TanzaniaGEOTEST_RanForrModel.ipynb

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  "metadata": {
    "colab": {
      "name": "TanzaniaGEOTEST_RanForrModel.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMz0TTsqmrshvVAFBvk/OT2",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kvinne-anc/Tanzania-Soil-Topography-Waterpumps-Project/blob/main/TanzaniaGEOTEST_RanForrModel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UxiyClCrt_lQ",
        "colab": {
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        },
        "outputId": "cf6d67e9-4c54-4ad4-e44f-1eb97d64e4e0"
      },
      "source": [
        "%%time \n",
        "\n",
        "# Important library for many geopython libraries\n",
        "!apt install gdal-bin python-gdal python3-gdal \n",
        "# Install rtree - Geopandas requirment\n",
        "!apt install python3-rtree \n",
        "# Install Geopandas\n",
        "!pip install git+git://github.com/geopandas/geopandas.git\n",
        "# Install descartes - Geopandas requirment\n",
        "!pip install descartes \n",
        "# Install Folium for Geographic data visualization\n",
        "!pip install folium\n",
        "# Install plotlyExpress\n",
        "!pip install plotly_express"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "gdal-bin is already the newest version (2.2.3+dfsg-2).\n",
            "python-gdal is already the newest version (2.2.3+dfsg-2).\n",
            "The following additional packages will be installed:\n",
            "  python3-numpy\n",
            "Suggested packages:\n",
            "  python-numpy-doc python3-nose python3-numpy-dbg\n",
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            "  python3-gdal python3-numpy\n",
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            "Reading package lists... Done\n",
            "Building dependency tree       \n",
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            "The following additional packages will be installed:\n",
            "  libspatialindex-c4v5 libspatialindex-dev libspatialindex4v5\n",
            "  python3-pkg-resources\n",
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            "  libspatialindex-c4v5 libspatialindex-dev libspatialindex4v5\n",
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            "\n",
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            "  Running command git clone -q git://github.com/geopandas/geopandas.git /tmp/pip-req-build-37xhncfm\n",
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            "  Downloading https://files.pythonhosted.org/packages/cc/ab/85d8da5c9a45e072301beb37ad7f833cd344e04c817d97e0cc75681d248f/munch-2.5.0-py2.py3-none-any.whl\n",
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            "  Downloading https://files.pythonhosted.org/packages/73/86/43fa9f15c5b9fb6e82620428827cd3c284aa933431405d1bcf5231ae3d3e/cligj-0.7.2-py3-none-any.whl\n",
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            "Building wheels for collected packages: geopandas\n",
            "  Building wheel for geopandas (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for geopandas: filename=geopandas-0.9.0+36.gcb88dd4-py2.py3-none-any.whl size=996489 sha256=4548a00cf38fd8aa39c189d222c70c44d81de9e95be7d7b7510656e56e42290e\n",
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            "Successfully built geopandas\n",
            "Installing collected packages: munch, cligj, click-plugins, fiona, pyproj, geopandas\n",
            "Successfully installed click-plugins-1.1.1 cligj-0.7.2 fiona-1.8.20 geopandas-0.9.0+36.gcb88dd4 munch-2.5.0 pyproj-3.1.0\n",
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            "Collecting plotly_express\n",
            "  Downloading https://files.pythonhosted.org/packages/d4/d6/8a2906f51e073a4be80cab35cfa10e7a34853e60f3ed5304ac470852a08d/plotly_express-0.4.1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: plotly>=4.1.0 in /usr/local/lib/python3.7/dist-packages (from plotly_express) (4.4.1)\n",
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            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.20.0->plotly_express) (2018.9)\n",
            "Installing collected packages: plotly-express\n",
            "Successfully installed plotly-express-0.4.1\n",
            "CPU times: user 475 ms, sys: 111 ms, total: 585 ms\n",
            "Wall time: 47.2 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YN9dl6b1uQ_1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2cef4523-a5b7-4d64-8b12-4ea73b884560"
      },
      "source": [
        "!pip install shapely"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: shapely in /usr/local/lib/python3.7/dist-packages (1.7.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0URy_tPIuRIZ"
      },
      "source": [
        "from shapely.geometry.polygon import Polygon\n",
        "from shapely.geometry.multipolygon import MultiPolygon"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MfqURLdDuRPN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "outputId": "fd2f50cb-91f9-4ea0-898b-acec4ffe1818"
      },
      "source": [
        "!pip install PyShp"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting PyShp\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ca/1f/e9cc2c3fce32e2926581f8b6905831165235464c858ba550b6e9b8ef78c3/pyshp-2.1.2.tar.gz (217kB)\n",
