Simplified_Notebook/ML_Models.ipynb

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      "source": [
        "## **Linear Regression**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Linear regression libraries\n",
        "from sklearn.linear_model import LinearRegression, LogisticRegression, Perceptron\n",
        "from sklearn.metrics import r2_score, f1_score, recall_score, precision_score\n",
        "from sklearn.metrics import silhouette_score, accuracy_score, mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "from sklearn.neural_network import MLPRegressor, MLPClassifier"
      ],
      "metadata": {
        "id": "7aIqgnt34161"
      },
      "execution_count": null,
      "outputs": []
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      "execution_count": null,
      "metadata": {
        "id": "43l7Ce4QRj-P"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "colab": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RestBP</th>\n",
              "      <th>Chol</th>\n",
              "      <th>Fbs</th>\n",
              "      <th>RestECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExAng</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>Slope</th>\n",
              "      <th>Ca</th>\n",
              "      <th>Thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>fixed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>160</td>\n",
              "      <td>286</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>108</td>\n",
              "      <td>1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>120</td>\n",
              "      <td>229</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>129</td>\n",
              "      <td>1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>reversable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
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            "text/plain": [
              "   RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  Oldpeak  Slope  Ca        Thal\n",
              "0     145   233    1        2    150      0      2.3      3   0       fixed\n",
              "1     160   286    0        2    108      1      1.5      2   3      normal\n",
              "2     120   229    0        2    129      1      2.6      2   2  reversable\n",
              "3     130   250    0        0    187      0      3.5      3   0      normal\n",
              "4     130   204    0        2    172      0      1.4      1   0      normal"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = pd.read_csv('Heart1.csv')\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uh1UyVLeSAU6"
      },
      "outputs": [],
      "source": [
        "x = data['MaxHR']\n",
        "y = data['Slope']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XcGejaPOSH3C"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size = 0.25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AMuTO9eLSe6d"
      },
      "outputs": [],
      "source": [
        "numerator = (len(xtrain) * (np.sum(xtrain * ytrain))) - (np.sum(xtrain) * np.sum(ytrain))\n",
        "denominator = (len(xtrain) * (np.sum(np.square(xtrain))) - np.square(np.sum(xtrain)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "-0.011661081556399383"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "slope = numerator / denominator\n",
        "slope\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gIjVZHbvTlVG",
        "outputId": "5712f284-95e3-4576-a0aa-4b9da3b10a01"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3.346894175301217"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "intercept = ytrain.mean() - (slope * xtrain.mean())\n",
        "intercept"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pgLZTJZbTuKC"
      },
      "outputs": [],
      "source": [
        "def linear(xtest):\n",
        "  ypred = (slope * xtest) + intercept\n",
        "  return ypred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NF63zfvrUF1c",
        "outputId": "af963dbc-6916-4c50-8899-12eb20017ec5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "277    1.574410\n",
              "256    1.691021\n",
              "105    1.527765\n",
              "171    2.052514\n",
              "63     1.364510\n",
              "         ...   \n",
              "128    1.364510\n",
              "198    1.457799\n",
              "234    1.446138\n",
              "15     1.317866\n",
              "208    1.539427\n",
              "Name: MaxHR, Length: 76, dtype: float64"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "yperd = linear(xtest)\n",
        "\n",
        "yperd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2MpMWdt2UJTm",
        "outputId": "806a9093-8f06-4778-f62d-8409820ab4bc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "27.525785366086396\n"
          ]
        }
      ],
      "source": [
        "#error calculation\n",
        "\n",
        "error = ytest - yperd\n",
        "SSE = (error ** 2).sum()\n",
        "print(SSE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "00cPt6vXUW3n",
        "outputId": "91b68089-0e0c-415a-f33e-1fe6c8ccc021"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.6018150765774097"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "rmse = np.sqrt(mean_squared_error(ytest, yperd))\n",
        "rmse"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fanR5hMTUtqR",
        "outputId": "5bde170e-93ce-4f65-bc73-4cc282c20c98"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.02244874400814656"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "r2 = r2_score(ytest, yperd)\n",
        "r2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "scsjXnzXVA22",
        "outputId": "cc58bb19-af65-4632-83fb-9545c6c6c185"
      },
      "outputs": [
        {
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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.scatter(xtest, ytest)\n",
        "\n",
        "plt.plot(xtest, yperd, color = 'red')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eGtodNg4VNCx",
        "outputId": "79095e8d-5d2d-4354-9bcb-2c7c3009d23b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "enter the number150\n",
            "1.5977319418413094\n"
          ]
        }
      ],
      "source": [
        "#checking the accuracy using user input\n",
        "\n",
        "user = float(input(\"enter the number\"))\n",
        "\n",
        "print(linear(user))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e-YEz3ApWfj2"
      },
      "source": [
        "## **Multilinear Regression**\n",
        " \n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QuVbdjRBWTF3",
        "outputId": "4a4426fd-d198-4a75-c37c-8b59731f764e"
      },
      "outputs": [
        {
          "data": {
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              "      <th>RestBP</th>\n",
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              "          const element = document.querySelector('#df-413980af-2b43-4743-a1c0-987a817f205a');\n",
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              "                                                     [key], {});\n",
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              "\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",
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              "          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": [
              "   RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  Oldpeak  Slope  Ca        Thal\n",
