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      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "This data set comes from UCI Machine Learning Repository: https://archive.ics.uci.edu/dataset/320/student%2Bperformance"
      ],
      "metadata": {
        "id": "_U2fp_HCDh4c"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ZG5aqpmeC23y"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import requests\n",
        "from io import BytesIO"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"https://www.iam.fmph.uniba.sk/ospm/Rosa/PDV/01_student-short.csv\"\n",
        "\n",
        "# iam.fmph.uniba.sk ma problem s bezpecnostnym certifikatom => obideme verifikaciu\n",
        "response = requests.get(path, verify=False)\n",
        "response.raise_for_status()\n",
        "data = pd.read_csv(BytesIO(response.content), sep=\";\")\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "XpfnAEOpC8zT",
        "outputId": "f531b397-8736-4993-cead-feea2a9fd7b7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.13/dist-packages/urllib3/connectionpool.py:1097: InsecureRequestWarning: Unverified HTTPS request is being made to host 'www.iam.fmph.uniba.sk'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#tls-warnings\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    school sex  age famsize  Medu  Fedu      Mjob      Fjob  studytime paid  \\\n",
              "0       GP   F   18     GT3     4     4   at_home   teacher          2   no   \n",
              "1       GP   F   17     GT3     1     1   at_home     other          2   no   \n",
              "2       GP   F   15     LE3     1     1   at_home     other          2  yes   \n",
              "3       GP   F   15     GT3     4     2    health  services          3  yes   \n",
              "4       GP   F   16     GT3     3     3     other     other          2  yes   \n",
              "..     ...  ..  ...     ...   ...   ...       ...       ...        ...  ...   \n",
              "390     MS   M   20     LE3     2     2  services  services          2  yes   \n",
              "391     MS   M   17     LE3     3     1  services  services          1   no   \n",
              "392     MS   M   21     GT3     1     1     other     other          1   no   \n",
              "393     MS   M   18     LE3     3     2  services     other          1   no   \n",
              "394     MS   M   19     LE3     1     1     other   at_home          1   no   \n",
              "\n",
              "     absences  G3  \n",
              "0           6   6  \n",
              "1           4   6  \n",
              "2          10  10  \n",
              "3           2  15  \n",
              "4           4  10  \n",
              "..        ...  ..  \n",
              "390        11   9  \n",
              "391         3  16  \n",
              "392         3   7  \n",
              "393         0  10  \n",
              "394         5   9  \n",
              "\n",
              "[395 rows x 12 columns]"
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GP</td>\n",
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              "      <td>GT3</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>390</th>\n",
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              "      <td>20</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>services</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
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              "      <td>MS</td>\n",
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              "      <td>1</td>\n",
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              "      <td>services</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
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              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>21</td>\n",
              "      <td>GT3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>other</td>\n",
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              "      <td>7</td>\n",
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              "      <td>M</td>\n",
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              "      <td>LE3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
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              "      <td>9</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 395,\n  \"fields\": [\n    {\n      \"column\": \"school\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"MS\",\n          \"GP\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"M\",\n          \"F\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 15,\n        \"max\": 22,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          17,\n          22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"famsize\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"LE3\",\n          \"GT3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Medu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fedu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mjob\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"health\",\n          \"teacher\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fjob\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"other\",\n          \"at_home\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"studytime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"paid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"absences\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 0,\n        \"max\": 75,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          56,\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"G3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 0,\n        \"max\": 20,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          6,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# chybajuce data\n",
        "missing_values_count = data.isnull().sum()\n",
        "print(missing_values_count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L6FVhTqGGsZr",
        "outputId": "d9ff7372-97ad-4ac2-d5e1-48f5d27902ba"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "school       0\n",
            "sex          0\n",
            "age          0\n",
            "famsize      0\n",
            "Medu         0\n",
            "Fedu         0\n",
            "Mjob         0\n",
            "Fjob         0\n",
            "studytime    0\n",
            "paid         0\n",
            "absences     0\n",
            "G3           0\n",
            "dtype: int64\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "data.describe().transpose()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "Yk-6bSlIG4Mf",
        "outputId": "74860939-c905-4468-96e6-2adaf56cfc11"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           count       mean       std   min   25%   50%   75%   max\n",
              "age        395.0  16.696203  1.276043  15.0  16.0  17.0  18.0  22.0\n",
              "Medu       395.0   2.749367  1.094735   0.0   2.0   3.0   4.0   4.0\n",
              "Fedu       395.0   2.521519  1.088201   0.0   2.0   2.0   3.0   4.0\n",
              "studytime  395.0   2.035443  0.839240   1.0   1.0   2.0   2.0   4.0\n",
              "absences   395.0   5.708861  8.003096   0.0   0.0   4.0   8.0  75.0\n",
              "G3         395.0  10.415190  4.581443   0.0   8.0  11.0  14.0  20.0"
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            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "peLLtRpSGzY6",
        "outputId": "f9a40adc-1824-4826-a941-e649a6d0f474"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(2, 6, figsize=(14, 6))\n",
        "axes = axes.flatten()\n",
        "\n",
        "for i, column in enumerate(data.columns):\n",
        "    sns.histplot(data[column], ax=axes[i])\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "2HzAEwU5HQVL",
        "outputId": "12976c4b-f37b-44b2-eab8-aa6dc69adc88"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(data, height=1.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 923
        },
        "id": "-VlmC082HlMu",
        "outputId": "c5b92c6e-1e3f-4882-d126-22d97dbf14be"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7b6b26ba41a0>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 42 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=data, x=\"Medu\", y=\"G3\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "qBo_yGmKIYej",
        "outputId": "1f0cd21a-3d49-4f26-e729-de9e608b0042"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Medu', ylabel='G3'>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=data, x=\"Fjob\", y=\"G3\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "Dz1jWL3tItXj",
        "outputId": "89b9c07c-34fe-42fa-faf8-2bc48477c350"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Fjob', ylabel='G3'>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podobne mozete vyskusat graficky analyzovat zavislosti G3 od ostatnych premennych."
      ],
      "metadata": {
        "id": "va_mgHNUI1kK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Medu vs. percento \"yes\" v \"paid\" (t.j. percento s platenymi hodinami navyse)\n",
        "paid_pct = (\n",
        "    data.groupby(\"Medu\")[\"paid\"]\n",
        "         .apply(lambda x: (x == \"yes\").mean() * 100)\n",
        "         .reset_index(name=\"percentage\")\n",
        ")\n",
        "\n",
        "sns.barplot(data=paid_pct, x=\"Medu\", y=\"percentage\")\n",
        "plt.ylabel(\"Paid classes: yes (%)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Z9Tx87AIJsr0",
        "outputId": "dd4334af-cc22-4736-f5c0-c2f4f2a96d08"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Paid classes: yes (%)')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# To iste pre Fjob\n",
        "paid_pct = (\n",
        "    data.groupby(\"Fjob\")[\"paid\"]\n",
        "         .apply(lambda x: (x == \"yes\").mean() * 100)\n",
        "         .reset_index(name=\"percentage\")\n",
        ")\n",
        "\n",
        "sns.barplot(data=paid_pct, x=\"Fjob\", y=\"percentage\")\n",
        "plt.ylabel(\"Paid classes: yes (%)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "qHvlsIy8KadD",
        "outputId": "ab76a538-3d41-4693-be1a-94be9bb17067"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Paid classes: yes (%)')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ako by ste vykreslili/znazornili zavislosti medzi Medu a Fjob? A ako medzi Fjob a Mjob?"
