midd_survey_log_reg .ipynb

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      "source": [
        "Logistic Regression model on Middlebury-college-dataset(985*17)"
      ],
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.linear_model import LogisticRegression"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "midd_data = pd.read_csv(\"midd_survey.csv\")"
      ],
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        "id": "1_VmRQENIjG9"
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        "midd_data.head()"
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              "   relationship  midd_find_relationship  midd_find_hookup  \\\n",
              "0             0                       4               5.0   \n",
              "1             0                       3               4.0   \n",
              "2             0                       5               3.0   \n",
              "3             0                       4               2.0   \n",
              "4             0                       2               5.0   \n",
              "\n",
              "   midd_goes_relationship  midd_goes_hookup  midd_lookingfor_relationship  \\\n",
              "0                     5.0               2.0                             2   \n",
              "1                     4.0               3.0                             2   \n",
              "2                     3.0               1.0                             1   \n",
              "3                     3.0               2.0                             4   \n",
              "4                     5.0               3.0                             5   \n",
              "\n",
              "   midd_lookingfor_hookup  midd_opps_newpeople  mrtl_potential_date  \\\n",
              "0                     4.0                    3                  2.0   \n",
              "1                     1.0                    4                  2.0   \n",
              "2                     1.0                    3                  5.0   \n",
              "3                     4.0                    3                  1.0   \n",
              "4                     4.0                    3                  2.0   \n",
              "\n",
              "  find_partner gender   gpa               class  siblings parents_married  \\\n",
              "0         None  Other  2.00         Lower Class         5             Yes   \n",
              "1      Friends  Woman  2.16         Lower Class         1             Yes   \n",
              "2      Friends  Woman  2.50         Lower Class         3             Yes   \n",
              "3       Family    Man  2.50         Upper Class         3             Yes   \n",
              "4      Friends  Woman  2.50  Upper Middle Class         2             Yes   \n",
              "\n",
              "                   race housing        year  \n",
              "0       Hispanic/Latino    Dorm  First Year  \n",
              "1                 White    Dorm   Sophomore  \n",
              "2  Asian/Asian American    Dorm  First Year  \n",
              "3                 White    Dorm      Senior  \n",
              "4                 White    Dorm  First Year  "
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            "text/plain": [
              "       relationship  midd_find_relationship  midd_find_hookup  \\\n",
              "count    985.000000              985.000000        984.000000   \n",
              "mean       0.300508                3.079188          4.036585   \n",
              "std        0.458712                1.083798          0.864516   \n",
              "min        0.000000                1.000000          1.000000   \n",
              "25%        0.000000                2.000000          4.000000   \n",
              "50%        0.000000                3.000000          4.000000   \n",
              "75%        1.000000                4.000000          5.000000   \n",
              "max        1.000000                5.000000          5.000000   \n",
              "\n",
              "       midd_goes_relationship  midd_goes_hookup  midd_lookingfor_relationship  \\\n",
              "count              983.000000        980.000000                    985.000000   \n",
              "mean                 3.568667          3.467347                      2.856853   \n",
              "std                  1.080806          1.035605                      1.314714   \n",
              "min                  1.000000          1.000000                      1.000000   \n",
              "25%                  3.000000          3.000000                      2.000000   \n",
              "50%                  4.000000          4.000000                      3.000000   \n",
              "75%                  4.000000          4.000000                      4.000000   \n",
