TanzaniaGEO_LogRegModel.ipynb

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  "metadata": {
    "colab": {
      "name": "TanzaniaGEO_LogRegModel.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyNVgqRO9p7X5yzLevwih8uN",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "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/TanzaniaGEO_LogRegModel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BUuL-KSTI7UU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "248ceccf-f5fc-48ad-df1f-700941e99012"
      },
      "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/'\n",
        "    !pip install category_encoders==2.*\n",
        "    !pip install pandas-profiling==2.*"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting category_encoders==2.*\n",
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            "Installing collected packages: category-encoders\n",
            "Successfully installed category-encoders-2.2.2\n",
            "Collecting pandas-profiling==2.*\n",
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            "Collecting htmlmin>=0.1.12\n",
            "  Downloading https://files.pythonhosted.org/packages/b3/e7/fcd59e12169de19f0131ff2812077f964c6b960e7c09804d30a7bf2ab461/htmlmin-0.1.12.tar.gz\n",
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            "Collecting phik>=0.11.1\n",
            "\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 multimethod==1.4\n",
            "  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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            "Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.4->visions[type_image_path]==0.7.1->pandas-profiling==2.*) (4.4.2)\n",
            "Requirement already satisfied: PyWavelets in /usr/local/lib/python3.7/dist-packages (from imagehash; extra == \"type_image_path\"->visions[type_image_path]==0.7.1->pandas-profiling==2.*) (1.1.1)\n",
            "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=27099 sha256=cbd20c6fe33e220db8da09c36de2020527e7240f34bd25d9d6fdf068a4dc42ae\n",
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            "Successfully built htmlmin phik\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: tangled-up-in-unicode, htmlmin, requests, tqdm, confuse, phik, 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",
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            "      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"
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          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gXFIlhE7U7MV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f37ddd57-0567-45a0-b29c-6b12ab59d003"
      },
      "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",
            "The following NEW packages will be installed:\n",
            "  python3-gdal python3-numpy\n",
            "0 upgraded, 2 newly installed, 0 to remove and 39 not upgraded.\n",
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            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  libspatialindex-c4v5 libspatialindex-dev libspatialindex4v5\n",
            "  python3-pkg-resources\n",
            "Suggested packages:\n",
            "  python3-setuptools\n",
            "The following NEW packages will be installed:\n",
            "  libspatialindex-c4v5 libspatialindex-dev libspatialindex4v5\n",
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            "Get:4 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libspatialindex-dev amd64 1.8.5-5 [285 kB]\n",
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            "/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n",
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            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from geopandas==0.9.0+36.gcb88dd4) (1.1.5)\n",
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            "Collecting fiona>=1.8\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9c/fc/9807326c37a6bfb2393ae3e1cca147aa74844562c4d5daa782d6e97ad2bc/Fiona-1.8.20-cp37-cp37m-manylinux1_x86_64.whl (15.4MB)\n",
            "\u001b[K     |████████████████████████████████| 15.4MB 264kB/s \n",
            "\u001b[?25hCollecting pyproj>=2.2.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/11/1d/1c54c672c2faf08d28fe78e15d664c048f786225bef95ad87b6c435cf69e/pyproj-3.1.0-cp37-cp37m-manylinux2010_x86_64.whl (6.6MB)\n",
            "\u001b[K     |████████████████████████████████| 6.6MB 45.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->geopandas==0.9.0+36.gcb88dd4) (2.8.1)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->geopandas==0.9.0+36.gcb88dd4) (1.19.5)\n",
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            "Collecting munch\n",
            "  Downloading https://files.pythonhosted.org/packages/cc/ab/85d8da5c9a45e072301beb37ad7f833cd344e04c817d97e0cc75681d248f/munch-2.5.0-py2.py3-none-any.whl\n",
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            "Collecting cligj>=0.5\n",
            "  Downloading https://files.pythonhosted.org/packages/73/86/43fa9f15c5b9fb6e82620428827cd3c284aa933431405d1bcf5231ae3d3e/cligj-0.7.2-py3-none-any.whl\n",
            "Collecting click-plugins>=1.0\n",
