HackHers.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Riaaggarwal06/Hackbattle-/blob/main/HackHers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 337,
      "metadata": {
        "id": "ndzDy63IT1Ce"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 338,
      "metadata": {
        "id": "AvWAOpD0Wwh9"
      },
      "outputs": [],
      "source": [
        "!pip install -q streamlit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 339,
      "metadata": {
        "id": "Y3hNshNMYuxM"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('heart_disease.csv')"
      ],
      "metadata": {
        "id": "jzgBNbehiiVO"
      },
      "execution_count": 340,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mapping = {'yes': 1, 'no': 0}\n",
        "dataset['prevalentStroke'] = dataset['prevalentStroke'].map(mapping)\n",
        "dataset['Heart_ stroke'] = dataset['Heart_ stroke'].map(mapping)\n",
        "mapping = {'Yes': 1, 'No': 0}\n",
        "dataset['ever_married'] = dataset['ever_married'].map(mapping)\n",
        "mapping = {'Male': 1, 'Female': 0}\n",
        "dataset['Gender'] = dataset['Gender'].map(mapping)\n",
        "mapping = {'Urban': 1, 'Rural': 0}\n",
        "dataset['Residence_type'] = dataset['Residence_type'].map(mapping)"
      ],
      "metadata": {
        "id": "wWdKFkpyq1KO"
      },
      "execution_count": 341,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 342,
      "metadata": {
        "id": "JfJtoJpaZGbT"
      },
      "outputs": [],
      "source": [
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 343,
      "metadata": {
        "id": "BwSf1bDjZWjE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3acefaf4-73de-4392-dd82-377b89f6bc5c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.   32.    1.   ... 29.84 80.   88.  ]\n",
            " [ 0.   33.    0.   ... 21.61 75.   93.  ]\n",
            " [ 0.   33.    1.   ... 19.84 86.   69.  ]\n",
            " ...\n",
            " [ 0.   69.    0.   ... 26.27 60.   77.  ]\n",
            " [ 0.   70.    1.   ...   nan 68.   62.  ]\n",
            " [ 0.   70.    0.   ... 31.78 60.   95.  ]]\n"
          ]
        }
      ],
      "source": [
        "print(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 344,
      "metadata": {
        "id": "qzELoFXyZd9i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7cd806f-78c9-466f-b827-65113abbc5a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nan nan nan ... nan  1. nan]\n"
          ]
        }
      ],
      "source": [
        "print(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 345,
      "metadata": {
        "id": "Ptmo2APCgLyz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5c03eb0-cd74-40cd-ab8e-b2782d2b298d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender                0\n",
              "age                   0\n",
              "Residence_type        0\n",
              "heart_disease         0\n",
              "ever_married          0\n",
              "currentSmoker         0\n",
              "cigsPerDay           29\n",
              "BPMeds               53\n",
              "prevalentStroke       0\n",
              "prevalentHyp          0\n",
              "diabetes              0\n",
              "totChol              50\n",
              "sysBP                 0\n",
              "diaBP                 0\n",
              "BMI                  19\n",
              "heartRate             1\n",
              "glucose             388\n",
              "Heart_ stroke      3594\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 345
        }
      ],
      "source": [
        "dataset.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "id": "ZNSwx6StoQBY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "0ca1ce31-210a-4d30-9ac6-3814ea82482f"
      },
      "execution_count": 346,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Gender  age  Residence_type  heart_disease  ever_married  currentSmoker  \\\n",
              "0          0   32               1              0             1              1   \n",
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              "3          1   33               1              0             1              0   \n",
              "4          0   33               1              0             1              1   \n",
              "...      ...  ...             ...            ...           ...            ...   \n",
              "4233       1   69               1              1             0              1   \n",
              "4234       1   69               0              0             1              1   \n",
              "4235       0   69               0              0             1              0   \n",
              "4236       0   70               1              0             1              0   \n",
              "4237       0   70               0              1             1              0   \n",
              "\n",
              "      cigsPerDay  BPMeds  prevalentStroke  prevalentHyp  diabetes  totChol  \\\n",
