cohorts/2022/04-evaluation/homework_4.ipynb

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      "source": [
        "#@ IMPORTING LIBRARIES AND DEPENDENCIES:\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import roc_curve\n",
        "\n",
        "from sklearn.feature_extraction import DictVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import KFold\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
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      "execution_count": 6,
      "metadata": {
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          "text": [
            "--2022-09-25 12:19:55--  https://raw.githubusercontent.com/alexeygrigorev/datasets/master/AER_credit_card_data.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 73250 (72K) [text/plain]\n",
            "Saving to: ‘data.csv’\n",
            "\n",
            "\rdata.csv              0%[                    ]       0  --.-KB/s               \rdata.csv            100%[===================>]  71.53K  --.-KB/s    in 0.001s  \n",
            "\n",
            "2022-09-25 12:19:55 (78.0 MB/s) - ‘data.csv’ saved [73250/73250]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#@ GETTING THE DATASET:\n",
        "!wget https://raw.githubusercontent.com/alexeygrigorev/datasets/master/AER_credit_card_data.csv -O data.csv"
      ]
    },
    {
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              "  card  reports       age  income     share  expenditure owner selfemp  \\\n",
              "0  yes        0  37.66667  4.5200  0.033270   124.983300   yes      no   \n",
              "1  yes        0  33.25000  2.4200  0.005217     9.854167    no      no   \n",
              "2  yes        0  33.66667  4.5000  0.004156    15.000000   yes      no   \n",
              "3  yes        0  30.50000  2.5400  0.065214   137.869200    no      no   \n",
              "4  yes        0  32.16667  9.7867  0.067051   546.503300   yes      no   \n",
              "5  yes        0  23.25000  2.5000  0.044438    91.996670    no      no   \n",
              "6  yes        0  27.91667  3.9600  0.012576    40.833330    no      no   \n",
              "7  yes        0  29.16667  2.3700  0.076434   150.790000   yes      no   \n",
              "8  yes        0  37.00000  3.8000  0.245628   777.821700   yes      no   \n",
              "9  yes        0  28.41667  3.2000  0.019780    52.580000    no      no   \n",
              "\n",
              "   dependents  months  majorcards  active  \n",
              "0           3      54           1      12  \n",
              "1           3      34           1      13  \n",
              "2           4      58           1       5  \n",
              "3           0      25           1       7  \n",
              "4           2      64           1       5  \n",
              "5           0      54           1       1  \n",
              "6           2       7           1       5  \n",
              "7           0      77           1       3  \n",
              "8           0      97           1       6  \n",
              "9           0      65           1      18  "
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          "metadata": {},
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      "source": [
        "#@ READING THE DATASET:\n",
        "data = pd.read_csv('data.csv')\n",
        "data.columns = data.columns.str.lower()\n",
        "data.head(10)"
      ]
    },
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>29.16667</td>\n",
              "      <td>2.3700</td>\n",
