trees.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "68df697e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "93d2e2a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Life Expectancy Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b6819694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>...</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>65.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0.01</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1154</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.16</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.479</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>62.0</td>\n",
       "      <td>492</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8.18</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.476</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>268.0</td>\n",
       "      <td>66</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>64.0</td>\n",
       "      <td>430</td>\n",
       "      <td>...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>8.13</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.470</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.5</td>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2787</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8.52</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.2</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3013</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>7.87</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>0.454</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year      Status  Life expectancy   Adult Mortality  \\\n",
       "0  Afghanistan  2015  Developing              65.0            263.0   \n",
       "1  Afghanistan  2014  Developing              59.9            271.0   \n",
       "2  Afghanistan  2013  Developing              59.9            268.0   \n",
       "3  Afghanistan  2012  Developing              59.5            272.0   \n",
       "4  Afghanistan  2011  Developing              59.2            275.0   \n",
       "\n",
       "   infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles   ...  \\\n",
       "0             62     0.01               71.279624         65.0      1154  ...   \n",
       "1             64     0.01               73.523582         62.0       492  ...   \n",
       "2             66     0.01               73.219243         64.0       430  ...   \n",
       "3             69     0.01               78.184215         67.0      2787  ...   \n",
       "4             71     0.01                7.097109         68.0      3013  ...   \n",
       "\n",
       "   Polio  Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
       "0    6.0               8.16         65.0        0.1  584.259210  33736494.0   \n",
       "1   58.0               8.18         62.0        0.1  612.696514    327582.0   \n",
       "2   62.0               8.13         64.0        0.1  631.744976  31731688.0   \n",
       "3   67.0               8.52         67.0        0.1  669.959000   3696958.0   \n",
       "4   68.0               7.87         68.0        0.1   63.537231   2978599.0   \n",
       "\n",
       "    thinness  1-19 years   thinness 5-9 years  \\\n",
       "0                   17.2                 17.3   \n",
       "1                   17.5                 17.5   \n",
       "2                   17.7                 17.7   \n",
       "3                   17.9                 18.0   \n",
       "4                   18.2                 18.2   \n",
       "\n",
       "   Income composition of resources  Schooling  \n",
       "0                            0.479       10.1  \n",
       "1                            0.476       10.0  \n",
       "2                            0.470        9.9  \n",
       "3                            0.463        9.8  \n",
       "4                            0.454        9.5  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "142b3f42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Year', 'Status', 'Life expectancy ', 'Adult Mortality',\n",
       "       'infant deaths', 'Alcohol', 'percentage expenditure', 'Hepatitis B',\n",
       "       'Measles ', ' BMI ', 'under-five deaths ', 'Polio', 'Total expenditure',\n",
       "       'Diphtheria ', ' HIV/AIDS', 'GDP', 'Population',\n",
       "       ' thinness  1-19 years', ' thinness 5-9 years',\n",
       "       'Income composition of resources', 'Schooling'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "94a5ed75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2938 entries, 0 to 2937\n",
      "Data columns (total 22 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   Country                          2938 non-null   object \n",
      " 1   Year                             2938 non-null   int64  \n",
      " 2   Status                           2938 non-null   object \n",