              "0     145   233    1        2    150      0      2.3      3   0       fixed\n",
              "1     160   286    0        2    108      1      1.5      2   3      normal\n",
              "2     120   229    0        2    129      1      2.6      2   2  reversable\n",
              "3     130   250    0        0    187      0      3.5      3   0      normal\n",
              "4     130   204    0        2    172      0      1.4      1   0      normal"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv('Heart1.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_F847H3Wxmd",
        "outputId": "0d5d4608-cc27-4d3b-d2a8-715250cfc338"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "RestBP      0\n",
              "Chol        0\n",
              "Fbs         0\n",
              "RestECG     0\n",
              "MaxHR       0\n",
              "ExAng       0\n",
              "Oldpeak    21\n",
              "Slope       0\n",
              "Ca          0\n",
              "Thal        0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tBOM6XxSXB_w",
        "outputId": "e9eb8b6f-86eb-42f6-f079-bae74936eff5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "RestBP     0\n",
              "Chol       0\n",
              "Fbs        0\n",
              "RestECG    0\n",
              "MaxHR      0\n",
              "ExAng      0\n",
              "Oldpeak    0\n",
              "Slope      0\n",
              "Ca         0\n",
              "Thal       0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.dropna(inplace = True)\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ha3WeM7DXFMX"
      },
      "outputs": [],
      "source": [
        "#Spiltting dataset into dependent and independent variables \n",
        "x1 = df['Oldpeak']\n",
        "x2 = df['MaxHR']\n",
        "y = df['Slope']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aYEpIpflXUuO",
        "outputId": "e18bf44c-ce12-4361-8a97-8a1b55dc6a87"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.1958776608044737\n",
            "0.25549244273183686\n",
            "-0.005642582470618569\n"
          ]
        }
      ],
      "source": [
        "#Which one is easy You can choose the one below also does the same job\n",
        "x1_mean = np.mean(x1)\n",
        "x2_mean = np.mean(x2)\n",
        "y_mean = np.mean(y)\n",
        "n = x1.count()\n",
        "a = sum(x1**2) - (sum(x1)**2/n)\n",
        "b = sum(x2**2) - (sum(x2)**2/n)\n",
        "c = sum(x1*y) - (sum(x1)*sum(y)/n)\n",
        "d = sum(x2*y) - (sum(x2)*sum(y)/n)\n",
        "e = sum(x1*x2) - (sum(x1)*sum(x2)/n)\n",
        "\n",
        "\n",
        "b1 = ((b * c) - (e * d))/((a * b) - (e**2))\n",
        "b2 = ((a * d) - (e * c))/((a * b) - (e**2))\n",
        "b0 = y_mean - (b1*x1_mean) - (b2*x2_mean)\n",
        "\n",
        "print(b0)\n",
        "print(b1)\n",
        "print(b2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1FseBLxmXslo",
        "outputId": "4dcec65a-d73f-46e5-9184-d7ec9c29273f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.195877660804472\n",
            "0.2554924427318379\n",
            "-0.005642582470618591\n"
          ]
        }
      ],
      "source": [
        "X1 = np.array(x1)\n",
        "X2 = np.array(x2)\n",
        "Y = np.array(y)\n",
        "X = np.column_stack((np.ones_like(X1), X1, X2))\n",
        "beta = np.linalg.inv(X.T @ X) @ X.T @ Y\n",
        "b0, b1, b2 = beta\n",
        "print(b0)\n",
        "print(b1)\n",
        "print(b2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PqWmVqT6YKon",
        "outputId": "2ca4ec3b-b00e-4704-be86-15e631d6152b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0      1.937123\n",
              "1      1.969717\n",
              "2      2.132265\n",
              "3      2.034938\n",
              "4      1.583043\n",
              "         ...   \n",
              "298    1.757648\n",
              "299    2.268948\n",
              "300    1.853572\n",
              "301    1.214068\n",
              "302    1.219711\n",
              "Length: 282, dtype: float64"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred = b0 + (b1 * x1) + (b2 * x2)\n",
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gWiAJepFYspK",
        "outputId": "3b63e7c1-f22b-4e38-919d-15e0503618ff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.24794183571406173 0.49793758214665995\n"
          ]
        }
      ],
      "source": [
        "#Performance metrics\n",
        "\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "mse = mean_squared_error(y, y_pred)\n",
        "\n",
        "rmse = np.sqrt(mse)\n",
        "\n",
        "print(mse, rmse)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "jNj826RzZE3l",
        "outputId": "fe622c9b-56f3-4ebb-f052-c1a093043181"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.scatter(x2, y, color = 'red')\n",
        "plt.scatter(x2, y_pred, color = 'blue')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iemqYw6IZYaT",
        "outputId": "7a5a52d7-a430-44bc-9d5f-f3baf3f51ff3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.3\n",
            "150\n",
            "1.9371229084949102\n"
          ]
        }
      ],
      "source": [
        "user = float(input())\n",
        "user1 = float(input())\n",
        "print(b0 + (b1 * user) + (b2 * user1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Nx6n7XDachY0",
        "outputId": "66f013a5-2df0-4dd9-a7f2-ef92c4aa7ef8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9ce324fd-7aa0-448b-adec-359098b2d5e9\">\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>RestBP</th>\n",
              "      <th>Chol</th>\n",
              "      <th>Fbs</th>\n",
              "      <th>RestECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExAng</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>Slope</th>\n",
              "      <th>Ca</th>\n",
              "      <th>Thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>fixed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>160</td>\n",
              "      <td>286</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>108</td>\n",
              "      <td>1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>120</td>\n",
              "      <td>229</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>129</td>\n",
              "      <td>1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>reversable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>110</td>\n",
              "      <td>264</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>132</td>\n",
              "      <td>0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>reversable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>144</td>\n",
              "      <td>193</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>141</td>\n",
              "      <td>0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>reversable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>130</td>\n",
              "      <td>131</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>115</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>reversable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>130</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>174</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>138</td>\n",
              "      <td>175</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>173</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>282 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ce324fd-7aa0-448b-adec-359098b2d5e9')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      display: none;\n",
