StrokePredicition.ipynb

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  "metadata": {
    "colab": {
      "name": "StrokePredicition",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBFWBmLoLKVI"
      },
      "source": [
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "id": "dPQ7xU9ULf3d",
        "outputId": "54f2f542-f9e5-4b32-a90e-cd9c81a71522"
      },
      "source": [
        "df = pd.read_csv('/content/stroke.csv')\r\n",
        "df.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>work_type</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoking_status</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9046</td>\n",
              "      <td>Male</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>228.69</td>\n",
              "      <td>36.6</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>51676</td>\n",
              "      <td>Female</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>202.21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31112</td>\n",
              "      <td>Male</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>105.92</td>\n",
              "      <td>32.5</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>60182</td>\n",
              "      <td>Female</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>171.23</td>\n",
              "      <td>34.4</td>\n",
              "      <td>smokes</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1665</td>\n",
              "      <td>Female</td>\n",
              "      <td>79.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>174.12</td>\n",
              "      <td>24.0</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id  gender   age  ...   bmi   smoking_status stroke\n",
              "0   9046    Male  67.0  ...  36.6  formerly smoked      1\n",
              "1  51676  Female  61.0  ...   NaN     never smoked      1\n",
              "2  31112    Male  80.0  ...  32.5     never smoked      1\n",
              "3  60182  Female  49.0  ...  34.4           smokes      1\n",
              "4   1665  Female  79.0  ...  24.0     never smoked      1\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BJ4XeqicL1xG"
      },
      "source": [
        "# Preprocessing\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SHGDxcFZLm6R",
        "outputId": "995d069c-583e-48aa-a86e-eb1ffc1d5e3c"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5110 entries, 0 to 5109\n",
            "Data columns (total 12 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   id                 5110 non-null   int64  \n",
            " 1   gender             5110 non-null   object \n",
            " 2   age                5110 non-null   float64\n",
            " 3   hypertension       5110 non-null   int64  \n",
            " 4   heart_disease      5110 non-null   int64  \n",
            " 5   ever_married       5110 non-null   object \n",
            " 6   work_type          5110 non-null   object \n",
            " 7   Residence_type     5110 non-null   object \n",
            " 8   avg_glucose_level  5110 non-null   float64\n",
            " 9   bmi                4909 non-null   float64\n",
            " 10  smoking_status     5110 non-null   object \n",
            " 11  stroke             5110 non-null   int64  \n",
            "dtypes: float64(3), int64(4), object(5)\n",
            "memory usage: 479.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2PUBExjLrNg",
        "outputId": "4b6d7f20-7bce-4599-f001-b77fa3b1c40d"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                     0\n",
              "gender                 0\n",
              "age                    0\n",
              "hypertension           0\n",
              "heart_disease          0\n",
              "ever_married           0\n",
              "work_type              0\n",
              "Residence_type         0\n",
              "avg_glucose_level      0\n",
              "bmi                  201\n",
              "smoking_status         0\n",
              "stroke                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cz3BVZR6wUOt"
      },
      "source": [
        "df.fillna(df['bmi'].mean(),inplace=True)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "id": "w_G85FSNwbGe",
        "outputId": "0037b192-4c5f-4a63-96f8-ce5a7d2845c3"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>work_type</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoking_status</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9046</td>\n",
              "      <td>Male</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>228.69</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>51676</td>\n",
              "      <td>Female</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>202.21</td>\n",
              "      <td>28.893237</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31112</td>\n",
              "      <td>Male</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>105.92</td>\n",
              "      <td>32.500000</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>60182</td>\n",
              "      <td>Female</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>171.23</td>\n",
              "      <td>34.400000</td>\n",
              "      <td>smokes</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1665</td>\n",
              "      <td>Female</td>\n",
              "      <td>79.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>174.12</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id  gender   age  ...        bmi   smoking_status stroke\n",
              "0   9046    Male  67.0  ...  36.600000  formerly smoked      1\n",