      " 3   Life expectancy                  2928 non-null   float64\n",
      " 4   Adult Mortality                  2928 non-null   float64\n",
      " 5   infant deaths                    2938 non-null   int64  \n",
      " 6   Alcohol                          2744 non-null   float64\n",
      " 7   percentage expenditure           2938 non-null   float64\n",
      " 8   Hepatitis B                      2385 non-null   float64\n",
      " 9   Measles                          2938 non-null   int64  \n",
      " 10   BMI                             2904 non-null   float64\n",
      " 11  under-five deaths                2938 non-null   int64  \n",
      " 12  Polio                            2919 non-null   float64\n",
      " 13  Total expenditure                2712 non-null   float64\n",
      " 14  Diphtheria                       2919 non-null   float64\n",
      " 15   HIV/AIDS                        2938 non-null   float64\n",
      " 16  GDP                              2490 non-null   float64\n",
      " 17  Population                       2286 non-null   float64\n",
      " 18   thinness  1-19 years            2904 non-null   float64\n",
      " 19   thinness 5-9 years              2904 non-null   float64\n",
      " 20  Income composition of resources  2771 non-null   float64\n",
      " 21  Schooling                        2775 non-null   float64\n",
      "dtypes: float64(16), int64(4), object(2)\n",
      "memory usage: 505.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f3c5a257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                              0\n",
       "Year                                 0\n",
       "Status                               0\n",
       "Life expectancy                     10\n",
       "Adult Mortality                     10\n",
       "infant deaths                        0\n",
       "Alcohol                            194\n",
       "percentage expenditure               0\n",
       "Hepatitis B                        553\n",
       "Measles                              0\n",
       " BMI                                34\n",
       "under-five deaths                    0\n",
       "Polio                               19\n",
       "Total expenditure                  226\n",
       "Diphtheria                          19\n",
       " HIV/AIDS                            0\n",
       "GDP                                448\n",
       "Population                         652\n",
       " thinness  1-19 years               34\n",
       " thinness 5-9 years                 34\n",
       "Income composition of resources    167\n",
       "Schooling                          163\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f8219aec",
   "metadata": {},
   "outputs": [],
   "source": [
    "y= df[\"Life expectancy \"]\n",
    "X=  df.drop([\"Life expectancy \"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bbfd85e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y.fillna(y.median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5c51c4e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X.Year = pd.to_datetime(X.Year).dt.year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dedaa17",
   "metadata": {},
   "source": [
    "## Coding categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "236f74c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import category_encoders as ce\n",
    "bin_enc = ce.BinaryEncoder(drop_invariant=True)\n",
    "X = bin_enc.fit_transform(X) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "43d02fa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country_0</th>\n",
       "      <th>Country_1</th>\n",
       "      <th>Country_2</th>\n",
       "      <th>Country_3</th>\n",
       "      <th>Country_4</th>\n",
       "      <th>Country_5</th>\n",
       "      <th>Country_6</th>\n",
       "      <th>Country_7</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status_0</th>\n",
       "      <th>...</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.16</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.479</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8.18</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.476</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>8.13</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.470</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8.52</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>7.87</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>0.454</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country_0  Country_1  Country_2  Country_3  Country_4  Country_5  \\\n",
       "0          0          0          0          0          0          0   \n",
       "1          0          0          0          0          0          0   \n",
       "2          0          0          0          0          0          0   \n",
       "3          0          0          0          0          0          0   \n",