              "      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",
              "    }\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-9ce324fd-7aa0-448b-adec-359098b2d5e9 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-9ce324fd-7aa0-448b-adec-359098b2d5e9');\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": [
              "     RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  Oldpeak  Slope  Ca        Thal\n",
              "0       145   233    1        2    150      0      2.3      3   0       fixed\n",
              "1       160   286    0        2    108      1      1.5      2   3      normal\n",
              "2       120   229    0        2    129      1      2.6      2   2  reversable\n",
              "3       130   250    0        0    187      0      3.5      3   0      normal\n",
              "4       130   204    0        2    172      0      1.4      1   0      normal\n",
              "..      ...   ...  ...      ...    ...    ...      ...    ...  ..         ...\n",
              "298     110   264    0        0    132      0      1.2      2   0  reversable\n",
              "299     144   193    1        0    141      0      3.4      2   2  reversable\n",
              "300     130   131    0        0    115      1      1.2      2   1  reversable\n",
              "301     130   236    0        2    174      0      0.0      2   1      normal\n",
              "302     138   175    0        0    173      0      0.0      1   0      normal\n",
              "\n",
              "[282 rows x 10 columns]"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "5sY_TdFca4ZI",
        "outputId": "37553c14-c05f-4225-9158-bf08062fb226"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#MULTILINEAR WITH LIBRARY\n",
        "from sklearn.linear_model import LinearRegression\n",
        "newX = df[['Oldpeak','MaxHR']]\n",
        "newY = df['Slope']\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(newX, newY, test_size = 0.25)\n",
        "model = LinearRegression()\n",
        "model.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IAtvqyjUcNT3",
        "outputId": "587bb22c-39f4-42a8-df55-801e9dac521f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.3\n",
            "150\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([1.95005594])"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "user = float(input())\n",
        "user1 = float(input())\n",
        "\n",
        "model.predict([[user, user1]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LHHVrOA5d0NX"
      },
      "source": [
        "# **LOGISTIC REGRESSION** "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "nOTKvE4fcuHa",
        "outputId": "f8210b8a-71ac-4c84-8ca1-63f0db040ec5"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "\n",
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RestBP</th>\n",
              "      <th>Chol</th>\n",
              "      <th>Fbs</th>\n",
              "      <th>RestECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExAng</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>Slope</th>\n",
              "      <th>Ca</th>\n",
              "      <th>Thal</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>145</td>\n",
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>150</td>\n",
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              "      <td>2.3</td>\n",
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              "      <th>1</th>\n",
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              "      <td>1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>120</td>\n",
              "      <td>229</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>129</td>\n",
              "      <td>1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>reversable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0dc9f72c-0ff3-411f-9796-b54034f8d35a')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      display: none;\n",
              "      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",
              "    }\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-0dc9f72c-0ff3-411f-9796-b54034f8d35a 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-0dc9f72c-0ff3-411f-9796-b54034f8d35a');\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": [
              "   RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  Oldpeak  Slope  Ca        Thal\n",
              "0     145   233    1        2    150      0      2.3      3   0       fixed\n",
              "1     160   286    0        2    108      1      1.5      2   3      normal\n",
              "2     120   229    0        2    129      1      2.6      2   2  reversable\n",
              "3     130   250    0        0    187      0      3.5      3   0      normal\n",
              "4     130   204    0        2    172      0      1.4      1   0      normal"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1 = pd.read_csv('Heart1.csv')\n",
        "df1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TL_3RXTBdzWE",
        "outputId": "8ac23492-97c0-4237-efa4-9a23627491c1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "RestBP      0\n",
              "Chol        0\n",
              "Fbs         0\n",
              "RestECG     0\n",
              "MaxHR       0\n",
              "ExAng       0\n",
              "Oldpeak    21\n",
              "Slope       0\n",
              "Ca          0\n",
              "Thal        0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#PREPROCESSING\n",
        "\n",
        "df1.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5P8VCawReL-X",
        "outputId": "9a8e3c55-37cf-44a0-fa90-18415c4cc812"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "RestBP     0\n",
              "Chol       0\n",
              "Fbs        0\n",
              "RestECG    0\n",
              "MaxHR      0\n",
              "ExAng      0\n",
              "Oldpeak    0\n",
              "Slope      0\n",
              "Ca         0\n",
              "Thal       0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#HENCE THE NULL VALUES ARE MORE THAN 10% they are removed\n",
        "df1.dropna(inplace = True)\n",
        "df1.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ojUmvwRFeWIV"
      },
      "outputs": [],
      "source": [
        "x = df1[['MaxHR']]\n",
        "y = df1['Thal']\n",
        "\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size = 0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "EZOyxsyOeuXa",
        "outputId": "da2d6478-bb13-43a6-e664-172ede7bff6e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LogisticRegression(solver='liblinear')"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#USING LIBRARY\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "model = LogisticRegression(solver = 'liblinear')\n",
        "model.fit(xtrain, ytrain)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "OEJtnzE8fNfX",
        "outputId": "d672ad7a-4ce3-4764-ec81-43997ac52afb"
      },
      "outputs": [
        {
