world_happiness_analysis_2021.ipynb

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      "name": "world happiness analysis 2021.ipynb",
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        "<a href=\"https://colab.research.google.com/github/Michaelzats/world-happiness-analysis-2021/blob/main/world_happiness_analysis_2021.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "\n",
        "\n",
        "## Why are we happy in 2021?\n",
        "\n",
        "People have always been trying to find the answer to that question. So they made a questionnaire to explore these in [\"The World Happiness Report\"](https://worldhappiness.report/). Six measurements are taken per country:\n",
        "+ GDP per Capita\n",
        "+ Family\n",
        "+ Life Expectancy\n",
        "+ Freedom\n",
        "+ Generosity\n",
        "+ Trust Government Corruption\n",
        "\n",
        "The following study analyses the correlations and presents the results on why people are happy around the world in 2021 \n"
      ],
      "metadata": {
        "id": "gxgsq8LUIvb5"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the important required libraries.\n",
        "\n"
      ],
      "metadata": {
        "id": "bok2cmc8Ivb7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. READING THE DATASET."
      ],
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        "id": "iQI9LEkX4z48"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np \n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import plotly.graph_objs as go\n",
        "from plotly.offline import iplot\n",
        "\n",
        "sns.set_style('dark')\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "trusted": true,
        "id": "TTR-oaolIvb8"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us load the data set.\n",
        "\n"
      ],
      "metadata": {
        "id": "t-OrkWnCIvb9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "happy20=pd.read_csv('2021.csv')"
      ],
      "metadata": {
        "trusted": true,
        "id": "WQ5Qb4IPIvb-"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets check first 5 and last 5 elements in dsta set"
      ],
      "metadata": {
        "id": "5gz_wFCgIvb-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "happy20.head(5)"
      ],
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        "outputId": "516977f1-051b-431d-b8fc-ace5c9839e20"
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      "execution_count": 56,
      "outputs": [
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          "data": {
            "text/plain": [
              "  Country name Regional indicator  Ladder score  \\\n",
              "0      Finland     Western Europe         7.842   \n",
              "1      Denmark     Western Europe         7.620   \n",
              "2  Switzerland     Western Europe         7.571   \n",
              "3      Iceland     Western Europe         7.554   \n",
              "4  Netherlands     Western Europe         7.464   \n",
              "\n",
              "   Standard error of ladder score  upperwhisker  lowerwhisker  \\\n",
              "0                           0.032         7.904         7.780   \n",
              "1                           0.035         7.687         7.552   \n",
              "2                           0.036         7.643         7.500   \n",
              "3                           0.059         7.670         7.438   \n",
              "4                           0.027         7.518         7.410   \n",
              "\n",
              "   Logged GDP per capita  Social support  Healthy life expectancy  \\\n",
              "0                 10.775           0.954                     72.0   \n",
              "1                 10.933           0.954                     72.7   \n",
              "2                 11.117           0.942                     74.4   \n",
              "3                 10.878           0.983                     73.0   \n",
              "4                 10.932           0.942                     72.4   \n",
              "\n",
              "   Freedom to make life choices  Generosity  Perceptions of corruption  \\\n",
              "0                         0.949      -0.098                      0.186   \n",
              "1                         0.946       0.030                      0.179   \n",
              "2                         0.919       0.025                      0.292   \n",
              "3                         0.955       0.160                      0.673   \n",
              "4                         0.913       0.175                      0.338   \n",
              "\n",
              "   Ladder score in Dystopia  Explained by: Log GDP per capita  \\\n",
              "0                      2.43                             1.446   \n",
              "1                      2.43                             1.502   \n",
              "2                      2.43                             1.566   \n",
              "3                      2.43                             1.482   \n",
              "4                      2.43                             1.501   \n",
              "\n",
              "   Explained by: Social support  Explained by: Healthy life expectancy  \\\n",
              "0                         1.106                                  0.741   \n",
              "1                         1.108                                  0.763   \n",
              "2                         1.079                                  0.816   \n",
              "3                         1.172                                  0.772   \n",
              "4                         1.079                                  0.753   \n",
              "\n",
              "   Explained by: Freedom to make life choices  Explained by: Generosity  \\\n",
              "0                                       0.691                     0.124   \n",
              "1                                       0.686                     0.208   \n",
              "2                                       0.653                     0.204   \n",
              "3                                       0.698                     0.293   \n",
              "4                                       0.647                     0.302   \n",
              "\n",
              "   Explained by: Perceptions of corruption  Dystopia + residual  \n",
              "0                                    0.481                3.253  \n",
              "1                                    0.485                2.868  \n",
              "2                                    0.413                2.839  \n",
              "3                                    0.170                2.967  \n",
              "4                                    0.384                2.798  "
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              "      <th>Country name</th>\n",
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              "      <th>Generosity</th>\n",
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              "      <th>Explained by: Log GDP per capita</th>\n",
              "      <th>Explained by: Social support</th>\n",
              "      <th>Explained by: Healthy life expectancy</th>\n",
              "      <th>Explained by: Freedom to make life choices</th>\n",
              "      <th>Explained by: Generosity</th>\n",
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              "      <th>0</th>\n",
              "      <td>Finland</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>7.842</td>\n",
              "      <td>0.032</td>\n",
              "      <td>7.904</td>\n",
              "      <td>7.780</td>\n",
              "      <td>10.775</td>\n",
              "      <td>0.954</td>\n",
              "      <td>72.0</td>\n",
              "      <td>0.949</td>\n",
              "      <td>-0.098</td>\n",
              "      <td>0.186</td>\n",
              "      <td>2.43</td>\n",
              "      <td>1.446</td>\n",
              "      <td>1.106</td>\n",
              "      <td>0.741</td>\n",
              "      <td>0.691</td>\n",
              "      <td>0.124</td>\n",
              "      <td>0.481</td>\n",
              "      <td>3.253</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Denmark</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>7.620</td>\n",
              "      <td>0.035</td>\n",
              "      <td>7.687</td>\n",
              "      <td>7.552</td>\n",
              "      <td>10.933</td>\n",
              "      <td>0.954</td>\n",
              "      <td>72.7</td>\n",
              "      <td>0.946</td>\n",
              "      <td>0.030</td>\n",
              "      <td>0.179</td>\n",
              "      <td>2.43</td>\n",
              "      <td>1.502</td>\n",
              "      <td>1.108</td>\n",
              "      <td>0.763</td>\n",
              "      <td>0.686</td>\n",
              "      <td>0.208</td>\n",
              "      <td>0.485</td>\n",
              "      <td>2.868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Switzerland</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>7.571</td>\n",
              "      <td>0.036</td>\n",
              "      <td>7.643</td>\n",
              "      <td>7.500</td>\n",
              "      <td>11.117</td>\n",
              "      <td>0.942</td>\n",
              "      <td>74.4</td>\n",
              "      <td>0.919</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.292</td>\n",
              "      <td>2.43</td>\n",
              "      <td>1.566</td>\n",
              "      <td>1.079</td>\n",
              "      <td>0.816</td>\n",
              "      <td>0.653</td>\n",
              "      <td>0.204</td>\n",
              "      <td>0.413</td>\n",
              "      <td>2.839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Iceland</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>7.554</td>\n",
              "      <td>0.059</td>\n",
              "      <td>7.670</td>\n",
              "      <td>7.438</td>\n",
              "      <td>10.878</td>\n",
              "      <td>0.983</td>\n",
              "      <td>73.0</td>\n",
              "      <td>0.955</td>\n",
              "      <td>0.160</td>\n",
              "      <td>0.673</td>\n",
              "      <td>2.43</td>\n",
              "      <td>1.482</td>\n",
              "      <td>1.172</td>\n",
              "      <td>0.772</td>\n",
              "      <td>0.698</td>\n",