       "4          0          0          0          0          0          0   \n",
       "\n",
       "   Country_6  Country_7  Year  Status_0  ...  Polio  Total expenditure  \\\n",
       "0          0          1  1970         0  ...    6.0               8.16   \n",
       "1          0          1  1970         0  ...   58.0               8.18   \n",
       "2          0          1  1970         0  ...   62.0               8.13   \n",
       "3          0          1  1970         0  ...   67.0               8.52   \n",
       "4          0          1  1970         0  ...   68.0               7.87   \n",
       "\n",
       "   Diphtheria    HIV/AIDS         GDP  Population   thinness  1-19 years  \\\n",
       "0         65.0        0.1  584.259210  33736494.0                   17.2   \n",
       "1         62.0        0.1  612.696514    327582.0                   17.5   \n",
       "2         64.0        0.1  631.744976  31731688.0                   17.7   \n",
       "3         67.0        0.1  669.959000   3696958.0                   17.9   \n",
       "4         68.0        0.1   63.537231   2978599.0                   18.2   \n",
       "\n",
       "    thinness 5-9 years  Income composition of resources  Schooling  \n",
       "0                 17.3                            0.479       10.1  \n",
       "1                 17.5                            0.476       10.0  \n",
       "2                 17.7                            0.470        9.9  \n",
       "3                 18.0                            0.463        9.8  \n",
       "4                 18.2                            0.454        9.5  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e698a100",
   "metadata": {},
   "outputs": [],
   "source": [
    "X.fillna(X.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a592a0b",
   "metadata": {},
   "source": [
    "## Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "72e31016",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test= train_test_split(X, y, test_size= 0.30, random_state=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5474c852",
   "metadata": {},
   "source": [
    "## Linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b0c9477e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficient of determination: 0.8246578016681347\n",
      "intercept: 55.06661652520731\n",
      "slope: [ 5.39868078e-01  3.00562333e-01  7.53268236e-01 -1.75790685e-01\n",
      " -4.00059136e-01 -1.16217061e-01  2.54676690e-01  1.92664428e-02\n",
      "  1.58206781e-15  8.82491412e-01 -8.82491412e-01 -1.96947113e-02\n",
      "  1.00510203e-01  9.71513717e-02 -3.83416146e-05 -1.61047720e-02\n",
      " -2.10888131e-05  4.48954665e-02 -7.46079555e-02  2.41455560e-02\n",
      "  1.77176532e-02  4.56656976e-02 -4.47773693e-01  4.23917554e-05\n",
      " -5.49815561e-11 -6.89028877e-02 -3.17531192e-03  4.96058442e+00\n",
      "  7.22793633e-01]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "sc = lr.score(X_train, y_train)\n",
    "print('coefficient of determination:', sc)\n",
    "print('intercept:', lr.intercept_)\n",
    "print('slope:', lr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1840fdaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_lr = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db51d2bc",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f8dd760a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "s = svm.SVR()\n",
    "s.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "337c9d08",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_svr = s.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a190abe",
   "metadata": {},
   "source": [
    "## Decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "28be1d15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(random_state=0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dt = DecisionTreeRegressor(random_state=0)\n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0070a9d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_dt = dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3075f3c6",
   "metadata": {},
   "source": [
    "## Evaluation of the quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1228d01e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear regression:  17.062129156285682\n",
      "SVM:  100.89982155896014\n",
      "Decision tree:  7.4750000000000005\n"
     ]
    }
   ],
   "source": [
    "# mean squared error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(\"Linear regression: \",mean_squared_error(y_test, y_lr))\n",
    "print(\"SVM: \",mean_squared_error(y_test, y_svr))\n",
    "print(\"Decision tree: \",mean_squared_error(y_test, y_dt))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0c68a67f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear regression:  3.0889831815712046\n",
      "SVM:  7.579025871219417\n",