              "max                  5.000000          5.000000                      5.000000   \n",
              "\n",
              "       midd_lookingfor_hookup  midd_opps_newpeople  mrtl_potential_date  \\\n",
              "count              976.000000           985.000000           981.000000   \n",
              "mean                 2.571721             3.164467             2.316004   \n",
              "std                  1.282217             1.102176             1.157072   \n",
              "min                  1.000000             1.000000             1.000000   \n",
              "25%                  1.000000             2.000000             1.000000   \n",
              "50%                  3.000000             3.000000             2.000000   \n",
              "75%                  4.000000             4.000000             3.000000   \n",
              "max                  5.000000             5.000000             5.000000   \n",
              "\n",
              "              gpa    siblings  \n",
              "count  985.000000  985.000000  \n",
              "mean     3.509472    1.549239  \n",
              "std      0.301945    1.040766  \n",
              "min      2.000000    0.000000  \n",
              "25%      3.340000    1.000000  \n",
              "50%      3.550000    1.000000  \n",
              "75%      3.700000    2.000000  \n",
              "max      4.000000    5.000000  "
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>relationship</th>\n",
              "      <th>midd_find_relationship</th>\n",
              "      <th>midd_find_hookup</th>\n",
              "      <th>midd_goes_relationship</th>\n",
              "      <th>midd_goes_hookup</th>\n",
              "      <th>midd_lookingfor_relationship</th>\n",
              "      <th>midd_lookingfor_hookup</th>\n",
              "      <th>midd_opps_newpeople</th>\n",
              "      <th>mrtl_potential_date</th>\n",
              "      <th>gpa</th>\n",
              "      <th>siblings</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>985.000000</td>\n",
              "      <td>985.000000</td>\n",
              "      <td>984.000000</td>\n",
              "      <td>983.000000</td>\n",
              "      <td>980.000000</td>\n",
              "      <td>985.000000</td>\n",
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              "      <td>981.000000</td>\n",
              "      <td>985.000000</td>\n",
              "      <td>985.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.300508</td>\n",
              "      <td>3.079188</td>\n",
              "      <td>4.036585</td>\n",
              "      <td>3.568667</td>\n",
              "      <td>3.467347</td>\n",
              "      <td>2.856853</td>\n",
              "      <td>2.571721</td>\n",
              "      <td>3.164467</td>\n",
              "      <td>2.316004</td>\n",
              "      <td>3.509472</td>\n",
              "      <td>1.549239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.458712</td>\n",
              "      <td>1.083798</td>\n",
              "      <td>0.864516</td>\n",
              "      <td>1.080806</td>\n",
              "      <td>1.035605</td>\n",
              "      <td>1.314714</td>\n",
              "      <td>1.282217</td>\n",
              "      <td>1.102176</td>\n",
              "      <td>1.157072</td>\n",
              "      <td>0.301945</td>\n",
              "      <td>1.040766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.340000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.550000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.700000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ce0b48f-2810-4274-af0f-3cb3eade7b45')\"\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",
              "\n",
              "\n",
              "    <div id=\"df-c2f9a0e8-7cbd-42a1-a6f8-906fe40ed759\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c2f9a0e8-7cbd-42a1-a6f8-906fe40ed759')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-c2f9a0e8-7cbd-42a1-a6f8-906fe40ed759 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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-0ce0b48f-2810-4274-af0f-3cb3eade7b45 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-0ce0b48f-2810-4274-af0f-3cb3eade7b45');\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": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "midd_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3PUdA_JnKyCh",
        "outputId": "afe22940-36f9-4395-a1aa-fd5a2a9269fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 985 entries, 0 to 984\n",
            "Data columns (total 18 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   relationship                  985 non-null    int64  \n",
            " 1   midd_find_relationship        985 non-null    int64  \n",
            " 2   midd_find_hookup              984 non-null    float64\n",
