DECISION_TREE_ON_LIFE_BY_MS.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/Life Expectancy Data.csv\")"
      ],
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        "id": "SGGg-03pnUNr"
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      "outputs": []
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            "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",
              "2933     Zimbabwe  2004  Developing              44.3            723.0   \n",
              "2934     Zimbabwe  2003  Developing              44.5            715.0   \n",
              "2935     Zimbabwe  2002  Developing              44.8             73.0   \n",
              "2936     Zimbabwe  2001  Developing              45.3            686.0   \n",
              "2937     Zimbabwe  2000  Developing              46.0            665.0   \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",
              "2933             27     4.36                0.000000         68.0        31   \n",
              "2934             26     4.06                0.000000          7.0       998   \n",
              "2935             25     4.43                0.000000         73.0       304   \n",
              "2936             25     1.72                0.000000         76.0       529   \n",
              "2937             24     1.68                0.000000         79.0      1483   \n",
              "\n",
              "      ...  Polio  Total expenditure  Diphtheria    HIV/AIDS         GDP  \\\n",
              "0     ...    6.0               8.16         65.0        0.1  584.259210   \n",
              "1     ...   58.0               8.18         62.0        0.1  612.696514   \n",
              "2     ...   62.0               8.13         64.0        0.1  631.744976   \n",
              "3     ...   67.0               8.52         67.0        0.1  669.959000   \n",
              "4     ...   68.0               7.87         68.0        0.1   63.537231   \n",
              "...   ...    ...                ...          ...        ...         ...   \n",
              "2933  ...   67.0               7.13         65.0       33.6  454.366654   \n",
              "2934  ...    7.0               6.52         68.0       36.7  453.351155   \n",
              "2935  ...   73.0               6.53         71.0       39.8   57.348340   \n",
              "2936  ...   76.0               6.16         75.0       42.1  548.587312   \n",
              "2937  ...   78.0               7.10         78.0       43.5  547.358878   \n",
              "\n",
              "      Population   thinness  1-19 years   thinness 5-9 years  \\\n",
              "0     33736494.0                   17.2                 17.3   \n",
              "1       327582.0                   17.5                 17.5   \n",
              "2     31731688.0                   17.7                 17.7   \n",
              "3      3696958.0                   17.9                 18.0   \n",
              "4      2978599.0                   18.2                 18.2   \n",
              "...          ...                    ...                  ...   \n",
              "2933  12777511.0                    9.4                  9.4   \n",
              "2934  12633897.0                    9.8                  9.9   \n",
              "2935    125525.0                    1.2                  1.3   \n",
              "2936  12366165.0                    1.6                  1.7   \n",
              "2937  12222251.0                   11.0                 11.2   \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",
              "2933                            0.407        9.2  \n",
              "2934                            0.418        9.5  \n",
              "2935                            0.427       10.0  \n",
              "2936                            0.427        9.8  \n",
              "2937                            0.434        9.8  \n",
              "\n",
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              "      <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",
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              "      <th>Measles</th>\n",
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              "      <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",
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              "      <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",
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              "      <td>6.0</td>\n",
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              "      <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",
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              "    <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",
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              "    <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",
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              "      <th>2933</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2004</td>\n",
              "      <td>Developing</td>\n",
              "      <td>44.3</td>\n",
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              "      <td>68.0</td>\n",
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              "      <td>67.0</td>\n",
              "      <td>7.13</td>\n",
              "      <td>65.0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>454.366654</td>\n",
              "      <td>12777511.0</td>\n",
              "      <td>9.4</td>\n",
              "      <td>9.4</td>\n",
              "      <td>0.407</td>\n",
              "      <td>9.2</td>\n",
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              "      <th>2934</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2003</td>\n",
              "      <td>Developing</td>\n",
              "      <td>44.5</td>\n",
              "      <td>715.0</td>\n",
              "      <td>26</td>\n",
              "      <td>4.06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.0</td>\n",
              "      <td>998</td>\n",
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              "      <td>7.0</td>\n",
              "      <td>6.52</td>\n",
              "      <td>68.0</td>\n",
              "      <td>36.7</td>\n",
              "      <td>453.351155</td>\n",
              "      <td>12633897.0</td>\n",
              "      <td>9.8</td>\n",
              "      <td>9.9</td>\n",
              "      <td>0.418</td>\n",
              "      <td>9.5</td>\n",
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              "      <th>2935</th>\n",
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              "      <td>125525.0</td>\n",
              "      <td>1.2</td>\n",
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              "      <td>0.427</td>\n",
              "      <td>10.0</td>\n",
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              "      <th>2936</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2001</td>\n",
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              "      <td>45.3</td>\n",
              "      <td>686.0</td>\n",
              "      <td>25</td>\n",
              "      <td>1.72</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>76.0</td>\n",
              "      <td>529</td>\n",
              "      <td>...</td>\n",
              "      <td>76.0</td>\n",
              "      <td>6.16</td>\n",
              "      <td>75.0</td>\n",
              "      <td>42.1</td>\n",
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              "      <td>1.6</td>\n",
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              "      <th>2937</th>\n",
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              "      <td>24</td>\n",
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              "      <td>1483</td>\n",
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              "      <td>78.0</td>\n",
              "      <td>7.10</td>\n",
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              "      <td>11.0</td>\n",
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              "      <td>9.8</td>\n",
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              "type": "dataframe",
              "variable_name": "df"
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          "metadata": {},
          "execution_count": 4
        }
      ]
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      "cell_type": "code",
      "source": [
        "df.dtypes"
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        },
        "id": "3s9r7zoeoBU5",
        "outputId": "a57ae6e2-511b-4752-a036-b896abd54b5a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country                             object\n",
              "Year                                 int64\n",
              "Status                              object\n",
              "Life expectancy                    float64\n",
              "Adult Mortality                    float64\n",
              "infant deaths                        int64\n",
              "Alcohol                            float64\n",
              "percentage expenditure             float64\n",
              "Hepatitis B                        float64\n",
              "Measles                              int64\n",
              " BMI                               float64\n",
              "under-five deaths                    int64\n",
              "Polio                              float64\n",
              "Total expenditure                  float64\n",
              "Diphtheria                         float64\n",
              " HIV/AIDS                          float64\n",
              "GDP                                float64\n",
              "Population                         float64\n",
              " thinness  1-19 years              float64\n",
              " thinness 5-9 years                float64\n",
              "Income composition of resources    float64\n",
              "Schooling                          float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(\"Country\",axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "PP42DQvJntPB"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "LdgK5Wi1oTtT",
        "outputId": "7f8d7758-ef4b-4500-f448-e636a0405cda"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year      Status  Life expectancy   Adult Mortality  infant deaths  \\\n",
              "0  2015  Developing              65.0            263.0             62   \n",
              "1  2014  Developing              59.9            271.0             64   \n",
              "2  2013  Developing              59.9            268.0             66   \n",
              "3  2012  Developing              59.5            272.0             69   \n",
              "4  2011  Developing              59.2            275.0             71   \n",
              "\n",
              "   Alcohol  percentage expenditure  Hepatitis B  Measles    BMI   ...  Polio  \\\n",
              "0     0.01               71.279624         65.0      1154   19.1  ...    6.0   \n",
              "1     0.01               73.523582         62.0       492   18.6  ...   58.0   \n",
              "2     0.01               73.219243         64.0       430   18.1  ...   62.0   \n",
              "3     0.01               78.184215         67.0      2787   17.6  ...   67.0   \n",
              "4     0.01                7.097109         68.0      3013   17.2  ...   68.0   \n",
              "\n",
              "   Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
              "0               8.16         65.0        0.1  584.259210  33736494.0   \n",
              "1               8.18         62.0        0.1  612.696514    327582.0   \n",
              "2               8.13         64.0        0.1  631.744976  31731688.0   \n",
              "3               8.52         67.0        0.1  669.959000   3696958.0   \n",
              "4               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 21 columns]"
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              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Status</th>\n",
              "      <th>Life expectancy</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015</td>\n",
              "      <td>Developing</td>\n",
              "      <td>65.0</td>\n",
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              "      <td>65.0</td>\n",
              "      <td>1154</td>\n",
              "      <td>19.1</td>\n",
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              "      <td>6.0</td>\n",
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              "      <td>65.0</td>\n",
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              "      <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>2014</td>\n",
              "      <td>Developing</td>\n",
              "      <td>59.9</td>\n",
              "      <td>271.0</td>\n",
              "      <td>64</td>\n",
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              "      <td>73.523582</td>\n",
              "      <td>62.0</td>\n",
              "      <td>492</td>\n",
              "      <td>18.6</td>\n",
              "      <td>...</td>\n",
              "      <td>58.0</td>\n",
              "      <td>8.18</td>\n",
              "      <td>62.0</td>\n",
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              "      <td>327582.0</td>\n",
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              "      <td>17.5</td>\n",
              "      <td>0.476</td>\n",
              "      <td>10.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\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>18.1</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>2012</td>\n",
              "      <td>Developing</td>\n",
              "      <td>59.5</td>\n",
              "      <td>272.0</td>\n",
              "      <td>69</td>\n",
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              "      <td>67.0</td>\n",
              "      <td>2787</td>\n",
