classifier_model_gender.ipynb

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        "pip install pandas-profiling==2.7.1 "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pandas-profiling==2.7.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/50/8a/25da481171f4912e2515a76fe31b7a4f036a443b8858b244ef7daaffd5b6/pandas_profiling-2.7.1-py2.py3-none-any.whl (252kB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/6d/55/b4726d81e5d6509fa3441f770f8a9524612627dc1b2a7d6209d1d20083fe/confuse-1.4.0-py2.py3-none-any.whl\n",
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            "  Downloading https://files.pythonhosted.org/packages/b3/e7/fcd59e12169de19f0131ff2812077f964c6b960e7c09804d30a7bf2ab461/htmlmin-0.1.12.tar.gz\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/13/fe/7614dec3db3f20882ff12dae0a58b579e97b590f2994ce9c953fe179d512/visions-0.4.1-py3-none-any.whl (58kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b7/ce/193e8ddf62d4be643b9b4b20e8e9c63b2f6a20f92778c0410c629f89bdaa/phik-0.11.2.tar.gz (1.1MB)\n",
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            "Collecting imagehash; extra == \"type_image_path\"\n",
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            "Building wheels for collected packages: htmlmin, phik\n",
            "  Building wheel for htmlmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for htmlmin: filename=htmlmin-0.1.12-cp37-none-any.whl size=27085 sha256=fefa7b2b2de1708235485712e653223c8f580de05ca49b0781f7ddc2f1795c43\n",
            "  Stored in directory: /root/.cache/pip/wheels/43/07/ac/7c5a9d708d65247ac1f94066cf1db075540b85716c30255459\n",
            "  Building wheel for phik (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for phik: filename=phik-0.11.2-cp37-none-any.whl size=1107413 sha256=06f9b98f8dda7f53e1b15090f98180a7acd7c27a4ef49d38880903f5ec7d92b5\n",
            "  Stored in directory: /root/.cache/pip/wheels/c0/a3/b0/f27b1cfe32ea131a3715169132ff6d85653789e80e966c3bf6\n",
            "Successfully built htmlmin phik\n",
            "\u001b[31mERROR: phik 0.11.2 has requirement scipy>=1.5.2, but you'll have scipy 1.4.1 which is incompatible.\u001b[0m\n",
            "Installing collected packages: tqdm, confuse, htmlmin, tangled-up-in-unicode, imagehash, visions, phik, pandas-profiling\n",
            "  Found existing installation: tqdm 4.41.1\n",
            "    Uninstalling tqdm-4.41.1:\n",
            "      Successfully uninstalled tqdm-4.41.1\n",
            "  Found existing installation: pandas-profiling 1.4.1\n",
            "    Uninstalling pandas-profiling-1.4.1:\n",
            "      Successfully uninstalled pandas-profiling-1.4.1\n",
            "Successfully installed confuse-1.4.0 htmlmin-0.1.12 imagehash-4.2.0 pandas-profiling-2.7.1 phik-0.11.2 tangled-up-in-unicode-0.1.0 tqdm-4.60.0 visions-0.4.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GZ7NMFYCJmC4"
      },
      "source": [
        "import pandas as pd\n",
        "import pandas_profiling\n",
        "import numpy as np\n",
        "from pandas_profiling import ProfileReport\n",
        "pd.set_option('display.max_columns', None)\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC, LinearSVC\n",
        "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2PrCO6iWMr_2",
        "outputId": "a7f1f200-92f1-4306-cfe6-7f1adaf11e6c"
      },
      "source": [
        "df=pd.read_csv('training_data.csv')\n",
        "print(df)\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      patient_id standard_lab_parameter_name  parameter_value      unit  \\\n",
            "0              0                         WBC           579.00   10^3/μl   \n",
            "1              1                          T4             5.00     μg/dl   \n",
            "2              2                         WBC             6.81   10^3/μl   \n",
            "3              3                          T4             5.70     μg/dl   \n",
            "4              4                         WBC             4.64   10^3/μl   \n",
            "...          ...                         ...              ...       ...   \n",
            "9995        9995                         WBC             9.14   10^3/μl   \n",
            "9996        9996                         WBC             4.90  thou/mm3   \n",
            "9997        9997                          T4             9.80     μg/dl   \n",
            "9998        9998                         WBC             4.93   10^3/μl   \n",
            "9999        9999                          T4             6.00     μg/dl   \n",
            "\n",
            "                            created_at  reference_high  reference_low  \\\n",
            "0              2019-07-02 01:19:03.581            10.0            4.0   \n",
            "1              2019-07-02 01:19:03.581            12.0            4.5   \n",
            "2              2019-05-06 14:07:51.925            10.0            4.0   \n",
            "3              2019-05-06 14:07:51.925            12.0            4.5   \n",
            "4     2019-07-21 13:41:00.528523+00:00            10.0            4.0   \n",
            "...                                ...             ...            ...   \n",
            "9995  2019-09-06 16:07:32.846153+00:00            10.0            4.0   \n",
            "9996  2019-11-25 11:53:17.630406+00:00            10.0            4.0   \n",
