Diabetes_Prediction_using_ML.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EqiL06vbk5m4"
      },
      "outputs": [],
      "source": [
        "!pip install StandardScaler\n",
        "import StandardScaler"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rI7Pv3jdlBy5"
      },
      "source": [
        "Importing the dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RXGu-sxslFZo"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_zPqtgMvnluN"
      },
      "source": [
        "Data Collection and Data preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cpmW4jfglSkO"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "JUqX2JxNoacO",
        "outputId": "88ab5297-9b7c-48dc-8ffd-55e292a891f3"
      },
      "outputs": [
        {
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-3f1e6142fac4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#loading the dataset to a pandas Dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdiabetes_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'/content/diabetes.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\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    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    584\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\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    587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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    483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\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/python3.7/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    809\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 811\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\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    812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    813\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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/python3.7/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1038\u001b[0m             )\n\u001b[1;32m   1039\u001b[0m         \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1042\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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/python3.7/dist-packages/pandas/io/parsers/c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\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     52\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\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[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m    227\u001b[0m             \u001b[0mmemory_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"memory_map\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\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    228\u001b[0m             \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"storage_options\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\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--> 229\u001b[0;31m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"encoding_errors\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"strict\"\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    230\u001b[0m         )\n\u001b[1;32m    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    705\u001b[0m                 \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    706\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[0;32m--> 707\u001b[0;31m                 \u001b[0mnewline\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[0m\u001b[1;32m    708\u001b[0m             )\n\u001b[1;32m    709\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;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/diabetes.csv'"
          ]
        }
      ],
      "source": [
        "#loading the dataset to a pandas Dataframe\n",
        "diabetes_dataset = pd.read_csv('/content/diabetes.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9_fiqlNGo2Lm"
      },
      "outputs": [],
      "source": [
        "pd.read_csv?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "KgPiJbrepPQH",
        "outputId": "23ea0509-b340-4614-c6ce-bd6278237d26"
      },
      "outputs": [
        {
          "data": {
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              "      <td>23.3</td>\n",
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              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1af5c69d-f0f6-4504-b59e-bb26f8154550')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-1af5c69d-f0f6-4504-b59e-bb26f8154550 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-1af5c69d-f0f6-4504-b59e-bb26f8154550');\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": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
              "1            1       85             66             29        0  26.6   \n",
              "2            8      183             64              0        0  23.3   \n",
              "3            1       89             66             23       94  28.1   \n",
              "4            0      137             40             35      168  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#printing the first five rows of dataset \n",
        "diabetes_dataset.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QwB0l1ltpfd2",
        "outputId": "dd6d41df-2706-4d16-b526-50ec6c463bfc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(768, 9)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#numbers of rows and columns in this dataset\n",
        "diabetes_dataset.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "MQeMF4NMp_y6",
        "outputId": "6bbcdc2a-5b82-49f8-ff54-cb1f820fffcc"
      },
      "outputs": [
        {
          "data": {
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              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
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              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.845052</td>\n",
              "      <td>120.894531</td>\n",
              "      <td>69.105469</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>31.992578</td>\n",
              "      <td>0.471876</td>\n",
              "      <td>33.240885</td>\n",
              "      <td>0.348958</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.369578</td>\n",
              "      <td>31.972618</td>\n",