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            "\u001b[?25hBuilding wheels for collected packages: PyShp\n",
            "  Building wheel for PyShp (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for PyShp: filename=pyshp-2.1.2-cp36-none-any.whl size=36216 sha256=fd8990e52b5563f13b412baa1b1e3df8501667e895d532e4da992ec37705f647\n",
            "  Stored in directory: /root/.cache/pip/wheels/96/6c/53/4112475adf3b831da97f083163d0f38ee6daac9c1b13f7afea\n",
            "Successfully built PyShp\n",
            "Installing collected packages: PyShp\n",
            "Successfully installed PyShp-2.1.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJCd_E8IuRVs"
      },
      "source": [
        "import pandas as pd\n",
        "import geopandas as gdp \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns \n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dUSzeUMcuyjG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e294f84-3c1d-4261-e72c-734102b318e5"
      },
      "source": [
        "  !pip install category_encoders==2.*\n",
        "  !pip install pandas-profiling==2.*"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting category_encoders==2.*\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/44/57/fcef41c248701ee62e8325026b90c432adea35555cbc870aff9cfba23727/category_encoders-2.2.2-py2.py3-none-any.whl (80kB)\n",
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            "Installing collected packages: category-encoders\n",
            "Successfully installed category-encoders-2.2.2\n",
            "Collecting pandas-profiling==2.*\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/2d/e6/c3ad340cbbf8b93f84922c80f92359a6e6c7f88919c4d4353b8b7fe17f8d/pandas_profiling-2.13.0-py2.py3-none-any.whl (245kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/29/c1/24814557f1d22c56d50280771a17307e6bf87b70727d975fd6b2ce6b014a/requests-2.25.1-py2.py3-none-any.whl (61kB)\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 tqdm>=4.48.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/42/d7/f357d98e9b50346bcb6095fe3ad205d8db3174eb5edb03edfe7c4099576d/tqdm-4.61.0-py2.py3-none-any.whl (75kB)\n",
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            "\u001b[?25hCollecting tangled-up-in-unicode>=0.0.6\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/93/3e/cb354fb2097fcf2fd5b5a342b10ae2a6e9363ba435b64e3e00c414064bc7/tangled_up_in_unicode-0.1.0-py3-none-any.whl (3.1MB)\n",
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            "Collecting confuse>=1.0.0\n",
            "  Downloading https://files.pythonhosted.org/packages/6d/55/b4726d81e5d6509fa3441f770f8a9524612627dc1b2a7d6209d1d20083fe/confuse-1.4.0-py2.py3-none-any.whl\n",
            "Collecting htmlmin>=0.1.12\n",
            "  Downloading https://files.pythonhosted.org/packages/b3/e7/fcd59e12169de19f0131ff2812077f964c6b960e7c09804d30a7bf2ab461/htmlmin-0.1.12.tar.gz\n",
            "Collecting visions[type_image_path]==0.7.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/80/96/01e4ba22cef96ae5035dbcf0451c2f4f859f8f17393b98406b23f0034279/visions-0.7.1-py3-none-any.whl (102kB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/7a/d0/ce5ad0392aa12645b7ad91a5983d6b625b704b021d9cd48c587630c1a9ac/multimethod-1.4-py2.py3-none-any.whl\n",
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            "Collecting imagehash; extra == \"type_image_path\"\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8e/18/9dbb772b5ef73a3069c66bb5bf29b9fb4dd57af0d5790c781c3f559bcca6/ImageHash-4.2.0-py2.py3-none-any.whl (295kB)\n",
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            "Building wheels for collected packages: phik, htmlmin\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=1107437 sha256=87d68bea39ccd02a2f39a359660e81c09ca525532a19e8f36002751cff83bdd5\n",
            "  Stored in directory: /root/.cache/pip/wheels/c0/a3/b0/f27b1cfe32ea131a3715169132ff6d85653789e80e966c3bf6\n",
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            "  Created wheel for htmlmin: filename=htmlmin-0.1.12-cp37-none-any.whl size=27099 sha256=cd317b57c76bb177c2cc79883bebbc4cec750858b3961afc4aa5ece329c48dea\n",
            "  Stored in directory: /root/.cache/pip/wheels/43/07/ac/7c5a9d708d65247ac1f94066cf1db075540b85716c30255459\n",
            "Successfully built phik htmlmin\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\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: requests, phik, tqdm, tangled-up-in-unicode, confuse, htmlmin, multimethod, imagehash, visions, pandas-profiling\n",
            "  Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\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 multimethod-1.4 pandas-profiling-2.13.0 phik-0.11.2 requests-2.25.1 tangled-up-in-unicode-0.1.0 tqdm-4.61.0 visions-0.7.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GnCmntKouRc1"
      },
      "source": [
        "import sys\n",
        "\n",
        "# If you're on Colab:\n",
        "if 'google.colab' in sys.modules:\n",
        "    DATA_PATH = 'https://raw.githubusercontent.com/LambdaSchool/DS-Unit-2-Kaggle-Challenge/master/data/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T45jkj-auRrd"
      },
      "source": [
        "train = pd.merge(pd.read_csv('/content/final_waterpumps.csv'), \n",
        "                 pd.read_csv(DATA_PATH+'waterpumps/train_labels.csv'))\n",
        "\n",