            "9997  2019-09-16 13:21:24.372339+00:00            12.0            4.5   \n",
            "9998  2019-09-16 13:21:24.372339+00:00            10.0            4.0   \n",
            "9999  2019-09-25 09:43:03.695340+00:00            12.0            4.5   \n",
            "\n",
            "     age_group gender  \n",
            "0          old   male  \n",
            "1          old   male  \n",
            "2        adult   male  \n",
            "3        adult   male  \n",
            "4        adult   male  \n",
            "...        ...    ...  \n",
            "9995     adult   male  \n",
            "9996     adult   male  \n",
            "9997       old   male  \n",
            "9998       old   male  \n",
            "9999     adult   male  \n",
            "\n",
            "[10000 rows x 9 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nt6uwgaMxAEm"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "46f3d8b9007541a0a2b1c58471b693ac",
            "03122822944047c19e2f57aa363d34b0",
            "71e04657880a4890a1baed65441bbd24",
            "a168a51028434e91b533497c59c87326",
            "84d91c1a0cf34c9e8714e555ef56a0b3",
            "fecbef553a0f4c958772ab218848c7c4",
            "5daada0ffb41428880c10b9c905bbcb4",
            "c4149dfde7e2484ea001fb4aeda81ef7",
            "177a070c184b47708a1913ecc056100b",
            "01e7032fb77b4204944303862df15f4b",
            "d59619cde9074e24a571209d15b7ac9e",
            "64da52e040714e0bb491ffa229cf3524",
            "067c6cd9f47941e5a624d3e214b82499",
            "62222347ea784fa692730a295dccdc8b",
            "64ff4c9eac054435bb895cdae84c4f48",
            "790e513869a5491796d609606b62b751",
            "765d5cf590b74e5aa88da50afd4657f8",
            "01c6b88ae91b457fafd16a072e7f4cbe",
            "7f3ad868bf4c4af9b0659a7bdf6b9304",
            "12217b00d379417eb29bd55f38584284",
            "bb9a14b18e8c4b4b89e916a4c2e21c7e",
            "88112c1cc614475096b399fb6809c1b4",
            "39b9e0303a8b48a8a9f2fb08c55f6d6a",
            "81b1653dfdce4bab989a08f1aaee99a3",
            "fb73fd1ae1724a02bd348834bede7611",
            "42b19383e715449d8c4a822a75afd989",
            "9a9cfa6e259542b59b6a3ac661c41740",
            "d2331cf2628d4bcbbbf4eb9ea52e1878",
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            "d626ebf25442485f83ee65379beda11b",
            "758b2d4269a9401fbecd04be049667a6",
            "8f41d1c02f02410c84a6d27ee22b66ba",
            "811ef389c65f4a5087015577a6eef62d",
            "9a380873a1db4d738637ac47e8339a3b",
            "0a43764bb7774af1872586b8a7e837d3",
            "43d35156a9cf4fad811f2cedd7029eea",
            "24a7020dbb2e4e56bceb1527c5598d9f",
            "07743633b8fc4106b5151148ebcaf4b1",
            "caf0b6c18e8544acbdec70baf26466e4",
            "c12637d4b2a34b24b20645895e3be80a",
            "ba7fd99896fa4a97a1a1aa1a64b8c216",
            "fb299b991065470d9268d0b640cbf6e3",
            "cf96ec4e16af4a798d2211691b355ed3",
            "3ea1f15c8b244a47a1a23740e0239dfb"
          ]
        },
        "id": "Sd2kT2RmNHDh",
        "outputId": "e00da2bb-31c1-4b37-c462-654fc3515d36"
      },
      "source": [
        "#generate profile report\n",
        "profile= ProfileReport(df)\n",
        "profile.to_file(output_file=\"profile1.html\")"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "46f3d8b9007541a0a2b1c58471b693ac",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Summarize dataset:   0%|          | 0/21 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "64da52e040714e0bb491ffa229cf3524",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "39b9e0303a8b48a8a9f2fb08c55f6d6a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9a380873a1db4d738637ac47e8339a3b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGRDGQk7MXrH",
        "outputId": "81b3fe43-8066-4114-e260-f82a2caad353"
      },
      "source": [
        "# Read in data and display first 5 rows\n",
        "features = pd.read_csv('training_data.csv')\n",
        "test_file_data= pd.read_csv('test_data.csv')\n",
        "print(features.head(5))\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   patient_id standard_lab_parameter_name  parameter_value     unit  \\\n",
            "0           0                         WBC           579.00  10^3/μl   \n",
            "1           1                          T4             5.00    μg/dl   \n",
            "2           2                         WBC             6.81  10^3/μl   \n",
            "3           3                          T4             5.70    μg/dl   \n",
            "4           4                         WBC             4.64  10^3/μl   \n",
            "\n",
            "                         created_at  reference_high  reference_low age_group  \\\n",
            "0           2019-07-02 01:19:03.581            10.0            4.0       old   \n",
            "1           2019-07-02 01:19:03.581            12.0            4.5       old   \n",
            "2           2019-05-06 14:07:51.925            10.0            4.0     adult   \n",
            "3           2019-05-06 14:07:51.925            12.0            4.5     adult   \n",
            "4  2019-07-21 13:41:00.528523+00:00            10.0            4.0     adult   \n",
            "\n",
            "  gender  \n",
            "0   male  \n",
            "1   male  \n",