              "      <td>0.076434</td>\n",
              "      <td>150.790000</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>77</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>37.00000</td>\n",
              "      <td>3.8000</td>\n",
              "      <td>0.245628</td>\n",
              "      <td>777.821700</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>97</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>28.41667</td>\n",
              "      <td>3.2000</td>\n",
              "      <td>0.019780</td>\n",
              "      <td>52.580000</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6aaeab21-4054-4d92-9e7b-c339881c8818')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      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-6aaeab21-4054-4d92-9e7b-c339881c8818 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-6aaeab21-4054-4d92-9e7b-c339881c8818');\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",
              "  "
            ],
            "text/plain": [
              "   card  reports       age  income     share  expenditure owner selfemp  \\\n",
              "0     1        0  37.66667  4.5200  0.033270   124.983300   yes      no   \n",
              "1     1        0  33.25000  2.4200  0.005217     9.854167    no      no   \n",
              "2     1        0  33.66667  4.5000  0.004156    15.000000   yes      no   \n",
              "3     1        0  30.50000  2.5400  0.065214   137.869200    no      no   \n",
              "4     1        0  32.16667  9.7867  0.067051   546.503300   yes      no   \n",
              "5     1        0  23.25000  2.5000  0.044438    91.996670    no      no   \n",
              "6     1        0  27.91667  3.9600  0.012576    40.833330    no      no   \n",
              "7     1        0  29.16667  2.3700  0.076434   150.790000   yes      no   \n",
              "8     1        0  37.00000  3.8000  0.245628   777.821700   yes      no   \n",
              "9     1        0  28.41667  3.2000  0.019780    52.580000    no      no   \n",
              "\n",
              "   dependents  months  majorcards  active  \n",
              "0           3      54           1      12  \n",
              "1           3      34           1      13  \n",
              "2           4      58           1       5  \n",
              "3           0      25           1       7  \n",
              "4           2      64           1       5  \n",
              "5           0      54           1       1  \n",
              "6           2       7           1       5  \n",
              "7           0      77           1       3  \n",
              "8           0      97           1       6  \n",
              "9           0      65           1      18  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@ CREATING THE TARGET VARIABLE:\n",
        "card_values = {\n",
        "    \"yes\": 1,\n",
        "    \"no\": 0\n",
        "}\n",
        "data[\"card\"] = data.card.map(card_values)\n",
        "data.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "NLoLOYlQOIY_"
      },
      "outputs": [],
      "source": [
        "#@ INITIALIZING NUMERICAL AND CATEGORICAL VARIABLES:\n",
        "numerical = [\"reports\", \"age\", \"income\", \"share\", \"expenditure\", \"dependents\", \"months\", \"majorcards\", \"active\"]\n",
        "categorical = [\"owner\", \"selfemp\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "DFH1e1dP73t9"
      },
      "outputs": [],
      "source": [
        "#@ SPLITTING THE DATASET:\n",
        "df_full_train, df_test = train_test_split(data, test_size=0.2, random_state=1)\n",
