diff --git a/2-Regression/4-Logistic/solution/ft.py b/2-Regression/4-Logistic/solution/ft.py
new file mode 100644
index 00000000..c6054a7f
--- /dev/null
+++ b/2-Regression/4-Logistic/solution/ft.py
@@ -0,0 +1,810 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Untitled3.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "S8kryK7XUvDB",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 338
+        },
+        "outputId": "bb788b28-7c9e-4ee4-8795-fe0f12c4ed95"
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "pumpkins = pd.read_csv('pump.csv')\n",
+        "\n",
+        "pumpkins.head()"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City Name</th>\n",
+              "      <th>Type</th>\n",
+              "      <th>Package</th>\n",
+              "      <th>Variety</th>\n",
+              "      <th>Sub Variety</th>\n",
+              "      <th>Grade</th>\n",
+              "      <th>Date</th>\n",
+              "      <th>Low Price</th>\n",
+              "      <th>High Price</th>\n",
+              "      <th>Mostly Low</th>\n",
+              "      <th>Mostly High</th>\n",
+              "      <th>Origin</th>\n",
+              "      <th>Origin District</th>\n",
+              "      <th>Item Size</th>\n",
+              "      <th>Color</th>\n",
+              "      <th>Environment</th>\n",
+              "      <th>Unit of Sale</th>\n",
+              "      <th>Quality</th>\n",
+              "      <th>Condition</th>\n",
+              "      <th>Appearance</th>\n",
+              "      <th>Storage</th>\n",
+              "      <th>Crop</th>\n",
+              "      <th>Repack</th>\n",
+              "      <th>Trans Mode</th>\n",
+              "      <th>Unnamed: 24</th>\n",
+              "      <th>Unnamed: 25</th>\n",
+              "      <th>Unnamed: 26</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>BALTIMORE</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>24 inch bins</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>4/29/17</td>\n",
+              "      <td>270.0</td>\n",
+              "      <td>280.0</td>\n",
+              "      <td>270.0</td>\n",
+              "      <td>280.0</td>\n",
+              "      <td>MARYLAND</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>lge</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>E</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>BALTIMORE</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>24 inch bins</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>42891</td>\n",
+              "      <td>270.0</td>\n",
+              "      <td>280.0</td>\n",
+              "      <td>270.0</td>\n",
+              "      <td>280.0</td>\n",
+              "      <td>MARYLAND</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>lge</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>E</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>BALTIMORE</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>24 inch bins</td>\n",
+              "      <td>HOWDEN TYPE</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>9/24/16</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>DELAWARE</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>med</td>\n",
