Classification_Model_!.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart=pd.read_csv('/content/heart.csv')"
      ],
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        "id": "nmhTkOercaqc"
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      "outputs": []
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        "heart.head()"
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      "execution_count": 82,
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              "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
              "0   40   M           ATA        140          289          0     Normal    172   \n",
              "1   49   F           NAP        160          180          0     Normal    156   \n",
              "2   37   M           ATA        130          283          0         ST     98   \n",
              "3   48   F           ASY        138          214          0     Normal    108   \n",
              "4   54   M           NAP        150          195          0     Normal    122   \n",
              "\n",
              "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
              "0              N      0.0       Up             0  \n",
              "1              N      1.0     Flat             1  \n",
              "2              N      0.0       Up             0  \n",
              "3              Y      1.5     Flat             1  \n",
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            "text/plain": [
              "              Age   RestingBP  Cholesterol   FastingBS       MaxHR  \\\n",
              "count  918.000000  918.000000   918.000000  918.000000  918.000000   \n",
              "mean    53.510893  132.396514   198.799564    0.233115  136.809368   \n",
              "std      9.432617   18.514154   109.384145    0.423046   25.460334   \n",
              "min     28.000000    0.000000     0.000000    0.000000   60.000000   \n",
              "25%     47.000000  120.000000   173.250000    0.000000  120.000000   \n",
              "50%     54.000000  130.000000   223.000000    0.000000  138.000000   \n",
              "75%     60.000000  140.000000   267.000000    0.000000  156.000000   \n",
              "max     77.000000  200.000000   603.000000    1.000000  202.000000   \n",
              "\n",
              "          Oldpeak  HeartDisease  \n",
              "count  918.000000    918.000000  \n",
              "mean     0.887364      0.553377  \n",
              "std      1.066570      0.497414  \n",
              "min     -2.600000      0.000000  \n",
              "25%      0.000000      0.000000  \n",
              "50%      0.600000      1.000000  \n",
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              "      <th>mean</th>\n",
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              "      <th>25%</th>\n",
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              "      <th>max</th>\n",
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              "      <td>202.000000</td>\n",
              "      <td>6.200000</td>\n",
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        "heart[['Age']].value_counts"