            "2   male  \n",
            "3   male  \n",
            "4   male  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UjBY7xCXVYoY"
      },
      "source": [
        "features=features.drop(['unit','patient_id','created_at'],axis=1)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "a26fzOBbV9ac",
        "outputId": "29c32c2b-18be-41d9-eff0-7f397cf36e41"
      },
      "source": [
        "features.head(5)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>standard_lab_parameter_name</th>\n",
              "      <th>parameter_value</th>\n",
              "      <th>reference_high</th>\n",
              "      <th>reference_low</th>\n",
              "      <th>age_group</th>\n",
              "      <th>gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>WBC</td>\n",
              "      <td>579.00</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>old</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>T4</td>\n",
              "      <td>5.00</td>\n",
              "      <td>12.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>old</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>WBC</td>\n",
              "      <td>6.81</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>adult</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>T4</td>\n",
              "      <td>5.70</td>\n",
              "      <td>12.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>adult</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>WBC</td>\n",
              "      <td>4.64</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>adult</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  standard_lab_parameter_name  parameter_value  reference_high  reference_low  \\\n",
              "0                         WBC           579.00            10.0            4.0   \n",
              "1                          T4             5.00            12.0            4.5   \n",
              "2                         WBC             6.81            10.0            4.0   \n",
              "3                          T4             5.70            12.0            4.5   \n",
              "4                         WBC             4.64            10.0            4.0   \n",
              "\n",
              "  age_group gender  \n",
              "0       old   male  \n",
              "1       old   male  \n",
              "2     adult   male  \n",
              "3     adult   male  \n",
              "4     adult   male  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ra_TtK5zMTbn",
        "outputId": "23f7f681-a6a0-49c8-f421-c8e085133aae"
      },
      "source": [
        "print('The shape of our features is:', features.shape)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The shape of our features is: (10000, 6)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "EuMMkLwxuiq9",
        "outputId": "2dbe3b38-7eb4-4e34-ff9e-94b801a6b07c"
      },
      "source": [
        "# Descriptive statistics for each column\n",
        "features.describe()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>parameter_value</th>\n",
              "      <th>reference_high</th>\n",
              "      <th>reference_low</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>356.897325</td>\n",
              "      <td>372.848728</td>\n",
              "      <td>162.951938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9289.389443</td>\n",
              "      <td>1800.575300</td>\n",
              "      <td>712.506331</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-71.000000</td>\n",
              "      <td>1.120000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6.700000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.830000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>4.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>101.000000</td>\n",
              "      <td>107.000000</td>\n",
              "      <td>98.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>920000.000000</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       parameter_value  reference_high  reference_low\n",
              "count     10000.000000    10000.000000   10000.000000\n",
              "mean        356.897325      372.848728     162.951938\n",
              "std        9289.389443     1800.575300     712.506331\n",
              "min         -71.000000        1.120000       0.000000\n",
              "25%           6.700000       10.000000       4.000000\n",
              "50%           8.830000       12.000000       4.500000\n",
              "75%         101.000000      107.000000      98.000000\n",
              "max      920000.000000    15000.000000    5000.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sh6_foUiUnWJ",
        "outputId": "41dbf153-f8f4-4b5e-ecc5-5eb1b505a287"
      },
      "source": [
        "print(features.values)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['WBC' 579.0 10.0 4.0 'old' 'male']\n",
            " ['T4' 5.0 12.0 4.5 'old' 'male']\n",
            " ['WBC' 6.81 10.0 4.0 'adult' 'male']\n",
            " ...\n",
            " ['T4' 9.8 12.0 4.5 'old' 'male']\n",
            " ['WBC' 4.93 10.0 4.0 'old' 'male']\n",
            " ['T4' 6.0 12.0 4.5 'adult' 'male']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_gVl0j44cGP-"
      },
      "source": [
        "gender_list = features['gender'].unique().tolist()\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9llq1VsNcs4F",