              "      <td>0.293</td>\n",
              "      <td>0.170</td>\n",
              "      <td>2.967</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>7.464</td>\n",
              "      <td>0.027</td>\n",
              "      <td>7.518</td>\n",
              "      <td>7.410</td>\n",
              "      <td>10.932</td>\n",
              "      <td>0.942</td>\n",
              "      <td>72.4</td>\n",
              "      <td>0.913</td>\n",
              "      <td>0.175</td>\n",
              "      <td>0.338</td>\n",
              "      <td>2.43</td>\n",
              "      <td>1.501</td>\n",
              "      <td>1.079</td>\n",
              "      <td>0.753</td>\n",
              "      <td>0.647</td>\n",
              "      <td>0.302</td>\n",
              "      <td>0.384</td>\n",
              "      <td>2.798</td>\n",
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        "happy20.tail(5)"
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          "data": {
            "text/plain": [
              "    Country name  Regional indicator  Ladder score  \\\n",
              "144      Lesotho  Sub-Saharan Africa         3.512   \n",
              "145     Botswana  Sub-Saharan Africa         3.467   \n",
              "146       Rwanda  Sub-Saharan Africa         3.415   \n",
              "147     Zimbabwe  Sub-Saharan Africa         3.145   \n",
              "148  Afghanistan          South Asia         2.523   \n",
              "\n",
              "     Standard error of ladder score  upperwhisker  lowerwhisker  \\\n",
              "144                           0.120         3.748         3.276   \n",
              "145                           0.074         3.611         3.322   \n",
              "146                           0.068         3.548         3.282   \n",
              "147                           0.058         3.259         3.030   \n",
              "148                           0.038         2.596         2.449   \n",
              "\n",
              "     Logged GDP per capita  Social support  Healthy life expectancy  \\\n",
              "144                  7.926           0.787                   48.700   \n",
              "145                  9.782           0.784                   59.269   \n",
              "146                  7.676           0.552                   61.400   \n",
              "147                  7.943           0.750                   56.201   \n",
              "148                  7.695           0.463                   52.493   \n",
              "\n",
              "     Freedom to make life choices  Generosity  Perceptions of corruption  \\\n",
              "144                         0.715      -0.131                      0.915   \n",
              "145                         0.824      -0.246                      0.801   \n",
              "146                         0.897       0.061                      0.167   \n",
              "147                         0.677      -0.047                      0.821   \n",
              "148                         0.382      -0.102                      0.924   \n",
              "\n",
              "     Ladder score in Dystopia  Explained by: Log GDP per capita  \\\n",
              "144                      2.43                             0.451   \n",
              "145                      2.43                             1.099   \n",
              "146                      2.43                             0.364   \n",
              "147                      2.43                             0.457   \n",
              "148                      2.43                             0.370   \n",
              "\n",
              "     Explained by: Social support  Explained by: Healthy life expectancy  \\\n",
              "144                         0.731                                  0.007   \n",
              "145                         0.724                                  0.340   \n",
              "146                         0.202                                  0.407   \n",
              "147                         0.649                                  0.243   \n",
              "148                         0.000                                  0.126   \n",
              "\n",
              "     Explained by: Freedom to make life choices  Explained by: Generosity  \\\n",
              "144                                       0.405                     0.103   \n",
              "145                                       0.539                     0.027   \n",
              "146                                       0.627                     0.227   \n",
              "147                                       0.359                     0.157   \n",
              "148                                       0.000                     0.122   \n",
              "\n",
              "     Explained by: Perceptions of corruption  Dystopia + residual  \n",
              "144                                    0.015                1.800  \n",
              "145                                    0.088                0.648  \n",
              "146                                    0.493                1.095  \n",
              "147                                    0.075                1.205  \n",
              "148                                    0.010                1.895  "
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              "      <th>Standard error of ladder score</th>\n",
              "      <th>upperwhisker</th>\n",
              "      <th>lowerwhisker</th>\n",
              "      <th>Logged GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "      <th>Generosity</th>\n",
              "      <th>Perceptions of corruption</th>\n",
              "      <th>Ladder score in Dystopia</th>\n",
              "      <th>Explained by: Log GDP per capita</th>\n",
              "      <th>Explained by: Social support</th>\n",
              "      <th>Explained by: Healthy life expectancy</th>\n",
              "      <th>Explained by: Freedom to make life choices</th>\n",
              "      <th>Explained by: Generosity</th>\n",
              "      <th>Explained by: Perceptions of corruption</th>\n",
              "      <th>Dystopia + residual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>Lesotho</td>\n",
              "      <td>Sub-Saharan Africa</td>\n",
              "      <td>3.512</td>\n",
              "      <td>0.120</td>\n",
              "      <td>3.748</td>\n",
              "      <td>3.276</td>\n",
              "      <td>7.926</td>\n",
              "      <td>0.787</td>\n",
              "      <td>48.700</td>\n",
              "      <td>0.715</td>\n",
              "      <td>-0.131</td>\n",
              "      <td>0.915</td>\n",
              "      <td>2.43</td>\n",
              "      <td>0.451</td>\n",
              "      <td>0.731</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.405</td>\n",
              "      <td>0.103</td>\n",
              "      <td>0.015</td>\n",
              "      <td>1.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>Botswana</td>\n",
              "      <td>Sub-Saharan Africa</td>\n",
              "      <td>3.467</td>\n",
              "      <td>0.074</td>\n",
              "      <td>3.611</td>\n",
              "      <td>3.322</td>\n",
              "      <td>9.782</td>\n",
              "      <td>0.784</td>\n",
              "      <td>59.269</td>\n",
              "      <td>0.824</td>\n",
              "      <td>-0.246</td>\n",
              "      <td>0.801</td>\n",
              "      <td>2.43</td>\n",
              "      <td>1.099</td>\n",
              "      <td>0.724</td>\n",
              "      <td>0.340</td>\n",
              "      <td>0.539</td>\n",
              "      <td>0.027</td>\n",
              "      <td>0.088</td>\n",
              "      <td>0.648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>Rwanda</td>\n",
              "      <td>Sub-Saharan Africa</td>\n",
              "      <td>3.415</td>\n",
              "      <td>0.068</td>\n",
              "      <td>3.548</td>\n",
              "      <td>3.282</td>\n",
              "      <td>7.676</td>\n",
              "      <td>0.552</td>\n",
              "      <td>61.400</td>\n",
              "      <td>0.897</td>\n",
              "      <td>0.061</td>\n",
              "      <td>0.167</td>\n",
              "      <td>2.43</td>\n",
              "      <td>0.364</td>\n",
              "      <td>0.202</td>\n",
              "      <td>0.407</td>\n",
              "      <td>0.627</td>\n",
              "      <td>0.227</td>\n",
              "      <td>0.493</td>\n",
              "      <td>1.095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Sub-Saharan Africa</td>\n",
              "      <td>3.145</td>\n",
              "      <td>0.058</td>\n",
              "      <td>3.259</td>\n",
              "      <td>3.030</td>\n",
              "      <td>7.943</td>\n",
              "      <td>0.750</td>\n",
              "      <td>56.201</td>\n",
              "      <td>0.677</td>\n",
              "      <td>-0.047</td>\n",
              "      <td>0.821</td>\n",
              "      <td>2.43</td>\n",
              "      <td>0.457</td>\n",
              "      <td>0.649</td>\n",
              "      <td>0.243</td>\n",
              "      <td>0.359</td>\n",
              "      <td>0.157</td>\n",
              "      <td>0.075</td>\n",
              "      <td>1.205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>2.523</td>\n",
              "      <td>0.038</td>\n",
              "      <td>2.596</td>\n",
              "      <td>2.449</td>\n",
              "      <td>7.695</td>\n",
              "      <td>0.463</td>\n",
              "      <td>52.493</td>\n",
              "      <td>0.382</td>\n",
              "      <td>-0.102</td>\n",
              "      <td>0.924</td>\n",
              "      <td>2.43</td>\n",
              "      <td>0.370</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.126</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.122</td>\n",
              "      <td>0.010</td>\n",
              "      <td>1.895</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "happy20.shape"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZAphoYbDIvcA",
        "outputId": "4e95ad2a-0e0a-4528-b243-a6864c089fd7"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(149, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's check the duplicate data in data set."