              "1  51676  Female  61.0  ...  28.893237     never smoked      1\n",
              "2  31112    Male  80.0  ...  32.500000     never smoked      1\n",
              "3  60182  Female  49.0  ...  34.400000           smokes      1\n",
              "4   1665  Female  79.0  ...  24.000000     never smoked      1\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "0yHrdYUDwcFq",
        "outputId": "223eba3d-c9c7-450c-c437-960b6770c045"
      },
      "source": [
        "sns.countplot(x='stroke',data=df)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9f8a6f61d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3txyGkiTNI8NCktRlWGiPfMyKFqIklyTZluTW+e5lsTAsNC0fs6IF7FJg9Xw3sZgYFtoTH7OiBamqvgTsmO8+FhPDQnsy1WNWls9TL5LmkWEhSeoyLLQnPmZFEmBYaM98zIokwLDQHlTVLmD3Y1ZuB67wMStaCJJ8DPgK8IIkW5Ksne+enup83Ickqcs9C0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkW0ixJck6SZzzBdVb65FTtDQwLafacA0wZFu0JvtJey7CQnoQkByT59yQ3J7k1yXnAYcC1Sa5tYx5M8tdJbgZenuSP29hbk5wzxTZ/McmNSX41yfOSfDbJpiRfTvLCOf6K0qMsme8GpL3UauA7VfVqgCTPAc4CXlVV32tjDgCuq6q3JXlZW34sEOC6JF8Edrb1X8DkI+DfUlU3J7kG+P2qujPJscCHgOPn8PtJj+Id3NKTkOSXgM8BHwc+XVVfTvItYGx3WCTZBexfVY8keSvw3Kp6R1t2AbCdyWdtXcdkaLyuqjYneWZbdsfIR+5fVb88R19Pehz3LKQnoar+K8nRwCnAO9uewGM9XFWPzGBzDwDfBl4JbGby8PD9VXXUrDUs/T95zkJ6EpIcBjxUVf8AvAc4GvgB8KxpVvkycFqSZyQ5APitVgP4cZs/M8lvV9X3gW8mOb19VpK8ZMCvI3W5ZyE9OS8G3pPkJ8D/AH8AvBz4bJLvVNWrRgdX1deSXApc30ofrqobk6xsy3+Y5DXAxiQPAm8CLkryF8DTmDyfcfPwX0uamucsJEldHoaSJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEld/wt1jkL2vsLfTgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "x7JDDnrewpKd",
        "outputId": "f38e08b2-c7a4-4783-b19e-046d5c0aabc4"
      },
      "source": [
        "sns.countplot(x='stroke',hue='gender',data=df)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9f8a6cce80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "YeNTJ_BKwvRQ",
        "outputId": "591f5a54-7c5f-4db6-8f77-792e9da5cc3f"
      },
      "source": [
        "sns.countplot(x='stroke',hue='smoking_status',data=df)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9f8a6cc668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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un7n7t8ArwVwRKQe2b9/O6NGj2bx5MzVq1GDevHkAjBw5kmeffZa0tDSmTZvG3XffzUUXXURiYiLvvfceAIsXL6ZHjx5Urly5wPk5MjIyWL16NU899VS+954xYwb33nsv69evJzU1NfcGTDt27OD+++9n27ZtbNu2jZdeeomVK1cybdo0nnjiCYAi26kDPPfccyxevJjXXnuN9PR0kpOTWbVqFevXr6dixYrMmTOH3bt3M3HiRFatWsXKlSvZsmVLif2eoxGLcxY/M7PbgVTgfnf/B3AZ8EGeORnBGMCXJ423L2zDZjYSGAlQv3794qxZRGIgLi6OxMREANq0aUN6ejpHjhxh9erVDBw4MHdezk2CfvzjH5OcnEzXrl155ZVXuPvuu4ucDzBw4EAqVqx4yntfc801TJkyhYyMDG666SYaN26cW1N8fOT8TYsWLbj++usxs9xW4xBpp54TbCe3U589ezaXX345r732GpUrV+btt98mLS2Ntm3bApGbF/3whz/kww8/pEuXLtSqVSv3s3366aff/5d6hko7LKYDjwEe/Pw1MKK4Nu7uzwPPQ6Q3VHFtV8qG9zp1Dp9Uwjovfy/WJZQrOW29ASpWrMg333xDdnY2NWrUyL0JUV59+/blF7/4BQcPHiQtLY1u3bpx9OjRQudD4e3Ib731Vtq3b09KSgq9e/fmD3/4Aw0bNsxXU4UKFfK1Ho/mvEd8fDzr168nIyODuLg43J1hw4bx3//93/nmvfbaa6HbKk2leumsu+9x9yx3zwZeIHKYCWAXcHmeqfWCscLGRaScuvDCC4mLi+Ovf/0rAO7Ohg0bAKhWrRpt27bl3nvvpU+fPlSsWLHI+UX57LPPaNiwIWPGjKFfv36n1eq7qHbqrVu35g9/+AN9+/blq6++4vrrr2fu3Lns3bsXiJzv+OKLL2jfvj3vvfceBw4c4Pjx47n1x0qp7lmYWR133x28HADkXCm1CHjJzJ4C6gKNgTWAAY3NLI5ISAwCTr01lYiUuLJ0qeucOXP46U9/yuOPP87x48cZNGgQrVq1AiKHawYOHMiyZcuiml+YV199lT//+c9UrlyZSy+9lF/84he5h5LChLVTv+6665g2bRo33ngjS5cu5fHHH6d79+5kZ2dTuXJlfve733H11VczadIkrrnmGmrUqJF7OC5WSqxFuZm9DHQBagJ7gInB60Qih6HSgbtywsPMHiZySOoEMNbd/xaM9wZ+A1QEZrr7lGjeXy3Ki09Z+UdCh6FKl1qUn9vKTItydx9cwPAfi5g/BTglCILLa98oxtJEROQ0qd2HiIiEUliIiEgohYWIiIRSWIiISCiFhYiIhFKLchGJSodnOxTr9lbdU/qN8ZYtW8a0adNYvHhxqb/32U57FiIiEkphISJl1tGjR7nxxhtp1aoVLVu2JDk5mQYNGjBhwgQSExNJSkpi3bp19OjRg0aNGjFjxgwg0tJj3LhxtGzZkvj4eJKTk0/Z9tq1a2ndujU7d+4kLS2Nzp0706ZNG3r06MHu3ZFGE8888wzNmzcnISGBQYMGlepnL2t0GEpEyqwlS5ZQt25dUlJSAMjMzGT8+PHUr1+f9evXc9999zF8+HBWrVrFsWPHaNmyJaNGjWL+/PmsX7+eDRs2sH//ftq2bUunTp1yt7t69WruueceFi5cSJ06dbjttttYuHAhtWrVIjk5mYcffpiZM2fy5JNP8vnnn3Peeedx6NChWP0aygSFhYiUWfHx8dx///2MHz+ePn360LFjRyDSXTZn+ZEjR6hevTrVq1fP/Ud95cqVDB48mIoVK1K7dm06d+7M2rVrufDCC9m6dSsjR47kzTffpG7dumzatIlNmzZxww03AJCVlUWdOnUASEhIYMiQIfTv35/+/fvH5pdQRigsRKTMatKkCevWreONN97gkUce4frrrwfI1xb85JbhYW3C69Spw7Fjx/joo4+oW7cu7k6LFi14//33T5mbkpLC8uXLef3115kyZQobN26kUqXy+c+mzlmISJn11VdfUbVqVYYOHcq4ceNYt25dVOt17NiR5ORksrKy2LdvH8uXL6ddu8gdEWrUqEFKSgoTJkxg2bJlXHnllezbty83LI4fP87mzZvJzs7myy+/pGvXrkydOpXMzEyOHDlSYp+1rCufESkipy0Wl7pu3LiRcePGUaFCBSpXrsz06dO55ZZbQtcbMGAA77//Pq1atcLM+OUvf8mll16ae+/t2rVrs3jxYnr16sXMmTOZO3cuY8aMITMzkxMnTjB27FiaNGnC0KFDyczMxN0ZM2YMNWrUKOmPXGaVWIvyWFOL8uKjFuXfUYtyOVecbotyHYYSEZFQCgsREQmlsBARkVAKCxERCaWwEBGRUAoLEREJVWLfszCzmUAfYK+7twzGfgX8CPgW2Anc4e6HzKwBsBX4JFj9A3cfFazTBpgFnA+8Adzr5+r1viJlWHFfuhzNZcjp6en06dOHTZs25Y5NmjSJatWq8cADDxS4zqxZs0hNTeW5554rtlqlZPcsZgE9TxpbCrR09wTgU2BCnmU73T0xeIzKMz4duBNoHDxO3qaIiJSwEgsLd18OHDxp7E13z2nc8gFQr6htmFkd4EJ3/yDYm5gNlO9uXiICQJcuXRg/fjzt2rWjSZMmrFix4pQ5KSkpXHPNNezfv5/hw4czZswYrr32Who2bMjcuXOBwtuZjx49mkWLFgGRb4SPGDECgJkzZ/Lwww+Tnp5Os2bNuPPOO2nRogXdu3fnm2++KaVPX/piec5iBPC3PK/jzOwjM3vPzDoGY5cBGXnmZARjBTKzkWaWamap+/btK/6KRaRMOXHiBGvWrOE3v/kNkydPzrdswYIFPPnkk7zxxhvUrFkTgN27d7Ny5UoWL17MQw89BJCvnflbb73FuHHj2L17Nx07dswNoF27drFlyxYAVqxYkdvufPv27YwePZrNmzdTo0YN5s2bV1ofvdTFJCzM7GHgBDAnGNoN1Hf31sDPgZfM7MLT3a67P+/uSe6eVKtWreIrWERiwsyKHL/pppsAaNOmDenp6bnL33nnHaZOnUpKSgoXX3xx7nj//v2pUKECzZs3Z8+ePQCFtjPPCYstW7bQvHlzateuze7du3n//fe59tprAYiLiyMxMbHAGs41pR4WZjacyInvITknqt39X+5+IHieRuTkdxNgF/kPVdULxkSkHLjkkkv4xz/+kW/s4MGDuXsKOe3JK1asmK81eaNGjTh8+DCffvppvnXztjMPu07msssu49ChQyxZsoROnTrRsWNHXn31VapVq0b16tVP2d7JNZxrSjUszKwn8CDQ193/mWe8lplVDJ43JHIi+zN33w18bWZXW+S/ErcDC0uzZhGJnWrVqlGnTh3eeecdIBIUS5Ys4brrrityvSuuuIJ58+Zx++23s3nz5iLnFtXO/Oqrr+Y3v/lNblhMmzYt9wZM5U1JXjr7MtAFqGlmGcBEIlc/nQcsDXYjcy6R7QQ8ambHgWxglLvnnBy/m+8unf0b+c9ziEgpiVXH3dmzZzN69Gh+/vOfAzBx4kQaNWoUul7Tpk2ZM2cOAwcO5PXXXy90XmHtzCESJG+++Sb/9m//xhVXXMHBgwfLbVioRXkZpxbl31GL8tKlFuXnNrUoFxGRYqewEBGRUAoLESnUuXqYurw7kz9XhYWIFKhKlSocOHBAgXGOcXcOHDhAlSpVTmu9ErsaSkTObvXq1SMjIwN1Qzj3VKlShXr1iuy2dAqFhYgUqHLlysTFxcW6DCkjdBhKRERCKSxERCSUwkJEREIpLEREJJTCQkREQiksREQklMJCRERCKSxERCSUwkJEREIpLEREJJTCQkREQiksREQkVFRhYWZvRzMmIiLnpiK7zppZFaAqUNPMLgYsWHQhcFkJ1yYiImVE2J7FXUAa0DT4mfNYCDwXtnEzm2lme81sU56x/2NmS81se/Dz4mDczOwZM9thZh+b2VV51hkWzN9uZsNO/2OKiMj3UWRYuPtv3T0OeMDdG7p7XPBo5e6hYQHMAnqeNPYQ8La7NwbeDl4D9AIaB4+RwHSIhAswEWgPtAMm5gSMiIiUjqhufuTuz5rZtUCDvOu4++yQ9ZabWYOThvsBXYLnfwKWAeOD8dkeuYfjB2ZWw8zqBHOXuvtBADNbSiSAXo6mdhER+f6iCgsz+zPQCFgPZAXDDhQZFoWo7e67g+f/C9QOnl8GfJlnXkYwVth4QXWOJLJXQv369c+gNBERKUi0t1VNApp7Md+53d3dzIptm+7+PPA8QFJSku4yLyJSTKL9nsUm4NJies89weElgp97g/FdwOV55tULxgobFxGRUhJtWNQEtpjZ/5jZopzHGb7nIiDniqZhRK6syhm/Pbgq6mogMzhc9T9AdzO7ODix3T0YExGRUhLtYahJZ7JxM3uZyAnqmmaWQeSqpieBV83sJ8AXwL8H098AegM7gH8CdwC4+0EzewxYG8x7NOdkt4iIlI5or4Z670w27u6DC1l0fQFzHRhdyHZmAjPPpAYREfn+or0a6jCRq58AfgBUBo66+4UlVZiIiJQd0e5ZVM95bmZG5DsRV5dUUSIiUracdtdZj3gN6FEC9YiISBkU7WGom/K8rEDkexfHSqQiEREpc6K9GupHeZ6fANKJHIoSEZFyINpzFneUdCEiIlJ2RXvzo3pmtiBoN77XzOaZWb2SLk5ERMqGaE9wv0jkG9Z1g8frwZiIiJQD0YZFLXd/0d1PBI9ZQK0SrEtERMqQaMPigJkNNbOKwWMocKAkCxMRkbIj2rAYQaSH0/8Cu4FbgOElVJOIiJQx0V46+ygwzN3/Abm3Op1GJEREROQcF+2eRUJOUECkEyzQumRKEhGRsibasKgQ3EsCyN2ziHavREREznLR/oP/a+B9M/tr8HogMKVkShIRkbIm2m9wzzazVKBbMHSTu28pubJir8242bEuAYAF1cPniIiUtKgPJQXhcE4HhIiIFOy0W5SLiEj5o7AQEZFQpR4WZnalma3P8/jazMaa2SQz25VnvHeedSaY2Q4z+8TMdNMlEZFSVuqXv7r7J0AigJlVBHYBC4A7gKfdfVre+WbWHBgEtCDSxPAtM2vi7lmlWriISDkW68NQ1wM73f2LIub0A15x93+5++fADqBdqVQnIiJA7MNiEPByntc/M7OPzWxmni8BXgZ8mWdORjAmIiKlJGbfwjazHwB9gQnB0HTgMcCDn7/mNHtPmdlIYCRA/fr1i63W8q7Dsx1iXQIAT6hpgEjMxHLPohewzt33ALj7HnfPcvds4AW+O9S0C7g8z3r1grFTuPvz7p7k7km1aul2GyIixSWWYTGYPIegzKxOnmUDgE3B80XAIDM7z8zigMbAmlKrUkREYrNfb2YXADcAd+UZ/qWZJRI5DJWes8zdN5vZq0S+PX4CGK0roURESldMwsLdjwKXnDR2WxHzp6DGhSIiMRPrq6FEROQsoLAQEZFQCgsREQmlsBARkVAKCxERCaWwEBGRUAoLEREJpbAQEZFQCgsREQmlsBARkVAKCxERCaWwEBGRUAoLEREJpbAQEZFQCgsREQmlsBARkVAKCxERCaWwEBGRUAoLEREJpbAQEZFQMQsLM0s3s41mtt7MUoOx/2NmS81se/Dz4mDczOwZM9thZh+b2VWxqltEpDyK9Z5FV3dPdPek4PVDwNvu3hh4O3gN0AtoHDxGAtNLvVIRkXIs1mFxsn7An4LnfwL65xmf7REfADXMrE4sChQRKY9iGRYOvGlmaWY2Mhir7e67g+f/C9QOnl8GfJln3YxgLB8zG2lmqWaWum/fvpKqW0Sk3KkUw/e+zt13mdkPgaVmti3vQnd3M/PT2aC7Pw88D5CUlHRa64qISOFitmfh7ruCn3uBBUA7YE/O4aXg595g+i7g8jyr1wvGRESkFMQkLMzsAjOrnvMc6A5sAhYBw4Jpw4CFwfNFwO3BVVFXA5l5DleJiEgJi9VhqNrAAjPLqeEld19iZmuBV83sJ8AXwL8H898AegM7gH8Cd5R+ySIi5VdMwsLdPwNaFTB+ALi+gHEHRpdCaSIiUoCydumsiIiUQQoLEREJpbAQEZFQCgsREQmlsBARkVAKCxERCdBwn2YAAAXdSURBVKWwEBGRUAoLEREJpbAQEZFQCgsREQmlsBARkVAKCxERCaWwEBGRUAoLEREJpbAQEZFQCgsREQmlsBARkVAKCxERCaWwEBGRUAoLEREJVephYWaXm9m7ZrbFzDab2b3B+CQz22Vm64NH7zzrTDCzHWb2iZn1KO2aRUTKu0oxeM8TwP3uvs7MqgNpZrY0WPa0u0/LO9nMmgODgBZAXeAtM2vi7lmlWrWISDlW6nsW7r7b3dcFzw8DW4HLililH/CKu//L3T8HdgDtSr5SERHJEdNzFmbWAGgNfBgM/czMPjazmWZ2cTB2GfBlntUyKCRczGykmaWaWeq+fftKqGoRkfInZmFhZtWAecBYd/8amA40AhKB3cCvT3eb7v68uye5e1KtWrWKtV4RkfIsJmFhZpWJBMUcd58P4O573D3L3bOBF/juUNMu4PI8q9cLxkREpJTE4mooA/4IbHX3p/KM18kzbQCwKXi+CBhkZueZWRzQGFhTWvWKiEhsrobqANwGbDSz9cHYL4DBZpYIOJAO3AXg7pvN7FVgC5ErqUbrSigRkdJV6mHh7isBK2DRG0WsMwWYUmJFiYhIkfQNbhERCaWwEBGRUAoLEREJpbAQEZFQCgsREQmlsBARkVAKCxERCaWwEBGRUAoLEREJpbAQEZFQCgsREQmlsBARkVCx6DorIueAvz8aH+sSAKj/XxtjXUK5oD0LEREJpbAQEZFQOgwlchZqM252rEtgQfVYVyClSXsWIiISSnsWIiLf03udOse6BAA6L3+vxLatsBCRs1qHZzvEugSeKAf/lOowlIiIhDprwsLMeprZJ2a2w8weinU9IiLlyVkRFmZWEfgd0AtoDgw2s+axrUpEpPw4K8ICaAfscPfP3P1b4BWgX4xrEhEpN86WszKXAV/meZ0BtD95kpmNBEYGL4+Y2SelUFuJuiLWBUTUBPbHuogusS4AwCzWFZQZZeTvJpSBv59dYvnmeX3/v5+F/rGeLWERFXd/Hng+1nWca8ws1d2TYl2HSEH097N0nC2HoXYBl+d5XS8YExGRUnC2hMVaoLGZxZnZD4BBwKIY1yQiUm6cFYeh3P2Emf0M+B+gIjDT3TfHuKzyRIf2pCzT389SYO4e6xpERKSMO1sOQ4mISAwpLEREJJTCQoqkNitSVpnZTDPba2abYl1LeaCwkEKpzYqUcbOAnrEuorxQWEhR1GZFyix3Xw4cjHUd5YXCQopSUJuVy2JUi4jEkMJCRERCKSykKGqzIiKAwkKKpjYrIgIoLKQI7n4CyGmzshV4VW1WpKwws5eB94ErzSzDzH4S65rOZWr3ISIiobRnISIioRQWIiISSmEhIiKhFBYiIhJKYSEiIqEUFiLFxMzGmlnV01yngbqmytlAYSFSfMYCBYZF0MFX5KylsBA5A2Z2gZmlmNkGM9tkZhOBusC7ZvZuMOeImf3azDYA15jZz4O5m8xsbAHbbGhmH5lZWzNrZGZLzCzNzFaYWdNS/ogi+VSKdQEiZ6mewFfufiOAmV0E3AF0dff9wZwLgA/d/X4zaxMsbw8Y8KGZvQf8I1j/SiIt4Ie7+wYzexsY5e7bzaw98HugWyl+PpF89A1ukTNgZk2AN4FkYLG7rzCzdCApJyzM7ARwnrtnmdm9wCXu/l/BsseAfUR6bX1IJDRucvctZlYtWPZJnrc8z92bldLHEzmF9ixEzoC7f2pmVwG9gceDPYGTHXP3rCg2lwn8HbgO2ELk8PAhd08stoJFviedsxA5A2ZWF/inu/8F+BVwFXAYqF7IKiuA/mZW1cwuAAYEYwDfBq9vN7Nb3f1r4HMzGxi8l5lZqxL8OCKhtGchcmbigV+ZWTZwHPgpcA2wxMy+cveueSe7+zozmwWsCYb+n7t/ZGYNguVHzawPsNTMjgBDgOlm9ghQmcj5jA0l/7FECqZzFiIiEkqHoUREJJTCQkREQiksREQklMJCRERCKSxERCSUwkJEREIpLEREJNT/Bw+BCPeGXmxZAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "aqMhAAvyw-EV",
        "outputId": "e0ac7aff-7240-437a-bfb5-e9da8c871428"
      },
      "source": [
        "sns.displot(df['age'],bins=40)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f9f8972e9e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zCueNCVBxnyV"
      },
      "source": [
        "df = pd.get_dummies(df,drop_first=True)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-xuSila1uQ7"
      },
      "source": [
        "df.drop(labels='id',inplace=True,axis=1)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "id": "LP0Q7EK41jNq",
        "outputId": "57847499-9a6d-4749-f47a-b082cdd04c07"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 42,
      "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>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>stroke</th>\n",
              "      <th>gender_Male</th>\n",
              "      <th>gender_Other</th>\n",
              "      <th>ever_married_Yes</th>\n",
              "      <th>work_type_Never_worked</th>\n",