        "outputId": "cc353386-883e-49fd-c45e-d1e8d1a2faa2"
      },
      "source": [
        "gender_list"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['male', 'female', 'Female', 'mAle']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CcmB9KoQdSoM"
      },
      "source": [
        "features[\"gender\"] = features[\"gender\"].str.lower()"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LC3ElCgqdX9W",
        "outputId": "ee0027bd-d9a9-4190-b068-3a1323b5dda0"
      },
      "source": [
        "gender_list_new=features['gender'].unique().tolist()\n",
        "print(gender_list_new)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['male', 'female']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JEMCSoKwej6i",
        "outputId": "c023e66b-649c-4167-cc07-44ec7f77187a"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "  \n",
        "# label_encoder object knows how to understand word labels.\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "  \n",
        "# Encode labels in column 'species'.\n",
        "features['gender']= label_encoder.fit_transform(features['gender'])\n",
        "  \n",
        "features['gender'].unique()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gz2PhClJbEhr"
      },
      "source": [
        "labels = np.array(features['gender'])\n",
        "\n",
        "# Remove the labels from the features\n",
        "# axis 1 refers to the columns\n",
        "features= features.drop('gender', axis = 1)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uu1qldhmenee",
        "outputId": "1e411407-c424-4584-d416-93a31ca39c75"
      },
      "source": [
        "labels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 1, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NoI3bofzaaPs"
      },
      "source": [
        "features_encoded = pd.get_dummies(features)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "id": "7Wz1UHb0anDa",
        "outputId": "ea892439-b42c-4ab9-f4e5-2133cb026542"
      },
      "source": [
        "features_encoded.head(5)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>parameter_value</th>\n",
              "      <th>reference_high</th>\n",
              "      <th>reference_low</th>\n",
              "      <th>standard_lab_parameter_name_Chloride</th>\n",
              "      <th>standard_lab_parameter_name_SGPT</th>\n",
              "      <th>standard_lab_parameter_name_Sodium</th>\n",
              "      <th>standard_lab_parameter_name_T4</th>\n",
              "      <th>standard_lab_parameter_name_WBC</th>\n",
              "      <th>age_group_adult</th>\n",
              "      <th>age_group_child</th>\n",
              "      <th>age_group_old</th>\n",
              "      <th>age_group_teen</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>579.00</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.00</td>\n",
              "      <td>12.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6.81</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.70</td>\n",
              "      <td>12.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.64</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   parameter_value  reference_high  reference_low  \\\n",
              "0           579.00            10.0            4.0   \n",
              "1             5.00            12.0            4.5   \n",
              "2             6.81            10.0            4.0   \n",
              "3             5.70            12.0            4.5   \n",
              "4             4.64            10.0            4.0   \n",
              "\n",
              "   standard_lab_parameter_name_Chloride  standard_lab_parameter_name_SGPT  \\\n",
              "0                                     0                                 0   \n",
              "1                                     0                                 0   \n",
              "2                                     0                                 0   \n",
              "3                                     0                                 0   \n",
              "4                                     0                                 0   \n",
              "\n",
              "   standard_lab_parameter_name_Sodium  standard_lab_parameter_name_T4  \\\n",
              "0                                   0                               0   \n",
              "1                                   0                               1   \n",
              "2                                   0                               0   \n",
              "3                                   0                               1   \n",
              "4                                   0                               0   \n",
              "\n",
              "   standard_lab_parameter_name_WBC  age_group_adult  age_group_child  \\\n",
              "0                                1                0                0   \n",
              "1                                0                0                0   \n",
              "2                                1                1                0   \n",
              "3                                0                1                0   \n",
              "4                                1                1                0   \n",
              "\n",