      ],
      "metadata": {
        "id": "rsWTbiLeMgEz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Korelacie G3 s numerickymi premennymi\n",
        "correlations = (\n",
        "    data.corr(numeric_only=True)[\"G3\"]\n",
        "         .drop(\"G3\")\n",
        "         .round(2)\n",
        ")\n",
        "\n",
        "print(correlations)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Npde9KsNNri",
        "outputId": "8ccc9ca8-3c56-4aef-91bd-f0e06b6be1af"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age         -0.16\n",
            "Medu         0.22\n",
            "Fedu         0.15\n",
            "studytime    0.10\n",
            "absences     0.03\n",
            "Name: G3, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Korelacie G3 so vsetkymi\n",
        "data_corr = data.copy()\n",
        "coding = {}\n",
        "\n",
        "# Konverzia na ciselne\n",
        "for col in data_corr.select_dtypes(include=[\"object\", \"category\"]).columns:\n",
        "    codes, uniques = pd.factorize(data_corr[col])\n",
        "    data_corr[col] = codes\n",
        "\n",
        "    # ulozit kody\n",
        "    coding[col] = {category: code for code, category in enumerate(uniques)}\n",
        "\n",
        "correlations = (\n",
        "    data_corr.corr()[\"G3\"]\n",
        "             .drop(\"G3\")\n",
        "             .round(2)\n",
        ")\n",
        "\n",
        "print(correlations)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hktwX-q5N3kM",
        "outputId": "4fa6d5ef-462c-4bbc-d9c0-48dc6b48e048"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "school      -0.05\n",
            "sex          0.10\n",
            "age         -0.16\n",
            "famsize      0.08\n",
            "Medu         0.22\n",
            "Fedu         0.15\n",
            "Mjob         0.10\n",
            "Fjob        -0.02\n",
            "studytime    0.10\n",
            "paid         0.10\n",
            "absences     0.03\n",
            "Name: G3, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pouzite kodovanie\n",
        "for col, mapping in coding.items():\n",
        "    print(col, \":\", mapping)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oJ2CI_JEdP1k",
        "outputId": "7228857a-35e5-474f-e8a1-3197fe934cfe"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "school : {'GP': 0, 'MS': 1}\n",
            "sex : {'F': 0, 'M': 1}\n",
            "famsize : {'GT3': 0, 'LE3': 1}\n",
            "Mjob : {'at_home': 0, 'health': 1, 'other': 2, 'services': 3, 'teacher': 4}\n",
            "Fjob : {'teacher': 0, 'other': 1, 'services': 2, 'health': 3, 'at_home': 4}\n",
            "paid : {'no': 0, 'yes': 1}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=data, x=\"Mjob\", y=\"G3\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "suSEm8sYXuka",
        "outputId": "8af22da0-2914-4302-a585-1ba2068583fa"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Mjob', ylabel='G3'>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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VqlWrdOjQIf3mN7+5bL8dOnRQZmam47F169bqKB8AAHgol44Bio2NVWxsbJnLgoKCtG7dOqd5c+fO1a233qqMjAxFRESU22+dOnUUGhpapbUCAIDaw6PGAOXk5Mhms6lBgwYVtjty5IjCwsLUqlUrjRgxQhkZGTVTIAAA8Age81dg+fn5euqppxQXF6fAwMBy20VFRSkpKUlt2rRRZmamZs6cqTvuuEP79+9XQEBAmesUFBSooOA/9yzJzc294rq44dvV42ZvAABX8YgAVFRUpPvvv1/GGL355psVtv3lJbXOnTsrKipKkZGRev/99zVu3Lgy10lMTNTMmTMrXRc3fLs23OwNAOAqbh+ASsNPenq6vvzyywrP/pSlQYMGat26tVJSUsptk5CQoPj4eMd0bm6uwsPDL9s3N3y7etzsDQDgSm4dgErDz5EjR7Rx40Y1bty40n3k5eUpNTVVDz30ULlt/Pz85Ofnd9V1csM3AAA8i0sHQefl5Sk5OVnJycmSpKNHjyo5OVkZGRkqKirS0KFDtXv3bv3tb39TcXGxsrKylJWVpcLCQkcfffv21dy5cx3TU6dO1ebNm5WWlqbt27frnnvukbe3t+Li4mp68wAAgJty6Rmg3bt3q0+fPo7p0stQo0aN0owZM/TRRx9Jkrp27eq03saNG9W7d29JUmpqqk6dOuVYdvz4ccXFxenHH39UcHCwevbsqa+//lrBwcHVuzEAAMBjuDQA9e7dW8aYcpdXtKxUWlqa0/Ty5cuvtSwAAFDLedR9gAAAAKoCAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFhOHVcXAKB28rpw1tUleKTqet3YH5XHvnAf1fGaEYAAVAv/o1tcXQJ+gf3hPtgX7oEABKBaXGh5p0r8G7i6DI/jdeFstXxAsj8qj33hPqpjXxCAAFSLEv8GKqnXxNVl4P9jf7gP9oV7YBA0AACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwnEoHIGOMjh49qosXL0qSCgsL9d577+ndd9/VqVOnqrxAAACAqlapGyEeOnRIMTExOnbsmFq1aqW1a9dq2LBhOnjwoIwxqlu3rrZv364bb7yxuuoFAAC4ZpU6A/TUU0+pS5cuSk5O1sCBA3X33XerefPmOnPmjE6fPq0ePXro+eefv+L+tmzZokGDBiksLEw2m02rV692Wm6M0XPPPadmzZrJ399f0dHROnLkyGX7nTdvnlq0aCG73a6oqCh98803ldlMAABQy1UqAG3fvl0zZ85Up06d9MILL+jgwYOaOnWqfHx85Ofnp6efflpbtlz5d3WcP39eXbp00bx588pc/tJLL+mNN97Q/PnztXPnTtWrV08xMTHKz88vt8/33ntP8fHxmj59uvbu3asuXbooJiZGP/zwQ2U2FQAA1GKVCkB5eXlq1KiRJKlevXqqV6+emjVr5lgeHh6u7OzsK+4vNjZWL7zwgu65555Llhlj9Nprr+nZZ5/V4MGD1blzZ7377rs6ceLEJWeKfumVV17R+PHjNWbMGLVv317z589X3bp1tWjRoivfUAAAUKtVKgCFhYUpIyPDMf3SSy+padOmjumTJ0+qYcOGVVLY0aNHlZWVpejoaMe8oKAgRUVFaceOHWWuU1hYqD179jit4+Xlpejo6HLXAQAA1lOpQdDR0dE6ePCgevbsKUmaOHGi0/K1a9eqW7duVVJYVlaWJCkkJMRpfkhIiGPZfzt16pSKi4vLXOfgwYPlPldBQYEKCgoc07m5uZWq1evC2Uq1R/W9Zl75OdXSb23H6wbAaioVgObPn1/h8gceeECjRo26poJcITExUTNnzrzq9f2PXvm4J1SPoKAg+fj6Sd9vdnUpHsvH109BQUGuLgMAakSlAlB+fr7