              "      <td>17.6</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",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011</td>\n",
              "      <td>Developing</td>\n",
              "      <td>59.2</td>\n",
              "      <td>275.0</td>\n",
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              "      <td>68.0</td>\n",
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              "      <td>63.537231</td>\n",
              "      <td>2978599.0</td>\n",
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              "      <td>18.2</td>\n",
              "      <td>0.454</td>\n",
              "      <td>9.5</td>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-90de8cf5-028f-4669-b0d4-aa8087cf8686 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-90de8cf5-028f-4669-b0d4-aa8087cf8686');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-da07b922-1578-4d9e-b238-391485438e73\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-da07b922-1578-4d9e-b238-391485438e73')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-da07b922-1578-4d9e-b238-391485438e73 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhgJhf1DoVvV",
        "outputId": "32ec7cbf-9851-4a85-81b7-4007053e3c43"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v3pJLd9_pDFJ",
        "outputId": "15973ba7-13f6-4967-b020-0cf3729598de"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['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')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "columns = df[['Life expectancy ', 'Adult Mortality', 'Alcohol',\n",
        "           'Hepatitis B', ' BMI ', 'Polio', 'Total expenditure',\n",
        "           'Diphtheria ', 'GDP', 'Population', ' thinness  1-19 years', ' thinness 5-9 years',\n",
        "           'Income composition of resources', 'Schooling']]\n",
        "\n",
        "# Create subplots\n",
        "fig, axes = plt.subplots(nrows=4, ncols=4, figsize=(20, 20))\n",
        "\n",
        "# Flatten the axes array for easy iteration\n",
        "axes = axes.flatten()\n",
        "\n",
        "# Iterate through columns and plot histograms\n",
        "for i, column in enumerate(columns):\n",
        "    ax = axes[i]\n",
        "    ax.hist(df[column].dropna(), bins=20, color='skyblue', edgecolor='black')  # Adjust the number of bins as needed\n",
        "    ax.set_title(column)\n",
        "    ax.set_xlabel('Value')\n",
        "    ax.set_ylabel('Frequency')\n",
        "\n",
        "# Adjust layout\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OB6-zC8Cp9VF",
        "outputId": "a1ae50a6-cf04-4c4a-f86f-b6c738efc05a"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 16 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-7Ie7QQPs7UZ",
        "outputId": "4d64f7cd-f89e-4056-c346-14b2b5873116"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['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')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in ['Adult Mortality','Alcohol','Hepatitis B','Diphtheria ', 'Polio','GDP','Population',' thinness  1-19 years',' thinness 5-9 years']:\n",
        "  df[i] = df[i].fillna(df[i].median())"
      ],
      "metadata": {
        "id": "fi__vq55reHI"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in ['Life expectancy ',' BMI ','Total expenditure','Income composition of resources', 'Schooling']:\n",
        "  df[i]=df[i].fillna(df[i].mean())"
      ],
      "metadata": {
        "id": "Fc53m4Vwt-vK"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c8HUp0d6ucky",
        "outputId": "6ef12982-38b0-4da5-ac95-9e695a2ed0c2"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year                               0\n",
              "Status                             0\n",
              "Life expectancy                    0\n",
              "Adult Mortality                    0\n",
              "infant deaths                      0\n",
              "Alcohol                            0\n",
              "percentage expenditure             0\n",
              "Hepatitis B                        0\n",
              "Measles                            0\n",
              " BMI                               0\n",
              "under-five deaths                  0\n",
              "Polio                              0\n",
              "Total expenditure                  0\n",
              "Diphtheria                         0\n",
              " HIV/AIDS                          0\n",
              "GDP                                0\n",
              "Population                         0\n",
              " thinness  1-19 years              0\n",
              " thinness 5-9 years                0\n",
              "Income composition of resources    0\n",
              "Schooling                          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# encoding\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "WmO_ZUBOuixB",
        "outputId": "dbf605b6-6796-4a87-dce4-0f668d1cbf7f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year      Status  Life expectancy   Adult Mortality  infant deaths  \\\n",
              "0  2015  Developing              65.0            263.0             62   \n",
              "1  2014  Developing              59.9            271.0             64   \n",
              "2  2013  Developing              59.9            268.0             66   \n",
              "3  2012  Developing              59.5            272.0             69   \n",
              "4  2011  Developing              59.2            275.0             71   \n",
              "\n",
              "   Alcohol  percentage expenditure  Hepatitis B  Measles    BMI   ...  Polio  \\\n",
              "0     0.01               71.279624         65.0      1154   19.1  ...    6.0   \n",
              "1     0.01               73.523582         62.0       492   18.6  ...   58.0   \n",
              "2     0.01               73.219243         64.0       430   18.1  ...   62.0   \n",
              "3     0.01               78.184215         67.0      2787   17.6  ...   67.0   \n",
              "4     0.01                7.097109         68.0      3013   17.2  ...   68.0   \n",
              "\n",
              "   Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
              "0               8.16         65.0        0.1  584.259210  33736494.0   \n",
              "1               8.18         62.0        0.1  612.696514    327582.0   \n",
              "2               8.13         64.0        0.1  631.744976  31731688.0   \n",
              "3               8.52         67.0        0.1  669.959000   3696958.0   \n",
              "4               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 21 columns]"
            ],
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              "  <div id=\"df-44b21e27-6f60-4f67-ae13-f558a067d599\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></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>BMI</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>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>19.1</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>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>18.6</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>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>18.1</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>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>17.6</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>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>17.2</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 × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44b21e27-6f60-4f67-ae13-f558a067d599')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-44b21e27-6f60-4f67-ae13-f558a067d599 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-44b21e27-6f60-4f67-ae13-f558a067d599');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
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              "    30% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-445c1473-1e9d-4db6-84b0-735ca2115620 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EFWTAFcIA5P1",
        "outputId": "62fc10a9-15a0-4ab7-973d-38fa68913142"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year                                 16\n",
              "Status                                2\n",
              "Life expectancy                     363\n",
              "Adult Mortality                     425\n",
              "infant deaths                       209\n",
              "Alcohol                            1077\n",
              "percentage expenditure             2328\n",
              "Hepatitis B                          87\n",
              "Measles                             958\n",
              " BMI                                609\n",
              "under-five deaths                   252\n",
              "Polio                                73\n",
              "Total expenditure                   819\n",
              "Diphtheria                           81\n",
              " HIV/AIDS                           200\n",
              "GDP                                2491\n",
              "Population                         2279\n",
              " thinness  1-19 years               200\n",
              " thinness 5-9 years                 207\n",
              "Income composition of resources     626\n",
              "Schooling                           174\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "\n",
        "df['Status'] = le.fit_transform(df['Status'])"
      ],
      "metadata": {
        "id": "iWlT8ej_BMXd"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "8tv-7nkkBhtf",
        "outputId": "08bd9d4c-54f1-4216-dcfc-eaa5b3e8e1e5"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year  Status  Life expectancy   Adult Mortality  infant deaths  Alcohol  \\\n",
              "0  2015       1              65.0            263.0             62     0.01   \n",
              "1  2014       1              59.9            271.0             64     0.01   \n",
              "2  2013       1              59.9            268.0             66     0.01   \n",
              "3  2012       1              59.5            272.0             69     0.01   \n",
              "4  2011       1              59.2            275.0             71     0.01   \n",
              "\n",
              "   percentage expenditure  Hepatitis B  Measles    BMI   ...  Polio  \\\n",
              "0               71.279624         65.0      1154   19.1  ...    6.0   \n",
              "1               73.523582         62.0       492   18.6  ...   58.0   \n",
              "2               73.219243         64.0       430   18.1  ...   62.0   \n",
              "3               78.184215         67.0      2787   17.6  ...   67.0   \n",
              "4                7.097109         68.0      3013   17.2  ...   68.0   \n",
              "\n",
              "   Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
              "0               8.16         65.0        0.1  584.259210  33736494.0   \n",
              "1               8.18         62.0        0.1  612.696514    327582.0   \n",
              "2               8.13         64.0        0.1  631.744976  31731688.0   \n",
              "3               8.52         67.0        0.1  669.959000   3696958.0   \n",
              "4               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",
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>18.6</td>\n",
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              "      <td>8.18</td>\n",
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              "      <td>612.696514</td>\n",
              "      <td>327582.0</td>\n",
              "      <td>17.5</td>\n",
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              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013</td>\n",
              "      <td>1</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>18.1</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>2012</td>\n",
              "      <td>1</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>17.6</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>2011</td>\n",
              "      <td>1</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>17.2</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",
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    {
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      "source": [
        "df.corr()"
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          "height": 999
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        "id": "Nh3A440dBi2N",
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      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     Year    Status  Life expectancy   \\\n",
              "Year                             1.000000  0.001864          0.169623   \n",
              "Status                           0.001864  1.000000         -0.481962   \n",
              "Life expectancy                  0.169623 -0.481962          1.000000   \n",
              "Adult Mortality                 -0.079535  0.314894         -0.696326   \n",
              "infant deaths                   -0.037415  0.112252         -0.196535   \n",
              "Alcohol                         -0.069147 -0.579662          0.389847   \n",
              "percentage expenditure           0.031400 -0.454261          0.381791   \n",
              "Hepatitis B                      0.021302 -0.127008          0.170555   \n",
              "Measles                         -0.082493  0.076955         -0.157574   \n",
              " BMI                             0.108327 -0.310873          0.559255   \n",
              "under-five deaths               -0.042937  0.115195         -0.222503   \n",
              "Polio                            0.091388 -0.218628          0.458449   \n",
              "Total expenditure                0.081860 -0.289985          0.207981   \n",
              "Diphtheria                       0.131373 -0.215281          0.472253   \n",
              " HIV/AIDS                       -0.139741  0.148590         -0.556457   \n",