        "df_train, df_val = train_test_split(df_full_train, test_size=0.25, random_state=1)\n",
        "\n",
        "df_train = df_train.reset_index(drop=True)\n",
        "df_val = df_val.reset_index(drop=True)\n",
        "df_test = df_test.reset_index(drop=True)\n",
        "\n",
        "y_train = df_train.card\n",
        "y_val = df_val.card\n",
        "y_test = df_test.card\n",
        "\n",
        "del df_train['card']\n",
        "del df_val['card']\n",
        "del df_test['card']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1e8h-5_8BY2f",
        "outputId": "ed9e07a6-59ca-4dd1-ec47-26824fd5f652"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  reports, 0.717\n",
            "      age, 0.524\n",
            "   income, 0.591\n",
            "    share, 0.989\n",
            "expenditure, 0.991\n",
            "dependents, 0.533\n",
            "   months, 0.529\n",
            "majorcards, 0.534\n",
            "   active, 0.604\n"
          ]
        }
      ],
      "source": [
        "#@ CALCULATING ROC AUC SCORE:\n",
        "for c in numerical:\n",
        "    auc = roc_auc_score(y_train, df_train[c])\n",
        "    if auc < 0.5:\n",
        "        auc = roc_auc_score(y_train, -df_train[c])\n",
        "    print('%9s, %.3f' % (c, auc))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u0OKzaY-B6en"
      },
      "source": [
        "- Answer for Question1: **share**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "Grw27ZjXBxJR",
        "outputId": "0bc255ca-1c8d-4505-9e1f-fdfeb60ba911"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@ PLOTTING THE ROC CURVE:\n",
        "plt.figure(figsize=(5, 5))\n",
        "\n",
        "fpr, tpr, _ = roc_curve(y_train, df_train.share)\n",
        "plt.plot(fpr, tpr, label='+share')\n",
        "\n",
        "fpr, tpr, _ = roc_curve(y_train, -df_train.share)\n",
        "plt.plot(fpr, tpr, label='-share')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], color='grey', linestyle='--')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PoF7l4A6DSFp"
      },
      "source": [
        "**TRAINING THE MODEL**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "HkaRbbupDBQj"
      },
      "outputs": [],
      "source": [
        "#@ TRAINING LOGISTIC REGRESSION MODEL:\n",
        "columns = categorical + numerical\n",
        "\n",
        "train_dicts = df_train[columns].to_dict(orient='records')\n",
        "dv = DictVectorizer(sparse=False)\n",
        "X_train = dv.fit_transform(train_dicts)\n",
        "\n",
        "model = LogisticRegression(solver='liblinear', C=1.0, max_iter=1000)\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "val_dicts = df_val[columns].to_dict(orient='records')\n",
        "X_val = dv.transform(val_dicts)\n",
        "\n",
        "y_pred = model.predict_proba(X_val)[:, 1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZiLlLqWBEuK4",
        "outputId": "b2d7866e-7fd8-44f5-a539-7d1d932a7fb9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.995171242063847"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@ INSPECTING ROC AUC SCORE:\n",
        "roc_auc_score(y_val, y_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PFwijne6E1q9"
      },
      "source": [
        "- Answer for Question2: 0.995"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wF7B4VPYEy93",
        "outputId": "fea03d5d-85a1-4b59-f473-4f9ba5435239"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9739783600107306"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@ INSPECTING THE ROC AUC SCORE:\n",