Wr1+vgQMHSpISEhKczpx4e3tr1qxZVVJYaGioJCk7O9tpnFF2dra6du1a5jpNmjSRt7f3JeOQsrOzHf2VJSEhQfHx8Y7p3NxchYeHX3GtF1reqRL/BlfcHj+fAarK4BgSEqKlf31XOTnueSYjPT1ds2fP1rRp0xQZGenqcsoUFBR0ydlTAKitKhWAkpKS9OmnnzoC0Ny5c9WhQwf5+/tLkg4ePKiwsDA9/vjj11xYy5YtFRoaqg0bNjgCT25urnbu3HnJpbdSvr6+6t69uzZs2KAhQ4ZIkkpKSrRhwwZNnjy53Ofy8/OTn5/fVdda4t9AJfWaXPX6qBohISFu/wEeGRmp1q1bu7oMALC8Sg2C/tvf/qYJEyY4zVu2bJk2btyojRs3as6cOXr//fevuL+8vDwlJycrOTlZ0s8Dn5OTk5WRkSGbzaYpU6bohRde0EcffaR9+/Zp5MiRCgsLc4QbSerbt6/mzp3rmI6Pj9fChQu1ZMkSHThwQBMnTtT58+c1ZsyYymwqAACoxSp1BiglJUWdOnVyTNvtdnl5/SdD3XrrrZo0adIV97d792716dPHMV16GWrUqFFKSkrSk08+qfPnz2vChAk6e/asevbsqTVr1shutzvWSU1NdfoKjgceeEAnT57Uc889p6ysLHXt2lVr1qxx+zMDAACg5lQqAJ09e9ZpzM/JkyedlpeUlDgtv5zevXvLGFPucpvNpueff77Cu0unpaVdMm/y5MkVXvICAADWVqlLYM2bN9f+/fvLXf6vf/1LzZs3v+aiAAAAqlOlAtBdd92l5557rsyvorhw4YJmzpypu+++u8qKAwAAqA6VugT2zDPP6P3331ebNm00efJkx1+zHDp0SHPnztXFixf1zDPPVEuhAAAAVaVSASgkJETbt2/XxIkT9fTTTzvG79hsNvXr109/+ctfGGwMAADcXqUCkPTz/XnWrFmj06dPKyUlRZJ0ww03OL4kFQAAwN1VOgCVatSokW699daqrAUAAKBGVGoQNAAAQG1AAAIAAJZDAAIAAJZDAAIAAJZz1YOgAQCewys/x9UleJzqes3YF5VXHa8ZAQgAarGgoCD5+PpJ3292dSkeycfXT0FBQVXSF/vi2lTlvpAIQABQq4WEhGjpX99VTo77nXVIT0/X7NmzNW3aNEVGRrq6nDIFBQVV2Q1+2RfXpir3hUQAAoBaLyQkxK3v0h8ZGen4aqXajn3hPhgEDQAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcABAAALIcvQ60CXvnu982+7s7dX7P8/HxlZGRUWX/p6elOP6tKRESE7HZ7lfZZVdx9H7srXjegZhCArkFQUJB8fP2k7ze7uhSP5OPrp6CgIFeXUaaMjAxNmDChyvudPXt2lfa3YMECt/vmZt4X186d3xtAbUEAugYhISFa+td3lZPjnr+xpaena/bs2Zo2bZoiIyNdXc4lgoKCFBIS4uoyyhQREaEFCxa4uozLioiIcHUJl+B9ce3c+b0B1BYEoGsUEhLi9geqyMhItztL4O7sdjuv2TXgfQHA3TEIGgAAWI7bB6AWLVrIZrNd8pg0aVKZ7ZOSki5p666DRAEAgGu4/SWwXbt2qbi42DG9f/9+9evXT8OGDSt3ncDAQB06dMgxbbPZqrVGAADgWdw+AAUHBztNv/jii7r++uvVq1evctex2WwKDQ2t7tIAAICHcvtLYL9UWFiopUuXauzYsRWe1cnLy1NkZKTCw8M1ePBgfffddzVYJQAAcHceFYBWr16ts2fPavTo0eW2adOmjRYtWqQPP/xQS5cuVUlJiW677TYdP3683HUKCgqUm5vr9AAAALWXRwWgd955R7GxsQoLCyu3TY8ePTRy5Eh17dpVvXr10qpVqxQcHKy33nqr3HUSExMVFBTkeISHh1dH+QAAwE14TABKT0/X+vXr9fDDD1dqPR8fH910001KSUkpt01CQoJycnIcj2PHjl1ruQAAwI15TABavHixmjZtqrvvvrtS6xUXF2vfvn1q1qxZuW38/PwUGBjo9AAAALWXRwSgkpISLV68WKNGjVKdOs5/uDZy5EglJCQ4pp9//nmtXbtW33//vfbu3asHH3xQ6enplT5zBAAAai+3/zN4SVq/fr0yMjI0duzYS5ZlZGTIy+s/Oe7MmTMaP368srKy1LBhQ3Xv3l3bt29X+/bta7JkAADgxjwiAPXv31/GmDKXbdq0yWn61Vdf1auvvloDVQEAAE/lEZfAAAAAqhIBCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWE4dVxcAAAAqLz8/XxkZGVXSV3p6utPPqhIRESG73V6lfVYVAhAAAB4oIyNDEyZMqNI+Z8+eXaX9LViwQK1bt67SPqsKAQgAAA8UERGhBQsWuLqMCkVERLi6hHIRgAAA8EB2u91tz654AgZBAwAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAy3HrADRjxgzZbDanR9u2bStcZ8WKFWrbtq3sdrs6deqkzz77rIaqBQAAnsKtA5AkdejQQZmZmY7H1q1by227fft2xcXFady4cfrnP/+pIUOGaMiQIdq/f38NVgwAANyd2wegOnXqKDQ01PFo0qRJuW1ff/11DRgwQE888YTatWunWbNmqVu3bpo7d24NVgwAANyd2wegI0eOKCwsTK1atdKIESOUkZFRbtsdO3YoOjraaV5MTIx27NhR3WUCAAAP4tbfBh8VFaWkpCS1adNGmZmZmjlzpu644w7t379fAQEBl7TPyspSSEiI07yQkBBlZWVV+DwFBQUqKChwTOfm5lbNBlRSfn5+hQGvstLT051+VoWIiAjZ7fYq6w+A5/CEY5TEcQpXxq0DUGxsrOPfnTt3VlRUlCIjI/X+++9r3LhxVfY8iYmJmjlzZpX1d7UyMjI0YcKEKu939uzZVdbXggUL1Lp16yrrD4Dn8IRjlMRxClfGrQPQf2vQoIFat26tlJSUMpeHhoYqOzvbaV52drZCQ0Mr7DchIUHx8fGO6dzcXIWHh197wZUUERGhBQsW1PjzVkZERISrSwDgIp5wjJI4TuHKeFQAysvLU2pqqh566KEyl/fo0UMbNmzQlClTHPPWrVunHj16VNivn5+f/Pz8qrLUq2K32/mtBYDb4hiF2sStB0FPnTpVmzdvVlpamrZv36577rlH3t7eiouLkySNHDlSCQkJjvaPPfaY1qxZo5dfflkHDx7UjBkztHv3bk2ePNlVmwAAANyQW58BOn78uOLi4vTjjz8qODhYPXv21Ndff63g4GBJP1+P9vL6T4a77bbbtGzZMj377LN65plndOONN2r16tXq2LGjqzYBAAC4IbcOQMuXL69w+aZNmy6ZN2zYMA0bNqyaKgIAALWBW18CAwAAqA4EIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDlu/VUYAJCfn6+MjIwq6y89Pd3pZ1WJiIiQ3W6v0j4BVB8CEAC3lpGRoQkTJlR5v7Nnz67S/hYsWKDWrVtXaZ8Aqg8BCIBbi4iI0IIFC1xdxmVFRES4ugQAlUAAAuDW7HY7Z1YAVDkGQQMAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMshAAEAAMtx6wCUmJioW265RQEBAWratKmGDBmiQ4cOVbhOUlKSbDab08Nut9dQxQAAwBO4dQDavHmzJk2apK+//lrr1q1TUVGR+vfvr/Pnz1e4XmBgoDIzMx2P9PT0GqoYAAB4gjquLqAia9ascZpOSkpS06ZNtWfPHt15553lrmez2RQaGlrd5QEAAA/l1meA/ltOTo4kqVGjRhW2y8vLU2RkpMLDwzV48GB99913NVEeAADwEB4TgEpKSjRlyhTdfvvt6tixY7nt2rRpo0WLFunDDz/U0qVLVVJSottuu03Hjx8vd52CggLl5uY6PQAAQO1lM8YYVxdxJSZOnKjPP/9cW7duVfPmza94vaKiIrVr105xcXGaNWtWmW1mzJihmTNnXjI/JydHgYGBV10zAACoObm5uQoKCrqiz2+POAM0efJkffLJJ9q4cWOlwo8k+fj46KabblJKSkq5bRISEpSTk+N4HDt27FpLBgAAbsytB0EbY/T73/9eH3zwgTZt2qSWLVtWuo/i4mLt27dPd911V7lt/Pz85Ofndy2lAgAAD+LWAWjSpElatmyZPvzwQwUEBCgrK0uSFBQUJH9/f0nSyJEjdd111ykxMVGS9Pzzz+tXv/qVbrjhBp09e1Zz5sxRenq6Hn74YZdtBwAAcC9uHYDefPNNSVLv3r2d5i9evFijR4+WJGVkZMjL6z9X8s6cOaPx48crKytLDRs2VPfu3bV9+3a1b9++psoGAABuzmMGQdekygyiAgAA7qHWDYIGAACoSgQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOR4RgObNm6cWLVrIbrcrKipK33zzTYXtV6xYobZt28put6tTp0767LPPaqhS93HixAkNHDhQffv21cCBA3XixAlXl2RZhYWFWrFihV5//XWtWLFChYWFri7JslJSUtSnTx/17t1bffr0UUpKiqtLsiyOUe4jLy9P06ZN05gxYzRt2jTl5eW5uqQaYTPGGFcXUZH33ntPI0eO1Pz58xUVFaXXXntNK1as0KFDh9S0adNL2m/fvl133nmnEhMTNXDgQC1btkx//OMftXfvXnXs2PGKnjM3N1dBQUHKyclRYGBgVW9StevXr5+Kiooume/j46N169a5oCLrmj9/vlasWKHi4mLHPG9vbw0bNkyPPPKICyuznt69e5e7bNOmTTVWBzhGuZNHHnlEBw8evGR+27ZtNX/+fBdUdG0q8/nt9meAXnnlFY0fP15jxoxR+/btNX/+fNWtW1eLFi0qs/3rr7+uAQMG6IknnlC7du00a9YsdevWTXPnzq3hyl3jlweWRo0aKSEhQY0aNZIkFRUVqV+/fq4sz1Lmz5+v5cuXKzAwUFOnTtXKlSs1depUBQYGavny5R55cPFUvww/3t7eeuihh+Tt7V3mclQvjlHuozT82Gw29e/fX2+//bb69+8vm82mgwcP1vpf0uq4uoCKFBYWas+ePUpISHDM8/LyUnR0tHbs2FHmOjt27FB8fLzTvJiYGK1evbo6S3ULJ06ccBxYVq1a5TioxMTE6PTp07r33ntVVFSkEydOKCwszJWl1nqll70aNmyoFStWqE6dn99qAwcO1IABAzRs2DCtWLFCY8eOla+vr4urrd1+eZlr6dKlat68uSRp3LhxOn78uB588EFHuxtuuMElNVoFxyj3kZeX5wg/n3/+uex2uyTpmWeeUXx8vGJjY3Xw4EHl5eWpfv36Lq62erj1GaBTp06puLhYISEhTvNDQkKUlZVV5jpZWVmVai9JBQUFys3NdXp4ogkTJkj6+beq0gNLqV/OK22H6vPhhx+quLhY48aNc4SfUnXq1NHYsWNVXFysDz/80EUVWsf48eMl/XzmpzT8lGrevLnjTFBpO1QfjlHuIzExUdLPZ+RKw08pu92u6Ohop3a1kVsHoJqSmJiooKAgxyM8PNzVJV2VCxcuSJJ+97vflbl87NixTu1QfUoHdPbo0aPM5aXzGfhZ/UqHOf72t78tc/mwYcOc2qH6cIxyH6XHnvvvv7/M5aXvi9p8jHLrANSkSRN5e3srOzvbaX52drZCQ0PLXCc0NLRS7SUpISFBOTk5jsexY8euvXgX8Pf3lyS99dZbZS4vHTdV2g7Vp/T0fUWXan/ZDtXHZrNJkpYtW1bm8hUrVji1Q/XhGOU+So8977//fpnLS98XtfkY5dYByNfXV927d9eGDRsc80pKSrRhw4YKf7P+ZXtJWrduXbntJcnPz0+BgYFOD0+0YMECSdLp06d1+vRpp2W/nFfaDtVn8ODB8vb21jvvvKOLFy86Lbt48aIWLVokb29vDR482EUVWsfChQslScXFxTp+/LjTsuPHjzv+Qq+0HaoPxyj3UTq2dt26dcrPz3dalp+fr/Xr1zu1q43cehC0JMXHx2vUqFG6+eabdeutt+q1117T+fPnNWbMGEnSyJEjdd111zmuUz722GPq1auXXn75Zd19991avny5du/ebYk3VFhYmHx8fFRUVKR7771XjRo10tixY7Vo0SLHgcXHx6dWJ3p34evrq2HDhmn58uUaNmyYxo4dqx49emjHjh1atGiRzpw5o+HDhzMAugb8cmDzgw8+6LgNwX/fnoAB0NWPY5T7qF+/vtq2bauDBw8qNjZW0dHRjvfF+vXrZYxR27Zta+0AaMkD7gMkSXPnztWcOXOUlZWlrl276o033lBUVJSkn/98tUWLFkpKSnK0X7FihZ599lmlpaXpxhtv1EsvvaS77rrrip+P+wChqnAfIPfBfYDcB8co92Hl+wB5RACqaZ4egKSfB65NmDBBFy5ckL+/vxYsWMBvVS5SWFioDz/80PGnvYMHD+bMj4ukpKRo/PjxMsbIZrNp4cKFnPlxEY5R7iMvL0+JiYmOY1RCQoLHnvkhAF2j2hCAAACwmlp1J2gAAICqRgACAACWQwACAACWQwACAACWQwACAACWQwACAACWQwACAACWQwACAACWQwACAACW4/ZfhuoKpTfHzs3NdXElAADgSpV+bl/Jl1wQgMpw7tw5SVJ4eLiLKwEAAJV17tw5BQUFVdiG7wIrQ0lJiU6cOKGAgADZbDZXl3PVcnNzFR4ermPHjvGdZi7GvnAf7Av3wb5wH7VlXxhjdO7cOYWFhcnLq+JRPpwBKoOXl5eaN2/u6jKqTGBgoEf/h65N2Bfug33hPtgX7qM27IvLnfkpxSBoAABgOQQgAABgOQSgWszPz0/Tp0+Xn5+fq0uxPPaF+2BfuA/2hfuw4r5gEDQAALAczgABAADLIQABAADLIQB5sNGjR2vIkCGuLqNW6t27t6ZMmVKtz9GiRQu99tprFbaZMWOGunbtWq11WEFN7E9cm7S0NNlsNiUnJ7u6FPx/tf34QwByE7z5YbPZtHr1aleX4dE2bdokm82ms2fPuroUVFJ4eLgyMzPVsWNHV5filgjxVY8ABABXobCw0NUleIyioqLLtvH29lZoaKjq1OH+vLWZMUYXL150dRmSCEA1as2aNerZs6caNGigxo0ba+DAgUpNTZUktWzZUpJ00003yWazqXfv3lfc75/+9Cc1a9ZMjRs31qRJk5wONmfOnNHIkSPVsGFD1a1bV7GxsTpy5IhjeVJSkho0aKBPPvlEbdq0Ud26dTV06FD99NNPWrJkiVq0aKGGDRvq0UcfVXFxsWO9goICTZ06Vdddd53q1aunqKgobdq06dpeIDdTUlKiJ598Uo0aNVJoaKhmzJjhWHb27Fk9/PDDCg4OVmBgoH7961/r22+/dSxPTU3V4MGDFRISovr16+uWW27R+vXry32uFi1aSJLuuece2Ww2x3Spv/71r2rRooWCgoI0fPhwx/fVWVFBQYEeffRRNW3aVHa7XT179tSuXbuUlpamPn36SJIaNmwom82m0aNHO9araH9Kl9+npZcD3n77bbVs2VJ2u70mNtdl/vGPf6hTp07y9/dX48aNFR0drfPnz0uS3n77bbVr1052u11t27bVX/7yF8d6pWez33vvPfXq1Ut2u11vvvmm/P399fnnnzs9xwcffKCAgAD99NNPZZ4F/+677zRw4EAFBgYqICBAd9xxh+OYebk6CgsLNXnyZDVr1kx2u12RkZFKTEysplereo0ePVqbN2/W66+/LpvNJpvNprS0NO3fv1+xsbGqX7++QkJC9NBDD+nUqVOO9Sr6zCl1/PhxxcXFqVGjRqpXr55uvvlm7dy506lNRcefkpISJSYmqmXLlvL391eXLl30j3/8w7G89Kzs559/ru7du8vPz09bt26tpleqkgxqzD/+8Q+zcuVKc+TIEfPPf/7TDBo0yHTq1MkUFxebb775xkgy69evN5mZmebHH3+8bH+jRo0ygYGB5pFHHjEHDhwwH3/8salbt65ZsGCBo81vfvMb065dO7NlyxaTnJxsYmJizA033GAKCwuNMcYsXrzY+Pj4mH79+pm9e/eazZs3m8aNG5v+/fub+++/33z33Xfm448/Nr6+vmb58uWOfh9++GFz2223mS1btpiUlBQzZ84c4+fnZw4fPlz1L5wL9OrVywQGBpoZM2aYw4cPmyVLlhibzWbWrl1rjDEmOjraDBo0yOzatcscPnzY/OEPfzCNGzd27Lfk5GQzf/58s2/fPnP48GHz7LPPGrvdbtLT0x3PERkZaV599VVjjDE//PCDkWQWL15sMjMzzQ8//GCMMWb69Ommfv365t577zX79u0zW7ZsMaGhoeaZZ56p2RfEjTz66KMmLCzMfPbZZ+a7774zo0aNMg0bNjSnTp0yK1euNJLMoUOHTGZmpjl79qwx5vL705jL79Pp06ebevXqmQEDBpi9e/eab7/91iXbXxNOnDhh6tSpY1555RVz9OhR869//cvMmzfPnDt3zixdutQ0a9bMrFy50nz//fdm5cqVplGjRiYpKckYY8zRo0eNJNOiRQtHmxMnTpihQ4eaBx980Ol57rvvPse80vX++c9/GmOMOX78uGnUqJG59957za5du8yhQ4fMokWLzMGDB40x5rJ1zJkzx4SHh5stW7aYtLQ089VXX5lly5bV0CtYtc6ePWt69Ohhxo8fbzIzM01mZqY5deqUCQ4ONgkJCebAgQNm7969pl+/fqZPnz6O9Sr6zDHGmHPnzplWrVqZO+64w3z11VfmyJEj5r333jPbt283xlzZ8eeFF14wbdu2NWvWrDGpqalm8eLFxs/Pz2zatMkYY8zGjRuNJNO5c2ezdu1ak5KSckWfbzWBAORCJ0+eNJLMvn37LnnzX4lRo0aZyMhIc/HiRce8YcOGmQceeMAYY8zhw4eNJLNt2zbH8lOnThl/f3/z/vvvG2N+DkCSTEpKiqPN7373O1O3bl1z7tw5x7yYmBjzu9/9zhhjTHp6uvH29jb/93//51RP3759TUJCwpW/AG6sV69epmfPnk7zbrnlFvPUU0+Zr776ygQGBpr8/Hyn5ddff7156623yu2zQ4cO5s9//rNj+pcByBhjJJkPPvjAaZ3p06ebunXrmtzcXMe8J554wkRFRV3FVnm+vLw84+PjY/72t7855hUWFpqwsDDz0ksvOQ62Z86ccVqvov1pjLmifTp9+nTj4+PjCKe12Z49e4wkk5aWdsmy66+//pIgMWvWLNOjRw9jzH+CzGuvvebU5oMPPjD169c358+fN8YYk5OTY+x2u/n888+d1is9BiYkJJiWLVs6flmrbB2///3vza9//WtTUlJSya13T7169TKPPfaYY3rWrFmmf//+Tm2OHTvm+AWgLL/8zDHGmLfeessEBASUG0gud/zJz883devWdQSmUuPGjTNxcXHGmP8EoNWrV1dug2sAF1tr0JEjR/Tcc89p586dOnXqlEpKSiRJGRkZat++/VX12aFDB3l7ezummzVrpn379kmSDhw4oDp16igqKsqxvHHjxmrTpo0OHDjgmFe3bl1df/31jumQkBC1aNFC9evXd5r3ww8/SJL27dun4uJitW7d2qmWgoICNW7c+Kq2wx117tzZabpZs2b64Ycf9O233yovL++Sbb1w4YLj9HJeXp5mzJihTz/9VJmZmbp48aIuXLigjIyMStfRokULBQQEXFKHFaWmpqqoqEi33367Y56Pj49uvfVWHThwQLfccku565a3PyVd0T6VpMjISAUHB1fFpri1Ll26qG/fvurUqZNiYmLUv39/DR06VL6+vkpNTdW4ceM0fvx4R/uLFy9e8gWUN998s9P0XXfdJR8fH3300UcaPny4Vq5cqcDAQEVHR5dZQ3Jysu644w75+Phcsuz8+fOXrWP06NHq16+f2rRpowEDBmjgwIHq37//Vb8m7ubbb7/Vxo0bnY7TpVJTU9W