+              "      <td>ORANGE</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>N</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>BALTIMORE</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>24 inch bins</td>\n",
+              "      <td>HOWDEN TYPE</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>9/24/16</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>VIRGINIA</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>med</td>\n",
+              "      <td>ORANGE</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>N</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>BALTIMORE</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>24 inch bins</td>\n",
+              "      <td>HOWDEN TYPE</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>42501</td>\n",
+              "      <td>90.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>90.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>MARYLAND</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>lge</td>\n",
+              "      <td>ORANGE</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>N</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   City Name Type       Package  ... Unnamed: 24 Unnamed: 25  Unnamed: 26\n",
+              "0  BALTIMORE  NaN  24 inch bins  ...         NaN         NaN          NaN\n",
+              "1  BALTIMORE  NaN  24 inch bins  ...         NaN         NaN          NaN\n",
+              "2  BALTIMORE  NaN  24 inch bins  ...         NaN         NaN          NaN\n",
+              "3  BALTIMORE  NaN  24 inch bins  ...         NaN         NaN          NaN\n",
+              "4  BALTIMORE  NaN  24 inch bins  ...         NaN         NaN          NaN\n",
+              "\n",
+              "[5 rows x 27 columns]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tzurtgU8U59o"
+      },
+      "source": [
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "\n",
+        "new_columns = ['Color','Origin','Item Size','Variety','City Name','Package']\n",
+        "\n",
+        "new_pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n",
+        "\n",
+        "new_pumpkins.dropna(inplace=True)\n",
+        "\n",
+        "new_pumpkins = new_pumpkins.apply(LabelEncoder().fit_transform)"
+      ],
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fjM82HmFU-tf",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0c1d401c-f1fd-495c-956f-c5395b94b94b"
+      },
+      "source": [
+        "new_pumpkins.info"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<bound method DataFrame.info of       City Name  Package  Variety  Origin  Item Size  Color\n",
+              "2             1        3        4       3          3      0\n",
+              "3             1        3        4      17          3      0\n",
+              "4             1        3        4       5          2      0\n",
+              "5             1        3        4       5          2      0\n",
+              "6             1        4        4       5          3      0\n",
+              "...         ...      ...      ...     ...        ...    ...\n",
+              "1694         12        3        5       4          6      1\n",
+              "1695         12        3        5       4          6      1\n",
+              "1696         12        3        5       4          6      1\n",