              "GDP                              0.091480 -0.445981          0.430895   \n",
              "Population                       0.014027  0.037606         -0.028845   \n",
              " thinness  1-19 years           -0.047827  0.365814         -0.467267   \n",
              " thinness 5-9 years             -0.050859  0.364179         -0.461739   \n",
              "Income composition of resources  0.236333 -0.457302          0.692483   \n",
              "Schooling                        0.203471 -0.491444          0.715066   \n",
              "\n",
              "                                 Adult Mortality  infant deaths   Alcohol  \\\n",
              "Year                                   -0.079535      -0.037415 -0.069147   \n",
              "Status                                  0.314894       0.112252 -0.579662   \n",
              "Life expectancy                        -0.696326      -0.196535  0.389847   \n",
              "Adult Mortality                         1.000000       0.078890 -0.190191   \n",
              "infant deaths                           0.078890       1.000000 -0.112918   \n",
              "Alcohol                                -0.190191      -0.112918  1.000000   \n",
              "percentage expenditure                 -0.242628      -0.085612  0.344410   \n",
              "Hepatitis B                            -0.123287      -0.167421  0.090850   \n",
              "Measles                                 0.031293       0.501128 -0.049752   \n",
              " BMI                                   -0.382143      -0.227220  0.314697   \n",
              "under-five deaths                       0.094280       0.996629 -0.109790   \n",
              "Polio                                  -0.270002      -0.170896  0.211744   \n",
              "Total expenditure                      -0.111385      -0.126564  0.294590   \n",
              "Diphtheria                             -0.270301      -0.175376  0.212388   \n",
              " HIV/AIDS                               0.523886       0.025231 -0.046235   \n",
              "GDP                                    -0.281434      -0.102829  0.313060   \n",
              "Population                             -0.005292       0.551667 -0.026836   \n",
              " thinness  1-19 years                   0.296135       0.464811 -0.413021   \n",
              " thinness 5-9 years                     0.301634       0.470445 -0.402034   \n",
              "Income composition of resources        -0.440143      -0.143663  0.414507   \n",
              "Schooling                              -0.434756      -0.191757  0.497105   \n",
              "\n",
              "                                 percentage expenditure  Hepatitis B  \\\n",
              "Year                                           0.031400     0.021302   \n",
              "Status                                        -0.454261    -0.127008   \n",
              "Life expectancy                                0.381791     0.170555   \n",
              "Adult Mortality                               -0.242628    -0.123287   \n",
              "infant deaths                                 -0.085612    -0.167421   \n",
              "Alcohol                                        0.344410     0.090850   \n",
              "percentage expenditure                         1.000000     0.039528   \n",
              "Hepatitis B                                    0.039528     1.000000   \n",
              "Measles                                       -0.056596    -0.073021   \n",
              " BMI                                           0.228537     0.112438   \n",
              "under-five deaths                             -0.087852    -0.170549   \n",
              "Polio                                          0.146148     0.364890   \n",
              "Total expenditure                              0.173414     0.056180   \n",
              "Diphtheria                                     0.142506     0.449151   \n",
              " HIV/AIDS                                     -0.097857    -0.085497   \n",
              "GDP                                            0.901819     0.076660   \n",
              "Population                                    -0.016888    -0.125006   \n",
              " thinness  1-19 years                         -0.249610    -0.097576   \n",
              " thinness 5-9 years                           -0.251144    -0.100576   \n",
              "Income composition of resources                0.380374     0.117516   \n",
              "Schooling                                      0.388105     0.140961   \n",
              "\n",
              "                                 Measles       BMI   ...     Polio  \\\n",
              "Year                            -0.082493  0.108327  ...  0.091388   \n",
              "Status                           0.076955 -0.310873  ... -0.218628   \n",
              "Life expectancy                 -0.157574  0.559255  ...  0.458449   \n",
              "Adult Mortality                  0.031293 -0.382143  ... -0.270002   \n",
              "infant deaths                    0.501128 -0.227220  ... -0.170896   \n",
              "Alcohol                         -0.049752  0.314697  ...  0.211744   \n",
              "percentage expenditure          -0.056596  0.228537  ...  0.146148   \n",
              "Hepatitis B                     -0.073021  0.112438  ...  0.364890   \n",
              "Measles                          1.000000 -0.175925  ... -0.136669   \n",
              " BMI                            -0.175925  1.000000  ...  0.282285   \n",
              "under-five deaths                0.507809 -0.237586  ... -0.188864   \n",
              "Polio                           -0.136669  0.282285  ...  1.000000   \n",
              "Total expenditure               -0.104569  0.231814  ...  0.130630   \n",
              "Diphtheria                      -0.142385  0.281188  ...  0.673977   \n",
              " HIV/AIDS                        0.030899 -0.243548  ... -0.159128   \n",
              "GDP                             -0.069253  0.275231  ...  0.189926   \n",
              "Population                       0.237188 -0.069700  ... -0.044813   \n",
              " thinness  1-19 years            0.224870 -0.531651  ... -0.218401   \n",
              " thinness 5-9 years              0.221138 -0.538533  ... -0.219168   \n",
              "Income composition of resources -0.115764  0.479837  ...  0.347759   \n",
              "Schooling                       -0.122609  0.508105  ...  0.376597   \n",
              "\n",
              "                                 Total expenditure  Diphtheria    HIV/AIDS  \\\n",
              "Year                                      0.081860     0.131373  -0.139741   \n",
              "Status                                   -0.289985    -0.215281   0.148590   \n",
              "Life expectancy                           0.207981     0.472253  -0.556457   \n",
              "Adult Mortality                          -0.111385    -0.270301   0.523886   \n",
              "infant deaths                            -0.126564    -0.175376   0.025231   \n",
              "Alcohol                                   0.294590     0.212388  -0.046235   \n",
              "percentage expenditure                    0.173414     0.142506  -0.097857   \n",
              "Hepatitis B                               0.056180     0.449151  -0.085497   \n",
              "Measles                                  -0.104569    -0.142385   0.030899   \n",
              " BMI                                      0.231814     0.281188  -0.243548   \n",
              "under-five deaths                        -0.128269    -0.195808   0.038062   \n",
              "Polio                                     0.130630     0.673977  -0.159128   \n",
              "Total expenditure                         1.000000     0.146091  -0.001383   \n",
              "Diphtheria                                0.146091     1.000000  -0.164419   \n",
              " HIV/AIDS                                -0.001383    -0.164419   1.000000   \n",
              "GDP                                       0.111600     0.181953  -0.122590   \n",
              "Population                               -0.061882    -0.033128  -0.017094   \n",
              " thinness  1-19 years                    -0.267820    -0.226409   0.203684   \n",
              " thinness 5-9 years                      -0.274332    -0.219699   0.206899   \n",
              "Income composition of resources           0.149095     0.364008  -0.247454   \n",
              "Schooling                                 0.218310     0.380619  -0.218620   \n",
              "\n",
              "                                      GDP  Population   thinness  1-19 years  \\\n",
              "Year                             0.091480    0.014027              -0.047827   \n",
              "Status                          -0.445981    0.037606               0.365814   \n",
              "Life expectancy                  0.430895   -0.028845              -0.467267   \n",
              "Adult Mortality                 -0.281434   -0.005292               0.296135   \n",
              "infant deaths                   -0.102829    0.551667               0.464811   \n",
              "Alcohol                          0.313060   -0.026836              -0.413021   \n",
              "percentage expenditure           0.901819   -0.016888              -0.249610   \n",
              "Hepatitis B                      0.076660   -0.125006              -0.097576   \n",
              "Measles                         -0.069253    0.237188               0.224870   \n",
              " BMI                             0.275231   -0.069700              -0.531651   \n",
              "under-five deaths               -0.106093    0.539260               0.466697   \n",
              "Polio                            0.189926   -0.044813              -0.218401   \n",
              "Total expenditure                0.111600   -0.061882              -0.267820   \n",
              "Diphtheria                       0.181953   -0.033128              -0.226409   \n",
              " HIV/AIDS                       -0.122590   -0.017094               0.203684   \n",
              "GDP                              1.000000   -0.025269              -0.263323   \n",
              "Population                      -0.025269    1.000000               0.236410   \n",
              " thinness  1-19 years           -0.263323    0.236410               1.000000   \n",
              " thinness 5-9 years             -0.266935    0.235278               0.939187   \n",
              "Income composition of resources  0.441657   -0.015014              -0.400021   \n",
              "Schooling                        0.434243   -0.035967              -0.436545   \n",
              "\n",
              "                                  thinness 5-9 years  \\\n",
              "Year                                       -0.050859   \n",
              "Status                                      0.364179   \n",
              "Life expectancy                            -0.461739   \n",
              "Adult Mortality                             0.301634   \n",
              "infant deaths                               0.470445   \n",
              "Alcohol                                    -0.402034   \n",
              "percentage expenditure                     -0.251144   \n",
              "Hepatitis B                                -0.100576   \n",
              "Measles                                     0.221138   \n",
              " BMI                                       -0.538533   \n",
              "under-five deaths                           0.471167   \n",
              "Polio                                      -0.219168   \n",
              "Total expenditure                          -0.274332   \n",
              "Diphtheria                                 -0.219699   \n",
              " HIV/AIDS                                   0.206899   \n",
              "GDP                                        -0.266935   \n",
              "Population                                  0.235278   \n",
              " thinness  1-19 years                       0.939187   \n",
              " thinness 5-9 years                         1.000000   \n",
              "Income composition of resources            -0.389147   \n",
              "Schooling                                  -0.426192   \n",
              "\n",
              "                                 Income composition of resources  Schooling  \n",
              "Year                                                    0.236333   0.203471  \n",
              "Status                                                 -0.457302  -0.491444  \n",
              "Life expectancy                                         0.692483   0.715066  \n",
              "Adult Mortality                                        -0.440143  -0.434756  \n",
              "infant deaths                                          -0.143663  -0.191757  \n",
              "Alcohol                                                 0.414507   0.497105  \n",
              "percentage expenditure                                  0.380374   0.388105  \n",
              "Hepatitis B                                             0.117516   0.140961  \n",
              "Measles                                                -0.115764  -0.122609  \n",
              " BMI                                                    0.479837   0.508105  \n",
              "under-five deaths                                      -0.161533  -0.207111  \n",
              "Polio                                                   0.347759   0.376597  \n",
              "Total expenditure                                       0.149095   0.218310  \n",
              "Diphtheria                                              0.364008   0.380619  \n",
              " HIV/AIDS                                              -0.247454  -0.218620  \n",
              "GDP                                                     0.441657   0.434243  \n",
