n",
+    "data[pd.isnull(data['normalized-losses'])].head()  #查看某列缺失的情况，这里没缺少，我们假设其缺失"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([13., 24., 36., 26., 16., 17., 24., 25.,  7., 12.,  0.,  0.,  1.,\n",
+       "         4.]), array([ 65.        ,  78.64285714,  92.28571429, 105.92857143,\n",
+       "        119.57142857, 133.21428571, 146.85714286, 160.5       ,\n",
+       "        174.14285714, 187.78571429, 201.42857143, 215.07142857,\n",
+       "        228.71428571, 242.35714286, 256.        ]), <a list of 14 Patch objects>)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12,5))\n",
+    "c = '#376DE8'  # 指定颜色\n",
+    "\n",
+    "#ECDF，画出占比\n",
+    "plt.subplot(121)  # 分两边，画在左边\n",
+    "cdf = ECDF(data['normalized-losses'])\n",
+    "plt.plot(cdf.x, cdf.y,label='statmodels',color=c)\n",
+    "plt.xlabel('normalized-losses')\n",
+    "plt.ylabel('ECDF')\n",
+    "\n",
+    "#overall distribution，画出占比\n",
+    "plt.subplot(122)  # 分两边，画在右边\n",
+    "plt.hist(data['normalized-losses'].dropna(),\n",
+    "        bins=int(np.sqrt(len(data['normalized-losses']))),\n",
+    "        color = c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以发现**80%的 normalized losses是低于200**并且绝大多数低于125。\n",
+    "<br>\n",
+    "<br>\n",
+    "一个基本的想法就是用中位数来进行填充,但是我们得来想一想,这个特征(保险损失值)跟哪些因素可能有关呢？应该是保险的情况吧,所以我们可以分组来进行填充这样会更精确一些。\n",
+    "<br><br>\n",
+    "首先来看一下对于不同保险情况的统计指标："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>symboling</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>-2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>103.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>103.0</td>\n",
+       "      <td>103.0</td>\n",
+       "      <td>103.0</td>\n",
+       "      <td>103.00</td>\n",
+       "      <td>103.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-1</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>86.136364</td>\n",
+       "      <td>17.715464</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>91.5</td>\n",
+       "      <td>95.00</td>\n",
+       "      <td>137.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>67.0</td>\n",
+       "      <td>128.776119</td>\n",
+       "      <td>44.511429</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>161.00</td>\n",
+       "      <td>256.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>54.0</td>\n",
+       "      <td>132.037037</td>\n",
+       "      <td>29.599823</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>108.5</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>154.00</td>\n",
+       "      <td>231.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>128.000000</td>\n",
+       "      <td>31.285367</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>101.5</td>\n",
+       "      <td>134.0</td>\n",
+       "      <td>158.75</td>\n",
+       "      <td>192.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>162.222222</td>\n",
+       "      <td>34.033166</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>186.00</td>\n",
+       "      <td>256.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           count        mean        std    min    25%    50%     75%    max\n",
+       "symboling                                                                  \n",
+       "-2           3.0  103.000000   0.000000  103.0  103.0  103.0  103.00  103.0\n",
+       "-1          22.0   86.136364  17.715464   65.0   74.0   91.5   95.00  137.0\n",
+       " 0          67.0  128.776119  44.511429   77.0   91.0  110.0  161.00  256.0\n",
+       " 1          54.0  132.037037  29.599823   74.0  108.5  128.0  154.00  231.0\n",
+       " 2          32.0  128.000000  31.285367   83.0  101.5  134.0  158.75  192.0\n",
+       " 3          27.0  162.222222  34.033166   74.0  150.0  153.0  186.00  256.0"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.groupby('symboling')['normalized-losses'].describe()  #查看风险等级"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这样，我们可以对应不同的组，去填充对应的均值\n",
+    "<br><br>如-2对应着103，-1对应86"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "In total: (205, 27)\n"
+     ]
+    },
+    {
+     "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>symboling</th>\n",
+       "      <th>normalized-losses</th>\n",
+       "      <th>make</th>\n",
+       "      <th>fuel-type</th>\n",
+       "      <th>aspiration</th>\n",
+       "      <th>num-of-doors</th>\n",
+       "      <th>body-style</th>\n",
+       "      <th>drive-wheels</th>\n",
+       "      <th>engine-location</th>\n",
+       "      <th>wheel-base</th>\n",
+       "      <th>length</th>\n",
+       "      <th>width</th>\n",
+       "      <th>height</th>\n",
+       "      <th>curb-weight</th>\n",
+       "      <th>engine-type</th>\n",