            "  Downloading https://files.pythonhosted.org/packages/e9/da/824b92d9942f4e472702488857914bdd50f73021efea15b4cad9aca8ecef/click_plugins-1.1.1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas==0.9.0+36.gcb88dd4) (2020.12.5)\n",
            "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=df63544fdd3d315772e153c05b141433124566b23fba22da4207640069fdff40\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-ulj1z4_s/wheels/91/24/71/376c9c67192694168352afcccc2d264248f7e2cc6192997186\n",
            "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",
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            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly>=4.1.0->plotly_express) (1.3.3)\n",
            "Installing collected packages: plotly-express\n",
            "Successfully installed plotly-express-0.4.1\n",
            "CPU times: user 411 ms, sys: 108 ms, total: 519 ms\n",
            "Wall time: 39.3 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JqfNmMVNU7Po"
      },
      "source": [
        "\n",
        "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\n",
        "import sys"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fCsfu53CGWET",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "1bd25ed3-666c-48d0-bbd4-7ffbf320f31b"
      },
      "source": [
        "pumps = pd.read_csv('/content/new_tza_waterpumps.csv')\n",
        "labels = pd.read_csv(DATA_PATH+'waterpumps/train_labels.csv')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ParserError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mParserError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-46-708c4f397ca4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpumps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/new_tza_waterpumps.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDATA_PATH\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'waterpumps/train_labels.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    674\u001b[0m         )\n\u001b[1;32m    675\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 676\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    678\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m         \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   1131\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m         \u001b[0mnrows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_validate_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"nrows\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1133\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1135\u001b[0m         \u001b[0;31m# May alter columns / col_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   2035\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2036\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2037\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2038\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2039\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_first_chunk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.read\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_low_memory\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.raise_parser_error\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mParserError\u001b[0m: Error tokenizing data. C error: EOF inside string starting at row 2744"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TirIgl-CGf7B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        },
        "outputId": "3292b15e-b733-4840-c99a-f875a4ca48c8"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>34.32677841186523 -4.757298469543457</th>\n",
              "      <th>34.28800201416016 -4.774341583251953</th>\n",
              "      <th>34.27829742431641 -4.772695541381836</th>\n",
              "      <th>34.25322341918945 -4.757996559143066</th>\n",
              "      <th>34.24434280395508 -4.761241912841797</th>\n",
              "      <th>34.24274444580078 -4.772641181945801</th>\n",
              "      <th>34.27273941040039 -4.843512535095215</th>\n",
              "      <th>34.26709365844727 -4.851646423339844</th>\n",
              "      <th>34.25820159912109 -4.850813388824463</th>\n",
              "      <th>34.24848937988281 -4.8459153175354</th>\n",
              "      <th>34.2202262878418 -4.867846488952637</th>\n",
              "      <th>34.21457290649414 -4.877600193023682</th>\n",
              "      <th>34.20973587036133 -4.885727882385254</th>\n",
              "      <th>34.20489883422852 -4.894674777984619</th>\n",
              "      <th>34.19924926757812 -4.901995658874512</th>\n",
              "      <th>34.19441223144531 -4.911752700805664</th>\n",
              "      <th>34.19118499755859 -4.921509742736816</th>\n",
              "      <th>34.18634796142578 -4.931266307830811</th>\n",
              "      <th>34.18069839477539 -4.940207004547119</th>\n",
              "      <th>34.17424011230469 -4.947522640228271</th>\n",
              "      <th>34.16535186767578 -4.949134349822998</th>\n",
              "      <th>34.16049194335938 -4.93853759765625</th>\n",
              "      <th>34.16209030151367 -4.927961826324463</th>\n",
              "      <th>34.16369247436523 -4.918195724487305</th>\n",
              "      <th>34.16368103027344 -4.905989170074463</th>\n",
              "      <th>34.16285705566406 -4.892965793609619</th>\n",
              "      <th>34.16608428955078 -4.881569862365723</th>\n",