              "Population                                             -0.015014  -0.035967  \n",
              " thinness  1-19 years                                  -0.400021  -0.436545  \n",
              " thinness 5-9 years                                    -0.389147  -0.426192  \n",
              "Income composition of resources                         1.000000   0.796207  \n",
              "Schooling                                               0.796207   1.000000  \n",
              "\n",
              "[21 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></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>BMI</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>Year</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.001864</td>\n",
              "      <td>0.169623</td>\n",
              "      <td>-0.079535</td>\n",
              "      <td>-0.037415</td>\n",
              "      <td>-0.069147</td>\n",
              "      <td>0.031400</td>\n",
              "      <td>0.021302</td>\n",
              "      <td>-0.082493</td>\n",
              "      <td>0.108327</td>\n",
              "      <td>...</td>\n",
              "      <td>0.091388</td>\n",
              "      <td>0.081860</td>\n",
              "      <td>0.131373</td>\n",
              "      <td>-0.139741</td>\n",
              "      <td>0.091480</td>\n",
              "      <td>0.014027</td>\n",
              "      <td>-0.047827</td>\n",
              "      <td>-0.050859</td>\n",
              "      <td>0.236333</td>\n",
              "      <td>0.203471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Status</th>\n",
              "      <td>0.001864</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.481962</td>\n",
              "      <td>0.314894</td>\n",
              "      <td>0.112252</td>\n",
              "      <td>-0.579662</td>\n",
              "      <td>-0.454261</td>\n",
              "      <td>-0.127008</td>\n",
              "      <td>0.076955</td>\n",
              "      <td>-0.310873</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.218628</td>\n",
              "      <td>-0.289985</td>\n",
              "      <td>-0.215281</td>\n",
              "      <td>0.148590</td>\n",
              "      <td>-0.445981</td>\n",
              "      <td>0.037606</td>\n",
              "      <td>0.365814</td>\n",
              "      <td>0.364179</td>\n",
              "      <td>-0.457302</td>\n",
              "      <td>-0.491444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Life expectancy</th>\n",
              "      <td>0.169623</td>\n",
              "      <td>-0.481962</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.696326</td>\n",
              "      <td>-0.196535</td>\n",
              "      <td>0.389847</td>\n",
              "      <td>0.381791</td>\n",
              "      <td>0.170555</td>\n",
              "      <td>-0.157574</td>\n",
              "      <td>0.559255</td>\n",
              "      <td>...</td>\n",
              "      <td>0.458449</td>\n",
              "      <td>0.207981</td>\n",
              "      <td>0.472253</td>\n",
              "      <td>-0.556457</td>\n",
              "      <td>0.430895</td>\n",
              "      <td>-0.028845</td>\n",
              "      <td>-0.467267</td>\n",
              "      <td>-0.461739</td>\n",
              "      <td>0.692483</td>\n",
              "      <td>0.715066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Adult Mortality</th>\n",
              "      <td>-0.079535</td>\n",
              "      <td>0.314894</td>\n",
              "      <td>-0.696326</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.078890</td>\n",
              "      <td>-0.190191</td>\n",
              "      <td>-0.242628</td>\n",
              "      <td>-0.123287</td>\n",
              "      <td>0.031293</td>\n",
              "      <td>-0.382143</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.270002</td>\n",
              "      <td>-0.111385</td>\n",
              "      <td>-0.270301</td>\n",
              "      <td>0.523886</td>\n",
              "      <td>-0.281434</td>\n",
              "      <td>-0.005292</td>\n",
              "      <td>0.296135</td>\n",
              "      <td>0.301634</td>\n",
              "      <td>-0.440143</td>\n",
              "      <td>-0.434756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>infant deaths</th>\n",
              "      <td>-0.037415</td>\n",
              "      <td>0.112252</td>\n",
              "      <td>-0.196535</td>\n",
              "      <td>0.078890</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.112918</td>\n",
              "      <td>-0.085612</td>\n",
              "      <td>-0.167421</td>\n",
              "      <td>0.501128</td>\n",
              "      <td>-0.227220</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.170896</td>\n",
              "      <td>-0.126564</td>\n",
              "      <td>-0.175376</td>\n",
              "      <td>0.025231</td>\n",
              "      <td>-0.102829</td>\n",
              "      <td>0.551667</td>\n",
              "      <td>0.464811</td>\n",
              "      <td>0.470445</td>\n",
              "      <td>-0.143663</td>\n",
              "      <td>-0.191757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alcohol</th>\n",
              "      <td>-0.069147</td>\n",
              "      <td>-0.579662</td>\n",
              "      <td>0.389847</td>\n",
              "      <td>-0.190191</td>\n",
              "      <td>-0.112918</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.344410</td>\n",
              "      <td>0.090850</td>\n",
              "      <td>-0.049752</td>\n",
              "      <td>0.314697</td>\n",
              "      <td>...</td>\n",
              "      <td>0.211744</td>\n",
              "      <td>0.294590</td>\n",
              "      <td>0.212388</td>\n",
              "      <td>-0.046235</td>\n",
              "      <td>0.313060</td>\n",
              "      <td>-0.026836</td>\n",
              "      <td>-0.413021</td>\n",
              "      <td>-0.402034</td>\n",
              "      <td>0.414507</td>\n",
              "      <td>0.497105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>percentage expenditure</th>\n",
              "      <td>0.031400</td>\n",
              "      <td>-0.454261</td>\n",
              "      <td>0.381791</td>\n",
              "      <td>-0.242628</td>\n",
              "      <td>-0.085612</td>\n",
              "      <td>0.344410</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.039528</td>\n",
              "      <td>-0.056596</td>\n",
              "      <td>0.228537</td>\n",
              "      <td>...</td>\n",
              "      <td>0.146148</td>\n",
              "      <td>0.173414</td>\n",
              "      <td>0.142506</td>\n",
              "      <td>-0.097857</td>\n",
              "      <td>0.901819</td>\n",
              "      <td>-0.016888</td>\n",
              "      <td>-0.249610</td>\n",
              "      <td>-0.251144</td>\n",
              "      <td>0.380374</td>\n",
              "      <td>0.388105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hepatitis B</th>\n",
              "      <td>0.021302</td>\n",
              "      <td>-0.127008</td>\n",
              "      <td>0.170555</td>\n",
              "      <td>-0.123287</td>\n",
              "      <td>-0.167421</td>\n",
              "      <td>0.090850</td>\n",
              "      <td>0.039528</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.073021</td>\n",
              "      <td>0.112438</td>\n",
              "      <td>...</td>\n",
              "      <td>0.364890</td>\n",
              "      <td>0.056180</td>\n",
              "      <td>0.449151</td>\n",
              "      <td>-0.085497</td>\n",
              "      <td>0.076660</td>\n",
              "      <td>-0.125006</td>\n",
              "      <td>-0.097576</td>\n",
              "      <td>-0.100576</td>\n",
              "      <td>0.117516</td>\n",
              "      <td>0.140961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Measles</th>\n",
              "      <td>-0.082493</td>\n",
              "      <td>0.076955</td>\n",
              "      <td>-0.157574</td>\n",
              "      <td>0.031293</td>\n",
              "      <td>0.501128</td>\n",
              "      <td>-0.049752</td>\n",
              "      <td>-0.056596</td>\n",
              "      <td>-0.073021</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.175925</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.136669</td>\n",
              "      <td>-0.104569</td>\n",
              "      <td>-0.142385</td>\n",
              "      <td>0.030899</td>\n",
              "      <td>-0.069253</td>\n",
              "      <td>0.237188</td>\n",
              "      <td>0.224870</td>\n",
              "      <td>0.221138</td>\n",
              "      <td>-0.115764</td>\n",
              "      <td>-0.122609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMI</th>\n",
              "      <td>0.108327</td>\n",
              "      <td>-0.310873</td>\n",
              "      <td>0.559255</td>\n",
              "      <td>-0.382143</td>\n",
              "      <td>-0.227220</td>\n",
              "      <td>0.314697</td>\n",
              "      <td>0.228537</td>\n",
              "      <td>0.112438</td>\n",
              "      <td>-0.175925</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.282285</td>\n",
              "      <td>0.231814</td>\n",
              "      <td>0.281188</td>\n",
              "      <td>-0.243548</td>\n",
              "      <td>0.275231</td>\n",
              "      <td>-0.069700</td>\n",
              "      <td>-0.531651</td>\n",
              "      <td>-0.538533</td>\n",
              "      <td>0.479837</td>\n",
              "      <td>0.508105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>under-five deaths</th>\n",
              "      <td>-0.042937</td>\n",
              "      <td>0.115195</td>\n",
              "      <td>-0.222503</td>\n",
              "      <td>0.094280</td>\n",
              "      <td>0.996629</td>\n",
              "      <td>-0.109790</td>\n",
              "      <td>-0.087852</td>\n",
              "      <td>-0.170549</td>\n",
              "      <td>0.507809</td>\n",
              "      <td>-0.237586</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.188864</td>\n",
              "      <td>-0.128269</td>\n",
              "      <td>-0.195808</td>\n",
              "      <td>0.038062</td>\n",
              "      <td>-0.106093</td>\n",
              "      <td>0.539260</td>\n",
              "      <td>0.466697</td>\n",
              "      <td>0.471167</td>\n",
              "      <td>-0.161533</td>\n",
              "      <td>-0.207111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Polio</th>\n",
              "      <td>0.091388</td>\n",
              "      <td>-0.218628</td>\n",
              "      <td>0.458449</td>\n",
              "      <td>-0.270002</td>\n",
              "      <td>-0.170896</td>\n",
              "      <td>0.211744</td>\n",
              "      <td>0.146148</td>\n",
              "      <td>0.364890</td>\n",
              "      <td>-0.136669</td>\n",
              "      <td>0.282285</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.130630</td>\n",
              "      <td>0.673977</td>\n",
              "      <td>-0.159128</td>\n",
              "      <td>0.189926</td>\n",
              "      <td>-0.044813</td>\n",
              "      <td>-0.218401</td>\n",
              "      <td>-0.219168</td>\n",
              "      <td>0.347759</td>\n",
              "      <td>0.376597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total expenditure</th>\n",
              "      <td>0.081860</td>\n",
              "      <td>-0.289985</td>\n",
              "      <td>0.207981</td>\n",
              "      <td>-0.111385</td>\n",
              "      <td>-0.126564</td>\n",
              "      <td>0.294590</td>\n",
              "      <td>0.173414</td>\n",
              "      <td>0.056180</td>\n",
              "      <td>-0.104569</td>\n",
              "      <td>0.231814</td>\n",
              "      <td>...</td>\n",
              "      <td>0.130630</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.146091</td>\n",
              "      <td>-0.001383</td>\n",
              "      <td>0.111600</td>\n",
              "      <td>-0.061882</td>\n",
              "      <td>-0.267820</td>\n",
              "      <td>-0.274332</td>\n",
              "      <td>0.149095</td>\n",
              "      <td>0.218310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Diphtheria</th>\n",
              "      <td>0.131373</td>\n",
              "      <td>-0.215281</td>\n",
              "      <td>0.472253</td>\n",
              "      <td>-0.270301</td>\n",
              "      <td>-0.175376</td>\n",
              "      <td>0.212388</td>\n",
              "      <td>0.142506</td>\n",
              "      <td>0.449151</td>\n",
              "      <td>-0.142385</td>\n",
              "      <td>0.281188</td>\n",
              "      <td>...</td>\n",
              "      <td>0.673977</td>\n",
              "      <td>0.146091</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.164419</td>\n",
              "      <td>0.181953</td>\n",
              "      <td>-0.033128</td>\n",
              "      <td>-0.226409</td>\n",
              "      <td>-0.219699</td>\n",
              "      <td>0.364008</td>\n",
              "      <td>0.380619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HIV/AIDS</th>\n",
              "      <td>-0.139741</td>\n",
              "      <td>0.148590</td>\n",
              "      <td>-0.556457</td>\n",
              "      <td>0.523886</td>\n",
              "      <td>0.025231</td>\n",
              "      <td>-0.046235</td>\n",
              "      <td>-0.097857</td>\n",
              "      <td>-0.085497</td>\n",
              "      <td>0.030899</td>\n",
              "      <td>-0.243548</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.159128</td>\n",
              "      <td>-0.001383</td>\n",