              "      <td>19.355807</td>\n",
              "      <td>15.952218</td>\n",
              "      <td>115.244002</td>\n",
              "      <td>7.884160</td>\n",
              "      <td>0.331329</td>\n",
              "      <td>11.760232</td>\n",
              "      <td>0.476951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.078000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.300000</td>\n",
              "      <td>0.243750</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.372500</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.626250</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-842da292-d2de-4bda-8a73-8ddd2ba6b774');\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",
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              "        }\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
              "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
              "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
              "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
              "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
              "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
              "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
              "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
              "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
              "\n",
              "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
              "count  768.000000                768.000000  768.000000  768.000000  \n",
              "mean    31.992578                  0.471876   33.240885    0.348958  \n",
              "std      7.884160                  0.331329   11.760232    0.476951  \n",
              "min      0.000000                  0.078000   21.000000    0.000000  \n",
              "25%     27.300000                  0.243750   24.000000    0.000000  \n",
              "50%     32.000000                  0.372500   29.000000    0.000000  \n",
              "75%     36.600000                  0.626250   41.000000    1.000000  \n",
              "max     67.100000                  2.420000   81.000000    1.000000  "
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#getting the statistical measures of the data \n",
        "diabetes_dataset.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ojLcHBfeqWpb",
        "outputId": "79fcdd11-e1d2-4c1d-bc17-052fbb9da992"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    500\n",
              "1    268\n",
              "Name: Outcome, dtype: int64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "diabetes_dataset['Outcome'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XyPfBDcKrGHd"
      },
      "source": [
        "0--> Non-Diabetes\n",
        "\n",
        "1--> Diabetes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "H5fY1iR-rU2_",
        "outputId": "36d837c9-883f-4249-f32d-b9687ccd196b"
      },
      "outputs": [
        {
          "data": {
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              "      <th>0</th>\n",
              "      <td>3.298000</td>\n",
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              "              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-13ebcc1b-ef9d-4a7e-964b-4708335e80bc 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-13ebcc1b-ef9d-4a7e-964b-4708335e80bc');\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": [
              "         Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
              "Outcome                                                                      \n",
              "0           3.298000  109.980000      68.184000      19.664000   68.792000   \n",
              "1           4.865672  141.257463      70.824627      22.164179  100.335821   \n",
              "\n",
              "               BMI  DiabetesPedigreeFunction        Age  \n",
              "Outcome                                                  \n",
              "0        30.304200                  0.429734  31.190000  \n",
              "1        35.142537                  0.550500  37.067164  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "diabetes_dataset.groupby('Outcome').mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RVdv-yvYrxmA"
      },
      "outputs": [],
      "source": [
        "#separating the data  and labels\n",
        "X = diabetes_dataset.drop(columns = 'Outcome', axis=1)\n",
        "Y = diabetes_dataset['Outcome']\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HpGZBUOxiZAI",
        "outputId": "27c20ca8-8ab2-4338-af2c-f7bc81c73eed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
            "0              6      148             72             35        0  33.6   \n",
            "1              1       85             66             29        0  26.6   \n",
            "2              8      183             64              0        0  23.3   \n",
            "3              1       89             66             23       94  28.1   \n",
            "4              0      137             40             35      168  43.1   \n",
            "..           ...      ...            ...            ...      ...   ...   \n",
            "763           10      101             76             48      180  32.9   \n",
            "764            2      122             70             27        0  36.8   \n",
            "765            5      121             72             23      112  26.2   \n",
            "766            1      126             60              0        0  30.1   \n",
            "767            1       93             70             31        0  30.4   \n",
            "\n",
            "     DiabetesPedigreeFunction  Age  \n",
            "0                       0.627   50  \n",
            "1                       0.351   31  \n",
            "2                       0.672   32  \n",
            "3                       0.167   21  \n",
            "4                       2.288   33  \n",
            "..                        ...  ...  \n",
            "763                     0.171   63  \n",
            "764                     0.340   27  \n",
            "765                     0.245   30  \n",
            "766                     0.349   47  \n",
            "767                     0.315   23  \n",
            "\n",
            "[768 rows x 8 columns]\n"
