D209_Task_1.ipynb

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  "metadata": {
    "colab": {
      "name": "D209 Task 1.ipynb",
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      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "F6MtpOfEaXK1"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('ggplot')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "3KeB4l_JaeG-",
        "outputId": "e8f8914c-1d77-4e7c-c3e7-2b5409dc73b7"
      },
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/medical_clean.csv')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CaseOrder</th>\n",
              "      <th>Customer_id</th>\n",
              "      <th>Interaction</th>\n",
              "      <th>UID</th>\n",
              "      <th>City</th>\n",
              "      <th>State</th>\n",
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              "      <th>Population</th>\n",
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              "      <th>TimeZone</th>\n",
              "      <th>Job</th>\n",
              "      <th>Children</th>\n",
              "      <th>Age</th>\n",
              "      <th>Income</th>\n",
              "      <th>Marital</th>\n",
              "      <th>Gender</th>\n",
              "      <th>ReAdmis</th>\n",
              "      <th>VitD_levels</th>\n",
              "      <th>Doc_visits</th>\n",
              "      <th>Full_meals_eaten</th>\n",
              "      <th>vitD_supp</th>\n",
              "      <th>Soft_drink</th>\n",
              "      <th>Initial_admin</th>\n",
              "      <th>HighBlood</th>\n",
              "      <th>Stroke</th>\n",
              "      <th>Complication_risk</th>\n",
              "      <th>Overweight</th>\n",
              "      <th>Arthritis</th>\n",
              "      <th>Diabetes</th>\n",
              "      <th>Hyperlipidemia</th>\n",
              "      <th>BackPain</th>\n",
              "      <th>Anxiety</th>\n",
              "      <th>Allergic_rhinitis</th>\n",
              "      <th>Reflux_esophagitis</th>\n",
              "      <th>Asthma</th>\n",
              "      <th>Services</th>\n",
              "      <th>Initial_days</th>\n",
              "      <th>TotalCharge</th>\n",
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              "      <td>-86.72508</td>\n",
              "      <td>2951</td>\n",
              "      <td>Suburban</td>\n",
              "      <td>America/Chicago</td>\n",
              "      <td>Psychologist, sport and exercise</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
              "      <td>86575.93</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Male</td>\n",
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              "      <td>19.141466</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>Emergency Admission</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
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              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
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              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Blood Work</td>\n",
              "      <td>10.585770</td>\n",
              "      <td>3726.702860</td>\n",
              "      <td>17939.403420</td>\n",
              "      <td>3</td>\n",
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              "      <td>176354c5eef714957d486009feabf195</td>\n",
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              "      <td>30.84513</td>\n",
              "      <td>-85.22907</td>\n",
              "      <td>11303</td>\n",
              "      <td>Urban</td>\n",
              "      <td>America/Chicago</td>\n",
              "      <td>Community development worker</td>\n",
              "      <td>3</td>\n",
              "      <td>51</td>\n",
              "      <td>46805.99</td>\n",
              "      <td>Married</td>\n",
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              "      <td>No</td>\n",
              "      <td>18.940352</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
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              "      <td>No</td>\n",
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              "      <td>No</td>\n",
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              "      <td>Yes</td>\n",
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              "      <td>14370.14</td>\n",
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              "      <td>No</td>\n",
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              "      <td>2434.234222</td>\n",
              "      <td>17505.192460</td>\n",
              "      <td>2</td>\n",
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              "      <td>New Richland</td>\n",
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              "      <td>43.89744</td>\n",
              "      <td>-93.51479</td>\n",
              "      <td>2162</td>\n",
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              "      <td>America/Chicago</td>\n",
              "      <td>Early years teacher</td>\n",
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              "      <td>78</td>\n",