              "      <td>-0.164419</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.122590</td>\n",
              "      <td>-0.017094</td>\n",
              "      <td>0.203684</td>\n",
              "      <td>0.206899</td>\n",
              "      <td>-0.247454</td>\n",
              "      <td>-0.218620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GDP</th>\n",
              "      <td>0.091480</td>\n",
              "      <td>-0.445981</td>\n",
              "      <td>0.430895</td>\n",
              "      <td>-0.281434</td>\n",
              "      <td>-0.102829</td>\n",
              "      <td>0.313060</td>\n",
              "      <td>0.901819</td>\n",
              "      <td>0.076660</td>\n",
              "      <td>-0.069253</td>\n",
              "      <td>0.275231</td>\n",
              "      <td>...</td>\n",
              "      <td>0.189926</td>\n",
              "      <td>0.111600</td>\n",
              "      <td>0.181953</td>\n",
              "      <td>-0.122590</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.025269</td>\n",
              "      <td>-0.263323</td>\n",
              "      <td>-0.266935</td>\n",
              "      <td>0.441657</td>\n",
              "      <td>0.434243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Population</th>\n",
              "      <td>0.014027</td>\n",
              "      <td>0.037606</td>\n",
              "      <td>-0.028845</td>\n",
              "      <td>-0.005292</td>\n",
              "      <td>0.551667</td>\n",
              "      <td>-0.026836</td>\n",
              "      <td>-0.016888</td>\n",
              "      <td>-0.125006</td>\n",
              "      <td>0.237188</td>\n",
              "      <td>-0.069700</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.044813</td>\n",
              "      <td>-0.061882</td>\n",
              "      <td>-0.033128</td>\n",
              "      <td>-0.017094</td>\n",
              "      <td>-0.025269</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.236410</td>\n",
              "      <td>0.235278</td>\n",
              "      <td>-0.015014</td>\n",
              "      <td>-0.035967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thinness  1-19 years</th>\n",
              "      <td>-0.047827</td>\n",
              "      <td>0.365814</td>\n",
              "      <td>-0.467267</td>\n",
              "      <td>0.296135</td>\n",
              "      <td>0.464811</td>\n",
              "      <td>-0.413021</td>\n",
              "      <td>-0.249610</td>\n",
              "      <td>-0.097576</td>\n",
              "      <td>0.224870</td>\n",
              "      <td>-0.531651</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.218401</td>\n",
              "      <td>-0.267820</td>\n",
              "      <td>-0.226409</td>\n",
              "      <td>0.203684</td>\n",
              "      <td>-0.263323</td>\n",
              "      <td>0.236410</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.939187</td>\n",
              "      <td>-0.400021</td>\n",
              "      <td>-0.436545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thinness 5-9 years</th>\n",
              "      <td>-0.050859</td>\n",
              "      <td>0.364179</td>\n",
              "      <td>-0.461739</td>\n",
              "      <td>0.301634</td>\n",
              "      <td>0.470445</td>\n",
              "      <td>-0.402034</td>\n",
              "      <td>-0.251144</td>\n",
              "      <td>-0.100576</td>\n",
              "      <td>0.221138</td>\n",
              "      <td>-0.538533</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.219168</td>\n",
              "      <td>-0.274332</td>\n",
              "      <td>-0.219699</td>\n",
              "      <td>0.206899</td>\n",
              "      <td>-0.266935</td>\n",
              "      <td>0.235278</td>\n",
              "      <td>0.939187</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.389147</td>\n",
              "      <td>-0.426192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income composition of resources</th>\n",
              "      <td>0.236333</td>\n",
              "      <td>-0.457302</td>\n",
              "      <td>0.692483</td>\n",
              "      <td>-0.440143</td>\n",
              "      <td>-0.143663</td>\n",
              "      <td>0.414507</td>\n",
              "      <td>0.380374</td>\n",
              "      <td>0.117516</td>\n",
              "      <td>-0.115764</td>\n",
              "      <td>0.479837</td>\n",
              "      <td>...</td>\n",
              "      <td>0.347759</td>\n",
              "      <td>0.149095</td>\n",
              "      <td>0.364008</td>\n",
              "      <td>-0.247454</td>\n",
              "      <td>0.441657</td>\n",
              "      <td>-0.015014</td>\n",
              "      <td>-0.400021</td>\n",
              "      <td>-0.389147</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.796207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Schooling</th>\n",
              "      <td>0.203471</td>\n",
              "      <td>-0.491444</td>\n",
              "      <td>0.715066</td>\n",
              "      <td>-0.434756</td>\n",
              "      <td>-0.191757</td>\n",
              "      <td>0.497105</td>\n",
              "      <td>0.388105</td>\n",
              "      <td>0.140961</td>\n",
              "      <td>-0.122609</td>\n",
              "      <td>0.508105</td>\n",
              "      <td>...</td>\n",
              "      <td>0.376597</td>\n",
              "      <td>0.218310</td>\n",
              "      <td>0.380619</td>\n",
              "      <td>-0.218620</td>\n",
              "      <td>0.434243</td>\n",
              "      <td>-0.035967</td>\n",
              "      <td>-0.436545</td>\n",
              "      <td>-0.426192</td>\n",
              "      <td>0.796207</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f07a92d6-6504-4297-9a40-997960007681');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </script>\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-41375b5b-2878-4e1c-9996-a7fd10092527')\"\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-41375b5b-2878-4e1c-9996-a7fd10092527 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr(),cmap='coolwarm',linewidth=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "OfZp08QOBlxs",
        "outputId": "f7f76b0b-d9b5-46da-ec6d-4065d743f088"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# scaling"
      ],
      "metadata": {
        "id": "aF9mo4l8B-XN"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "ktqHOzjDCHB7",
        "outputId": "87cdc0bc-fba5-4ff1-fbd1-2908c66080d8"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Year  Status  Life expectancy   Adult Mortality  infant deaths  Alcohol  \\\n",
              "2933  2004       1              44.3            723.0             27     4.36   \n",
              "2934  2003       1              44.5            715.0             26     4.06   \n",
              "2935  2002       1              44.8             73.0             25     4.43   \n",
              "2936  2001       1              45.3            686.0             25     1.72   \n",
              "2937  2000       1              46.0            665.0             24     1.68   \n",
              "\n",
              "      percentage expenditure  Hepatitis B  Measles    BMI   ...  Polio  \\\n",
              "2933                     0.0         68.0        31   27.1  ...   67.0   \n",
              "2934                     0.0          7.0       998   26.7  ...    7.0   \n",
              "2935                     0.0         73.0       304   26.3  ...   73.0   \n",
              "2936                     0.0         76.0       529   25.9  ...   76.0   \n",
              "2937                     0.0         79.0      1483   25.5  ...   78.0   \n",
              "\n",
              "      Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
              "2933               7.13         65.0       33.6  454.366654  12777511.0   \n",
              "2934               6.52         68.0       36.7  453.351155  12633897.0   \n",
              "2935               6.53         71.0       39.8   57.348340    125525.0   \n",
              "2936               6.16         75.0       42.1  548.587312  12366165.0   \n",
              "2937               7.10         78.0       43.5  547.358878  12222251.0   \n",
              "\n",
              "       thinness  1-19 years   thinness 5-9 years  \\\n",
              "2933                    9.4                  9.4   \n",
              "2934                    9.8                  9.9   \n",
              "2935                    1.2                  1.3   \n",
              "2936                    1.6                  1.7   \n",
              "2937                   11.0                 11.2   \n",
              "\n",
              "      Income composition of resources  Schooling  \n",
              "2933                            0.407        9.2  \n",
              "2934                            0.418        9.5  \n",
              "2935                            0.427       10.0  \n",
              "2936                            0.427        9.8  \n",
              "2937                            0.434        9.8  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e2a958fd-c896-42cf-bf3e-93e7bb74cbf2\" class=\"colab-df-container\">\n",
              "    <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>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>BMI</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>2933</th>\n",
              "      <td>2004</td>\n",
              "      <td>1</td>\n",
              "      <td>44.3</td>\n",
              "      <td>723.0</td>\n",
              "      <td>27</td>\n",
              "      <td>4.36</td>\n",
              "      <td>0.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>31</td>\n",
              "      <td>27.1</td>\n",
              "      <td>...</td>\n",
              "      <td>67.0</td>\n",
              "      <td>7.13</td>\n",
              "      <td>65.0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>454.366654</td>\n",
              "      <td>12777511.0</td>\n",
              "      <td>9.4</td>\n",
              "      <td>9.4</td>\n",
              "      <td>0.407</td>\n",
              "      <td>9.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2934</th>\n",
              "      <td>2003</td>\n",
              "      <td>1</td>\n",
              "      <td>44.5</td>\n",
              "      <td>715.0</td>\n",
              "      <td>26</td>\n",
              "      <td>4.06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>998</td>\n",
              "      <td>26.7</td>\n",
              "      <td>...</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.52</td>\n",
              "      <td>68.0</td>\n",
              "      <td>36.7</td>\n",
              "      <td>453.351155</td>\n",
              "      <td>12633897.0</td>\n",
              "      <td>9.8</td>\n",
              "      <td>9.9</td>\n",
              "      <td>0.418</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2935</th>\n",
              "      <td>2002</td>\n",
              "      <td>1</td>\n",
              "      <td>44.8</td>\n",
              "      <td>73.0</td>\n",
              "      <td>25</td>\n",
              "      <td>4.43</td>\n",
              "      <td>0.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>304</td>\n",
              "      <td>26.3</td>\n",
              "      <td>...</td>\n",
              "      <td>73.0</td>\n",
              "      <td>6.53</td>\n",
              "      <td>71.0</td>\n",
              "      <td>39.8</td>\n",
              "      <td>57.348340</td>\n",
              "      <td>125525.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.427</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2936</th>\n",
              "      <td>2001</td>\n",
              "      <td>1</td>\n",
              "      <td>45.3</td>\n",
              "      <td>686.0</td>\n",
              "      <td>25</td>\n",
              "      <td>1.72</td>\n",
              "      <td>0.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>529</td>\n",
              "      <td>25.9</td>\n",
              "      <td>...</td>\n",
              "      <td>76.0</td>\n",
              "      <td>6.16</td>\n",
              "      <td>75.0</td>\n",
              "      <td>42.1</td>\n",
              "      <td>548.587312</td>\n",
              "      <td>12366165.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.427</td>\n",
              "      <td>9.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2937</th>\n",
              "      <td>2000</td>\n",
              "      <td>1</td>\n",
              "      <td>46.0</td>\n",
              "      <td>665.0</td>\n",
              "      <td>24</td>\n",
              "      <td>1.68</td>\n",
              "      <td>0.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>1483</td>\n",
              "      <td>25.5</td>\n",
              "      <td>...</td>\n",
              "      <td>78.0</td>\n",
              "      <td>7.10</td>\n",
              "      <td>78.0</td>\n",
              "      <td>43.5</td>\n",
              "      <td>547.358878</td>\n",
              "      <td>12222251.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>11.2</td>\n",
              "      <td>0.434</td>\n",
              "      <td>9.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e2a958fd-c896-42cf-bf3e-93e7bb74cbf2')\"\n",
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              "    </button>\n",
              "\n",
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              "      display:flex;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
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              "\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "\n",
              "<div id=\"df-0ba2fe44-ab9b-449e-8d8d-a477a28be435\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0ba2fe44-ab9b-449e-8d8d-a477a28be435')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "\n",
