Raghav_K_Regression_Linear_Regression_Algorithm.ipynb

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      "name": "Raghav K_Regression_Linear Regression_Algorithm.ipynb",
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        "id": "_HGr0_t9e-HG"
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      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns \n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "from sklearn import datasets\n",
        "from sklearn import tree\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from scipy import stats\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        },
        "id": "FJI9iKs-fOTZ",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset imported is \"heart.csv\" which can be done by .read_csv() function"
      ],
      "metadata": {
        "id": "TrLB742AfXTl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/heart.csv')\n",
        "data"
      ],
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          "height": 423
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            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0     63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1     37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2     41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3     56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4     57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "..   ...  ...  ..       ...   ...  ...  ...    ...      ...    ...  ..   ...     ...\n",
              "298   57    0   0       140   241    0  ...      1      0.2      1   0     3       0\n",
              "299   45    1   3       110   264    0  ...      0      1.2      1   0     3       0\n",
              "300   68    1   0       144   193    1  ...      0      3.4      1   2     3       0\n",
              "301   57    1   0       130   131    0  ...      1      1.2      1   1     3       0\n",
              "302   57    0   1       130   236    0  ...      0      0.0      1   1     2       0\n",
              "\n",
              "[303 rows x 14 columns]"
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    {
      "cell_type": "markdown",
      "source": [
        ".head() function returns the top 5 entries with maximum value which is tresbps column found in the dataset"
      ],
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        "id": "hexHL5eJgGsb"
      }
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    {
      "cell_type": "code",
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        "data.head()"
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a6555231-f516-43c7-a104-cf6fd86e46b8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ".nunique() function returns the unique columns in the dataset\n",
        "\n"
      ],
      "metadata": {
        "id": "-WdE4PpWfk9J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VGJC9ppyflmx",
        "outputId": "90756c9f-8447-4695-dda7-4a5253b66c62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age          41\n",
              "sex           2\n",
              "cp            4\n",
              "trestbps     49\n",
              "chol        152\n",
              "fbs           2\n",
              "restecg       3\n",
              "thalach      91\n",
              "exang         2\n",
              "oldpeak      40\n",
              "slope         3\n",
              "ca            5\n",
              "thal          4\n",
              "target        2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ".describe() function is used to calculate many of the data like mean, count, std"
      ],
      "metadata": {
        "id": "3RhlVwJof7S6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "xc6_Aa8Pfo3R",
        "outputId": "555defcb-6aa3-43aa-e109-188e632e9d32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3dc17108-9858-4487-aa1e-1c2a8647b399\">\n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
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              "      <th>exang</th>\n",
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              "      <th>slope</th>\n",
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              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3dc17108-9858-4487-aa1e-1c2a8647b399');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              age         sex          cp  ...          ca        thal      target\n",
              "count  303.000000  303.000000  303.000000  ...  303.000000  303.000000  303.000000\n",
              "mean    54.366337    0.683168    0.966997  ...    0.729373    2.313531    0.544554\n",
              "std      9.082101    0.466011    1.032052  ...    1.022606    0.612277    0.498835\n",
              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     47.500000    0.000000    0.000000  ...    0.000000    2.000000    0.000000\n",
              "50%     55.000000    1.000000    1.000000  ...    0.000000    2.000000    1.000000\n",
              "75%     61.000000    1.000000    2.000000  ...    1.000000    3.000000    1.000000\n",
              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000    1.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check for null values"
      ],
      "metadata": {
        "id": "NirJk1-PxuLC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZvS1yU9Oxvqr",
        "outputId": "d6959141-5c61-4494-ae0d-121bf19ecc5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "trestbps    0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalach     0\n",
              "exang       0\n",
              "oldpeak     0\n",
              "ca          0\n",
              "target      0\n",
              "cp_0        0\n",
              "cp_1        0\n",
              "cp_2        0\n",
              "cp_3        0\n",
              "thal_0      0\n",
              "thal_1      0\n",
              "thal_2      0\n",
              "thal_3      0\n",
              "slope_0     0\n",
              "slope_1     0\n",
              "slope_2     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "target.value_counts() function returns the count of unique objects in the dataset provided"
      ],
      "metadata": {