        "y_pred_bin = model.predict(X_val)\n",
        "roc_auc_score(y_val, y_pred_bin)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "io7CXh0IFJiO",
        "outputId": "c320ea33-6c75-4648-9173-622681799d7a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@ INSPECTING THE ROC CURVE:\n",
        "plt.figure(figsize=(5, 5))\n",
        "\n",
        "fpr, tpr, _ = roc_curve(y_val, y_pred)\n",
        "plt.plot(fpr, tpr, label='probability')\n",
        "\n",
        "fpr, tpr, _ = roc_curve(y_val, y_pred_bin)\n",
        "plt.plot(fpr, tpr, label='hard prediction')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], color='grey', linestyle='--')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "XBBnC2UdFqN9",
        "outputId": "da5d1b49-e20e-4fca-b735-82d8e4df5d8e"
      },
      "outputs": [
        {
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              "      <th>threshold</th>\n",
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            "text/plain": [
              "     threshold   tp  fp  fn  tn\n",
              "0          0.0  211  53   0   0\n",
              "10         0.1  210  17   1  36\n",
              "20         0.2  207   5   4  48\n",
              "30         0.3  205   5   6  48\n",
              "40         0.4  205   1   6  52\n",
              "50         0.5  204   1   7  52\n",
              "60         0.6  204   1   7  52\n",
              "70         0.7  204   1   7  52\n",
              "80         0.8  204   1   7  52\n",
              "90         0.9  204   0   7  53\n",
              "100        1.0  179   0  32  53"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@ CREATING CONFUSION MATRIX:\n",
        "def confusion_matrix_dataframe(y_val, y_pred):\n",
        "    scores = []\n",
        "\n",
        "    thresholds = np.linspace(0, 1, 101)\n",
        "\n",
        "    for t in thresholds:\n",
        "        actual_positive = (y_val == 1)\n",
        "        actual_negative = (y_val == 0)\n",
        "\n",
        "        predict_positive = (y_pred >= t)\n",
        "        predict_negative = (y_pred < t)\n",
        "\n",
        "        tp = (predict_positive & actual_positive).sum()\n",
        "        tn = (predict_negative & actual_negative).sum()\n",
        "\n",
        "        fp = (predict_positive & actual_negative).sum()\n",
        "        fn = (predict_negative & actual_positive).sum()\n",
        "\n",
        "        scores.append((t, tp, fp, fn, tn))\n",
        "\n",
        "    columns = ['threshold', 'tp', 'fp', 'fn', 'tn']\n",
        "    df_scores = pd.DataFrame(scores, columns=columns)\n",
        "    \n",
        "    return df_scores\n",
        "\n",
        "\n",
        "#@ IMPLEMENTATION OF FUNCTION:\n",
        "df_scores = confusion_matrix_dataframe(y_val, y_pred)\n",
        "df_scores[::10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "Xz7FM-2-KHPB"
      },
      "outputs": [],
      "source": [
        "#@ ADDING THE COLUMNS:\n",
        "df_scores['p'] = df_scores.tp / (df_scores.tp + df_scores.fp)\n",
        "df_scores['r'] = df_scores.tp / (df_scores.tp + df_scores.fn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
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        "id": "9nNXk0HtKkBC",
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        {