+       "      <th>num-of-cylinders</th>\n",
+       "      <th>engine-size</th>\n",
+       "      <th>fuel-system</th>\n",
+       "      <th>bore</th>\n",
+       "      <th>stroke</th>\n",
+       "      <th>compress-ratio</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>peak-rpm</th>\n",
+       "      <th>city-mpg</th>\n",
+       "      <th>highway-mpg</th>\n",
+       "      <th>price</th>\n",
+       "      <th>output</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>164</td>\n",
+       "      <td>alfa-romero</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>two</td>\n",
+       "      <td>convertible</td>\n",
+       "      <td>rwd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>88.6</td>\n",
+       "      <td>168.8</td>\n",
+       "      <td>64.1</td>\n",
+       "      <td>48.8</td>\n",
+       "      <td>2548</td>\n",
+       "      <td>dohc</td>\n",
+       "      <td>four</td>\n",
+       "      <td>130</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>3.47</td>\n",
+       "      <td>2.68</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>111</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>21</td>\n",
+       "      <td>27</td>\n",
+       "      <td>13495</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>164</td>\n",
+       "      <td>alfa-romero</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>two</td>\n",
+       "      <td>convertible</td>\n",
+       "      <td>rwd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>88.6</td>\n",
+       "      <td>168.8</td>\n",
+       "      <td>64.1</td>\n",
+       "      <td>48.8</td>\n",
+       "      <td>2548</td>\n",
+       "      <td>dohc</td>\n",
+       "      <td>four</td>\n",
+       "      <td>130</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>3.47</td>\n",
+       "      <td>2.68</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>111</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>21</td>\n",
+       "      <td>27</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>164</td>\n",
+       "      <td>alfa-romero</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>two</td>\n",
+       "      <td>hatchback</td>\n",
+       "      <td>rwd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>94.5</td>\n",
+       "      <td>171.2</td>\n",
+       "      <td>65.5</td>\n",
+       "      <td>52.4</td>\n",
+       "      <td>2823</td>\n",
+       "      <td>ohcv</td>\n",
+       "      <td>six</td>\n",
+       "      <td>152</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>2.68</td>\n",
+       "      <td>3.47</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>154</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>19</td>\n",
+       "      <td>26</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>164</td>\n",
+       "      <td>audi</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>four</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>fwd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>99.8</td>\n",
+       "      <td>176.6</td>\n",
+       "      <td>66.2</td>\n",
+       "      <td>54.3</td>\n",
+       "      <td>2337</td>\n",
+       "      <td>ohc</td>\n",
+       "      <td>four</td>\n",
+       "      <td>109</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>102</td>\n",
+       "      <td>5500</td>\n",
+       "      <td>24</td>\n",
+       "      <td>30</td>\n",
+       "      <td>13950</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>164</td>\n",
+       "      <td>audi</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>four</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>4wd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>99.4</td>\n",
+       "      <td>176.6</td>\n",
+       "      <td>66.4</td>\n",
+       "      <td>54.3</td>\n",
+       "      <td>2824</td>\n",
+       "      <td>ohc</td>\n",
+       "      <td>five</td>\n",
+       "      <td>136</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>115</td>\n",
+       "      <td>5500</td>\n",
+       "      <td>18</td>\n",
+       "      <td>22</td>\n",
+       "      <td>17450</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   symboling  normalized-losses         make fuel-type aspiration  ... peak-rpm city-mpg highway-mpg  price  output\n",
+       "0          3                164  alfa-romero       gas        std  ...     5000       21          27  13495      no\n",
+       "1          3                164  alfa-romero       gas        std  ...     5000       21          27  16500      no\n",
+       "2          1                164  alfa-romero       gas        std  ...     5000       19          26  16500      no\n",
+       "3          2                164         audi       gas        std  ...     5500       24          30  13950      no\n",
