Depression_student.ipynb

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      "cell_type": "markdown",
      "source": [
        "Importo le librerie"
      ],
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        "id": "a_Tot-DxY8i5"
      }
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    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import os.path\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as mpatches\n",
        "import seaborn as sb"
      ],
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      "source": [
        "# Descrizione problema\n",
        "\n",
        "Il dataset in esame, contiene informazioni utili per la previsione di assenza o presenza di depressione tra gli studenti\n",
        "\n",
        "L'obiettivo del progetto è quello di predire una variabile discreta binaria."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "student = pd.read_csv(\"students.csv\",  index_col=0)"
      ],
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      "execution_count": null,
      "outputs": []
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      "source": [
        "student.head()"
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              "        Timestamp  gender   age                           course  year  \\\n",
              "0  8/7/2020 17:46    Male  21.0                          Finance   3.0   \n",
              "1  8/7/2020 14:56  Female  18.0               Management Studies   2.0   \n",
              "2  8/7/2020 16:21  Female  21.0  General Business and Management   2.0   \n",
              "3  8/7/2020 14:31    Male  20.0                       Accounting   2.0   \n",
              "4  8/7/2020 17:50    Male  18.0                 Computer Science   1.0   \n",
              "\n",
              "      grades  marital  depression  anxiety  panic_attack  specialist  sport  \\\n",
              "0  20.252930    False       False     True         False       False   True   \n",
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              "   alchool  \n",
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      "source": [
        "Di seguito sono riportate le dimensioni in memoria, il numero di istanze non nulle e il tipo delle feature che compongono i dati raccolti nel dataset (training set) "
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2000 entries, 0 to 1999\n",
            "Data columns (total 13 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   Timestamp     2000 non-null   object \n",
            " 1   gender        2000 non-null   object \n",
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            " 3   course        2000 non-null   object \n",
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            " 5   grades        2000 non-null   float64\n",
            " 6   marital       2000 non-null   bool   \n",
            " 7   depression    2000 non-null   bool   \n",
            " 8   anxiety       2000 non-null   bool   \n",
            " 9   panic_attack  2000 non-null   bool   \n",
            " 10  specialist    2000 non-null   bool   \n",
            " 11  sport         2000 non-null   bool   \n",
            " 12  alchool       2000 non-null   bool   \n",
            "dtypes: bool(7), float64(3), object(3)\n",
            "memory usage: 474.6 KB\n"
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      ],
      "source": [
        "student.info(memory_usage=\"deep\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Cleaning"
      ],
      "metadata": {
        "id": "vPaA3a4D8EPV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Formatto i dati in modo da poter essere elaborati con maggiore facilità\n",
        "\n",
        "\n",
        "Elimino inoltre la colonna TimeStamp in quanto riguarda la data di compilazione del questionario e non ha nessuna correlazione con i dati"
      ],
      "metadata": {
        "id": "b_rN0tTb-MIQ"
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        "# Gender:  0 => Female\n",
        "# Gender:  1 => Male\n",
        "student[\"gender\"] = student['gender'].map({'Female': False, 'Male': True})\n",
        "\n",
        "student.drop(\"Timestamp\",axis = 1, inplace=True)\n",
        "\n"
      ],
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        "id": "cVOpaaV80nup"
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              "   gender   age                           course  year     grades  marital  \\\n",
              "0    True  21.0                          Finance   3.0  20.252930    False   \n",
              "1   False  18.0               Management Studies   2.0  29.959451    False   \n",
              "2   False  21.0  General Business and Management   2.0  25.429350     True   \n",
              "3    True  20.0                       Accounting   2.0  19.425978    False   \n",
              "4    True  18.0                 Computer Science   1.0  21.539774    False   \n",
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              "   depression  anxiety  panic_attack  specialist  sport  alchool  \n",
              "0       False     True         False       False   True    False  \n",
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              "2        True    False          True        True  False     True  \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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-96f78195-f2e6-4443-9aac-47dacda58ff0 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-96f78195-f2e6-4443-9aac-47dacda58ff0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3L_Sn5SDFtYT"
      },
      "source": [
        "### Significato delle feature"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wTb47myOFtYU"
      },
      "source": [
        "Dati di tipo categorico \n",
        "- **`gender`** Genere del soggetto in esame (True se maschio False se Femmina)\n",
        "\n",
        "- **`age`** Età del soggetto\n",
        "\n",
        "- **`course`** Il nome del corso di studio dello studente\n",
        "\n",
        "- **`year`** Anno di studio corrente\n",
        "\n",
        "- **`grades`** Media dei voti dello studente\n",
        "\n",
        "- **`marital`** Indica se il soggetto è sposato oppure no\n",
        "\n",
        "- **`depression`** indica la presenza o meno di depressione dello studente\n",
        "\n",
        "- **`anxiety`** indica se il soggetto soffre di ansia\n",
        "\n",
        "- **`panic_attack`** Indica se il soggetto soffre di attacchi di panico\n",
        "\n",
        "- **`specialist`** indica se il soggetto è seguito da uno specialista (es. psicologo) \n",
        "\n",
        "- **`sport`** indica se il soggetto pratica sport regolarmente\n",
        "\n",
        "- **`alchool`** indica se il soggetto fa uso regolarmente di alchol\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0kPIcYCQFtYU"
      },
      "source": [
        "Con il metodo _describe_ è possibile avere una rappresentazione statistica delle feature numeriche, ottenendo media, deviazione standard e la distribuzione in termini di massimi, minimi e percentili.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jIipdG7kFtYV"
      },
      "source": [
        "### Esplorazione singole feature"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jHcU7w91FtYV",
        "outputId": "7192a6e5-3fef-4565-918c-031742055d4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              age         year       grades\n",
              "count  2000.00000  2000.000000  2000.000000\n",
              "mean     20.77500     2.854500    24.071375\n",
              "std       1.93604     1.307358     3.161989\n",
              "min      18.00000     1.000000    18.000766\n",
              "25%      19.00000     2.000000    21.612770\n",
              "50%      21.00000     3.000000    24.168937\n",
              "75%      22.00000     4.000000    26.609599\n",
              "max      24.00000     5.000000    29.996210"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>year</th>\n",
              "      <th>grades</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2000.00000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>20.77500</td>\n",
              "      <td>2.854500</td>\n",
              "      <td>24.071375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.93604</td>\n",
              "      <td>1.307358</td>\n",
              "      <td>3.161989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>18.000766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>19.00000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>21.612770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>21.00000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>24.168937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>22.00000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>26.609599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>24.00000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>29.996210</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-476f67a1-8782-41b3-a145-9da6c4bdc31c')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\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",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-476f67a1-8782-41b3-a145-9da6c4bdc31c 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-476f67a1-8782-41b3-a145-9da6c4bdc31c');\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": 7
        }
      ],
      "source": [
        "student.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I dati sono distribuiti abbastanza uniformemente, i voti, riguardando studenti universitari si muovono tra un minimo di 18 ed un massimo di 30.\n",
        "Tra i dati non troviamo casi particolari che possano alterare l'elaborazione di un modello.\n"
      ],
      "metadata": {
        "id": "4UJJgzR8og9v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analisi dei dati"
      ],
      "metadata": {
        "id": "It_9qUhQ_SQJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "student['depression'].value_counts().plot.pie(autopct='%1.1f%%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "_Xg7Cp9ea87_",
        "outputId": "98511203-2b76-4d8f-d10c-a69a419e44fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f11ee2cacd0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WnL4gq0DFtYV"