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        },
        "id": "vJLlTC9roZ43",
        "outputId": "e0b2d561-ef08-44b4-aee8-04a31f3491d9"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.value_counts of      Age\n",
              "0     40\n",
              "1     49\n",
              "2     37\n",
              "3     48\n",
              "4     54\n",
              "..   ...\n",
              "913   45\n",
              "914   68\n",
              "915   57\n",
              "916   57\n",
              "917   38\n",
              "\n",
              "[918 rows x 1 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart[['Sex']].value_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HPoRXEbYyJ0P",
        "outputId": "812b5e0d-0089-4d8c-ab1f-22be0698caee"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.value_counts of     Sex\n",
              "0     M\n",
              "1     F\n",
              "2     M\n",
              "3     F\n",
              "4     M\n",
              "..   ..\n",
              "913   M\n",
              "914   M\n",
              "915   M\n",
              "916   F\n",
              "917   M\n",
              "\n",
              "[918 rows x 1 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart[['ChestPainType']].value_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G3kxIBzEyJ3G",
        "outputId": "64572bda-48b7-4fe3-a38f-8bf7d3f023b2"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.value_counts of     ChestPainType\n",
              "0             ATA\n",
              "1             NAP\n",
              "2             ATA\n",
              "3             ASY\n",
              "4             NAP\n",
              "..            ...\n",
              "913            TA\n",
              "914           ASY\n",
              "915           ASY\n",
              "916           ATA\n",
              "917           NAP\n",
              "\n",
              "[918 rows x 1 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart[['RestingBP']].value_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Iho4PrQyJ53",
        "outputId": "d1b165cc-0741-4852-a3c1-195d2e9e2d9a"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.value_counts of      RestingBP\n",
              "0          140\n",
              "1          160\n",
              "2          130\n",
              "3          138\n",
              "4          150\n",
              "..         ...\n",
              "913        110\n",
              "914        144\n",
              "915        130\n",
              "916        130\n",
              "917        138\n",
              "\n",
              "[918 rows x 1 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart[['Cholesterol']].value_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZshKDAt3yJ8b",
        "outputId": "31d69315-a34f-47cc-bceb-9fa9db361550"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.value_counts of      Cholesterol\n",
              "0            289\n",
              "1            180\n",
              "2            283\n",
              "3            214\n",