              "   age_group_old  age_group_teen  \n",
              "0              1               0  \n",
              "1              1               0  \n",
              "2              0               0  \n",
              "3              0               0  \n",
              "4              0               0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9FZqZK74fqwu"
      },
      "source": [
        "\n",
        "feature_encoded_list = list(features_encoded.columns)\n",
        "x_array = np.array(features_encoded)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RdrQpDpWf6D-",
        "outputId": "560c5705-9d5c-4d10-eeed-629b21e4a1b8"
      },
      "source": [
        "x_array.shape"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tu-zYi5HfN2m",
        "outputId": "9fafb623-61c6-4d6e-a66f-ca6e194ca0b7"
      },
      "source": [
        "labels.shape"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sFi9XN_AfQ7G"
      },
      "source": [
        "y_array=labels.reshape((10000,1))"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rIscz7XW0GmN"
      },
      "source": [
        "# Using Skicit-learn to split data into training and testing sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "#Split the data into training and testing sets\n",
        "x_train, x_test, y_train,y_test = train_test_split(x_array, y_array, test_size = 0.20, random_state = 42)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w729ywi71t6h",
        "outputId": "8f289ff0-4a57-41c0-8aa7-800e4aebebfd"
      },
      "source": [
        "print(x_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(8000, 12)\n",
            "(2000, 12)\n",
            "(8000, 1)\n",
            "(2000, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5iHwx0JOo_Pl"
      },
      "source": [
        "Using logistic regression\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bkb_Kceikejh",
        "outputId": "3591b205-a433-4100-f7f1-101d1ae7016e"
      },
      "source": [
        "#using logistic regression\n",
        "logreg = LogisticRegression()\n",
        "\n",
        "logreg.fit(x_train, y_train)\n",
        "\n",
        "y_pred_logreg = logreg.predict(x_test)\n",
        "\n",
        "logreg.score(x_train, y_train)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.884"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a8uuJhkwo7O3"
      },
      "source": [
        "Using support vector machines\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1sreG5qqkwzU",
        "outputId": "1193b494-c3d8-4db7-d299-b7ba621e919f"
      },
      "source": [
        "svc = SVC()\n",
        "\n",
        "svc.fit(x_train, y_train)\n",
        "\n",
        "y_pred_svm = svc.predict(x_test)\n",
        "\n",
        "svc.score(x_train, y_train)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.88425"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hb__Omt0pIhh"
      },
      "source": [
        "using random forest\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tGD8FR3Mk7ED",
        "outputId": "4b943b33-01bc-40fc-9fee-3856b4c1ec76"
      },
      "source": [
        "random_forest = RandomForestClassifier(n_estimators=100,oob_score=True,max_features=5)\n",
        "\n",
        "random_forest.fit(x_train, y_train)\n",
        "\n",
        "y_pred_rf = random_forest.predict(x_test)\n",
        "\n",
        "random_forest.score(x_train, y_train)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.920375"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G0Lz01s6pLTF"
      },
      "source": [
        "using gradeint boost\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AqwJQee-lIJ9",
        "outputId": "7c6fc48c-3cb6-4cda-d217-9a004347c681"
      },
      "source": [
        "grad_boost = GradientBoostingClassifier(n_estimators=1000)\n",
        "grad_boost.fit(x_train, y_train)\n",
        "y_pred_grad = grad_boost.predict(x_test)\n",
        "grad_boost.score(x_train, y_train)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_gb.py:1454: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9095"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jlcBRgP0pbZy"
      },
      "source": [
        "predictions using test file\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4MdqYZldgTX5"
      },
      "source": [
        "test_data=test_file_data.drop(['unit','patient_id','created_at'],axis=1)\n",
        "test_data = test_data.iloc[: , 1:]\n",
        "test_data_encoded = pd.get_dummies(test_data)\n",
        "x_test_file = np.array(test_data_encoded)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "id": "2n_fn6l9gy6l",
        "outputId": "90fbe979-0a14-4a13-8b6b-90e2b4c3948b"
      },
      "source": [
        "test_data_encoded.head(5)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>parameter_value</th>\n",
              "      <th>reference_high</th>\n",
              "      <th>reference_low</th>\n",
              "      <th>standard_lab_parameter_name_Chloride</th>\n",
              "      <th>standard_lab_parameter_name_SGPT</th>\n",
              "      <th>standard_lab_parameter_name_Sodium</th>\n",
              "      <th>standard_lab_parameter_name_T4</th>\n",
              "      <th>standard_lab_parameter_name_WBC</th>\n",
              "      <th>age_group_adult</th>\n",
              "      <th>age_group_child</th>\n",
              "      <th>age_group_old</th>\n",