        "test = pd.read_csv('/content/test_final.csv')\n",
        "\n",
        "sample_submission = pd.read_csv(DATA_PATH+'waterpumps/sample_submission.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ka4vbNh-yWv"
      },
      "source": [
        "train['date_recorded'] = train['date_recorded'].astype('datetime64[ns]')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzfLUzW6-yg3"
      },
      "source": [
        "train['date_recorded'] = train['date_recorded'].dt.year"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HKWSncpt-ykd"
      },
      "source": [
        "train['age'] = train['date_recorded']-train['construction_year']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KJxM2mTc_BRX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "6c7c02d1-375c-4757-e0ed-5b9edb508334"
      },
      "source": [
        "train['age']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0          12\n",
              "1           3\n",
              "2           4\n",
              "3          27\n",
              "4        2011\n",
              "         ... \n",
              "59395      14\n",
              "59396      15\n",
              "59397    2011\n",
              "59398    2011\n",
              "59399       9\n",
              "Name: age, Length: 59400, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 729
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FVqfOSNz_BU-"
      },
      "source": [
        "train['age'] = train['age'].replace([2011],0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fW8jTo1t_laQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "0bb5491d-750f-474c-a8a7-beb04e461004"
      },
      "source": [
        "train['age']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        12\n",
              "1         3\n",
              "2         4\n",
              "3        27\n",
              "4         0\n",
              "         ..\n",
              "59395    14\n",
              "59396    15\n",
              "59397     0\n",
              "59398     0\n",
              "59399     9\n",
              "Name: age, Length: 59400, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 731
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KJgppHTrAisF"
      },
      "source": [
        "test['date_recorded'] = test['date_recorded'].astype('datetime64[ns]')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nyQyF96vAivs"
      },
      "source": [
        "test['date_recorded'] = test['date_recorded'].dt.year"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L4SodDsCAi3y"
      },
      "source": [
        "test['age'] = test['date_recorded']-test['construction_year']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HttrgRNPh1k5"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2jgfqUx6Ai7G"
      },
      "source": [
        "test['age'] = test['age'].replace([2011],0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G6gIfQn8Ai0V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "dbd553e2-75a1-4049-db30-c7f66abebe62"
      },
      "source": [
        "test['age']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         1\n",
              "1        13\n",
              "2         3\n",
              "3        26\n",
              "4        13\n",
              "         ..\n",
              "14353    23\n",
              "14354    17\n",
              "14355     3\n",
              "14356     4\n",
              "14357     5\n",
              "Name: age, Length: 14358, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 736
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BegzzNXWuRzE"
      },
      "source": [
        "train, val = train_test_split(train, train_size=0.90, test_size=0.10, \n",
        "                              stratify=train['status_group'], random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YkpdmtGbuqdr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "49efc105-48b7-48b0-ee95-8afb65e36418"
      },
      "source": [
        "train.shape, val.shape, test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((53460, 48), (5940, 48), (14358, 47))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 738
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LK8NrybSuqlx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "fce3675e-6e97-4bd1-cc03-3e3807cebdcb"
      },
      "source": [
        "train['status_group'].value_counts(normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "functional                 0.543079\n",
              "non functional             0.384250\n",
              "functional needs repair    0.072671\n",
              "Name: status_group, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 739
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8KZRNZTfuqrI"
      },
      "source": [
        "#trained = train.drop(['extraction_type', 'id', 'index_right', 'quantity_group', 'num_private', 'basin', 'region', 'AREA', 'PERIMETER', 'public_meeting', 'recorded_by', 'scheme_management', 'permit', 'extraction_type_group', 'extraction_type_class', 'management_group', 'payment', 'payment_type', 'quality_group', 'source_type', 'source_class', 'MAIN_TYPE', 'date_recorded', 'waterpoint_type_group', 'status_group'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-G0yvpZH49zW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "outputId": "9ac32149-fbda-4ae1-df6f-42dab428938d"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": null,