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-3ef4f17f0c0e>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#WITHOUT LIBRARY\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnumerator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxtrain\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mytrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mytrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdenominator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxtrain\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/numpy/core/overrides.py\u001b[0m in \u001b[0;36msum\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36msum\u001b[0;34m(a, axis, dtype, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m   2294\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2295\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2296\u001b[0;31m     return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims,\n\u001b[0m\u001b[1;32m   2297\u001b[0m                           initial=initial, where=where)\n\u001b[1;32m   2298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapreduction\u001b[0;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[1;32m     84\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpasskwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mufunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpasskwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "#WITHOUT LIBRARY\n",
        "numerator = (len(xtrain) * (np.sum(xtrain.values * ytrain.values))) - (np.sum(xtrain.values) * np.sum(ytrain.values))\n",
        "denominator = (len(xtrain) * np.sum(np.square(xtrain.values)) - np.square(np.sum(xtrain.values)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "amp_uDY8gb6X"
      },
      "outputs": [],
      "source": [
        "slope = numerator / denominator\n",
        "intercept = ytrain.mean() - (slope * xtrain.mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A7Zi9ZFDgj6l"
      },
      "outputs": [],
      "source": [
        "def logistic_regression(xtest):\n",
        "    ypred = 1 / ( 1 + np.exp(-( (slope * xtest.values) + intercept ) ) )\n",
        "    ypred = ypred.squeeze()\n",
        "    ypred = np.where(ypred < 0.5, 0, 1)\n",
        "    return ypred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V3uRqzEtguvo"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
        "conf = confusion_matrix(ytest, ypred)\n",
        "print(classification_report(ytest, ypred))\n",
        "acc = accuracy_score(ytest, ypred)\n",
        "\n",
        "print(conf)\n",
        "print(acc)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Single Layer Perceptron**"
      ],
      "metadata": {
        "id": "rduZo2a9t0w3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RoHJyWPTg6_i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "25039c60-7db9-4c38-97cb-e7ab4c3fe616"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  Oldpeak  Slope  Ca        Thal\n",
              "0     145   233    1        2    150      0      2.3      3   0       fixed\n",
              "1     160   286    0        2    108      1      1.5      2   3      normal\n",
              "2     120   229    0        2    129      1      2.6      2   2  reversable\n",
              "3     130   250    0        0    187      0      3.5      3   0      normal\n",
              "4     130   204    0        2    172      0      1.4      1   0      normal"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RestBP</th>\n",
              "      <th>Chol</th>\n",
              "      <th>Fbs</th>\n",
              "      <th>RestECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExAng</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>Slope</th>\n",
              "      <th>Ca</th>\n",
              "      <th>Thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>fixed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>160</td>\n",
              "      <td>286</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>108</td>\n",
              "      <td>1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>120</td>\n",
              "      <td>229</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>129</td>\n",
              "      <td>1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>reversable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5182a126-e557-4ac8-a917-8f52ed739599')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display: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",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\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",
              "    }\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-5182a126-e557-4ac8-a917-8f52ed739599 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-5182a126-e557-4ac8-a917-8f52ed739599');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.linear_model import Perceptron\n",
        "\n",
        "data = pd.read_csv('Heart1.csv')\n",
        "data.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()\n",
        "data.dropna(inplace = True)"
      ],
      "metadata": {
        "id": "P_WXkeQBwVbU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.drop('Thal', axis = 1)\n",
        "y = data['Thal']"
      ],
      "metadata": {
        "id": "VCHgN4BnupXN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ],
      "metadata": {
        "id": "nxZSVZ6nz_x2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size = 0.2)"
      ],
      "metadata": {
        "id": "I0ABdkDU04Gq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#eta0 is the Learning rate\n",
        "\n",
        "model = Perceptron(random_state= 100, eta0 = 0.5)\n",
        "\n",
        "model.fit(xtrain, ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "TEZn8MAPvNnI",
        "outputId": "878c8d91-65d3-4365-e8ba-598f4ccbd319"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Perceptron(eta0=0.5, random_state=100)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Perceptron(eta0=0.5, random_state=100)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Perceptron</label><div class=\"sk-toggleable__content\"><pre>Perceptron(eta0=0.5, random_state=100)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ypred = model.predict(xtest)\n",
        "ypred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0WrvUYHwNIG",
        "outputId": "2e9306ca-dd69-43fb-ffdc-7e567d0e425d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 1, 1, 1, 2, 1,\n",
              "       2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model accuracy\n",
        "model.score(xtest, ytest)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eAdT3kGnwgiF",
        "outputId": "2402e9a5-73a2-4a78-c2ed-c858bd267efd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5087719298245614"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(ytest)\n",
        "plt.plot(ypred, color = 'red')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "sLRIMNSdyXYK",
        "outputId": "ea8169f8-067b-4ecc-b9a4-89d28c92134d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Multi Layer Perceptron**\n",
        "\n"
      ],
      "metadata": {
        "id": "wxWre27h1Qoi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from sklearn.neural_network import MLPRegressor, MLPClassifier\n",
        "\n",
        "data = pd.read_csv('Heart1.csv')\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "OWoMi83s1QTb",
        "outputId": "7adb6ace-3736-45e4-dd71-b2c1c889c4e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  Oldpeak  Slope  Ca        Thal\n",
              "0     145   233    1        2    150      0      2.3      3   0       fixed\n",
              "1     160   286    0        2    108      1      1.5      2   3      normal\n",
              "2     120   229    0        2    129      1      2.6      2   2  reversable\n",
              "3     130   250    0        0    187      0      3.5      3   0      normal\n",