      },
      "source": [
        "Circa il 70% degli studenti che fanno parte del Dataset non soffrono di depressione, questo potrebbe essere un problema nella fase di modellazione e valutazione del modello. \n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "student.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_TW6O67a_Tq",
        "outputId": "f9aae7dc-fcf7-49b2-fb9a-7db7f04382ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender          0\n",
              "age             0\n",
              "course          0\n",
              "year            0\n",
              "grades          0\n",
              "marital         0\n",
              "depression      0\n",
              "anxiety         0\n",
              "panic_attack    0\n",
              "specialist      0\n",
              "sport           0\n",
              "alchool         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Il dataset non contiene valori nulli"
      ],
      "metadata": {
        "id": "6XNULyubBWvl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(nrows=1, ncols=2, sharex=False, sharey=False, figsize=(20, 10))\n",
        "\n",
        "\n",
        "axes[0].boxplot(student[\"grades\"], showmeans=True)\n",
        "axes[1].boxplot(student[\"age\"], showmeans=True)\n",
        "axes[0].set_title(\"grades Boxplot\")\n",
        "axes[1].set_title(\"age Boxplot\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "e0qYsjiUBiNd",
        "outputId": "4f320269-dfba-4cbd-dfe1-e719ba49e81f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'age Boxplot')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Come abbiamo già  potuto constatare i dati sono distribuiti uniformemente, non ci sono eccezioni o elementi fuori dal range di valori accettabili"
      ],
      "metadata": {
        "id": "ajxbufeD-7qa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 10))\n",
        "\n",
        "plt.subplot(2, 3, 1)\n",
        "plt.title('Age')\n",
        "plt.hist(student['age'], label=\"age\", color='gray', bins=7)\n",
        "plt.ylabel('count')\n",
        "\n",
        "plt.subplot(2, 3, 2)\n",
        "plt.title('course')\n",
        "plt.hist(student['course'], label=\"course\")\n",
        "plt.xticks(rotation=90)\n",
        "plt.ylabel('count')\n",
        "\n",
        "plt.subplot(2, 3, 3)\n",
        "plt.title('grades')\n",
        "plt.hist(student['grades'], label=\"grades\", color=\"red\")\n",
        "plt.ylabel('count')\n",
        "\n",
        "plt.subplot(2, 3, 4)\n",
        "plt.title('year')\n",
        "plt.hist(student['year'], label=\"year\", color=\"green\", bins=5)\n",
        "plt.ylabel('count')\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "ZRHJRmtsdE3g",
        "outputId": "cf92428c-4132-4062-de1b-8d06ffa1e83a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 4 Axes>"
            ],
            "image/png": 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tVwBPmaL8Orr5kjYt/zHw8kHFI0mSJEmSpK0zlDmSJEmSJEmSNPcNcmibJEmSFqilR39+1CFIkqQBsEeSJEmSJEmS+mIiSZIkSZIkSX0xkSRJkiRJkqS+mEiSJEmSJElSX0wkSZIkSZIkqS+u2iZpVqxevXrUIfRt1apVow5BkiRJkuYkeyRJkiRJkiSpLyaSJEmSJEmS1BcTSZIkSZIkSeqLiSRJkiRJkiT1xUSSJEmSJEmS+mIiSZIkSZIkSX0xkSRJkiRp7CRZkuT8JNckuTrJm1r5LknOTfLt9rhzK0+SE5KsS3JFkv1GWwNJmp9MJEmSJEkaR/cAb6mqfYEDgKOS7AscDZxXVfsA57XnAAcB+7RtJXDi8EOWpPnPRJIkSZKksVNVG6rqsrZ/F3AtsAewHDi1nXYqcEjbXw6cVp2LgJ2S7DbksCX1KxnNpq1mIkmSJEnSWEuyFHgKcDGwuKo2tEO3AIvb/h7ATT2X3dzKNn2tlUnWJlm7cePGgcUsSfOViSRJkiRJYyvJDsBngDdX1Z29x6qqgNqc16uqk6pqoqomFi1aNIuRStLCYCJJkiRJ0lhKsh1dEuljVXVGK751cshae7ytla8HlvRcvmcrkyTNIhNJkiRJksZOkgAnA9dW1fE9h84GVrT9FcBZPeWvaqu3HQDc0TMETpI0S7YddQCSJEmSNIVnAK8ErkxyeSt7B3AcsCbJkcCNwKHt2DnAwcA64EfAa4YbriQtDCaSJEmSJI2dqroQmG6JpedOcX4BRw00KEmSQ9skSZIkSZLUHxNJkiRJkiRJ6svAEklJliQ5P8k1Sa5O8qZWfkyS9Ukub9vBPde8Pcm6JN9K8oJBxSZJkiRJkhagZPjbPDPIOZLuAd5SVZcl2RG4NMm57dj7quo9vScn2Rc4DHgisDvwpSSPq6p7BxijJEmSJEmS+jSwHklVtaGqLmv7dwHXAnvMcMly4PSquruqrqdbbWH/QcUnSZIkSZKkzTOUOZKSLAWeAlzcit6Y5IokpyTZuZXtAdzUc9nNTJF4SrIyydokazdu3DjAqCVJkiRJktRr4ImkJDsAnwHeXFV3AicCjwWWARuA927O61XVSVU1UVUTixYtmvV4JUmSJEmSNLWBJpKSbEeXRPpYVZ0BUFW3VtW9VXUf8CHuH762HljSc/merUySJEmSJEljYJCrtgU4Gbi2qo7vKd+t57SXAle1/bOBw5Jsn2RvYB/gkkHFJ0mSJEmSpM0zyFXbngG8ErgyyeWt7B3A4UmWAQXcALwOoKquTrIGuIZuxbejXLFNgtWrV486BEmSJC1083AJc0lbZmCJpKq6EJjqr805M1xzLHDsoGKSJI2PJKcALwZuq6pfaWXHAL8NTK6m8I6qOqcdeztwJHAv8HtV9YWhBy1JkiQtcENZtU2SpCl8BHjhFOXvq6plbZtMIu0LHAY8sV3z10m2GVqkkiRJkgATSZKkEamqC4Dv9Xn6cuD0qrq7qq4H1nH/Yg2SJEmShsREkiRp3LwxyRVJTkmycyvbA7ip55ybW9kDJFmZZG2StRs3bpzqFEmSJElbyESSJGmcnAg8FlgGbADeu7kvUFUnVdVEVU0sWrRotuOTJEmSFjQTSZKksVFVt1bVvVV1H/Ah7h++th5Y0nPqnq1MkiRJ0hCZSJIkjY0ku/U8fSlwVds/GzgsyfZJ9gb2AS4ZdnySJEnSQrftqAOQJC1MST4BHAjsmuRmYBVwYJJlQAE3AK8DqKqrk6wBrgHuAY6qqntHEbckSZK0kJlIkiSNRFUdPkXxyTOcfyxw7OAikiRJkvRgHNomSZIkSZKkvphIkiRJkiRJUl9MJEmSJEmSJKkvJpIkSZIkSZLUFxNJkiRJkiRJ6ouJJEmSJEmSJPXFRJIkSZIkSZL6YiJJkiRJkiRJfTGRJEmSJEmSpL5sO+oAJEmSJEl9SkYdgaQFzh5JkiRJkiRJ6ouJJEmSJEmSJPXFRJIkSZIkSZL64hxJkhac1atXjzqEvq1atWrUIUiSJEnST9kjSZIkSZIkSX0xkSRJkiRJkqS+mEiSJEmSJElSXwaWSEqyJMn5Sa5JcnWSN7XyXZKcm+Tb7XHnVp4kJyRZl+SKJPsNKjZJkiRJkiRtvkH2SLoHeEtV7QscAByVZF/gaOC8qtoHOK89BzgI2KdtK4ETBxibJEmSpDGW5JQktyW5qqfsmCTrk1zetoN7jr29fSn9rSQvGE3UkjT/DSyRVFUbquqytn8XcC2wB7AcOLWddipwSNtfDpxWnYuAnZLsNqj4JEmSJI21jwAvnKL8fVW1rG3nALQvrA8Dntiu+esk2wwtUklaQIYyR1KSpcBTgIuBxVW1oR26BVjc9vcAbuq57OZWtulrrUyyNsnajRs3DixmSZIkSaNTVRcA3+vz9OXA6VV1d1VdD6wD9h9YcJK0gA08kZRkB+AzwJur6s7eY1VVQG3O61XVSVU1UVUTixYtmsVIJUmSJM0Bb2xzqp4yOd8qfX4pDbP4xXQymk2SRqyvRFKS8/opm+Kc7eiSSB+rqjNa8a2TQ9ba422tfD2wpOfyPVuZJGnMbWk7IUma/2a5jTgReCywDNgAvHdzX8AvpiVp68yYSErysCS7ALsm2bmtuLZLG6o2ZYa/59oAJwPXVtXxPYfOBla0/RXAWT3lr2qrtx0A3NEzBE6SNIa2pp2QJM1vg2gjqurWqrq3qu4DPsT9w9f8UlqShmTbBzn+OuDNwO7ApcBkX8o7gQ8+yLXPAF4JXJnk8lb2DuA4YE2SI4EbgUPbsXOAg+nGM/8IeE3/1ZAkjcjWtBOSpPlt1tuIJLv1fNn8UmByRbezgY8nOb7dbx/gki2MW5I0gxkTSVX1AeADSX63qv5yc164qi7k/sZiU8+d4vwCjtqce0iSRmtr2glJ0vy2tW1Ekk8AB9L1aLoZWAUcmGQZ3TyrN9Alq6iqq5OsAa4B7gGOqqp7Z6UikqSf8WA9kgCoqr9M8mvA0t5rquq0AcUlSZpDbCckSdPZ0jaiqg6fovjkGc4/Fjh2C8OUJPWpr0RSko/STWp3OTCZ2S/ADwiSJNsJSdK0bCMkaX7pK5EETAD7tuFnkiRtynZCkjQd2whJmkdmXLWtx1XAowcZiCRpTrOdkCRNxzZCkuaRfnsk7Qpck+QS4O7Jwqr6jYFEJUmaa2wnJEnTsY2QtLBlunXIBmxAHUH7TSQdM5C7S5Lmi2NGHYAkaWwdM+oAJEmzp99V2/550IFIkuYu2wlJ0nRsIyRpful31ba76FZWAHgosB3ww6p65KACkyTNHbYTkqTp2EZI0vzSb4+kHSf3kwRYDhwwqKAkSXOL7YQkaTq2EZI0v/S7attPVecfgBcMIB5J0hxnOyFJmo5thCTNff0ObXtZz9OHABPAjwcSkSRpzrGdkCRNxzZCkuaXfldte0nP/j3ADXRdUiVJAtsJSdL0bCMkaR7pd46k1ww6EEnS3GU7IUmajm2EJM0vfc2RlGTPJGcmua1tn0my56CDkyTNDbYTkqTp2EZI0vzS72TbHwbOBnZv22dbmSRJYDshSZqebYQkzSP9JpIWVdWHq+qetn0EWDTAuCRJc4vthCRpOrYRkjSP9JtI+m6SI5Js07YjgO8OMjBJ0pxiOyFJmo5thCTNI/0mkl4LHArcAmwAfhN49YBikiTNPbYTkqTp2EZI0jzS16ptwJ8BK6rq+wBJdgHeQ9coSJJkOyFJmo5thCTNI/32SHrS5B9+gKr6HvCUwYQkSZqDNrudSHJKW73nqp6yXZKcm+Tb7XHnVp4kJyRZl+SKJPsNrCaSpNnmZwlJmkf6TSQ9ZPLNPPz0W4R+ezNJkua/LWknPgK8cJOyo4Hzqmof4Lz2HOAgYJ+2rQROnIWYJUnD4WcJSZpH+v0D/l7gq0k+1Z6/HDh2MCFJkuagzW4nquqCJEs3KV4OHNj2TwW+ArytlZ9WVQVclGSnJLtV1YZZiV6SNEh+lpCkeaSvRFJVnZZkLfCcVvSyqrpmcGFJkuaSWWwnFvckh24BFrf9PYCbes67uZU9IJGUZCVdryX22muvLQhBkjSb/CwhSfNL311K2x97/+BLkqY02+1EVVWS2oLrTgJOApiYmNjs6yVJs8/PEpI0f/Q7R5IkScNwa5LdANrjba18PbCk57w9W5kkSZKkIRpYImma1XiOSbI+yeVtO7jn2NvbajzfSvKCQcUlSRprZwMr2v4K4Kye8le11dsOAO5wfiRJkiRp+Aa5WsJHgA8Cp21S/r6qek9vQZJ9gcOAJwK7A19K8riquneA8UmSRijJJ+gm1t41yc3AKuA4YE2SI4EbgUPb6ecABwPrgB8Brxl6wJIkSZIGl0iaZjWe6SwHTq+qu4Hrk6wD9ge+OqDwJEkjVlWHT3PouVOcW8BRg41IkiRJ0oMZxRxJb0xyRRv6tnMrm241ngdIsjLJ2iRrN27cOOhYJUmSJEmS1Aw7kXQi8FhgGd2Sze/d3BeoqpOqaqKqJhYtWjTb8UmSJEmSJGkaQ00kVdWtVXVvVd0HfIhu+Bq4Go8kSZIkSdLYG2oiaXJJ5+alwOSKbmcDhyXZPsnewD7AJcOMTZIkSZIkSTMb2GTb06zGc2CSZUABNwCvA6iqq5OsAa4B7gGOcsU2SZIkSZKk8TLIVdumWo3n5BnOPxY4dlDxSJIkSZIkaeuMYtU2SZIkSZIkzUEmkiRJkiRJktQXE0mSJEmSJEnqi4kkSZIkSWMnySlJbktyVU/ZLknOTfLt9rhzK0+SE5KsS3JFkv1GF7kkzW8mkiRJkiSNo48AL9yk7GjgvKraBzivPQc4CNinbSuBE4cUoyQtOCaSJEmSJI2dqroA+N4mxcuBU9v+qcAhPeWnVeciYKckuw0nUklaWEwkSZIkSZorFlfVhrZ/C7C47e8B3NRz3s2t7AGSrEyyNsnajRs3Di5SSZqnTCRJkiRJmnOqqoDagutOqqqJqppYtGjRACKTpPnNRJIkSZKkueLWySFr7fG2Vr4eWNJz3p6tTJI0y0wkSZIkSZorzgZWtP0VwFk95a9qq7cdANzRMwROkjSLth11AJIkSZK0qSSfAA4Edk1yM7AKOA5Yk+RI4Ebg0Hb6OcDBwDrgR8Brhh6wJC0QJpIkSZIkjZ2qOnyaQ8+d4twCjhpsRJIkcGibJEmSJEmS+mQiSZIkSZIkSX0xkSRJkiRJkqS+mEiSJEmSJElSX0wkSZIkSZIkqS8mkiRJkiRJktQXE0mSJEmSJEnqi4kkSZIkSZIk9cVEkiRJkiRJkvpiIkmSJEmSJEl9MZEkSZIkSZKkvphIkiRJkiRJUl9MJEmSJEmSJKkvA0skJTklyW1Jruop2yXJuUm+3R53buVJckKSdUmuSLLfoOKSJEmSJEnSlhlkj6SPAC/cpOxo4Lyq2gc4rz0HOAjYp20rgRMHGJckSZIkSZK2wMASSVV1AfC9TYqXA6e2/VOBQ3rKT6vORcBOSXYbVGySJEmSJEnafMOeI2lxVW1o+7cAi9v+HsBNPefd3MoeIMnKJGuTrN24cePgIpUkSZIkSdLPGNlk21VVQG3BdSdV1URVTSxatGgAkUmSJEmSJGkqw04k3To5ZK093tbK1wNLes7bs5VJkiRJkiRpTAw7kXQ2sKLtrwDO6il/VVu97QDgjp4hcJIkSZIkSRoD2w7qhZN8AjgQ2DXJzcAq4DhgTZIjgRuBQ9vp5wAHA+uAHwGvGVRckiRJkiRJ2jIDSyRV1eHTHHruFOcWcNSgYpEkSZIkSdLWG9lk25IkSZIkSZpbTCRJkiRJkiSpLyaSJEmSJEmS1BcTSZIkSZIkSeqLiSRJkiRJkiT1ZWCrtkmStKWS3ADcBdwL3FNVE0l2AT4JLAVuAA6tqu+PKkZJkiRpIbJHkiRpXD27qpZV1UR7fjRwXlXtA5zXnkuSJEkaIhNJkqS5Yjlwats/FThkhLFIkiRJC5KJJEnSOCrgi0kuTbKylS2uqg1t/xZg8VQXJlmZZG2StRs3bhxGrJIkSdKC4RxJkqRx9MyqWp/kUcC5Sb7Ze7CqKklNdWFVnQScBDAxMTHlOZIkSZK2jD2SJEljp6rWt8fbgDOB/YFbk+wG0B5vG12EkiRJ0sJkIl+c0nwAACAASURBVEmSNFaSPCLJjpP7wPOBq4CzgRXttBXAWaOJUJIkSVq4HNomSRo3i4Ezk0DXTn28qv4pydeANUmOBG4EDh1hjJIkSdKCZCJJkjRWquo64MlTlH8XeO7wI5IkSZI0yUSSJEmSpDklyQ3AXcC9wD1VNZFkF+CTwFLgBuDQqvr+qGKUpPnKOZIkSZIkzUXPrqplVTXRnh8NnFdV+wDnteeSpFlmIkmSJEnSfLAcOLXtnwocMsJYJGneMpEkSZIkaa4p4ItJLk2yspUtrqoNbf8WusUbHiDJyiRrk6zduHHjMGKVpHnFOZIkSZIkzTXPrKr1SR4FnJvkm70Hq6qS1FQXVtVJwEkAExMTU54jSZqePZIkSZIkzSlVtb493gacCewP3JpkN4D2eNvoIpSk+ctEkiRJkqQ5I8kjkuw4uQ88H7gKOBtY0U5bAZw1mgglaX5zaJskSZKkuWQxcGYS6D7PfLyq/inJ14A1SY4EbgQOHWGMkjRvmUiSJEmSNGdU1XXAk6co/y7w3OFHJEkLi0PbJEmSJEmS1BcTSZIkSZIkSerLSIa2JbkBuAu4F7inqiaS7AJ8ElgK3AAcWlXfH0V8kiRJkiRJeqBR9kh6dlUtq6qJ9vxo4Lyq2gc4rz2XJEmSJEnSmBinoW3LgVPb/qnAISOMRZIkSZIkSZsYVSKpgC8muTTJyla2uKo2tP1b6Jb1fIAkK5OsTbJ248aNw4hVkiRJkiRJjGiOJOCZVbU+yaOAc5N8s/dgVVWSmurCqjoJOAlgYmJiynMkSZIkSZI0+0bSI6mq1rfH24Azgf2BW5PsBtAebxtFbJIkSZIkSZra0BNJSR6RZMfJfeD5wFXA2cCKdtoK4KxhxyZJkiRJkqTpjWJo22LgzCST9/94Vf1Tkq8Ba5IcCdwIHDqC2CRJkiRJkjSNoSeSquo64MlTlH8XeO6w45EkSZIkSVJ/RrVqmyRJkiRJkuYYE0mSJEmSJEnqi4kkSZIkSZIk9cVEkiRJkiRJkvpiIkmSJEmSJEl9MZEkSZIkSZKkvphIkiRJkiRJUl9MJEmSJEmSJKkvJpIkSZIkSZLUFxNJkiRJkiRJ6ouJJEmSJEmSJPXFRJIkSZIkSZL6YiJJkiRJkiRJfTGRJEmSJEmSpL6YSJIkSZIkSVJfTCRJkiRJkiSpLyaSJEmSJEmS1BcTSZIkSZIkSeqLiSRJkiRJkiT1xUSSJEmSJEmS+mIiSZIkSZIkSX0xkSRJkiRJkqS+mEiSJEmSJElSX0wkSZIkSZIkqS8mkiRJkiRJktSXsUskJXlhkm8lWZfk6FHHI0kaH7YRkqSZ2E5I0uCNVSIpyTbAXwEHAfsChyfZd7RRSZLGgW2EJGkmthOSNBxjlUgC9gfWVdV1VfVfwOnA8hHHJEkaD7YRkqSZ2E5I0hBsO+oANrEHcFPP85uBp/eekGQlsLI9/UGSb23hvXYFvrOF144T6zE+5kMdwHqMlWOOOWZL6/GY2Y5lDDxoGwHzrp0whvGJAcYjDmMwhp/Ku7cqBtuJzlxvJ2bDfKjHfKgDWI9xM/frkQyknRi3RNKDqqqTgJO29nWSrK2qiVkIaaSsx/iYD3UA6zFu5ks9hmk+tRPGMD4xjEscxmAM4xbDXDSf2onZMB/qMR/qANZj3MyHegyqDuM2tG09sKTn+Z6tTJIk2whJ0kxsJyRpCMYtkfQ1YJ8keyd5KHAYcPaIY5IkjQfbCEnSTGwnJGkIxmpoW1Xdk+SNwBeAbYBTqurqAd1uq7uzjgnrMT7mQx3Aeoyb+VKPrTbkNgLG49/eGDrjEAOMRxzG0DGGzjjEMDYWaDsxG+ZDPeZDHcB6jJv5UI+B1CFVNYjXlSRJkiRJ0jwzbkPbJEmSJEmSNKZMJEmSJEmSJKkvCyKRlOSUJLcluaqnbFmSi5JcnmRtkv1HGWM/pqnHk5N8NcmVST6b5JGjjPHBJFmS5Pwk1yS5OsmbWvkuSc5N8u32uPOoY53JDPV4eXt+X5KxXipyhjr8RZJvJrkiyZlJdhp1rDOZoR7vbHW4PMkXk+w+6lhnMl09eo6/JUkl2XVUMUqStFDNh88TC+B9+Lx4D9tzfOzf+81UhyS/2/4/rk7y56OM88HM8DM1137HH5bkkiTfaPVY3cr3TnJxknVJPpluMYKtU1XzfgP+O7AfcFVP2ReBg9r+wcBXRh3nFtbja8Cz2v5rgXeOOs4HqcNuwH5tf0fgX4F9gT8Hjm7lRwPvHnWsW1iPXwYeD3wFmBh1nFtYh+cD27byd8/h/4tH9pzze8DfjDrWLalHe76EbuLQG4FdRx2r20B/Dh4LbN/2D2w/uzsNOYYH/M6P+9+BAf5bBDgC+N/t+V7A/qOOa6FvdF+EPnLUcbgtrG0+fJ5YAO/D58V72PZ8Trz3m+H/4tnAl3re0zxq1LFuYT3m2u94gB3a/nbAxcABwBrgsFb+N8DvbO29FkSPpKq6APjepsXAZO+dnwf+Y6hBbYFp6vE44IK2fy7wP4ca1Gaqqg1VdVnbvwu4FtgDWA6c2k47FThkNBH2Z7p6VNW1VfWt0UbXnxnq8MWquqeddhGw56hi7McM9biz57RH0P3Oj60ZfjcA3gf8EWNeB82KzwD3JvklulU2lgAfH3IMz5ui7KAhxzD5Dfmm23ZDDuOvgV8FDm/P7wL+asgxCEjy8SSPTPII4CrgmiRvHXVco5DkuUkePuo4Fpr58HliAbwPnxfvYdvhOfHeb4Y6/A5wXFXd3Y7dNrooH9wM9Zhrv+NVVT9oT7drWwHPAT7dymfld3xBJJKm8WbgL5LcBLwHePuI49lSV9P98Qd4Od2HjjkhyVLgKXSZ0sVVtaEdugVYPKKwNtsm9ZiTZqjDa4F/HHY8W2rTeiQ5tv2OvwL436OLbPP01iPJcmB9VX1jpEEtEEkWJzk5yT+25/smOXKIIdzX3gS/FPjLqnor3bdkA5fkd5JcCTy+DQuY3K4HrhhGDJu4DNhI963gt9v+DUkuS/LUIcXw9Ko6CvgxQFV9H9j67uCbKcmbWhIl7efzsiTPH3IMz2hDXv41yXVJrk9y3RBD2Ld9QXAIXbu0N/DKId4fgCR3Jblzk+2mNozmF4cUxquAb7ThFn+R5CXjPhRpHpuznycWwPvwOfsedq6+99vk/+JxwK+34VT/nORpo4xtc2xSjzn3O55kmySXA7fRdTT5N+D2niTrzdyfsNxiCzmR9DvA71fVEuD3gZNHHM+Wei3whiSX0nXD+68Rx9OXJDvQffP+5k16jlBdn7uxzr5Pmqkec8V0dUjyx8A9wMdGFdvmmKoeVfXH7Xf8Y8AbRxlfv3rrQffv/w7mUBJsHvgIXVfyyTm1/pXu/2JYfpLkcGAF8LlWNqxeOB8HXgKc3R4nt6dW1RFDiqHXucDBVbVrVf0CXa+ozwFvoOspNAw/SbINrU1Ksgi4b0j37vXa9nft+cDOdAmU44Ycw8nA8cAzgacBE+1xWLZrPdIOAc6uqp8wmvcK7wfeSvcmfE/gD+l+d04HThlGAFW1oqoeB7wMuImul9zGYdxbDzAnP0/M9/fhc/k9LHP0vd8U/xfbArvQDat6K7AmSUYYYl+mqMec+x2vqnurahldG7U/8IRB3GchJ5JWAGe0/U/R/SPPOVX1zap6flU9FfgEXcZxrLU3gp8BPlZVk/8HtybZrR3fjS6DOtamqcecMl0dkrwaeDHwivaGYqz18X/xMcZ82CdMWY/H0n3r/o0kN9A1CJclefToopz3dq2qNbRkQfv25t4h3v81dEOpjq2q65PsDXx0GDeuqjuq6oaqOpzu26rJD+o7JNlrGDFs4oCq+kJPfF8EfrWqLgK2H1IMJwBnAo9KcixwIfB/hnTvXpNvvg8GPlpVV/eUDcsdVfWPVXVbVX13chvi/f8WuIFuqPIFSR4DjOILnN+oqr+tqruq6s6qOgl4QVV9ki7JN3BJjkjyt3TDFP4H8EHg14dxbz3AnPs8Md/fh8+D97Bz7r3fNP8XNwNntKFWl9C9rxrbScNh2nrMud/xSVV1O3A+3fvKnZJs2w7tCazf2tdfyImk/wCe1fafQ9dtfs5J8qj2+BDgT+gmzxpbLRN9MnBtVR3fc+hsul9U2uNZw45tc8xQjzljujokeSHdmOzfqKofjSq+fs1Qj316TlsOfHPYsW2OqepRVVdW1aOqamlVLaVrlPerqltGGOp898Mkv8D9PVAOAO4Y1s2r6hrgbXTDuqiq66vq3cO6P0CSNwK30vUI+nzbPjfjRYOxIcnbkjymbX9E92FnG4bUK6iqPkb39/D/AzYAh1TVp4Zx701cmuSLdImkLyTZkeH3jDq/DaP61ST7TW7DunlVnVBVe1TVwe2DyY10k7kO24+SHJrkIW07lDb0keH14ng/sAz4EPB7VfXnVfXVId1bP2tOfZ6Y7+/D58N72Ln23m+Gn6l/oP2NTvI4umHh3xl+hP2ZoR5z7Xd8Udpqhenm0nse3XxP5wO/2U6bld/xzIFE7VZL8gm61W92pXtzvAr4FvABum53PwbeUFWXjirGfkxTjx2Ao9opZwBvH+fse5JnAv8XuJL73wS/g24M6hq6FXFuBA6tqk0nNBwbM9Rje+AvgUXA7cDlVfWCkQT5IGaowwl09Zj8pvmiqnr98CPszwz1OJJuBb376H6mXl9VW519H5Tp6lFV5/SccwPdaoBj2xDPde2D8V8Cv0I3oe8i4DeraihzBCV5Cd0Y/IdW1d5JlgF/VlW/MYz7txjW0c0NNMzeJlPFsStdO/fMVvT/gNV0ib29qmrdEGI4ALi6uok3SfJI4Jeraqhz4rUvi5YB11XV7S3Zucewfi5bDOdPUVxV9Zwh3X8xXW+w3avqoCT70vVQG+owg3TzIH2A7hveopvQ9/fpvt19alVdOKQ4nki3itgzgX2Ab1XV0OeMWkjmw+eJBfA+fF68h51L7/1m+L/4Et1w32V0U6/8YVV9eSRB9mGGetzJ3PodfxLdZNrb0HUaWlNVf9bartPphht+HTii2kToW3yvMc45SJI0dK3r7+Pphg59q7q5WIZ170vpvvH6SlU9pZVdVVW/MsQYzgeeV/dPyrhgJfk63TfBkz3UHgKsraqh9cRp930p8OWquqM93wk4sKr+YZhxjFK6CfA/DPxxVT25/Z5+var+24hDG7qW0HwG3bfkv06X2LioqlbMeKEkSbNk2wc/RZKkhSHJUXRj469uz3dOcnhVDW1y56q6Y5P5KIc9hOk64CtJPg/89NuqYQ/jbV3h/xBYSs/7lWH1gJkMo7eXb1Xd1zPHwDCtqqoze+K4PckquqEDQ5HkD6YovgO4tKouH0IIu1bVmiRvh27+siTDnL8M+OmE67/NA38uXzvEMC7s2T5YVTcP8d6SJJlIkiSpx29X1V9NPqmq7yf5bYa3StjVSX4L2KbN8/V7wL8M6d6T/r1tD2UES933+BTdvH9/x3AnPO91XZLfA05sz99Al2gbtqnmtBz2e7iJtn22PX8xcAXw+iSfqqo/H/D9Rzp/WY+z6IY/fIkR/VxW1ZNGcV9JkiY5tE2SpCbJlcCTeoYybQNcUVVPHNL9fw74Y7pl3gN8AXhnVf14xgsHFMsoJytNcml1K5KOTFvQ4gS64YYFnEe3JPBQVzRKcgrdvHuTSc6jgF2q6tVDjOEC4OCq+kF7vgPdROwvpOuVtO+A7z/S+ct64ri8umWVR6b1ivoj4InAwybLh9xbT5K0gJlIkiSpSfIXwGPolhoHeB1wU1W9ZXRRDVeSX6VbuWSHqtoryZOB11XVG4YcxzF0S1Cfyc8OsRvbCWAHJckjgD+lW+oduhX13lVVPxxiDN8E/tvknGFJtge+UVVPSPL1yTm9BhzDyOYv64nhXcC/9E6GO4IYvgh8km7o5+vpVuDZWFVvG1VMkqSFxUSSJElNm0z5dcBzW9G5wN9V1UCHsCR5f1W9OclnmWIJ8SGv2nYx3RKxZ49qwu92z+unKK6q+sUhxjAO8+GMhSR/CryU+5cMfgndkuHvBU6qqlcM6L7PqaovJ3nZVMer6oxB3HeGeO4CHkGX3PwJXVKrquqRQ4zh0qp6apIrJoe5JflaVT1tWDFIkhY250iSJKmpqvvo5sM58cHOnWUfbY/vGfJ9p1RVN20y4ffQ54Kpqr2Hfc8pjHQ+nHFKMFbVO9vKac9oRa+vqrVtfyBJpOZZwJfpElcPCAsYaiKpqnYc5v2mMdkTa0OSFwH/QbeksyRJQ2GPJEmSmiTPAI6hG962Lff3NhhaL5hRS/Jp4Hjgg8DTgTcBE1V12JDuPzY9UEY9H06Sp1bVpUmeNdXxqvrnIcTwyKq6M8mUiYqFMtQwyROq6pttrqYHqKrLhhjLi+kSnEvo5o16JHBMVX12xgslSZolJpIkSWraPDC/D1xKTw+UqvrugO97JVP0OOm5/9BWaUqyK/ABuvl4AnwReNOg/w167r+6qlYl+fAUh2uYw8rGYT6cUUvyuap6cRtq2PszOpQka5I/mOl4VR0/yPv3xHFSVa1Mcv7UYYx2ouskb66q948yBknSwmEiSZKkJsnFVfX0Edz3MW33qPY4OdTtCLoPqUcPO6ZRS7J3VV3/YGUDjmFyPpz/atvQ58NpcbwYeCcP7Ck31DhGIcmqtvt44Gl08zJBN9Ttkqo6YsjxPGzTVRSnKhu2JP9eVXuNMgZJ0sJhIkmSpCbJccA2dPOu9K4UNpRhK1OtfpXksqqacjjNgGLYG/hdHjjB9NDm42lxPKDek5MMDzOOcZBkHfAy4Moa0Ru3Nuzz8qr6YZIjgP2A91fVvw/p/hcAL6qqu9rzHYHPV9V/H8b9e+KY6udyqL+jU0lyU1UtGWUMkqSFw8m2JUm632RvpImesgKGNWwlSZ5RVf+vPfk14CFDuvekfwBOBj4L3Dfke5PkCcATgZ/fZJ6kRwIPG3IsoZtIeu822fQSYLequmSYcQA3AVeNKonUnAg8OcmTgbcAf0fXc27K+ZsGYDFdr7BJ/9XKhiLJo4E9gIcneQpdrzDofi5/blhxzMBvhiVJQ2MiSZKkpqqePeIQjoT/v717j9e1nvM//nrvvVNJR21JB4XwSyrZyGFQxiFSMaGIpJEhYhhGzEzKb34G41Q5RSjnUCSVkuQw02HvNJ0wklCipMPuoNr1+f1xXat972XtuqP7uq5ar+fjcT/WdbjX+r6723vttT739/v58qkkq9P8onol0PVW83+qqgM7HnPUw4DtgTVYdqeuxcArO87yEZpi2rY0S8uuBT5Ms8SqS28Bjk1yCsvOlOukP1BrSVVVkh2Bg6vq0CR7djj+4cDpSY5qz3cCDutw/GcCLwfWp2lGP+Ua4G1dBGiXWs5UMAqwchcZJEkCl7ZJkrSMdjvtRzAy+6WqDug4w+rtuFd3OW479ouBTWiabHe+vG8kx+Or6r+7HHOGDGdW1VajSw6T/E9VbdFxjhNoiljnMDJLrKr27zDDKcDxNIXNvwEuA/6nqh7ZYYat2rEBvl9VP+5q7JEMf1dVX+t6XEmShsQZSZIktZJ8jGaZyjY0S3d2BjpbxpTk36adA50Xsh4JvJRmFs5U0aLL5X1TLkjyNv68V1OXM7RuTjKXdhZIkvn0sNwPeEBVbdbDuKNeBLwYeEVV/S7JhsB7uxq8He8PwFGj17rq0TTiR0kOpfl/sl2STYHHV9WhHeeQJKk3FpIkSVrqCVW1eZKzq2r/JO8Djutw/OtGjleiWeL1kw7HB3gB8KCquukOnzlZ3wB+AHwHuKWnDAfSFC7ul+TfaQqL/9JDjmOTPKOqTuhhbADa4tHXaGarwbSiTge+xdJlXSsDGwM/o5k92KVPt4+3t+f/C3yZpq+YJEmzgoUkSZKWuqH9eH2SBwBXAOt2NXhVvW/0PMl/At/uavzWuTT9iS7reNzp7l1V/9xngKr6fJJFwNNo+tDsVFVdF/YAXg38U5KbgJuXxqvVugqQ5JXAXsBawINpGk9/jOa1mbjpS+jaZW6v6WLsadauqiOS7NvmWpKkr0KnJEm9sJAkSdJSxyRZg2bJzpk0MyA+2WOee9M09+3SGsBPk5zBsj2Sdug4xzFJnl1Vx3Y8LklWq6prkqxFU1D74si9tarqj13mqapVuxxvOfYGHgucBlBVP09yv77CVNWZSR53x8+8y12X5L4sXe64NdB5LzNJkvpks21JkmaQZEVgpS4bXic5h6XLd+YC84F3VtVBHWaYcTv3qjqlqwxtjsXAKjTFrJtpZgR1MgsnyTFVtX2SX7LsLllTGR406QwzZNoBeHJ7+r2qOqbj8U+rqsdNNR5PMg84s6o272j8N46czgG2Au5bVc/sYvyRHFsBBwGb0czemw/sXFVnd5lDkqQ+OSNJkqQRSZ7ASIPnJFTV4R0Nv/3I8RLg91W1pKOxgaZglOSBwCZV9Z0k96YpanWqz1k4VbV9+3HjvjKMSvIfwGOAz7eXXp/kiVW1b4cxTmmbn6+c5Ok0y8q+2eH4o38eltD0TOpj97QrgacAD6MpLP4M2LKHHJIk9cYZSZIktZJ8lqb/y1ksbfBcVbVPV+NX1Uvv6NqEM9zWC6eqHpxkE+BjVdVJL5yRHE+e6XpVfb/DDM8Dvjs1K61d9vjUqvp6Vxnacc8GtqyqW9vzucCPu5oN1I45B9gTeAZNAeXbwCerox8kk7ygqr5yR9c6yLEI2KGqLmnPnwx8eHoPJ0mS7sksJEk9STKv65kGkm5fkp8Am3b1y/EM459ZVVuNnM8Dzq6qTTvMcBZtL5yqelR77Zyuf1FOMjrbZaU206Kq2rbDDGdV1ZbTrv146nXpMMfZNAWsP7bna9Esb+uskNSOOx+gqi7vctx27GX+bizvWgc5HgN8BHguzfK6dwHbV9VvuswhSVKfXNomzSDJm4Gtq+rvRq4dSNMr49+A9wPPBm6l2QZ4v6q6JcmDgU8AW7TP/Tawd1Vd1X6Ni4CPAi8BHpZkFYtJ0qCcC9wfuLTLQdsdoKaWDV1DM+MD4CbgkC6zADdW1U1JprLNY9k+QZ2oqueOnifZAPhgxzHmzHCtj5+d3gX8OMnJNH82ngy8tYuB0/xB2A94Le3r0e5SdlBVHdDB+NvR/Hu7Xvvv8JTVaJa4daqqzkiyD3AC8Cfgb/sorEmS1CdnJEkzSLIucAGwXlVd1f4i9VtgO+BfaHbxeSNNI9hjgEOr6uNJHgJsDHyf5ofcr9E0I31D+3UvAq6ieSfzD1V1A5IGo/1FfUvgdHrYsSzJuzruezNThvfQfJ96GfA6ml4451fV23vOFeC8jmdnfYrmtfhwe+m1wJpV9fKuMoxkWZemTxLA6VX1u47GfSPNv317VdUv22sPonlT5Piq+sCEx9+C5u/kATRv5ExZDJxcVVdOcvyRHN9k2YLqpjQF5yuhl10NJUnqjYUkaTmSHAccWVWfSLI98B5gG+DXwBpTRaAku9L8gL3NDF9jJ5rZSlPLQy4CDqiqT3X0nyHpTuhrx7K2ufVVI714tgF2Ai6i6b9y0yTHn5al1144IzkOYukv7nNoigkXVdVuHWZYBfhX4G/bSycC/7eqrusqw0iW9YAHMjIjqot+UUl+DDy9qv4w7fp84ISulvklWaGqbk6yAs2OaZdU1WVdjN2OP+P3hild72ooSVKfLCRJy5FkF+DVVfWUJF8C/gc4CTgVuGbkqXOA31TVI5KsA3wI+BuaHWbmAFdW1Qbt17wIeGVVndjdf4mkoUtyGvC8qvptki2B79AsZ9ocuLmq/r7XgD1IsvvI6RKaItKPesyzJk2xr/MfnJK8G3gRcB7NkmpomsBPfBZMknOrarM7e+8uHP9jNMvozkuyOvDfNI3w1wL+qaq+OMnxp2WZC3xnpjeOJEmaTeyRJC3f14GPJtmMZkvutwA30yx3WXs5vY3+H8076I+sqj+2M5IOnvYcq7fSwCT5YVU9Kclilv07Gppf2FebcISVq+q37fFuwKeq6n3t7KCzJjz2MpKcw59/n7oaWEgzG+eKLnJU1WFJ7gU8tL30sy7GBUjyb8ARVfXTJCsCx9H0vrslyYur6jtdZWntBDysqm68w2fe9W5vNlwXM+X+pqr+oT3eA/jfqtopyf1p/r90VkhqeyHemmT1qdmDkiTNRhaSpOWoqj8l+SrwBZp+FL8GSHIC8L4k/wpcS9MTaf12WvuqNL9wXd0uQ3hzP+kl3UkvAaiqVXsaPyPH2wL7AlTVrVNNrzt0HM2Mjy+057sA9wZ+B3yGpsfbxCV5KnAYzfK+ABsk2b2L5Vw0s3/e2R7vTjO79H40Ra3DaGaMdelCYAVG+nZ1aIu2Afx0odlNb9JGi1VPB74CUFW/6+HvBjT/7p+T5ETgtiWOVbVPH2EkSeqDhSTp9h0G/D3wipFrLwP+AzifpnB0IfDu9t7+wOE0xaQLgM8C/9hVWEl/saNotvImyddGd2zsyHeTHEHTvHdN4LttlnXpZtbHqL+dtqX6OVPbrCfprD8R8D7gGVX1M4AkD6WZffLoDsa+aWQJ2zOBL1bVLcBP2s0XunY9cFaSk1i2CfzEixdVNXfSY9yBq9o+hZcAT6Tp3zW1m+DKPeQ5sn1IkjRrWUiSbt+vgRtodl8DoJ3O/ur2sYyqOo8//yXnfSP3N5pISkl/rdGpDQ/qYfw30MyCWRd4UlXd3F6/P9D1bmlzkzy2qk4HSPIYYKqY0OV26ytMFZEAqup/20bLXbixXdb8e5pNFv5p5N69O8ow6uj2MRu9CjiQ5u/CG0Z2q3sa8K2uw1TVYV2PKUnS0NhsW1qOtjfJ+4HVquoVd/R8SXdfUzNuph/PRm3h6FPAfWgKbNfQzMw8D3hOVR3RUY5P0TSW/lx76SXA3C6+Hyd5HM2M1PnAB6vqne31ZwMvrapdJ51hhkzL9IsaKTaqQ0k2oWmEvykjS/uqqo8CtCRJvbCQJM2g3fL5TLwTawAAIABJREFU98CvgGdV1W96jiRpgpLcQtPvJDTLZa6fukU3zbYHp90ha2oWZh/jrwjsDTypvfQD4CM9NZzu1Uz9ooCu+kVpRJIfAvsBH6DpF7YHMKeq/q3XYJIkdchCkiRJWkaS5wCPYNkZFwd0nGEV4E9tb6KprddXrKrrb/8z73mSLAJePL1fVFV10S9KI5IsqqpHJzmnqh45eq3vbJIkdWVO3wEkSVIjyevHuTbhDB+j6df0OprZLy8AHthlhtZJLNtMeWW63y1tKP6sXxTNLm7q3o3t0vefJ3ltkufRLAOVJGnWuFvPSFp77bVro4026juGJA3OokWL/lBV8/vOoTtnpv5MSX5cVY/qMMPZVbX5yMf7AMdV1d90laHNcVZVbXlH1yY4/hxg66r6ry7Gu4MsvfWLGoq2oPppYDHwSeBRwFur6oSOczwG+AmwBvBOYHXgPVV1apc5JEnq091617aNNtqIhQsX9h1DkgYnya/6zqDxJdkVeDGwcZLR3blWBf7YcZwb2o/XJ3kAcAXNbnJduy7JVlV1JkCSR49km7iqujXJh2kKFn17NU2/qH3a8x8AH+kvTi9eUVUfSvJMYE3gpcBngU4LSVV1Rnt4LU1/JEmSZp27dSFJkqR7iP8CLgXWBt43cn0xcHbHWY5JsgbwXuBMoGhmgHTtDcBXkvyWZond/WmW3HXppCR/BxxZ/U7hngd8qKreD0v7RfWYpw9pPz4b+GxVnZckt/cJd+ngyxZ4/0xV7dBVFkmS+na3Xtq2YMGCckaSJP25tvnrgr5z6O6t3TltpR53blsBeFh72vmW90kWA6sAt9DMhuplF78kpwJ/W1XXtuf3AU6oqid0maNPST4NrAdsDGwBzAW+11WT6ySXA78BvgicxtLCFgBVdUoXOSRJGoKJzkhKchHNu6m3AEuqakGStYAvAxvRbGP7wqq6sn1X6UM07zRdD7x8ajq7JEmzQZLnA+8G7kfzi2pfhYsn0Pw7Pa89p6oO7zjD3sDnq+rc9nzNJLtWVWdLuqpq1a7GugMrTRWRAKrq2iT37jNQD/YEtgQurKrr258nu1xadn/g6cDUMtRv0eycd16HGSRJGoQudm3bpqq2HHln/K3ASVW1Cc2OLG9tr28HbNI+9gI+2kE2SZKG5D3ADlW1elWtVlWr9lBE+izwn8CTgMe0jz5mt72yqq6aOqmqK4FXdhkgjd2S/Gt7vkGSx3aZoXVdktuasHfdL2ogHk8zK+2qJLsB/wJ0NlOuqm6pquOrandga+AC4HtJXttVBkmShqKPHkk7Ak9tjw8Dvgf8c3v98LYHwalJ1kiyblVd2kNGSZL68Puq+knPGRYAm/bcEwhgbpJM5Wj7At2r4wwfodktbVuaHbquBT5MU1zr0kz9onbpOEPfPgpskWQL4E00fbsOB57SVYB2qedzaGYlbQQcCBzV1fiSJA3FpAtJBZyQpICPV9UhwDojxaHfAeu0x+vRrD2fcnF7bZlCUpK9aGYsseGGG04wuiRJnVuY5MvA14Ebpy5W1ZEdZjiXplDR9xs5xwNfTvLx9vxVwHEdZ3hcVW2V5MfQzIpK0nUxi6o6I8nD6bFf1AAsqapKsiNwcFUdmmTPrgZPcjiwGXAssP/UkktJkmajSReSnlRVlyS5H3Bikp+O3mx/ILhT73i2xahDoGm2fddFlSSpd6vR9Al8xsi1ArosJK0NnJ/kdJYtZnW9K9U/07xx9A/t+dk0Ba4u3dzOhJqaFTWfZoZSJ5K8pare057uVFVfGbn3/6rqbV1lGYDFSfYFXgr8TZI5wAodjr8bcB3wemCfkQ3jeuljJklSnyZaSKqqS9qPlyU5Cngs8PupJWtJ1gUua59+CbDByKev316TJGlWqKoumwcvzzv6DgBQVbcmOQ14MPBCmgLX1zqOMbV06X5J/h3YGfjXDsffhaZvFsC+wFdG7j0LmE2FpBfRNLl+RVX9LsmGwHu7GryquugrKknS3cLECklJVgHmVNXi9vgZwAHA0cDuwH+0H7/RfsrRwGuTfAl4HHC1/ZEkSbNJkofS9IJZp6o2S7I5TfPt/9tVhr63MW9fg13bxx9odnqlqrbpOktVfT7JIuBpNDNPduq4h1WWczzT+T1aWzz6Gs2mLND82bA/kSRJPZjkjKR1gKPaqb/zgC9U1fFJzgCOaNe1/4rmXUZo1pw/m2YXjOvpdktX3YNk/1n1szW1nys8pXuQTwBvBj4OUFVnJ/kC0FkhKcnWwEHA/6Fpbj0XuK7DpTs/BX4AbF9VF7SZ/rGjsZeR5LNV9dI20/RrXajlHM90fo+W5JU0Sx3Xopmlth7wMZoinyRJ6tDECklVdSGwxQzXr2CGf/TbXVn2nlQeSZLuBu5dVaeP9F8BWNJxhoNpllR9hWYHt5cBD+1w/Oe345+c5HjgS/Q3++YRoydtv6RHdzj+FkmuofnvX7k9pj1fqcMcQ7A3TYuE0wCq6udtD05JktQx13tLkjQcf0jyYJY2d96ZHnZPa2cCza2qW6rq0zT9eLoa++tVtQvwcOBk4A00PYo+muQZt//Zd40k+yZZDGye5Joki9vzy1i6JH/iqmpuVa1WVatW1bz2eOq8y0bTQ3BjVd00dZJkHrNsVpYkSUMx6V3bJEnS+Pam2Zn04UkuAX5Js1tUl65vt7g/K8l7aApZnb/xVFXXAV8AvpBkTeAFNDu5ndDB2O8C3pXkXVW176TH01hOSfI2mplZTwdeA3yz50ySJM1KaVaU3T0tWLCgFi5c2HcMDYw9kiRIsqiqFvSdQ3+Z0Q0rehj7gTQzb1YA/hFYHfjIVL+i2STJk2e6XlXf7zrLbJdkDrAnzeYtAb4NfLLuzj/ISpJ0N+WMJEmSBiLJGjQ9iTYC5k31SqqqfbrKUFW/ag9vAPbvatyBevPI8Uo0PXoWAdv2E2f2qqpbaZrRf6LvLJIkzXYWkiRJGo5jgVOBc4Bbuxw4ydm3d7+qNu8qy1BU1XNHz5NsAHywpzizWpInAu8AHkjz82to9mp5UJ+5JEmajSwkSZI0HCtV1Rt7GvtWmubFX6DpPXNDTzmG7GLg//QdYpY6lGap5SLglp6zSJI0q1lImiVmW98gSbqb+mySVwLHADdOXayqP0564KraMsnDgV1piknntx9PqKolkx5/iJIcxNKdweYAWwJn9pdoVru6qo7rO4QkSbKQJEnSkNwEvBd4O0sLGAV0snynqn4K7Afsl+RFwOHAu9tMs9Hojh5LgC9W1Y/6CjPLnZzkvcCRLFtktbAnSVLHLCRJkjQcbwIeUlV/6GPwJOsBuwDPA66kWUp0VB9ZhqCqDktyL+DhNAW9n/UcaTZ7XPtxdDfKwsbnkiR1zkKSJEnDcQFwfR8DJzkFWBU4AtgDuKK9da8ka3WxvG5okjwb+DjwC5rmzhsneZVLrLpXVdv0nUGSJDUsJEmSNBzXAWclOZlll+/s08HYD6SZ4fEqYK+R66HD5XUD835gm6q6ACDJg4FvARaSOpJkt6r6XJIZm9BX1fu7ziRJ0mxnIUmSpOH4evvoXFVt1Me4A7d4qojUuhBY3FeYWWqV9uOqvaaQJEm3sZAkSdJAtD15VgY2rCr78fRvYZJjaZb7FfAC4IwkzweoqiP7DDcbVNXH24/7951FkiQ15vQdQJIkNZI8FzgLOL493zLJ0f2mmtVWAn4PPAV4KnA5sDLwXGD7/mLNPknek2S1JCskOSnJ5Ul26zuXJEmzkTOSJEkajncAjwW+B1BVZyWZjb2JBqGq9ug7g27zjKp6S5LnARcBzwe+D3yu11SSJM1CFpIkSRqOm6vq6iSj127tYuAka93e/Vm6a9vGwOuAjRj5mamqdugr0yw29fo/B/jKDH9PJElSRywkSZI0HOcleTEwN8kmwD7Af3U09iKaPkABNgSubI/XAH4NbNxRjiH5OnAo8E06KuhpuY5J8lPgBuDVSeYDf+o5kyRJs5KFJEmShuN1wNuBG4EvAt8G3tnFwFW1MUCSTwBHVdWx7fl2wE5dZBigP1XVgX2HEFTVW5O8B7i6qm5Jch2wY9+5JEmajSwkSZI0EFV1PU0h6e09xti6ql45dVJVx7W/wM9GH0qyH3ACTXEPgKo6s79Is1OSl40cj946vPs0kiTNbhaSJEkaiCTfpFleNupqYCHw8arqYinPb5P8C0ubGL8E+G0H4w7RI4GXAtuydGlbtefq1mNGjlcCngaciYUkSZI6ZyFJkqThuBCYT7OsDeBFwGLgocAnaIoak7YrsB9wVHv+/fbabPQC4EFVdVPfQWa7qnrd6HmSNYAv9RRHkqRZzUKSJEnD8YSqGp158c0kZ1TVY5Kc10WAdne213cx1t3AuTTNxi/rO4j+zHXMzgbwkiT1zkKSJEnDcZ8kG1bVrwGSbAjcp73XyayYJA8F/ok/3/J+Ni7nWgP4aZIzWLZH0g79RZqdpi37nANsChzRXyJJkmYvC0mSJA3Hm4AfJvkFEJoZF69JsgpwWEcZvgJ8DPgkcEtHYw7Vfn0H0G3+c+R4CfCrqrq4rzCSJM1mFpIkSRqIqjo2ySbAw9tLPxtpsP3BjmIsqaqPdjTWoFXVKUnWYWmj59OrymVuPaiqU6aOk6wNXNFjHEmSZrU5fQeQJEnL2AR4GLAF8MLRbc878s0kr0mybpK1ph4dZxiEJC8ETqdpuv1C4LQkO/ebanZJsnWS7yU5MsmjkpxL07vq90me1Xc+SZJmI2ckSZI0EEn2A55K0//lWGA74Id0u8X57u3HN49cK+BBHWYYircDj5mahZRkPvAd4Ku9pppdDgbeBqwOfBfYrqpOTfJwmt0Nj+8znCRJs5GFJEmShmNnmplIP66qPdplVZ/rMkBVuRPWUnOmLWW7Amdzd21eVZ0AkOSAqjoVoKp+mqTfZJIkzVIWkiRJGo4bqurWJEuSrEaz7fwGXYdIshnNrKiVpq5VVZezoobi+CTfppn5AvAi4Lge88xGt44c3zDtXiFJkjpnIUmSpOFYmGQN4BPAIuBa4L+7DDCQ5XWDUFVvTvJ84EntpUOq6qg+M81CWyS5hmYXw5XbY9rzlZb/aZIkaVIsJEmSNBBV9Zr28GNJjgdWq6qzO47R+/K6viV5CLBOVf2oqo4EjmyvPynJg6vqF/0mnD2qam7fGSRJ0rJc5y9J0oAkWS/JE4ANgTWSPLnjCDdU1a1Ar8vrevZB4JoZrl/d3pMkSZq1nJEkSdJAJHk3TR+e84Fb2ssFfL/DGL0vrxuAdarqnOkXq+qcJBt1H0eSJGk4LCRJkjQcOwEPq6ob+wowkOV1fVvjdu6t3FkKSZKkAXJpmyRJw3EhsELfIaZU1UWzsIgEzaysV06/mOTvaWZpSZIkzVrOSJIkaTiuB85KchJw26ykqtqnv0iz0huAo5K8hKWFowXAvYDn9ZZKkiRpACwkSZI0HEe3D/Woqn4PPCHJNsBm7eVvVdV3e4wlSZI0CBaSJEkaiKo6rO8MSR4MXFxVNyZ5KrA5cHhVXdVvsu5V1cnAyX3nkCRJGhJ7JEmSNBBJNkny1STnJ7lw6tFxjK8BtyR5CHAIsAHwhY4zSJIkaaAmXkhKMjfJj5Mc055vnOS0JBck+XKSe7XXV2zPL2jvbzTpbJIkDcyngY8CS4BtgMOBz3Wc4daqWkLTC+igqnozsG7HGSRJkjRQXcxIej3wk5HzdwMfqKqHAFcCe7bX9wSubK9/oH2eJEmzycpVdRKQqvpVVb0DeE7HGW5OsiuwO3BMe20wO8lJkiSpXxMtJCVZn+YH4E+25wG2Bb7aPuUwYKf2eMf2nPb+09rnS5I0W9yYZA7w8ySvTfI84D4dZ9gDeDzw71X1yyQbA5/tOIMkSZIGatIzkj4IvAW4tT2/L3BVO2Ue4GJgvfZ4PeA3AO39q9vnLyPJXkkWJll4+eWXTzK7JEldez1wb2Af4NHAS2lmBnWmqs6vqn2q6otJ1gRWrSpnCUuSJAmYYCEpyfbAZVW16K78ulV1SFUtqKoF8+fPvyu/tCRJvaqqM6rq2qq6uKr2qKrnV9WpXWZI8r0kqyVZCzgT+ESS93eZQZIkScM1b4Jf+4nADkmeDawErAZ8CFgjybx21tH6wCXt8y+h2Rnm4iTzgNWBKyaYT5KkQUhy9O3dr6odusoCrF5V1yT5e+Dwqtovydkdji9JkqQBm1ghqar2BfYFSPJU4J+q6iVJvgLsDHyJZrr+N9pPObo9/+/2/nerqiaVT5KkAXk8zfLuLwKnAX32CJyXZF3ghcDbe8whSZKkAepi17bp/hl4Y5ILaHogHdpePxS4b3v9jcBbe8gmSVIf7g+8DdiMZvbu04E/VNUpVXVKx1kOAL4N/KKqzkjyIODnHWeQJEnSQOXuPOlnwYIFtXDhwr5j3C1kfzfAu6eq/e6+f4c1OUkWVdWCvnPozkuyIrAr8F5g/6o6uOdIkiRJ0m36mJEkSZKmSbJikucDnwP2Bg4Ejuohx0OTnJTk3PZ88yT/0nUOSZIkDZOFJEmSepbkcJoegVvRzEJ6TFW9s6ouuYNPnYRP0PQ4vBmgqs4GdukhhyRJkgZokru2SerAbFu26FI+3UPtBlwHvB7YJ7nt73WAqqrVOsxy76o6fSQDwJIOx5ckSdKAWUiSJKlnVTWkGcJ/SPJgoACS7Axc2m8kSZIkDYWFJEmSNGpv4BDg4UkuAX5JM2NKkiRJspAkSZKWqqoLgb9Nsgowp6oW951JkiRJwzFrC0mzra+MJEnjSLIi8HfARsC8qV5JVXVAj7EkSZI0ELO2kCRJkmb0DeBqYBFwY89ZJEmSNDAWkiRJ0qj1q+pZfYeQJEnSMA1plxhJktS//0ryyL5DSJIkaZickSRJkkY9CXh5kl/SLG0LUFW1eb+xJEmSNAQWkiRJ0qjt+g4gSZKk4bKQJEmSSLJaVV0DLO47iyRJkobLQpIkSQL4ArA9zW5tRbOkbUoBD+ojlCRJkobFQpIkSaKqtm8/btx3FkmSJA2Xu7ZJkqTbJHliklXa492SvD/Jhn3nkiRJ0jBYSJIkSaM+ClyfZAvgTcAvgM/2G0mSJElDYSFJkiSNWlJVBewIHFxVHwZW7TmTJEmSBsIeSZIkadTiJPsCuwFPTjIHWKHnTJIkSRoIZyRJkqRRLwJuBPasqt8B6wPv7TeSJEmShsIZSZIk6TZt8ej9I+e/Bg7vL5EkSZKGxEKSJEm6TZLFQLWn96JZ1nZtVa3eXypJkiQNhYUkSZJ0m6q6rbF2ktA03d66v0SSJEkaEnskSZKkGVXj68Az+84iSZKkYXBGkiRJuk2S54+czgEWAH/qKY4kSZIGxkKSJEka9dyR4yXARTTL2yRJkiQLSZIkaamq2qPvDJIkSRoueyRJkiSSrJRk9yQ7pPGWJMck+VCStfvOJ0mSpGGwkCRJkgAOB54BvAL4HvBA4GBgMfCZ3lJJkiRpUFzaJkmSADatqs2SzAMurqqntNePT/I/fQaTJEnScDgjSZIkAdwEUFVLgN9Ou3dL93EkSZI0RM5IkiRJAOsnORDIyDHt+Xr9xZIkSdKQWEiSJEkAbx45Xjjt3vRzSZIkzVIWkiRJElV1WN8ZJEmSNHz2SJIkSZIkSdJYLCRJkiRJkiRpLBaSJEmSJEmSNBZ7JEmSJJIcBNTy7lfVPh3GkSRJ0kBZSJIkSeDObJIkSRrDxApJSVYCvg+s2I7z1araL8nGwJeA+wKLgJdW1U1JVgQOBx4NXAG8qKoumlQ+SZK0lLu2SZIkaRyT7JF0I7BtVW0BbAk8K8nWwLuBD1TVQ4ArgT3b5+8JXNle/0D7PEmS1KEk85P8Z5Jjk3x36tF3LkmSJA3DxApJ1bi2PV2hfRSwLfDV9vphwE7t8Y7tOe39pyXJpPJJkqQZfR74CbAxsD9wEXBGn4EkSZI0HBPdtS3J3CRnAZcBJwK/AK6qqiXtUy4G1muP1wN+A9Dev5pm+dv0r7lXkoVJFl5++eWTjC9J0mx036o6FLi5qk6pqlfQvAkkSZIkTbaQVFW3VNWWwPrAY4GH3wVf85CqWlBVC+bPn/9XZ5QkScu4uf14aZLnJHkUsFafgSRJkjQcnezaVlVXJTkZeDywRpJ57ayj9YFL2qddAmwAXJxkHrA6TdNtSZLUnf+bZHXgTcBBwGrAP/YbSZIkSUMxsRlJbbPONdrjlYGn0/RcOBnYuX3a7sA32uOj23Pa+9+tqppUPkmStKwkc4FNqurqqjq3qrapqkdX1dF9Z5MkSdIwTHJp27rAyUnOpmnSeWJVHQP8M/DGJBfQ9EA6tH3+ocB92+tvBN46wWySJGmaqroF2LXvHJIkSRquiS1tq6qzgUfNcP1Cmn5J06//CXjBpPJIkqSx/CjJwcCXgeumLlbVmf1FkiRJ0lB00iNJkiTdbWzZfjxg5Frhzm2SJEnCQpIkSRpRVdv0nUGSJEnDNckeSZIk6W4myTpJDk1yXHu+aZI9+84lSZKkYRirkJTkpHGuSZKku73PAN8GHtCe/y/wht7SSJIkaVBut5CUZKUkawFrJ1kzyVrtYyNgvS4CSpKkTq1dVUcAtwJU1RLgln4jSZIkaSjuqEfSq2jehXwAsAhIe/0a4OAJ5pIkSf24Lsl9aRpsk2Rr4Op+I0mSJGkobreQVFUfAj6U5HVVdVBHmSRpubJ/7vhJ9yC1X/UdQbPPG4GjgQcn+REwH9i530iSJEkairF2bauqg5I8Adho9HOq6vAJ5ZIkST2oqjOTPAV4GM1M5J9V1c09x5IkSdJAjFVISvJZ4MHAWSztk1CAhSRJku55HsvSN4+2SuKbR5IkSQLGLCQBC4BNq8o1FpIk3YP55pEkSZJuz7iFpHOB+wOXTjCLJEnqn28eSZIkabnGLSStDZyf5HTgxqmLVbXDRFJJkqS++OaRJEmSlmvcQtI7JhlCkiQNhm8eSZIkabnG3bXtlEkHkSRJg/COvgNIkiRpuMbdtW0xTaNNgHsBKwDXVdVqkwomSZK6V1WnJHkgsElVfSfJvYG5feeSJEnSMIw7I2nVqeMkAXYEtp5UKEmS1I8krwT2Atai2b1tPeBjwNP6zCVJkqRhmHNnP6EaXweeOYE8kiSpX3sDTwSuAaiqnwP36zWRJEmSBmPcpW3PHzmdQ7M18J8mkkiSJPXpxqq6qZmADEnmsXR5uyRJkma5cXdte+7I8RLgIprlbZIk6Z7llCRvA1ZO8nTgNcA3e84kSZKkgRi3R9Iekw4iSZIG4a3AnsA5wKuAY4FP9ppIkiRJgzHu0rb1gYNoeiYA/AB4fVVdPKlgkiSpe1V1K/CJ9iFJkiQtY9xm258GjgYe0D6+2V6TJEn3AEl2TLL3yPlpSS5sHy/oM5skSZKGY9xC0vyq+nRVLWkfnwHmTzCXJEnq1lto3jSasiLwGOCpwD/0EUiSJEnDM24h6YokuyWZ2z52A66YZDBJktSpe1XVb0bOf1hVV1TVr4FV+golSZKkYRm3kPQK4IXA74BLgZ2Bl08okyRJ6t6aoydV9dqRU2chS5IkCRi/kHQAsHtVza+q+9EUlvafXCxJktSx05K8cvrFJK8CTu8hjyRJkgZorF3bgM2r6sqpk6r6Y5JHTSiTJEnq3j8CX0/yYuDM9tqjaXol7dRbKkmSJA3KuIWkOUnWnComJVnrTnyuJEkauKq6DHhCkm2BR7SXv1VV3+0xliRJkgZm3GLQ+4D/TvKV9vwFwL9PJpIkSepLWziyeCRJkqQZjVVIqqrDkywEtm0vPb+qzp9cLEmSJEmSJA3N2MvT2sKRxSNJkiRJkqRZatxd2yRJkiRJkjTLWUiSJEmSJEnSWCwkSZIkSZIkaSwWkiRJkiRJkjQWC0mSJEmSJEkai4UkSZIkSZIkjcVCkiRJkiRJksZiIUmSJEmSJEljmVghKckGSU5Ocn6S85K8vr2+VpITk/y8/bhmez1JDkxyQZKzk2w1qWySJEmSJEm68yY5I2kJ8Kaq2hTYGtg7yabAW4GTqmoT4KT2HGA7YJP2sRfw0QlmkyRJkiRJ0p00sUJSVV1aVWe2x4uBnwDrATsCh7VPOwzYqT3eETi8GqcCayRZd1L5JEmSJEmSdOd00iMpyUbAo4DTgHWq6tL21u+Addrj9YDfjHzaxe216V9rryQLkyy8/PLLJ5ZZkiRJkiRJy5p4ISnJfYCvAW+oqmtG71VVAXVnvl5VHVJVC6pqwfz58+/CpJIkSZIkSbo9Ey0kJVmBpoj0+ao6sr38+6kla+3Hy9rrlwAbjHz6+u01SZIkSZIkDcAkd20LcCjwk6p6/8ito4Hd2+PdgW+MXH9Zu3vb1sDVI0vgJEmSJEmS1LN5E/zaTwReCpyT5Kz22tuA/wCOSLIn8Cvghe29Y4FnAxcA1wN7TDCbJEmSJEmS7qSJFZKq6odAlnP7aTM8v4C9J5VHkiRJkiRJf51Odm2TJEmSJEnS3Z+FJEmSJEmSJI3FQpIkSZIkSZLGYiFJkiRJkiRJY7GQJEmSJEmSpLFYSJIkSZIkSdJYLCRJkiRJkiRpLBaSJEmSJEmSNBYLSZIkSZIkSRqLhSRJkiRJkiSNxUKSJEmSJEmSxmIhSZIkSZIkSWOxkCRJkiRJkqSxWEiSJEmSJEnSWCwkSZIkSZIkaSwWkiRJkiRJkjQWC0mSJEmSJEkai4UkSZIkSZIkjcVCkiRJkiRJksZiIUmSJEmSJEljsZAkSZIkSZKksVhIkiRJkiRJ0lgsJEmSJEmSJGksFpIkSZIkSZI0FgtJkiRJkiRJGouFJEmSJEmSJI3FQpIkSZIkSZLGYiFJkiRJkiRJY7GQJEmSJEmSpLFYSJIkSZIkSdJYLCRJkiRJkiRpLBaSJEmSJEmSNBYLSZIkSZIkSRqLhSRJkiRJkiSNxUKSJEmSJEmSxmIhSZIkSZIkSWOxkCRJkiRJkqSxWEiSJEmSJEnSWCwkSZIkSZIkaSwdqWJxAAAIUklEQVQTKyQl+VSSy5KcO3JtrSQnJvl5+3HN9nqSHJjkgiRnJ9lqUrkkSZIkSZL0l5nkjKTPAM+adu2twElVtQlwUnsOsB2wSfvYC/joBHNJkiRJkiTpLzCxQlJVfR/447TLOwKHtceHATuNXD+8GqcCayRZd1LZJEmSJEmSdOd13SNpnaq6tD3+HbBOe7we8JuR513cXvszSfZKsjDJwssvv3xySSVJkiRJkrSM3pptV1UB9Rd83iFVtaCqFsyfP38CySRJkiRJkjSTrgtJv59astZ+vKy9fgmwwcjz1m+vSZIkSZIkaSC6LiQdDezeHu8OfGPk+sva3du2Bq4eWQInSZIkSZKkAZg3qS+c5IvAU4G1k1wM7Af8B3BEkj2BXwEvbJ9+LPBs4ALgemCPSeWSJEmSJEnSX2ZihaSq2nU5t542w3ML2HtSWSRJkiRJkvTX663ZtiRJkiRJku5eLCRJkiRJkiRpLBaSJEmSJEmSNBYLSZIkSZIkSRqLhSRJkiRJkiSNxUKSJEmSJEmSxmIhSZIkSZIkSWOxkCRJkiRJkqSxWEiSJEmSJEnSWCwkSZIkSZIkaSwWkiRJkiRJkjQWC0mSJEmSJEkai4UkSZIkSZIkjcVCkiRJkiRJksZiIUmSJEmSJEljsZAkSZIkSZKksVhIkiRJkiRJ0lgsJEmSJEmSJGksFpIkSZIkSZI0FgtJkiRJkiRJGouFJEmSJEmSJI3FQpIkSZIkSZLGYiFJkiRJkiRJY7GQJEmSJEmSpLFYSJIkSZIkSdJYLCRJkiRJkiRpLBaSJEmSJEmSNBYLSZIkSZIkSRqLhSRJkiRJkiSNxUKSJEmSJEmSxmIhSZIkSZIkSWOxkCRJkiRJkqSxWEiSJEmSJEnSWCwkSZIkSZIkaSwWkiRJkiRJkjQWC0mSJEmSJEkai4UkSZIkSZIkjcVCkiRJkiRJksZiIUmSJEmSJEljGVwhKcmzkvwsyQVJ3tp3HkmSJEmSJDUGVUhKMhf4MLAdsCmwa5JN+00lSZIkSZIkGFghCXgscEFVXVhVNwFfAnbsOZMkSZIkSZKAeX0HmGY94Dcj5xcDjxt9QpK9gL3a02uT/OwvHGtt4A9/4edOyhAzwTBzmWl8Q8xlpjHlHflLcz3wrs4iSZIkSUMrJN2hqjoEOOSv/TpJFlbVgrsg0l1miJlgmLnMNL4h5jLT+IaaS5IkSdLsNLSlbZcAG4ycr99ekyRJkiRJUs+GVkg6A9gkycZJ7gXsAhzdcyZJkiRJkiQxsKVtVbUkyWuBbwNzgU9V1XkTGu6vXh43AUPMBMPMZabxDTGXmcY31FySJEmSZqFUVd8ZJEmSJEmSdDcwtKVtkiRJkiRJGigLSZIkSZIkSRrLPbqQlORTSS5Lcu5y7ifJgUkuSHJ2kq0GkuupSa5Oclb7+LcOMm2Q5OQk5yc5L8nrZ3hOp6/XmJk6fa2SrJTk9CT/02baf4bnrJjky+3rdFqSjQaQ6eVJLh95nf5+kplGxp2b5MdJjpnhXqev053I1flrleSiJOe04y2c4X4v36skSZIkabpBNduegM8ABwOHL+f+dsAm7eNxwEfbj33nAvhBVW3fQZYpS4A3VdWZSVYFFiU5sarOH3lO16/XOJmg29fqRmDbqro2yQrAD5McV1WnjjxnT+DKqnpIkl2AdwMv6jkTwJer6rUTzDGT1wM/AVab4V7Xr9O4uaCf12qbqvrDcu719b1KkiRJkpZxj56RVFXfB/54O0/ZETi8GqcCayRZdwC5OldVl1bVme3xYppfsteb9rROX68xM3Wq/W+/tj1doX1M71i/I3BYe/xV4GlJ0nOmziVZH3gO8MnlPKXT1+lO5BqiXr5XSZIkSdJ09+hC0hjWA34zcn4xPRcqRjy+Xap0XJJHdDlwu8ToUcBp02719nrdTibo+LVql0WdBVwGnFhVy32dqmoJcDVw354zAfxduyzqq0k2mGSe1geBtwC3Lud+56/TmLmg+9eqgBOSLEqy1wz3h/y9SpIkSdIsMtsLSUN1JvDAqtoCOAj4elcDJ7kP8DXgDVV1TVfj3p47yNT5a1VVt1TVlsD6wGOTbDbpMe+CTN8ENqqqzYETWToTaCKSbA9cVlWLJjnOnTVmrk5fq9aTqmormiVseyd5cgdjSpIkSdKdNtsLSZcAo7MN1m+v9aqqrplaqlRVxwIrJFl70uO2/XW+Bny+qo6c4Smdv153lKmv16od7yrgZOBZ027d9jolmQesDlzRZ6aquqKqbmxPPwk8esJRngjskOQi4EvAtkk+N+05fbxOd5irh9eKqrqk/XgZcBTw2GlPGeT3KkmSJEmzz2wvJB0NvKzdEWlr4OqqurTvUEnuP9UrJsljaf4/TfQX7Ha8Q4GfVNX7l/O0Tl+vcTJ1/VolmZ9kjfZ4ZeDpwE+nPe1oYPf2eGfgu1U1sZ5F42Sa1k9nB5p+UxNTVftW1fpVtRGwC81rsNu0p3X6Oo2bq+vXKskqbTN5kqwCPAOYvqPjIL9XSZIkSZp97tG7tiX5IvBUYO0kFwP70TQipqo+BhwLPBu4ALge2GMguXYGXp1kCXADsMukf8GmmanxUuCcttcOwNuADUdydf16jZOp69dqXeCwJHNpilZHVNUxSQ4AFlbV0TTFr88muYCmqfouE8wzbqZ9kuxAsxPeH4GXTzjTjHp+ncbN1fVrtQ5wVFsPnQd8oaqOT/IP0O/3KkmSJEmaLpOvT0iSJEmSJOmeYLYvbZMkSZIkSdKYLCRJkiRJkiRpLBaSJEmSJEmSNBYLSZIkSZIkSRqLhSRJkiRJkiSNxUKSJEmSJEmSxmIhSZIkSZIkSWP5/6srUJa0l4zeAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 10))\n",
        "\n",
        "plt.subplot(2, 3, 1)\n",
        "plt.title('anxiety')\n",
        "student['anxiety'].value_counts().plot.pie(autopct='%1.1f%%')\n",
        "\n",
        "plt.subplot(2, 3, 2)\n",
        "plt.title('panic_attack')\n",
        "student['panic_attack'].value_counts().plot.pie(autopct='%1.1f%%')\n",
        "plt.ylabel('count')\n",
        "\n",
        "plt.subplot(2, 3, 3)\n",
        "plt.title('marital')\n",
        "student['marital'].value_counts().plot.pie(autopct='%1.1f%%')\n",
        "plt.ylabel('count')\n",
        "\n",
        "plt.subplot(2, 3, 4)\n",
        "plt.title('specialist')\n",
        "student['specialist'].value_counts().plot.pie(autopct='%1.1f%%')\n",
        "plt.ylabel('count')\n",
        "\n",
        "plt.subplot(2, 3, 5)\n",
        "plt.title('sport')\n",
        "student['sport'].value_counts().plot.pie(autopct='%1.1f%%')\n",
        "plt.ylabel('count')\n",
        "\n",
        "plt.subplot(2, 3, 6)\n",
        "plt.title('alchool')\n",
        "student['alchool'].value_counts().plot.pie(autopct='%1.1f%%')\n",
        "plt.ylabel('count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "uMbKuxJaDEni",
        "outputId": "ff3f47c3-6d86-4a0c-a024-a21657128b75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'count')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "from matplotlib import rcParams\n",
        "plt.figure(figsize=(20, 10))\n",
        "plt.subplot(3, 3, 1)\n",
        "sns.countplot(x='marital', hue='depression', data = student, palette=\"Set2\");\n",
        "plt.subplot(3, 3, 2)\n",
        "sns.countplot(x='anxiety', hue='depression', data = student, palette=\"Set2\");\n",
        "plt.subplot(3, 3, 3)\n",
        "sns.countplot(x='panic_attack', hue='depression', data = student, palette=\"Set2\");\n",
        "plt.subplot(3, 3, 4)\n",
        "sns.countplot(x='specialist', hue='depression', data = student, palette=\"Set2\");\n",
        "plt.subplot(3, 3, 5)\n",
        "sns.countplot(x='age', hue='depression', data = student, palette=\"Set2\");\n",
        "plt.subplot(3, 3, 6)\n",
        "sns.countplot(x='gender', hue='depression', data = student, palette=\"Set2\");\n",
        "plt.subplot(3, 3, 7)\n",
        "sns.countplot(x='sport', hue='depression', data = student, palette=\"Set2\");\n",
        "plt.subplot(3, 3, 8)\n",
        "sns.countplot(x='alchool', hue='depression', data = student, palette=\"Set2\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "826eho8hFDdQ",
        "outputId": "a7f6ed62-1645-4643-f2dc-ff493e540066"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dai grafici appena mostrati notiamo chiaramente che ci sono diverse correlazioni tra i dati e la presenza o meno di depressione.\n",
        "Le feature che sembrano essere più correlate alla depressione sono  **`sport`** e **`marital`**.\n",
        "\n",
        "Notiamo che tra tutti gli studenti chi fa sport ha una bassissima probabilità di soffrire di depressione.\n",
        "\n",
        "Notiamo inoltre che ci sono più ragazze che soffrono di depressione di quanto siano i maschi.\n",
        "\n",
        "Dal grafico centrale notiamo chiaramente che al crescere dell'età crescono anche i casi di depressione.\n",
        "\n",
        "Infine si può osservare che chi fa uso regolarmente di alchol, chi soffre di attacchi di panico o di ansia e chi è seguito da uno specialista ha più probabilità di soffrire di depressione.\n"
      ],
      "metadata": {
        "id": "YTW7wF16qNUb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "_ = student.groupby([pd.cut(student[\"grades\"], bins=10), \"depression\"]).size().unstack(\"depression\").plot.bar(stacked=False, figsize=(15,5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "ik9pAQmID8MG",
        "outputId": "f5a5b511-88f8-4008-b8a2-db470d9da64c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Notiamo che c'è una forte correlazione tra la media dei voti di uno studente con la presenza di depressione.\n",
        "Più ci muoviamo verso gli estremi e più i casi di depressione aumentano."
      ],
      "metadata": {
        "id": "YKKrxjCcsX2W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "\n",
        "sns.countplot(x='course', hue='depression', data = student, palette=\"Set2\")\n",
        "plt.xticks(rotation=90)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "id": "G0kbRNRzL9MP",
        "outputId": "fef7ff62-80ed-435d-ce00-3aa37ec0ef86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
              " <a list of 10 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Il corso di studi dello studente non sembra influire significativamente sulla depressione se non leggermente per i corsi di legge."
      ],
      "metadata": {
        "id": "wS9KTS2VGOx8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZhGysJNiFuNw"
      },
      "outputs": [],
      "source": [
        "def plot_correlation(dataset):\n",
        "    cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
        "    # Generate a mask for the upper triangle\n",
        "    mask = np.zeros_like(dataset, dtype=np.bool)\n",
        "    mask[np.triu_indices_from(mask)] = True\n",
        "\n",
        "    # Set up the matplotlib figure\n",
        "    f, ax = plt.subplots(figsize=(11, 9))\n",
        "    # Draw the heatmap with the mask and correct aspect ratio\n",
        "    sns.heatmap(dataset, mask=mask, cmap=cmap, vmax=.3, center=0,annot = True, square=True, linewidths=.5, cbar_kws={\"shrink\": .5});"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_correlation(student.corr())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "Zka-pnz4hRdR",
        "outputId": "eb00371e-3847-45d9-96a0-e14fb13a472d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  after removing the cwd from sys.path.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 792x648 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zyvjag-_ijMt"
      },
      "source": [
        "Producendo una matrice di correlazione delle features, possiamo osservare le seguenti correlazioni rilevanti:  \n",
        "\n",
        "* `sport` vs `depression`: è molto evidente come chi faccia sport abbia molta meno probabilità di soffrire di depressione, ciò ci conferma le analisi fatte poco fa.\n",
        "* `alchool` vs `depression`: notiamo correlazione tra chi fa uso regolarmente di alchol e chi soffre di depressione, ciò si riflette inversamente sulla correlazione tra alchool e sport \n",
        "* `age` vs `year`: età e anno di corrente di studi sono fortemente correlati, come ci si poteva immaginare.\n",
        "* `depression` vs `marited`: Come abbiamo notato con analisi precedenti, depression è fortemente correlato dal campo marital.\n",
        "* `specialist` `panic_attack` `anxiety` conguentemente alle analisi fatte poco fa sono tutti più o meno correlati al campo depression \n",
        "\n",
        "\n",
        "Notiamo una inconguenza dall'analisi dei voti fatta poco fa dalla matrice di correlazione.\n",
        "Ci si aspettava infatti una correlazione tra `grades` e `depression` che non viene però esplicitata dalla matrice.  \n",
        "Questo potrebbe essere dovuto al fatto che la correlazione non è diretta, ma i casi di depressione aumentano sia nel caso in cui i voti siano sotto la media che quando i voti sono sopra la media, diminuendo quando grades è intorno alla media.\n",