6desKP3M6duyo5ORk3XTTTWrUqFG5z1PR8SclJUU//fST+vXr57ROYWGhbrrpJqd5//3/wR0QgGrQoEGDFBkZqYULFyosLEwlJSXq2LHjNQ2m/O+Dg81mc/wnv5Y+Kuo3Ly9P3t7e2rNnj1P4klTmm9FTlfca5OXlqVmzZmWOeWrQoIEkaerUqVq3bp3+9Kc/6YYbbpC/v7+GDh16Vfu6KvYxKn4dr2SfSlK9evWqs0S34e3trXXr1mn79u1au3at/vznP2vatGn6+OOPJUkLFy50+sWqdJ1f+u/XytfXV0OHDtWyZcs0fPhwLVu2TA888EC5g579/f3LrS8vL++ydXTr1k1Hjx7V559/rvXr1+v+++9XdHS00/gUT5aXl6dBgwbpj3/84yXLmjVrJunynzkVvcalLve+kaRPP/1U1113nVO7//5KDXd87xCAasiPP/6oQ4cOaeHChbrjjjskyWkgmK+vryQ5DTS+Vu3atdPFixe1c+dO3XbbbU51XO0ZJ+nngdrFxcX64YcfHNtiJd26dVNWVpbq1KlzyWDlUtu2bdPo0aN1zz33SPr5QJGWllZhvz4+PlW6/2uj66+/Xr6+vtq2bZsiIyMl/fwXRrt27dKUKVOu+n10JfvUamw2m26//Xbdfvvteu655xQZGalt27YpLCxM33//vUaMGFHpPkeMGKF+/frpu+++05dffqkXXnih3LadO3fWkiVLVFRUdMmHcEhIyBXVERgYqAceeEAPPPCAhg4dqgEDBuj06dMVnvFwV76+vk7/r7t166aVK1eqRYsWZYbIy33mSD+/xm+//fZVvybt27eXn5+fMjIy1KtXr0qv72r8FVgNadiwoRo3bqwFCxYoJSVFX375peLj4x3LmzZtKn9/f61Zs0bZ2dnKycm55ue88cYbNXjwYI0fP15bt27Vt99+qwcffFDXXXedBg8efNX9tm7dWiNGjNDIkSO1atUqHT16VN98840SExP16aefXnPd7i46Olo9evTQkCFDtHbtWqWlpWn79u2aNm2adu/eLenn137VqlVKTk7Wt99+q9/+9reXPWvTokULbdiwQVlZWTpz5kxNbIrHqVevniZOnKgnnnhCa9as0b///W+NHz9eP/30k8aNG6fIyEjZbDZ98sknOnnypOM31Mu5kn1qJTt37tT//u//avfu3crIyNCqVat08uRJtWvXTjNnzlRiYqLeeOMNHT58WPv27dPixYv1yiuvXLbfO++8U6GhoRoxYoRatmx5ydmbX5o8ebJyc3M1fPhw7d69W0eOHNFf//pXHTp0SJIuW8crr7yiv//97zp48KAOHz6sFStWKDQ01OmMnidp0aKFdu7cqbS0NJ06dUqTJk3S6dOnFRcXp127dik1NVVffPGFxowZo+Li4st+5khSXFycQkNDNWTIEG3btk3ff/+9Vq5cqR07dlxRTQEBAZo6daoef/xxLVmyRKmpqdq7d6/+/Oc/a8mSJdXxMlQpAlAN8fLy0vLly7Vnzx517NhRjz/+uObMmeNYXqdOHb3xxht66623FBYWdk0B5ZcWL16s7t27a+DAgerRo4eMMfrss8/KvK5e2X5HjhypP/zhD2rTpo2GDBmiXbt2KSIiokrqdmc2m02fffaZ7rzzTo0ZM0atW7fW8OHDlZ6erpCQEEk/H3wbNmyo2267TYMGDVJMTIy6detWYb8vv/yy1q1bp/Dw8Euun+M/XnzxRd1333166KGH1K1bN6WkpOiLL75Qw4YNdd1112nmzJl6+umnFRISosmTJ19Rn1eyT60kMDBQW7Zs0V133aXWrVvr2Wef1csvv6zY2Fg9/PDDevvtt7V48WJ16tRJvXr1UlJSkuNWHhWx2WyKi4vTt99+e9kzSI0bN9aXX36pvLw89erVS927d9fChQsdx67L1REQEKCXXnpJN998s2655RalpaXps88+k5eXZ37sTZ06Vd7e3mrfvr2Cg4NVWFiobdu2qbi4WP3791enTp00ZcoUNWjQQF5eXpf9zJF+Pqu0du1aNW3aVHfddZc6deqkF1988ZLLmRWZNWuW/ud//keJiYlq166dBgwYoE8//fSK/j+4Gt8GDwAALMczozAAAMA1IAC5sfr165f7+Oqrr1xdHgAAHotLYG4sJSWl3GXXXXfdFf0JIwAAuBQBCAAAWA6XwAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgADUeqNHj9aQIUOuuP2mTZtks9l09uzZaqsJgGsRgAB4pNGjR8tms+mRRx65ZNmkSZNks9k0evRoSdLrr7+upKSkmi0QgFsjAAHwWOHh4Vq+fLkuXLjgmJefn69ly5Y5fS9dUFCQx34JJoDqQQAC4LG6deum8PBwrVq1yjFv1apVioiIcPpC2f++BFZQUKBHH31UTZs2ld1uV8+ePbVr165L+t+2bZs6d+4su92uX/3qV9q/f3+1bg+AmkMAAuDRxo4dq8WLFzumFy1apDFjxlS4zpNPPqmVK1dqyZIl2rt3r2644QbFxMTo9OnTTu2eeOIJvfzyy9q1a5eCg4M1aNAgFRUVVct2AKhZBCAAHu3BBx/U1q1blZ6ervT0dG3btk0PPvhgue3Pnz+vN998U3PmzFFsbKzat2+vhQsXyt/fX++8845T2+nTp6tfv37q1KmTlixZouzsbH3wwQfVvUkAakAdVxcAANciODhYd999t5KSkmSM0d13360mTZqU2z41NVVFRUW6/fbbHfN8fHx066236sCBA05te/To4fh3o0aN1KZNm0vaAPBMBCAAHm/s2LGaPHmyJGnevHkurgaAJ+ASGACPN2DAABUWFqqoqEgxMTEVtr3++uvl6+urbdu2OeYVFRVp165dat++vVPbr7/+2vHvM2fO6PDhw2rXrl3VFg/AJTgDBMDjeXt7Oy5NeXt7V9i2Xr16mjhxop544gk1atRIEREReumll/TTTz9p3LhxTm2ff/55NW7cWCEhIZo2bZqaNGlSqRsqAnBfBCAAtUJgYOAVt33xxRdVUlKihx56SOfOndPNN9+sL774Qg0bNryk3WOPPaYjR46oa9eu+vjjj+Xr61vVpQNwAZsxxri6CACoTnFxcfL29tbSpUtdXQoAN8EYIAC11sWLF/Xvf/9bO3bsUIcOHVxdDgA3QgACUGvt379fN998szp06FDmd4YBsC4ugQEAAMvhDBAAALAcAhAAALAcAhAAALAcAhAAALAcAhAAALAcAhAAALAcAhAAALAcAhAAALAcAhAAALCc/wdBrDpy6CNSIAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Lenze pri inom kodovani:\n",
        "\n",
        "data_corr2 = data.copy()\n",
        "data_corr2[\"Mjob\"] = data[\"Mjob\"].map({\n",
        "    \"health\": 0,\n",
        "    \"services\": 1,\n",
        "    \"teacher\": 2,\n",
        "    \"at_home\": 3,\n",
        "    \"other\": 4\n",
        "})\n",
        "\n",
        "corr1 = data_corr[\"G3\"].corr(data_corr[\"Mjob\"])\n",
        "corr2 = data_corr2[\"G3\"].corr(data_corr2[\"Mjob\"])\n",
        "\n",
        "print(\"data_corr: \", round(corr1, 2))\n",
        "print(\"data_corr2:\", round(corr2, 2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AOnPJjzYeRby",
        "outputId": "54da0a34-8b07-40de-946f-880903e9a788"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data_corr:  0.1\n",
            "data_corr2: -0.17\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# One-hot-encoding\n",
        "data_onehot = pd.get_dummies(\n",
        "    data,\n",
        "    columns=[\"Mjob\"],\n",
        "    dtype=int\n",
        ")\n",
        "data_onehot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "-gCpO0liOy2J",
        "outputId": "e2e5bd07-1e63-49d1-ef6b-ee643f250c7f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    school sex  age famsize  Medu  Fedu      Fjob  studytime paid  absences  \\\n",
              "0       GP   F   18     GT3     4     4   teacher          2   no         6   \n",
              "1       GP   F   17     GT3     1     1     other          2   no         4   \n",
              "2       GP   F   15     LE3     1     1     other          2  yes        10   \n",
              "3       GP   F   15     GT3     4     2  services          3  yes         2   \n",