          ]
        }
      ],
      "source": [
        "print(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JkbJgpimkuaP",
        "outputId": "530b979b-ccb2-4b90-b8fa-87a6677a0948"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0      1\n",
            "1      0\n",
            "2      1\n",
            "3      0\n",
            "4      1\n",
            "      ..\n",
            "763    0\n",
            "764    0\n",
            "765    0\n",
            "766    1\n",
            "767    0\n",
            "Name: Outcome, Length: 768, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bA4hUjXekyaB"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SanVvzR6k6WQ"
      },
      "source": [
        "Data Standardization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P-g3nY61k-PB"
      },
      "outputs": [],
      "source": [
        "scaler = StandardScaler()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dmdVu2RElW0S",
        "outputId": "1b6c70c2-4130-4c23-94f4-2f5db5b32939"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "StandardScaler()"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "scaler.fit(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "54_50YfWlbSR"
      },
      "outputs": [],
      "source": [
        "standardized_data = scaler.transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ZjGymvElpNa",
        "outputId": "583198fa-6a3c-4cd3-f6fd-07eb41f612fc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 0.63994726  0.84832379  0.14964075 ...  0.20401277  0.46849198\n",
            "   1.4259954 ]\n",
            " [-0.84488505 -1.12339636 -0.16054575 ... -0.68442195 -0.36506078\n",
            "  -0.19067191]\n",
            " [ 1.23388019  1.94372388 -0.26394125 ... -1.10325546  0.60439732\n",
            "  -0.10558415]\n",
            " ...\n",
            " [ 0.3429808   0.00330087  0.14964075 ... -0.73518964 -0.68519336\n",
            "  -0.27575966]\n",
            " [-0.84488505  0.1597866  -0.47073225 ... -0.24020459 -0.37110101\n",
            "   1.17073215]\n",
            " [-0.84488505 -0.8730192   0.04624525 ... -0.20212881 -0.47378505\n",
            "  -0.87137393]]\n"
          ]
        }
      ],
      "source": [
        "print(standardized_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GEvQ9S6BlwD2"
      },
      "outputs": [],
      "source": [
        "X = standardized_data\n",
        "Y = diabetes_dataset['Outcome']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kfpIAfTHmMpB",
        "outputId": "d65e68b5-2a7b-4f8f-a995-ded578dcfeaf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 0.63994726  0.84832379  0.14964075 ...  0.20401277  0.46849198\n",
            "   1.4259954 ]\n",
            " [-0.84488505 -1.12339636 -0.16054575 ... -0.68442195 -0.36506078\n",
            "  -0.19067191]\n",
            " [ 1.23388019  1.94372388 -0.26394125 ... -1.10325546  0.60439732\n",
            "  -0.10558415]\n",
            " ...\n",
            " [ 0.3429808   0.00330087  0.14964075 ... -0.73518964 -0.68519336\n",
            "  -0.27575966]\n",
            " [-0.84488505  0.1597866  -0.47073225 ... -0.24020459 -0.37110101\n",
            "   1.17073215]\n",
            " [-0.84488505 -0.8730192   0.04624525 ... -0.20212881 -0.47378505\n",
            "  -0.87137393]]\n",
            "0      1\n",
            "1      0\n",
            "2      1\n",
            "3      0\n",
            "4      1\n",
            "      ..\n",
            "763    0\n",
            "764    0\n",
            "765    0\n",
            "766    1\n",
            "767    0\n",
            "Name: Outcome, Length: 768, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(X)\n",
        "print(Y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IcL4WEypmxBe"
      },
      "source": [
        "Train Test split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8-Hvranum1aY"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, Y_train,Y_test = train_test_split(X,Y, test_size=0.2, stratify = Y, random_state=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BoNORVqVxCDo",
        "outputId": "69ac7b40-3c93-4519-9c71-16430e44b115"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(768, 8) (614, 8) (154, 8)\n"
          ]
        }
      ],
      "source": [
        "print(X.shape, X_train.shape,X_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RegZtHn4yZpu"
      },
      "source": [
        "Training the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D7verpLJyb5f"
      },
      "outputs": [],
      "source": [
        "classifier = svm.SVC(kernel='linear')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J4TlBngLys0v",
        "outputId": "7cd66f05-652f-4257-82b4-2a43bacdbe4d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#training the support vector Machine Classifier\n",
        "classifier.fit(X_train, Y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aoEog54ozb6W"
      },
      "source": [
        "Model Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1bBmgQpkzgia"
      },
      "source": [
        "Accuracy Score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "id": "UKLae2Asy-mj",
        "outputId": "54271a27-7905-4bc0-ddd2-478568996a38"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-541ffd85bf48>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#accuracy score on the training data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mX_train_prediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\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[0mtraining_data_accuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_prediction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'classifier' is not defined"
          ]
        }
      ],
      "source": [
        "#accuracy score on the training data\n",
        "X_train_prediction = classifier.predict(X_train)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PRDSoDSzVpEI",
        "outputId": "3f5043f3-880b-4c79-b227-eecb1b4efeed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy score of the training data\n"
          ]
        }
      ],
      "source": [
        "print(\"Accuracy score of the training data\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j6t5QLW8Vuhw"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Diabetes Prediction using ML",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}