              "4            195\n",
              "..           ...\n",
              "913          264\n",
              "914          193\n",
              "915          131\n",
              "916          236\n",
              "917          175\n",
              "\n",
              "[918 rows x 1 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart[['RestingECG']].value_counts"
      ],
      "metadata": {
        "id": "6uEsdb9NyJ_G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "heart[['MaxHR']].value_counts"
      ],
      "metadata": {
        "id": "z2Zf88SJyKCd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "heart[['ExerciseAngina']].value_counts"
      ],
      "metadata": {
        "id": "N_wac8kTyKFC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "heart[['Oldpeak']].value_counts"
      ],
      "metadata": {
        "id": "0alvgGZUyKI5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "heart[['ST_Slope']].value_counts"
      ],
      "metadata": {
        "id": "1PSTlICJzQ2r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "heart[['HeartDisease']].value_counts"
      ],
      "metadata": {
        "id": "jSf3jB6azQ6Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "heart = pd.read_csv('heart.csv')\n",
        "heart['HeartDisease'].astype(float)\n",
        "\n",
        "     "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t3IADrAb0qPc",
        "outputId": "4d3f684f-3c35-4298-8724-c48f799a3469"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0.0\n",
              "1      1.0\n",
              "2      0.0\n",
              "3      1.0\n",
              "4      0.0\n",
              "      ... \n",
              "913    1.0\n",
              "914    1.0\n",
              "915    1.0\n",
              "916    1.0\n",
              "917    0.0\n",
              "Name: HeartDisease, Length: 918, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corrmat = heart.corr()\n",
        "top_corr_features = corrmat.index\n",
        "plt.figure(figsize=(20,20))\n",
        "g=sns.heatmap(heart[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OOSn7poM0qaj",
        "outputId": "6d0956ff-5d22-489f-8c14-29c73a0e849e"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "h8rJeeel0qiw",
        "outputId": "219cd3fc-6d81-414b-919a-676024aba230"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f2a1e783070>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2a1e6f5f10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2a1e6ad370>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2a1e65a760>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2a1e688b50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2a1e63ae80>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2a1e63af70>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2a1e5a23d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2a1e5bebb0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "sns.countplot(x='HeartDisease',data=heart,palette='BuGn_r')\n",
        "     "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "6d4C4h8587w5",