              "4       GP   F   16     GT3     3     3     other          2  yes         4   \n",
              "..     ...  ..  ...     ...   ...   ...       ...        ...  ...       ...   \n",
              "390     MS   M   20     LE3     2     2  services          2  yes        11   \n",
              "391     MS   M   17     LE3     3     1  services          1   no         3   \n",
              "392     MS   M   21     GT3     1     1     other          1   no         3   \n",
              "393     MS   M   18     LE3     3     2     other          1   no         0   \n",
              "394     MS   M   19     LE3     1     1   at_home          1   no         5   \n",
              "\n",
              "     G3  Mjob_at_home  Mjob_health  Mjob_other  Mjob_services  Mjob_teacher  \n",
              "0     6             1            0           0              0             0  \n",
              "1     6             1            0           0              0             0  \n",
              "2    10             1            0           0              0             0  \n",
              "3    15             0            1           0              0             0  \n",
              "4    10             0            0           1              0             0  \n",
              "..   ..           ...          ...         ...            ...           ...  \n",
              "390   9             0            0           0              1             0  \n",
              "391  16             0            0           0              1             0  \n",
              "392   7             0            0           1              0             0  \n",
              "393  10             0            0           0              1             0  \n",
              "394   9             0            0           1              0             0  \n",
              "\n",
              "[395 rows x 16 columns]"
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              "      <th></th>\n",
              "      <th>school</th>\n",
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              "      <th>390</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>20</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>11</td>\n",
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              "      <td>MS</td>\n",
              "      <td>M</td>\n",
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              "      <td>LE3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>services</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 16 columns</p>\n",
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},\n    {\n      \"column\": \"famsize\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"LE3\",\n          \"GT3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Medu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fedu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fjob\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"other\",\n          \"at_home\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"studytime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"paid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"absences\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 0,\n        \"max\": 75,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          56,\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"G3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 0,\n        \"max\": 20,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          6,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mjob_at_home\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mjob_health\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mjob_other\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mjob_services\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mjob_teacher\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlations = (\n",
        "    data_onehot.corr(numeric_only=True)[\"G3\"]\n",
        "         .drop(\"G3\")\n",
        "         .round(2)\n",
        ")\n",
        "\n",
        "print(correlations)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YoiHhcLoP6t5",
        "outputId": "6754b184-266d-4e13-9a3c-d901f4002708"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age             -0.16\n",
            "Medu             0.22\n",
            "Fedu             0.15\n",
            "studytime        0.10\n",
            "absences         0.03\n",
            "Mjob_at_home    -0.12\n",
            "Mjob_health      0.12\n",
            "Mjob_other      -0.10\n",
            "Mjob_services    0.08\n",
            "Mjob_teacher     0.06\n",
            "Name: G3, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dummy premenne\n",
        "data_dummy = pd.get_dummies(\n",
        "    data,\n",
        "    columns=[\"Mjob\"],\n",
        "    drop_first=True,\n",
        "    dtype=int\n",
        ")\n",
        "data_dummy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "YBImh-orPpOK",