      "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>id</th>\n",
              "      <th>amount_tsh</th>\n",
              "      <th>date_recorded</th>\n",
              "      <th>funder</th>\n",
              "      <th>gps_height</th>\n",
              "      <th>installer</th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>wpt_name</th>\n",
              "      <th>num_private</th>\n",
              "      <th>basin</th>\n",
              "      <th>subvillage</th>\n",
              "      <th>region</th>\n",
              "      <th>region_code</th>\n",
              "      <th>district_code</th>\n",
              "      <th>lga</th>\n",
              "      <th>ward</th>\n",
              "      <th>population</th>\n",
              "      <th>public_meeting</th>\n",
              "      <th>recorded_by</th>\n",
              "      <th>scheme_management</th>\n",
              "      <th>scheme_name</th>\n",
              "      <th>permit</th>\n",
              "      <th>construction_year</th>\n",
              "      <th>extraction_type</th>\n",
              "      <th>extraction_type_group</th>\n",
              "      <th>extraction_type_class</th>\n",
              "      <th>management</th>\n",
              "      <th>management_group</th>\n",
              "      <th>payment</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>water_quality</th>\n",
              "      <th>quality_group</th>\n",
              "      <th>quantity</th>\n",
              "      <th>quantity_group</th>\n",
              "      <th>source</th>\n",
              "      <th>source_type</th>\n",
              "      <th>source_class</th>\n",
              "      <th>waterpoint_type</th>\n",
              "      <th>waterpoint_type_group</th>\n",
              "      <th>geometry</th>\n",
              "      <th>index_right</th>\n",
              "      <th>AREA</th>\n",
              "      <th>PERIMETER</th>\n",
              "      <th>SOIL_TYPE</th>\n",
              "      <th>MAIN_TYPE</th>\n",
              "      <th>status_group</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>779</th>\n",
              "      <td>2940</td>\n",
              "      <td>50.0</td>\n",
              "      <td>2013</td>\n",
              "      <td>Jaica</td>\n",
              "      <td>57</td>\n",
              "      <td>JAICA</td>\n",
              "      <td>39.581692</td>\n",
              "      <td>-9.725771</td>\n",
              "      <td>Michungwani</td>\n",
              "      <td>0</td>\n",
              "      <td>Ruvuma / Southern Coast</td>\n",
              "      <td>Legezamwendo</td>\n",
              "      <td>Lindi</td>\n",
              "      <td>80</td>\n",
              "      <td>23</td>\n",
              "      <td>Lindi Rural</td>\n",
              "      <td>Mchinga</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>VWC</td>\n",
              "      <td>JAICA Borehole Scheme</td>\n",
              "      <td>False</td>\n",
              "      <td>2000</td>\n",
              "      <td>submersible</td>\n",
              "      <td>submersible</td>\n",
              "      <td>submersible</td>\n",
              "      <td>vwc</td>\n",
              "      <td>user-group</td>\n",
              "      <td>pay per bucket</td>\n",
              "      <td>per bucket</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>enough</td>\n",
              "      <td>enough</td>\n",
              "      <td>machine dbh</td>\n",
              "      <td>borehole</td>\n",
              "      <td>groundwater</td>\n",
              "      <td>communal standpipe multiple</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>POINT (39.58169206 -9.725770900000001)</td>\n",
              "      <td>527.0</td>\n",
              "      <td>0.106389</td>\n",
              "      <td>2.326321</td>\n",
              "      <td>VRe15</td>\n",
              "      <td>Eutric Vertisols</td>\n",
              "      <td>non functional</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11237</th>\n",
              "      <td>55887</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2011</td>\n",
              "      <td>World Bank</td>\n",
              "      <td>0</td>\n",
              "      <td>Rhobi</td>\n",
              "      <td>36.894605</td>\n",
              "      <td>-5.960452</td>\n",
              "      <td>Kwa Saidi</td>\n",
              "      <td>0</td>\n",
              "      <td>Wami / Ruvu</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Dodoma</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Kongwa</td>\n",
              "      <td>Pandambili</td>\n",
              "      <td>0</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>VWC</td>\n",
              "      <td>Kite</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>submersible</td>\n",
              "      <td>submersible</td>\n",
              "      <td>submersible</td>\n",
              "      <td>vwc</td>\n",
              "      <td>user-group</td>\n",
              "      <td>never pay</td>\n",
              "      <td>never pay</td>\n",
              "      <td>salty</td>\n",
              "      <td>salty</td>\n",
              "      <td>dry</td>\n",
              "      <td>dry</td>\n",
              "      <td>machine dbh</td>\n",