              "      <td>39741.49</td>\n",
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              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>Yes</td>\n",
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              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
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              "      <td>37.59894</td>\n",
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              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Low</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
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              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
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              "      <td>No</td>\n",
              "      <td>CT Scan</td>\n",
              "      <td>1.254807</td>\n",
              "      <td>2113.073274</td>\n",
              "      <td>3716.525786</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CaseOrder Customer_id  ... Item7 Item8\n",
              "0          1     C412403  ...     3     4\n",
              "1          2     Z919181  ...     3     3\n",
              "2          3     F995323  ...     3     3\n",
              "3          4     A879973  ...     5     5\n",
              "4          5     C544523  ...     4     3\n",
              "\n",
              "[5 rows x 50 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 224
        },
        "id": "X6Tn8pn1ajm-",
        "outputId": "b0bb537c-1082-4e32-eb80-a72e9b5a97ad"
      },
      "source": [
        "df = df.drop(['Customer_id','Interaction','UID','City','State','County','Zip','Lat','Lng', \n",
        "             \"Area\",'TimeZone','Job','Marital', 'Gender','Soft_drink','Initial_admin','HighBlood','Stroke',\n",
        "              'Complication_risk','Overweight','Arthritis','Diabetes',\n",
        "              'Hyperlipidemia','BackPain','Anxiety','Allergic_rhinitis',\n",
        "              'Reflux_esophagitis','Asthma','Services','Item1',\n",
        "              'Item2','Item3','Item4','Item5','Item6',\n",
        "              'Item7','Item8'], axis=1)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5287</td>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>1209.56</td>\n",
              "      <td>No</td>\n",
              "      <td>17.439069</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.254807</td>\n",
              "      <td>2113.073274</td>\n",
              "      <td>3716.525786</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CaseOrder  Population  Children  Age  ...  Item5 Item6  Item7  Item8\n",
              "0          1        2951         1   53  ...      4     3      3      4\n",
              "1          2       11303         3   51  ...      4     4      3      3\n",
              "2          3       17125         3   53  ...      3     4      3      3\n",
              "3          4        2162         0   78  ...      4     5      5      5\n",
              "4          5        5287         1   22  ...      5     3      4      3\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "_k-zXDP-WGuR",
        "outputId": "2c65df13-77ae-41a9-ba7d-98432ec3964e"
      },
      "source": [
        "y = df['ReAdmis']\n",
        "y = pd.get_dummies(y)\n",
        "y = y['Yes']\n",
        "df['ReAdmis'] = y\n",
        "df.to_csv('/content/drive/MyDrive/Colab Notebooks/medical_clean_prepped.csv')"
      ],
      "execution_count": null,
      "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",
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              "    }\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>CaseOrder</th>\n",
              "      <th>Population</th>\n",
              "      <th>Children</th>\n",
              "      <th>Age</th>\n",
              "      <th>Income</th>\n",
              "      <th>ReAdmis</th>\n",
              "      <th>VitD_levels</th>\n",
              "      <th>Doc_visits</th>\n",
              "      <th>Full_meals_eaten</th>\n",
              "      <th>vitD_supp</th>\n",
              "      <th>Initial_days</th>\n",
              "      <th>TotalCharge</th>\n",
              "      <th>Additional_charges</th>\n",
              "      <th>Item1</th>\n",
              "      <th>Item2</th>\n",
              "      <th>Item3</th>\n",
              "      <th>Item4</th>\n",
              "      <th>Item5</th>\n",
              "      <th>Item6</th>\n",
              "      <th>Item7</th>\n",
              "      <th>Item8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>2951</td>\n",
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              "      <td>53</td>\n",
              "      <td>86575.93</td>\n",
              "      <td>0</td>\n",
              "      <td>19.141466</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10.585770</td>\n",
              "      <td>3726.702860</td>\n",
              "      <td>17939.403420</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>11303</td>\n",
              "      <td>3</td>\n",
              "      <td>51</td>\n",
              "      <td>46805.99</td>\n",
              "      <td>0</td>\n",
              "      <td>18.940352</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>15.129562</td>\n",