      ],
      "metadata": {
        "id": "Isu1WHf8IvcA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "happy20.duplicated().sum()\n"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UPr3-aEfIvcB",
        "outputId": "d3dd4d1b-9f15-41dd-d679-5730a38a3e8a"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "happy20.describe()"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "OF6VkJmWIvcB",
        "outputId": "35725bf9-1f76-4989-e860-b6fe2cbdfd84"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Ladder score  Standard error of ladder score  upperwhisker  \\\n",
              "count    149.000000                      149.000000    149.000000   \n",
              "mean       5.532839                        0.058752      5.648007   \n",
              "std        1.073924                        0.022001      1.054330   \n",
              "min        2.523000                        0.026000      2.596000   \n",
              "25%        4.852000                        0.043000      4.991000   \n",
              "50%        5.534000                        0.054000      5.625000   \n",
              "75%        6.255000                        0.070000      6.344000   \n",
              "max        7.842000                        0.173000      7.904000   \n",
              "\n",
              "       lowerwhisker  Logged GDP per capita  Social support  \\\n",
              "count    149.000000             149.000000      149.000000   \n",
              "mean       5.417631               9.432208        0.814745   \n",
              "std        1.094879               1.158601        0.114889   \n",
              "min        2.449000               6.635000        0.463000   \n",
              "25%        4.706000               8.541000        0.750000   \n",
              "50%        5.413000               9.569000        0.832000   \n",
              "75%        6.128000              10.421000        0.905000   \n",
              "max        7.780000              11.647000        0.983000   \n",
              "\n",
              "       Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
              "count               149.000000                    149.000000  149.000000   \n",
              "mean                 64.992799                      0.791597   -0.015134   \n",
              "std                   6.762043                      0.113332    0.150657   \n",
              "min                  48.478000                      0.382000   -0.288000   \n",
              "25%                  59.802000                      0.718000   -0.126000   \n",
              "50%                  66.603000                      0.804000   -0.036000   \n",
              "75%                  69.600000                      0.877000    0.079000   \n",
              "max                  76.953000                      0.970000    0.542000   \n",
              "\n",
              "       Perceptions of corruption  Ladder score in Dystopia  \\\n",
              "count                 149.000000              1.490000e+02   \n",
              "mean                    0.727450              2.430000e+00   \n",
              "std                     0.179226              5.347044e-15   \n",
              "min                     0.082000              2.430000e+00   \n",
              "25%                     0.667000              2.430000e+00   \n",
              "50%                     0.781000              2.430000e+00   \n",
              "75%                     0.845000              2.430000e+00   \n",
              "max                     0.939000              2.430000e+00   \n",
              "\n",
              "       Explained by: Log GDP per capita  Explained by: Social support  \\\n",
              "count                        149.000000                    149.000000   \n",
              "mean                           0.977161                      0.793315   \n",
              "std                            0.404740                      0.258871   \n",
              "min                            0.000000                      0.000000   \n",
              "25%                            0.666000                      0.647000   \n",
              "50%                            1.025000                      0.832000   \n",
              "75%                            1.323000                      0.996000   \n",
              "max                            1.751000                      1.172000   \n",
              "\n",
              "       Explained by: Healthy life expectancy  \\\n",
              "count                             149.000000   \n",
              "mean                                0.520161   \n",
              "std                                 0.213019   \n",
              "min                                 0.000000   \n",
              "25%                                 0.357000   \n",
              "50%                                 0.571000   \n",
              "75%                                 0.665000   \n",
              "max                                 0.897000   \n",
              "\n",
              "       Explained by: Freedom to make life choices  Explained by: Generosity  \\\n",
              "count                                  149.000000                149.000000   \n",
              "mean                                     0.498711                  0.178047   \n",
              "std                                      0.137888                  0.098270   \n",
              "min                                      0.000000                  0.000000   \n",
              "25%                                      0.409000                  0.105000   \n",
              "50%                                      0.514000                  0.164000   \n",
              "75%                                      0.603000                  0.239000   \n",
              "max                                      0.716000                  0.541000   \n",
              "\n",
              "       Explained by: Perceptions of corruption  Dystopia + residual  \n",
              "count                               149.000000           149.000000  \n",
              "mean                                  0.135141             2.430329  \n",
              "std                                   0.114361             0.537645  \n",
              "min                                   0.000000             0.648000  \n",
              "25%                                   0.060000             2.138000  \n",
              "50%                                   0.101000             2.509000  \n",
              "75%                                   0.174000             2.794000  \n",
              "max                                   0.547000             3.482000  "
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Ladder score</th>\n",
              "      <th>Standard error of ladder score</th>\n",
              "      <th>upperwhisker</th>\n",
              "      <th>lowerwhisker</th>\n",
              "      <th>Logged GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "      <th>Generosity</th>\n",
              "      <th>Perceptions of corruption</th>\n",
              "      <th>Ladder score in Dystopia</th>\n",
              "      <th>Explained by: Log GDP per capita</th>\n",
              "      <th>Explained by: Social support</th>\n",
              "      <th>Explained by: Healthy life expectancy</th>\n",
              "      <th>Explained by: Freedom to make life choices</th>\n",
              "      <th>Explained by: Generosity</th>\n",
              "      <th>Explained by: Perceptions of corruption</th>\n",
              "      <th>Dystopia + residual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>149.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>1.490000e+02</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>149.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.532839</td>\n",
              "      <td>0.058752</td>\n",
              "      <td>5.648007</td>\n",
              "      <td>5.417631</td>\n",
              "      <td>9.432208</td>\n",
              "      <td>0.814745</td>\n",
              "      <td>64.992799</td>\n",
              "      <td>0.791597</td>\n",
              "      <td>-0.015134</td>\n",
              "      <td>0.727450</td>\n",
              "      <td>2.430000e+00</td>\n",
              "      <td>0.977161</td>\n",
              "      <td>0.793315</td>\n",
              "      <td>0.520161</td>\n",
              "      <td>0.498711</td>\n",
              "      <td>0.178047</td>\n",
              "      <td>0.135141</td>\n",
              "      <td>2.430329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.073924</td>\n",
              "      <td>0.022001</td>\n",
              "      <td>1.054330</td>\n",
              "      <td>1.094879</td>\n",
              "      <td>1.158601</td>\n",