+       "4          2                164         audi       gas        std  ...     5500       18          22  17450      no\n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = data.dropna(subset=['price','bore','stroke'])  #对缺失值少量的可以之间删除\n",
+    "#对于大量缺失情况的，补相关特征不同组对应的均值\n",
+    "data['normalized-losses'] = data.groupby('symboling')['normalized-losses'].transform(lambda x:x.fillna(x.mean()))\n",
+    "\n",
+    "print('In total:', data.shape)  # 205条数据，26个特征，NaN表示为缺失值\n",
+    "data.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebook_必备数学基础/案例：汽车价格预测任务.ipynb b/notebook_必备数学基础/案例：汽车价格预测任务.ipynb
new file mode 100644
index 0000000..dcbbd35
--- /dev/null
+++ b/notebook_必备数学基础/案例：汽车价格预测任务.ipynb
@@ -0,0 +1,1211 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**数据集简介**\n",
+    "<br>\n",
+    "主要包括3类指标:\n",
+    "<br>\n",
+    "<ul>\n",
+    "    <li>汽车的各种特性\n",
+    "    <li>保险风险评级:(-3,-2,-1,0,1,2,3)\n",
+    "    <li>每辆保险车辆年平均相对损失支付.\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**类别属性**\n",
+    "<ul>\n",
+    "    <li>make:汽车的商标(爽迪,宝马。。。)\n",
+    "    <li>fuel-type:汽油还是天然气\n",
+    "    <li>aspiration:涡轮\n",
+    "    <li>num- of-doors:两门还是四门\n",
+    "    <li>body-style:硬顶车、轿车、菰背车、散篷车\n",
+    "    <li>drive- wheels:驱动轮\n",
+    "    <li>engine-location:发动机位置\n",
+    "    <li>engine-type:发动机类型\n",
+    "    <li>num- of-cylinders:几个气缸\n",
+    "    <li>fuel- system:燃油系统\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**连续指标**\n",
+    "<ul>\n",
+    "<li>bore: continuous from 2.54 to 3.94.\n",
+    "<li>stroke: continuous from 2.07 to 4.17.\n",
+    "<li>compression-ratio: continuous from 7 to 23.\n",
+    "<li>horsepower: continuous from 48 to 288.\n",
+    "<li>peak-rpm: continuous from 4150 to 6600.\n",
+    "<li>city-mpg: continuous from 13 to 49.\n",
+    "<li>highway-mpg: continuous from 16 to 54.\n",
+    "<li>price: continuous from 5118 to 45400.\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration',\n",
+       "       'num-of-doors', 'body-style', 'drive-wheels', 'engine-location',\n",
+       "       'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type',\n",
+       "       'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke',\n",
+       "       'compress-ratio', 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg',\n",
+       "       'price', 'output'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# loading packages\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from pandas import datetime\n",
+    "\n",
+    "#data visualization and missing values\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns #基于Matplotlib，更高级的\n",
+    "import missingno as msno  #提供了一个灵活且易于使用的缺失数据可视化和实用程序的小工具集\n",
+    "%matplotlib inline\n",
+    "\n",
+    "#stats\n",
+    "from statsmodels.distributions.empirical_distribution import ECDF #对许多不同统计模型估计的类和函数，可以进行统计测试和统计数据的探索。\n",
+    "from sklearn.metrics import mean_squared_error,r2_score #常用的机器学习方法，包括回归(Regression)、降维(Dimensionality Reduction)、分类(Classfication)、聚类(Clustering)等方法\n",
+    "\n",
+    "#mechine learning \n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.linear_model import Lasso,LassoCV\n",
+    "from sklearn.model_selection import train_test_split,cross_val_score\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "seed = 123  # 随机种子，使随机策略结果一致，不指定则每次随机结果不一样\n",
+    "\n",
+    "#import data\n",
+    "data = pd.read_csv('auto_data.csv', na_values='?')\n",
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "symboling              int64\n",
+       "normalized-losses      int64\n",
+       "make                  object\n",
+       "fuel-type             object\n",
+       "aspiration            object\n",
+       "num-of-doors          object\n",
+       "body-style            object\n",
+       "drive-wheels          object\n",
+       "engine-location       object\n",
+       "wheel-base           float64\n",
+       "length               float64\n",
+       "width                float64\n",
+       "height               float64\n",
+       "curb-weight            int64\n",
+       "engine-type           object\n",
+       "num-of-cylinders      object\n",
+       "engine-size            int64\n",
+       "fuel-system           object\n",
+       "bore                 float64\n",
+       "stroke               float64\n",
+       "compress-ratio       float64\n",
+       "horsepower             int64\n",
+       "peak-rpm               int64\n",
+       "city-mpg               int64\n",
+       "highway-mpg            int64\n",
+       "price                  int64\n",
+       "output                object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "In total: (205, 27)\n"
+     ]