              "      <th>34.19917297363281 -4.840131759643555</th>\n",
              "      <th>34.19997024536133 -4.83035945892334</th>\n",
              "      <th>34.19834136962891 -4.820590972900391</th>\n",
              "      <th>34.17487335205078 -4.79774808883667</th>\n",
              "      <th>34.17243957519531 -4.788784503936768</th>\n",
              "      <th>34.17323684692383 -4.779018878936768</th>\n",
              "      <th>34.21844863891602 -4.735957622528076</th>\n",
              "      <th>34.22086334228516 -4.726191997528076</th>\n",
              "      <th>...</th>\n",
              "      <th>34.19171524047852 -4.673216342926025</th>\n",
              "      <th>34.18927383422852 -4.664261341094971</th>\n",
              "      <th>34.18357086181641 -4.625976085662842</th>\n",
              "      <th>34.17629623413086 -4.618640422821045</th>\n",
              "      <th>34.13589096069336 -4.612046718597412</th>\n",
              "      <th>34.12942123413086 -4.60389518737793</th>\n",
              "      <th>34.19154739379883 -4.533167362213135</th>\n",
              "      <th>34.19234466552734 -4.522579193115234</th>\n",
              "      <th>34.19070816040039 -4.511990547180176</th>\n",
              "      <th>34.19151306152344 -4.502222537994385</th>\n",
              "      <th>34.19958114624023 -4.494901180267334</th>\n",
              "      <th>34.20846176147461 -4.49247407913208</th>\n",
              "      <th>34.21412658691406 -4.501440048217773</th>\n",
              "      <th>34.21817779541016 -4.510415077209473</th>\n",
              "      <th>34.22545623779297 -4.516939163208008</th>\n",
              "      <th>34.23433303833008 -4.512062549591064</th>\n",
              "      <th>34.23836517333984 -4.503923892974854</th>\n",
              "      <th>34.23916244506836 -4.494967460632324</th>\n",
              "      <th>34.2391471862793 -4.485195159912109</th>\n",
              "      <th>34.24479293823242 -4.475435256958008</th>\n",
              "      <th>34.25205612182617 -4.469746589660645</th>\n",
              "      <th>34.2577018737793 -4.461607933044434</th>\n",
              "      <th>34.25849151611328 -4.452650547027588</th>\n",
              "      <th>34.2512092590332 -4.443676471710205</th>\n",
              "      <th>34.24312973022461 -4.437966823577881</th>\n",
              "      <th>34.2447395324707 -4.428194046020508</th>\n",
              "      <th>34.25437927246094 -4.392378807067871</th>\n",
              "      <th>34.26244735717773 -4.387494087219238</th>\n",
              "      <th>34.27132797241211 -4.385884284973145</th>\n",
              "      <th>34.27778625488281 -4.376927852630615</th>\n",
              "      <th>34.27852630615234 -4.328063488006592</th>\n",
              "      <th>34.25508499145508 -4.310112953186035</th>\n",
              "      <th>34.28166198730469 -4.257199764251709</th>\n",
              "      <th>34.27111053466797 -4.218892097473145</th>\n",
              "      <th>34.25092315673828 -4.215616226196289</th>\n",
              "      <th>34.16861343383789 -4.249701499938965</th>\n",
              "      <th>34.16456604003906 -4.233400344848633</th>\n",
              "      <th>34.17343902587891 -4.229350566864014</th>\n",
              "      <th>34.24038314819336 -4.17730712890625</th>\n",
              "      <th>34.2554817199707 -4.150426864624023))\"</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>50318.0</td>\n",
              "      <td>27377.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2013-01-17</td>\n",
              "      <td>Rwssp</td>\n",
              "      <td>1170.0</td>\n",
              "      <td>DWE</td>\n",
              "      <td>34.642644</td>\n",
              "      <td>-4.054159</td>\n",
              "      <td>Nyegere</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Internal</td>\n",
              "      <td>Nyegere</td>\n",
              "      <td>Singida</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Iramba</td>\n",
              "      <td>Ibaga</td>\n",
              "      <td>245.0</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>VWC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>2009.0</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>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>hand pump</td>\n",
              "      <td>...</td>\n",
              "      <td>0.186032</td>\n",
              "      <td>7.419713</td>\n",
              "      <td>LPe2</td>\n",
              "      <td>Eutric Leptosols</td>\n",
              "      <td>POLYGON ((34.2554817199707 -4.150426864624023,...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>51288.0</td>\n",
              "      <td>52689.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>2013-01-16</td>\n",
              "      <td>Tasaf</td>\n",
              "      <td>1088.0</td>\n",
              "      <td>SHY BUILDERS</td>\n",
              "      <td>34.339135</td>\n",
              "      <td>-4.117980</td>\n",
              "      <td>Msele</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Internal</td>\n",
              "      <td>Msele</td>\n",
              "      <td>Singida</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Iramba</td>\n",
              "      <td>Tulya</td>\n",