        "id": "ly9euNt9gW8z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.target.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fl32ud-_geMK",
        "outputId": "aef5c61a-992c-4168-e0c2-817881c5ed3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    165\n",
              "0    138\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizing data using Seaborn"
      ],
      "metadata": {
        "id": "qnSF62l7yBmT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_style('darkgrid')\n",
        "sns.set_palette('Set2')"
      ],
      "metadata": {
        "id": "N3DIpeCjyECT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prepare Data"
      ],
      "metadata": {
        "id": "d39YScjOyH30"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_rev = data.copy()\n",
        "\n",
        "def chng(sex):\n",
        "    if sex == 0:\n",
        "        return 'female'\n",
        "    else:\n",
        "        return 'male'\n",
        "\n",
        "data_rev['sex'] = data_rev['sex'].apply(chng)\n",
        "\n",
        "def chng2(prob):\n",
        "    if prob == 0:\n",
        "        return \"Heart Disease\"\n",
        "    else:\n",
        "        return \"No Heart Disease\"\n",
        "        \n",
        "data_rev['target'] = data_rev['target'].apply(chng2)"
      ],
      "metadata": {
        "id": "s4TTbwFzyG5z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x = \"target\", data = data, palette = \"bwr\")\n",
        "plt.show() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "fTQRX918gk60",
        "outputId": "77b94526-2dd2-48dc-cbab-9677c66dd050"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remove the entries with duplicates"
      ],
      "metadata": {
        "id": "2XgeY3ZWoylh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.drop_duplicates()\n",
        "duplicate_rows = data[data.duplicated()]\n",
        "print(\"Number of duplicate rows :: \", duplicate_rows.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vfZrh3ewo1DO",
        "outputId": "00697fc9-76e3-40cc-fc9b-addd7ba4052b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicate rows ::  (0, 22)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Print the percentage value of the people found with and without heart disease in the given dataset"
      ],
      "metadata": {
        "id": "gSqpOeTdgwIM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Percentage of people who don't have heart disease: {:.2f}%\".format((len(data[data.target == 0]) / (len(data.target))*100)))\n",
        "print(\"Percentage of people who have heart disease: {:.2f}%\".format((len(data[data.target == 1]) / (len(data.target))*100)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "im3pe6MAg2xm",
        "outputId": "a201f74e-5091-4d61-96c3-2acaa1b9a2e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of people who don't have heart disease: 45.54%\n",
            "Percentage of people who have heart disease: 54.46%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "sns.countplot() defines the number of observations in each of the category using the bar chart"
      ],
      "metadata": {
        "id": "73ZXZ82Hh03g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Print the number of Male and Female patients who have heart disease"
      ],
      "metadata": {
        "id": "GKJ2b9lZjOUD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Percentage of Female Patients: {:.2f}%\".format((len(data[data.sex == 0]) / (len(data.sex))*100)))\n",
        "print(\"Percentage of Male Patients: {:.2f}%\".format((len(data[data.sex == 1]) / (len(data.sex))*100)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yAm7q9HNh1d2",
        "outputId": "27d0ec01-37c8-4186-a7ea-42509c86680a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of Female Patients: 31.68%\n",
            "Percentage of Male Patients: 68.32%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ".groupby() function defines the data into specified group on some criteria"
      ],
      "metadata": {
        "id": "dqKUxk89j2k3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby('target').mean()"
      ],
      "metadata": {
        "id": "NxPaVOyHjp5y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Representation of the above methods (Age, Percentage of people with heart disease and the Frequency)"
      ],
      "metadata": {
        "id": "ByJW7dRRkCEK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(data.age, data.target).plot(kind=\"bar\", figsize=(20,6))\n",
        "plt.title('Heart Disease Frequency for Ages')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "4pgOuSl3kBhz",
        "outputId": "f81be57f-b75e-41b8-9e06-e697865285cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x = data.age[data.target==1], y = data.thalach[(data.target==1)], c=\"red\")\n",
        "plt.scatter(x = data.age[data.target==0], y = data.thalach[(data.target==0)])\n",
        "plt.legend([\"Disease\", \"No Disease\"])\n",
        "plt.xlabel(\"Age\")\n",
        "plt.ylabel(\"Maximum Heart Rate\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "kxkfVTypkmL0",
        "outputId": "ae329e35-b409-43ff-d6c2-e46e9bd91077"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Maximum Heart Rate')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create dummy values instead of \"cp\", \"slope\" and \"thal\" which are categorical values"
      ],
      "metadata": {
        "id": "PvptXpVflddH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = pd.get_dummies(data['cp'], prefix = \"cp\")\n",
        "y = pd.get_dummies(data['thal'], prefix = \"thal\")\n",
        "z = pd.get_dummies(data['slope'], prefix = \"slope\")\n",
        "\n",
        "frames = [data, x, y, z]\n",
        "data = pd.concat(frames, axis = 1)\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WrXcMeU8lTdO",
        "outputId": "91dbddee-a5cb-416a-c4b3-cb8aa60ee374"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1fd995c8-6a5c-44ff-95cd-bc1e6b8eabb3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "      <th>cp_0</th>\n",
              "      <th>cp_1</th>\n",
              "      <th>cp_2</th>\n",
              "      <th>cp_3</th>\n",
              "      <th>thal_0</th>\n",
              "      <th>thal_1</th>\n",
              "      <th>thal_2</th>\n",