+    },
+    {
+     "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>symboling</th>\n",
+       "      <th>normalized-losses</th>\n",
+       "      <th>make</th>\n",
+       "      <th>fuel-type</th>\n",
+       "      <th>aspiration</th>\n",
+       "      <th>num-of-doors</th>\n",
+       "      <th>body-style</th>\n",
+       "      <th>drive-wheels</th>\n",
+       "      <th>engine-location</th>\n",
+       "      <th>wheel-base</th>\n",
+       "      <th>length</th>\n",
+       "      <th>width</th>\n",
+       "      <th>height</th>\n",
+       "      <th>curb-weight</th>\n",
+       "      <th>engine-type</th>\n",
+       "      <th>num-of-cylinders</th>\n",
+       "      <th>engine-size</th>\n",
+       "      <th>fuel-system</th>\n",
+       "      <th>bore</th>\n",
+       "      <th>stroke</th>\n",
+       "      <th>compress-ratio</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>peak-rpm</th>\n",
+       "      <th>city-mpg</th>\n",
+       "      <th>highway-mpg</th>\n",
+       "      <th>price</th>\n",
+       "      <th>output</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>164</td>\n",
+       "      <td>alfa-romero</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>two</td>\n",
+       "      <td>convertible</td>\n",
+       "      <td>rwd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>88.6</td>\n",
+       "      <td>168.8</td>\n",
+       "      <td>64.1</td>\n",
+       "      <td>48.8</td>\n",
+       "      <td>2548</td>\n",
+       "      <td>dohc</td>\n",
+       "      <td>four</td>\n",
+       "      <td>130</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>3.47</td>\n",
+       "      <td>2.68</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>111</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>21</td>\n",
+       "      <td>27</td>\n",
+       "      <td>13495</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>164</td>\n",
+       "      <td>alfa-romero</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>two</td>\n",
+       "      <td>convertible</td>\n",
+       "      <td>rwd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>88.6</td>\n",
+       "      <td>168.8</td>\n",
+       "      <td>64.1</td>\n",
+       "      <td>48.8</td>\n",
+       "      <td>2548</td>\n",
+       "      <td>dohc</td>\n",
+       "      <td>four</td>\n",
+       "      <td>130</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>3.47</td>\n",
+       "      <td>2.68</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>111</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>21</td>\n",
+       "      <td>27</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>164</td>\n",
+       "      <td>alfa-romero</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>two</td>\n",
+       "      <td>hatchback</td>\n",
+       "      <td>rwd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>94.5</td>\n",
+       "      <td>171.2</td>\n",
+       "      <td>65.5</td>\n",
+       "      <td>52.4</td>\n",
+       "      <td>2823</td>\n",
+       "      <td>ohcv</td>\n",
+       "      <td>six</td>\n",
+       "      <td>152</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>2.68</td>\n",
+       "      <td>3.47</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>154</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>19</td>\n",
+       "      <td>26</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>164</td>\n",
+       "      <td>audi</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>four</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>fwd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>99.8</td>\n",
+       "      <td>176.6</td>\n",
+       "      <td>66.2</td>\n",
+       "      <td>54.3</td>\n",
+       "      <td>2337</td>\n",
+       "      <td>ohc</td>\n",
+       "      <td>four</td>\n",
+       "      <td>109</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>102</td>\n",
+       "      <td>5500</td>\n",
+       "      <td>24</td>\n",
+       "      <td>30</td>\n",
+       "      <td>13950</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>164</td>\n",
+       "      <td>audi</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>four</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>4wd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>99.4</td>\n",
+       "      <td>176.6</td>\n",
+       "      <td>66.4</td>\n",
+       "      <td>54.3</td>\n",
+       "      <td>2824</td>\n",
+       "      <td>ohc</td>\n",
+       "      <td>five</td>\n",
+       "      <td>136</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>115</td>\n",
+       "      <td>5500</td>\n",
+       "      <td>18</td>\n",
+       "      <td>22</td>\n",