              "4     130   204    0        2    172      0      1.4      1   0      normal"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1ca3831e-e7a3-43b0-894b-cf8a9b0a0f6b\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RestBP</th>\n",
              "      <th>Chol</th>\n",
              "      <th>Fbs</th>\n",
              "      <th>RestECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExAng</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>Slope</th>\n",
              "      <th>Ca</th>\n",
              "      <th>Thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>145</td>\n",
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              "      <td>2</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>fixed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>160</td>\n",
              "      <td>286</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>108</td>\n",
              "      <td>1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>120</td>\n",
              "      <td>229</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>129</td>\n",
              "      <td>1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>reversable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-1ca3831e-e7a3-43b0-894b-cf8a9b0a0f6b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-1ca3831e-e7a3-43b0-894b-cf8a9b0a0f6b');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()\n",
        "data.dropna(inplace = True)"
      ],
      "metadata": {
        "id": "VjRZRCon1yUC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.drop('Thal', axis = 1)\n",
        "y = data['Thal']"
      ],
      "metadata": {
        "id": "rbfnA7sy11uE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ],
      "metadata": {
        "id": "3-gsFxiE13iw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size = 0.2, shuffle = True)"
      ],
      "metadata": {
        "id": "lVj8JdgG192H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ss = StandardScaler()\n",
        "xtrain = ss.fit_transform(xtrain)\n",
        "xtest = ss.fit_transform(xtest)"
      ],
      "metadata": {
        "id": "2r9oaRZg1_Tb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mlp = MLPClassifier(hidden_layer_sizes=(10,20), max_iter=100)\n",
        "\n",
        "mlp.fit(xtrain, ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "JV2oe-2U2O1Y",
        "outputId": "6b2e63ab-d22d-4cfb-b89e-9cec5cb3b6fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MLPClassifier(hidden_layer_sizes=(10, 20), max_iter=100)"
            ],
            "text/html": [
              "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(hidden_layer_sizes=(10, 20), max_iter=100)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(hidden_layer_sizes=(10, 20), max_iter=100)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = mlp.predict(xtest)\n",
        "pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "niNuUAhG2dDC",
        "outputId": "fe44971c-2474-455c-a2c7-e1f3b828e735"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2,\n",
              "       2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1,\n",
              "       2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "mse = mean_squared_error(ytest, pred)\n",
        "rmse = np.sqrt(mse)\n",
        "mae = mean_absolute_error(ytest, pred)\n",
        "print(mse, rmse, mae)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kI7Y6vb42pPB",
        "outputId": "aa17c64a-c3b8-4e98-8901-1c89ab9c0f4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.42105263157894735 0.6488856845230502 0.38596491228070173\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(ytest, pred))\n",
        "print(classification_report(ytest, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v1xBQedJ5ffr",
        "outputId": "abae6809-045f-4313-a0cc-1d44ab4ee206"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0  1  1]\n",
            " [ 0 24  4]\n",
            " [ 0 15 12]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00         2\n",
            "           1       0.60      0.86      0.71        28\n",
            "           2       0.71      0.44      0.55        27\n",
            "\n",
            "    accuracy                           0.63        57\n",
            "   macro avg       0.44      0.43      0.42        57\n",
            "weighted avg       0.63      0.63      0.61        57\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(ytest)\n",
        "plt.plot(pred)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "JQ5H6YCV2tHi",
        "outputId": "73782a26-74dc-496f-efaa-941a62305be7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**SVM**"
      ],
      "metadata": {
        "id": "dXrlherJ4W3D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "data = pd.read_csv('Heart1.csv')\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "p4LmbGk84WJo",
        "outputId": "14e59012-d9f2-4e72-d95d-f2f3792cbe9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  Oldpeak  Slope  Ca        Thal\n",
              "0     145   233    1        2    150      0      2.3      3   0       fixed\n",
              "1     160   286    0        2    108      1      1.5      2   3      normal\n",
              "2     120   229    0        2    129      1      2.6      2   2  reversable\n",
              "3     130   250    0        0    187      0      3.5      3   0      normal\n",
              "4     130   204    0        2    172      0      1.4      1   0      normal"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e7950704-9a06-414f-a037-b09e9f18a2a5\">\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>RestBP</th>\n",
              "      <th>Chol</th>\n",
              "      <th>Fbs</th>\n",
              "      <th>RestECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExAng</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>Slope</th>\n",
              "      <th>Ca</th>\n",
              "      <th>Thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>fixed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>160</td>\n",
              "      <td>286</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>108</td>\n",
              "      <td>1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>120</td>\n",
              "      <td>229</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>129</td>\n",
              "      <td>1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>reversable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7950704-9a06-414f-a037-b09e9f18a2a5')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      display: none;\n",
              "      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",
              "    }\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-e7950704-9a06-414f-a037-b09e9f18a2a5 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-e7950704-9a06-414f-a037-b09e9f18a2a5');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()\n",
        "data.dropna(inplace = True)"
      ],
      "metadata": {
        "id": "Drtc_hPQ4ch0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.drop('Thal', axis = 1)\n",
        "y = data['Thal']"
      ],
      "metadata": {
        "id": "yGJxocbU4eSf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ],
      "metadata": {
        "id": "5qWEXQ3i4jxQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size = 0.2)"
      ],
      "metadata": {
        "id": "ReuxDfDD4lFH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ss = StandardScaler()\n",