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              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "    fill: var(--fill-color);\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
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              "\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0ba2fe44-ab9b-449e-8d8d-a477a28be435 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.drop(['Year','Status'],axis=1)\n",
        "y = df[['Status','Year']]"
      ],
      "metadata": {
        "id": "I-gt4u4jCIYd"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "25MiYnlHCiOH",
        "outputId": "1664c16e-0dea-4b55-e658-209672dde1f2"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Status  Year\n",
              "0       1  2015\n",
              "1       1  2014\n",
              "2       1  2013\n",
              "3       1  2012\n",
              "4       1  2011"
            ],
            "text/html": [
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ab038982-1866-4fc7-a327-36f6679b8115 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "y",
              "summary": "{\n  \"name\": \"y\",\n  \"rows\": 2938,\n  \"fields\": [\n    {\n      \"column\": \"Status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 2000,\n        \"max\": 2015,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          2015,\n          2014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "minmax = MinMaxScaler()"
      ],
      "metadata": {
        "id": "nYsQoQKTCjeH"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newx = minmax.fit_transform(x)"
      ],
      "metadata": {
        "id": "zPaeFutdCzbt"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9uKu9ImVDETV",
        "outputId": "00f8379f-a33f-474b-f03c-59d8845f830e"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['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')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = pd.DataFrame(newx,columns=['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'])"
      ],
      "metadata": {
        "id": "ORVVyDbnC-AC"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "d3_2Q1w-DKxV",
        "outputId": "cae26f04-b8bb-4e1c-d20f-cd7742d23c0f"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Life expectancy   Adult Mortality  infant deaths  Alcohol  \\\n",
              "0          0.544592         0.362881       0.034444      0.0   \n",
              "1          0.447818         0.373961       0.035556      0.0   \n",
              "2          0.447818         0.369806       0.036667      0.0   \n",
              "3          0.440228         0.375346       0.038333      0.0   \n",
              "4          0.434535         0.379501       0.039444      0.0   \n",
              "\n",
              "   percentage expenditure  Hepatitis B  Measles       BMI   \\\n",
              "0                0.003659     0.653061  0.005439  0.209733   \n",
              "1                0.003774     0.622449  0.002319  0.203940   \n",
              "2                0.003759     0.642857  0.002027  0.198146   \n",
              "3                0.004014     0.673469  0.013135  0.192352   \n",
              "4                0.000364     0.683673  0.014200  0.187717   \n",
              "\n",
              "   under-five deaths      Polio  Total expenditure  Diphtheria    HIV/AIDS  \\\n",
              "0              0.0332  0.031250           0.452118     0.649485        0.0   \n",
              "1              0.0344  0.572917           0.453279     0.618557        0.0   \n",
              "2              0.0356  0.614583           0.450377     0.639175        0.0   \n",
              "3              0.0372  0.666667           0.473012     0.670103        0.0   \n",
              "4              0.0388  0.677083           0.435287     0.680412        0.0   \n",
              "\n",
              "        GDP  Population   thinness  1-19 years   thinness 5-9 years  \\\n",
              "0  0.004889    0.026074               0.619565             0.603509   \n",
              "1  0.005127    0.000253               0.630435             0.610526   \n",
              "2  0.005287    0.024525               0.637681             0.617544   \n",
              "3  0.005608    0.002857               0.644928             0.628070   \n",
              "4  0.000519    0.002302               0.655797             0.635088   \n",
              "\n",
              "   Income composition of resources  Schooling  \n",
              "0                         0.505274   0.487923  \n",
              "1                         0.502110   0.483092  \n",
              "2                         0.495781   0.478261  \n",
              "3                         0.488397   0.473430  \n",
              "4                         0.478903   0.458937  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-70fad0f9-7efa-4aa3-ae29-9a6764aa7ec7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>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>BMI</th>\n",
              "      <th>under-five deaths</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>0.544592</td>\n",
              "      <td>0.362881</td>\n",
              "      <td>0.034444</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003659</td>\n",
              "      <td>0.653061</td>\n",
              "      <td>0.005439</td>\n",
              "      <td>0.209733</td>\n",
              "      <td>0.0332</td>\n",
              "      <td>0.031250</td>\n",
              "      <td>0.452118</td>\n",
              "      <td>0.649485</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.004889</td>\n",
              "      <td>0.026074</td>\n",
              "      <td>0.619565</td>\n",
              "      <td>0.603509</td>\n",
              "      <td>0.505274</td>\n",
              "      <td>0.487923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.447818</td>\n",
              "      <td>0.373961</td>\n",
              "      <td>0.035556</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003774</td>\n",
              "      <td>0.622449</td>\n",
              "      <td>0.002319</td>\n",
              "      <td>0.203940</td>\n",
              "      <td>0.0344</td>\n",
              "      <td>0.572917</td>\n",
              "      <td>0.453279</td>\n",
              "      <td>0.618557</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.005127</td>\n",
              "      <td>0.000253</td>\n",
              "      <td>0.630435</td>\n",
              "      <td>0.610526</td>\n",
              "      <td>0.502110</td>\n",
              "      <td>0.483092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.447818</td>\n",
              "      <td>0.369806</td>\n",
              "      <td>0.036667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003759</td>\n",
              "      <td>0.642857</td>\n",
              "      <td>0.002027</td>\n",
              "      <td>0.198146</td>\n",
              "      <td>0.0356</td>\n",
              "      <td>0.614583</td>\n",
              "      <td>0.450377</td>\n",
              "      <td>0.639175</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.005287</td>\n",
              "      <td>0.024525</td>\n",
              "      <td>0.637681</td>\n",
              "      <td>0.617544</td>\n",
              "      <td>0.495781</td>\n",
              "      <td>0.478261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.440228</td>\n",
              "      <td>0.375346</td>\n",
              "      <td>0.038333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.004014</td>\n",
              "      <td>0.673469</td>\n",
              "      <td>0.013135</td>\n",
              "      <td>0.192352</td>\n",
              "      <td>0.0372</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.473012</td>\n",
              "      <td>0.670103</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.005608</td>\n",
              "      <td>0.002857</td>\n",
              "      <td>0.644928</td>\n",
              "      <td>0.628070</td>\n",
              "      <td>0.488397</td>\n",
              "      <td>0.473430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.434535</td>\n",
              "      <td>0.379501</td>\n",
              "      <td>0.039444</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000364</td>\n",
              "      <td>0.683673</td>\n",
              "      <td>0.014200</td>\n",
              "      <td>0.187717</td>\n",
              "      <td>0.0388</td>\n",
              "      <td>0.677083</td>\n",
              "      <td>0.435287</td>\n",
              "      <td>0.680412</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000519</td>\n",
              "      <td>0.002302</td>\n",
              "      <td>0.655797</td>\n",
              "      <td>0.635088</td>\n",
              "      <td>0.478903</td>\n",
              "      <td>0.458937</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70fad0f9-7efa-4aa3-ae29-9a6764aa7ec7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-70fad0f9-7efa-4aa3-ae29-9a6764aa7ec7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-70fad0f9-7efa-4aa3-ae29-9a6764aa7ec7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
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              "type": "dataframe",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 2938,\n  \"fields\": [\n    {\n      \"column\": \"Life expectancy \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18041062742095568,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 363,\n        \"samples\": [\n          0.3643263757115749,\n          0.7324478178368123,\n          0.7817836812144212\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adult Mortality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1718645639348416,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 425,\n        \"samples\": [\n          0.8490304709141274,\n          0.19390581717451524,\n          0.590027700831025\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"infant deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06551472295188837,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 209,\n        \"samples\": [\n          0.005,\n          0.2683333333333333,\n          0.009444444444444445\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21959382541259584,\n        \"min\": 0.0,\n        \"max\": 1.0000000000000002,\n        \"num_unique_values\": 1077,\n        \"samples\": [\n          0.5055991041433371,\n          0.5207166853303472,\n          0.30515117581187007\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percentage expenditure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1020494803988585,\n        \"min\": 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0.012446802995527447,\n          0.024455305090417236,\n          0.00034404264243601045\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" BMI \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23091166293631485,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 609,\n        \"samples\": [\n          0.27809965237543455,\n          0.6071842410196987,\n          0.27462340672074165\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"under-five deaths \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06417821936229347,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 252,\n        \"samples\": [\n          0.0184,\n          0.0424,\n          0.030000000000000002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Polio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.243407975530817,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          0.6770833333333333,\n          0.41666666666666663,\n          0.9270833333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total expenditure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13930782631137195,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 819,\n        \"samples\": [\n          0.5229251305861868,\n          0.22983168891468367,\n          0.12130005803830526\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Diphtheria \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24387177276271207,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          0.020618556701030927,\n          0.6494845360824741,\n          0.7731958762886597\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" HIV/AIDS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1005501887343871,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          0.17425742574257427,\n          0.009900990099009901,\n          0.2495049504950495\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11157577518535719,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2491,\n        \"samples\": [\n          0.0021538077200184886,\n          0.27130567293697355,\n          0.04156878670286264\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04175293181073342,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2279,\n        \"samples\": [\n          0.00335844255580008,\n          0.00020962944609601513,\n          0.0359983998568747\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" thinness  1-19 years\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15933408412314112,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          0.6485507246376812,\n          0.07971014492753623,\n          0.22826086956521743\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" thinness 5-9 years\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1573983792905637,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 207,\n        \"samples\": [\n          0.49122807017543857,\n          0.08421052631578947,\n          0.143859649122807\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income composition of resources\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2160545787382687,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 626,\n        \"samples\": [\n          0.9662447257383967,\n          0.46729957805907174,\n          0.7025316455696203\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Schooling\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1576995789860972,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 174,\n        \"samples\": [\n          0.821256038647343,\n          0.9275362318840579,\n          0.8985507246376813\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([y,x],axis=1)"