              "      <th>work_type_Private</th>\n",
              "      <th>work_type_Self-employed</th>\n",
              "      <th>work_type_children</th>\n",
              "      <th>Residence_type_Urban</th>\n",
              "      <th>smoking_status_formerly smoked</th>\n",
              "      <th>smoking_status_never smoked</th>\n",
              "      <th>smoking_status_smokes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>228.69</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>61.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>202.21</td>\n",
              "      <td>28.893237</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>105.92</td>\n",
              "      <td>32.500000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>49.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>171.23</td>\n",
              "      <td>34.400000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>79.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>174.12</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    age  hypertension  ...  smoking_status_never smoked  smoking_status_smokes\n",
              "0  67.0             0  ...                            0                      0\n",
              "1  61.0             0  ...                            1                      0\n",
              "2  80.0             0  ...                            1                      0\n",
              "3  49.0             0  ...                            0                      1\n",
              "4  79.0             1  ...                            1                      0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZMs0h9b13lh"
      },
      "source": [
        "X = df.drop(labels='stroke',axis=1)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "id": "8RyYagD93tGV",
        "outputId": "09506b93-c82f-4881-b56e-b00862682557"
      },
      "source": [
        "X.head()"
      ],
      "execution_count": 44,
      "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>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>gender_Male</th>\n",
              "      <th>gender_Other</th>\n",
              "      <th>ever_married_Yes</th>\n",
              "      <th>work_type_Never_worked</th>\n",
              "      <th>work_type_Private</th>\n",
              "      <th>work_type_Self-employed</th>\n",
              "      <th>work_type_children</th>\n",
              "      <th>Residence_type_Urban</th>\n",
              "      <th>smoking_status_formerly smoked</th>\n",
              "      <th>smoking_status_never smoked</th>\n",
              "      <th>smoking_status_smokes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>228.69</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>61.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>202.21</td>\n",
              "      <td>28.893237</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>105.92</td>\n",
              "      <td>32.500000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>49.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>171.23</td>\n",
              "      <td>34.400000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>79.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>174.12</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    age  hypertension  ...  smoking_status_never smoked  smoking_status_smokes\n",
              "0  67.0             0  ...                            0                      0\n",
              "1  61.0             0  ...                            1                      0\n",
              "2  80.0             0  ...                            1                      0\n",
              "3  49.0             0  ...                            0                      1\n",
              "4  79.0             1  ...                            1                      0\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E3w6Ev-f3ufn"
      },
      "source": [
        "Y = df[['stroke']]"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "QK02SD5u32Pt",
        "outputId": "00d719aa-d148-45f6-da31-d67e82db0df6"
      },
      "source": [
        "Y.head()"
      ],
      "execution_count": 46,
      "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>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   stroke\n",
              "0       1\n",
              "1       1\n",
              "2       1\n",
              "3       1\n",
              "4       1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zkWbnaVc40SJ",
        "outputId": "3f4dc1e9-9ff7-4ca5-e9e7-1bddc6d26c3f"
      },
      "source": [
        "Y.value_counts()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "stroke\n",
              "0         4861\n",
              "1          249\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0mKFYyT049ym"
      },
      "source": [
        "handling imbalanced data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vM8KCaZQ40UK"
      },
      "source": [
        "from imblearn.combine import SMOTETomek"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E9QmUV3p40WX",
        "outputId": "710d5323-81c6-454f-a010-fc38d921cdfd"
      },
      "source": [
        "smk = SMOTETomek(random_state=40)\r\n",
        "X_res,Y_res = smk.fit_sample(X,Y)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49saEt_040YU",
        "outputId": "761c77cd-9975-44fb-9426-eece1d5db72e"
      },
      "source": [
        "X_res.shape"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9636, 16)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2lr1rSoE40b1",
        "outputId": "478d6c3b-a365-426a-8656-cbbcad026962"