        "xtrain = ss.fit_transform(xtrain)\n",
        "xtest = ss.fit_transform(xtest)"
      ],
      "metadata": {
        "id": "yfW5OCDK4pUW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svc = SVC()\n",
        "svc.fit(xtrain, ytrain)"
      ],
      "metadata": {
        "id": "Ks95nEzH4tlN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "c028bed9-1fb2-49af-b2b3-78c538b9e1b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict = svc.predict(xtest)\n",
        "predict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x_WpDAeo74-k",
        "outputId": "c5e1443d-4b34-41e7-ac79-19d48183312e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 2, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2,\n",
              "       1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 209
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(ytest, predict))\n",
        "print(classification_report(ytest, predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VfX_lHuP8CfX",
        "outputId": "948e276a-30b1-4def-b8d9-86d885eb1698"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0  2  1]\n",
            " [ 0 25  4]\n",
            " [ 0 14 11]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00         3\n",
            "           1       0.61      0.86      0.71        29\n",
            "           2       0.69      0.44      0.54        25\n",
            "\n",
            "    accuracy                           0.63        57\n",
            "   macro avg       0.43      0.43      0.42        57\n",
            "weighted avg       0.61      0.63      0.60        57\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(accuracy_score(ytest, predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XojqOVTh8nkX",
        "outputId": "731d24de-5dc0-42f0-fce5-d9b131deff01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.631578947368421\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(ytest)\n",
        "plt.plot(predict)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "qBn5UNE8812O",
        "outputId": "0dec0418-512e-4784-cc56-3bafc9889ce7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Agglomertive clustering**"
      ],
      "metadata": {
        "id": "2k1aNMOj9Zzg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from sklearn.cluster import AgglomerativeClustering \n",
        "\n",
        "data = pd.read_csv('Heart1.csv')\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "t75qLAHI9N1-",
        "outputId": "0a88408b-b2d6-4d45-d527-21aa256223b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  Oldpeak  Slope  Ca        Thal\n",
              "0     145   233    1        2    150      0      2.3      3   0       fixed\n",
              "1     160   286    0        2    108      1      1.5      2   3      normal\n",
              "2     120   229    0        2    129      1      2.6      2   2  reversable\n",
              "3     130   250    0        0    187      0      3.5      3   0      normal\n",
              "4     130   204    0        2    172      0      1.4      1   0      normal"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RestBP</th>\n",
              "      <th>Chol</th>\n",
              "      <th>Fbs</th>\n",
              "      <th>RestECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExAng</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>Slope</th>\n",
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              "      <th>0</th>\n",
              "      <td>145</td>\n",
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              "      <td>reversable</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
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              "      display: none;\n",
              "      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",
              "    }\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-10bfd61b-d1ee-4d31-ba14-21f78ec39f7d 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-10bfd61b-d1ee-4d31-ba14-21f78ec39f7d');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()\n",
        "data.dropna(inplace = True)"
      ],
      "metadata": {
        "id": "q0fYGgtEAJdQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.drop('Thal', axis = 1)\n",
        "y = data['Thal']"
      ],
      "metadata": {
        "id": "oNmlMI5O_d9Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ],
      "metadata": {
        "id": "B_sKjziHAiYT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.cluster.hierarchy as sch\n",
        "dendogram = sch.dendrogram(sch.linkage(x, method= 'ward',metric = 'euclidean'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "MBAehy7IA9eA",
        "outputId": "c6fcec97-d4a3-44f9-b275-7a2ba28d307b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = AgglomerativeClustering(n_clusters = 3)\n",
        "\n",
        "model.fit(x)\n",
        "labels = model.labels_\n",
        "predicted = model.fit_predict(x)"
      ],
      "metadata": {
        "id": "OiL-hdze__h7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x1 = list(range(1,283))\n",
        "df = pd.DataFrame(labels, columns = ['cluster'])\n",
        "df.insert(0, 'No', x1)\n",
        "df.head() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "B49YZPBRAGs9",
        "outputId": "5198ac63-fe37-48ae-c853-37a6a9da3a2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   No  cluster\n",
              "0   1        2\n",
              "1   2        0\n",
              "2   3        1\n",
              "3   4        2\n",
              "4   5        1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-416ca4d9-67aa-4901-870f-5b17232b5570\">\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>No</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-416ca4d9-67aa-4901-870f-5b17232b5570')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      display: none;\n",
              "      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",
              "    }\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-416ca4d9-67aa-4901-870f-5b17232b5570 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-416ca4d9-67aa-4901-870f-5b17232b5570');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 256
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df['No'], df['cluster'], c= predicted, cmap = 'rainbow')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "NbUA48ztCDEN",
        "outputId": "4dfbf338-8fc1-4bed-f552-a966c208a6b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import silhouette_score, davies_bouldin_score\n",
        "\n",
        "s = silhouette_score(x, labels, metric = 'euclidean')\n",
        "d = davies_bouldin_score(x, labels)\n",
        "print(s, d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yHvpl6BECzuE",
        "outputId": "82ad93ad-bcb4-4a28-8d60-263ce51b394e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.2455812327822541 1.2049602347033048\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Fuzzy - C - Mean**"
      ],
      "metadata": {
        "id": "ib7Uhw8jECF6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from fcmeans import FCM\n",
        "\n",
        "data = pd.read_csv('Heart1.csv')\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "TqMS0aC-Deba",
        "outputId": "22520178-8b0f-4fba-a57f-6effe0556817"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  Oldpeak  Slope  Ca        Thal\n",
              "0     145   233    1        2    150      0      2.3      3   0       fixed\n",
              "1     160   286    0        2    108      1      1.5      2   3      normal\n",