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RKJEssEjy+wAkcco3B0NAlQBEZC9ZOWoCas/KtigBiMg+RLLVBNSelYU1gD491AksIk1k5aoG0J6Vb95Bx4hxaFfNASQiTWRlKwG0Z2Wbt3NEj0Po0EHPAIhIE5GclJgNVBIkeAZA7f8i0oysnJSYDVQSRM8AiMg+ZakG0G7t3FVP5bYaPQUsIs2LaBhou1WxpXEEkGoAItIMdQK3X+V6BkBE9icrV01A7dXuZwCUAESkOZFsdQK3V+WbdxDpYBzeLTfZoYhIKsrK0VxA7VXZ5u0c3i2XrIj+CUSkGZEcTQXRXpVt1jMAIrIfqTIZnJmNM7MVZlZqZlOa2d/fzF42s7fM7BUzKwy3H2tmr5vZ0nDfxKhzZpjZu2a2MHwdG7/LSn3lW3ao/V9E9i0VEoCZRYDpwDnAUOBSMxva5LBpwEx3HwlMBX4Sbt8OXOHuw4BxwM/NrEfUed9192PD18KDvJa0UVvXwAdbd+oZABHZt0h2wkcBxbIk5GigNFzCETN7AhgPLIs6ZihwQ/h+LvA0gLuvbDzA3SvMbD1QAGw5+NBT3+rKKqY+t4yqnXV7bN9V34A7FOoZABHZl6ywD8AdErRmeCxNQH2AtVGfy8Jt0RYBF4TvJwBdzSwv+gAzGw1kA6uiNt8ZNg39zMyanRLTzK4xsxIzK6msrIwh3NSwo7aeax99gzfe30xOxw57vLrkZnHmMYdy8qD8ZIcpIqkqK/HLQsa0KHwMbgL+n5ldBbwKlAP1jTvNrDfwCHCluzeEm28BPiBICvcDNxM0H+3B3e8P91NUVORxijeh3J0fPL2Eleu3MePLozn16IJkhyQi6aZxXeC6nR8ngziLJQGUA32jPheG23Zz9wrCGoCZdQEudPct4eduwF+B77v7vKhz1oVva8zsIYIk0i48VbKWP71RxrfOHKSbv4i0TuNNP4EPg8WSAOYDg8xsIMGN/xLgi9EHmFk+sCn8dn8L8GC4PRv4C0EH8awm5/R293VmZsDngSUHezHJsnDtFu56fjk1dUGlZ0nFVk4+Kp/rzxyU5MhEJG1FsoOfCewIbrEPwN3rgEnAHOBt4Cl3X2pmU83s/PCw04AVZrYSOAy4M9x+MfBp4Kpmhns+ZmaLgcVAPnBHvC6qLa3dtJ2vPjyfVZVVdM7JonNOFuOGHc7PLzmWiBZ6EZHWygpnCUjgUNCY+gDcfTYwu8m2W6PezwJmNXPeo8Cj+yjzjAOKNAVt27mLrz5cQm1dA3++bgxHHdol2SGJSHuRFdYAkp0AJNDQ4KzeUE2DB33RP579NqWVVcz8ymjd/EUkvho7gRPYBKQEcAAemfc+txUv3WPbjyeMYMxRGs4pInG2uwaQ+sNAM8Lflq+nX69O3DzuGAAO7ZbDqAG9khyViLRLjX0AqgEkX21dA/95dxNfKCrkvJG9kx2OiLR3u58DSOIoIAksXLuFHbvq+a8j1dwjIm2gDTqBlQBi9K/SDXQwOOkTeS0fLCJysNqgE1gJIEavrdrA8D7d6d6pY7JDEZFM0AZPAisBxKC6po4312xR84+ItJ2sqLmAEkQJIAb/eW8TdQ3OmKPU/CMibSSS+NlAlQBi8FrpBrIjHSjqryGfItJG1AmcGl5btZFP9e/BIdmRZIciIpmiDeYCUgJowebqWpat28oYtf+LSFvqkAWYHgRra6Xrq5i1oAzHKd+8A3f4L033ICJtySzhC8MrATTjzr8u45WVlWRHggrS0Yd1YWRh9yRHJSIZp3Fd4EQVn7CS01TZ5u28srKSSacfxY1jByc7HBHJZJEcDQNtS0/OXwvAxFF9WzhSRCTBsnKS/yCYmY0zsxVmVmpmU5rZ39/MXjazt8zsFTMrjNp3pZm9E76ujNp+vJktDsu8L1waMqnq6ht4cv5aTju6gMKenZIdjohkukh2cqeCMLMIMB04BxgKXGpmQ5scNo1g3d+RwFTgJ+G5vYDbgBOA0cBtZtYzPOfXwNeAQeFr3EFfzUF6efl61m+r4Ysn9E92KCIiwVDQJA8DHQ2Uuvtqd68FngDGNzlmKPC38P3cqP1nAy+6+yZ33wy8CIwzs95AN3ef5+4OzCRYGD6pHv/3Gg7vlsvpgwuSHYqISPAwWJITQB9gbdTnsnBbtEXABeH7CUBXM8vbz7l9wvf7KxMAM7vGzErMrKSysjKGcFtn7abtvPpOJReP6ktWRF0jIpICIjlpMRvoTcCpZvYmcCpQDtTHo2B3v9/di9y9qKAgcd/M//xGOQZcos5fEUkVWdlJ7wQuB6LvioXhtt3cvcLdL3D344Dvh9u27Ofc8vD9Pstsays+3MqA/M4c0eOQZIYhIvKxrNyk1wDmA4PMbKCZZQOXAMXRB5hZvpk1lnUL8GD4fg4w1sx6hp2/Y4E57r4O2GpmJ4ajf64AnonD9bRa+Zad9NHNX0RSSSTJfQDuXgdMIriZvw085e5LzWyqmZ0fHnYasMLMVgKHAXeG524CfkSQROYDU8NtANcBDwClwCrg+XhdVGuUb96hBCAiqSUVpoJw99nA7Cbbbo16PwuYtY9zH+TjGkH09hJg+IEEmyg7d9WzoapGzT8ikloiiZ0KQsNdgA8+Ch61Vg1ARFJKgmsASgBA+ZYdAKoBiEhqUQJIvMYEUNhTCUBEUkiyp4LIBOWbd2AGh3XLTXYoIiIfa6wBuCekeCUAoGLLDg7rmkt2lv4cIpJCsnIAh/pdCSledzyCJqAjeujbv4ikmEhO8DNBzUBKAAQ1AHUAi0jKyQoTQIKmg8j4BNDQ4FRs2UkfdQCLSKqJZAc/VQNIjA3VNdTWN+gZABFJPVlh03SCloXM+ARQvjkYAqoEICIpJyusAagJKDEqtgSZVX0AIpJy1AmcWBXhQ2DqAxCRlKNO4MQq37KDrjlZdMvtmOxQRET2tDsBqA8gIcq37NC3fxFJTWoCSqzyzXoGQERSVCp0ApvZODNbYWalZjalmf39zGyumb1pZm+Z2bnh9svMbGHUq8HMjg33vRKW2bjv0PheWmwqPtJCMCKSohJcA2hxQRgziwDTgbOAMmC+mRW7+7Kow35AsFLYr81sKMHiMQPc/THgsbCcEcDT7r4w6rzLwoVhkqK6po4t23epBiAiqWl3H0DymoBGA6Xuvtrda4EngPFNjnGgW/i+O1DRTDmXhuemDI0AEpGUlgIJoA+wNupzWbgt2u3A5WZWRvDtf3Iz5UwE/tBk20Nh88//hIvD78XMrjGzEjMrqaysjCHc2JU1JgBNBCciqWh3E1BqDwO9FJjh7oXAucAjZra7bDM7Adju7kuizrnM3UcAp4SvLzVXsLvf7+5F7l5UUFAQp3ADFVoJTERSWQrUAMqBvlGfC8Nt0a4GngJw99eBXCA/av8lNPn27+7l4c9twOMETU1tqnzzDrI6GId2VQ1ARFJQCjwHMB8YZGYDzSyb4GZe3OSYNcCZAGY2hCABVIafOwAXE9X+b2ZZZpYfvu8IfBZYQhtb+WEVfXt1ItKh2dYnEZHk2j0baJKagNy9DpgEzAHeJhjts9TMpprZ+eFhNwJfM7NFBN/0r3LfvYbZp4G17r46qtgcYI6ZvQUsJKhR/C4uVxSjmrp6Xlu1gTFH5bXlrxURiZ1ZkAQS1ATU4jBQAHefTdC5G73t1qj3y4Ax+zj3FeDEJtuqgeMPMNa4mv/uZrbX1nPGMUl5/EBEJDZZuUntA2iX5q5YT3ZWB076RH7LB4uIJEskW1NBxNvcFes56RN5HJIdSXYoIiL7lpWj2UDj6f2N1ayurOb0wfEdVioiEneqAcTX3OXrAThtsNr/RSTFZeVqOuh4mruikk/kd2ZAfudkhyIisn9Z2WoCipcdtfW8vnqjvv2LSHqI5KgJKF5eX72B2roGTj9G7f8ikgbUCRw/r6yopFN2hNEDeyU7FBGRlmXlqA8gXso372BAXmdysjT8U0TSQCQn5WcDTRtVNXV0yYnpAWgRkeTLStxUEBmXAKpr6+ico2//IpIm1AkcP9tr6umsGoCIpIusHNUA4qWqpo7O2UoAIpImlADip7qmTjUAEUkfkWx1AsdDQ4NTXVtPF/UBiEi6UA0gPnbsqgdQDUBE0kdWLng91NfFveiYEoCZjTOzFWZWamZTmtnfz8zmmtmbZvaWmZ0bbh9gZjvMbGH4+k3UOceb2eKwzPvMLOHrMlbXBH9AJQARSRu7l4WMfy2gxQRgZhFgOnAOMBS41MyGNjnsBwRLRR5HsGbwr6L2rXL3Y8PXN6K2/xr4GjAofI1r/WXEpmp3AlATkIikid0LwychAQCjgVJ3X+3utQSLu49vcowD3cL33YGK/RVoZr2Bbu4+L1w7eCbw+QOKvBWqa8ImII0CEpF00VgDSFIC6AOsjfpcFm6LdjtwuZmVEawdPDlq38CwaejvZnZKVJllLZQJgJldY2YlZlZSWVkZQ7j7Vl0b1AD0JLCIpI2s3OBnMpqAYnQpMMPdC4FzgUfMrAOwDugXNg3dADxuZt32U85e3P1+dy9y96KCgoObwVN9ACKSdnY3AcV/KGgsd8JyoG/U58JwW7SrCdvw3f11M8sF8t19PVATbl9gZquAo8PzC1soM+7UByAiaaffiXDxI9D18LgXHUsNYD4wyMwGmlk2QSdvcZNj1gBnApjZECAXqDSzgrATGTP7BEFn72p3XwdsNbMTw9E/VwDPxOWK9mN3H4BqACKSLroXwtDzIfeAGk9i0uKd0N3rzGwSMAeIAA+6+1IzmwqUuHsxcCPwOzP7DkGH8FXu7mb2aWCqme0CGoBvuPumsOjrgBnAIcDz4Suh1AQkIvKxmO6E7j6boHM3etutUe+XAWOaOe9PwJ/2UWYJMPxAgj1YjZ3AGgUkIpJhTwJX19SR27EDkQ4Jf+ZMRCTlZVQCqKqp1xBQEZFQRiUAzQQqIvKxjEoA22u1FoCISKOMSgBaD1hE5GMZlQCqa+rppIfARESAjEsA6gMQEWmUWQmgto4u6gMQEQEyLQHU1KsGICISypgE4O5U19ZpIjgRkVDGJIDttfW4ax4gEZFGGZMAds8DpAQgIgJkUgIIp4LuoiYgEREgoxKAZgIVEYmWMQmgSmsBiIjsIaYEYGbjzGyFmZWa2ZRm9vczs7nh4u9vmdm54fazzGyBmS0Of54Rdc4rYZkLw9eh8busvW1XH4CIyB5avBuGSzpOB84CyoD5ZlYcLgLT6AfAU+7+azMbSrB4zABgA/A5d68ws+EEq4r1iTrvsnBhmISrUh+ASMrbtWsXZWVl7Ny5M9mhpKXc3FwKCwvp2LFjTMfH8nV4NFDq7qsBzOwJYDwQnQAcaFywsjtQAeDub0YdsxQ4xMxy3L0mpujiSMtBiqS+srIyunbtyoABAwiWC5dYuTsbN26krKyMgQMHxnROLE1AfYC1UZ/L2PNbPMDtwOVmVkbw7X9yM+VcCLzR5Ob/UNj88z+W4H/txgTQSZ3AIilr586d5OXl6ebfCmZGXl7eAdWe4tUJfCkww90LgXOBR8xsd9lmNgy4C/h61DmXufsI4JTw9aXmCjaza8ysxMxKKisrWx1g4zDQztlqAhJJZbr5t96B/u1iSQDlQN+oz4XhtmhXA08BuPvrQC6QHwZUCPwFuMLdVzWe4O7l4c9twOMETU17cff73b3I3YsKCgpiuaZmVdcG6wFnRTJm4JOIyH7FcjecDwwys4Fmlg1cAhQ3OWYNcCaAmQ0hSACVZtYD+Cswxd3/1XiwmWWZWWOC6Ah8FlhysBezP1oMRkSSoaSkhG9961v73F9RUcFFF13UhhF9rMU7orvXmdkkghE8EeBBd19qZlOBEncvBm4Efmdm3yHoEL7K3T087yjgVjO7NSxyLFANzAlv/hHgJeB38b64aFoLQETiob6+nkgk9qbkoqIiioqK9rn/iCOOYNasWfEI7YDFdEd099kEnbvR226Ner8MGNPMeXcAd+yj2ONjD/PgVdfUqwNYJI388NmlLKvYGtcyhx7Rjds+N2yf+9977z3GjRvH8ccfzxtvvMGwYcOYOXMmQ4cOZeLEibz44ot873vfo1evXtx2223U1NRw5JFH8tBDD9GlSxfmz5/P9ddfT3V1NTk5Obz88sssWLCAadOm8dxzz/H3v/+d66+/HrYhvYAAAAr6SURBVAja61999VU2btzIZz/7WZYsWcLOnTu59tprKSkpISsri3vvvZfTTz+dGTNmUFxczPbt21m1ahUTJkzg7rvvPui/R8bcEatr6vQMgIi0aMWKFfz+979nzJgxfOUrX+FXv/oVAHl5ebzxxhts2LCBCy64gJdeeonOnTtz1113ce+99zJlyhQmTpzIk08+yahRo9i6dSuHHHLIHmVPmzaN6dOnM2bMGKqqqsjNzd1j//Tp0zEzFi9ezPLlyxk7diwrV64EYOHChbz55pvk5OQwePBgJk+eTN++fTkYmZMAauvo1Tk72WGISIz29009kfr27cuYMUGDxuWXX859990HwMSJEwGYN28ey5Yt231MbW0tJ510EitWrKB3796MGjUKgG7duu1V9pgxY7jhhhu47LLLuOCCCygsLNxj/z//+U8mTw5G0R9zzDH0799/dwI488wz6d69OwBDhw7l/fffVwKIVVVNHX17dUp2GCKS4poOpWz83LlzZyB44Oqss87iD3/4wx7HLV68uMWyp0yZwnnnncfs2bMZM2YMc+bM2asWsC85OTm730ciEerq6mI6b38yZkzk9pp6PQMgIi1as2YNr7/+OgCPP/44J5988h77TzzxRP71r39RWloKQHV1NStXrmTw4MGsW7eO+fPnA7Bt27a9btKrVq1ixIgR3HzzzYwaNYrly5fvsf+UU07hscceA2DlypWsWbOGwYMHJ+Q6IYMSgEYBiUgsBg8ezPTp0xkyZAibN2/m2muv3WN/QUEBM2bM4NJLL2XkyJGcdNJJLF++nOzsbJ588kkmT57MJz/5Sc4666y9nsr9+c9/zvDhwxk5ciQdO3bknHPO2WP/ddddR0NDAyNGjGDixInMmDFjj2/+8WbunrDC462oqMhLSg587jh358j/ns03Tz+KG8cmLpuKyMF5++23GTJkSNJ+/3vvvbd7RE66au5vaGYL3H2vsagZUQPYsaueBq0HLCKyh4xIAJoHSERiMWDAgLT+9n+gMiQBaCpoEZGmMiIBaDlIEZG9ZUQCaKwBaDI4EZGPZUQC2F4b9gEoAYiI7JYRCWB3E5A6gUWkjc2YMYNJkyYBcPvttzNt2rQkR/SxjEgA6gQWkQPl7jQ0NCQ7jITKiDuiOoFF0tDzU+CDlufXOSCHj4BzfrrP3e+99x5nn302J5xwAgsWLODiiy/mueeeo6amhgkTJvDDH/4QgJkzZzJt2jTMjJEjR/LII4/w7LPPcscdd1BbW0teXh6PPfYYhx12WHzjj7OMuCPu7gNQE5CItOCdd97h4YcfZuvWrcyaNYv//Oc/uDvnn38+r776Knl5edxxxx289tpr5Ofns2nTJgBOPvlk5s2bh5nxwAMPcPfdd3PPPfck+Wr2L6YEYGbjgF8QrN71gLv/tMn+fsDDQI/wmCnhIjKY2S0EawbXA99y9zmxlBlP1TV15GRpPWCRtLKfb+qJ1L9/f0488URuuukmXnjhBY477jgAqqqqeOedd1i0aBFf+MIXyM/PB6BXr14AlJWVMXHiRNatW0dtbS0DBw5MSvwHosU7oplFgOnAOcBQ4FIzG9rksB8AT7n7cQRrBv8qPHdo+HkYMA74lZlFYiwzbrQesIjEKnra51tuuYWFCxeycOFCSktLufrqq/d53uTJk5k0aRKLFy/mt7/97V4TwaWiWL4SjwZK3X21u9cCTwDjmxzjQOPqB92BivD9eOAJd69x93eB0rC8WMqMG80EKiIH6uyzz+bBBx+kqqoKgPLyctavX88ZZ5zBH//4RzZu3Aiwuwnoo48+ok+fPgA8/PDDyQn6AMVyV+wDrI36XAac0OSY24EXzGwy0Bn4TNS585qc2yd831KZAJjZNcA1AP369Ysh3L1V19YrAYjIARk7dixvv/02J510EgBdunTh0UcfZdiwYXz/+9/n1FNPJRKJcNxxxzFjxgxuv/12vvCFL9CzZ0/OOOMM3n333SRfQctanA7azC4Cxrn7V8PPXwJOcPdJUcfcEJZ1j5mdBPweGA7cB8xz90fD434PPB+ett8ym9Pa6aCnzy2lqqaOm8cdc8DnikjbSfZ00O3BgUwHHcvX4nIgeuHJwnBbtKsJ2vhx99fNLBfIb+HclsqMm2+eflSiihYRSVux9AHMBwaZ2UAzyybo1C1ucswa4EwAMxsC5AKV4XGXmFmOmQ0EBgH/ibFMERFJoBZrAO5eZ2aTgDkEQzYfdPelZjYVKHH3YuBG4Hdm9h2CDuGrPGhbWmpmTwHLgDrgm+5eD9BcmQm4PhFJM+6+18LsEpsDXeExI5aEFJH08O6779K1a1fy8vKUBA6Qu7Nx40a2bdu21zMIB9MHICLSJgoLCykrK6OysjLZoaSl3NxcCgsLYz5eCUBEUkbHjh3T4gna9kJzI4iIZCglABGRDKUEICKSodJqFJCZVQLvt/L0fGBDHMNJB7rmzKBrbv8O9nr7u3tB041plQAOhpmVNDcMqj3TNWcGXXP7l6jrVROQiEiGUgIQEclQmZQA7k92AEmga84Muub2LyHXmzF9ACIisqdMqgGIiEgUJQARkQzV7hKAmY0zsxVmVmpmU5rZn2NmT4b7/21mA9o+yviK4ZpvMLNlZvaWmb1sZv2TEWc8tXTNUcddaGZuZmk9ZDCW6zWzi8N/56Vm9nhbxxhvMfx33c/M5prZm+F/2+cmI854MrMHzWy9mS3Zx34zs/vCv8lbZvapg/qF7t5uXgRrC6wCPgFkA4uAoU2OuQ74Tfj+EuDJZMfdBtd8OtApfH9tJlxzeFxX4FWCdamLkh13gv+NBwFvAj3Dz4cmO+42uOb7gWvD90OB95Iddxyu+9PAp4Al+9h/LsGyugacCPz7YH5fe6sBjAZK3X21u9cCTwDjmxwzHng4fD8LONPSe+LxFq/Z3ee6+/bw4zyCJTjTWSz/zgA/Au4CdrZlcAkQy/V+DZju7psB3H19G8cYb7FcswPdwvfdgYo2jC8h3P1VYNN+DhkPzPTAPKCHmfVu7e9rbwmgD7A26nNZuK3ZY9y9DvgIyGuT6BIjlmuOdjXBN4h01uI1h1Xjvu7+17YMLEFi+Tc+GjjazP5lZvPMbFybRZcYsVzz7cDlZlYGzAYmt01oSXWg/7/vl9YDyCBmdjlQBJya7FgSycw6APcCVyU5lLaURdAMdBpBDe9VMxvh7luSGlViXQrMcPd7zOwk4BEzG+7uDckOLF20txpAOdA36nNhuK3ZY8wsi6DquLFNokuMWK4ZM/sM8H3gfHevaaPYEqWla+4KDAdeMbP3CNpKi9O4IziWf+MyoNjdd7n7u8BKgoSQrmK55quBpwDc/XUgl2DStPYspv/fY9XeEsB8YJCZDTSzbIJO3uImxxQDV4bvLwL+5mHvSppq8ZrN7DjgtwQ3/3RvG4YWrtndP3L3fHcf4O4DCPo9znf3dF1QOpb/rp8m+PaPmeUTNAmtbssg4yyWa14DnAlgZkMIEkB7X0uyGLgiHA10IvCRu69rbWHtqgnI3evMbBIwh2AUwYPuvtTMpgIl7l4M/J6gqlhK0NlySfIiPngxXvP/Al2AP4b93Wvc/fykBX2QYrzmdiPG650DjDWzZUA98F13T9uabYzXfCPwOzP7DkGH8FVp/mUOM/sDQSLPD/s2bgM6Arj7bwj6Os4FSoHtwJcP6vel+d9LRERaqb01AYmISIyUAEREMpQSgIhIhlICEBHJUEoAIiIZSglARCRDKQGIiGSo/w+jNHsmoXQOzgAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@ INSPECTING THE CURVES:\n",
        "plt.plot(df_scores.threshold, df_scores.p, label='precision')\n",
        "plt.plot(df_scores.threshold, df_scores.r, label='recall')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFMJZ8fWLV3a"
      },
      "source": [
        "- Answer for Question3: ~= 0.3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "WwBzhzngK8QI"
      },
      "outputs": [],
      "source": [
        "#@ GETTING F1 SCORES:\n",
        "df_scores['f1'] = 2 * df_scores.p * df_scores.r / (df_scores.p + df_scores.r)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "XUZYKM99MD5-",
        "outputId": "b5c2f0e4-c972-4615-e8aa-732c6a40acd4"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@ INSPECTING F1 SCORE:\n",
        "plt.plot(df_scores.threshold, df_scores.f1)\n",
        "plt.xticks(np.linspace(0, 1, 11))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FN9QStJpMtip"
      },
      "source": [
        "- Answer for Question4: ~=0.4 or ~=0.8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "7BUnlQQSMTHE"