+              "1697         12        3        5       4          6      1\n",
+              "1698         12        3        5       4          6      1\n",
+              "\n",
+              "[991 rows x 6 columns]>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 892
+        },
+        "id": "ozP-7W_cX-Ew",
+        "outputId": "ae12f8f9-4467-487b-e1ce-fd7c2402e953"
+      },
+      "source": [
+        "import seaborn as sns\n",
+        "\n",
+        "g = sns.PairGrid(new_pumpkins)\n",
+        "g.map(sns.scatterplot)"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<seaborn.axisgrid.PairGrid at 0x7f36dc4a4750>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1080x1080 with 36 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 380
+        },
+        "id": "xOACXOb-YHkY",
+        "outputId": "17f632f3-650f-412c-bfa3-e97f3d12d82e"
+      },
+      "source": [
+        "sns.swarmplot(x=\"Color\", y=\"Item Size\", data=new_pumpkins)"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py:1296: UserWarning: 80.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+            "  warnings.warn(msg, UserWarning)\n",
+            "/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py:1296: UserWarning: 37.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+            "  warnings.warn(msg, UserWarning)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36db883190>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 398
+        },
+        "id": "pjGnRqR2YXRI",
+        "outputId": "f45c1974-d98e-4086-9672-0f6d60381731"
+      },
+      "source": [
+        "sns.catplot(x=\"Color\", y=\"Item Size\",\n",
+        "            kind=\"violin\", data=new_pumpkins)"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<seaborn.axisgrid.FacetGrid at 0x7f36db7e1f10>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 360x360 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BwCyVF8yY9UG"
+      },
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "Selected_features = ['Origin','Item Size','Variety','City Name','Package']\n",
+        "\n",
+        "X = new_pumpkins[Selected_features]\n",
+        "y = new_pumpkins['Color']\n",
+        "\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
+      ],
+      "execution_count": 21,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ryYCdSuHZ6ww",
+        "outputId": "c2b81357-25da-4018-b9de-8208bdeb85d9"
+      },
+      "source": [
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.model_selection import GridSearchCV\n",
+        "pipe = Pipeline([('classifier' , RandomForestClassifier())])\n",
+        "# pipe = Pipeline([('classifier', RandomForestClassifier())])\n",
+        "\n",
+        "# Create param grid.\n",
+        "\n",
+        "param_grid = [\n",
+        "    {'classifier' : [LogisticRegression()],\n",
+        "     'classifier__penalty' : ['l1', 'l2'],\n",
+        "    'classifier__C' : np.logspace(-4, 4, 20),\n",
+        "    'classifier__solver' : ['liblinear']},\n",
+        "    {'classifier' : [RandomForestClassifier()],\n",
+        "    'classifier__n_estimators' : list(range(10,101,10)),\n",
+        "    'classifier__max_features' : list(range(6,32,5))}\n",
+        "]\n",
+        "\n",
+        "# Create grid search object\n",