              "2     120   229    0        2    129      1      2.6      2   2  reversable\n",
              "3     130   250    0        0    187      0      3.5      3   0      normal\n",
              "4     130   204    0        2    172      0      1.4      1   0      normal"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0d581783-5f55-4157-ad1a-8331b1f8ea8a\">\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>RestBP</th>\n",
              "      <th>Chol</th>\n",
              "      <th>Fbs</th>\n",
              "      <th>RestECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExAng</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>Slope</th>\n",
              "      <th>Ca</th>\n",
              "      <th>Thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>fixed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>160</td>\n",
              "      <td>286</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>108</td>\n",
              "      <td>1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>120</td>\n",
              "      <td>229</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>129</td>\n",
              "      <td>1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>reversable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d581783-5f55-4157-ad1a-8331b1f8ea8a')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      display: none;\n",
              "      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",
              "    }\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-0d581783-5f55-4157-ad1a-8331b1f8ea8a 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-0d581783-5f55-4157-ad1a-8331b1f8ea8a');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 290
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()\n",
        "data.dropna(inplace = True)"
      ],
      "metadata": {
        "id": "DSn7JyOzEaFC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.drop('Thal', axis = 1)\n",
        "y = data['Thal']"
      ],
      "metadata": {
        "id": "ZpkDhH9gEMlr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "xtrain, ytrain, xtest, ytest = train_test_split(x, y, test_size = 0.25)"
      ],
      "metadata": {
        "id": "z_5YxXjQEdI7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = FCM(n_clusters = 7)\n",
        "\n",
        "model.fit(np.array(xtrain))"
      ],
      "metadata": {
        "id": "rbEUD7jYExe-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "centeriod = model.centers\n",
        "label = model.predict(np.array(xtrain))\n",
        "model.partition_coefficient"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5yco28HNFFVB",
        "outputId": "c674ceb9-9007-45e6-962d-d7bdadbe8586"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.057899107758463506"
            ]
          },
          "metadata": {},
          "execution_count": 300
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array(xtrain)\n",
        "f, axes = plt.subplots(1,2, figsize=(15, 5))\n",
        "axes[0].scatter(x[:,0], x[:,1], color = 'red', alpha = 1)\n",
        "axes[1].scatter(x[:,0], x[:,1], c = label, alpha = 1)\n",
        "axes[1].scatter(centeriod[:,0], centeriod[:,1], s = 500, color = 'green')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "loN5elVKFbHT",
        "outputId": "fcc1d317-29cb-4550-bc12-724e22393544"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f504445bfa0>"
            ]
          },
          "metadata": {},
          "execution_count": 301
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Metrics for performance\n",
        "print(model.partition_coefficient)\n",
        "print(model.partition_entropy_coefficient)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wg0AHrEIGYMe",
        "outputId": "9d996ea3-531a-4f0c-de25-5cbf709507ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.057899107758463506\n",
            "0.2622480030892326\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Self Organizing Map**"
      ],
      "metadata": {
        "id": "hrkqSSAnP01T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from minisom import MiniSom\n",
        "data = pd.read_csv('Heart1.csv')\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pixdKI02MPOY",
        "outputId": "12c6c24a-5ced-49df-d4b3-5e3598b3e37f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  Oldpeak  Slope  Ca        Thal\n",
              "0     145   233    1        2    150      0      2.3      3   0       fixed\n",
              "1     160   286    0        2    108      1      1.5      2   3      normal\n",
              "2     120   229    0        2    129      1      2.6      2   2  reversable\n",
              "3     130   250    0        0    187      0      3.5      3   0      normal\n",
              "4     130   204    0        2    172      0      1.4      1   0      normal"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-11ea8ec3-3089-40c7-8260-26268f7eb61d\">\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>RestBP</th>\n",
              "      <th>Chol</th>\n",
              "      <th>Fbs</th>\n",
              "      <th>RestECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExAng</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>Slope</th>\n",
              "      <th>Ca</th>\n",
              "      <th>Thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>fixed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>160</td>\n",
              "      <td>286</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>108</td>\n",
              "      <td>1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>120</td>\n",
              "      <td>229</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>129</td>\n",
              "      <td>1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>reversable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-11ea8ec3-3089-40c7-8260-26268f7eb61d')\"\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",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      display: none;\n",
              "      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",
              "    }\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-11ea8ec3-3089-40c7-8260-26268f7eb61d 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-11ea8ec3-3089-40c7-8260-26268f7eb61d');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()\n",
        "data.dropna(inplace = True)"
      ],
      "metadata": {
        "id": "SjTHPSS4QUOC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.drop('Thal', axis = 1)\n",
        "y = data['Thal']"
      ],
      "metadata": {
        "id": "wNKH5aiGP7YK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ],
      "metadata": {
        "id": "YXiyp4VrhpEt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "minmax = MinMaxScaler()\n",
        "x = minmax.fit_transform(x)"
      ],
      "metadata": {
        "id": "ywOVa4QvQVMt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = MiniSom(x=10, y=10, input_len= 9, sigma= 1.0, learning_rate = 0.5)\n",
        "\n",
        "model.random_weights_init(x)\n",
        "model.train_random(data = x, num_iteration=100)"
      ],
      "metadata": {
        "id": "wvKkVK3JQczf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pylab import bone, pcolor, colorbar, plot, show\n",
        "\n",
        "bone()\n",
        "pcolor(model.distance_map().T)\n",
        "colorbar()\n",
        "markers = ['o', 's', 'o']\n",
        "for i, x in enumerate(x):\n",