              "      <td>borehole</td>\n",
              "      <td>groundwater</td>\n",
              "      <td>communal standpipe multiple</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>POINT (36.89460495 -5.96045209)</td>\n",
              "      <td>148.0</td>\n",
              "      <td>4.323920</td>\n",
              "      <td>31.871850</td>\n",
              "      <td>LVx2</td>\n",
              "      <td>Chromic Luvisols</td>\n",
              "      <td>non functional</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24337</th>\n",
              "      <td>28440</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2013</td>\n",
              "      <td>Private Individual</td>\n",
              "      <td>1861</td>\n",
              "      <td>Dmdd</td>\n",
              "      <td>35.838990</td>\n",
              "      <td>-3.233549</td>\n",
              "      <td>Kwa Paulo Marmo</td>\n",
              "      <td>0</td>\n",
              "      <td>Internal</td>\n",
              "      <td>Barai</td>\n",
              "      <td>Arusha</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>Karatu</td>\n",
              "      <td>Mbulumbulu</td>\n",
              "      <td>150</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>VWC</td>\n",
              "      <td>Jumuhiya ya watumia  maji</td>\n",
              "      <td>True</td>\n",
              "      <td>2009</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>vwc</td>\n",
              "      <td>user-group</td>\n",
              "      <td>pay per bucket</td>\n",
              "      <td>per bucket</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>seasonal</td>\n",
              "      <td>seasonal</td>\n",
              "      <td>river</td>\n",
              "      <td>river/lake</td>\n",
              "      <td>surface</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>POINT (35.83899012 -3.23354937)</td>\n",
              "      <td>113.0</td>\n",
              "      <td>0.232842</td>\n",
              "      <td>7.016327</td>\n",
              "      <td>SNh3</td>\n",
              "      <td>Haplic Solonetz</td>\n",
              "      <td>functional</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40212</th>\n",
              "      <td>23480</td>\n",
              "      <td>250.0</td>\n",
              "      <td>2013</td>\n",
              "      <td>World Bank</td>\n",
              "      <td>1331</td>\n",
              "      <td>World Bank</td>\n",
              "      <td>30.017050</td>\n",
              "      <td>-4.335164</td>\n",
              "      <td>Kwa Muyoga</td>\n",
              "      <td>0</td>\n",
              "      <td>Lake Tanganyika</td>\n",
              "      <td>Rulemba</td>\n",
              "      <td>Kigoma</td>\n",
              "      <td>16</td>\n",
              "      <td>2</td>\n",
              "      <td>Kasulu</td>\n",
              "      <td>Munyegera</td>\n",
              "      <td>320</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>Water authority</td>\n",
              "      <td>Kabuye</td>\n",
              "      <td>True</td>\n",
              "      <td>1982</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>water board</td>\n",
              "      <td>user-group</td>\n",
              "      <td>pay monthly</td>\n",
              "      <td>monthly</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>insufficient</td>\n",
              "      <td>insufficient</td>\n",
              "      <td>river</td>\n",
              "      <td>river/lake</td>\n",
              "      <td>surface</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>POINT (30.01704985 -4.33516446)</td>\n",
              "      <td>172.0</td>\n",
              "      <td>0.114816</td>\n",
              "      <td>3.358702</td>\n",
              "      <td>FRrX2</td>\n",
              "      <td>Rhodic Ferrasols</td>\n",
              "      <td>functional</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15237</th>\n",
              "      <td>31935</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2013</td>\n",
              "      <td>Lifetime</td>\n",
              "      <td>27</td>\n",
              "      <td>Artisan</td>\n",
              "      <td>39.172887</td>\n",
              "      <td>-8.528037</td>\n",
              "      <td>Kwahuseni 2</td>\n",
              "      <td>0</td>\n",
              "      <td>Ruvuma / Southern Coast</td>\n",
              "      <td>Plot</td>\n",
              "      <td>Lindi</td>\n",
              "      <td>80</td>\n",
              "      <td>13</td>\n",
              "      <td>Kilwa</td>\n",
              "      <td>Mingumbi</td>\n",
              "      <td>330</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>2007</td>\n",
              "      <td>afridev</td>\n",
              "      <td>afridev</td>\n",
              "      <td>handpump</td>\n",
              "      <td>vwc</td>\n",
              "      <td>user-group</td>\n",
              "      <td>never pay</td>\n",
              "      <td>never pay</td>\n",
              "      <td>salty</td>\n",
              "      <td>salty</td>\n",
              "      <td>enough</td>\n",
              "      <td>enough</td>\n",
              "      <td>shallow well</td>\n",
              "      <td>shallow well</td>\n",
              "      <td>groundwater</td>\n",