              "      <th>age_group_teen</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.10</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.00</td>\n",
              "      <td>12.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.10</td>\n",
              "      <td>12.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.62</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9350.00</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   parameter_value  reference_high  reference_low  \\\n",
              "0             8.10            10.0            4.0   \n",
              "1            13.00            12.0            4.5   \n",
              "2             7.10            12.0            4.5   \n",
              "3             5.62            10.0            4.0   \n",
              "4          9350.00         10000.0         4000.0   \n",
              "\n",
              "   standard_lab_parameter_name_Chloride  standard_lab_parameter_name_SGPT  \\\n",
              "0                                     0                                 0   \n",
              "1                                     0                                 0   \n",
              "2                                     0                                 0   \n",
              "3                                     0                                 0   \n",
              "4                                     0                                 0   \n",
              "\n",
              "   standard_lab_parameter_name_Sodium  standard_lab_parameter_name_T4  \\\n",
              "0                                   0                               0   \n",
              "1                                   0                               1   \n",
              "2                                   0                               1   \n",
              "3                                   0                               0   \n",
              "4                                   0                               0   \n",
              "\n",
              "   standard_lab_parameter_name_WBC  age_group_adult  age_group_child  \\\n",
              "0                                1                1                0   \n",
              "1                                0                1                0   \n",
              "2                                0                1                0   \n",
              "3                                1                1                0   \n",
              "4                                1                0                0   \n",
              "\n",
              "   age_group_old  age_group_teen  \n",
              "0              0               0  \n",
              "1              0               0  \n",
              "2              0               0  \n",
              "3              0               0  \n",
              "4              1               0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LiGWNYiDhdHT"
      },
      "source": [
        "\n",
        "predictions = random_forest.predict(x_test_file)\n",
        "\n",
        "\n"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZsbSdxf_naBJ",
        "outputId": "72759f3f-d1f2-423f-837f-98b342042dd7"
      },
      "source": [
        "print(predictions)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 1 1 ... 1 1 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HFRcYJfMopja",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b2cf96b-594a-4e61-c249-c4629e9c46ea"
      },
      "source": [
        "\n",
        "print(label_encoder.inverse_transform(predictions)[:10])"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['male' 'male' 'male' 'male' 'male' 'male' 'male' 'male' 'male' 'male']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pC5SASLdpfNI"
      },
      "source": [
        "Accuracy measures"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2KKVK48Rlhrc",
        "outputId": "fdf1e0b8-60e9-4320-dbe1-b2c6e8fad816"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "confusion_matrix(y_test, y_pred_rf)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  41,  191],\n",
              "       [  53, 1715]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xQ9rDB4khnJc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f52561fc-e734-4d47-ba08-571574c1aa98"
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "print(classification_report(y_pred_rf,y_test))\n",
        "print(accuracy_score(y_pred_rf, y_test))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.18      0.44      0.25        94\n",
            "           1       0.97      0.90      0.93      1906\n",
            "\n",
            "    accuracy                           0.88      2000\n",
            "   macro avg       0.57      0.67      0.59      2000\n",
            "weighted avg       0.93      0.88      0.90      2000\n",
            "\n",
            "0.878\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "poPbFPQGp2OC",
        "outputId": "c78b7a50-6cd9-4186-e2f7-19776cacd695"
      },
      "source": [
        "from sklearn import datasets, metrics, model_selection, svm\n",
        "metrics.plot_roc_curve(random_forest, x_test, y_test) "
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7f1f9b371f50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WeFmf2Nqp49K"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}