      },
      "source": [
        "Y_res.shape"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9636,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bRcQ9vMS6WXr"
      },
      "source": [
        "from collections import Counter"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kGpV9mAD6bjp",
        "outputId": "5683c238-be60-4f63-d646-4d793241f9dc"
      },
      "source": [
        "Counter(Y_res)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({0: 4818, 1: 4818})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u0xoMEEr6bnC"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0kfJ9bm234xT"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3NdBTdjq4E0v"
      },
      "source": [
        "xtrain,xtest,ytrain,ytest = train_test_split(X_res,Y_res,random_state=10,shuffle=True,test_size=0.2)"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ioPLQ9Q_6_CB",
        "outputId": "3ec7d05b-aa0c-46ce-fc1d-53894b5c3d8c"
      },
      "source": [
        "print('Train :{}'.format(xtrain.shape))\r\n",
        "print('Test :{}'.format(xtest.shape))"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train :(7708, 16)\n",
            "Test :(1928, 16)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6MC66hK7SkQ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EsGoUHST7jFo"
      },
      "source": [
        "Modeling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3e0ADeuq7kTC"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q46LFFi-D8Gz"
      },
      "source": [
        "Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1oozeZYB7nf0"
      },
      "source": [
        "lr = LogisticRegression()"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XabP_-DI7p7e",
        "outputId": "271746cb-c989-4fb8-8237-271983f81bf4"
      },
      "source": [
        "lr.fit(xtrain,ytrain)"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/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": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qbNcXy1T7u9Q",
        "outputId": "9f77e437-cc26-46a1-f5ff-705494ff2add"
      },
      "source": [
        "lr.score(xtrain,ytrain)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7859366891541256"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFNwsQqO8Yyd",
        "outputId": "08ba4b13-c95f-4545-aab0-de420ecde8cf"
      },
      "source": [
        "lr.score(xtest,ytest)"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7759336099585062"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Js3TpVps_eHv"
      },
      "source": [
        "params = {\r\n",
        "    'penalty':['l1', 'l2', 'elasticnet', 'none'],\r\n",
        "    'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],\r\n",
        "    'solver':['newton-cg', 'liblinear', 'sag', 'saga']\r\n",
        "\r\n",
        "\r\n",
        "}"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4rnHvWXhAwVo"
      },
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV"
      ],
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9clG3BhZA_TO"
      },
      "source": [
        "random_search = RandomizedSearchCV(lr,param_distributions=params,n_iter=5,scoring='roc_auc',n_jobs=-1,verbose=3)"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5qhWE45_BzRp",
        "outputId": "914277a7-8949-4fbc-a8ac-074f9b6f3154"
      },
      "source": [
        "random_search.fit(xtrain,ytrain)"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  22 out of  25 | elapsed:    2.7s remaining:    0.4s\n",
            "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:    3.3s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=None, error_score=nan,\n",
              "                   estimator=LogisticRegression(C=1.0, class_weight=None,\n",
              "                                                dual=False, fit_intercept=True,\n",
              "                                                intercept_scaling=1,\n",
              "                                                l1_ratio=None, max_iter=100,\n",
              "                                                multi_class='auto', n_jobs=None,\n",
              "                                                penalty='l2', random_state=None,\n",
              "                                                solver='lbfgs', tol=0.0001,\n",
              "                                                verbose=0, warm_start=False),\n",
              "                   iid='deprecated', n_iter=5, n_jobs=-1,\n",
              "                   param_distributions={'C': [0.001, 0.01, 0.1, 1, 10, 100,\n",
              "                                              1000],\n",
              "                                        'penalty': ['l1', 'l2', 'elasticnet',\n",
              "                                                    'none'],\n",
              "                                        'solver': ['newton-cg', 'liblinear',\n",
              "                                                   'sag', 'saga']},\n",
              "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
              "                   return_train_score=False, scoring='roc_auc', verbose=3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RG_4FyzDCfid",
        "outputId": "a807b3d4-2f48-4941-8c0f-7d212e3ab630"
      },
      "source": [
        "random_search.best_estimator_"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1, 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='none',\n",
              "                   random_state=None, solver='newton-cg', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYbdXs32DD_j"
      },
      "source": [