      ],
      "metadata": {
        "id": "6gOBNU37DLsF"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "pFqdeVbnDXqH",
        "outputId": "cd6c9aeb-cef4-4c91-cd4f-08dab4c2cd65"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Status  Year  Life expectancy   Adult Mortality  infant deaths  \\\n",
              "2933       1  2004          0.151803         1.000000       0.015000   \n",
              "2934       1  2003          0.155598         0.988920       0.014444   \n",
              "2935       1  2002          0.161290         0.099723       0.013889   \n",
              "2936       1  2001          0.170778         0.948753       0.013889   \n",
              "2937       1  2000          0.184061         0.919668       0.013333   \n",
              "\n",
              "       Alcohol  percentage expenditure  Hepatitis B  Measles       BMI   ...  \\\n",
              "2933  0.243561                     0.0     0.683673  0.000146  0.302433  ...   \n",
              "2934  0.226764                     0.0     0.061224  0.004703  0.297798  ...   \n",
              "2935  0.247480                     0.0     0.734694  0.001433  0.293163  ...   \n",
              "2936  0.095745                     0.0     0.765306  0.002493  0.288528  ...   \n",
              "2937  0.093505                     0.0     0.795918  0.006989  0.283893  ...   \n",
              "\n",
              "         Polio  Total expenditure  Diphtheria    HIV/AIDS       GDP  \\\n",
              "2933  0.666667           0.392339     0.649485   0.663366  0.003799   \n",
              "2934  0.041667           0.356936     0.680412   0.724752  0.003790   \n",
              "2935  0.729167           0.357516     0.711340   0.786139  0.000467   \n",
              "2936  0.760417           0.336042     0.752577   0.831683  0.004589   \n",
              "2937  0.781250           0.390598     0.783505   0.859406  0.004579   \n",
              "\n",
              "      Population   thinness  1-19 years   thinness 5-9 years  \\\n",
              "2933    0.009875               0.336957             0.326316   \n",
              "2934    0.009764               0.351449             0.343860   \n",
              "2935    0.000097               0.039855             0.042105   \n",
              "2936    0.009558               0.054348             0.056140   \n",
              "2937    0.009446               0.394928             0.389474   \n",
              "\n",
              "      Income composition of resources  Schooling  \n",
              "2933                         0.429325   0.444444  \n",
              "2934                         0.440928   0.458937  \n",
              "2935                         0.450422   0.483092  \n",
              "2936                         0.450422   0.473430  \n",
              "2937                         0.457806   0.473430  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b6bdcf72-06ad-447d-ba96-cd47a93ccc95\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Status</th>\n",
              "      <th>Year</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>BMI</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>2933</th>\n",
              "      <td>1</td>\n",
              "      <td>2004</td>\n",
              "      <td>0.151803</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.015000</td>\n",
              "      <td>0.243561</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.683673</td>\n",
              "      <td>0.000146</td>\n",
              "      <td>0.302433</td>\n",
              "      <td>...</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.392339</td>\n",
              "      <td>0.649485</td>\n",
              "      <td>0.663366</td>\n",
              "      <td>0.003799</td>\n",
              "      <td>0.009875</td>\n",
              "      <td>0.336957</td>\n",
              "      <td>0.326316</td>\n",
              "      <td>0.429325</td>\n",
              "      <td>0.444444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2934</th>\n",
              "      <td>1</td>\n",
              "      <td>2003</td>\n",
              "      <td>0.155598</td>\n",
              "      <td>0.988920</td>\n",
              "      <td>0.014444</td>\n",
              "      <td>0.226764</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.061224</td>\n",
              "      <td>0.004703</td>\n",
              "      <td>0.297798</td>\n",
              "      <td>...</td>\n",
              "      <td>0.041667</td>\n",
              "      <td>0.356936</td>\n",
              "      <td>0.680412</td>\n",
              "      <td>0.724752</td>\n",
              "      <td>0.003790</td>\n",
              "      <td>0.009764</td>\n",
              "      <td>0.351449</td>\n",
              "      <td>0.343860</td>\n",
              "      <td>0.440928</td>\n",
              "      <td>0.458937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2935</th>\n",
              "      <td>1</td>\n",
              "      <td>2002</td>\n",
              "      <td>0.161290</td>\n",
              "      <td>0.099723</td>\n",
              "      <td>0.013889</td>\n",
              "      <td>0.247480</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.734694</td>\n",
              "      <td>0.001433</td>\n",
              "      <td>0.293163</td>\n",
              "      <td>...</td>\n",
              "      <td>0.729167</td>\n",
              "      <td>0.357516</td>\n",
              "      <td>0.711340</td>\n",
              "      <td>0.786139</td>\n",
              "      <td>0.000467</td>\n",
              "      <td>0.000097</td>\n",
              "      <td>0.039855</td>\n",
              "      <td>0.042105</td>\n",
              "      <td>0.450422</td>\n",
              "      <td>0.483092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2936</th>\n",
              "      <td>1</td>\n",
              "      <td>2001</td>\n",
              "      <td>0.170778</td>\n",
              "      <td>0.948753</td>\n",
              "      <td>0.013889</td>\n",
              "      <td>0.095745</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.765306</td>\n",
              "      <td>0.002493</td>\n",
              "      <td>0.288528</td>\n",
              "      <td>...</td>\n",
              "      <td>0.760417</td>\n",
              "      <td>0.336042</td>\n",
              "      <td>0.752577</td>\n",
              "      <td>0.831683</td>\n",
              "      <td>0.004589</td>\n",
              "      <td>0.009558</td>\n",
              "      <td>0.054348</td>\n",
              "      <td>0.056140</td>\n",
              "      <td>0.450422</td>\n",
              "      <td>0.473430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2937</th>\n",
              "      <td>1</td>\n",
              "      <td>2000</td>\n",
              "      <td>0.184061</td>\n",
              "      <td>0.919668</td>\n",
              "      <td>0.013333</td>\n",
              "      <td>0.093505</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.795918</td>\n",
              "      <td>0.006989</td>\n",
              "      <td>0.283893</td>\n",
              "      <td>...</td>\n",
              "      <td>0.781250</td>\n",
              "      <td>0.390598</td>\n",
              "      <td>0.783505</td>\n",
              "      <td>0.859406</td>\n",
              "      <td>0.004579</td>\n",
              "      <td>0.009446</td>\n",
              "      <td>0.394928</td>\n",
              "      <td>0.389474</td>\n",
              "      <td>0.457806</td>\n",
              "      <td>0.473430</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b6bdcf72-06ad-447d-ba96-cd47a93ccc95')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b6bdcf72-06ad-447d-ba96-cd47a93ccc95 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b6bdcf72-06ad-447d-ba96-cd47a93ccc95');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-340ba0d1-1841-413d-89a5-09df01a6380f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-340ba0d1-1841-413d-89a5-09df01a6380f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-340ba0d1-1841-413d-89a5-09df01a6380f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# DECISION TREE WITH REGRESSOR"
      ],
      "metadata": {
        "id": "OoQOEy_oDZR1"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " x = df.drop('Life expectancy ',axis=1)\n",
        " x1 = pd.DataFrame(x)\n",
        " y = df['Life expectancy ']\n",
        " y1 = pd.DataFrame(y)"
      ],
      "metadata": {
        "id": "L1V7FhgyDjrX"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x1_train,x1_test,y1_train,y1_test= train_test_split(x1,y1,test_size=0.25,random_state=42)"
      ],
      "metadata": {
        "id": "mwoALxz7D8Z9"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "DTR = DecisionTreeRegressor(random_state=42)\n",
        "\n",
        "model = DTR.fit(x1_train,y1_train)\n",
        "y1_pred= model.predict(x1_test)\n"
      ],
      "metadata": {
        "id": "Z44D3fVZEW8w"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y1_test)\n",
        "print(y1_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9QbwM1UpE06e",
        "outputId": "f5b08fd3-5168-4fa6-c8fa-0086e3a4ee06"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      Life expectancy \n",
            "2546          0.709677\n",
            "650           0.751423\n",
            "1740          0.719165\n",
            "177           0.768501\n",
            "1377          0.296015\n",
            "...                ...\n",
            "2284          0.690702\n",
            "1554          0.554080\n",
            "862           0.426945\n",
            "1413          0.637571\n",
            "1762          0.620493\n",
            "\n",
            "[735 rows x 1 columns]\n",
            "[0.71157495 0.73434535 0.71157495 0.75711575 0.24288425 0.28273245\n",
            " 0.58444023 0.71537002 0.76850095 0.32827324 0.28273245 0.29032258\n",
            " 0.35483871 0.63187856 0.61669829 0.47058824 0.42504744 0.79886148\n",
            " 0.68311195 0.80834915 0.81783681 1.         0.68690702 0.59582543\n",
            " 0.77229602 0.60151803 0.22390892 0.5597723  0.92409867 0.73244782\n",
            " 0.58823529 0.72296015 0.67362429 0.68880455 0.80455408 0.17836812\n",
            " 0.40986717 0.66034156 0.5142315  0.67931689 0.35104364 0.40037951\n",
            " 0.73244782 0.82732448 0.74762808 0.09867173 0.82732448 0.74193548\n",
            " 0.54459203 0.66603416 0.35483871 0.71537002 0.82352941 0.86717268\n",
            " 0.72296015 0.44971537 0.69639469 0.70208729 0.55218216 0.5711575\n",
            " 0.79506641 0.34914611 0.71726755 0.79127135 0.31499051 0.41176471\n",
            " 0.41366224 0.77229602 0.58254269 0.61859583 0.90512334 0.78178368\n",
            " 0.70398482 0.27893738 0.30360531 0.59203036 0.55218216 0.75332068\n",
            " 0.42694497 0.48766603 0.5028463  0.47817837 0.5028463  0.51043643\n",
            " 0.67362429 0.84819734 0.69639469 0.58823529 0.67552182 0.80645161\n",
            " 0.69639469 0.28842505 0.71726755 0.74762808 0.75521822 0.61290323\n",
            " 0.77419355 0.70398482 0.55218216 0.52941176 0.81783681 0.28842505\n",
            " 0.73244782 0.29791271 0.79127135 0.92409867 0.62428843 0.59772296\n",
            " 0.43643264 0.50474383 0.52371917 0.4459203  0.37381404 0.5540797\n",
            " 0.53510436 0.77798861 0.35104364 0.28842505 0.49335863 0.47058824\n",
            " 0.5540797  0.66223909 0.87286528 0.40037951 0.4288425  0.81024668\n",
            " 0.68880455 0.47248577 0.71347249 0.67362429 0.71157495 0.85199241\n",
            " 0.55028463 0.59203036 0.44971537 0.55218216 0.75521822 0.80834915\n",
            " 0.74573055 0.74003795 0.77229602 0.58064516 0.48197343 0.42694497\n",
            " 0.22201139 0.5142315  0.55028463 0.62476151 0.58254269 0.80834915\n",
            " 0.74003795 0.92409867 0.58633776 0.42314991 1.         0.78178368\n",
            " 0.90512334 0.69449715 0.80265655 0.85009488 0.71157495 0.943074\n",
            " 0.62808349 0.81783681 0.71157495 0.47058824 0.54459203 0.84819734\n",
            " 0.77609108 0.88614801 0.86717268 0.61100569 0.67362429 0.4345351\n",
            " 0.67172676 0.57874763 0.67741935 0.56356736 0.32637571 0.73434535\n",
            " 0.90512334 0.67362429 0.81404175 0.69259962 0.81973435 0.15180266\n",
            " 0.5597723  0.30740038 0.69639469 0.68690702 0.40037951 0.60531309\n",
            " 0.84819734 0.5256167  0.79127135 0.62808349 0.72675522 0.60721063\n",
            " 0.80834915 0.68880455 0.79127135 0.44971537 0.74762808 0.62808349\n",
            " 0.5597723  0.27893738 0.73055028 0.59392789 0.69449715 0.86717268\n",
            " 0.48956357 0.30550285 0.73434535 0.41176471 0.43074004 0.5085389\n",
            " 0.56736243 0.71347249 0.4345351  0.56736243 0.79127135 0.70967742\n",
            " 0.86717268 0.82542694 0.72865275 0.21821632 0.20493359 0.76091082\n",
            " 0.37381404 0.60151803 0.67172676 0.74573055 0.5426945  0.72675522\n",
            " 0.66793169 0.29981025 0.76850095 0.80265655 0.24288425 0.75521822\n",
            " 0.71726755 0.77229602 0.71347249 0.62476151 0.40986717 0.29791271\n",
            " 0.85768501 0.71726755 0.72865275 0.47817837 0.63567362 0.79506641\n",
            " 0.40796964 0.98102467 0.70588235 0.60721063 0.36053131 0.84819734\n",
            " 0.42694497 0.30740038 0.69639469 0.60531309 0.51233397 0.22011385\n",
            " 0.69259962 0.84819734 0.59582543 0.79316888 0.25616698 0.31878558\n",
            " 0.43833017 0.29601518 0.70398482 0.70588235 0.17836812 0.69070209\n",
            " 0.43643264 0.71347249 0.5654649  0.52751423 0.5426945  0.81214421\n",
            " 0.43074004 0.71537002 0.78368121 0.48387097 0.66982922 0.15749526\n",
            " 0.48956357 0.73244782 0.55218216 0.79127135 0.5028463  0.61100569\n",
            " 0.75711575 0.60151803 0.77229602 0.69449715 0.19165085 0.79127135\n",
            " 0.76660342 0.67552182 0.68880455 0.71726755 0.78368121 0.25616698\n",