+       "      <td>17450</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   symboling  normalized-losses         make fuel-type aspiration  ... peak-rpm city-mpg highway-mpg  price  output\n",
+       "0          3                164  alfa-romero       gas        std  ...     5000       21          27  13495      no\n",
+       "1          3                164  alfa-romero       gas        std  ...     5000       21          27  16500      no\n",
+       "2          1                164  alfa-romero       gas        std  ...     5000       19          26  16500      no\n",
+       "3          2                164         audi       gas        std  ...     5500       24          30  13950      no\n",
+       "4          2                164         audi       gas        std  ...     5500       18          22  17450      no\n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('In total:', data.shape)  # 205条数据，26个特征，NaN表示为缺失值\n",
+    "data.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>symboling</th>\n",
+       "      <th>normalized-losses</th>\n",
+       "      <th>wheel-base</th>\n",
+       "      <th>length</th>\n",
+       "      <th>width</th>\n",
+       "      <th>height</th>\n",
+       "      <th>curb-weight</th>\n",
+       "      <th>engine-size</th>\n",
+       "      <th>bore</th>\n",
+       "      <th>stroke</th>\n",
+       "      <th>compress-ratio</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>peak-rpm</th>\n",
+       "      <th>city-mpg</th>\n",
+       "      <th>highway-mpg</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.834146</td>\n",
+       "      <td>128.965854</td>\n",
+       "      <td>98.756585</td>\n",
+       "      <td>174.049268</td>\n",
+       "      <td>65.907805</td>\n",
+       "      <td>53.724878</td>\n",
+       "      <td>2555.565854</td>\n",
+       "      <td>126.907317</td>\n",
+       "      <td>3.324878</td>\n",
+       "      <td>3.253366</td>\n",
+       "      <td>10.142537</td>\n",
+       "      <td>106.048780</td>\n",
+       "      <td>5131.463415</td>\n",
+       "      <td>25.219512</td>\n",
+       "      <td>30.751220</td>\n",
+       "      <td>13293.331707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.245307</td>\n",
+       "      <td>39.421600</td>\n",
+       "      <td>6.021776</td>\n",
+       "      <td>12.337289</td>\n",
+       "      <td>2.145204</td>\n",
+       "      <td>2.443522</td>\n",
+       "      <td>520.680204</td>\n",
+       "      <td>41.642693</td>\n",
+       "      <td>0.273049</td>\n",
+       "      <td>0.313937</td>\n",
+       "      <td>3.972040</td>\n",
+       "      <td>43.468803</td>\n",
+       "      <td>480.933330</td>\n",
+       "      <td>6.542142</td>\n",
+       "      <td>6.886443</td>\n",
+       "      <td>8115.709527</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-2.000000</td>\n",
+       "      <td>65.000000</td>\n",
+       "      <td>86.600000</td>\n",
+       "      <td>141.100000</td>\n",
+       "      <td>60.300000</td>\n",
+       "      <td>47.800000</td>\n",
+       "      <td>1488.000000</td>\n",
+       "      <td>61.000000</td>\n",
+       "      <td>2.540000</td>\n",
+       "      <td>2.070000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>48.000000</td>\n",
+       "      <td>4150.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>5000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>95.000000</td>\n",
+       "      <td>94.500000</td>\n",
+       "      <td>166.300000</td>\n",
+       "      <td>64.100000</td>\n",
+       "      <td>52.000000</td>\n",
+       "      <td>2145.000000</td>\n",
+       "      <td>97.000000</td>\n",
+       "      <td>3.130000</td>\n",
+       "      <td>3.110000</td>\n",
+       "      <td>8.600000</td>\n",
+       "      <td>70.000000</td>\n",
+       "      <td>4800.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>7775.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>122.000000</td>\n",
+       "      <td>97.000000</td>\n",
+       "      <td>173.200000</td>\n",
+       "      <td>65.500000</td>\n",
+       "      <td>54.100000</td>\n",
+       "      <td>2414.000000</td>\n",
+       "      <td>120.000000</td>\n",
+       "      <td>3.310000</td>\n",
+       "      <td>3.290000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>95.000000</td>\n",
+       "      <td>5200.000000</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>10295.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>102.400000</td>\n",
+       "      <td>183.100000</td>\n",
+       "      <td>66.900000</td>\n",
+       "      <td>55.500000</td>\n",
+       "      <td>2935.000000</td>\n",
+       "      <td>141.000000</td>\n",
+       "      <td>3.580000</td>\n",
+       "      <td>3.410000</td>\n",