        "outputId": "bd4935c0-17c8-48df-844e-71b342b88627"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2a1ccda1f0>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3sAAAE9CAYAAACyU3u7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAUmklEQVR4nO3df7BndX3f8dcbFmLiL0S2lLCQZSLRMlMFZ4ehsaZU1IIxLk2Uapq4GGa2TlGT2rTSpp3atGlNpw3FaExJIC5pIpJYYZthaihgTVp/sPyQ8CNMtowObBE2gMQfwQzk3T/u2Xqli7nL7tnv3s99PGbu3HM+53y/9w3/7DznnO/5VncHAACAsRy26AEAAAA48MQeAADAgMQeAADAgMQeAADAgMQeAADAgMQeAADAgNYteoD9ccwxx/TGjRsXPQYAAMBC3HzzzX/c3ev3dmxVx97GjRuzY8eORY8BAACwEFX1xac75jZOAACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAa2b882r6gtJvpLkySRPdPemqjo6yUeTbEzyhSTndfejVVVJLknyuiRfT3J+d98y53wAsBZsv+e2RY8AsOq84cWnLnqE/XYwruz9ze4+tbs3TfsXJbm+u09Ocv20nyTnJDl5+tma5EMHYTYAAIAhLeI2zs1Jtk3b25Kcu2z9il7ymSRHVdVxC5gPAABg1Zs79jrJ71bVzVW1dVo7trsfmLa/lOTYafv4JPcte+390xoAAAD7aNbP7CX56929q6r+UpLrquoPlx/s7q6q3pc3nKJxa5KceOKJB25SAACAgcx6Za+7d02/H0ry8SSnJ3lwz+2Z0++HptN3JTlh2cs3TGtPfc9Lu3tTd29av379nOMDAACsWrPFXlU9u6qeu2c7yWuT3JFke5It02lbklwzbW9P8tZackaSx5bd7gkAAMA+mPM2zmOTfHzpGxWyLslvdvd/q6qbklxVVRck+WKS86bzr83S1y7szNJXL7xtxtkAAACGNlvsdfe9SV62l/WHk5y1l/VOcuFc8wAAAKwli/jqBQAAAGYm9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAYk9gAAAAa0btEDrAWv+aW3L3oEgFXnur//y4seAQBWtdmv7FXV4VV1a1X9zrR/UlV9tqp2VtVHq+rIaf07pv2d0/GNc88GAAAwqoNxG+dPJrl72f7PJ7m4u1+U5NEkF0zrFyR5dFq/eDoPAACAZ2DW2KuqDUl+MMmvTvuV5FVJfns6ZVuSc6ftzdN+puNnTecDAACwj+a+svcfk/zjJH8+7b8wyZe7+4lp//4kx0/bxye5L0mm449N5wMAALCPZou9qnp9koe6++YD/L5bq2pHVe3YvXv3gXxrAACAYcx5Ze8VSd5QVV9IcmWWbt+8JMlRVbXnKaAbkuyatnclOSFJpuPPT/LwU9+0uy/t7k3dvWn9+vUzjg8AALB6zRZ73f1PuntDd29M8uYkN3T3301yY5I3TqdtSXLNtL192s90/Ibu7rnmAwAAGNkivlT9PUneXVU7s/SZvMum9cuSvHBaf3eSixYwGwAAwBAOypeqd/cnk3xy2r43yel7OefxJG86GPMAAACMbhFX9gAAAJiZ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABiQ2AMAABjQbLFXVc+qqs9V1eer6s6q+pfT+klV9dmq2llVH62qI6f175j2d07HN841GwAAwOjmvLL3jSSv6u6XJTk1ydlVdUaSn09ycXe/KMmjSS6Yzr8gyaPT+sXTeQAAADwDs8VeL/nqtHvE9NNJXpXkt6f1bUnOnbY3T/uZjp9VVTXXfAAAACOb9TN7VXV4Vd2W5KEk1yX530m+3N1PTKfcn+T4afv4JPclyXT8sSQvnHM+AACAUc0ae939ZHefmmRDktOTvGR/37OqtlbVjqrasXv37v2eEQAAYEQH5Wmc3f3lJDcm+WtJjqqqddOhDUl2Tdu7kpyQJNPx5yd5eC/vdWl3b+ruTevXr599dgAAgNVozqdxrq+qo6bt70zymiR3Zyn63jidtiXJNdP29mk/0/Eburvnmg8AAGBk6/7iU56x45Jsq6rDsxSVV3X371TVXUmurKp/neTWJJdN51+W5NerameSR5K8ecbZAAAAhjZb7HX37UlO28v6vVn6/N5T1x9P8qa55gEAAFhLVnQbZ1Vdv5I1AAAADg3f9speVT0ryXclOaaqXpBkz/fePS/f/MoEAAAADjF/0W2cfy/JTyX57iQ355ux9ydJPjDjXAAAAOyHbxt73X1Jkkuq6p3d/YsHaSYAAAD204oe0NLdv1hV359k4/LXdPcVM80FAADAflhR7FXVryf53iS3JXlyWu4kYg8AAOAQtNKvXtiU5BRfcg4AALA6rOirF5LckeQvzzkIAAAAB85Kr+wdk+Suqvpckm/sWezuN8wyFQAAAPtlpbH33jmHAAAA4MBa6dM4/8fcgwAAAHDgrPRpnF/J0tM3k+TIJEck+Vp3P2+uwQAAAHjmVnpl77l7tquqkmxOcsZcQwEAALB/Vvo0zv+nl1yd5G/NMA8AAAAHwEpv4/zhZbuHZel79x6fZSIAAAD220qfxvlDy7afSPKFLN3KCQAAwCFopZ/Ze9vcgwAAAHDgrOgze1W1oao+XlUPTT8fq6oNcw8HAADAM7PSB7T8WpLtSb57+vmv0xoAAACHoJXG3vru/rXufmL6+XCS9TPOBQAAwH5Yaew9XFU/VlWHTz8/luThOQcDAADgmVtp7P1EkvOSfCnJA0nemOT8mWYCAABgP630qxd+NsmW7n40Sarq6CT/PksRCAAAwCFmpVf2Xron9JKkux9Jcto8IwEAALC/Vhp7h1XVC/bsTFf2VnpVEAAAgINspcH2H5J8uqp+a9p/U5Kfm2ckAAAA9teKYq+7r6iqHUleNS39cHffNd9YAAAA7I8V34o5xZ3AAwAAWAVW+pk9AAAAVhGxBwAAMCCxBwAAMCCxBwAAMCCxBwAAMCCxBwAAMCCxBwAAMCCxBwAAMCCxBwAAMCCxBwAAMCCxBwAAMCCxBwAAMCCxBwAAMCCxBwAAMKDZYq+qTqiqG6vqrqq6s6p+clo/uqquq6o/mn6/YFqvqnp/Ve2sqtur6uVzzQYAADC6Oa/sPZHkH3b3KUnOSHJhVZ2S5KIk13f3yUmun/aT5JwkJ08/W5N8aMbZAAAAhjZb7HX3A919y7T9lSR3Jzk+yeYk26bTtiU5d9renOSKXvKZJEdV1XFzzQcAADCyg/KZvaramOS0JJ9Ncmx3PzAd+lKSY6ft45Pct+xl909rAAAA7KPZY6+qnpPkY0l+qrv/ZPmx7u4kvY/vt7WqdlTVjt27dx/ASQEAAMYxa+xV1RFZCr3f6O7/Mi0/uOf2zOn3Q9P6riQnLHv5hmntW3T3pd29qbs3rV+/fr7hAQAAVrE5n8ZZSS5Lcnd3/8KyQ9uTbJm2tyS5Ztn6W6encp6R5LFlt3sCAACwD9bN+N6vSPLjSf6gqm6b1v5pkvcluaqqLkjyxSTnTceuTfK6JDuTfD3J22acDQAAYGizxV53/36SeprDZ+3l/E5y4VzzAAAArCUH5WmcAAAAHFxiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYECzxV5VXV5VD1XVHcvWjq6q66rqj6bfL5jWq6reX1U7q+r2qnr5XHMBAACsBXNe2ftwkrOfsnZRkuu7++Qk10/7SXJOkpOnn61JPjTjXAAAAMObLfa6+1NJHnnK8uYk26btbUnOXbZ+RS/5TJKjquq4uWYDAAAY3cH+zN6x3f3AtP2lJMdO28cnuW/ZefdPawAAADwDC3tAS3d3kt7X11XV1qraUVU7du/ePcNkAAAAq9/Bjr0H99yeOf1+aFrfleSEZedtmNb+P919aXdv6u5N69evn3VYAACA1epgx972JFum7S1Jrlm2/tbpqZxnJHls2e2eAAAA7KN1c71xVX0kyZlJjqmq+5P8iyTvS3JVVV2Q5ItJzptOvzbJ65LsTPL1JG+bay4AAIC1YLbY6+63PM2hs/Zybie5cK5ZAAAA1pqFPaAFAACA+Yg9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAYk9AACAAR1SsVdVZ1fVPVW1s6ouWvQ8AAAAq9UhE3tVdXiSDyY5J8kpSd5SVacsdioAAIDV6ZCJvSSnJ9nZ3fd2958luTLJ5gXPBAAAsCodSrF3fJL7lu3fP60BAACwj9YteoB9VVVbk2yddr9aVfcsch5Y5Y5J8seLHgL2pi78T4seATg4/FsE++d7nu7AoRR7u5KcsGx/w7T2Lbr70iSXHqyhYGRVtaO7Ny16DgDWLv8WwXwOpds4b0pyclWdVFVHJnlzku0LngkAAGBVOmSu7HX3E1X1jiSfSHJ4ksu7+84FjwUAALAqHTKxlyTdfW2Saxc9B6whbokGYNH8WwQzqe5e9AwAAAAcYIfSZ/YAAAA4QMQerFFVdXZV3VNVO6vqokXPA8DaUlWXV9VDVXXHomeBUYk9WIOq6vAkH0xyTpJTkrylqk5Z7FQArDEfTnL2ooeAkYk9WJtOT7Kzu+/t7j9LcmWSzQueCYA1pLs/leSRRc8BIxN7sDYdn+S+Zfv3T2sAAAxC7AEAAAxI7MHatCvJCcv2N0xrAAAMQuzB2nRTkpOr6qSqOjLJm5NsX/BMAAAcQGIP1qDufiLJO5J8IsndSa7q7jsXOxUAa0lVfSTJp5O8uKrur6oLFj0TjKa6e9EzAAAAcIC5sgcAADAgsQcAADAgsQcAADAgsQcAADAgsQcAADAgsQfAqlVVX33K/vlV9YED9N4bq+pHl+2fWVWPVdWtVXVPVX2qql6/7Pjbq+qtB+JvA8CBsG7RAwDAoaaq1iXZmORHk/zmskO/192vn845NcnVVfWn3X19d//ywZ8UAJ6eK3sADKmq1lfVx6rqpunnFdP66VX16ekK3f+qqhdP6+dX1faquiHJ9Unel+SVVXVbVf2Dp75/d9+W5GeTvGN6/Xur6qen7XdV1V1VdXtVXTmtPbuqLq+qz01/e/O0vrGqfq+qbpl+vn9aP266enhbVd1RVa+c1l87zX9LVf1WVT1n5v+VAKxSruwBsJp9Z1Xdtmz/6CTbp+1Lklzc3b9fVScm+USSv5LkD5O8srufqKpXJ/k3SX5kes3Lk7y0ux+pqjOT/PSyK3ln7uXv35LkH+1l/aIkJ3X3N6rqqGntZ5Lc0N0/Ma19rqr+e5KHkrymux+vqpOTfCTJpixdVfxEd/9cVR2e5Luq6pgk/yzJq7v7a1X1niTvzlJ0AsC3EHsArGZ/2t2n7tmpqvOzFEpJ8uokp1TVnsPPm66CPT/JtimsOskRy97vuu5+ZB/+fj3N+u1JfqOqrk5y9bT22iRv2HP1L8mzkpyY5P8k+cB0W+iTSb5vOn5Tksur6ogkV3f3bVX1N5KckuR/Tv9dRyb59D7MC8AaIvYAGNVhSc7o7seXL04PcLmxu/92VW1M8sllh7+2j3/jtCR372X9B5P8QJIfSvIzVfVXsxSGP9Ld9zxlnvcmeTDJy6aZH0+S7v5UVf3A9F4frqpfSPJoloL0Lfs4JwBrkM/sATCq303yzj0705WzZOnK3q5p+/xv8/qvJHnu0x2sqpcm+edJPviU9cOSnNDdNyZ5z/T3npOl20jfWdMluao6bdk8D3T3nyf58SSHT8e/J8mD3f0rSX41S7eYfibJK6rqRdM5z66q7wsA7IXYA2BU70qyaXpIyl1J3j6t/7sk/7aqbs23v8Pl9iRPVtXnlz2g5ZV7vnohS5H3ru6+/imvOzzJf66qP0hya5L3d/eXk/yrLN0yentV3TntJ8kvJdlSVZ9P8pJ88+rimUk+P835d5Jc0t27sxSoH6mq27N0C+dL9u1/CwBrRXX3omcAAADgAHNlDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYEBiDwAAYED/F0v99sNkvJqCAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BB_v9-64-IOO",
        "outputId": "1c95cf54-9703-42d5-ece7-f49766590cd7"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 918 entries, 0 to 917\n",