              "      <td>0.114889</td>\n",
              "      <td>6.762043</td>\n",
              "      <td>0.113332</td>\n",
              "      <td>0.150657</td>\n",
              "      <td>0.179226</td>\n",
              "      <td>5.347044e-15</td>\n",
              "      <td>0.404740</td>\n",
              "      <td>0.258871</td>\n",
              "      <td>0.213019</td>\n",
              "      <td>0.137888</td>\n",
              "      <td>0.098270</td>\n",
              "      <td>0.114361</td>\n",
              "      <td>0.537645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.523000</td>\n",
              "      <td>0.026000</td>\n",
              "      <td>2.596000</td>\n",
              "      <td>2.449000</td>\n",
              "      <td>6.635000</td>\n",
              "      <td>0.463000</td>\n",
              "      <td>48.478000</td>\n",
              "      <td>0.382000</td>\n",
              "      <td>-0.288000</td>\n",
              "      <td>0.082000</td>\n",
              "      <td>2.430000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.648000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.852000</td>\n",
              "      <td>0.043000</td>\n",
              "      <td>4.991000</td>\n",
              "      <td>4.706000</td>\n",
              "      <td>8.541000</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>59.802000</td>\n",
              "      <td>0.718000</td>\n",
              "      <td>-0.126000</td>\n",
              "      <td>0.667000</td>\n",
              "      <td>2.430000e+00</td>\n",
              "      <td>0.666000</td>\n",
              "      <td>0.647000</td>\n",
              "      <td>0.357000</td>\n",
              "      <td>0.409000</td>\n",
              "      <td>0.105000</td>\n",
              "      <td>0.060000</td>\n",
              "      <td>2.138000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.534000</td>\n",
              "      <td>0.054000</td>\n",
              "      <td>5.625000</td>\n",
              "      <td>5.413000</td>\n",
              "      <td>9.569000</td>\n",
              "      <td>0.832000</td>\n",
              "      <td>66.603000</td>\n",
              "      <td>0.804000</td>\n",
              "      <td>-0.036000</td>\n",
              "      <td>0.781000</td>\n",
              "      <td>2.430000e+00</td>\n",
              "      <td>1.025000</td>\n",
              "      <td>0.832000</td>\n",
              "      <td>0.571000</td>\n",
              "      <td>0.514000</td>\n",
              "      <td>0.164000</td>\n",
              "      <td>0.101000</td>\n",
              "      <td>2.509000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.255000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>6.344000</td>\n",
              "      <td>6.128000</td>\n",
              "      <td>10.421000</td>\n",
              "      <td>0.905000</td>\n",
              "      <td>69.600000</td>\n",
              "      <td>0.877000</td>\n",
              "      <td>0.079000</td>\n",
              "      <td>0.845000</td>\n",
              "      <td>2.430000e+00</td>\n",
              "      <td>1.323000</td>\n",
              "      <td>0.996000</td>\n",
              "      <td>0.665000</td>\n",
              "      <td>0.603000</td>\n",
              "      <td>0.239000</td>\n",
              "      <td>0.174000</td>\n",
              "      <td>2.794000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.842000</td>\n",
              "      <td>0.173000</td>\n",
              "      <td>7.904000</td>\n",
              "      <td>7.780000</td>\n",
              "      <td>11.647000</td>\n",
              "      <td>0.983000</td>\n",
              "      <td>76.953000</td>\n",
              "      <td>0.970000</td>\n",
              "      <td>0.542000</td>\n",
              "      <td>0.939000</td>\n",
              "      <td>2.430000e+00</td>\n",
              "      <td>1.751000</td>\n",
              "      <td>1.172000</td>\n",
              "      <td>0.897000</td>\n",
              "      <td>0.716000</td>\n",
              "      <td>0.541000</td>\n",
              "      <td>0.547000</td>\n",
              "      <td>3.482000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-14fe58b1-1a84-427b-ba4c-f9d3f34c100c')\"\n",
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              "\n",
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              "\n",
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              "          document.querySelector('#df-14fe58b1-1a84-427b-ba4c-f9d3f34c100c 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-14fe58b1-1a84-427b-ba4c-f9d3f34c100c');\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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. DATA TREATMENT: CLEANING AND FORMATTING."
      ],
      "metadata": {
        "id": "gqv2ZUhV47J0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "happy20.info()"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T3Z-d44sIvcB",
        "outputId": "1d3bbad8-cbaf-445d-dcfd-f758cc07aa91"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 149 entries, 0 to 148\n",
            "Data columns (total 20 columns):\n",
            " #   Column                                      Non-Null Count  Dtype  \n",
            "---  ------                                      --------------  -----  \n",
            " 0   Country name                                149 non-null    object \n",
            " 1   Regional indicator                          149 non-null    object \n",
            " 2   Ladder score                                149 non-null    float64\n",
            " 3   Standard error of ladder score              149 non-null    float64\n",
            " 4   upperwhisker                                149 non-null    float64\n",
            " 5   lowerwhisker                                149 non-null    float64\n",
            " 6   Logged GDP per capita                       149 non-null    float64\n",
            " 7   Social support                              149 non-null    float64\n",
            " 8   Healthy life expectancy                     149 non-null    float64\n",
            " 9   Freedom to make life choices                149 non-null    float64\n",
            " 10  Generosity                                  149 non-null    float64\n",
            " 11  Perceptions of corruption                   149 non-null    float64\n",
            " 12  Ladder score in Dystopia                    149 non-null    float64\n",
            " 13  Explained by: Log GDP per capita            149 non-null    float64\n",
            " 14  Explained by: Social support                149 non-null    float64\n",
            " 15  Explained by: Healthy life expectancy       149 non-null    float64\n",
            " 16  Explained by: Freedom to make life choices  149 non-null    float64\n",
            " 17  Explained by: Generosity                    149 non-null    float64\n",
            " 18  Explained by: Perceptions of corruption     149 non-null    float64\n",
            " 19  Dystopia + residual                         149 non-null    float64\n",
            "dtypes: float64(18), object(2)\n",
            "memory usage: 23.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "happy20.isnull().sum()"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kkz-RpPJIvcC",
        "outputId": "9337adf5-d11a-417e-ca2c-908b57c3f36f"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country name                                  0\n",
              "Regional indicator                            0\n",
              "Ladder score                                  0\n",
              "Standard error of ladder score                0\n",
              "upperwhisker                                  0\n",
              "lowerwhisker                                  0\n",
              "Logged GDP per capita                         0\n",
              "Social support                                0\n",
              "Healthy life expectancy                       0\n",
              "Freedom to make life choices                  0\n",
              "Generosity                                    0\n",
              "Perceptions of corruption                     0\n",
              "Ladder score in Dystopia                      0\n",
              "Explained by: Log GDP per capita              0\n",
              "Explained by: Social support                  0\n",
              "Explained by: Healthy life expectancy         0\n",
              "Explained by: Freedom to make life choices    0\n",
              "Explained by: Generosity                      0\n",
              "Explained by: Perceptions of corruption       0\n",
              "Dystopia + residual                           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No Null records found here."