            " 3   midd_goes_relationship        983 non-null    float64\n",
            " 4   midd_goes_hookup              980 non-null    float64\n",
            " 5   midd_lookingfor_relationship  985 non-null    int64  \n",
            " 6   midd_lookingfor_hookup        976 non-null    float64\n",
            " 7   midd_opps_newpeople           985 non-null    int64  \n",
            " 8   mrtl_potential_date           981 non-null    float64\n",
            " 9   find_partner                  985 non-null    object \n",
            " 10  gender                        985 non-null    object \n",
            " 11  gpa                           985 non-null    float64\n",
            " 12  class                         985 non-null    object \n",
            " 13  siblings                      985 non-null    int64  \n",
            " 14  parents_married               985 non-null    object \n",
            " 15  race                          985 non-null    object \n",
            " 16  housing                       985 non-null    object \n",
            " 17  year                          985 non-null    object \n",
            "dtypes: float64(6), int64(5), object(7)\n",
            "memory usage: 138.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Psuedo Categories -find_rel, find_hook, goes_rel, goes_hook,\n",
        "# look_rel, look_hook, opps_new, pot_date, siblings\n",
        "# categories - gender, class, year, housing, race, find_partner, pareents\n",
        "# target(label) - relationship\n",
        "#visualizing target using hues\n",
        "midd_data[\"relationship\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V7Ca6ddxMCfv",
        "outputId": "0161adfd-abfa-4b17-b761-2e048291865b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    689\n",
              "1    296\n",
              "Name: relationship, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=\"relationship\", data=midd_data , hue=\"gender\",palette=\"mako\")\n",
        "#more women not in relationship than men, same for in relationship"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "HlTAz123SuGG",
        "outputId": "2f8803b3-f87e-4257-b0e2-9ff88c531d26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='relationship', ylabel='count'>"
            ]
          },
          "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": "code",
      "source": [
        "sns.countplot(x=\"relationship\",data=midd_data,hue=\"race\",palette = \"mako\")\n",
        "#most count:non-rel - white, rel - white"
      ],
      "metadata": {
        "id": "J6iGBlCxTiDo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "2c3680a7-3550-4c3d-e012-39524b31161a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='relationship', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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I60xMTODg4ICrV68K63R0dNClSxdh2cLCQrhkZmxsjAkTJsDPzw9Dhw7FmjVrFC5xAU8vS+nr69e4f21kMlm15WdrIqLmw7BDRE1CV1dX+LmoqAhDhw5FWlqawuvatWvo27dvtX0fPXoEPz8/GBgYYMeOHUhOTsb+/fsBPJ3s3NT+/k0piUQCuVwuLEdHRyMpKQmenp7YtWsX7O3tce7cuTr3r6ysbPI6iahp8NtYRNTkXnrpJXz33Xfo1KkT1NT++Z+Z3377Dffv38eyZctgZWUFAEhJSVFoU/XtroqKilqP4+TkhPLycpw/fx6enp4AgPv37yMjIwPOzs7P1YeePXuiZ8+eCA8Ph0wmwzfffIM+ffo81zGe9WxYqlp2cnJq8PGIqP44skNETS44OBgPHjxAYGAgkpOTcePGDZw4cQITJ06sMaxYW1tDQ0MDX3zxBf744w8cOnQIS5YsUWhjY2MDiUSCw4cP46+//kJRUVG143Tt2hXDhg3DlClT8NNPPyE9PR3jxo1Dhw4dMGzYsHrVnpmZifDwcCQlJeHWrVs4efIkrl271uhgkpiYiOXLl+P3339HZGQk9uzZg5kzZzbqmERUPww7RNTkLC0tkZiYiIqKCgwYMAAuLi6YNWsWjIyMoKJS/Z8dU1NTxMTEYM+ePXB2dsayZcuwYsUKhTYdOnTAokWLMG/ePJibmyMkJKTGc0dHR8Pd3R1DhgyBTCaDXC7H0aNH632TPx0dHfz2228ICAiAvb093nvvPQQHB+P9999//jfiGf/617+QkpKCnj174pNPPsGqVavg5+fXqGMSUf1I5M9eqCYioibXqVMnzJo1i4+QIFISjuwQERGRqDHsEBERkajxMhYRERGJGkd2iIiISNQYdoiIiEjUGHaIiIhI1Bh2iIiISNQYdoiIiEjUGHaIiIhI1Bh2iIiISNQYdoiIiEjU/h+5/4QMNCA6ZQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=\"relationship\",data=midd_data,hue=\"housing\",palette=\"mako\")\n",
        "#most people not in R are from dorms,same with in R"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "oNg_fcYncn29",
        "outputId": "fa0e8643-fdad-4c64-c898-1bc4c082e9fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='relationship', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "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.countplot(x=\"relationship\",data=midd_data,hue=\"find_partner\",palette=\"mako\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "9iaIDOKydFJl",
        "outputId": "22d13d57-f629-43bf-ce69-53cbbf573982"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='relationship', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "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": [
        "from analyzing we get - most people not in a relationship are white,women,hosuing in dorms and looking for partner in friends\n",
        "same case for in relationship but numbers are smaller for this case"