            "Data columns (total 12 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Age             918 non-null    int64  \n",
            " 1   Sex             918 non-null    object \n",
            " 2   ChestPainType   918 non-null    object \n",
            " 3   RestingBP       918 non-null    int64  \n",
            " 4   Cholesterol     918 non-null    int64  \n",
            " 5   FastingBS       918 non-null    int64  \n",
            " 6   RestingECG      918 non-null    object \n",
            " 7   MaxHR           918 non-null    int64  \n",
            " 8   ExerciseAngina  918 non-null    object \n",
            " 9   Oldpeak         918 non-null    float64\n",
            " 10  ST_Slope        918 non-null    object \n",
            " 11  HeartDisease    918 non-null    int64  \n",
            "dtypes: float64(1), int64(6), object(5)\n",
            "memory usage: 86.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart = pd.get_dummies(heart, columns = ['Sex','ExerciseAngina','RestingECG','ST_Slope','ChestPainType'])"
      ],
      "metadata": {
        "id": "8g3OYwS0-AEP"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "     "
      ],
      "metadata": {
        "id": "TH9b96IS-1Zk"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Label_Encoder= LabelEncoder()\n",
        "heart['HeartDisease'] = Label_Encoder.fit_transform(heart['HeartDisease'])\n",
        "     "
      ],
      "metadata": {
        "id": "daDHSWy9-k2O"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X=heart.drop('HeartDisease',axis= 1)\n",
        "Y = heart['HeartDisease']"
      ],
      "metadata": {
        "id": "UvpPETrA-8NQ"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y=heart[['HeartDisease']]"
      ],
      "metadata": {
        "id": "qQhsgFdo_AAH"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=heart[['Oldpeak','RestingBP','Cholesterol','MaxHR','HeartDisease']]\n",
        "     \n"
      ],
      "metadata": {
        "id": "9h2GepWN_EZk"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size = 0.3)"
      ],
      "metadata": {
        "id": "D-Xu5Uu8_Eg2"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "leojL7Da__mb"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "accuracy_rate=[]\n",
        "\n",
        "for i in range(1,40):\n",
        "    classifier= KNeighborsClassifier(n_neighbors =i)\n",
        "    score = cross_val_score(classifier,X,Y,cv=10)\n",
        "    accuracy_rate.append(score.mean())"
      ],
      "metadata": {
        "id": "QDvP7C2JAFny"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(1,40),accuracy_rate, color= 'blue',linestyle = 'dashed',marker='*', markersize=10)\n",
        "plt.title('Accuracy Rate vs K-Value')\n",
        "plt.xlabel('K-Value')\n",
        "plt.ylabel('Accuracy_Rate')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "ZbPK50-uAhnr",
        "outputId": "2e1cf873-8422-4d2d-e832-51333f6f8602"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Accuracy_Rate')"
            ]
          },
          "metadata": {},
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}