+       "      <td>9.400000</td>\n",
+       "      <td>120.000000</td>\n",
+       "      <td>5500.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>34.000000</td>\n",
+       "      <td>16503.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>256.000000</td>\n",
+       "      <td>120.900000</td>\n",
+       "      <td>208.100000</td>\n",
+       "      <td>72.300000</td>\n",
+       "      <td>59.800000</td>\n",
+       "      <td>4066.000000</td>\n",
+       "      <td>326.000000</td>\n",
+       "      <td>3.940000</td>\n",
+       "      <td>4.170000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>288.000000</td>\n",
+       "      <td>6600.000000</td>\n",
+       "      <td>49.000000</td>\n",
+       "      <td>54.000000</td>\n",
+       "      <td>45400.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        symboling  normalized-losses  wheel-base      length  ...     peak-rpm    city-mpg  highway-mpg         price\n",
+       "count  205.000000         205.000000  205.000000  205.000000  ...   205.000000  205.000000   205.000000    205.000000\n",
+       "mean     0.834146         128.965854   98.756585  174.049268  ...  5131.463415   25.219512    30.751220  13293.331707\n",
+       "std      1.245307          39.421600    6.021776   12.337289  ...   480.933330    6.542142     6.886443   8115.709527\n",
+       "min     -2.000000          65.000000   86.600000  141.100000  ...  4150.000000   13.000000    16.000000   5000.000000\n",
+       "25%      0.000000          95.000000   94.500000  166.300000  ...  4800.000000   19.000000    25.000000   7775.000000\n",
+       "50%      1.000000         122.000000   97.000000  173.200000  ...  5200.000000   24.000000    30.000000  10295.000000\n",
+       "75%      2.000000         158.000000  102.400000  183.100000  ...  5500.000000   30.000000    34.000000  16503.000000\n",
+       "max      3.000000         256.000000  120.900000  208.100000  ...  6600.000000   49.000000    54.000000  45400.000000\n",
+       "\n",
+       "[8 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()  #查看数据描述，count统计、mean均值、std标准差、min最小值、50%中位数、max最大值"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**缺失值处理**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x20cfd85d7f0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1800x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(style='ticks')  #指定风格\n",
+    "msno.matrix(data)  #查看缺少情况"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这里是没缺失值的，你可以手动删掉一些数据造成缺少的情况"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>symboling</th>\n",
+       "      <th>normalized-losses</th>\n",
+       "      <th>make</th>\n",
+       "      <th>fuel-type</th>\n",
+       "      <th>aspiration</th>\n",
+       "      <th>num-of-doors</th>\n",
+       "      <th>body-style</th>\n",
+       "      <th>drive-wheels</th>\n",
+       "      <th>engine-location</th>\n",
+       "      <th>wheel-base</th>\n",
+       "      <th>length</th>\n",
+       "      <th>width</th>\n",
+       "      <th>height</th>\n",
+       "      <th>curb-weight</th>\n",
+       "      <th>engine-type</th>\n",
+       "      <th>num-of-cylinders</th>\n",
+       "      <th>engine-size</th>\n",
+       "      <th>fuel-system</th>\n",
+       "      <th>bore</th>\n",
+       "      <th>stroke</th>\n",
+       "      <th>compress-ratio</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>peak-rpm</th>\n",
+       "      <th>city-mpg</th>\n",
+       "      <th>highway-mpg</th>\n",
+       "      <th>price</th>\n",
+       "      <th>output</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [symboling, normalized-losses, make, fuel-type, aspiration, num-of-doors, body-style, drive-wheels, engine-location, wheel-base, length, width, height, curb-weight, engine-type, num-of-cylinders, engine-size, fuel-system, bore, stroke, compress-ratio, horsepower, peak-rpm, city-mpg, highway-mpg, price, output]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#缺失值填充（也可以删掉，但是我们数据不多删掉就更少了）\n",
+    "data[pd.isnull(data['normalized-losses'])].head()  #查看某列缺失的情况，这里没缺少，我们假设其缺失"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([13., 24., 36., 26., 16., 17., 24., 25.,  7., 12.,  0.,  0.,  1.,\n",
+       "         4.]), array([ 65.        ,  78.64285714,  92.28571429, 105.92857143,\n",
+       "        119.57142857, 133.21428571, 146.85714286, 160.5       ,\n",
+       "        174.14285714, 187.78571429, 201.42857143, 215.07142857,\n",
+       "        228.71428571, 242.35714286, 256.        ]), <a list of 14 Patch objects>)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12,5))\n",
+    "c = '#376DE8'  # 指定颜色\n",
+    "\n",
+    "#ECDF，画出占比\n",
+    "plt.subplot(121)  # 分两边，画在左边\n",