+        "\n",
+        "clf = GridSearchCV(pipe, param_grid = param_grid, cv = 5, verbose=True, n_jobs=-1)\n",
+        "\n",
+        "# Fit on data\n",
+        "\n",
+        "best_clf = clf.fit(X_train, y_train)"
+      ],
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
+            "[Parallel(n_jobs=-1)]: Done 300 tasks      | elapsed:    7.5s\n",
+            "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:   12.5s finished\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_j0_jBUNa6Rw"
+      },
+      "source": [
+        "model_params= {\n",
+        "    'logistic_regression': {\n",
+        "        'model': LogisticRegression(multi_class='auto'),\n",
+        "        'params': {\n",
+        "            'C': [1,5,10],\n",
+        "            'solver':['liblinear','lbfgs']\n",
+        "        }\n",
+        "},\n",
+        "      'random_forest': {\n",
+        "        'model': RandomForestClassifier(),\n",
+        "        'params' : {\n",
+        "            'n_estimators': [1,5,10]\n",
+        "        }\n",
+        "    }\n",
+        "}"
+      ],
+      "execution_count": 30,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "sZvakxQ7bu_d"
+      },
+      "source": [
+        "score = []\n",
+        "\n",
+        "for model_name,mv in model_params.items():\n",
+        "    clf = GridSearchCV(mv['model'],mv['params'],cv=5,return_train_score=False)\n",
+        "    clf.fit(X_train, y_train)\n",
+        "    score.append(\n",
+        "    {'model_name': model_name,\n",
+        "    'best_score': clf.best_score_,   \n",
+        "    'best_params': clf.best_params_ \n",
+        "    })\n",
+        "    \n",
+        "\n",
+        "df = pd.DataFrame(score)"
+      ],
+      "execution_count": 32,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 108
+        },
+        "id": "F-pfESRRcGNv",
+        "outputId": "c66bbac3-cde1-42bf-84dc-7942495d8e32"
+      },
+      "source": [
+        "df"
+      ],
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>model_name</th>\n",
+              "      <th>best_score</th>\n",
+              "      <th>best_params</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>logistic_regression</td>\n",
+              "      <td>0.832083</td>\n",
+              "      <td>{'C': 1, 'solver': 'liblinear'}</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>random_forest</td>\n",
+              "      <td>0.929241</td>\n",
+              "      <td>{'n_estimators': 5}</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            model_name  best_score                      best_params\n",
+              "0  logistic_regression    0.832083  {'C': 1, 'solver': 'liblinear'}\n",
+              "1        random_forest    0.929241              {'n_estimators': 5}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iRrhcp08ZGP_",
+        "outputId": "d361eb92-b81b-434f-88ea-7c931f14fa3a"
+      },
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import accuracy_score, classification_report \n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "model = LogisticRegression(solver= 'liblinear', C = 1.0)\n",
+        "model.fit(X_train, y_train)\n",
+        "predictions = model.predict(X_test)\n",
+        "\n",