        "Non essendo una correlazione lineare la matrice di correlazione non evidenzia questa correlazione."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7j_WHuxoDvHt"
      },
      "source": [
        "### Standardizzazione, regolarizzazione e multicollinearità\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FIx543uCFuNx"
      },
      "outputs": [],
      "source": [
        "features = ['gender','age','year','grades','marital','anxiety','panic_attack','specialist','sport','alchool']\n",
        "target = ['depression']"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Convertiamo le variabili categoriche in numeriche.\n",
        "\n",
        "Converiamo la colonna `Course` in più colonne, una per ogni corso esistente.\n",
        "\n"
      ],
      "metadata": {
        "id": "2VA_Sx4CCjF4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bi7w3R6hFuNx"
      },
      "outputs": [],
      "source": [
        "categorical_features = ['course']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aaFIWScwFuNx"
      },
      "outputs": [],
      "source": [
        "dataset = pd.get_dummies(student, columns=categorical_features, prefix=categorical_features)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "tV3_H0mS9MOp",
        "outputId": "594635dc-2b2d-409b-83f1-e6754652a389"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   gender   age  year     grades  marital  depression  anxiety  panic_attack  \\\n",
              "0    True  21.0   3.0  20.252930    False       False     True         False   \n",
              "1   False  18.0   2.0  29.959451    False        True     True         False   \n",
              "2   False  21.0   2.0  25.429350     True        True    False          True   \n",
              "3    True  20.0   2.0  19.425978    False       False     True         False   \n",
              "4    True  18.0   1.0  21.539774    False       False     True         False   \n",
              "\n",
              "   specialist  sport  ...  course_Accounting  course_Business Studies  \\\n",
              "0       False   True  ...                  0                        0   \n",
              "1        True  False  ...                  0                        0   \n",
              "2        True  False  ...                  0                        0   \n",
              "3       False   True  ...                  1                        0   \n",
              "4       False   True  ...                  0                        0   \n",
              "\n",
              "   course_Computer Science  course_Design  course_Economics  course_Finance  \\\n",
              "0                        0              0                 0               1   \n",
              "1                        0              0                 0               0   \n",
              "2                        0              0                 0               0   \n",
              "3                        0              0                 0               0   \n",
              "4                        1              0                 0               0   \n",
              "\n",
              "   course_General Business and Management  course_Law  \\\n",
              "0                                       0           0   \n",
              "1                                       0           0   \n",
              "2                                       1           0   \n",
              "3                                       0           0   \n",
              "4                                       0           0   \n",
              "\n",
              "   course_Management Studies  course_Marketing  \n",
              "0                          0                 0  \n",
              "1                          1                 0  \n",
              "2                          0                 0  \n",
              "3                          0                 0  \n",
              "4                          0                 0  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8edfadad-b938-48cd-9101-ec0c1eb2019d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>year</th>\n",
              "      <th>grades</th>\n",
              "      <th>marital</th>\n",
              "      <th>depression</th>\n",
              "      <th>anxiety</th>\n",
              "      <th>panic_attack</th>\n",
              "      <th>specialist</th>\n",
              "      <th>sport</th>\n",
              "      <th>...</th>\n",
              "      <th>course_Accounting</th>\n",
              "      <th>course_Business Studies</th>\n",
              "      <th>course_Computer Science</th>\n",
              "      <th>course_Design</th>\n",
              "      <th>course_Economics</th>\n",
              "      <th>course_Finance</th>\n",
              "      <th>course_General Business and Management</th>\n",
              "      <th>course_Law</th>\n",
              "      <th>course_Management Studies</th>\n",
              "      <th>course_Marketing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>True</td>\n",
              "      <td>21.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>20.252930</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>29.959451</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>21.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>25.429350</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>True</td>\n",
              "      <td>20.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>19.425978</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>True</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>21.539774</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8edfadad-b938-48cd-9101-ec0c1eb2019d')\"\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",
              "       width=\"24px\">\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-8edfadad-b938-48cd-9101-ec0c1eb2019d 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-8edfadad-b938-48cd-9101-ec0c1eb2019d');\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": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pCPLrgrCijMu"
      },
      "source": [
        "Si convertono ora le features categoriche splittando i loro valori nelle rispettive classi di appartenenza: `course` prevede 10 classi, ognuna per un corso di studi. \n",
        "\n",
        "Sarà presente un 1 solo in corrispondenza della classe di appartenenza e 0 negli altri casi. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VkmvrJKcFuNx"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import Perceptron\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "from scipy import stats\n",
        "import math"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UAp4nCnaFuNx"
      },
      "outputs": [],
      "source": [
        "X = dataset.drop('depression', axis=1)\n",
        "y = dataset['depression']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(X, y.values.ravel(), test_size=0.3, random_state=43, stratify=y.values)"
      ],
      "metadata": {
        "id": "6MNi26PIHaKV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Addestriamo un semplice modello per verificare quali sono le feature più importanti."
      ],
      "metadata": {
        "id": "MbMfBVPvDI_T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "std_model = Pipeline([\n",
        "    (\"scaler\", StandardScaler()),\n",
        "    (\"model\",  Perceptron())\n",
        "])\n",
        "\n",
        "std_model.fit(X_train,y_train)\n",
        "print(\"R-squared coefficient: {:.5}\".format(std_model.score(X_val, y_val)))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zxC4tXPy2XT7",
        "outputId": "b31bc4a3-36e4-4eeb-8f78-83e01db1786e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R-squared coefficient: 0.93667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.Series(std_model[\"model\"].coef_[0], index=X_train.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W4IEd2qu5jgh",
        "outputId": "ce747b87-daa9-49e2-a3c9-c4af51d8937c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender                                    -3.593228\n",
              "age                                       -2.471399\n",
              "year                                       0.935539\n",
              "grades                                     4.885987\n",
              "marital                                   10.614456\n",
              "anxiety                                   -4.558651\n",
              "panic_attack                               9.419883\n",
              "specialist                                 4.045463\n",
              "sport                                    -13.943321\n",
              "alchool                                    7.859229\n",
              "course_Accounting                         -2.144940\n",
              "course_Business Studies                   -2.773398\n",
              "course_Computer Science                    1.476347\n",
              "course_Design                             -2.489068\n",
              "course_Economics                           0.830342\n",
              "course_Finance                             0.944120\n",
              "course_General Business and Management    -2.333333\n",
              "course_Law                                 1.136936\n",
              "course_Management Studies                  4.194408\n",
              "course_Marketing                           1.082622\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Sport` e `Marital` sono le variabili che ancora una volta risultano più influenti per il modello."
      ],
      "metadata": {
        "id": "O2Q73jcGDlcV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Parte 3 - Modellazione\n",
        "\n",
        "Sono testati diversi modelli di classificazione. \n",
        "\n",
        "Tramite Grid Search e StratifiedKFold sono generati 5 modelli differenti in grado di stimare gli iperparametri e permettere il calcolo dell'accuratezza dei modelli studiati.\n",
        "\n",
        "In alcune GridSearch sono stati volontariamente omessi alcuni valori di iperparametri che sono stati testati individualmente su altri notebook. Questa decisione è stata fatta per alleggerire i tempi di calcolo di questo notebook in modo da presentare solo risultati rilevanti."