              "0           15.0     0.0                0             0         0    242.0   \n",
              "1           15.0     0.0                0             0         0    199.0   \n",
              "2            0.0     0.0                0             0         0    158.0   \n",
              "3            0.0     0.0                0             1         0    165.0   \n",
              "4            5.0     0.0                0             0         0    200.0   \n",
              "...          ...     ...              ...           ...       ...      ...   \n",
              "4233         1.0     0.0                0             0         0    245.0   \n",
              "4234        23.0     0.0                0             1         0    186.0   \n",
              "4235         0.0     1.0                0             1         0    220.0   \n",
              "4236         0.0     0.0                1             1         0    107.0   \n",
              "4237         0.0     1.0                0             1         0    231.0   \n",
              "\n",
              "      sysBP  diaBP    BMI  heartRate  glucose  Heart_ stroke  \n",
              "0     111.0   70.0  29.84       80.0     88.0            NaN  \n",
              "1     116.0   81.0  21.61       75.0     93.0            NaN  \n",
              "2     108.0   67.0  19.84       86.0     69.0            NaN  \n",
              "3     141.5   95.0  26.74       54.0     77.0            NaN  \n",
              "4     119.0   74.0  23.80       75.0     74.0            NaN  \n",
              "...     ...    ...    ...        ...      ...            ...  \n",
              "4233  123.0   77.0  26.58       70.0     81.0            1.0  \n",
              "4234  179.0   93.0  26.64       72.0     67.0            NaN  \n",
              "4235  143.0   81.0  26.27       60.0     77.0            NaN  \n",
              "4236  143.0   93.0    NaN       68.0     62.0            1.0  \n",
              "4237  136.0   84.0  31.78       60.0     95.0            NaN  \n",
              "\n",
              "[4238 rows x 18 columns]"
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              "<p>4238 rows × 18 columns</p>\n",
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              "                                                    [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",
              "\n",
              "\n",
              "<div id=\"df-f040da52-b57d-4856-a72b-4f412b062d5f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f040da52-b57d-4856-a72b-4f412b062d5f')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f040da52-b57d-4856-a72b-4f412b062d5f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 346
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['prevalentStroke'].value_counts()"
      ],
      "metadata": {
        "id": "_ZF-qenMmgVZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b7f26f1-f4d6-46cc-da74-2e2b8dd4175b"
      },
      "execution_count": 347,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4213\n",
              "1      25\n",
              "Name: prevalentStroke, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 347
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
        "imputer.fit(X[:, 5:])\n",
        "X[:, 5:] = imputer.transform(X[:, 5:])"
      ],
      "metadata": {
        "id": "Z2xyUou0msoQ"
      },
      "execution_count": 348,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.isnull().sum()"
      ],
      "metadata": {
        "id": "78fOwFWveBir",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0faf207-180e-4723-d776-778e7de76aef"
      },
      "execution_count": 349,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender                0\n",
              "age                   0\n",
              "Residence_type        0\n",
              "heart_disease         0\n",
              "ever_married          0\n",
              "currentSmoker         0\n",
              "cigsPerDay           29\n",
              "BPMeds               53\n",
              "prevalentStroke       0\n",
              "prevalentHyp          0\n",
              "diabetes              0\n",
              "totChol              50\n",
              "sysBP                 0\n",
              "diaBP                 0\n",
              "BMI                  19\n",
              "heartRate             1\n",
              "glucose             388\n",
              "Heart_ stroke      3594\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 349
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "id": "jzpDb1o-w8Bw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3f3d811-79dd-4f41-b90b-ee0d4c230d1e"
      },
      "execution_count": 350,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.         32.          1.         ... 29.84       80.\n",
            "  88.        ]\n",
            " [ 0.         33.          0.         ... 21.61       75.\n",
            "  93.        ]\n",
            " [ 0.         33.          1.         ... 19.84       86.\n",