      "Decision tree:  1.6577097505668936\n"
     ]
    }
   ],
   "source": [
    "# mean absolute error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(\"Linear regression: \",mean_absolute_error(y_test, y_lr))\n",
    "print(\"SVM: \",mean_absolute_error(y_test, y_svr))\n",
    "print(\"Decision tree: \",mean_absolute_error(y_test, y_dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75810488",
   "metadata": {},
   "source": [
    "## The importance of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e6acb553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Country_0', 0.003586939599600534),\n",
       " ('Country_1', 0.00043528641487956213),\n",
       " ('Country_2', 0.0012445965189790977),\n",
       " ('Country_3', 0.0002955266491424785),\n",
       " ('Country_4', 0.0007594598269913248),\n",
       " ('Country_5', 0.0004473477471656755),\n",
       " ('Country_6', 5.904270988838898e-05),\n",
       " ('Country_7', 0.0004912794219232724),\n",
       " ('Year', 0.0),\n",
       " ('Status_0', 2.2158014778123868e-05),\n",
       " ('Status_1', 0.00015274333443504666),\n",
       " ('Adult Mortality', 0.13575760786841157),\n",
       " ('infant deaths', 0.002369175986155562),\n",
       " ('Alcohol', 0.008467301710122648),\n",
       " ('percentage expenditure', 0.006271696143048364),\n",
       " ('Hepatitis B', 0.0019369721723790198),\n",
       " ('Measles ', 0.0015214550273649533),\n",
       " (' BMI ', 0.006154391731776707),\n",
       " ('under-five deaths ', 0.029396373097045313),\n",
       " ('Polio', 0.003143098419361568),\n",
       " ('Total expenditure', 0.004901823552178708),\n",
       " ('Diphtheria ', 0.003381902051066721),\n",
       " (' HIV/AIDS', 0.5912459304592244),\n",
       " ('GDP', 0.0020723185008717114),\n",
       " ('Population', 0.002860086568717561),\n",
       " (' thinness  1-19 years', 0.006544627632363877),\n",
       " (' thinness 5-9 years', 0.0024582661817284606),\n",
       " ('Income composition of resources', 0.16736158716070615),\n",
       " ('Schooling', 0.016661005499693057)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns.values, dt.feature_importances_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d1bafc76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(dt.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0cced552",
   "metadata": {},
   "outputs": [],
   "source": [
    "from operator import itemgetter\n",
    "\n",
    "def draw_feature_importances(tree_model, X_dataset, figsize=(18,5)):\n",
    "    # Sorting the values of the importance of features in descending order\n",
    "    list_to_sort = list(zip(X_dataset.columns.values, tree_model.feature_importances_))\n",
    "    sorted_list = sorted(list_to_sort, key=itemgetter(1), reverse = True)\n",
    "    # Features names\n",
    "    labels = [x for x,_ in sorted_list]\n",
    "    # Features importance\n",
    "    data = [x for _,x in sorted_list]\n",
    "    # Graph output\n",
    "    fig, ax = plt.subplots(figsize=figsize)\n",
    "    ind = np.arange(len(labels))\n",
    "    plt.bar(ind, data)\n",
    "    plt.xticks(ind, labels, rotation='vertical')\n",
    "    # Values output\n",
    "    for a,b in zip(ind, data):\n",
    "        plt.text(a-0.05, b+0.01, str(round(b,3)))\n",
    "    plt.show()\n",
    "    return labels, data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a4ffe301",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_fl, dt_fd = draw_feature_importances(dt, X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6e0345c",
   "metadata": {},
   "source": [
    "## Defining hyperparameters for a tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e283192",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "params = {\n",
    "    'max_depth': [3, 4, 5, 6],\n",
    "    'min_samples_leaf': [0.04, 0.06, 0.08],\n",
    "    'max_features': [0.2, 0.4, 0.6, 0.8]\n",
    "}\n",
    "grid = GridSearchCV(estimator=DecisionTreeRegressor(random_state=1),\n",
    "                    param_grid=params, scoring='neg_mean_squared_error', cv=3, n_jobs=-1)\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9219fafc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12.980019491379105,\n",
       " {'max_depth': 6, 'max_features': 0.8, 'min_samples_leaf': 0.04})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-grid.best_score_, grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5185880d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=6, random_state=0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_opt = DecisionTreeRegressor(random_state=0, max_depth=6)\n",
    "dt_opt.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6d522f4a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>|---  HIV/AIDS <= 0.65\n",
       "|   |--- Income composition of resources <= 0.80\n",
       "|   |   |--- Adult Mortality <= 198.50\n",
       "|   |   |   |--- Income composition of resources <= 0.62\n",