              "      <td>223.0</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>VWC</td>\n",
              "      <td>None</td>\n",
              "      <td>True</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>india mark ii</td>\n",
              "      <td>india mark ii</td>\n",
              "      <td>handpump</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>hand pump</td>\n",
              "      <td>...</td>\n",
              "      <td>0.186032</td>\n",
              "      <td>7.419713</td>\n",
              "      <td>LPe2</td>\n",
              "      <td>Eutric Leptosols</td>\n",
              "      <td>POLYGON ((34.2554817199707 -4.150426864624023,...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>52457.0</td>\n",
              "      <td>27155.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>2013-02-13</td>\n",
              "      <td>World Bank</td>\n",
              "      <td>1304.0</td>\n",
              "      <td>Singida General  Supplies  Ltd</td>\n",
              "      <td>34.591327</td>\n",
              "      <td>-4.470296</td>\n",
              "      <td>Zahanati</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Internal</td>\n",
              "      <td>Mpande</td>\n",
              "      <td>Singida</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Iramba</td>\n",
              "      <td>Kinyagiri</td>\n",
              "      <td>306.0</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>WUA</td>\n",
              "      <td>Lyelembo water supply</td>\n",
              "      <td>True</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>submersible</td>\n",
              "      <td>submersible</td>\n",
              "      <td>submersible</td>\n",
              "      <td>wua</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>...</td>\n",
              "      <td>0.186032</td>\n",
              "      <td>7.419713</td>\n",
              "      <td>LPe2</td>\n",
              "      <td>Eutric Leptosols</td>\n",
              "      <td>POLYGON ((34.2554817199707 -4.150426864624023,...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>52503.0</td>\n",
              "      <td>36429.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>2013-02-09</td>\n",
              "      <td>Sema</td>\n",
              "      <td>1053.0</td>\n",
              "      <td>SEMA</td>\n",
              "      <td>34.221142</td>\n",
              "      <td>-4.195763</td>\n",
              "      <td>Ngunga</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Internal</td>\n",
              "      <td>Ngunga</td>\n",
              "      <td>Singida</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Iramba</td>\n",
              "      <td>Ntwike</td>\n",
              "      <td>189.0</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>WUA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>2011.0</td>\n",
              "      <td>nira/tanira</td>\n",
              "      <td>nira/tanira</td>\n",
              "      <td>handpump</td>\n",
              "      <td>wua</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>enough</td>\n",
              "      <td>enough</td>\n",
              "      <td>hand dtw</td>\n",
              "      <td>borehole</td>\n",
              "      <td>groundwater</td>\n",
              "      <td>hand pump</td>\n",
              "      <td>...</td>\n",
              "      <td>0.186032</td>\n",
              "      <td>7.419713</td>\n",
              "      <td>LPe2</td>\n",
              "      <td>Eutric Leptosols</td>\n",
              "      <td>POLYGON ((34.2554817199707 -4.150426864624023,...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53420.0</td>\n",
              "      <td>31380.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2013-02-17</td>\n",
              "      <td>Nsc</td>\n",
              "      <td>1250.0</td>\n",
              "      <td>NSC</td>\n",
              "      <td>34.550671</td>\n",
              "      <td>-4.341323</td>\n",
              "      <td>Mkusi</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Internal</td>\n",
              "      <td>Mademu</td>\n",
              "      <td>Singida</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Iramba</td>\n",
              "      <td>Msingi</td>\n",
              "      <td>385.0</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>Parastatal</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>parastatal</td>\n",
              "      <td>parastatal</td>\n",
              "      <td>never pay</td>\n",
              "      <td>never pay</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>seasonal</td>\n",
              "      <td>seasonal</td>\n",
              "      <td>rainwater harvesting</td>\n",
              "      <td>rainwater harvesting</td>\n",
              "      <td>surface</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>...</td>\n",
              "      <td>0.186032</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
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              "<p>2491 rows × 81 columns</p>\n",
              "</div>"
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              "      24058532714844  ...   34.2554817199707 -4.150426864624023))\"\n",
              "0            50318.0  ...                                      NaN\n",