      ],
      "metadata": {
        "id": "piFPV9p7IvcC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Renaming Columns\n"
      ],
      "metadata": {
        "id": "C0UYkSNoIvcC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "col_names_dict = {'Country name':'Country', 'Regional indicator':'Region',\n",
        "                  'Standard error of ladder score':'Standard Error', 'Logged GDP per capita':'Logged GDPPC',\n",
        "                  'Social support':'Social Support', 'Healthy life expectancy':'Life Expectancy',\n",
        "                  'Freedom to make life choices':'Freedom', 'Perceptions of corruption': 'Corruption'}\n",
        "\n",
        "happy20.rename(columns = col_names_dict, inplace = True)"
      ],
      "metadata": {
        "trusted": true,
        "id": "ftUCyloPIvcC"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "happy20.isnull().sum()"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LGa1xudCIvcD",
        "outputId": "00be50ce-641e-4d05-9716-3647f7b2aa13"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country                                       0\n",
              "Region                                        0\n",
              "Ladder score                                  0\n",
              "Standard Error                                0\n",
              "upperwhisker                                  0\n",
              "lowerwhisker                                  0\n",
              "Logged GDPPC                                  0\n",
              "Social Support                                0\n",
              "Life Expectancy                               0\n",
              "Freedom                                       0\n",
              "Generosity                                    0\n",
              "Corruption                                    0\n",
              "Ladder score in Dystopia                      0\n",
              "Explained by: Log GDP per capita              0\n",
              "Explained by: Social support                  0\n",
              "Explained by: Healthy life expectancy         0\n",
              "Explained by: Freedom to make life choices    0\n",
              "Explained by: Generosity                      0\n",
              "Explained by: Perceptions of corruption       0\n",
              "Dystopia + residual                           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us see which factors have maximum correlation with happiness in 2021\n",
        "\n"
      ],
      "metadata": {
        "id": "LIZOpjdXIvcD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Dropping columns which just numerically add up to the happiness index\n",
        "happy20 = happy20.drop(['Explained by: Log GDP per capita', 'Explained by: Social support',\n",
        "       'Explained by: Healthy life expectancy',\n",
        "       'Explained by: Freedom to make life choices',\n",
        "       'Explained by: Generosity', 'Explained by: Perceptions of corruption',\n",
        "       'Dystopia + residual','upperwhisker','lowerwhisker'],axis=1)"
      ],
      "metadata": {
        "trusted": true,
        "id": "4ZnemrEPIvcD"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "happy20.head()"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "MH5ee6k-IvcD",
        "outputId": "2b7553a6-e155-4517-f71a-e3a9af5a42d1"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Country          Region  Ladder score  Standard Error  Logged GDPPC  \\\n",
              "0      Finland  Western Europe         7.842           0.032        10.775   \n",
              "1      Denmark  Western Europe         7.620           0.035        10.933   \n",
              "2  Switzerland  Western Europe         7.571           0.036        11.117   \n",
              "3      Iceland  Western Europe         7.554           0.059        10.878   \n",
              "4  Netherlands  Western Europe         7.464           0.027        10.932   \n",
              "\n",
              "   Social Support  Life Expectancy  Freedom  Generosity  Corruption  \\\n",
              "0           0.954             72.0    0.949      -0.098       0.186   \n",
              "1           0.954             72.7    0.946       0.030       0.179   \n",
              "2           0.942             74.4    0.919       0.025       0.292   \n",
              "3           0.983             73.0    0.955       0.160       0.673   \n",
              "4           0.942             72.4    0.913       0.175       0.338   \n",
              "\n",
              "   Ladder score in Dystopia  \n",
              "0                      2.43  \n",
              "1                      2.43  \n",
              "2                      2.43  \n",
              "3                      2.43  \n",
              "4                      2.43  "
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>Region</th>\n",
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              "      <th>Standard Error</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Finland</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>7.842</td>\n",
              "      <td>0.032</td>\n",
              "      <td>10.775</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Denmark</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>7.620</td>\n",
              "      <td>0.035</td>\n",
              "      <td>10.933</td>\n",
              "      <td>0.954</td>\n",
              "      <td>72.7</td>\n",
              "      <td>0.946</td>\n",
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              "      <td>2.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Switzerland</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>7.571</td>\n",
              "      <td>0.036</td>\n",
              "      <td>11.117</td>\n",
              "      <td>0.942</td>\n",
              "      <td>74.4</td>\n",
              "      <td>0.919</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.292</td>\n",
              "      <td>2.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Iceland</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>7.554</td>\n",
              "      <td>0.059</td>\n",
              "      <td>10.878</td>\n",
              "      <td>0.983</td>\n",
              "      <td>73.0</td>\n",
              "      <td>0.955</td>\n",
              "      <td>0.160</td>\n",
              "      <td>0.673</td>\n",
              "      <td>2.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>7.464</td>\n",
              "      <td>0.027</td>\n",
              "      <td>10.932</td>\n",
              "      <td>0.942</td>\n",
              "      <td>72.4</td>\n",
              "      <td>0.913</td>\n",
              "      <td>0.175</td>\n",
              "      <td>0.338</td>\n",
              "      <td>2.43</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-2669cb66-88c5-4388-ad39-51600a5c2e54 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-2669cb66-88c5-4388-ad39-51600a5c2e54');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. EXPLORATORY DATA ANALYSIS (EDA) AND VISUALISATION.\n",
        "\n"
      ],
      "metadata": {
        "id": "g5L9XIoz_fsH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Correlation Matrix**"
      ],
      "metadata": {
        "id": "PAw13YTTIvcD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's now look at the relationships between each of the six measured values (Logged GDP per capita, social support, etc.) and the overall ladder score to perhaps highlight which features may be more/less important."