+    "cdf = ECDF(data['normalized-losses'])\n",
+    "plt.plot(cdf.x, cdf.y,label='statmodels',color=c)\n",
+    "plt.xlabel('normalized-losses')\n",
+    "plt.ylabel('ECDF')\n",
+    "\n",
+    "#overall distribution，画出占比\n",
+    "plt.subplot(122)  # 分两边，画在右边\n",
+    "plt.hist(data['normalized-losses'].dropna(),\n",
+    "        bins=int(np.sqrt(len(data['normalized-losses']))),\n",
+    "        color = c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以发现**80%的 normalized losses是低于200**并且绝大多数低于125。\n",
+    "<br>\n",
+    "<br>\n",
+    "一个基本的想法就是用中位数来进行填充,但是我们得来想一想,这个特征(保险损失值)跟哪些因素可能有关呢？应该是保险的情况吧,所以我们可以分组来进行填充这样会更精确一些。\n",
+    "<br><br>\n",
+    "首先来看一下对于不同保险情况的统计指标："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>symboling</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>-2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>103.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>103.0</td>\n",
+       "      <td>103.0</td>\n",
+       "      <td>103.0</td>\n",
+       "      <td>103.00</td>\n",
+       "      <td>103.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-1</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>86.136364</td>\n",
+       "      <td>17.715464</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>91.5</td>\n",
+       "      <td>95.00</td>\n",
+       "      <td>137.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>67.0</td>\n",
+       "      <td>128.776119</td>\n",
+       "      <td>44.511429</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>161.00</td>\n",
+       "      <td>256.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>54.0</td>\n",
+       "      <td>132.037037</td>\n",
+       "      <td>29.599823</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>108.5</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>154.00</td>\n",
+       "      <td>231.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>128.000000</td>\n",
+       "      <td>31.285367</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>101.5</td>\n",
+       "      <td>134.0</td>\n",
+       "      <td>158.75</td>\n",
+       "      <td>192.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>162.222222</td>\n",
+       "      <td>34.033166</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>186.00</td>\n",
+       "      <td>256.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           count        mean        std    min    25%    50%     75%    max\n",
+       "symboling                                                                  \n",
+       "-2           3.0  103.000000   0.000000  103.0  103.0  103.0  103.00  103.0\n",
+       "-1          22.0   86.136364  17.715464   65.0   74.0   91.5   95.00  137.0\n",
+       " 0          67.0  128.776119  44.511429   77.0   91.0  110.0  161.00  256.0\n",
+       " 1          54.0  132.037037  29.599823   74.0  108.5  128.0  154.00  231.0\n",
+       " 2          32.0  128.000000  31.285367   83.0  101.5  134.0  158.75  192.0\n",
+       " 3          27.0  162.222222  34.033166   74.0  150.0  153.0  186.00  256.0"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.groupby('symboling')['normalized-losses'].describe()  #查看风险等级"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这样，我们可以对应不同的组，去填充对应的均值\n",
+    "<br><br>如-2对应着103，-1对应86"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "In total: (205, 27)\n"
+     ]
+    },
+    {
+     "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>symboling</th>\n",
+       "      <th>normalized-losses</th>\n",
+       "      <th>make</th>\n",
+       "      <th>fuel-type</th>\n",
+       "      <th>aspiration</th>\n",
+       "      <th>num-of-doors</th>\n",
+       "      <th>body-style</th>\n",
+       "      <th>drive-wheels</th>\n",
+       "      <th>engine-location</th>\n",
+       "      <th>wheel-base</th>\n",
+       "      <th>length</th>\n",
+       "      <th>width</th>\n",
+       "      <th>height</th>\n",
+       "      <th>curb-weight</th>\n",
+       "      <th>engine-type</th>\n",
+       "      <th>num-of-cylinders</th>\n",
+       "      <th>engine-size</th>\n",
+       "      <th>fuel-system</th>\n",
+       "      <th>bore</th>\n",
+       "      <th>stroke</th>\n",
+       "      <th>compress-ratio</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>peak-rpm</th>\n",
+       "      <th>city-mpg</th>\n",
+       "      <th>highway-mpg</th>\n",
+       "      <th>price</th>\n",
+       "      <th>output</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>164</td>\n",
+       "      <td>alfa-romero</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>two</td>\n",
+       "      <td>convertible</td>\n",
+       "      <td>rwd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>88.6</td>\n",