            "  69.        ]\n",
            " ...\n",
            " [ 0.         69.          0.         ... 26.27       60.\n",
            "  77.        ]\n",
            " [ 0.         70.          1.         ... 25.80200758 68.\n",
            "  62.        ]\n",
            " [ 0.         70.          0.         ... 31.78       60.\n",
            "  95.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ],
      "metadata": {
        "id": "zCV91GrFhnNJ"
      },
      "execution_count": 351,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y)"
      ],
      "metadata": {
        "id": "EF3D-Ru9hrg3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf2dd9b5-e217-413e-b889-5c35b5aac2a2"
      },
      "execution_count": 352,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 1 1 ... 1 0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "id": "m0MdCp8WHczc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3966fa25-24c0-470f-9692-17912223fef3"
      },
      "execution_count": 353,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.         32.          1.         ... 29.84       80.\n",
            "  88.        ]\n",
            " [ 0.         33.          0.         ... 21.61       75.\n",
            "  93.        ]\n",
            " [ 0.         33.          1.         ... 19.84       86.\n",
            "  69.        ]\n",
            " ...\n",
            " [ 0.         69.          0.         ... 26.27       60.\n",
            "  77.        ]\n",
            " [ 0.         70.          1.         ... 25.80200758 68.\n",
            "  62.        ]\n",
            " [ 0.         70.          0.         ... 31.78       60.\n",
            "  95.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n"
      ],
      "metadata": {
        "id": "bgE4PV61Ye1Z"
      },
      "execution_count": 354,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)\n"
      ],
      "metadata": {
        "id": "DRCUs0w7ZTPt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d737990-d057-48d9-912a-e6dd32035492"
      },
      "execution_count": 355,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.         61.          0.         ... 23.16       74.\n",
            "  62.        ]\n",
            " [ 1.         48.          1.         ... 32.58       75.\n",
            "  77.        ]\n",
            " [ 1.         48.          0.         ... 25.83       80.\n",
            "  78.        ]\n",
            " ...\n",
            " [ 1.         46.          0.         ... 18.         78.\n",
            "  81.96675325]\n",
            " [ 1.         52.          1.         ... 23.78       69.\n",
            "  70.        ]\n",
            " [ 0.         53.          1.         ... 20.31       72.\n",
            "  76.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_train)"
      ],
      "metadata": {
        "id": "RhI6IAIqZmvE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dcc8654a-6fcd-4431-b771-5895f5010e0d"
      },
      "execution_count": 356,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 1 1 ... 1 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test)"
      ],
      "metadata": {
        "id": "LwyHpATSZnt8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e08dd6a-b409-40d1-8316-575829eca5da"
      },
      "execution_count": 357,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.         46.          1.         ... 34.52       80.\n",
            "  72.        ]\n",
            " [ 0.         37.          1.         ... 22.71       76.\n",
            "  63.        ]\n",
            " [ 0.         36.          0.         ... 33.36       55.\n",
            "  71.        ]\n",
            " ...\n",
            " [ 1.         61.          0.         ... 31.31       60.\n",
            "  81.96675325]\n",
            " [ 0.         52.          1.         ... 26.03       75.\n",
            "  82.        ]\n",
            " [ 1.         49.          0.         ... 25.4        67.\n",
            "  95.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_test)"
      ],
      "metadata": {
        "id": "54XyoG9XZpvx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ece115af-913a-445f-85fc-7e8001b8b3fd"
      },
      "execution_count": 358,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 1 1 ... 1 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "metadata": {
        "id": "5FhLRbI9Zr3p"
      },
      "execution_count": 359,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "id": "3wgcp7DkZuFW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5390b8b-10f5-4a51-89be-1d0fa7a7643f"
      },
      "execution_count": 360,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.86992626  1.33542571 -1.01394237 ... -0.64746883 -0.16083175\n",
            "  -0.88302585]\n",
            " [ 1.14952272 -0.19304774  0.98624935 ...  1.65212758 -0.07766654\n",
            "  -0.21566189]\n",
            " [ 1.14952272 -0.19304774 -1.01394237 ...  0.0043276   0.33815951\n",
            "  -0.17117096]\n",
            " ...\n",