       "|   |   |   |   |--- Alcohol <= 1.60\n",
       "|   |   |   |   |   |---  BMI  <= 37.00\n",
       "|   |   |   |   |   |   |--- value: [66.11]\n",
       "|   |   |   |   |   |---  BMI  >  37.00\n",
       "|   |   |   |   |   |   |--- value: [69.78]\n",
       "|   |   |   |   |--- Alcohol >  1.60\n",
       "|   |   |   |   |   |--- under-five deaths  <= 14.50\n",
       "|   |   |   |   |   |   |--- value: [72.55]\n",
       "|   |   |   |   |   |--- under-five deaths  >  14.50\n",
       "|   |   |   |   |   |   |--- value: [69.20]\n",
       "|   |   |   |--- Income composition of resources >  0.62\n",
       "|   |   |   |   |--- Adult Mortality <= 150.00\n",
       "|   |   |   |   |   |--- Income composition of resources <= 0.63\n",
       "|   |   |   |   |   |   |--- value: [78.09]\n",
       "|   |   |   |   |   |--- Income composition of resources >  0.63\n",
       "|   |   |   |   |   |   |--- value: [74.34]\n",
       "|   |   |   |   |--- Adult Mortality >  150.00\n",
       "|   |   |   |   |   |--- Alcohol <= 3.53\n",
       "|   |   |   |   |   |   |--- value: [70.49]\n",
       "|   |   |   |   |   |--- Alcohol >  3.53\n",
       "|   |   |   |   |   |   |--- value: [72.61]\n",
       "|   |   |--- Adult Mortality >  198.50\n",
       "|   |   |   |--- Income composition of resources <= 0.54\n",
       "|   |   |   |   |--- Adult Mortality <= 267.50\n",
       "|   |   |   |   |   |--- percentage expenditure <= 232.28\n",
       "|   |   |   |   |   |   |--- value: [63.50]\n",
       "|   |   |   |   |   |--- percentage expenditure >  232.28\n",
       "|   |   |   |   |   |   |--- value: [67.12]\n",
       "|   |   |   |   |--- Adult Mortality >  267.50\n",
       "|   |   |   |   |   |--- infant deaths <= 76.50\n",
       "|   |   |   |   |   |   |--- value: [59.65]\n",
       "|   |   |   |   |   |--- infant deaths >  76.50\n",
       "|   |   |   |   |   |   |--- value: [56.43]\n",
       "|   |   |   |--- Income composition of resources >  0.54\n",
       "|   |   |   |   |--- Schooling <= 14.95\n",
       "|   |   |   |   |   |--- Adult Mortality <= 262.50\n",
       "|   |   |   |   |   |   |--- value: [67.50]\n",
       "|   |   |   |   |   |--- Adult Mortality >  262.50\n",
       "|   |   |   |   |   |   |--- value: [64.86]\n",
       "|   |   |   |   |--- Schooling >  14.95\n",
       "|   |   |   |   |   |--- Polio <= 94.50\n",
       "|   |   |   |   |   |   |--- value: [78.00]\n",
       "|   |   |   |   |   |--- Polio >  94.50\n",
       "|   |   |   |   |   |   |--- value: [71.30]\n",
       "|   |--- Income composition of resources >  0.80\n",
       "|   |   |--- Income composition of resources <= 0.84\n",
       "|   |   |   |---  thinness  1-19 years <= 0.95\n",
       "|   |   |   |   |--- Total expenditure <= 9.43\n",
       "|   |   |   |   |   |--- Population <= 17308169.00\n",
       "|   |   |   |   |   |   |--- value: [79.35]\n",
       "|   |   |   |   |   |--- Population >  17308169.00\n",
       "|   |   |   |   |   |   |--- value: [81.50]\n",
       "|   |   |   |   |--- Total expenditure >  9.43\n",
       "|   |   |   |   |   |--- Polio <= 98.50\n",
       "|   |   |   |   |   |   |--- value: [87.33]\n",
       "|   |   |   |   |   |--- Polio >  98.50\n",
       "|   |   |   |   |   |   |--- value: [81.05]\n",
       "|   |   |   |---  thinness  1-19 years >  0.95\n",
       "|   |   |   |   |---  BMI  <= 4.30\n",
       "|   |   |   |   |   |--- Schooling <= 13.25\n",
       "|   |   |   |   |   |   |--- value: [82.00]\n",
       "|   |   |   |   |   |--- Schooling >  13.25\n",
       "|   |   |   |   |   |   |--- value: [87.00]\n",
       "|   |   |   |   |---  BMI  >  4.30\n",
       "|   |   |   |   |   |--- Adult Mortality <= 129.50\n",
       "|   |   |   |   |   |   |--- value: [76.37]\n",
       "|   |   |   |   |   |--- Adult Mortality >  129.50\n",
       "|   |   |   |   |   |   |--- value: [74.24]\n",
       "|   |   |--- Income composition of resources >  0.84\n",
       "|   |   |   |--- Adult Mortality <= 78.50\n",
       "|   |   |   |   |--- Adult Mortality <= 33.00\n",
       "|   |   |   |   |   |--- Income composition of resources <= 0.88\n",
       "|   |   |   |   |   |   |--- value: [78.94]\n",
       "|   |   |   |   |   |--- Income composition of resources >  0.88\n",
       "|   |   |   |   |   |   |--- value: [81.95]\n",
       "|   |   |   |   |--- Adult Mortality >  33.00\n",
       "|   |   |   |   |   |--- Total expenditure <= 1.64\n",
       "|   |   |   |   |   |   |--- value: [84.61]\n",
       "|   |   |   |   |   |--- Total expenditure >  1.64\n",
       "|   |   |   |   |   |   |--- value: [82.27]\n",
       "|   |   |   |--- Adult Mortality >  78.50\n",
       "|   |   |   |   |--- Country_5 <= 0.50\n",
       "|   |   |   |   |   |---  BMI  <= 63.10\n",
       "|   |   |   |   |   |   |--- value: [78.39]\n",