              "      <td>4193.190458</td>\n",
              "      <td>17612.998120</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>17125</td>\n",
              "      <td>3</td>\n",
              "      <td>53</td>\n",
              "      <td>14370.14</td>\n",
              "      <td>0</td>\n",
              "      <td>18.057507</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4.772177</td>\n",
              "      <td>2434.234222</td>\n",
              "      <td>17505.192460</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2162</td>\n",
              "      <td>0</td>\n",
              "      <td>78</td>\n",
              "      <td>39741.49</td>\n",
              "      <td>0</td>\n",
              "      <td>16.576858</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.714879</td>\n",
              "      <td>2127.830423</td>\n",
              "      <td>12993.437350</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5287</td>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>1209.56</td>\n",
              "      <td>0</td>\n",
              "      <td>17.439069</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.254807</td>\n",
              "      <td>2113.073274</td>\n",
              "      <td>3716.525786</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CaseOrder  Population  Children  Age  ...  Item5  Item6  Item7  Item8\n",
              "0          1        2951         1   53  ...      4      3      3      4\n",
              "1          2       11303         3   51  ...      4      4      3      3\n",
              "2          3       17125         3   53  ...      3      4      3      3\n",
              "3          4        2162         0   78  ...      4      5      5      5\n",
              "4          5        5287         1   22  ...      5      3      4      3\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2kMly8--anVw"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors=6)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6CaKjXWZaz10"
      },
      "source": [
        "y = df['ReAdmis'].values\n",
        "X = df.drop('ReAdmis', axis=1).values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1vajoEoha-Kn",
        "outputId": "4834ee95-bf57-498f-94b4-9ebc7e8f8534"
      },
      "source": [
        "knn.fit(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_sdwvDl3fxCE"
      },
      "source": [
        "y_pred = knn.predict(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CASmD2wlnMhG",
        "outputId": "6f98fbf6-5e60-4a98-e0ba-26363860a123"
      },
      "source": [
        "print(\"Prediction: {}\".format(y_pred))\n",
        "knn.score(X, y) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction: [0 0 0 ... 1 1 1]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9162"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMVyQYiSpHMU"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lnw5K4wIXxKb"
      },
      "source": [
        "pd.DataFrame(X_train).to_csv('/content/drive/MyDrive/Colab Notebooks/X_train.csv')\n",
        "pd.DataFrame(X_test).to_csv('/content/drive/MyDrive/Colab Notebooks/X_test.csv')\n",
        "pd.DataFrame(y_train).to_csv('/content/drive/MyDrive/Colab Notebooks/y_train.csv')\n",
        "pd.DataFrame(y_test).to_csv('/content/drive/MyDrive/Colab Notebooks/y_test.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8vDqBCutpLTf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1658b01b-2006-40b9-8498-94e130154304"
      },
      "source": [
        "knn.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0RVGvPm6siFP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67d7e99a-2215-4d7e-a13f-1d6122916f95"
      },
      "source": [
        "y_pred = knn.predict(X_test)\n",
        "print(\\\n",
        "      \"Test set predictions:\\\n",
        "      \\\n",
        "      n {}\\\n",
        "      \".format(y_pred))\n",
        "knn.score(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test set predictions:            n [1 1 0 ... 1 1 0]      \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8525"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXzcAtjRu04O"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import roc_auc_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6HD18Kiu-Fp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc08a0c2-bd67-4458-bf51-e14eff33896f"
      },
      "source": [
        "logreg = LogisticRegression()\n",
        "logreg.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2s0ckDYAvJHn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2f840e8-ca26-425d-9d8a-14e50244943e"
      },
      "source": [
        "y_pred_prob = logreg.predict_proba(X_test)[:,1]\n",
        "roc_auc_score(y_test, y_pred_prob)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9908872158442777"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcRwi_nErlRu"
      },
      "source": [
        "from sklearn.metrics import roc_curve"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "l-IjvVayrn2Q",
        "outputId": "2d5f4c36-2233-4bd4-a12d-4e32ed49fb18"
      },
      "source": [
        "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.plot(fpr, tpr)\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}