              "      <th>thal_3</th>\n",
              "      <th>slope_0</th>\n",
              "      <th>slope_1</th>\n",
              "      <th>slope_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</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",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</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",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1fd995c8-6a5c-44ff-95cd-bc1e6b8eabb3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1fd995c8-6a5c-44ff-95cd-bc1e6b8eabb3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1fd995c8-6a5c-44ff-95cd-bc1e6b8eabb3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   age  sex  cp  trestbps  chol  ...  thal_2  thal_3  slope_0  slope_1  slope_2\n",
              "0   63    1   3       145   233  ...       0       0        1        0        0\n",
              "1   37    1   2       130   250  ...       1       0        1        0        0\n",
              "2   41    0   1       130   204  ...       1       0        0        0        1\n",
              "3   56    1   1       120   236  ...       1       0        0        0        1\n",
              "4   57    0   0       120   354  ...       1       0        0        0        1\n",
              "\n",
              "[5 rows x 25 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Drop the columns that have been made to dummy values"
      ],
      "metadata": {
        "id": "gwUnTLw3lwVR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.drop(columns = ['cp', 'thal', 'slope'])\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "i3kaj4a1ly-y",
        "outputId": "2ef4ef08-1a74-49e6-e410-ff44808313fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>cp_0</th>\n",
              "      <th>cp_1</th>\n",
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              "      <th>thal_1</th>\n",
              "      <th>thal_2</th>\n",
              "      <th>thal_3</th>\n",
              "      <th>slope_0</th>\n",
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              "      <th>slope_2</th>\n",
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              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4aa830e1-0666-42c3-aa56-c0da9cae1d7a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4aa830e1-0666-42c3-aa56-c0da9cae1d7a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4aa830e1-0666-42c3-aa56-c0da9cae1d7a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   age  sex  trestbps  chol  fbs  ...  thal_2  thal_3  slope_0  slope_1  slope_2\n",
              "0   63    1       145   233    1  ...       0       0        1        0        0\n",
              "1   37    1       130   250    0  ...       1       0        1        0        0\n",
              "2   41    0       130   204    0  ...       1       0        0        0        1\n",
              "3   56    1       120   236    0  ...       1       0        0        0        1\n",
              "4   57    0       120   354    0  ...       1       0        0        0        1\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating Logistic Regression Model"
      ],
      "metadata": {
        "id": "dtMuLmcQl9Oj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.target.values\n",
        "x_data = data.drop(['target'], axis = 1)\n",
        "x\n",
        "x_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "eNFPZdIKl_to",
        "outputId": "6a83901c-0afe-4d1b-f8e6-4ebf586915fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>ca</th>\n",
              "      <th>cp_0</th>\n",
              "      <th>cp_1</th>\n",
              "      <th>cp_2</th>\n",
              "      <th>cp_3</th>\n",
              "      <th>thal_0</th>\n",
              "      <th>thal_1</th>\n",
              "      <th>thal_2</th>\n",
              "      <th>thal_3</th>\n",
              "      <th>slope_0</th>\n",
              "      <th>slope_1</th>\n",
              "      <th>slope_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</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",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</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",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>241</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>123</td>\n",
              "      <td>1</td>\n",
              "      <td>0.2</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>110</td>\n",
              "      <td>264</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>132</td>\n",
              "      <td>0</td>\n",
              "      <td>1.2</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>68</td>\n",
              "      <td>1</td>\n",
              "      <td>144</td>\n",
              "      <td>193</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>141</td>\n",
              "      <td>0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>131</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>115</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>174</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</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",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>303 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2cfc81b1-ef5f-428c-a1a2-1799b5367e26')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2cfc81b1-ef5f-428c-a1a2-1799b5367e26 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2cfc81b1-ef5f-428c-a1a2-1799b5367e26');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     age  sex  trestbps  chol  fbs  ...  thal_2  thal_3  slope_0  slope_1  slope_2\n",
              "0     63    1       145   233    1  ...       0       0        1        0        0\n",
              "1     37    1       130   250    0  ...       1       0        1        0        0\n",
              "2     41    0       130   204    0  ...       1       0        0        0        1\n",
              "3     56    1       120   236    0  ...       1       0        0        0        1\n",
              "4     57    0       120   354    0  ...       1       0        0        0        1\n",
              "..   ...  ...       ...   ...  ...  ...     ...     ...      ...      ...      ...\n",