      ],
      "metadata": {
        "id": "9brCRFYcIvcE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "feature_cols = ['Logged GDPPC', 'Social Support', 'Life Expectancy', 'Freedom', 'Generosity', 'Corruption']\n"
      ],
      "metadata": {
        "trusted": true,
        "id": "9F7kdNTZIvcE"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([happy20['Ladder score'], happy20[feature_cols]], axis = 1)\n",
        "\n",
        "fig = plt.figure(figsize = (13, 10))\n",
        "plt.style.use('seaborn-white')\n",
        "\n",
        "plt.matshow(df.corr(), fignum = fig.number, cmap = 'viridis')\n",
        "plt.xticks(range(df.shape[1]), df.columns, fontsize=14, rotation=45)\n",
        "plt.yticks(range(df.shape[1]), df.columns, fontsize=14)\n",
        "\n",
        "cb = plt.colorbar()\n",
        "cb.ax.tick_params(labelsize=14)\n",
        "\n",
        "plt.title('Correlation Matrix', fontsize = 24, y = 1.2);"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 724
        },
        "id": "9P_qAGjzIvcE",
        "outputId": "d2152731-6b62-44d7-b595-b91812a58563"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 936x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It looks like the Logged GDPPC, Social Support, and Life Expectancy metrics all have a relatively high correlation with the overall score a country received. Also, these factors each seem to have a pretty high correlation with each other (e.g., Social Support is well correlated with Life Expectancy, and so on). On the other end of the spectrum, Generosity does not seem to have a sizeable correlation with any other measurement, including the Ladde"
      ],
      "metadata": {
        "id": "KSrYro0wIvcE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "sns.kdeplot(happy20['Ladder score'],color='limegreen', label='2020')\n",
        "plt.title('Ladder Score Over The Year',size=20)\n",
        "plt.legend()\n",
        "plt.show"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "nSo2TypSIvcE",
        "outputId": "1d6141d1-4896-481f-f0ea-68bf7be5f043"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "sns.kdeplot(happy20['Logged GDPPC'],color='black', label='2020')\n",
        "plt.title('Logged GDPPC  Over The Year',size=20)\n",
        "plt.legend()\n",
        "plt.show"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "OExWqoGOIvcE",
        "outputId": "822274f0-9a50-464c-b321-5472ffa04ab8"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ladder score with top 10 country by different criterias - 2021"
      ],
      "metadata": {
        "id": "WAysyb_rIvcE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "happy20_top10 = happy20.groupby('Country')['Ladder score'].max().sort_values(ascending=False).head(20).reset_index()\n",
        "happy20.columns_top10 = ['Country','Ladder Score']\n",
        "happy20_top10\n"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "Hh_1t9uQIvcF",
        "outputId": "dad09801-804e-4a08-ae4d-cc551c4a484e"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Country  Ladder score\n",
              "0          Finland         7.842\n",
              "1          Denmark         7.620\n",
              "2      Switzerland         7.571\n",
              "3          Iceland         7.554\n",
              "4      Netherlands         7.464\n",
              "5           Norway         7.392\n",
              "6           Sweden         7.363\n",
              "7       Luxembourg         7.324\n",
              "8      New Zealand         7.277\n",
              "9          Austria         7.268\n",
              "10       Australia         7.183\n",
              "11          Israel         7.157\n",
              "12         Germany         7.155\n",
              "13          Canada         7.103\n",
              "14         Ireland         7.085\n",
              "15      Costa Rica         7.069\n",
              "16  United Kingdom         7.064\n",
              "17  Czech Republic         6.965\n",
              "18   United States         6.951\n",
              "19         Belgium         6.834"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-847dbaeb-c80c-4487-aca4-63d0b2563411\">\n",
              "    <div 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>Country</th>\n",
              "      <th>Ladder score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Finland</td>\n",
              "      <td>7.842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Denmark</td>\n",
              "      <td>7.620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Switzerland</td>\n",
              "      <td>7.571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Iceland</td>\n",
              "      <td>7.554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>7.464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Norway</td>\n",
              "      <td>7.392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>7.363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Luxembourg</td>\n",
              "      <td>7.324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>New Zealand</td>\n",
              "      <td>7.277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Austria</td>\n",
              "      <td>7.268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Australia</td>\n",
              "      <td>7.183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Israel</td>\n",
              "      <td>7.157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Germany</td>\n",
              "      <td>7.155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Canada</td>\n",
              "      <td>7.103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Ireland</td>\n",
              "      <td>7.085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Costa Rica</td>\n",
              "      <td>7.069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>7.064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Czech Republic</td>\n",
              "      <td>6.965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>United States</td>\n",
              "      <td>6.951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Belgium</td>\n",
              "      <td>6.834</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-847dbaeb-c80c-4487-aca4-63d0b2563411')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Finland, Denmark and Switzerland are found to be the happiest countries in the world "
      ],
      "metadata": {
        "id": "kA3KRBYNj4Qq"
      }
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    {
      "cell_type": "code",
      "source": [
        "happy20_GDP = happy20.groupby('Country')['Logged GDPPC'].max().sort_values(ascending=False).head(10).reset_index()\n",
        "happy20.columns_GDP = ['Logged GDPPC','Ladder Score']\n",
        "happy20_GDP"
      ],
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      "execution_count": 104,
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          "data": {
            "text/plain": [
              "                     Country  Logged GDPPC\n",
              "0                 Luxembourg        11.647\n",
              "1                  Singapore        11.488\n",
              "2                    Ireland        11.342\n",
              "3                Switzerland        11.117\n",
              "4       United Arab Emirates        11.085\n",
              "5                     Norway        11.053\n",
              "6              United States        11.023\n",
              "7  Hong Kong S.A.R. of China        11.000\n",
              "8                    Denmark        10.933\n",
              "9                Netherlands        10.932"
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      "cell_type": "markdown",
      "source": [
        "Luxembourg, Singapore and Ireland are found to be the richest countries by GDPPC in the world "
      ],
      "metadata": {
        "id": "D74-4lf-kH2B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "happy20_SC = happy20.groupby('Country')['Social Support'].max().sort_values(ascending=False).head(10).reset_index()\n",
        "happy20_SC"
      ],
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        "trusted": true,
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      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Country  Social Support\n",
              "0         Iceland           0.983\n",
              "1    Turkmenistan           0.983\n",
              "2         Finland           0.954\n",
              "3         Denmark           0.954\n",
              "4          Norway           0.954\n",
              "5      Kazakhstan           0.952\n",
              "6        Slovenia           0.948\n",
              "7     New Zealand           0.948\n",
              "8  Czech Republic           0.947\n",
              "9         Ireland           0.947"
            ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Iceland, Turkmenistan and Finland are found to be the richest countries by GDPPC in the world. Nonetheless, Turkmenistan is considered to be an authoritarian country, so the daya might be misleading\n"
      ],
      "metadata": {
        "id": "9RDsLDUSkUf8"
      }
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    {
      "cell_type": "code",
      "source": [
        "happy20_free = happy20.groupby('Country')['Freedom'].max().sort_values(ascending=False).head(10).reset_index()\n",
        "happy20.columns_free = ['Freedom','Ladder Score']\n",
        "happy20_free"
      ],
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      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Country  Freedom\n",