       "|   |   |   |   |   |---  BMI  >  63.10\n",
       "|   |   |   |   |   |   |--- value: [85.00]\n",
       "|   |   |   |   |--- Country_5 >  0.50\n",
       "|   |   |   |   |   |--- percentage expenditure <= 6871.23\n",
       "|   |   |   |   |   |   |--- value: [80.24]\n",
       "|   |   |   |   |   |--- percentage expenditure >  6871.23\n",
       "|   |   |   |   |   |   |--- value: [89.00]\n",
       "|---  HIV/AIDS >  0.65\n",
       "|   |--- Adult Mortality <= 333.00\n",
       "|   |   |--- under-five deaths  <= 24.50\n",
       "|   |   |   |--- Schooling <= 10.25\n",
       "|   |   |   |   |---  HIV/AIDS <= 2.80\n",
       "|   |   |   |   |   |--- Income composition of resources <= 0.54\n",
       "|   |   |   |   |   |   |--- value: [62.15]\n",
       "|   |   |   |   |   |--- Income composition of resources >  0.54\n",
       "|   |   |   |   |   |   |--- value: [71.45]\n",
       "|   |   |   |   |---  HIV/AIDS >  2.80\n",
       "|   |   |   |   |   |---  HIV/AIDS <= 28.80\n",
       "|   |   |   |   |   |   |--- value: [57.57]\n",
       "|   |   |   |   |   |---  HIV/AIDS >  28.80\n",
       "|   |   |   |   |   |   |--- value: [45.75]\n",
       "|   |   |   |--- Schooling >  10.25\n",
       "|   |   |   |   |---  HIV/AIDS <= 2.55\n",
       "|   |   |   |   |   |--- Adult Mortality <= 205.00\n",
       "|   |   |   |   |   |   |--- value: [71.72]\n",
       "|   |   |   |   |   |--- Adult Mortality >  205.00\n",
       "|   |   |   |   |   |   |--- value: [65.62]\n",
       "|   |   |   |   |---  HIV/AIDS >  2.55\n",
       "|   |   |   |   |   |--- Income composition of resources <= 0.53\n",
       "|   |   |   |   |   |   |--- value: [56.10]\n",
       "|   |   |   |   |   |--- Income composition of resources >  0.53\n",
       "|   |   |   |   |   |   |--- value: [62.98]\n",
       "|   |   |--- under-five deaths  >  24.50\n",
       "|   |   |   |---  HIV/AIDS <= 4.70\n",
       "|   |   |   |   |--- Income composition of resources <= 0.39\n",
       "|   |   |   |   |   |--- Country_0 <= 0.50\n",
       "|   |   |   |   |   |   |--- value: [55.18]\n",
       "|   |   |   |   |   |--- Country_0 >  0.50\n",
       "|   |   |   |   |   |   |--- value: [44.57]\n",
       "|   |   |   |   |--- Income composition of resources >  0.39\n",
       "|   |   |   |   |   |--- Population <= 23135387.00\n",
       "|   |   |   |   |   |   |--- value: [58.81]\n",
       "|   |   |   |   |   |--- Population >  23135387.00\n",
       "|   |   |   |   |   |   |--- value: [63.45]\n",
       "|   |   |   |---  HIV/AIDS >  4.70\n",
       "|   |   |   |   |--- percentage expenditure <= 72.62\n",
       "|   |   |   |   |   |---  HIV/AIDS <= 12.65\n",
       "|   |   |   |   |   |   |--- value: [50.83]\n",
       "|   |   |   |   |   |---  HIV/AIDS >  12.65\n",
       "|   |   |   |   |   |   |--- value: [46.08]\n",
       "|   |   |   |   |--- percentage expenditure >  72.62\n",
       "|   |   |   |   |   |--- Country_4 <= 0.50\n",
       "|   |   |   |   |   |   |--- value: [55.80]\n",
       "|   |   |   |   |   |--- Country_4 >  0.50\n",
       "|   |   |   |   |   |   |--- value: [58.47]\n",
       "|   |--- Adult Mortality >  333.00\n",
       "|   |   |--- Adult Mortality <= 504.50\n",
       "|   |   |   |--- Adult Mortality <= 379.50\n",
       "|   |   |   |   |--- Diphtheria  <= 71.50\n",
       "|   |   |   |   |   |--- under-five deaths  <= 77.50\n",
       "|   |   |   |   |   |   |--- value: [55.00]\n",
       "|   |   |   |   |   |--- under-five deaths  >  77.50\n",
       "|   |   |   |   |   |   |--- value: [52.71]\n",
       "|   |   |   |   |--- Diphtheria  >  71.50\n",
       "|   |   |   |   |   |--- percentage expenditure <= 401.58\n",
       "|   |   |   |   |   |   |--- value: [56.95]\n",
       "|   |   |   |   |   |--- percentage expenditure >  401.58\n",
       "|   |   |   |   |   |   |--- value: [64.03]\n",
       "|   |   |   |--- Adult Mortality >  379.50\n",
       "|   |   |   |   |--- percentage expenditure <= 68.17\n",
       "|   |   |   |   |   |---  HIV/AIDS <= 2.70\n",
       "|   |   |   |   |   |   |--- value: [48.17]\n",
       "|   |   |   |   |   |---  HIV/AIDS >  2.70\n",
       "|   |   |   |   |   |   |--- value: [52.13]\n",
       "|   |   |   |   |--- percentage expenditure >  68.17\n",
       "|   |   |   |   |   |--- Adult Mortality <= 454.00\n",
       "|   |   |   |   |   |   |--- value: [56.55]\n",
       "|   |   |   |   |   |--- Adult Mortality >  454.00\n",
       "|   |   |   |   |   |   |--- value: [54.19]\n",
       "|   |   |--- Adult Mortality >  504.50\n",
       "|   |   |   |---  HIV/AIDS <= 5.45\n",
       "|   |   |   |   |---  thinness 5-9 years <= 1.15\n",
       "|   |   |   |   |   |---  HIV/AIDS <= 1.80\n",
       "|   |   |   |   |   |   |--- value: [48.00]\n",
       "|   |   |   |   |   |---  HIV/AIDS >  1.80\n",
       "|   |   |   |   |   |   |--- value: [45.30]\n",