              "298   57    0       140   241    0  ...       0       1        0        1        0\n",
              "299   45    1       110   264    0  ...       0       1        0        1        0\n",
              "300   68    1       144   193    1  ...       0       1        0        1        0\n",
              "301   57    1       130   131    0  ...       0       1        0        1        0\n",
              "302   57    0       130   236    0  ...       1       0        0        1        0\n",
              "\n",
              "[303 rows x 21 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Change the name of the column"
      ],
      "metadata": {
        "id": "bIyWFDDxyiyV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.column = ['age', 'sex', 'chest_pain_type', 'resting_blood_pressure', 'cholesterol', 'fasting_blood_sugar', 'rest_ecg_type', 'max_heart_rate_achieved','exercise_induced_angina', 'st_depression', 'st_slope_type', 'num_major_vessels', 'thalassemia_type', 'target']\n",
        "data.column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hUrgXMPhynJf",
        "outputId": "c0fb909e-ce9f-473b-991a-f3a9284420f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['age',\n",
              " 'sex',\n",
              " 'chest_pain_type',\n",
              " 'resting_blood_pressure',\n",
              " 'cholesterol',\n",
              " 'fasting_blood_sugar',\n",
              " 'rest_ecg_type',\n",
              " 'max_heart_rate_achieved',\n",
              " 'exercise_induced_angina',\n",
              " 'st_depression',\n",
              " 'st_slope_type',\n",
              " 'num_major_vessels',\n",
              " 'thalassemia_type',\n",
              " 'target']"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analysis of the Algorithm"
      ],
      "metadata": {
        "id": "hwazoZgLmWK8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def initialize(dimension):\n",
        "    \n",
        "    weight = np.full((dimension,1),0.01)\n",
        "    bias = 0.0\n",
        "    return weight,bias"
      ],
      "metadata": {
        "id": "shF4JIx7mPZy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defining a sigmoid function "
      ],
      "metadata": {
        "id": "Hug2ERp8mfy6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def sigmoid(z):\n",
        "    \n",
        "    y_head = 1/(1+ np.exp(-z))\n",
        "    return y_head"
      ],
      "metadata": {
        "id": "kLC77vfFmjcr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define a function logistic_regression() to test the accuracy of the model"
      ],
      "metadata": {
        "id": "jYxkWvrynV9y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def logistic_regression(x_train,y_train,x_test,y_test,learningRate,iteration):\n",
        "    dimension = x_train.shape[0]\n",
        "    weight,bias = initialize(dimension)\n",
        "    \n",
        "    parameters, gradients = update(weight,bias,x_train,y_train,learningRate,iteration)\n",
        "\n",
        "    y_prediction = predict(parameters[\"weight\"],parameters[\"bias\"],x_test)\n",
        "    \n",
        "    print(\"Manuel Test Accuracy: {:.2f}%\".format((100 - np.mean(np.abs(y_prediction - y_test))*100)))"
      ],
      "metadata": {
        "id": "16xjYQAynbZS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remove the outliers using IQR"
      ],
      "metadata": {
        "id": "AoIhMySvpDb-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "q1 = data.quantile(0.25)\n",
        "q3 = data.quantile(0.75)\n",
        "print(q3-q1)\n",
        "data_rem = data[~((data<(q1-1.5*(q3-q1))))|(data>(q3+1.5*(q3-q1))).any(axis=1)]\n",
        "data_rem.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3EnvNkjpHx-",
        "outputId": "723e65e4-b52f-49ed-a7e4-ef122fb747b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age         13.00\n",
            "sex          1.00\n",
            "trestbps    20.00\n",
            "chol        63.75\n",
            "fbs          0.00\n",
            "restecg      1.00\n",
            "thalach     32.75\n",
            "exang        1.00\n",
            "oldpeak      1.60\n",
            "ca           1.00\n",
            "target       1.00\n",
            "cp_0         1.00\n",
            "cp_1         0.00\n",
            "cp_2         1.00\n",
            "cp_3         0.00\n",
            "thal_0       0.00\n",
            "thal_1       0.00\n",
            "thal_2       1.00\n",
            "thal_3       1.00\n",
            "slope_0      0.00\n",
            "slope_1      1.00\n",
            "slope_2      1.00\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(302, 22)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Assume or Gather column"
      ],
      "metadata": {
        "id": "AZOi4BhYzH8_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result = pd.concat(frames,axis=1)\n",
        "x = result.drop('target', axis = 1)\n",
        "y = result['target']"
      ],
      "metadata": {
        "id": "U0__HM6azOpg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split Data"
      ],
      "metadata": {
        "id": "Y1LHRLK1y7GP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)"
      ],
      "metadata": {
        "id": "OLcXnV_0y8mN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fit into the model"
      ],
      "metadata": {
        "id": "BMvTVbfRzXx_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "logre = LogisticRegression()\n",
        "logre.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NRNhxG9UzZlR",
        "outputId": "6f25c271-d058-4dad-860d-79339ec13e83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: 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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Predict the model which is around 86.88%"
      ],
      "metadata": {
        "id": "0Y1JLCdIzs73"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = logre.predict(x_test)\n",
        "print(accuracy_score(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DhkcxoXwzkGf",
        "outputId": "d3afef18-dd55-4049-a183-28c89bf374d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8688524590163934\n"
          ]
        }
      ]
    }
  ]
}