+       "      <td>168.8</td>\n",
+       "      <td>64.1</td>\n",
+       "      <td>48.8</td>\n",
+       "      <td>2548</td>\n",
+       "      <td>dohc</td>\n",
+       "      <td>four</td>\n",
+       "      <td>130</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>3.47</td>\n",
+       "      <td>2.68</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>111</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>21</td>\n",
+       "      <td>27</td>\n",
+       "      <td>13495</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>164</td>\n",
+       "      <td>alfa-romero</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>two</td>\n",
+       "      <td>convertible</td>\n",
+       "      <td>rwd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>88.6</td>\n",
+       "      <td>168.8</td>\n",
+       "      <td>64.1</td>\n",
+       "      <td>48.8</td>\n",
+       "      <td>2548</td>\n",
+       "      <td>dohc</td>\n",
+       "      <td>four</td>\n",
+       "      <td>130</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>3.47</td>\n",
+       "      <td>2.68</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>111</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>21</td>\n",
+       "      <td>27</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>164</td>\n",
+       "      <td>alfa-romero</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>two</td>\n",
+       "      <td>hatchback</td>\n",
+       "      <td>rwd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>94.5</td>\n",
+       "      <td>171.2</td>\n",
+       "      <td>65.5</td>\n",
+       "      <td>52.4</td>\n",
+       "      <td>2823</td>\n",
+       "      <td>ohcv</td>\n",
+       "      <td>six</td>\n",
+       "      <td>152</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>2.68</td>\n",
+       "      <td>3.47</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>154</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>19</td>\n",
+       "      <td>26</td>\n",
+       "      <td>16500</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>164</td>\n",
+       "      <td>audi</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>four</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>fwd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>99.8</td>\n",
+       "      <td>176.6</td>\n",
+       "      <td>66.2</td>\n",
+       "      <td>54.3</td>\n",
+       "      <td>2337</td>\n",
+       "      <td>ohc</td>\n",
+       "      <td>four</td>\n",
+       "      <td>109</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>102</td>\n",
+       "      <td>5500</td>\n",
+       "      <td>24</td>\n",
+       "      <td>30</td>\n",
+       "      <td>13950</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>164</td>\n",
+       "      <td>audi</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>std</td>\n",
+       "      <td>four</td>\n",
+       "      <td>sedan</td>\n",
+       "      <td>4wd</td>\n",
+       "      <td>front</td>\n",
+       "      <td>99.4</td>\n",
+       "      <td>176.6</td>\n",
+       "      <td>66.4</td>\n",
+       "      <td>54.3</td>\n",
+       "      <td>2824</td>\n",
+       "      <td>ohc</td>\n",
+       "      <td>five</td>\n",
+       "      <td>136</td>\n",
+       "      <td>mpfi</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>115</td>\n",
+       "      <td>5500</td>\n",
+       "      <td>18</td>\n",
+       "      <td>22</td>\n",
+       "      <td>17450</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   symboling  normalized-losses         make fuel-type aspiration  ... peak-rpm city-mpg highway-mpg  price  output\n",
+       "0          3                164  alfa-romero       gas        std  ...     5000       21          27  13495      no\n",
+       "1          3                164  alfa-romero       gas        std  ...     5000       21          27  16500      no\n",
+       "2          1                164  alfa-romero       gas        std  ...     5000       19          26  16500      no\n",
+       "3          2                164         audi       gas        std  ...     5500       24          30  13950      no\n",
+       "4          2                164         audi       gas        std  ...     5500       18          22  17450      no\n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = data.dropna(subset=['price','bore','stroke'])  #对缺失值少量的可以之间删除\n",
+    "#对于大量缺失情况的，补相关特征不同组对应的均值\n",
+    "data['normalized-losses'] = data.groupby('symboling')['normalized-losses'].transform(lambda x:x.fillna(x.mean()))\n",
+    "\n",
+    "print('In total:', data.shape)  # 205条数据，26个特征，NaN表示为缺失值\n",
+    "data.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}