            " [ 1.14952272 -0.42819751 -1.01394237 ... -1.90712037  0.17182909\n",
            "   0.00531359]\n",
            " [ 1.14952272  0.27725178  0.98624935 ... -0.49611535 -0.57665781\n",
            "  -0.5270984 ]\n",
            " [-0.86992626  0.39482666  0.98624935 ... -1.3432066  -0.32716218\n",
            "  -0.26015282]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test)"
      ],
      "metadata": {
        "id": "MOXiTvtQZv7H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc00cfc6-7c5f-41db-8ed7-cea38d44e766"
      },
      "execution_count": 361,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.86992626 -0.42819751  0.98624935 ...  2.1257175   0.33815951\n",
            "  -0.43811654]\n",
            " [-0.86992626 -1.48637143  0.98624935 ... -0.75732217  0.00549867\n",
            "  -0.83853492]\n",
            " [-0.86992626 -1.60394631 -1.01394237 ...  1.84254002 -1.74097076\n",
            "  -0.48260747]\n",
            " ...\n",
            " [ 1.14952272  1.33542571 -1.01394237 ...  1.34209707 -1.3251447\n",
            "   0.00531359]\n",
            " [-0.86992626  0.27725178  0.98624935 ...  0.05315131 -0.07766654\n",
            "   0.00679277]\n",
            " [ 1.14952272 -0.07547286 -1.01394237 ... -0.10064336 -0.74298823\n",
            "   0.58517487]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression(random_state = 0)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "MMcwfsueZx3m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "35189996-42d2-429a-ed3b-ec08146b3a7b"
      },
      "execution_count": 362,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=0)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 362
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)\n",
        "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
      ],
      "metadata": {
        "id": "3xQ77UqSZz-k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4db7b33d-f6f5-464a-f09a-85e963687e10"
      },
      "execution_count": 363,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 1]\n",
            " [1 1]\n",
            " [1 1]\n",
            " ...\n",
            " [1 1]\n",
            " [1 1]\n",
            " [1 0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "id": "rQJTdJafZ2Uf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33a992df-a16f-4474-97ca-032fd9508787"
      },
      "execution_count": 364,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  9 171]\n",
            " [  6 874]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8330188679245283"
            ]
          },
          "metadata": {},
          "execution_count": 364
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "tktRs3oKVzpf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "af517e7f-013e-4fad-d9b8-1bff2a8edecb"
      },
      "execution_count": 367,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import streamlit as st\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import pickle\n",
        "\n",
        "\n",
        "st.write(\"\"\"\n",
        "Hello Users,\n",
        "Welcome Heart Catalyst: It assist you with the risk prediction\n",
        "This app predicts heart stroke.\n",
        "\n",
        "\"\"\")\n",
        "\n",
        "st.sidebar.header('User Input Features')\n",
        "\n",
        "def user_input_features():\n",
        "    age = st.sidebar.number_input(\"Age:\")\n",
        "\n",
        "    sex = st.sidebar.selectbox(\"Sex\", (\"Male\", \"Female\"))\n",
        "    sex = 0 if sex == \"Female\" else 1\n",
        "\n",
        "    # education= st.sidebar.selectbox(\"Education:\", (\"PrimarySchool\",\"Graduate\",\"PostGraduate\",\"Uneducated\"))\n",
        "    # education = 0\n",
        "    # if education ==\"PrimarySchool\":\n",
        "\n",
        "    # elif \"PrimarySchool\",\"Graduate\",\"PostGraduate\",\"Uneducated\"\n",
        "\n",
        "    currsmoker = st.sidebar.selectbox('Current Smoker',(0,1))\n",
        "    nocig = st.sidebar.number_input(\"Number of Ciggrates\")\n",
        "    bpmed = st.sidebar.selectbox('blood pressure meds',(0,1))\n",
        "\n",
        "    prevstroke = st.sidebar.selectbox(\"Prevalent Stroke\", (\"Yes\", \"No\"))\n",
        "    prevstroke = 0 if prevstroke== \"Yes\" else 1\n",
        "\n",
        "    prevhyp = st.sidebar.selectbox(\"Prevalent HYP\", (0, 1))\n",
        "    diabetes = st.sidebar.selectbox(\"diabetes\", (0, 1))\n",
        "    cholestrol = st.sidebar.number_input(\"cholestrol\")\n",
        "\n",
        "    data = {'age': age,\n",
        "            'sex': sex,\n",
        "            'currentSmoker' : currsmoker,\n",
        "            'cigsPerDay' : nocig,\n",
        "            'BPMeds' : bpmed,\n",
        "            'prevalentStroke' : prevstroke,\n",
        "            'prevalentHyp' : prevhyp,\n",
        "            'diabetes' : diabetes,\n",
        "            'totChol' : cholestrol,\n",
        "            }\n",
        "\n",
        "    features = pd.DataFrame(data, index=[0])\n",