        "lr2 = LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\r\n",
        "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\r\n",
        "                   multi_class='auto', n_jobs=None, penalty='none',\r\n",
        "                   random_state=None, solver='newton-cg', tol=0.0001, verbose=0,\r\n",
        "                   warm_start=False)"
      ],
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BwkC1-wCDT-6",
        "outputId": "00754890-a18e-4db9-9bbf-38890b2ff308"
      },
      "source": [
        "lr2.fit(xtrain,ytrain)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1, 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='none',\n",
              "                   random_state=None, solver='newton-cg', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "et2JNEABDq-9",
        "outputId": "2998a250-f97d-4f0a-f8ff-390e079c2e44"
      },
      "source": [
        "lr2.score(xtrain,ytrain)"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7841203943954334"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KISfvU0mDyfk",
        "outputId": "2847ebb4-4226-4f62-b6d5-dc10177ed7b6"
      },
      "source": [
        "lr2.score(xtest,ytest)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7728215767634855"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hkp3oqTZD3Gj"
      },
      "source": [
        "yp_lr = lr2.predict(xtest)"
      ],
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xjqjCedKKeFz"
      },
      "source": [
        "KNN\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJHs214eKefR"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZiTAqR4_KiVG"
      },
      "source": [
        "kn = KNeighborsClassifier()"
      ],
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D_yyodcWKmbU",
        "outputId": "05c75316-a96c-4b28-8f88-b5feb7d80554"
      },
      "source": [
        "kn.fit(xtrain,ytrain)"
      ],
      "execution_count": 115,
      "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=5, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IlFZIsdiKrmX",
        "outputId": "d5a0a099-4829-4092-ae21-18804d920f6d"
      },
      "source": [
        "kn.score(xtrain,ytrain)"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9187856772184743"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3DUfyVGNKxaw",
        "outputId": "9f82fcff-ee79-4d08-e249-190fb91d6ace"
      },
      "source": [
        "kn.score(xtest,ytest)"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8983402489626556"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LNXkaEKSKzr9"
      },
      "source": [
        "params ={\r\n",
        "    'n_neighbors':[x for x in range(1,100,5)],\r\n",
        "    'leaf_size':[x for x in range(1,100,5)],\r\n",
        "    'algorithm':['auto', 'ball_tree', 'kd_tree', 'rute']\r\n",
        "}"
      ],
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eHs_6jpQLWuv"
      },
      "source": [
        "random_search = RandomizedSearchCV(kn,param_distributions=params,n_iter=10,scoring='roc_auc',n_jobs=-1,verbose=3)"
      ],
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aNIKw1G2LboQ",
        "outputId": "a6c9f303-5236-4c70-d4a1-5c4310b6dca3"
      },
      "source": [
        "random_search.fit(xtrain,ytrain)"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  47 out of  50 | elapsed:    3.1s remaining:    0.2s\n",
            "[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:    3.1s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=None, error_score=nan,\n",
              "                   estimator=KNeighborsClassifier(algorithm='auto',\n",
              "                                                  leaf_size=30,\n",
              "                                                  metric='minkowski',\n",
              "                                                  metric_params=None,\n",
              "                                                  n_jobs=None, n_neighbors=5,\n",
              "                                                  p=2, weights='uniform'),\n",
              "                   iid='deprecated', n_iter=10, n_jobs=-1,\n",
              "                   param_distributions={'algorithm': ['auto', 'ball_tree',\n",
              "                                                      'kd_tree', 'rute'],\n",
              "                                        'leaf_size': [1, 6, 11, 16, 21, 26, 31,\n",
              "                                                      36, 41, 46, 51, 56, 61,\n",
              "                                                      66, 71, 76, 81, 86, 91,\n",
              "                                                      96],\n",
              "                                        'n_neighbors': [1, 6, 11, 16, 21, 26,\n",
              "                                                        31, 36, 41, 46, 51, 56,\n",
              "                                                        61, 66, 71, 76, 81, 86,\n",
              "                                                        91, 96]},\n",
              "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
              "                   return_train_score=False, scoring='roc_auc', verbose=3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FVdLywyOLcI5",
        "outputId": "974c1364-6bfa-4eb9-c5f9-22e23880cac5"
      },
      "source": [
        "random_search.best_estimator_"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='kd_tree', leaf_size=16, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KK87dq2SMR7J"
      },
      "source": [
        "kn2 = KNeighborsClassifier(algorithm='kd_tree', leaf_size=16, metric='minkowski',\r\n",