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              "...              ...  ...                                      ...\n",
              "2486             NaN  ...                                      NaN\n",
              "2487             NaN  ...                                      NaN\n",
              "2488             NaN  ...                                      NaN\n",
              "2489             NaN  ...                                      NaN\n",
              "2490             NaN  ...                                      NaN\n",
              "\n",
              "[2491 rows x 81 columns]"
            ]
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          "metadata": {
            "tags": []
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      "metadata": {
        "id": "D6g7aVKRU7Sl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "outputId": "5d2114cf-84d8-47a8-a487-50b51e31d189"
      },
      "source": [
        "train = pd.merge(pd.read_csv('/content/tza_waterpumps.csv'), \n",
        "                 pd.read_csv(DATA_PATH+'waterpumps/train_labels.csv'))\n",
        "\n",
        "test = pd.read_csv('/content/new_test_waterpumps1.csv')\n",
        "\n",
        "sample_submission = pd.read_csv(DATA_PATH+'waterpumps/sample_submission.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "MergeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mMergeError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-f3dc3cd26193>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m train = pd.merge(pd.read_csv('/content/tza_waterpumps.csv'), \n\u001b[0;32m----> 2\u001b[0;31m                  pd.read_csv(DATA_PATH+'waterpumps/train_labels.csv'))\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/new_test_waterpumps1.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36mmerge\u001b[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m     84\u001b[0m         \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m         \u001b[0mindicator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindicator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m         \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     87\u001b[0m     )\n\u001b[1;32m     88\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m    618\u001b[0m             \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUserWarning\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 620\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_specification\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m         \u001b[0;31m# note this function has side effects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m_validate_specification\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1196\u001b[0m                             \u001b[0mron\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright_on\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1197\u001b[0m                             \u001b[0mlidx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft_index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1198\u001b[0;31m                             \u001b[0mridx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright_index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1199\u001b[0m                         )\n\u001b[1;32m   1200\u001b[0m                     )\n",
            "\u001b[0;31mMergeError\u001b[0m: No common columns to perform merge on. Merge options: left_on=None, right_on=None, left_index=False, right_index=False"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGzJeRLLU7XW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        },
        "outputId": "c808055b-74bb-493e-8baa-c197b1280de1"
      },
      "source": [
        "\n",
        "# Split train into train & val\n",
        "train, val = train_test_split(train, train_size=0.80, test_size=0.20, \n",
        "                              stratify=train['status_group'], random_state=42)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'status_group'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-06b1ba3c3f40>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# Split train into train & val\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m train, val = train_test_split(train, train_size=0.80, test_size=0.20, \n\u001b[0;32m----> 4\u001b[0;31m                               stratify=train['status_group'], random_state=42)\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2798\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2799\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2800\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2801\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2646\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'status_group'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wDNK9LrwU7bm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "outputId": "55c9edca-df42-4ca9-f2de-83f095557e1f"
      },
      "source": [
        "train.shape, val.shape, test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-493b12b07459>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'val' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b4mBuw9YU7ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "df167934-8113-44a6-d679-1dbcaefbef50"
      },
      "source": [