        "outputId": "3c15c491-1f1d-4cf0-ba20-640da3dcf364"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    school sex  age famsize  Medu  Fedu      Fjob  studytime paid  absences  \\\n",
              "0       GP   F   18     GT3     4     4   teacher          2   no         6   \n",
              "1       GP   F   17     GT3     1     1     other          2   no         4   \n",
              "2       GP   F   15     LE3     1     1     other          2  yes        10   \n",
              "3       GP   F   15     GT3     4     2  services          3  yes         2   \n",
              "4       GP   F   16     GT3     3     3     other          2  yes         4   \n",
              "..     ...  ..  ...     ...   ...   ...       ...        ...  ...       ...   \n",
              "390     MS   M   20     LE3     2     2  services          2  yes        11   \n",
              "391     MS   M   17     LE3     3     1  services          1   no         3   \n",
              "392     MS   M   21     GT3     1     1     other          1   no         3   \n",
              "393     MS   M   18     LE3     3     2     other          1   no         0   \n",
              "394     MS   M   19     LE3     1     1   at_home          1   no         5   \n",
              "\n",
              "     G3  Mjob_health  Mjob_other  Mjob_services  Mjob_teacher  \n",
              "0     6            0           0              0             0  \n",
              "1     6            0           0              0             0  \n",
              "2    10            0           0              0             0  \n",
              "3    15            1           0              0             0  \n",
              "4    10            0           1              0             0  \n",
              "..   ..          ...         ...            ...           ...  \n",
              "390   9            0           0              1             0  \n",
              "391  16            0           0              1             0  \n",
              "392   7            0           1              0             0  \n",
              "393  10            0           0              1             0  \n",
              "394   9            0           1              0             0  \n",
              "\n",
              "[395 rows x 15 columns]"
            ],
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>school</th>\n",
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              "      <th>famsize</th>\n",
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              "      <th>390</th>\n",
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              "      <td>services</td>\n",
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              "    <tr>\n",
              "      <th>391</th>\n",
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              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>services</td>\n",
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              "    <tr>\n",
              "      <th>392</th>\n",
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              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 15 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85de80ab-d78d-4c88-ac30-ed90757cff0e')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-85de80ab-d78d-4c88-ac30-ed90757cff0e 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-85de80ab-d78d-4c88-ac30-ed90757cff0e');\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",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_ddee1812-8070-4aac-bb46-96225a5ab76d\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
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              "\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data_dummy')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_dummy",
              "summary": "{\n  \"name\": \"data_dummy\",\n  \"rows\": 395,\n  \"fields\": [\n    {\n      \"column\": \"school\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"MS\",\n          \"GP\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"M\",\n          \"F\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 15,\n        \"max\": 22,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          17,\n          22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"famsize\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"LE3\",\n          \"GT3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Medu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fedu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fjob\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"other\",\n          \"at_home\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"studytime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"paid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"absences\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 0,\n        \"max\": 75,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          56,\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"G3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 0,\n        \"max\": 20,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          6,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mjob_health\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mjob_other\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mjob_services\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mjob_teacher\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlations2 = (\n",