+        "print(classification_report(y_test, predictions))\n",
+        "print('Predicted labels: ', predictions)\n",
+        "print('Accuracy: ', accuracy_score(y_test, predictions))"
+      ],
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.83      0.98      0.90       166\n",
+            "           1       0.00      0.00      0.00        33\n",
+            "\n",
+            "    accuracy                           0.81       199\n",
+            "   macro avg       0.42      0.49      0.45       199\n",
+            "weighted avg       0.69      0.81      0.75       199\n",
+            "\n",
+            "Predicted labels:  [0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0\n",
+            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0\n",
+            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+            " 0 0 0 0 0 1 0 0 0 0 0 0 0 0]\n",
+            "Accuracy:  0.8140703517587939\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ig1Lt-YxcRf2",
+        "outputId": "a8e963d4-8994-4fb3-fe90-ab291fdb2113"
+      },
+      "source": [
+        "from sklearn.metrics import confusion_matrix\n",
+        "confusion_matrix(y_test, predictions)"
+      ],
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[162,   4],\n",
+              "       [ 33,   0]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 366
+        },
+        "id": "I52UQMCNcgMY",
+        "outputId": "84881088-71db-4f8c-cca4-5beeec22c9c7"
+      },
+      "source": [
+        "from sklearn.metrics import roc_curve, roc_auc_score\n",
+        "\n",
+        "y_scores = model.predict_proba(X_test)\n",
+        "# calculate ROC curve\n",
+        "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
+        "sns.lineplot([0, 1], [0, 1])\n",
+        "sns.lineplot(fpr, tpr)"
+      ],
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
+            "  FutureWarning\n",
+            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
+            "  FutureWarning\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36d426b750>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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EtoeOvcyuTpxFSZWFF+L38cnOTHqFtuWTueMY17uT2WWJZiKB7q2stbD1NShKP/t2diukxhvzzoffaHRRbNcN0r+HzgONxlzCJX2VfJwnVidRUF7DPVN689C0/gT6S58dTyaB7m1Kc4wQ/vQWyNgCwWFAAzMbek2GC54wxspP6jwA2nVp1lLFuSkor2FRXDJrEnIY2C2Id26NZWi4NNPyBhLo3ib9e9j4jLHK8+p3YOi15/Y6QXL5N1ejtWb1z8d4+osUKmts/PGi/tw7tQ/+vnLC2ltIoHuTQ5sg7g/QuiPcvhbCRppdkXCS7BNVLFiVyKbUfEZEGs20+nWVtQHeRgLdm+z4p7EAaO63ECTT1TyB3a758MejvLh2Pza7ZuHl0dw6IUqaaXkpCXRvkpdsLNWXMPcIh/PLmb8ikR/Ti5jUN5QXro4hoqM0SfNmEujeoqYMThyFvheZXYloIqvNzjs/HOHVrw/Qys+Hl64ZyrWjwmXZvpBA9xp5+4zPYaPMrUM0SUp2KY+u2EvSsVIuGdyVxTOH0CVYmmkJgwS6t8hLMT5HTTS3DnFOaqw2Xv8mjX98e4j2bfx588aRXDqkmxyVi1+RQPcWuSng3xZCIs2uRDTS7owi5q1IJC2vnKtHhvHkZdF0kGZa4jQk0L1FXoo00XIzFTVWXl6fygfb0ukR0pr3bx/N1AGymEucmUN/3Uqp6UqpVKVUmlJq/hm2+Z1SKkUplayU+si5ZYom0Rpyk6FrtNmVCAd9fzCfS/66mfe3pnPLuJ6sf2iyhLloUINH6EopX+AN4CIgC9iplIrTWqfU26Yf8BgwUWtdrJSS3zxXUp4LVUXQZbDZlYgGlFRaePbLFD7bnUXvzm357N7xjI7qaHZZwk04MuQyBkjTWh8GUEp9AswEUuptczfwhta6GEBrnefsQkUTnDwhWr8Xi3A565KO8+TnSRRV1HL/1D48eGE/aaYlGsWRQA8DMuvdzwLGnrJNfwCl1BbAF1iktV536gsppeYCcwEiI+XkXIvJrQv0rnKE7oryyqpZFJdMfOJxorsH895toxkSFmJ2WcINOeukqB/QD5gKhAOblVIxWusT9TfSWi8FlgLExsZqJ723aEheCrTtAm1Dza5E1KO1ZsVPx1i8