      ],
      "metadata": {
        "id": "xr5zx9b3dcCl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=\"relationship\",data=midd_data,hue=\"year\",palette=\"mako\")\n",
        "#all year have almost equal people in relationship and not in relationship"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "7bcPEQrFd1hm",
        "outputId": "33232188-fcec-43ff-f791-267e3358d4c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='relationship', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "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": [
        "#comparing target with gpa\n",
        "sns.boxplot(x=\"relationship\",y=\"gpa\",data=midd_data,hue=\"year\",palette=\"mako\")\n",
        "#for all year most students not in a Relationship gpa lies in 3-4\n",
        "#similar for in reltionship but a bit higher starting point for 1st and 3rd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "Et6La3WPePzP",
        "outputId": "2aa14d0b-2490-4745-a202-5ed12acd0ba0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='relationship', ylabel='gpa'>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "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": [
        "#countplots for basic categories\n",
        "sns.countplot(x=\"gender\",data=midd_data,palette=\"mako\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "oeap7vPwgTc7",
        "outputId": "98cee4d8-517c-40a1-b2ef-ee9d7889a7c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "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.countplot(x=\"race\",data=midd_data,palette=\"mako\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "sJDuKRaRgxfi",
        "outputId": "eef1bc61-5fcb-45f2-f2d3-137b0b4fabac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='race', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "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.countplot(x=\"housing\",data=midd_data,palette=\"mako\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "1vvzwlvlgzSe",
        "outputId": "632fcfdf-d8cf-4144-ba2b-26486965bd66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='housing', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "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.countplot(x=\"midd_lookingfor_relationship\",data=midd_data,hue=\"relationship\",palette=\"mako\")\n",
        "#people not a in relationship and neutral (3,4) on looking for a relationship are more in population"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "J-ebimfXg14k",
        "outputId": "a4ae0ee3-09bb-4613-aec9-cf966be14eb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='midd_lookingfor_relationship', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "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.countplot(x=\"find_partner\",data=midd_data,palette=\"mako\",hue=\"relationship\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "sSZokNwsiyIA",
        "outputId": "03790040-d0bf-4896-aa3b-235216d80d2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='find_partner', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "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.countplot(x=\"midd_opps_newpeople\",data=midd_data,hue=\"relationship\",palette=\"mako\")\n",
        "#oppurtinities opinion lies in(2-4) for non Relationship"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "dFlFlPLkjQCV",
        "outputId": "fa226c7c-974a-4df4-cac0-529fb4dcf2f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='midd_opps_newpeople', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "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": [
        "# data visualization done\n",
        "# converting all category columns into dummy variables so that alogorithm can understand better\n",
        "midd_data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XLAoE9UzkI4y",
        "outputId": "222700e7-4bac-4088-fedb-a074708c3384"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['relationship', 'midd_find_relationship', 'midd_find_hookup',\n",
              "       'midd_goes_relationship', 'midd_goes_hookup',\n",
              "       'midd_lookingfor_relationship', 'midd_lookingfor_hookup',\n",
              "       'midd_opps_newpeople', 'mrtl_potential_date', 'find_partner', 'gender',\n",
              "       'gpa', 'class', 'siblings', 'parents_married', 'race', 'housing',\n",
              "       'year'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "find_r = pd.get_dummies(midd_data[\"midd_find_relationship\"],drop_first=True)\n",
        "find_h = pd.get_dummies(midd_data[\"midd_find_hookup\"],drop_first=True)\n",
        "goes_r = pd.get_dummies(midd_data[\"midd_goes_relationship\"],drop_first=True)\n",
        "goes_h = pd.get_dummies(midd_data[\"midd_goes_hookup\"],drop_first=True)\n",
        "look_r = pd.get_dummies(midd_data[\"midd_lookingfor_relationship\"],drop_first=True)\n",