        "    w = model.winner(x)\n",
        "    plot(w[0] + 0.5, \n",
        "         w[1] + 0.5,\n",
        "         #Target value 0 will have marker \"o\" with color \"r\"\n",
        "         #Target value 1 will have marker \"s\" with color \"g\"\n",
        "         markers[y[i]], \n",
        "         markeredgecolor = colors[y[i]],\n",
        "         markerfacecolor = 'None', #No color fill inside markers\n",
        "         markersize = 10,\n",
        "         markeredgewidth = 2)\n",
        "show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "dAWS-jMbQmOH",
        "outputId": "489d79d0-5968-418b-c15d-c08395c5351a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Apriori Algorithm**"
      ],
      "metadata": {
        "id": "ksBvrwGCXvji"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from mlxtend.frequent_patterns import apriori, association_rules\n",
        "\n",
        "data = pd.read_csv('Heart1.csv')\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KQPC0q_KX0i3",
        "outputId": "15453e76-cc4e-474c-d81c-24732185f7b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  Oldpeak  Slope  Ca        Thal\n",
              "0     145   233    1        2    150      0      2.3      3   0       fixed\n",
              "1     160   286    0        2    108      1      1.5      2   3      normal\n",
              "2     120   229    0        2    129      1      2.6      2   2  reversable\n",
              "3     130   250    0        0    187      0      3.5      3   0      normal\n",
              "4     130   204    0        2    172      0      1.4      1   0      normal"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-549cedf6-3e71-4590-b44d-02f886e4ec54\">\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>RestBP</th>\n",
              "      <th>Chol</th>\n",
              "      <th>Fbs</th>\n",
              "      <th>RestECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExAng</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>Slope</th>\n",
              "      <th>Ca</th>\n",
              "      <th>Thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>fixed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>160</td>\n",
              "      <td>286</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>108</td>\n",
              "      <td>1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>120</td>\n",
              "      <td>229</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>129</td>\n",
              "      <td>1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>reversable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-549cedf6-3e71-4590-b44d-02f886e4ec54')\"\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",
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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",
              "      display: none;\n",
              "      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",
              "    }\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-549cedf6-3e71-4590-b44d-02f886e4ec54 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-549cedf6-3e71-4590-b44d-02f886e4ec54');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.dropna(inplace = True)"
      ],
      "metadata": {
        "id": "1jNtSg1cX-0n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Ensemble learning**"
      ],
      "metadata": {
        "id": "a-bSEX50YfdN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier"
      ],
      "metadata": {
        "id": "od-HybPMYLgv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('Heart1.csv')\n",
        "data.dropna(inplace = True)"
      ],
      "metadata": {
        "id": "L-3UwhU5Y0v-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.drop('Thal', axis = 1)\n",
        "y = data[['Thal']]"
      ],
      "metadata": {
        "id": "PgN8IRWxY6ga"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size= 0.25)"
      ],
      "metadata": {
        "id": "1vUaVjeYY_7V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1 = RandomForestClassifier()\n",
        "model1.fit(xtrain, ytrain)\n",
        "predict1 = model1.predict(xtest)\n",
        "model1.score(xtest, ytest)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wpmZ-7_NZHFJ",
        "outputId": "57dbbfb8-6b74-4b63-dcf8-2dd00aed2ca5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-46-212701bbfb46>:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  model1.fit(xtrain, ytrain)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6619718309859155"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "conf = confusion_matrix(ytest, predict1)\n",
        "clas = classification_report(ytest, predict1)\n",
        "print(conf)\n",
        "print(clas)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ViWUaRCfZX2m",
        "outputId": "47206fea-fb3b-4d42-9bef-d43b0f5e26ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0  1  2]\n",
            " [ 0 31 10]\n",
            " [ 0 11 16]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       fixed       0.00      0.00      0.00         3\n",
            "      normal       0.72      0.76      0.74        41\n",
            "  reversable       0.57      0.59      0.58        27\n",
            "\n",
            "    accuracy                           0.66        71\n",
            "   macro avg       0.43      0.45      0.44        71\n",
            "weighted avg       0.63      0.66      0.65        71\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = accuracy_score(ytest, predict1)\n",
        "acc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xPcAqseoZjO9",
        "outputId": "75e6b49d-a1f8-4295-febb-05d07828443e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6619718309859155"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = AdaBoostClassifier(n_estimators = 100)\n",
        "model2.fit(xtrain, ytrain)\n",
        "\n",
        "predict2 = model2.predict(xtest)\n",
        "model2.score(xtest, ytest)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8M63f4xgaA0f",
        "outputId": "74f0cdf1-a81b-40d8-c937-8634a79de020"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5352112676056338"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "conf = confusion_matrix(ytest, predict2)\n",
        "clas = classification_report(ytest, predict2)\n",
        "print(conf)\n",
        "print(clas)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bzSucA_abGO",
        "outputId": "681aae63-27a5-4511-be9a-5dec69610268"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0  1  2]\n",
            " [ 1 30 10]\n",
            " [ 5 14  8]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       fixed       0.00      0.00      0.00         3\n",
            "      normal       0.67      0.73      0.70        41\n",
            "  reversable       0.40      0.30      0.34        27\n",
            "\n",
            "    accuracy                           0.54        71\n",
            "   macro avg       0.36      0.34      0.35        71\n",
            "weighted avg       0.54      0.54      0.53        71\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = accuracy_score(ytest, predict2)\n",
        "acc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ZSzrAi6akQq",
        "outputId": "0b670728-bbfb-4c7f-bca1-73e8c640e0cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5352112676056338"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "f4GG2Oy7amLb"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}