              "      <td>hand pump</td>\n",
              "      <td>hand pump</td>\n",
              "      <td>POINT (39.17288652 -8.528037300000001)</td>\n",
              "      <td>488.0</td>\n",
              "      <td>0.132291</td>\n",
              "      <td>3.413975</td>\n",
              "      <td>VRe15</td>\n",
              "      <td>Eutric Vertisols</td>\n",
              "      <td>functional</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          id  amount_tsh  date_recorded  ...         MAIN_TYPE    status_group age\n",
              "779     2940        50.0           2013  ...  Eutric Vertisols  non functional  13\n",
              "11237  55887         0.0           2011  ...  Chromic Luvisols  non functional   0\n",
              "24337  28440        10.0           2013  ...   Haplic Solonetz      functional   4\n",
              "40212  23480       250.0           2013  ...  Rhodic Ferrasols      functional  31\n",
              "15237  31935         0.0           2013  ...  Eutric Vertisols      functional   6\n",
              "\n",
              "[5 rows x 48 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 740
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V2nYGk3R4bRh"
      },
      "source": [
        "trained = train.drop(['id', 'num_private', 'extraction_type_class', 'management_group',\n",
        "       'region_code', 'recorded_by', 'geometry', 'date_recorded', 'permit', 'basin',\n",
        "       'scheme_management', 'payment_type', 'water_quality', 'construction_year', 'SOIL_TYPE',\n",
        "       'extraction_type_group', 'public_meeting', 'AREA', 'installer', 'PERIMETER',\n",
        "       'management', 'MAIN_TYPE', 'quality_group', 'quantity_group', 'waterpoint_type_group', \n",
        "       'source_type', 'source_class', 'index_right', 'status_group'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KV-CQjlP4QaT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "c5601b0e-733e-49a1-8168-4bf648b827f9"
      },
      "source": [
        "trained.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['amount_tsh', 'funder', 'gps_height', 'longitude', 'latitude',\n",
              "       'wpt_name', 'subvillage', 'region', 'district_code', 'lga', 'ward',\n",
              "       'population', 'scheme_name', 'extraction_type', 'payment', 'quantity',\n",
              "       'source', 'waterpoint_type', 'age'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 770
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZ8M4KmyuyoA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "14827c4b-0b6a-4c03-9e1e-dd00aad685da"
      },
      "source": [
        "numeric_features = trained.select_dtypes(include='number').columns.tolist()\n",
        "\n",
        "# Get a series with the cardinality of the nonnumeric features\n",
        "cardinality = trained.select_dtypes(exclude='number').nunique()\n",
        "\n",
        "# Get a list of all categorical features with cardinality <= 10000\n",
        "categorical_features = cardinality[cardinality <= 100000].index.tolist()\n",
        "\n",
        "# Combine the lists \n",
        "\n",
        "features = numeric_features + categorical_features\n",
        "print(features)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['amount_tsh', 'gps_height', 'longitude', 'latitude', 'district_code', 'population', 'age', 'funder', 'wpt_name', 'subvillage', 'region', 'lga', 'ward', 'scheme_name', 'extraction_type', 'payment', 'quantity', 'source', 'waterpoint_type']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aya8l5Qquyr7"
      },
      "source": [
        "target = train['status_group']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9pdomRGuR6d"
      },
      "source": [
        "X_train = trained[features]\n",
        "y_train = train['status_group']\n",
        "X_val = val[features]\n",
        "y_val = val['status_group']\n",
        "X_test = test[features]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q1xxSArrvP5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "703e6a38-0e88-461c-cd38-7faa2c80577a"
      },
      "source": [
        "import category_encoders as ce\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "pipeline = make_pipeline(\n",
        "    ce.OrdinalEncoder(),\n",
        "    SimpleImputer(strategy='mean'), \n",
        "    StandardScaler(), \n",
        "    RandomForestClassifier(n_estimators=310, \n",
        "                               bootstrap = True,\n",
        "                               max_features = 'sqrt')\n",
        ")\n",
        "\n",
        "\n",
        "# Fit on train\n",
        "pipeline.fit(X_train, y_train)\n",
        "\n",
        "# Score on val\n",
        "print('Validation Accuracy', pipeline.score(X_val, y_val))\n",
        "\n",
        "# Predict on test\n",
        "y_pred = pipeline.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Validation Accuracy 0.811952861952862\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CV7J0R8QvP-z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "f525ca7d-8da4-4329-bfca-96a2a5620bfa"
      },
      "source": [
        "Submission = pd.DataFrame(y_pred, columns = ['status_group'])\n",
        "Submission"
      ],
      "execution_count": null,
      "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>status_group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>non functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14353</th>\n",