        "look_h = pd.get_dummies(midd_data[\"midd_lookingfor_hookup\"],drop_first=True)\n",
        "opp_new = pd.get_dummies(midd_data[\"midd_opps_newpeople\"],drop_first=True)\n",
        "mrt_pot_date = pd.get_dummies(midd_data[\"mrtl_potential_date\"],drop_first=True)\n",
        "find_p = pd.get_dummies(midd_data[\"find_partner\"],drop_first=True)\n",
        "gndr = pd.get_dummies(midd_data[\"gender\"],drop_first=True)\n",
        "clss = pd.get_dummies(midd_data[\"class\"],drop_first=True)\n",
        "sib = pd.get_dummies(midd_data[\"siblings\"],drop_first=True)\n",
        "par_marr = pd.get_dummies(midd_data[\"parents_married\"],drop_first=True)\n",
        "rce = pd.get_dummies(midd_data[\"race\"],drop_first=True)\n",
        "house = pd.get_dummies(midd_data[\"housing\"],drop_first=True)\n",
        "yr = pd.get_dummies(midd_data[\"year\"],drop_first=True)\n",
        "#all categorical columns are converted into dummy\n",
        "#first we drop the orignal categorical columns and then add dummy\n"
      ],
      "metadata": {
        "id": "Q-L3ZHTClKAg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "midd_data.drop(['midd_find_relationship', 'midd_find_hookup','midd_goes_relationship', 'midd_goes_hookup',\n",
        "       'midd_lookingfor_relationship', 'midd_lookingfor_hookup','midd_opps_newpeople', 'mrtl_potential_date', 'find_partner', 'gender',\n",
        "        'class', 'siblings', 'parents_married', 'race', 'housing','year'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "mBxzKqkXrJf-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#adding dummies\n",
        "midd_data = pd.concat([midd_data,find_r,find_h,goes_r,goes_h,look_r,look_h,opp_new,mrt_pot_date,find_p,gndr,clss,sib,par_marr,rce,house,yr],axis=1)"
      ],
      "metadata": {
        "id": "fX9jg5Yvrivf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#existing categorical columns removed\n",
        "midd_data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PwJvtPjLrZxA",
        "outputId": "4027ab98-828b-4b31-9718-fe36223153f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['relationship', 'gpa', '2', '3', '4', '5', '2', '3', '4', '5', '2', '3',\n",
              "       '4', '5', '2', '3', '4', '5', '2', '3', '4', '5', '2', '3', '4', '5',\n",
              "       '2', '3', '4', '5', '2', '3', '4', '5', 'Friends', 'Graduate School',\n",
              "       'Job/Career', 'Middlebury', 'None', 'Other', 'Other', 'Woman',\n",
              "       'Middle Class', 'Upper Class', 'Upper Middle Class', '1', '2', '3', '4',\n",
              "       '5', 'Yes', 'Asian/Asian American', 'Hispanic/Latino', 'Other', 'White',\n",
              "       'House', 'Off Campus', 'Junior', 'Senior', 'Sophomore'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# column names cannot be numbers need to be a string\n",
        "midd_data.rename(columns = { 2:\"2\",3:\"3\",4:\"4\",5:\"5\",2.0:\"2\",3.0:\"3\",4.0:\"4\",5.0:\"5\",1:\"1\"},inplace=True)"
      ],
      "metadata": {
        "id": "k76Yw49msvOe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#now all categories are converted to dummies and column names are strings its time to split data\n",
        "X = midd_data.drop(\"relationship\",axis=1)\n",
        "#label\n",
        "y = midd_data[\"relationship\"]"
      ],
      "metadata": {
        "id": "9Ki2mHzquh8M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#splitting data into train and test\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)"
      ],
      "metadata": {
        "id": "1wPRaKP9u3jD"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating model\n",
        "log_model = LogisticRegression()"
      ],
      "metadata": {
        "id": "5guW-VB9wT_A"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#training model\n",
        "log_model.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "1D6Fy-tVwZ16",
        "outputId": "69f99bc5-8e79-4c3d-ff6f-fd129346d461"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#testing\n",
        "predictions = log_model.predict(X_test)"
      ],
      "metadata": {
        "id": "FuEOQNS1w1cQ"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# now model is trained and tested, we use metrics to evaluate model performance\n",
        "print(classification_report(y_test,predictions))\n",
        "#model has 80% accuracy"
      ],
      "metadata": {
        "id": "KhhTZr5Dw8iY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d552338-a612-496b-bb78-ef69ba3f0cc0"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.91      0.87       210\n",
            "           1       0.72      0.53      0.61        86\n",
            "\n",
            "    accuracy                           0.80       296\n",
            "   macro avg       0.77      0.72      0.74       296\n",
            "weighted avg       0.80      0.80      0.79       296\n",
            "\n"
          ]
        }
      ]
    }
  ]
}