       "|   |   |   |   |---  thinness 5-9 years >  1.15\n",
       "|   |   |   |   |   |--- infant deaths <= 26.00\n",
       "|   |   |   |   |   |   |--- value: [36.30]\n",
       "|   |   |   |   |   |--- infant deaths >  26.00\n",
       "|   |   |   |   |   |   |--- value: [40.00]\n",
       "|   |   |   |---  HIV/AIDS >  5.45\n",
       "|   |   |   |   |--- GDP <= 19.65\n",
       "|   |   |   |   |   |--- value: [58.00]\n",
       "|   |   |   |   |--- GDP >  19.65\n",
       "|   |   |   |   |   |--- Adult Mortality <= 600.00\n",
       "|   |   |   |   |   |   |--- value: [48.51]\n",
       "|   |   |   |   |   |--- Adult Mortality >  600.00\n",
       "|   |   |   |   |   |   |--- value: [45.68]\n",
       "</pre>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "from sklearn.tree import export_text\n",
    "tree_rules = export_text(dt_opt, feature_names=list(X.columns))\n",
    "HTML('<pre>' + tree_rules + '</pre>')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4d9b2559",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "212d704a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.514616935483871, 0.9285714285714286, 'X[22] <= 0.65\\nsquared_error = 89.76\\nsamples = 2056\\nvalue = 69.288'),\n",
       " Text(0.25806451612903225, 0.7857142857142857, 'X[27] <= 0.802\\nsquared_error = 34.271\\nsamples = 1527\\nvalue = 73.5'),\n",
       " Text(0.12903225806451613, 0.6428571428571429, 'X[11] <= 198.5\\nsquared_error = 23.592\\nsamples = 1144\\nvalue = 71.365'),\n",
       " Text(0.06451612903225806, 0.5, 'X[27] <= 0.623\\nsquared_error = 13.919\\nsamples = 933\\nvalue = 72.854'),\n",
       " Text(0.03225806451612903, 0.35714285714285715, 'X[13] <= 1.595\\nsquared_error = 14.833\\nsamples = 218\\nvalue = 69.158'),\n",
       " Text(0.016129032258064516, 0.21428571428571427, 'X[17] <= 37.0\\nsquared_error = 9.862\\nsamples = 127\\nvalue = 67.494'),\n",
       " Text(0.008064516129032258, 0.07142857142857142, 'squared_error = 5.847\\nsamples = 79\\nvalue = 66.108'),\n",
       " Text(0.024193548387096774, 0.07142857142857142, 'squared_error = 8.094\\nsamples = 48\\nvalue = 69.777'),\n",
       " Text(0.04838709677419355, 0.21428571428571427, 'X[18] <= 14.5\\nsquared_error = 12.515\\nsamples = 91\\nvalue = 71.48'),\n",
       " Text(0.04032258064516129, 0.07142857142857142, 'squared_error = 6.994\\nsamples = 62\\nvalue = 72.547'),\n",
       " Text(0.056451612903225805, 0.07142857142857142, 'squared_error = 16.688\\nsamples = 29\\nvalue = 69.2'),\n",
       " Text(0.0967741935483871, 0.35714285714285715, 'X[11] <= 150.0\\nsquared_error = 8.206\\nsamples = 715\\nvalue = 73.981'),\n",
       " Text(0.08064516129032258, 0.21428571428571427, 'X[27] <= 0.628\\nsquared_error = 6.924\\nsamples = 513\\nvalue = 74.761'),\n",
       " Text(0.07258064516129033, 0.07142857142857142, 'squared_error = 10.418\\nsamples = 58\\nvalue = 78.093'),\n",
       " Text(0.08870967741935484, 0.07142857142857142, 'squared_error = 4.883\\nsamples = 455\\nvalue = 74.336'),\n",
       " Text(0.11290322580645161, 0.21428571428571427, 'X[13] <= 3.53\\nsquared_error = 5.997\\nsamples = 202\\nvalue = 72.001'),\n",
       " Text(0.10483870967741936, 0.07142857142857142, 'squared_error = 7.282\\nsamples = 58\\nvalue = 70.493'),\n",
       " Text(0.12096774193548387, 0.07142857142857142, 'squared_error = 4.194\\nsamples = 144\\nvalue = 72.609'),\n",
       " Text(0.1935483870967742, 0.5, 'X[27] <= 0.544\\nsquared_error = 13.218\\nsamples = 211\\nvalue = 64.782'),\n",
       " Text(0.16129032258064516, 0.35714285714285715, 'X[11] <= 267.5\\nsquared_error = 7.733\\nsamples = 107\\nvalue = 62.306'),\n",
       " Text(0.14516129032258066, 0.21428571428571427, 'X[14] <= 232.281\\nsquared_error = 3.106\\nsamples = 75\\nvalue = 63.697'),\n",
       " Text(0.13709677419354838, 0.07142857142857142, 'squared_error = 2.56\\nsamples = 71\\nvalue = 63.504'),\n",
       " Text(0.1532258064516129, 0.07142857142857142, 'squared_error = 0.387\\nsamples = 4\\nvalue = 67.125'),\n",
       " Text(0.1774193548387097, 0.21428571428571427, 'X[12] <= 76.5\\nsquared_error = 3.397\\nsamples = 32\\nvalue = 59.044'),\n",
       " Text(0.1693548387096774, 0.07142857142857142, 'squared_error = 2.006\\nsamples = 26\\nvalue = 59.646'),\n",
       " Text(0.18548387096774194, 0.07142857142857142, 'squared_error = 1.039\\nsamples = 6\\nvalue = 56.433'),\n",
       " Text(0.22580645161290322, 0.35714285714285715, 'X[28] <= 14.95\\nsquared_error = 6.06\\nsamples = 104\\nvalue = 67.33'),\n",
       " Text(0.20967741935483872, 0.21428571428571427, 'X[11] <= 262.5\\nsquared_error = 3.45\\nsamples = 99\\nvalue = 66.994'),\n",
       " Text(0.20161290322580644, 0.07142857142857142, 'squared_error = 2.532\\nsamples = 80\\nvalue = 67.501'),\n",
       " Text(0.21774193548387097, 0.07142857142857142, 'squared_error = 1.671\\nsamples = 19\\nvalue = 64.858'),\n",