        "                     metric_params=None, n_jobs=None, n_neighbors=6, p=2,\r\n",
        "                     weights='uniform')"
      ],
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aEv9SNYlMVnk",
        "outputId": "02679ae8-4dbc-49ec-bf3b-e8b783596077"
      },
      "source": [
        "kn2.fit(xtrain,ytrain)"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='kd_tree', leaf_size=16, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i76ne7b1MY8-",
        "outputId": "3ed167fc-3989-40d3-9db7-dbe971a8baee"
      },
      "source": [
        "kn2.score(xtrain,ytrain)"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.922677737415672"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YL5rEf3PMfTA",
        "outputId": "308b2cd2-1da7-436f-a704-8e23a3f5cd65"
      },
      "source": [
        "kn2.score(xtest,ytest)"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9045643153526971"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uNKF0b0TZ2_U"
      },
      "source": [
        "yp_kn = kn2.predict(xtest)"
      ],
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rc5spYZcSOzP"
      },
      "source": [
        "Decision Tree\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "--PGFwc2MioT"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7SCzwTLSZpT"
      },
      "source": [
        "dt = DecisionTreeClassifier()"
      ],
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NMxBBL66Scpj",
        "outputId": "c1ec5c07-6216-4b33-c4ef-fe8c0d2c5906"
      },
      "source": [
        "dt.fit(xtrain,ytrain)"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O97CTU2LSsNr",
        "outputId": "d3923589-139e-485a-eb15-3229433f3be7"
      },
      "source": [
        "dt.score(xtrain,ytrain)"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dF230vcsSu67",
        "outputId": "b5190855-d542-4f2f-a997-e073cc0281e7"
      },
      "source": [
        "dt.score(xtest,ytest)"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9434647302904564"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cciD1TxsTo9K"
      },
      "source": [
        ""
      ],
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kf17iQlmSyrx"
      },
      "source": [
        "params = {\r\n",
        "    'criterion':['gini', 'entropy'],\r\n",
        "    'max_depth':[x for x in range(1,10)],\r\n",
        "    'min_samples_split':[x for x in range(1,10)],\r\n",
        "    'min_samples_leaf': [x for x in range(1,10)]\r\n",
        "\r\n",
        "}"
      ],
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eC_LJOtXWKYA"
      },
      "source": [
        "random_search = RandomizedSearchCV(dt,param_distributions=params,n_iter=10,scoring='roc_auc',n_jobs=-1,verbose=3)"
      ],
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1AwNRhBWVOG",
        "outputId": "a8e73c71-8d58-49bb-8743-6318cd3ce963"
      },
      "source": [
        "random_search.fit(xtrain,ytrain)"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:    1.9s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=None, error_score=nan,\n",
              "                   estimator=DecisionTreeClassifier(ccp_alpha=0.0,\n",
              "                                                    class_weight=None,\n",
              "                                                    criterion='gini',\n",
              "                                                    max_depth=None,\n",
              "                                                    max_features=None,\n",
              "                                                    max_leaf_nodes=None,\n",
              "                                                    min_impurity_decrease=0.0,\n",
              "                                                    min_impurity_split=None,\n",
              "                                                    min_samples_leaf=1,\n",
              "                                                    min_samples_split=2,\n",
              "                                                    min_weight_fraction_leaf=0.0,\n",
              "                                                    presort='deprecated',\n",
              "                                                    random_state=None,\n",
              "                                                    splitter='best'),\n",
              "                   iid='deprecated', n_iter=10, n_jobs=-1,\n",
              "                   param_distributions={'criterion': ['gini', 'entropy'],\n",
              "                                        'max_depth': [1, 2, 3, 4, 5, 6, 7, 8,\n",
              "                                                      9],\n",
              "                                        'min_samples_leaf': [1, 2, 3, 4, 5, 6,\n",
              "                                                             7, 8, 9],\n",
              "                                        'min_samples_split': [1, 2, 3, 4, 5, 6,\n",
              "                                                              7, 8, 9]},\n",
              "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
              "                   return_train_score=False, scoring='roc_auc', verbose=3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "egp0wNoOWb4l",
        "outputId": "547a746e-9721-400c-d276-a7b3e4698ce5"
      },
      "source": [
        "random_search.best_estimator_"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=9, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=9, min_samples_split=6,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hA9Ej4rQWlrG"
      },
      "source": [