              "0  Uzbekistan    0.970\n",
              "1      Norway    0.960\n",
              "2    Cambodia    0.959\n",
              "3     Iceland    0.955\n",
              "4    Slovenia    0.949\n",
              "5     Finland    0.949\n",
              "6     Denmark    0.946\n",
              "7      Sweden    0.945\n",
              "8     Vietnam    0.940\n",
              "9  Kyrgyzstan    0.935"
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      "cell_type": "markdown",
      "source": [
        "Uzbekistan, Norway and Cambodia are found to be the most free countries in the world. \n"
      ],
      "metadata": {
        "id": "5HmAq7I63L8a"
      }
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    {
      "cell_type": "code",
      "source": [
        "happy20_life = happy20.groupby('Country')['Life Expectancy'].max().sort_values(ascending=False).head(10).reset_index()\n",
        "happy20.columns_life = ['Life Expectancy','Ladder Score']\n",
        "happy20_life"
      ],
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      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Country  Life Expectancy\n",
              "0                  Singapore           76.953\n",
              "1  Hong Kong S.A.R. of China           76.820\n",
              "2                      Japan           75.100\n",
              "3                      Spain           74.700\n",
              "4                Switzerland           74.400\n",
              "5                     France           74.000\n",
              "6                South Korea           73.900\n",
              "7                  Australia           73.900\n",
              "8                     Cyprus           73.898\n",
              "9               North Cyprus           73.898"
            ],
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      "cell_type": "markdown",
      "source": [
        "Singapore, Hong Kong S.A.R. of China and Japan are found to be the countries with the biggest life expectancy in the world. \n"
      ],
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        "id": "dXG6nAJJ3bpC"
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        "happy20_gen = happy20.groupby('Country')['Generosity'].max().sort_values(ascending=False).head(10).reset_index()\n",
        "happy20.columns_gen = ['Generosity','Ladder Score']\n",
        "happy20_gen"
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      "execution_count": 108,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "          Country  Generosity\n",
              "0       Indonesia       0.542\n",
              "1         Myanmar       0.509\n",
              "2          Gambia       0.424\n",
              "3           Haiti       0.422\n",
              "4      Uzbekistan       0.311\n",
              "5           Kenya       0.287\n",
              "6        Thailand       0.287\n",
              "7    Turkmenistan       0.273\n",
              "8          Kosovo       0.257\n",
              "9  United Kingdom       0.233"
            ],
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              "\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Indonesia, Myanmar and Gambia are found to be the most generous countries in the world. \n"
      ],
      "metadata": {
        "id": "tRV1kbYj3mxi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "happy20_corr = happy20.groupby('Country')['Corruption'].max().sort_values(ascending=False).tail(10).reset_index()\n",
        "happy20.columns_corr = ['Corruption','Ladder Score']\n",
        "happy20_corr"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "Mzoz_DzvIvcF",
        "outputId": "d1505d3c-48d9-4ffb-e652-e14208873a0e"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Country  Corruption\n",
              "0      Ireland       0.363\n",
              "1  Netherlands       0.338\n",
              "2  Switzerland       0.292\n",
              "3       Norway       0.270\n",
              "4  New Zealand       0.242\n",
              "5       Sweden       0.237\n",
              "6      Finland       0.186\n",
              "7      Denmark       0.179\n",
              "8       Rwanda       0.167\n",
              "9    Singapore       0.082"
            ],
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ireland</td>\n",
              "      <td>0.363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>0.338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Switzerland</td>\n",
              "      <td>0.292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Norway</td>\n",
              "      <td>0.270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>New Zealand</td>\n",
              "      <td>0.242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>0.237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Finland</td>\n",
              "      <td>0.186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Denmark</td>\n",
              "      <td>0.179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Rwanda</td>\n",
              "      <td>0.167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Singapore</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Ireland, Netherlands and Switzerland are found to be the least corrupted countries in the world. \n"
      ],
      "metadata": {
        "id": "k9eNBcDW36js"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Happiness rate by the region."
      ],
      "metadata": {
        "id": "qkCiwrITIvcH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "regions =list(happy20[\"Region\"].unique())\n",
        "regions_happiness_rate = []\n",
        "\n",
        "for i in regions:\n",
        "    x = happy20[happy20[\"Region\"] == i]\n",
        "    happiness_rate = sum(x[\"Ladder score\"]) / len(x)\n",
        "    regions_happiness_rate.append(happiness_rate) # add list\n",
        "\n",
        "fig = {\n",
        "  \"data\": [\n",
        "    {\n",
        "      \"values\": regions_happiness_rate,\n",
        "      \"labels\": regions, \n",
        "      \"domain\": {\"x\": [0, .8]},\n",
        "      \"name\": \"Ladder score Rate\",\n",
        "      \"hoverinfo\":\"label+percent+name\",\n",
        "      \"hole\": .3,\n",
        "      \"type\": \"pie\" \n",
        "    },],\n",
        "  \"layout\": {\n",
        "        \"title\":\"Ladder score  Rates by Region\",\n",
        "        \"annotations\": [ \n",
        "            { \"font\": { \"size\": 13}\n",
        "    \n",
        "            },\n",
        "        ]\n",
        "    }\n",
        "}\n",
        "iplot(fig)\n"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "QqxH7CFPIvcI",
        "outputId": "1a737edc-bc73-4837-afb8-8b30bfa66050"
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      "execution_count": 126,
      "outputs": [
        {
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score  Rates by Region\"}},                        {\"responsive\": true}                    ).then(function(){\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above plot we can say that North Amrica has the  highest happiness rate,westren Europe has the  second highest happiness rate and sub- saharan Africa has the lowest happiness rate."
      ],
      "metadata": {
        "id": "eW2L83KoIvcI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Life Expectancy Across Countries"
      ],
      "metadata": {
        "id": "TosI3PFbIvcI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ax=sns.boxplot(x='Life Expectancy', y='Country', \n",
        "                 data=happy20, \n",
        "                 palette=\"BrBG\",\n",
        "                 dodge=False)\n",
        "#ax.set_xticklabels(ax.get_xticklabels(), fontsize=35, ha=\"right\")\n",
        "plt.yticks(fontsize=25)\n",
        "plt.xticks(fontsize=35)\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(30,80)"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "X7w689jIIvcI",
        "outputId": "2830b4c1-dabc-4f18-c850-6acf198acd71"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x5760 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As it can be seen above, the better country is, the more years people expect to live."
      ],
      "metadata": {
        "id": "IHFbtpKP6cZ1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Ladder Score Across The World"
      ],
      "metadata": {
        "id": "QMD8y0GfIvcI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "map_happy = dict(type = 'choropleth', \n",
        "           locations = happy20['Country'],\n",
        "           locationmode = 'country names',\n",
        "           z = happy20['Ladder score'], \n",
        "           text = happy20['Country'],\n",
        "           colorbar = {'title':'Ladder score'},\n",
        "           colorscale = 'Viridis')\n",
        "layout = dict(title = 'Ladder score Across The World', \n",
        "              geo = dict(showframe = False, projection = {'type': 'equirectangular'}))\n",
        "\n",
        "choromap3 = go.Figure(data = [map_happy], layout=layout)\n",
        "iplot(choromap3)"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "w40T2kDrIvcI",
        "outputId": "6a16f7a1-2427-4c0b-edc2-6cdeb181818e"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Happines Score Across The World. Most Central/Western and North America are seen to be happy at most. Meanwhile, the worst results are shown by African countries and most Asian countries. Interesting observation is that Latin American countries on average better than most of eastern europe countries."