        "    data_dummy.corr(numeric_only=True)[\"G3\"]\n",
        "         .drop(\"G3\")\n",
        "         .round(2)\n",
        ")\n",
        "\n",
        "print(correlations)\n",
        "print(\"\")\n",
        "print(correlations2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oX-i-0UPQfU7",
        "outputId": "95851675-4905-4ac6-e0fe-148a7f6a6c6e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age             -0.16\n",
            "Medu             0.22\n",
            "Fedu             0.15\n",
            "studytime        0.10\n",
            "absences         0.03\n",
            "Mjob_at_home    -0.12\n",
            "Mjob_health      0.12\n",
            "Mjob_other      -0.10\n",
            "Mjob_services    0.08\n",
            "Mjob_teacher     0.06\n",
            "Name: G3, dtype: float64\n",
            "\n",
            "age             -0.16\n",
            "Medu             0.22\n",
            "Fedu             0.15\n",
            "studytime        0.10\n",
            "absences         0.03\n",
            "Mjob_health      0.12\n",
            "Mjob_other      -0.10\n",
            "Mjob_services    0.08\n",
            "Mjob_teacher     0.06\n",
            "Name: G3, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Spat ku data:\n",
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LckUh0lHf1Xj",
        "outputId": "3876a7e4-abfc-4bcb-92e8-44cffef792b2"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    school sex  age famsize  Medu  Fedu      Mjob      Fjob  studytime paid  \\\n",
            "0       GP   F   18     GT3     4     4   at_home   teacher          2   no   \n",
            "1       GP   F   17     GT3     1     1   at_home     other          2   no   \n",
            "2       GP   F   15     LE3     1     1   at_home     other          2  yes   \n",
            "3       GP   F   15     GT3     4     2    health  services          3  yes   \n",
            "4       GP   F   16     GT3     3     3     other     other          2  yes   \n",
            "..     ...  ..  ...     ...   ...   ...       ...       ...        ...  ...   \n",
            "390     MS   M   20     LE3     2     2  services  services          2  yes   \n",
            "391     MS   M   17     LE3     3     1  services  services          1   no   \n",
            "392     MS   M   21     GT3     1     1     other     other          1   no   \n",
            "393     MS   M   18     LE3     3     2  services     other          1   no   \n",
            "394     MS   M   19     LE3     1     1     other   at_home          1   no   \n",
            "\n",
            "     absences  G3  \n",
            "0           6   6  \n",
            "1           4   6  \n",
            "2          10  10  \n",
            "3           2  15  \n",
            "4           4  10  \n",
            "..        ...  ..  \n",
            "390        11   9  \n",
            "391         3  16  \n",
            "392         3   7  \n",
            "393         0  10  \n",
            "394         5   9  \n",
            "\n",
            "[395 rows x 12 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pearson pri inom kodovani Medu:\n",
        "data2 = data.copy()\n",
        "\n",
        "data2[\"Medu\"] = data[\"Medu\"].map({\n",
        "    0: 0,\n",
        "    1: 4,\n",
        "    2: 9,\n",
        "    3: 13,\n",
        "    4: 16\n",
        "})\n",
        "\n",
        "corr1 = data[\"G3\"].corr(data[\"Medu\"])\n",
        "corr2 = data2[\"G3\"].corr(data2[\"Medu\"])\n",
        "\n",
        "print(\"data: \", round(corr1, 2))\n",
        "print(\"data2:\", round(corr2, 2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0kjNX6uygAS0",
        "outputId": "4acc6d92-35ed-4a47-f3b0-5cf0724fa860"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data:  0.22\n",
            "data2: 0.21\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Spearman pri inom kodovani:\n",
        "corr1 = data[\"G3\"].corr(data[\"Medu\"], method='spearman')\n",
        "corr2 = data2[\"G3\"].corr(data2[\"Medu\"], method='spearman')\n",
        "\n",
        "print(\"data: \", corr1)\n",
        "print(\"data2: \", corr2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vTa-VyMMhSos",
        "outputId": "423d65b4-87f8-4503-d798-cd65c1b7096d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data:  0.2250358528745645\n",
            "data2:  0.2250358528745645\n"
          ]
        }
      ]
    }
  ]
}