        "dt2 = DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\r\n",
        "                       max_depth=9, max_features=None, max_leaf_nodes=None,\r\n",
        "                       min_impurity_decrease=0.0, min_impurity_split=None,\r\n",
        "                       min_samples_leaf=9, min_samples_split=6,\r\n",
        "                       min_weight_fraction_leaf=0.0, presort='deprecated',\r\n",
        "                       random_state=None, splitter='best')"
      ],
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aSohhZtwWqMy",
        "outputId": "1f88da9b-5b74-46da-e8d9-92804600e373"
      },
      "source": [
        "dt2.fit(xtrain,ytrain)"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=9, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=9, min_samples_split=6,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M2C6c4g_WssE",
        "outputId": "2454884c-aec4-4622-d24b-b9e1e6c90faf"
      },
      "source": [
        "dt2.score(xtrain,ytrain)"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9368188894654904"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S357V4DcWu5g",
        "outputId": "c446f520-a6dc-4686-af4e-329ccaabef50"
      },
      "source": [
        "dt2.score(xtest,ytest)"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9268672199170125"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hD6N7oeUaLyN"
      },
      "source": [
        "yp_dt = dt2.predict(xtest)"
      ],
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jmGgJh8qWy0O"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wtF6EJIjY3-M"
      },
      "source": [
        "cm_lr = confusion_matrix(ytest,yp_lr)\r\n",
        "\r\n",
        "cm_kn = confusion_matrix(ytest,yp_kn)\r\n",
        "\r\n",
        "cm_dt = confusion_matrix(ytest,yp_dt)"
      ],
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "5sZgQUepakx-",
        "outputId": "683307cd-cf4c-4b15-95c8-7a442c4a97b5"
      },
      "source": [
        "plt.title(\"Logistic Regression Confusion Matrix\")\r\n",
        "sns.heatmap(cm_lr,annot=True,cmap='cool')"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9f819db668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8tLZB1z_a8ee",
        "outputId": "1e524e8d-59f6-4674-f8af-dc02f4ca77a4"
      },
      "source": [
        "xtest.shape"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1928, 16)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_bFq714_bF1h",
        "outputId": "9d4b520b-3d6b-491a-ac56-a658e2a39e52"
      },
      "source": [
        "cm_lr"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[732, 253],\n",
              "       [185, 758]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "EgwvocBDbJOx",
        "outputId": "2cb5df76-087a-410d-8bce-9511fcb18470"
      },
      "source": [
        "plt.figure(figsize=(24,12))\r\n",
        "\r\n",
        "plt.suptitle(\"Confusion Matrixes \",fontsize=24)\r\n",
        "plt.subplots_adjust(wspace = 0.4, hspace= 0.4)\r\n",
        "\r\n",
        "plt.subplot(2,3,1)\r\n",
        "plt.title(\"Logistic Regression Confusion Matrix\")\r\n",
        "sns.heatmap(cm_lr,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 24})\r\n",
        "\r\n",
        "plt.subplot(2,3,2)\r\n",
        "plt.title(\"Decision Tree Confusion Matrix\")\r\n",
        "sns.heatmap(cm_dt,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 24})\r\n",
        "\r\n",
        "plt.subplot(2,3,3)\r\n",
        "plt.title(\"KNN Confusion Matrix\")\r\n",
        "sns.heatmap(cm_kn,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 24})\r\n",
        "\r\n",
        "plt.show()"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1728x864 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NI5jUORYcjQP"
      },
      "source": [
        "from sklearn.metrics import classification_report"
      ],
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O8mCHomoeFPJ",
        "outputId": "61b5ecd2-4683-4673-9a0e-5dc534e1b6de"
      },
      "source": [
        "print(classification_report(ytest,yp_dt))"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.95      0.93       985\n",
            "           1       0.94      0.90      0.92       943\n",
            "\n",
            "    accuracy                           0.93      1928\n",
            "   macro avg       0.93      0.93      0.93      1928\n",
            "weighted avg       0.93      0.93      0.93      1928\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3iW8WfC1eKzR",
        "outputId": "e945be1c-8c67-4d8d-b4ff-848c0f4df7b2"
      },
      "source": [
        "print(classification_report(ytest,yp_kn))"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.84      0.90       985\n",
            "           1       0.85      0.97      0.91       943\n",
            "\n",
            "    accuracy                           0.90      1928\n",
            "   macro avg       0.91      0.91      0.90      1928\n",
            "weighted avg       0.91      0.90      0.90      1928\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7r80icseXfK"
      },
      "source": [
        "model = dt2"
      ],
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ed4v0fVzegCH"
      },
      "source": [
        "import pickle"
      ],
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "di7s5aQuekSN"
      },
      "source": [
        "pickle_out = open(\"classifier.pkl\",\"wb\")\r\n",
        "pickle.dump(model,pickle_out)\r\n",
        "pickle_out.close()"
      ],
      "execution_count": 171,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RARL49GbexL4"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}