      ],
      "metadata": {
        "id": "j2HEDl9eIvcI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "map_happy = dict(type = 'choropleth', \n",
        "           locations = happy20['Country'],\n",
        "           locationmode = 'country names',\n",
        "           z = happy20['Logged GDPPC'], \n",
        "           text = happy20['Country'],\n",
        "           colorbar = {'title':'Logged GDPPC'},\n",
        "           colorscale = 'Viridis')\n",
        "layout = dict(title = 'Logged GDPPC Across The World', \n",
        "              geo = dict(showframe = False, projection = {'type': 'equirectangular'}))\n",
        "\n",
        "choromap3 = go.Figure(data = [map_happy], layout=layout)\n",
        "iplot(choromap3)"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "gbj6JDXQIvcI",
        "outputId": "d645d425-f063-4757-8921-b36f74fd7650"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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GDPPC Across The World\"}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Economy (GDPPC Per Capita) Across The World. Most Central/Western and North America are seen to experience high GDPPC per Capital. Meanwhile, the worst results are shown by African countries and most Asian countries."
      ],
      "metadata": {
        "id": "bLNpHfB_7UIl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Family - Social Support Across The World"
      ],
      "metadata": {
        "id": "x1cfsmtlIvcJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "map_happy = dict(type = 'choropleth', \n",
        "           locations = happy20['Country'],\n",
        "           locationmode = 'country names',\n",
        "           z = happy20['Social Support'], \n",
        "           text = happy20['Country'],\n",
        "           colorbar = {'title':'Social Support'},\n",
        "           colorscale = 'Viridis')\n",
        "layout = dict(title = 'Social Support Across The World', \n",
        "              geo = dict(showframe = False, projection = {'type': 'equirectangular'}))\n",
        "\n",
        "choromap3 = go.Figure(data = [map_happy], layout=layout)\n",
        "iplot(choromap3)"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "xo-ywu5AIvcJ",
        "outputId": "92b0687b-eaa5-47fd-fbe3-f82d1a411fba"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "</body>\n",
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Social Support Across The World. The entire Europe and America are seen to expereince high social support. Meanwhile, Asian countries saw average results while in africa its the worst."
      ],
      "metadata": {
        "id": "2l8fCdTx8Cut"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Healthy Life Expectancy Across The World"
      ],
      "metadata": {
        "id": "LTt_AR7TIvcJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "map_happy = dict(type = 'choropleth', \n",
        "           locations = happy20['Country'],\n",
        "           locationmode = 'country names',\n",
        "           z = happy20['Life Expectancy'], \n",
        "           text = happy20['Country'],\n",
        "           colorbar = {'title':'Life Expectancy'},\n",
        "           colorscale = 'Viridis')\n",
        "layout = dict(title = ' Life Expectancy Across The World', \n",
        "              geo = dict(showframe = False, projection = {'type': 'equirectangular'}))\n",
        "\n",
        "choromap3 = go.Figure(data = [map_happy], layout=layout)\n",
        "iplot(choromap3)"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "WkVm1H_xIvcJ",
        "outputId": "25ebba8a-9d96-42eb-f868-3212a786be7a"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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Life Expectancy Across The World\"}},                        {\"responsive\": true}                    ).then(function(){\n",
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          },
          "metadata": {}
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Life expectancy across The World. The middle/western Europe and America are seen to expereince high life expectancy. Meanwhile, Asian and eastern Eurpe countries saw average results while in africa the results of life expectancy are the worst."
      ],
      "metadata": {
        "id": "jIqd1zCS9S3l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Freedom To Choose Things Across The World"
      ],
      "metadata": {
        "id": "hJlk-JrkIvcJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "map_happy = dict(type = 'choropleth', \n",
        "           locations = happy20['Country'],\n",
        "           locationmode = 'country names',\n",
        "           z = happy20['Freedom'], \n",
        "           text = happy20['Country'],\n",
        "           colorbar = {'title' :'Freedom'},\n",
        "           colorscale = 'Viridis')\n",
        "layout = dict(title = ' Freedom Across The World', \n",
        "              geo = dict(showframe = False, projection = {'type': 'equirectangular'}))\n",
        "\n",
        "choromap3 = go.Figure(data = [map_happy], layout=layout)\n",
        "iplot(choromap3)"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "KyW8ScJbIvcJ",
        "outputId": "d530afda-976f-4234-cdc5-6de18e96df15"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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Freedom Across The World\"}},                        {\"responsive\": true}                    ).then(function(){\n",
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              "\n",
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              "</body>\n",
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Life expectancy across The World. Most European and Latin American countries saw good freedom of speech scores. Latin America is a bit better with regards to freedom of speech than in previous countries. Africa and Asia are not free so much, China here is probably an exception from the statistic, as the score is probably less trustworthy and does not show the reality. Africa is shown to have the least freedom. Good examples of free countries are Scandinavian countries. "
      ],
      "metadata": {
        "id": "eJHy9nuU97x2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Corruption Across The World"
      ],
      "metadata": {
        "id": "gbduEFSdIvcJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "map_happy = dict(type = 'choropleth', \n",
        "           locations = happy20['Country'],\n",
        "           locationmode = 'country names',\n",
        "           z = happy20['Corruption'], \n",
        "           text = happy20['Country'],\n",
        "           colorbar = {'title' :'Corruption'},\n",
        "           colorscale = 'Viridis')\n",
        "layout = dict(title = 'Corruption Across The World', \n",
        "              geo = dict(showframe = False, projection = {'type': 'equirectangular'}))\n",
        "\n",
        "choromap3 = go.Figure(data = [map_happy], layout=layout)\n",
        "iplot(choromap3)"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "ki4d5rs0IvcJ",
        "outputId": "1d329935-3625-4921-d148-6964aabd7a7a"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "cell_type": "markdown",
      "source": [
        "Corruption across The World. Corruption is shown all around the world with the exception of the countries from the European Union, Australia, and North America. Nevertheless, the USA saw also pretty much corruption. "
      ],
      "metadata": {
        "id": "AFeaTa0p_9kW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. INTERFENCES AND CONCLUSIONS.\n"
      ],
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      }
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        "* . Most of the Asian and African countries has low happiness score\n",
        "\n",
        "* . American countries has comparitively better happiness score\n",
        "\n",
        "* . North America & western europe has highest happiness score\n",
        "\n",
        "\n"
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        "\n",
        "\n",
        "From the extensive analysis on happiness score for the country we can conclude that the happiness of citizens in the country depends on the efficiency of the government which includes GDPPC, corruption, freedom provided. Healthy life expectancy also significantly influences the happiness of citizens."
      ],
      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "## 5. REFERENCES.\n"
      ],
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        "id": "MAn4FgpaA2d-"
      }
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      "cell_type": "markdown",
      "source": [
        "https://www.kaggle.com/datasets/unsdsn/world-happiness\n",
        "\n",
        "https://worldhappiness.report/\n"
      ],
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        "id": "y56XadC1BcZC"
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