      },
      "outputs": [],
      "source": [
        "#@ DEFINING TRAIN AND PREDICT FUNCTION:\n",
        "def train(df_train, y_train, C=1.0):\n",
        "    dicts = df_train[columns].to_dict(orient='records')\n",
        "\n",
        "    dv = DictVectorizer(sparse=False)\n",
        "    X_train = dv.fit_transform(dicts)\n",
        "\n",
        "    model = LogisticRegression(solver='liblinear', C=C)\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    return dv, model\n",
        "\n",
        "def predict(df, dv, model):\n",
        "    dicts = df[columns].to_dict(orient='records')\n",
        "\n",
        "    X = dv.transform(dicts)\n",
        "    y_pred = model.predict_proba(X)[:, 1]\n",
        "\n",
        "    return y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "scPilB8sNQkF",
        "outputId": "9f184d71-4546-4472-e519-d8af21204c60"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.996 +- 0.003\n"
          ]
        }
      ],
      "source": [
        "#@ INITIALIZING KFOLD CROSS VALIDATION:\n",
        "scores = []\n",
        "\n",
        "kfold = KFold(n_splits=5, shuffle=True, random_state=1)\n",
        "\n",
        "for train_idx, val_idx in kfold.split(df_full_train):\n",
        "    df_train = df_full_train.iloc[train_idx]\n",
        "    df_val = df_full_train.iloc[val_idx]\n",
        "\n",
        "    y_train = df_train.card\n",
        "    y_val = df_val.card\n",
        "\n",
        "    dv, model = train(df_train, y_train, C=1.0)\n",
        "    y_pred = predict(df_val, dv, model)\n",
        "\n",
        "    auc = roc_auc_score(y_val, y_pred)\n",
        "    scores.append(auc)\n",
        "\n",
        "print('%.3f +- %.3f' % (np.mean(scores), np.std(scores)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9I6Txx8dNtAG"
      },
      "source": [
        "- Answer for Question5: 0.003"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TPfZn98TNh3Q",
        "outputId": "a56cd3a9-9efe-480b-ae57-b93d2099a11a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "C=0.01, 0.992 +- 0.006\n",
            "C= 0.1, 0.995 +- 0.004\n",
            "C=   1, 0.996 +- 0.003\n",
            "C=  10, 0.996 +- 0.003\n"
          ]
        }
      ],
      "source": [
        "#@ INSPECTING KFOLD CROSS VALIDATION:\n",
        "kfold = KFold(n_splits=5, shuffle=True, random_state=1)\n",
        "\n",
        "for C in [0.01, 0.1, 1, 10]:\n",
        "    scores = []\n",
        "\n",
        "    for train_idx, val_idx in kfold.split(df_full_train):\n",
        "        df_train = df_full_train.iloc[train_idx]\n",
        "        df_val = df_full_train.iloc[val_idx]\n",
        "\n",
        "        y_train = df_train.card\n",
        "        y_val = df_val.card\n",
        "\n",
        "        dv, model = train(df_train, y_train, C=C)\n",
        "        y_pred = predict(df_val, dv, model)\n",
        "\n",
        "        auc = roc_auc_score(y_val, y_pred)\n",
        "        scores.append(auc)\n",
        "\n",
        "    print('C=%4s, %.3f +- %.3f' % (C, np.mean(scores), np.std(scores)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CIa3rZUZONzY"
      },
      "source": [
        "- Answer for Question6: 1"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.8.13 ('chatbot')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.13"
    },
    "vscode": {
      "interpreter": {
        "hash": "4cbf2951cde2f569b578b1df868513fa15c696ba1c73b34f313a3fcf610bb9ca"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}