            " 0.28273245 0.29791271 0.71726755 0.80075901 0.60151803 0.37950664\n",
            " 0.70018975 0.4914611  0.5028463  0.55218216 0.17077799 0.68500949\n",
            " 0.43833017 0.86148008 0.35483871 0.80265655 0.33776091 0.70588235\n",
            " 0.61669829 0.77039848 0.72296015 0.67552182 0.72485769 0.85958254\n",
            " 0.5483871  0.57495256 0.49525617 0.58064516 0.70777989 0.96204934\n",
            " 0.60531309 0.72485769 0.70967742 0.68880455 0.54459203 0.71726755\n",
            " 0.71916509 0.66413662 0.77988615 0.37381404 0.41745731 0.74003795\n",
            " 0.61669829 0.76660342 0.5142315  0.61100569 0.48956357 0.41555977\n",
            " 0.73244782 0.86717268 0.57495256 0.62476151 0.74573055 0.81024668\n",
            " 0.36053131 0.69259962 0.77798861 0.56166983 0.61859583 0.48956357\n",
            " 0.38709677 0.73244782 0.43643264 0.73244782 0.71916509 0.41366224\n",
            " 0.70967742 0.74573055 0.74762808 0.66034156 0.38899431 0.68311195\n",
            " 0.73814042 0.57874763 0.27893738 0.85009488 0.84819734 0.86337761\n",
            " 0.47628083 0.41176471 0.54648956 0.40796964 0.59203036 0.73434535\n",
            " 0.29791271 0.80455408 0.69829222 0.81404175 0.5256167  0.5540797\n",
            " 0.87666034 0.68690702 0.943074   0.70588235 0.29411765 0.75901328\n",
            " 0.37381404 0.60531309 0.52182163 0.4459203  0.67362429 0.70398482\n",
            " 0.62618596 0.69449715 0.69070209 0.82922201 0.943074   0.69070209\n",
            " 0.59203036 0.61100569 0.51233397 0.66413662 0.62808349 0.81024668\n",
            " 0.53889943 0.40986717 0.57874763 0.54648956 0.71347249 0.76660342\n",
            " 0.32637571 0.44402277 0.56166983 0.60151803 0.22960152 0.75332068\n",
            " 0.79127135 0.61100569 0.15180266 0.33586338 0.33586338 0.5654649\n",
            " 0.65844402 0.73434535 0.71537002 0.69639469 0.71347249 0.81783681\n",
            " 0.88614801 0.59582543 0.76850095 0.77039848 0.81783681 0.37571157\n",
            " 0.92409867 0.40796964 0.61669829 0.72485769 0.29411765 0.96204934\n",
            " 0.85388994 0.33776091 0.60721063 0.71726755 0.69639469 0.34155598\n",
            " 0.51043643 0.53889943 0.44781784 0.31499051 0.86907021 0.62998102\n",
            " 0.84819734 0.62998102 0.71916509 0.76091082 0.61669829 0.68690702\n",
            " 0.85768501 0.72106262 0.51043643 0.59392789 0.82542694 0.74003795\n",
            " 0.76470588 0.69639469 0.69639469 0.69259962 0.58064516 0.80455408\n",
            " 0.72675522 0.77229602 0.67741935 0.75332068 0.67172676 0.61669829\n",
            " 0.28273245 0.57874763 0.63757116 0.51612903 0.98102467 0.70398482\n",
            " 0.50664137 0.69259962 0.73244782 0.39089184 0.86527514 0.69639469\n",
            " 0.69259962 0.90512334 0.70018975 0.79696395 0.74573055 0.74193548\n",
            " 0.62049336 0.67172676 0.67172676 0.62808349 0.28273245 0.4971537\n",
            " 0.47058824 0.4800759  0.77229602 0.37001898 0.63946869 0.89184061\n",
            " 0.59013283 0.72675522 0.73434535 0.48387097 0.50474383 0.62808349\n",
            " 0.78178368 0.84819734 0.36053131 0.81024668 0.69449715 0.79127135\n",
            " 0.70777989 0.85009488 0.68500949 0.74762808 1.         0.79696395\n",
            " 0.77609108 0.21821632 0.40986717 0.65844402 0.73434535 0.52941176\n",
            " 0.61859583 0.4345351  0.17077799 0.63757116 0.72675522 0.39278937\n",
            " 0.87096774 0.25426945 0.74003795 0.81024668 0.76850095 0.59582543\n",
            " 0.74193548 0.82352941 0.4288425  0.86527514 0.88614801 0.73244782\n",
            " 0.21442125 0.73434535 0.82922201 1.         0.40227704 0.69449715\n",
            " 0.67552182 0.50664137 0.78557875 0.65844402 0.71537002 0.60151803\n",
            " 0.67552182 0.63757116 0.31878558 0.71347249 0.40037951 0.80455408\n",
            " 0.79696395 0.43263757 0.70018975 0.60151803 0.62618596 0.72485769\n",
            " 0.68880455 0.67362429 0.72865275 0.74003795 0.75901328 0.33776091\n",
            " 0.23908918 0.73624288 0.53889943 0.73244782 0.72296015 0.80075901\n",
            " 0.62998102 0.70018975 0.17077799 0.72106262 0.60151803 0.33206831\n",
            " 0.60151803 0.33206831 0.67362429 0.50474383 0.60151803 0.84819734\n",
            " 0.86907021 0.67552182 0.60721063 0.80075901 0.71537002 0.71537002\n",
            " 0.87096774 0.29032258 0.69639469 0.60531309 0.82732448 0.69259962\n",
            " 0.55218216 0.72675522 0.33776091 0.81404175 0.66223909 0.32827324\n",
            " 0.74193548 0.81024668 0.33396584 0.76091082 0.69639469 0.86337761\n",
            " 0.82163188 0.85009488 0.5313093  0.58064516 0.943074   0.67362429\n",
            " 0.53320683 0.59772296 0.65844402 0.55028463 0.72106262 0.29791271\n",
            " 0.60531309 0.50474383 0.79506641 0.66223909 0.73244782 0.57874763\n",
            " 0.30740038 0.79696395 0.90512334 0.74762808 0.68121442 0.34535104\n",
            " 0.53510436 0.43263757 0.56166983 0.75332068 0.67741935 0.82732448\n",
            " 0.29601518 0.32637571 0.55028463 0.80645161 0.38519924 0.33206831\n",
            " 0.71347249 0.34155598 0.52941176 0.24667932 0.55218216 0.63567362\n",
            " 0.22770398 0.28273245 0.63757116 0.25996205 0.76091082 0.84819734\n",
            " 0.68690702 0.81973435 0.55028463 0.17457306 0.68121442 0.74762808\n",
            " 0.21821632 0.943074   0.74573055 0.47817837 0.74003795 0.82163188\n",
            " 0.98102467 0.5711575  0.86148008 0.67362429 0.4971537  0.68690702\n",
            " 0.21821632 0.21821632 0.36053131 0.65844402 1.         0.5597723\n",
            " 0.74952562 0.80834915 0.36812144 0.84819734 0.73624288 0.67552182\n",
            " 0.68311195 0.84819734 0.61290323 0.62476151 0.76660342 0.\n",
            " 0.47058824 0.5142315  0.77229602 0.62476151 0.34535104 0.50664137\n",
            " 0.22960152 0.74573055 0.55218216 0.67552182 0.69070209 0.69639469\n",
            " 0.41366224 0.70208729 0.58823529]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# validation with mse and r2 score"
      ],
      "metadata": {
        "id": "XgboMQUvE4-A"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "print(\"the mse is:\", mean_squared_error(y1_test,y1_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5tNy3L_BFAGw",
        "outputId": "00155a19-0f1a-4cd6-99aa-51975f1831fe"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the mse is: 0.0025629484545561334\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "print(\"the r2 score is:\", r2_score(y1_test,y1_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LnbyJQ86FN3L",
        "outputId": "27f1dc95-6e28-41c8-c1bf-6cbd37d96ea9"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the r2 score is: 0.9209388251407562\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "WCpdIZbAHMES",
        "outputId": "940911fe-2d24-4e03-d26c-f27ef1ec707d"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Status  Year  Adult Mortality  infant deaths  Alcohol  \\\n",
              "0       1  2015         0.362881       0.034444      0.0   \n",
              "1       1  2014         0.373961       0.035556      0.0   \n",
              "2       1  2013         0.369806       0.036667      0.0   \n",
              "3       1  2012         0.375346       0.038333      0.0   \n",
              "4       1  2011         0.379501       0.039444      0.0   \n",
              "\n",
              "   percentage expenditure  Hepatitis B  Measles       BMI   \\\n",
              "0                0.003659     0.653061  0.005439  0.209733   \n",
              "1                0.003774     0.622449  0.002319  0.203940   \n",
              "2                0.003759     0.642857  0.002027  0.198146   \n",
              "3                0.004014     0.673469  0.013135  0.192352   \n",
              "4                0.000364     0.683673  0.014200  0.187717   \n",
              "\n",
              "   under-five deaths      Polio  Total expenditure  Diphtheria    HIV/AIDS  \\\n",
              "0              0.0332  0.031250           0.452118     0.649485        0.0   \n",
              "1              0.0344  0.572917           0.453279     0.618557        0.0   \n",
              "2              0.0356  0.614583           0.450377     0.639175        0.0   \n",
              "3              0.0372  0.666667           0.473012     0.670103        0.0   \n",
              "4              0.0388  0.677083           0.435287     0.680412        0.0   \n",
              "\n",
              "        GDP  Population   thinness  1-19 years   thinness 5-9 years  \\\n",
              "0  0.004889    0.026074               0.619565             0.603509   \n",
              "1  0.005127    0.000253               0.630435             0.610526   \n",
              "2  0.005287    0.024525               0.637681             0.617544   \n",
              "3  0.005608    0.002857               0.644928             0.628070   \n",
              "4  0.000519    0.002302               0.655797             0.635088   \n",
              "\n",
              "   Income composition of resources  Schooling  \n",
              "0                         0.505274   0.487923  \n",
              "1                         0.502110   0.483092  \n",
              "2                         0.495781   0.478261  \n",
              "3                         0.488397   0.473430  \n",
              "4                         0.478903   0.458937  "
            ],
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              "      <th></th>\n",
              "      <th>Status</th>\n",
              "      <th>Year</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>BMI</th>\n",
              "      <th>under-five deaths</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>1</td>\n",
              "      <td>2015</td>\n",
              "      <td>0.362881</td>\n",
              "      <td>0.034444</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003659</td>\n",
              "      <td>0.653061</td>\n",
              "      <td>0.005439</td>\n",
              "      <td>0.209733</td>\n",
              "      <td>0.0332</td>\n",
              "      <td>0.031250</td>\n",
              "      <td>0.452118</td>\n",
              "      <td>0.649485</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.004889</td>\n",
              "      <td>0.026074</td>\n",
              "      <td>0.619565</td>\n",
              "      <td>0.603509</td>\n",
              "      <td>0.505274</td>\n",
              "      <td>0.487923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2014</td>\n",
              "      <td>0.373961</td>\n",
              "      <td>0.035556</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003774</td>\n",
              "      <td>0.622449</td>\n",
              "      <td>0.002319</td>\n",
              "      <td>0.203940</td>\n",
              "      <td>0.0344</td>\n",
              "      <td>0.572917</td>\n",
              "      <td>0.453279</td>\n",
              "      <td>0.618557</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.005127</td>\n",
              "      <td>0.000253</td>\n",
              "      <td>0.630435</td>\n",
              "      <td>0.610526</td>\n",
              "      <td>0.502110</td>\n",
              "      <td>0.483092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
              "      <td>0.369806</td>\n",
              "      <td>0.036667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003759</td>\n",
              "      <td>0.642857</td>\n",
              "      <td>0.002027</td>\n",
              "      <td>0.198146</td>\n",
              "      <td>0.0356</td>\n",
              "      <td>0.614583</td>\n",
              "      <td>0.450377</td>\n",
              "      <td>0.639175</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.005287</td>\n",
              "      <td>0.024525</td>\n",
              "      <td>0.637681</td>\n",
              "      <td>0.617544</td>\n",
              "      <td>0.495781</td>\n",
              "      <td>0.478261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>2012</td>\n",
              "      <td>0.375346</td>\n",
              "      <td>0.038333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.004014</td>\n",
              "      <td>0.673469</td>\n",
              "      <td>0.013135</td>\n",
              "      <td>0.192352</td>\n",
              "      <td>0.0372</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.473012</td>\n",
              "      <td>0.670103</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.005608</td>\n",
              "      <td>0.002857</td>\n",
              "      <td>0.644928</td>\n",
              "      <td>0.628070</td>\n",
              "      <td>0.488397</td>\n",
              "      <td>0.473430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>2011</td>\n",
              "      <td>0.379501</td>\n",
              "      <td>0.039444</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000364</td>\n",
              "      <td>0.683673</td>\n",
              "      <td>0.014200</td>\n",
              "      <td>0.187717</td>\n",
              "      <td>0.0388</td>\n",
              "      <td>0.677083</td>\n",
              "      <td>0.435287</td>\n",
              "      <td>0.680412</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000519</td>\n",
              "      <td>0.002302</td>\n",
              "      <td>0.655797</td>\n",
              "      <td>0.635088</td>\n",
              "      <td>0.478903</td>\n",
              "      <td>0.458937</td>\n",
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              "\n",
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              "\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([[1,\t2015,\t0.362881,\t0.034444,\t0.0,\t0.003659,\t0.653061,\t0.005439,\t0.209733,\t0.0332,\t0.031250,\t0.452118,\t0.649485,\t0.0,\t0.004889,\t0.026074,\t0.619565,\t0.603509,\t0.505274,\t0.487923]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "93WQ-63vFcLR",
        "outputId": "9477bddc-b05f-49f6-ee52-0b090d620285"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.54459203])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    }
  ]
}