       " Text(0.24193548387096775, 0.21428571428571427, 'X[19] <= 94.5\\nsquared_error = 11.282\\nsamples = 5\\nvalue = 73.98'),\n",
       " Text(0.23387096774193547, 0.07142857142857142, 'squared_error = 0.0\\nsamples = 2\\nvalue = 78.0'),\n",
       " Text(0.25, 0.07142857142857142, 'squared_error = 0.847\\nsamples = 3\\nvalue = 71.3'),\n",
       " Text(0.3870967741935484, 0.6428571428571429, 'X[27] <= 0.843\\nsquared_error = 11.928\\nsamples = 383\\nvalue = 79.874'),\n",
       " Text(0.3225806451612903, 0.5, 'X[25] <= 0.95\\nsquared_error = 8.322\\nsamples = 131\\nvalue = 77.052'),\n",
       " Text(0.2903225806451613, 0.35714285714285715, 'X[20] <= 9.43\\nsquared_error = 6.067\\nsamples = 31\\nvalue = 80.513'),\n",
       " Text(0.27419354838709675, 0.21428571428571427, 'X[24] <= 17308169.0\\nsquared_error = 0.978\\nsamples = 26\\nvalue = 79.685'),\n",
       " Text(0.2661290322580645, 0.07142857142857142, 'squared_error = 0.312\\nsamples = 22\\nvalue = 79.355'),\n",
       " Text(0.28225806451612906, 0.07142857142857142, 'squared_error = 0.75\\nsamples = 4\\nvalue = 81.5'),\n",
       " Text(0.3064516129032258, 0.21428571428571427, 'X[19] <= 98.5\\nsquared_error = 10.41\\nsamples = 5\\nvalue = 84.82'),\n",
       " Text(0.29838709677419356, 0.07142857142857142, 'squared_error = 1.556\\nsamples = 3\\nvalue = 87.333'),\n",
       " Text(0.31451612903225806, 0.07142857142857142, 'squared_error = 0.002\\nsamples = 2\\nvalue = 81.05'),\n",
       " Text(0.3548387096774194, 0.35714285714285715, 'X[17] <= 4.3\\nsquared_error = 4.157\\nsamples = 100\\nvalue = 75.979'),\n",
       " Text(0.3387096774193548, 0.21428571428571427, 'X[28] <= 13.25\\nsquared_error = 6.25\\nsamples = 2\\nvalue = 84.5'),\n",
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       " Text(0.9193548387096774, 0.21428571428571427, 'X[22] <= 1.8\\nsquared_error = 1.823\\nsamples = 2\\nvalue = 46.65'),\n",
       " Text(0.9112903225806451, 0.07142857142857142, 'squared_error = 0.0\\nsamples = 1\\nvalue = 48.0'),\n",
       " Text(0.9274193548387096, 0.07142857142857142, 'squared_error = 0.0\\nsamples = 1\\nvalue = 45.3'),\n",
       " Text(0.9516129032258065, 0.21428571428571427, 'X[12] <= 26.0\\nsquared_error = 3.709\\nsamples = 3\\nvalue = 38.767'),\n",
       " Text(0.9435483870967742, 0.07142857142857142, 'squared_error = 0.0\\nsamples = 1\\nvalue = 36.3'),\n",
       " Text(0.9596774193548387, 0.07142857142857142, 'squared_error = 1.0\\nsamples = 2\\nvalue = 40.0'),\n",
       " Text(0.9758064516129032, 0.35714285714285715, 'X[23] <= 19.653\\nsquared_error = 7.714\\nsamples = 35\\nvalue = 47.489'),\n",
       " Text(0.967741935483871, 0.21428571428571427, 'squared_error = 0.0\\nsamples = 1\\nvalue = 58.0'),\n",
       " Text(0.9838709677419355, 0.21428571428571427, 'X[11] <= 600.0\\nsquared_error = 4.595\\nsamples = 34\\nvalue = 47.179'),\n",
       " Text(0.9758064516129032, 0.07142857142857142, 'squared_error = 3.727\\nsamples = 18\\nvalue = 48.511'),\n",
       " Text(0.9919354838709677, 0.07142857142857142, 'squared_error = 1.333\\nsamples = 16\\nvalue = 45.681')]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(dt_opt) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "81eb0dd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0.875, 'X[22] <= 0.65\\nsquared_error = 89.76\\nsamples = 2056\\nvalue = 69.288'),\n",
       " Text(0.25, 0.625, 'X[27] <= 0.802\\nsquared_error = 34.271\\nsamples = 1527\\nvalue = 73.5'),\n",
       " Text(0.125, 0.375, 'X[11] <= 198.5\\nsquared_error = 23.592\\nsamples = 1144\\nvalue = 71.365'),\n",
       " Text(0.0625, 0.125, 'squared_error = 13.919\\nsamples = 933\\nvalue = 72.854'),\n",
       " Text(0.1875, 0.125, 'squared_error = 13.218\\nsamples = 211\\nvalue = 64.782'),\n",
       " Text(0.375, 0.375, 'X[27] <= 0.843\\nsquared_error = 11.928\\nsamples = 383\\nvalue = 79.874'),\n",
       " Text(0.3125, 0.125, 'squared_error = 8.322\\nsamples = 131\\nvalue = 77.052'),\n",
       " Text(0.4375, 0.125, 'squared_error = 7.51\\nsamples = 252\\nvalue = 81.341'),\n",
       " Text(0.75, 0.625, 'X[11] <= 333.0\\nsquared_error = 50.949\\nsamples = 529\\nvalue = 57.131'),\n",
       " Text(0.625, 0.375, 'X[18] <= 24.5\\nsquared_error = 47.204\\nsamples = 320\\nvalue = 60.175'),\n",
       " Text(0.5625, 0.125, 'squared_error = 34.322\\nsamples = 175\\nvalue = 63.726'),\n",
       " Text(0.6875, 0.125, 'squared_error = 29.154\\nsamples = 145\\nvalue = 55.888'),\n",
       " Text(0.875, 0.375, 'X[11] <= 504.5\\nsquared_error = 20.791\\nsamples = 209\\nvalue = 52.472'),\n",
       " Text(0.8125, 0.125, 'squared_error = 13.342\\nsamples = 169\\nvalue = 53.816'),\n",
       " Text(0.9375, 0.125, 'squared_error = 12.375\\nsamples = 40\\nvalue = 46.792')]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_new = DecisionTreeRegressor(random_state=0, max_depth=3)\n",
    "dt_new.fit(X_train, y_train)\n",
    "tree.plot_tree(dt_new) "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}