JoUqi41HLhnA3Mm9pZmWOGeOBPoxIKLe/fC6x+rLAnZorS3AEaXUAYyA3+mUKkXTyAlRl5NVXMnjq5LYfCCf2J4dWDJ7KH27tDO7LOHmHAn0nUA/pVQvjCCfA9xwyjargeuB95RSoRhDMIedWag4R3Yb5KdC7O1mVyIwmmn9e3sGL67bD8DTVw7m5nE98ZFmWsIJGgx0rbVVKfUAsB5jfPxdrXWyUuoZYJfWOq7uuYuVUimADXhEa13YnIULBxWng7UKusgRutkO5Zczb3kCuzKKmdy/M8/PGkJ4B2mmJZzHoTF0rXU8EH/KYwvr3dbAw3UfwpXkJhufZcjFNBabnaWbD/PaxoO09vflz9cOY/bIMFm2L5xOVop6urwUQBnX/xQtLulYCY8uTyAlp5QZMd1YdOVgugRJMy3RPCTQPV1uMnSIgoC2ZlfiVaotNl7beJClmw/ToU0Ab900kulDuptdlvBwEuieLm+fzD9vYTvTi5i3PIHDBRVcOyqcJy6LJqSNv9llCS8gge7JLFVQdAgGzzK7Eq9QXmPlpXX7WbYtg/AOrfn3nWM4r19ns8sSXkQC3RNZquGbxVBwELRdToi2gO8O5PP4ykSyS6q4bUIUj1wygLat5M9LtCz5jfNEW/4K216HTn2hxwjoKRe1aC4nKmt5Zk0KK386Rp/ObVl+73hG9ZRmWsIcEuiepvAQfP8XGDIbrnnX7Go8ltaatUnHWfh5EicqLTxwfl8euKCvNNMSppJA9yRaQ/wj4NcKLnne7Go8Vl5pNU9+nsT65FyGhAXzwR1jGNxDmmkJ80mge5KU1XBoI1z6EgR1M7saj6O15rPdWTy7JoUaq535lw7krkm98JNmWsJFSKB7iupSWPcYdBsKsXeaXY3HySyq5LGVifyQVsCYqI4smR1D787STEu4Fgl0T/HtC1B2HK77EHzlx+osNrtm2bZ0XlqXio+CxVcN4cYxkdJMS7gk+cv3BDkJsOMto6Ni+Cizq/EYaXllPLo8gZ+OnmDqgM48NyuGsPatzS5LiDOSQHd3djt8+TC07ggXLmx4e9Egi83OW98e4u/fpNGmlS+vXjeMq4ZLMy3h+iTQ3d2eZZC1E656C1p3MLsat5eYVcIjy/ey/3gZlw/tzqIrBxParpXZZQnhEAl0d1ZRAF8/BT0nwbA5Zlfj1qotNl7dcIC3Nx8mtF0rlt48iosHy0wh4V4k0N3Z1wuhthwuewVkOOCc7ThcyPyViRwpqGDO6AgemzGIkNbSTEu4Hwl0V1CcDt8uMcLZUXYbpMbDpIegi/Q6Pxdl1RZeXLef/2w/SkTH1nx411gm9pULaQv3JYFuhtIcCK7rjV2eB8uugop8aB/ZuNcZcBlMfsT59XmBTfvzeHxVIsdLq7lzUi/+eHF/2gTIn4Nwb/IbbIby40agV5fAf66G8ly4JQ4iRptdmccrqqjlmS+SWf1zNv26tGPFfRMYGSknk4VnkEA3i6UaPr4B8vbDDZ9ImDczrTVrEnJYFJdMSZWFBy/sx+/P70MrP2mmJTyHBLoZ7FZYfgdkbIHZ70DfaWZX5NFyS6tZsCqJDftyGRoewod3j2Vgt2CzyxLC6STQW5rWRnvb1HiY8WeIucbsijyW1pr/7szkufh91FrtLJgxiNsnRkkzLeGxJNBb2qbnjDCfMh/G3G12NR7raGEl81cmsPVQIWN7deTF2UOJCpULZQvPJoHekrL3wPevQP9LYep8s6vxSDa75r0tR/jzV6n4+fjw/KwY5oyOkGZawitIoLcUuw3WPAxtQmH8/bIQqBmkHi/j0RUJ7M08wQUDu/DcrCF0D5FmWsJ7SKC3lN3vQfZPcPU70CrI7Go8Sq3VzpvfpvHGpjSCAv15bc5wrhzWQ5ppCa8jgd4SyvNgwzPQa7JxEjTnZ7Mr8hh7M0/w6PIEUnPLmDm8Bwsvj6aTNNMSXkoCvSV89SRYKmGG9FxxlqpaG3/5OpV//XCELkGBvHNLLNOiu5pdlhCmkkBvLmW5sG4e1JRD2tdw3p+gc3+zq/IIWw8V8NjKRDIKK7lhbCTzLx1IcKA00xJCAr257P0YkldBtxiIvgrO++Mvz7WTtqznorTawgvx+/n4x6P07NSGj+4ey4Q+0kxLiJMk0JtL6lrjgs33fv/b50425hIO25CSy4LVieSX1TB3cm8emtaf1gGybF+I+hxaMqeUmq6USlVKpSmlzjiBWik1WymllVKxzivRDVUUQOYOGDDD7ErcXmF5DQ9+vIe7lu2iQ5sAVt0/kcdnDJIwF+I0GjxCV0r5Am8AFwFZwE6lVJzWOuWU7YKA/wN2NEehbuXAOkDDgEvNrsRtaa2J25vNorhkymusPDStP/dN7UOAnyzbF+JMHBlyGQOkaa0PAyilPgFmAimnbLcYeBGQBt2payE4DLoPM7sSt5RTUsUTq5LYuD+P4RHteemaofTvKnP3hWiII4EeBmTWu58FjK2/gVJqJBChtf5SKXXGQFdKzQXmAkRGNvJiDu7CUgWHvoHhN8gUxUay2zUf7zzKC/H7sdrtPHHZIG6f2AtfWbYvhEOafFJUKeUD/AW4raFttdZLgaUAsbGxuqnv7ZIOf2fMOZfx80Y5UlDB/BUJ7DhSxIQ+nVhy9VAiO7Uxuywh3IojgX4MiKh3P7zusZOCgCHAt3VLrbsBcUqpK7XWu5xVqNtIjYeAIIiaZHYlbsFqs/PuliO88tUBAvx8eHF2DL+LjZBl+0KcA0cCfSfQTynVCyPI5wA3nHxSa10C/G8ysFLqW+BPXhnmdrtxQrTvheAny88bsi+nlHkrEkjIKuGi6K48e9UQugYHml2WEG6rwUDXWluVUg8A6wFf4F2tdbJS6hlgl9Y6rrmLdBvZPxnXBx14mdmVuLQaq403Nh3izU1phLT25/UbRnBZTHc5KheiiRwaQ9daxwPxpzy28AzbTm16WW4qNR6Ur1xS7ix+OlrMvOUJHMwrZ9aIMBZeHk2HtgFmlyWER5CVoo1VXQLFGcaVh+zWXz937CfoOQHadDSnNhdWWWvlz+sP8N7WI3QLDuS920Zz/sAuZpclhEeRQG8Maw0UpBnj5AfWQ9jIXz/fsTeMf8Cc2lzYlrQC5q9MILOoipvGRTJv+kCCpJmWEE4ngd4YtRVgrYayHGjXFe7+xuyKXFpJlYXnv9zHf3dl0iu0Lf+dO46xvTuZXZYQHksCvTFqK8BWC2XHIUg6Jp7NV8nHeWJ1EoUVtdw7pQ//b1o/Av2l/4oQzUkCvTEqC0FjBHpIuNnVuKT8shoWfZHMlwk5DOoezL9uHU1MeIjZZQnhFSTQHaU1VBUbt8tyINy7G0qeSmvNqj3HeGZNCpU1Nv50cX/umdIHf19ppiVES5FAd5S1BmwW4wi9sgCCpKf5ScdOVLFgVSLfpuYzMtJoptW3izTTEqKlSaA7ylJpfD55lC5j6Njtmg93ZLBk7X7sGp66IppbxkdJMy0hTCKB7qiaMvDxgaoi435wD3PrMdnh/HLmr0jkx/QizusXyvOzYojoKM20hDCTBLqjKgvBL/CXQPfSI3Srzc7b3x/h1Q0HCPTz4eVrhnLNqHBZti+EC5BAd4TWUFUCge2g8mSge98Yekp2KY+u2EvSsVIuGdyVxTOH0EWaaQnhMiTQHWGtBm0F5WOMofv4Q2vvWd5fbbHx+jdpvPXdIdq3CeAfN47k0hjv+wdNCFcnge6I2kqgbkihqsgYbvHxjul4uzOKeHR5AofyK5g9MpwnLx9E+zbSTEsIVySB7oiTJ0TBGEv3gvHzihorL69P5YNt6fQIac0Hd4xhSv/OZpclhDgLCXRHVBb8csGKqiLoMcLceprZ5gP5PLYykeySKm4Z15NHpg+kXSv5VRHC1clfaUO0hupSaF23fL2yyGNPiJZUWlj8ZQrLd2fRu3NbPr1nPKOjvOdcgRDuTgK9IZZK0DbjhGhthXHfA4dc1iXl8OTnyRRV1HL/1D48eKE00xLC3UigN8RSxf9OiHrglMW8smqe+jyZtUnHie4ezHu3jWZImDTTEsIdSaA3pLoUfOqOVCsLjM8ecISutWb57iye/XIfVRYbj1wygLmTe0szLSHcmAR6QyoLwL9u8czxJONz50Hm1eMEmUWVPL4qke8PFhDbswNLZg+lb5d2ZpclhGgiCfSzsduNKYutOxj3M7ZAp34Q7J5DLna7Ztm2dF5an4oCnpk5mJvG9sRHmmkJ4REk0M/GUgnaDkoZ88/z98OwG8yu6pyk5ZUzf0UCuzKKmdy/M8/PGkJ4B2mmJYQnkUA/G0vVL7czthmfw8eYU8s5stjsLN18mNc2HKR1gC+vXDuMq0eGSTMtITyQBPrZVJeAb923KOMHaNcN2vc0t6ZGSDpWwqPLE0jJKWVGTDeevnIInYNamV2WEKKZSKCfTWUh+LU2jtSP7YaBVxjDLy6u2mLjtY0HWbr5MB3bBvDWTSOZPsQ9x/2FEI6TQD8Tuw1qy4yuihk/GJef6znB7KoatDO9iHnLEzhcUMHvYsNZMCOakDb+ZpclhGgBEuhnYqk0lv0rBelbIKAddB8KNZVmV3Za5TVWXlq3n2XbMgjv0Jr/3DmWSf1CzS5LCNGCJNDP5OQJUbsNjm6DyHHg45rfrk2peSxYmUhOaTW3T4ziTxcPoK000xLC68hf/ZlUnQBff8hLMU6OuuBwS3FFLYvXpLByzzH6dmnH8nsnMKpnB7PLEkKYRAL9TE5eQzR9i3FkHuE60xW11sQnHuepuCROVFr4wwV9eeCCvrTyk2ZaQngzhwJdKTUdeA3wBd7RWi855fmHgbsAK5AP3KG1znByrS3HbjM6K7bpaKwO7T7cGEPXdrMrI6+0midWJ/FVSi4xYSEsu2Ms0T2CzS5LCOECGgx0pZQv8AZwEZAF7FRKxWmtU+pttgeI1VpXKqXuA14CrmuOgltEbQWgoSTT+Bg8y+yK0Frz2a4sFn+ZQq3VzmOXDjlLhWgAAA0MSURBVOTOSb3wk2ZaQog6jhyhjwHStNaHAZRSnwAzgf8FutZ6U73ttwM3ObPIFnfyhGjGVuOzyePnmUWVPLYykR/SChjTqyNLro6hd2dppiWE+DVHAj0MyKx3PwsYe5bt7wTWnu4JpdRcYC5AZGSkgyWaoKoYfAPqmnH1Na1drs2u+WBrOi+vT8XXR/HsVUO4YUykNNMSQpyWU0+KKqVuAmKBKad7Xmu9FFgKEBsbq5353k5VVQTWashNhhE3m1LCwdwyHl2RwJ6jJ5g6oDPPz4qhR/vWptQihHAPjgT6MSCi3v3wusd+RSk1DVgATNFa1zinPBPYrMYY+rHdxknQFh5uqbXaeeu7Q7z+TRptW/ny1+uGM3N4D2mmJYRokCOBvhPop5TqhRHkc4Bf9ZBVSo0A/glM11rnOb3KlmSpMD5nbIW2oRDav8XeOiHrBI8uT2D/8TKuGNaDp66IJrSdNNMSQjimwUDXWluVUg8A6zGmLb6rtU5WSj0D7NJaxwEvA+2Az+qOJI9qra9sxrqbT9UJo29L1k7of8mvm3FVlTTLeHq1xcarXx/g7e8P0zmoFW/fEstF0V2d/j5CCM/m0Bi61joeiD/lsYX1bk9zcl3mKcuBwoPGGHr94RabxbhWdMfeTn277YcLmb8igfTCSq4fE8H8SwcR0lqaaQkhGk9WitZnqTIuOZe5E/xbQ48RvzxXfQK6DP7l+qJNVFZtYcna/Xy44yiRHdvw0V1jmdBXmmkJIc6dBHp91SXGidCMLcaViXwDjMdrK6BVCAQ5p6f4N/tzWbAqidzSau6a1IuHL+5PmwD5UQghmkZSpL6yHCjJMqYt9pxoPKbtRivdiHHg07RVmUUVtTzzRTKrf86mX5d2vHnfBEZESjMtIYRzSKCfZLNARQEc+wmUj9EuF4yTpO2jIPDc+6VorfkiIYdFccmUVVv4vwv7cf/5faSZlhDCqSTQT6ouBZQx3NItxghwaw0oX+gQdc4ve7zEaKa1YV8uw8JDePGasQzsJs20hBDOJ4F+Unmu0TK3+AiMu994rLrE6LToF9Dol9Na88nOTJ7/ch8Wu50FMwZxx6Re+MqyfSFEM5FAB7DbjUDP+dm4HzXROGJvGwrtujT65TIKK5i/IpFthwsZ17sjS64eSlRoWycXLYQQvyaBDlBTCnarcam5DlHQrhtUFkPYgF8vLGqAza55b8sR/vxVKv4+Pjw/K4Y5oyOkmZYQokVIoIMx1GKpgpy9MOx6o9tip77QyvEWtanHjWZaezNPcOHALjw7awjdQ6SZlhCi5Uigaw2lOca1Q7Udwkcbl55rH9Hw12I003rz2zTe2JRGUKA/f7t+BFcM7S7NtIQQLU4CvbYCrFVwdDu07gDBPYyjc9+GvzU/Z55g3vIEUnPLmDm8B09dMZiObRt/AlUIIZxBAr3qhHEN0cwd0Od8Yw566/Zn/5JaG698lcq7W47QJSiQf90ay4WDpJmWEMJcEuil2VB0yFgNGj4GAtoafVzOYOuhAuavSORoUSU3jI1k/qUDCQ6UZlpCCPN5d6Bbqo255pk/gm8ro/d5cNhpNy2ttvBC/D4+/jGTnp3a8PHd4xjfp1MLFyyEEGfm3YFeXQJo42IW4bHg6w9tOv5msw0puSxYnUh+WQ1zJ/fmoWn9aR0gy/aFEK7FuwO9LMf4qMiDkTcb3RUDfpmqWFhew6IvUvhibzYDuwWx9OZYhkWcfXxdCCHM4r2BbrPWNePaDSijf0tQD1AKrTWf/5zN018kU15j5eGL+nPvlD4E+DWt26IQQjQn7w30mlJAG6tDuw42jszbdiL7RBVPrE7im/15DI9oz0vXDKV/1yCzqxVCiAZ5b6CX5xlj6AUHYPTd2PHho70lLFm3E5td8+Tl0dw2IUqaaQkh3IZ3Brrdboyd1zXjygoezh/XaXYcS2Fi3068MGsokZ3amFykEEI0jncGem0Z2K3Yj26ntFUPLlzXngA/Ky/OjuF3sRGybF8I4Za8M9Ari0nNraR31k/81zqdyWE+PDtnAl07yoUnhBDuy+sCvcZq443vsjiyaxd/97cRM2oCc2NDURLmQgg351WBvjujmHkrEkjLK2d5xz3YbcFMiO4LIeFmlyaEEE3mFROrK2utPP1FMte8tZXKGivvX9GJWOsefHqOBx9fCJTFQkII9+fxR+g/HCxg/soEsoqruHlcTx6dPoCg5I+httxoxuXfBgJkRosQwv15bKCXVFl47ssUPt2VRa/Qtvx37jjG9q5rppXxA/j4Q+eBZ2zGJYQQ7sYjA3198nGeXJ1EYUUt903tw/9d2I9A/7pmWjYrHNkMPYaBX6vTNuMSQgh35FGBnl9Ww6K4ZL5MzGFQ92D+detoYsJDfr3Rj0uNRUWjbjO6K7aSZf1CCM/gEYGutWblT8d4Zk0KVbU2HrlkAHMn98bf95RzvqXZsOk5iBwPXYdAUHeQRURCCA/h9oF+7EQVj69M5LsD+YyMNJpp9e1yhqPudY+B3QqTHgJLFbTt3LLFCiFEM3Io0JVS04HXAF/gHa31klOebwUsA0YBhcB1Wut055b6a3a75j87Mnhx7X40sOiKaG4ef5ZmWmkbIGU1nP+EcSK0LAdayWIiIYTnaDDQlVK+wBvARUAWsFMpFae1Tqm32Z1Asda6r1JqDvAicF1zFAxwKL+c+SsS2JlezHn9Qnl+VgwRHc8y9dBSDfGPQKe+MPFBKDgI7bqCr9v/B0UIIf7HkUQbA6RprQ8DKKU+AWYC9QN9JrCo7vZy4HWllNJaayfWCsDOla/Rfu9SXlDQObQVwdX+qI8b+KLaSig5Crd8bsxs8fGT2S1CCI/jSKCHAZn17mcBY8+0jdbaqpQqAToBBfU3UkrNBeYCREZGnlPB7UO7UtquN4PDQghszBWExv8eek+te5EI8As8p/cXQghX1aJjDlrrpcBSgNjY2HM6eu83eQ5MntO0QgLaNu3rhRDCBTlyiHsMiKh3P7zusdNuo5TyA0IwTo4KIYRoIY4E+k6gn1Kql1IqAJgDxJ2yTRxwa93ta4BvmmP8XAghxJk1OORSNyb+ALAeY9riu1rrZKXUM8AurXUc8C/g30qpNKAII/SFEEK0IIfG0LXW8UD8KY8trHe7GrjWuaUJIYRoDK/ohy6EEN5AAl0IITyEBLoQQngICXQhhPAQyqzZhUqpfCDjHL88lFNWoXoB2WfvIPvsHZqyzz211qdtFWtaoDeFUmqX1jrW7Dpakuyzd5B99g7Ntc8y5CKEEB5CAl0IITyEuwb6UrMLMIHss3eQffYOzbLPbjmGLoQQ4rfc9QhdCCHEKSTQhRDCQ7h0oCulpiulUpVSaUqp+ad5vpVS6r91z+9QSkW1fJXO5cA+P6yUSlFKJSilNiqleppRpzM1tM/1tputlNJKKbef4ubIPiulflf3s05WSn3U0jU6mwO/25FKqU1KqT11v98zzKjTWZRS7yql8pRSSWd4Ximl/lb3/UhQSo1s8ptqrV3yA6NV7yGgNxAA7AWiT9nmfuCtuttzgP+aXXcL7PP5QJu62/d5wz7XbRcEbAa2A7Fm190CP+d+wB6gQ939LmbX3QL7vBS4r+52NJBudt1N3OfJwEgg6QzPzwDWAgoYB+xo6nu68hH6/y5OrbWuBU5enLq+mcAHdbeXAxcqpVQL1uhsDe6z1nqT1rqy7u52jCtIuTNHfs4Ai4EXgeqWLK6ZOLLPdwNvaK2LAbTWeS1co7M5ss8aCK67HQJkt2B9Tqe13oxxfYgzmQks04btQHulVPemvKcrB/rpLk4ddqZttNZW4OTFqd2VI/tc350Y/8K7swb3ue6/ohFa6y9bsrBm5MjPuT/QXym1RSm1XSk1vcWqax6O7PMi4CalVBbG9Rf+0DKlmaaxf+8NatGLRAvnUUrdBMQCU8yupTkppXyAvwC3mVxKS/PDGHaZivG/sM1KqRit9QlTq2pe1wPva61fUUqNx7gK2hCttd3swtyFKx+he+PFqR3ZZ5RS04AFwJVa65oWqq25NLTPQcAQ4FulVDrGWGOcm58YdeTnnAXEaa0tWusjwAGMgHdXjuzzncCnAFrrbUAgRhMrT+XQ33tjuHKge+PFqRvcZ6XUCOCfGGHu7uOq0MA+a61LtNahWusorXUUxnmDK7XWu8wp1ykc+d1ejXF0jlIqFGMI5nBLFulkjuzzUeBCAKXUIIxAz2/RKltWHHBL3WyXcUCJ1jqnSa9o9pngBs4Sz8A4MjkELKh77BmMP2gwfuCfAWnAj0Bvs2tugX3eAOQCP9d9xJldc3Pv8ynbfoubz3Jx8OesMIaaUoBEYI7ZNbfAPkcDWzBmwPwMXGx2zU3c34+BHMCC8T+uO4F7gXvr/YzfqPt+JDrj91qW/gshhIdw5SEXIYQQjSCBLoQQHkICXQghPIQEuhBCeAgJdCGE8BAS6EII4SEk0IUQwkP8f8ddFDzlRivzAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vzoouZHncjF7",
+        "outputId": "4ce14dca-3306-458a-c4fd-1322f82d5992"
+      },
+      "source": [
+        "auc = roc_auc_score(y_test,y_scores[:,1])\n",
+        "print(auc)"
+      ],
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.6997079225994889\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2RIw6vizcvzf"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file