              "      <td>non functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14354</th>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14355</th>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14356</th>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14357</th>\n",
              "      <td>non functional</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14358 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         status_group\n",
              "0          functional\n",
              "1          functional\n",
              "2          functional\n",
              "3      non functional\n",
              "4          functional\n",
              "...               ...\n",
              "14353  non functional\n",
              "14354      functional\n",
              "14355      functional\n",
              "14356      functional\n",
              "14357  non functional\n",
              "\n",
              "[14358 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 775
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TezttZvE3M_m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "1d4dfbb4-16c6-4f9e-8730-9246761eeab4"
      },
      "source": [
        "sample = pd.read_csv('/content/sample_submission.csv')\n",
        "sample"
      ],
      "execution_count": null,
      "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",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>status_group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>50785</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>51630</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17168</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45559</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>49871</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14353</th>\n",
              "      <td>39307</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14354</th>\n",
              "      <td>18990</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14355</th>\n",
              "      <td>28749</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14356</th>\n",
              "      <td>33492</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14357</th>\n",
              "      <td>68707</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14358 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id status_group\n",
              "0      50785   functional\n",
              "1      51630   functional\n",
              "2      17168   functional\n",
              "3      45559   functional\n",
              "4      49871   functional\n",
              "...      ...          ...\n",
              "14353  39307   functional\n",
              "14354  18990   functional\n",
              "14355  28749   functional\n",
              "14356  33492   functional\n",
              "14357  68707   functional\n",
              "\n",
              "[14358 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 776
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a7-F6auGvQDw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "3b9289e2-c220-4dbe-aea7-18dabb2518b4"
      },
      "source": [
        "Submission['id'] = sample['id']\n",
        "Submission = Submission[['id', 'status_group']]\n",
        "Submission"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>id</th>\n",
              "      <th>status_group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>50785</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>51630</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17168</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45559</td>\n",
              "      <td>non functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>49871</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14353</th>\n",
              "      <td>39307</td>\n",
              "      <td>non functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14354</th>\n",
              "      <td>18990</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14355</th>\n",
              "      <td>28749</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14356</th>\n",
              "      <td>33492</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14357</th>\n",
              "      <td>68707</td>\n",
              "      <td>non functional</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14358 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id    status_group\n",
              "0      50785      functional\n",
              "1      51630      functional\n",
              "2      17168      functional\n",
              "3      45559  non functional\n",
              "4      49871      functional\n",
              "...      ...             ...\n",
              "14353  39307  non functional\n",
              "14354  18990      functional\n",
              "14355  28749      functional\n",
              "14356  33492      functional\n",
              "14357  68707  non functional\n",
              "\n",
              "[14358 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 777
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0s-f_9-13E7Q"
      },
      "source": [
        "Submission.to_csv('GeoTest27.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vwXAfNn03Tv8"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}