        "    return features\n",
        "input_df = user_input_features()\n",
        "\n",
        "heart_dataset = pd.read_csv('heart_disease.csv')\n",
        "heart_dataset = heart_dataset.drop(columns=['target'])\n",
        "df = pd.concat([input_df,heart_dataset],axis=0)\n",
        "\n",
        "df = pd.get_dummies(df, columns = ['sex', 'education', 'currentSmoker', 'BPMeds', 'prevalentStroke', 'prevalentHyp', 'diabetes'])\n",
        "\n",
        "df = df[:1]\n",
        "\n",
        "st.write(input_df)\n",
        "load_clf = pickle.load(open('Random_forest_model.pkl', 'rb'))\n",
        "\n",
        "prediction = load_clf.predict(df)\n",
        "prediction_proba = load_clf.predict_proba(df)\n",
        "\n",
        "st.subheader('Prediction')\n",
        "st.write(prediction)\n",
        "\n",
        "st.subheader('Prediction Probability')\n",
        "st.write(prediction_proba)"
      ],
      "metadata": {
        "id": "uATkf1zjNwLE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "outputId": "83d7502e-0577-4b10-f094-3e655dfb9196"
      },
      "execution_count": 366,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-366-c0b5fa407772>\u001b[0m in \u001b[0;36m<cell line: 55>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0mheart_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'heart_disease.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m \u001b[0mheart_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheart_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'target'\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     56\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minput_df\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mheart_dataset\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\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[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5397\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[0;36m1.0\u001b[0m     \u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5398\u001b[0m         \"\"\"\n\u001b[0;32m-> 5399\u001b[0;31m         return super().drop(\n\u001b[0m\u001b[1;32m   5400\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5401\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\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[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4503\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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[1;32m   4504\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4505\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   4506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4507\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\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/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4544\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4545\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4546\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   4547\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4548\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6932\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\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[1;32m   6933\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6934\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{list(labels[mask])} not found in axis\"\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   6935\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6936\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['target'] not found in axis\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!npm install localtunnel"
      ],
      "metadata": {
        "id": "V6U0dgAnOo6B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!streamlit run /content/app.py &>/content/logs.txt &"
      ],
      "metadata": {
        "id": "X8ssWbt4OuNb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import urllib\n",
        "print(\"Password/Enpoint IP for localtunnel is:\",urllib.request.urlopen('https://ipv4.icanhazip.com').read().decode('utf8').strip(\"\\n\"))"
      ],
      "metadata": {
        "id": "3R71IgWvQ9Yp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!npx localtunnel --port 8501"
      ],
      "metadata": {
        "id": "jUn-qSnCOxOg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!npm install localtunnel"
      ],
      "metadata": {
        "id": "oo85hRXWa_wS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!streamlit run /content/app.py &>/content/logs.txt &"
      ],
      "metadata": {
        "id": "_axB73qBa_wT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import urllib\n",
        "print(\"Password/Enpoint IP for localtunnel is:\",urllib.request.urlopen('https://ipv4.icanhazip.com').read().decode('utf8').strip(\"\\n\"))"
      ],
      "metadata": {
        "id": "Z-KsjnmKa_wT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!npx localtunnel --port 8501"
      ],
      "metadata": {
        "id": "RIHwc6soa_wT"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}