      ],
      "metadata": {
        "id": "OPRMi4FsSbBm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2eKtTL23QT4s"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import KFold, StratifiedKFold\n",
        "skf = StratifiedKFold(3, shuffle=True, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3gbVM3RXFuNy"
      },
      "source": [
        "## Perceptron\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zsszpsStFuNy",
        "outputId": "ba15dd6e-680a-49bb-b041-0eb0aa39e61a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best cross validation score: 0.9079157139182427\n",
            "\n",
            "             Test set score: 0.8960396039603962\n",
            "\n",
            "                Best params: {'perceptron__alpha': 0.0001, 'perceptron__l1_ratio': 0.5, 'perceptron__penalty': 'elasticnet', 'std': StandardScaler()}\n",
            "\n"
          ]
        }
      ],
      "source": [
        "models = {}\n",
        "\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn import metrics\n",
        "\n",
        "std_perceptron = Pipeline([\n",
        "    ('std', None),\n",
        "    ('perceptron', Perceptron(n_jobs=-1, early_stopping=True, n_iter_no_change=5))\n",
        "])\n",
        "\n",
        "parameters = [\n",
        "    {\n",
        "        \"std\": [None, StandardScaler()],\n",
        "        \"perceptron__penalty\": [\"l2\", \"l1\"],\n",
        "        'perceptron__alpha': [0.0001, 0.001, 0.01, 1, 10],\n",
        "    },\n",
        "    {\n",
        "        \"std\": [None, StandardScaler()],\n",
        "        \"perceptron__penalty\": [\"none\"]\n",
        "    },\n",
        "    {\n",
        "        \"std\": [None, StandardScaler()],\n",
        "        \"perceptron__penalty\": [\"elasticnet\"],\n",
        "        'perceptron__alpha': [0.0001, 0.001, 0.01, 1, 10],\n",
        "        'perceptron__l1_ratio':np.linspace(0,1,5)\n",
        "\n",
        "    }\n",
        "]\n",
        "\n",
        "perceptron_cv = GridSearchCV(std_perceptron, parameters, cv=skf, n_jobs=-1, scoring='f1')\n",
        "perceptron_cv.fit(X_train, y_train)\n",
        "\n",
        "score = perceptron_cv.score(X_val, y_val)\n",
        "\n",
        "print(\"Best cross validation score: {}\\n\".format(perceptron_cv.best_score_))\n",
        "print(\"             Test set score: {}\\n\".format(score))\n",
        "print(\"                Best params: {}\\n\".format(perceptron_cv.best_params_))\n",
        "\n",
        "models[\"Perceptron\"] = {\"Model\": perceptron_cv.best_estimator_, \"Score\": perceptron_cv.best_score_}\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sN9HtxrtFuNy",
        "outputId": "4610b4ec-0bdb-4ca6-9c8c-f9a07f419a9a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender                                    -2.905432\n",
              "age                                        0.732725\n",
              "year                                       1.360329\n",
              "grades                                    -4.468663\n",
              "marital                                    8.044443\n",
              "anxiety                                   -0.249342\n",
              "panic_attack                              10.796813\n",
              "specialist                                 4.634021\n",
              "sport                                    -12.039631\n",
              "alchool                                    6.035412\n",
              "course_Accounting                         -2.053939\n",
              "course_Business Studies                    0.000000\n",
              "course_Computer Science                    3.151686\n",
              "course_Design                              0.000000\n",
              "course_Economics                          -2.668894\n",
              "course_Finance                             6.585283\n",
              "course_General Business and Management     0.000000\n",
              "course_Law                                 0.000000\n",
              "course_Management Studies                 -2.736769\n",
              "course_Marketing                          -2.294647\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "pd.Series(perceptron_cv.best_estimator_[1].coef_[0], index=X_train.columns)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = perceptron_cv.predict(X_val)\n",
        "print(classification_report(y_val, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JFjxAoF1OJHr",
        "outputId": "e34a0cd5-ccb7-4c8b-be5e-f95360f72aa2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.95      0.94      0.95       399\n",
            "        True       0.89      0.90      0.90       201\n",
            "\n",
            "    accuracy                           0.93       600\n",
            "   macro avg       0.92      0.92      0.92       600\n",
            "weighted avg       0.93      0.93      0.93       600\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kadoG7JMijMx"
      },
      "source": [
        "### Perceptron with Polynomial features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "940Vwsz6ijMx",
        "outputId": "d96db30d-dadd-42be-95ef-1ca4ef30b2f1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/joblib/externals/loky/process_executor.py:705: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
            "  \"timeout or by a memory leak.\", UserWarning\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best cross validation score: 0.9044047739699913\n",
            "\n",
            "             Test set score: 0.9227053140096618\n",
            "\n",
            "                Best params: {'perceptron__alpha': 0.0001, 'perceptron__l1_ratio': 0.5, 'perceptron__penalty': 'elasticnet', 'poly__degree': 4, 'std': None}\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "poly_perceptron = Pipeline([\n",
        "    ('poly', PolynomialFeatures()),\n",
        "    ('std', StandardScaler()),\n",
        "    ('perceptron', Perceptron(n_jobs=-1,))\n",
        "])\n",
        "\n",
        "\n",
        "parameters = [\n",
        "    {\n",
        "        \"std\": [None, StandardScaler()],\n",
        "        \"perceptron__penalty\": [\"l2\", \"l1\"],\n",
        "        'perceptron__alpha': [0.0001, 0.001, 0.01, 1],\n",
        "        'poly__degree': list(range(1,5))\n",
        "\n",
        "    },\n",
        "    {\n",
        "        \"std\": [None, StandardScaler()],\n",
        "        \"perceptron__penalty\": [\"none\"],\n",
        "        'poly__degree': list(range(1,5))\n",
        "\n",
        "    },\n",
        "    {\n",
        "        \"std\": [None, StandardScaler()],\n",
        "        \"perceptron__penalty\": [\"elasticnet\"],\n",
        "        'perceptron__alpha': [0.0001, 0.001, 0.01, 1],\n",
        "        'perceptron__l1_ratio':[1,0.5,0.1],\n",
        "        'poly__degree': list(range(1,5))\n",
        "\n",
        "\n",
        "    }\n",
        "]\n",
        "\n",
        "poly_perceptron_cv = GridSearchCV(poly_perceptron, parameters, cv=skf, n_jobs=-1, return_train_score=True, scoring='f1')\n",
        "poly_perceptron_cv.fit(X_train, y_train)\n",
        "\n",
        "score = poly_perceptron_cv.score(X_val, y_val)\n",
        "\n",
        "print(\"Best cross validation score: {}\\n\".format(poly_perceptron_cv.best_score_))\n",
        "print(\"             Test set score: {}\\n\".format(score))\n",
        "print(\"                Best params: {}\\n\".format(poly_perceptron_cv.best_params_))\n",
        "\n",
        "models[\"Polynomial Perceptron\"] = {\"Model\": poly_perceptron_cv.best_estimator_, \"Score\": poly_perceptron_cv.best_score_}\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = poly_perceptron_cv.predict(X_val)\n",
        "print(classification_report(y_val, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83120f13-0208-41ef-dabf-786589fa11de",
        "id": "aLMW3HPbEYKb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.97      0.94      0.96       399\n",
            "        True       0.90      0.95      0.92       201\n",
            "\n",
            "    accuracy                           0.95       600\n",
            "   macro avg       0.94      0.95      0.94       600\n",
            "weighted avg       0.95      0.95      0.95       600\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#poly_perceptron_cv.best_estimator_[2].coef_[0]"
      ],
      "metadata": {
        "id": "pB_V4umkbHSL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pM-LI6fGFuN5"
      },
      "source": [
        "\n",
        "# Logistic Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Metodo per ottenere un piano di separazione non ottimale ma lineare. Si può decidere:\n",
        "- Standardizzazione i dati sottraendo la media e scalando con la varianza\n",
        "- Metodo di penalizzazione/regolarizzazione\n",
        "- il valore di C, ovvero dell'inverso dell'intensità di regolarizzazione\n"
      ],
      "metadata": {
        "id": "tMUx_dll-8_x"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XVELNuytFuN6",
        "outputId": "ed6baca9-bacb-4c5f-9fd7-a8fce2f1b6ba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best cross validation score: 0.930781184914608\n",
            "\n",
            "             Test set score: 0.923469387755102\n",
            "\n",
            "                Best params: {'lr__C': 0.17500000000000002, 'lr__penalty': 'l1', 'std': StandardScaler()}\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "std_lr = Pipeline([\n",
        "    ('std', StandardScaler()),\n",
        "    ('lr', LogisticRegression(solver=\"saga\"))\n",
        "])\n",
        "\n",
        "\n",
        "parameters = [\n",
        "    {\n",
        "        \"std\": [None, StandardScaler()],\n",
        "        \"lr__penalty\": [\"none\"]\n",
        "    },\n",
        "    {\n",
        "        \"std\": [None, StandardScaler()],\n",
        "        \"lr__penalty\": [\"l2\", \"l1\"],\n",
        "        \"lr__C\": np.linspace(0.1, 0.4, 5),\n",
        "\n",
        "    },\n",
        "    {\n",
        "        \"std\": [None, StandardScaler()],\n",
        "        \"lr__penalty\": [\"elasticnet\"],\n",
        "        \"lr__C\": np.linspace(0.1, 0.4, 5),\n",
        "        \"lr__l1_ratio\":  [0.1,0.2,0.5,1],\n",
        "    }\n",
        "]\n",
        "\n",
        "\n",
        "\n",
        "lr_gs = GridSearchCV(std_lr, parameters,cv=skf, n_jobs=-1, return_train_score=False, scoring='f1')\n",
        "lr_gs.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "score = lr_gs.score(X_val, y_val)\n",
        "\n",
        "print(\"Best cross validation score: {}\\n\".format(lr_gs.best_score_))\n",
        "print(\"             Test set score: {}\\n\".format(score))\n",
        "print(\"                Best params: {}\\n\".format(lr_gs.best_params_))\n",
        "\n",
        "models[\"Logistic Regression\"] = {\"Model\": lr_gs.best_estimator_, \"Score\": lr_gs.best_score_}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "989AxZMkFuN8",
        "outputId": "3023d5d6-2466-4c0b-c3c0-3772eec26ac3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.95      0.97      0.96       399\n",
            "        True       0.95      0.90      0.92       201\n",
            "\n",
            "    accuracy                           0.95       600\n",
            "   macro avg       0.95      0.94      0.94       600\n",
            "weighted avg       0.95      0.95      0.95       600\n",
            "\n"
          ]
        }
      ],
      "source": [
        "pred = lr_gs.predict(X_val)\n",
        "print(classification_report(y_val, pred))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.Series(lr_gs.best_estimator_[1].coef_[0], index=X_train.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nqr4CCaSp9Dh",
        "outputId": "b39c2df7-bf06-4f2e-b6c4-9e1f2a1fbbb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender                                   -0.553024\n",
              "age                                       0.000000\n",
              "year                                      0.002008\n",
              "grades                                    0.088334\n",
              "marital                                   1.201913\n",
              "anxiety                                  -0.194466\n",
              "panic_attack                              1.392894\n",
              "specialist                                0.588103\n",
              "sport                                    -1.845944\n",
              "alchool                                   0.857343\n",
              "course_Accounting                        -0.002144\n",
              "course_Business Studies                   0.000000\n",
              "course_Computer Science                   0.021491\n",
              "course_Design                             0.016712\n",
              "course_Economics                          0.000000\n",
              "course_Finance                            0.000000\n",
              "course_General Business and Management    0.000000\n",
              "course_Law                                0.000000\n",
              "course_Management Studies                 0.000000\n",
              "course_Marketing                         -0.151352\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RandomForest"
      ],
      "metadata": {
        "id": "PY8L5yeUTuTV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MDVK9NyIFuOG",
        "outputId": "97a36f4d-a229-4aeb-b500-809ffc8245dd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best cross validation score: 0.9348175501775159\n",
            "\n",
            "             Test set score: 0.9360613810741688\n",
            "\n",
            "                Best params: {'rfc__max_depth': 8, 'rfc__min_samples_leaf': 2, 'rfc__min_samples_split': 2, 'rfc__n_estimators': 200}\n",
            "\n",
            "CPU times: user 3.83 s, sys: 276 ms, total: 4.11 s\n",
            "Wall time: 3min 14s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "rfc = Pipeline([\n",
        "    ('std', StandardScaler()),\n",
        "    ('rfc', RandomForestClassifier(n_jobs=-1, random_state=3))\n",
        "])\n",
        "\n",
        "parameters = {\n",
        "    'rfc__n_estimators': [100, 200, 300],\n",
        "    'rfc__max_depth': [2, 4, 6, 8, 10],\n",
        "    'rfc__min_samples_leaf': [2,2,10],\n",
        "    'rfc__min_samples_split': [2,10,20],\n",
        "}\n",
        "    \n",
        "\n",
        "rfc_gs = GridSearchCV(rfc, parameters, cv=skf, n_jobs=-1, return_train_score=True, scoring='f1')\n",
        "rfc_gs.fit(X_train, y_train)\n",
        "\n",
        "score = rfc_gs.score(X_val, y_val)\n",
        "\n",
        "print(\"Best cross validation score: {}\\n\".format(rfc_gs.best_score_))\n",
        "print(\"             Test set score: {}\\n\".format(score))\n",
        "print(\"                Best params: {}\\n\".format(rfc_gs.best_params_))\n",
        "\n",
        "models[\"RandomForest\"] = {\"Model\": rfc_gs.best_estimator_, \"Score\": rfc_gs.best_score_}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wENDtm5eFuOH",
        "outputId": "cd41fb59-cef8-40b1-fcbb-350558555fec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.96      0.98      0.97       399\n",
            "        True       0.96      0.91      0.94       201\n",
            "\n",
            "    accuracy                           0.96       600\n",
            "   macro avg       0.96      0.95      0.95       600\n",
            "weighted avg       0.96      0.96      0.96       600\n",
            "\n"
          ]
        }
      ],
      "source": [
        "pred = rfc_gs.predict(X_val)\n",
        "\n",
        "print(classification_report(y_val, pred))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.Series(rfc_gs.best_estimator_[1].feature_importances_, index=X_train.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YhVP_vKGF7En",
        "outputId": "e14f73ea-19c7-4dac-cc60-1470bf7c5ae8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender                                    0.018562\n",
              "age                                       0.028588\n",
              "year                                      0.016613\n",
              "grades                                    0.076406\n",
              "marital                                   0.143047\n",
              "anxiety                                   0.007407\n",
              "panic_attack                              0.206210\n",
              "specialist                                0.042575\n",
              "sport                                     0.347354\n",
              "alchool                                   0.085294\n",
              "course_Accounting                         0.002444\n",
              "course_Business Studies                   0.002251\n",
              "course_Computer Science                   0.003078\n",
              "course_Design                             0.002694\n",
              "course_Economics                          0.001805\n",
              "course_Finance                            0.002370\n",
              "course_General Business and Management    0.003414\n",
              "course_Law                                0.003587\n",
              "course_Management Studies                 0.002815\n",
              "course_Marketing                          0.003487\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a1uAlsSDFtYr"
      },
      "source": [
        "### Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JdXoA57vFtYr",
        "outputId": "ba1d22b2-db20-4eed-8f26-468fa63a010a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best cross validation score: 0.9087426926001195\n",
            "\n",
            "             Test set score: 0.9090909090909091\n",
            "\n",
            "                Best params: {'scaler': None, 'tree__max_depth': 4}\n",
            "\n",
            "CPU times: user 108 ms, sys: 2.4 ms, total: 110 ms\n",
            "Wall time: 477 ms\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "tree_model = Pipeline([\n",
        "    (\"scaler\", StandardScaler()),\n",
        "    (\"tree\", DecisionTreeClassifier(random_state=42))\n",
        "])\n",
        "\n",
        "#print(tree_model.get_params())\n",
        "\n",
        "tree_grid = {\"scaler\": [None, StandardScaler()],\n",
        "             'tree__max_depth': [i for i in range(2, 7)]\n",
        "             }\n",
        "\n",
        "tree_gs = GridSearchCV(tree_model, tree_grid, cv=skf, n_jobs=-1, return_train_score=True, scoring='f1')\n",
        "tree_gs.fit(X_train, y_train)\n",
        "\n",
        "score = tree_gs.score(X_val, y_val)\n",
        "\n",
        "print(\"Best cross validation score: {}\\n\".format(tree_gs.best_score_))\n",
        "print(\"             Test set score: {}\\n\".format(score))\n",
        "print(\"                Best params: {}\\n\".format(tree_gs.best_params_))\n",
        "\n",
        "\n",
        "models[\"Decision Tree\"] = {\"Model\": tree_gs.best_estimator_, \"Score\": tree_gs.best_score_}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "outputId": "64d31b03-4221-46a5-99db-3e31bffb76eb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kp2sqC5TR7Bj"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.95      0.96      0.96       399\n",
            "        True       0.92      0.90      0.91       201\n",
            "\n",
            "    accuracy                           0.94       600\n",
            "   macro avg       0.94      0.93      0.93       600\n",
            "weighted avg       0.94      0.94      0.94       600\n",
            "\n"
          ]
        }
      ],
      "source": [
        "pred = tree_gs.predict(X_val)\n",
        "\n",
        "print(classification_report(y_val, pred))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.Series(tree_gs.best_estimator_[1].feature_importances_, index=X_train.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TXdP9NimFzpK",
        "outputId": "7b10183f-6b49-4efc-c5c6-7cad5a14dec7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender                                    0.000000\n",
              "age                                       0.000000\n",
              "year                                      0.000000\n",
              "grades                                    0.020787\n",
              "marital                                   0.103975\n",
              "anxiety                                   0.000000\n",
              "panic_attack                              0.101136\n",
              "specialist                                0.000000\n",
              "sport                                     0.737182\n",
              "alchool                                   0.036804\n",
              "course_Accounting                         0.000000\n",
              "course_Business Studies                   0.000000\n",
              "course_Computer Science                   0.000000\n",
              "course_Design                             0.000000\n",
              "course_Economics                          0.000000\n",
              "course_Finance                            0.000117\n",
              "course_General Business and Management    0.000000\n",
              "course_Law                                0.000000\n",
              "course_Management Studies                 0.000000\n",
              "course_Marketing                          0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vvd4oT5SFtYr"
      },
      "source": [
        "# Riepilogo dll'accuratezza ottenuta"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x7MlBKbJFtYs",
        "outputId": "720050ed-7400-4ff4-bf80-82364345ec52",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Perceptron:\n",
            "\t\t\t\t0.9079157139182427\n",
            "Polynomial Perceptron:\n",
            "\t\t\t\t0.9044047739699913\n",
            "Logistic Regression:\n",
            "\t\t\t\t0.930781184914608\n",
            "RandomForest:\n",
            "\t\t\t\t0.9348175501775159\n",
            "Decision Tree:\n",
            "\t\t\t\t0.9087426926001195\n"
          ]
        }
      ],
      "source": [
        "for name, model in models.items():\n",
        "    print(name + \":\\n\\t\\t\\t\\t{}\".format(model[\"Score\"]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SlnbG_DBFtYs"
      },
      "source": [
        "# Parte 4 - Valutazione dei modelli di classificazione\n",
        "\n",
        "L'accuratezza non è una buona stima della capacità predittiva di un modello di classificazione, in particolare nei casi in cui il dataset è sbilanciato, perchè contiene maggiori record appartenenti ad una classe. In questo caso se si costruisse un modello che predica sempre l'assenza di depressione, esso avrebbe una accuratezza di circa il 70%"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VWNCt0SDFtYs"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xtUUlZa9FtYs"
      },
      "source": [
        "### Precision, Recall e F1-Score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XIVSUYwJFtYs"
      },
      "source": [
        "Si costruisce una **matrice di confusione** per ciascun modello creato in precedenza, calcolando la **precisione**, la **recall** e la **F1-score**.\n",
        "Il Perceptron ottiene nuovamente lo score peggiore mentre RandomForest ottiene lo score più alto di tutti"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calcoliamo i vari valori di score con un dataset che non è stato usato per il training dei modelli per verificare la generalità di essi."
      ],
      "metadata": {
        "id": "DXkiIlHX2dlt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wzeX_mhIFtYs"
      },
      "outputs": [],
      "source": [
        "def calculate_precision_recall_f1(name, X, y) :\n",
        "    model = models[name]\n",
        "    y_pred = model[\"Model\"].predict(X)\n",
        "    \n",
        "    model[\"Precision\"] = precision_score(y, y_pred)\n",
        "    model[\"Recall\"] = recall_score(y, y_pred)\n",
        "    model[\"F1_Score\"] = f1_score(y, y_pred, average=\"macro\")\n",
        "    \n",
        "    print(\"{}:\\n\\n \\\n",
        "    Confusion matrix:\\n{}\\n\\n \\\n",
        "           Precision: {}\\n \\\n",
        "              Recall: {}\\n \\\n",
        "    F1 Score (macro): {}\\n\\n\\n\".format(name, \n",
        "                                 pd.DataFrame(confusion_matrix(y, y_pred), index=classes, columns=classes), \n",
        "                                 model[\"Precision\"], \n",
        "                                 model[\"Recall\"], \n",
        "                                 model[\"F1_Score\"]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "id": "tAMF4r95FtYt",
        "outputId": "e1a0c947-76b7-41c2-f3d6-e7ebcfc0ec21",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Perceptron:\n",
            "\n",
            "     Confusion matrix:\n",
            "               not depressed  depressed\n",
            "not depressed            377         22\n",
            "depressed                 20        181\n",
            "\n",
            "            Precision: 0.8916256157635468\n",
            "               Recall: 0.900497512437811\n",
            "     F1 Score (macro): 0.9216378924324593\n",
            "\n",
            "\n",
            "\n",
            "Polynomial Perceptron:\n",
            "\n",
            "     Confusion matrix:\n",
            "               not depressed  depressed\n",
            "not depressed            377         22\n",
            "depressed                 10        191\n",
            "\n",
            "            Precision: 0.8967136150234741\n",
            "               Recall: 0.9502487562189055\n",
            "     F1 Score (macro): 0.9409964229081388\n",
            "\n",
            "\n",
            "\n",
            "Logistic Regression:\n",
            "\n",
            "     Confusion matrix:\n",
            "               not depressed  depressed\n",
            "not depressed            389         10\n",
            "depressed                 20        181\n",
            "\n",
            "            Precision: 0.9476439790575916\n",
            "               Recall: 0.900497512437811\n",
            "     F1 Score (macro): 0.9431703374419074\n",
            "\n",
            "\n",
            "\n",
            "RandomForest:\n",
            "\n",
            "     Confusion matrix:\n",
            "               not depressed  depressed\n",
            "not depressed            392          7\n",
            "depressed                 18        183\n",
            "\n",
            "            Precision: 0.9631578947368421\n",
            "               Recall: 0.9104477611940298\n",
            "     F1 Score (macro): 0.9525795162478384\n",
            "\n",
            "\n",
            "\n",
            "Decision Tree:\n",
            "\n",
            "     Confusion matrix:\n",
            "               not depressed  depressed\n",
            "not depressed            384         15\n",
            "depressed                 21        180\n",
            "\n",
            "            Precision: 0.9230769230769231\n",
            "               Recall: 0.8955223880597015\n",
            "     F1 Score (macro): 0.9321573948439621\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "classes = [\"not depressed\" , \"depressed\"]\n",
        "for name, model in models.items():\n",
        "    calculate_precision_recall_f1(name, X_val, y_val)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "81Ro5B-fFtYt"
      },
      "source": [
        "### Confronto modelli con intervallo di confidenza"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jOnGzi28FtYt"
      },
      "source": [
        "Si verifica con **confidenza** al 95% se la F1-Score dei modelli non è statisticamente significativa una dall'altra. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "3ZIdsumQFtYt",
        "outputId": "dd225b91-282c-4635-eec5-0c73965319e8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RandomForest vs Logistic Regression , intervallo di confidenza:\n",
            "Valore minimo: -0.011694986957135205\n",
            "Valore massimo: 0.03051334456899733\n",
            "\n",
            "RandomForest vs Decision Tree , intervallo di confidenza:\n",
            "Valore minimo: -0.0016879346813130705\n",
            "Valore massimo: 0.042532177489065744\n",
            "\n",
            "RandomForest vs Polynomial Perceptron , intervallo di confidenza:\n",
            "Valore minimo: -0.009725426890605387\n",
            "Valore massimo: 0.03289161357000468\n",
            "\n"
          ]
        }
      ],
      "source": [
        "def difference_between_two_models(error1, error2, confidence):\n",
        "    z_half_alfa = stats.norm.ppf(confidence)\n",
        "    variance = (((1 - error1) * error1) / len(y_val)) + (((1 - error2) * error2) / len(y_val))\n",
        "    d_minus = abs(error1 - error2) - z_half_alfa * (pow(variance, 0.5))\n",
        "    d_plus = abs(error1 - error2) + z_half_alfa * (pow(variance, 0.5))\n",
        "    print(\"Valore minimo: {}\\nValore massimo: {}\\n\".format(d_minus, d_plus))\n",
        "\n",
        "lre_error = 1 - models[\"Logistic Regression\"][\"F1_Score\"]\n",
        "tree_error = 1 - models[\"Decision Tree\"][\"F1_Score\"]\n",
        "forest_error = 1 - models[\"RandomForest\"][\"F1_Score\"]\n",
        "poly_per = 1 - models[\"Polynomial Perceptron\"][\"F1_Score\"]\n",
        "\n",
        "print(\"RandomForest vs Logistic Regression , intervallo di confidenza:\")\n",
        "difference_between_two_models(forest_error, lre_error, 0.95)\n",
        "\n",
        "print(\"RandomForest vs Decision Tree , intervallo di confidenza:\")\n",
        "difference_between_two_models(forest_error, tree_error, 0.95)\n",
        "\n",
        "print(\"RandomForest vs Polynomial Perceptron , intervallo di confidenza:\")\n",
        "difference_between_two_models(forest_error, poly_per, 0.95)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fcUvogU1FtYt"
      },
      "source": [
        "Riepilogo F1-Score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zPFmEutSFtYt",
        "outputId": "aab1f125-45b9-4dce-9656-021d9e0d1242",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Perceptron:\n",
            "\t\t\t\t0.9216378924324593\n",
            "Polynomial Perceptron:\n",
            "\t\t\t\t0.9409964229081388\n",
            "Logistic Regression:\n",
            "\t\t\t\t0.9431703374419074\n",
            "RandomForest:\n",
            "\t\t\t\t0.9525795162478384\n",
            "Decision Tree:\n",
            "\t\t\t\t0.9321573948439621\n"
          ]
        }
      ],
      "source": [
        "for name, model in models.items():\n",
        "    print(name + \":\\n\\t\\t\\t\\t{}\".format(model[\"F1_Score\"]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5SUVGO1MFtYt"
      },
      "source": [
        "Il modello che ha ottenuto il risultato migliore è Random Forest.\n",
        "Con score leggermente minore troviamo Logistic Regression e Polynomial perceptron \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A93E42KGFtYu"
      },
      "source": [
        "## Parte 5 - Analisi del modello migliore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MiPtcMc8FtYu"
      },
      "outputs": [],
      "source": [
        "from sklearn.dummy import DummyClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1L959a35FtYu"
      },
      "source": [
        "### Confronto con modello casuale"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6k-TW4yzFtYu"
      },
      "source": [
        "Viene creato un modello casuale"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "spRxXT9uFtYu",
        "outputId": "1d992611-1468-4f7d-db6c-efc33af74f9d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.475"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "random = DummyClassifier(strategy=\"uniform\", random_state=42)\n",
        "random.fit(X_train, y_train)\n",
        "\n",
        "random_score = random.score(X_val, y_val)\n",
        "models[\"Dummy\"] = {\"Model\" : random, \"Score\": random_score}\n",
        "random_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3qkue1aIFtYu"
      },
      "source": [
        "Ovviamente tutti i modelli scelti precedentemente sono statisticamente migliori di uno casuale, di seguito sono riportati gli intervalli di confidenza"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "id": "n31K9oRwFtYu",
        "outputId": "3edb9801-5bf3-4505-9dd9-dce4051e83ae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dummy:\n",
            "\n",
            "     Confusion matrix:\n",
            "               not depressed  depressed\n",
            "not depressed            186        213\n",
            "depressed                102         99\n",
            "\n",
            "            Precision: 0.3173076923076923\n",
            "               Recall: 0.4925373134328358\n",
            "     F1 Score (macro): 0.46372481421895345\n",
            "\n",
            "\n",
            "\n",
            "Valore minimo: 0.43737141886100317\n",
            "Valore massimo: 0.5403379851967669\n",
            "\n",
            "Valore minimo: 0.42722914683522983\n",
            "Valore massimo: 0.5316618996106781\n",
            "\n",
            "Valore minimo: 0.42488948393590104\n",
            "Valore massimo: 0.5296537334424697\n",
            "\n",
            "Valore minimo: 0.4041124087357935\n",
            "Valore massimo: 0.5117137476912182\n",
            "\n",
            "Valore minimo: 0.4153901403319104\n",
            "Valore massimo: 0.521475020918107\n",
            "\n"
          ]
        }
      ],
      "source": [
        "calculate_precision_recall_f1(\"Dummy\", X_val, y_val)\n",
        "\n",
        "difference_between_two_models(1 - models[\"RandomForest\"][\"F1_Score\"], 1 - models[\"Dummy\"][\"F1_Score\"], 0.99)\n",
        "\n",
        "difference_between_two_models(1 - models[\"Logistic Regression\"][\"F1_Score\"], 1 - models[\"Dummy\"][\"F1_Score\"], 0.99)\n",
        "\n",
        "difference_between_two_models(1 - models[\"Polynomial Perceptron\"][\"F1_Score\"], 1 - models[\"Dummy\"][\"F1_Score\"], 0.99)\n",
        "\n",
        "difference_between_two_models(1 - models[\"Perceptron\"][\"F1_Score\"], 1 - models[\"Dummy\"][\"F1_Score\"], 0.99)\n",
        "\n",
        "difference_between_two_models(1 - models[\"Decision Tree\"][\"F1_Score\"], 1 - models[\"Dummy\"][\"F1_Score\"], 0.99)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BEspGJoEFtYv",
        "outputId": "ec9509ef-84ca-48ce-9e0c-598f58eb1ddb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "\n",
        "fig = plt.figure(figsize=(15, 8))\n",
        "\n",
        "red_patch = mpatches.Patch(color='red', label='Random Forest')\n",
        "blue_patch = mpatches.Patch(color='blue', label='Logistic Regression')\n",
        "green_patch = mpatches.Patch(color='green', label='Dummy model')\n",
        "orange_patch = mpatches.Patch(color='orange', label='Decision Tree')\n",
        "\n",
        "\n",
        "plt.legend(handles=[red_patch, blue_patch,green_patch,orange_patch])\n",
        "\n",
        "precision_rf, recall_rf, tresholds_rf = precision_recall_curve(y_val, \n",
        "    models[\"RandomForest\"][\"Model\"].predict_proba(X_val)[:, 1])\n",
        "\n",
        "_ = plt.plot(precision_rf, recall_rf, color=\"red\")\n",
        "\n",
        "precision_knn, recall_knn, tresholds_knn = precision_recall_curve(y_val, \n",
        "    models[\"Logistic Regression\"][\"Model\"].predict_proba(X_val)[:, 1])\n",
        "\n",
        "_ = plt.plot(precision_knn, recall_knn, color=\"blue\")\n",
        "\n",
        "\n",
        "precision_knn, recall_knn, tresholds_knn = precision_recall_curve(y_val, \n",
        "    models[\"Dummy\"][\"Model\"].predict_proba(X_val)[:, 1])\n",
        "\n",
        "_ = plt.plot(precision_knn, recall_knn, color=\"green\")\n",
        "\n",
        "\n",
        "precision_knn, recall_knn, tresholds_knn = precision_recall_curve(y_val, \n",
        "    models[\"Decision Tree\"][\"Model\"].predict_proba(X_val)[:, 1])\n",
        "\n",
        "_ = plt.plot(precision_knn, recall_knn, color=\"orange\")\n",
        "\n",
        "\n",
        "\n",
        "_ = plt.xlabel(\"Precision\")\n",
        "_ = plt.ylabel(\"Recall\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Maggiore è l'area sotto la curva migliore è il modello, è evidente come RandomForest sia leggermente superiore a tutti gli altri modelli provati\n",
        "\n",
        "A seguito di queste considerazioni, è stato scelto il modello basato su RandomForest"
      ],
      "metadata": {
        "id": "yCckzi0-Jevd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "calculate_precision_recall_f1(\"RandomForest\", X_val, y_val)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8yhb61RwKBrR",
        "outputId": "8ce85b9d-4f93-45de-a041-59ca41d5e649"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RandomForest:\n",
            "\n",
            "     Confusion matrix:\n",
            "               not depressed  depressed\n",
            "not depressed            392          7\n",
            "depressed                 18        183\n",
            "\n",
            "            Precision: 0.9631578947368421\n",
            "               Recall: 0.9104477611940298\n",
            "     F1 Score (macro): 0.9525795162478384\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ]
    }
  ]
}