        "train['status_group'].value_counts(normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "functional                 0.545522\n",
              "non functional             0.386158\n",
              "functional needs repair    0.068321\n",
              "Name: status_group, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OJow4JtbU7hX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "outputId": "c944605a-c544-4520-a937-a10dac7752fb"
      },
      "source": [
        "cat_col = train.columns[train.dtypes=='object']\n",
        "\n",
        "(cat_col)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['date_recorded', 'funder', 'installer', 'wpt_name', 'basin',\n",
              "       'subvillage', 'region', 'lga', 'ward', 'public_meeting', 'recorded_by',\n",
              "       'scheme_management', 'scheme_name', 'permit', 'extraction_type',\n",
              "       'extraction_type_group', 'extraction_type_class', 'management',\n",
              "       'management_group', 'payment', 'payment_type', 'water_quality',\n",
              "       'quality_group', 'quantity', 'quantity_group', 'source', 'source_type',\n",
              "       'source_class', 'waterpoint_type', 'waterpoint_type_group', 'SOIL_TYPE',\n",
              "       'MAIN_TYPE', 'geometry', 'status_group'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3hQShtdU7km"
      },
      "source": [
        "train[cat_col] = train[cat_col].astype('category')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kHvGTiBpU7Vl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "26fc08c9-97bb-4ea0-ab61-716dd6699805"
      },
      "source": [
        "train.loc[0]\n",
        "train['lga'].cat.codes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2672      88\n",
              "49889    110\n",
              "52171     96\n",
              "45347    121\n",
              "53494     57\n",
              "        ... \n",
              "52516    123\n",
              "3278      11\n",
              "31958     16\n",
              "9083     103\n",
              "41732     12\n",
              "Length: 45989, dtype: int8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F3EhGEfpVdkq"
      },
      "source": [
        "trained = train.drop(['amount_tsh', 'index_left', 'AREA', 'PERIMETER', 'installer', 'extraction_type', 'construction_year', 'quantity', 'wpt_name', 'num_private', 'basin', 'region', 'district_code', 'population', 'public_meeting', 'recorded_by', 'id', 'scheme_management', 'scheme_name', 'permit', 'extraction_type_group', 'extraction_type_class', 'management_group', 'management', 'payment', 'payment_type', 'quality_group', 'source_type', 'source_class', 'MAIN_TYPE', 'source', 'date_recorded', 'waterpoint_type', 'waterpoint_type_group', 'status_group'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X5FWnkhqVdvF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "outputId": "c536251f-cd1b-490b-d9e8-1bb69ee4417c"
      },
      "source": [
        "trained.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>funder</th>\n",
              "      <th>gps_height</th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>subvillage</th>\n",
              "      <th>region_code</th>\n",
              "      <th>lga</th>\n",
              "      <th>ward</th>\n",
              "      <th>water_quality</th>\n",
              "      <th>quantity_group</th>\n",
              "      <th>SOIL_TYPE</th>\n",
              "      <th>geometry</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2672</th>\n",
              "      <td>Oxfam</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.691264</td>\n",
              "      <td>-2.847368</td>\n",
              "      <td>Nyakuguma</td>\n",
              "      <td>18.0</td>\n",
              "      <td>Ngara</td>\n",
              "      <td>Bugarama</td>\n",
              "      <td>soft</td>\n",
              "      <td>enough</td>\n",
              "      <td>FRh16</td>\n",
              "      <td>POLYGON ((30.87990951538086 -0.997727453708648...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49889</th>\n",
              "      <td>Mission</td>\n",
              "      <td>1466.0</td>\n",
              "      <td>35.297519</td>\n",
              "      <td>-9.760209</td>\n",
              "      <td>Ganga</td>\n",
              "      <td>10.0</td>\n",
              "      <td>Songea Rural</td>\n",
              "      <td>Wino</td>\n",
              "      <td>soft</td>\n",
              "      <td>enough</td>\n",
              "      <td>AChX4</td>\n",
              "      <td>POLYGON ((36.40432739257812 -8.525897026062012...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52171</th>\n",
              "      <td>Watu Wa Ujerumani</td>\n",
              "      <td>1188.0</td>\n",
              "      <td>34.031656</td>\n",
              "      <td>-1.146791</td>\n",
              "      <td>Kabwana</td>\n",
              "      <td>20.0</td>\n",
              "      <td>Rorya</td>\n",
              "      <td>Mkoma</td>\n",
              "      <td>soft</td>\n",
              "      <td>enough</td>\n",
              "      <td>CMx3</td>\n",
              "      <td>POLYGON ((34.09883117675781 -1.023067951202393...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45347</th>\n",
              "      <td>Dhv</td>\n",
              "      <td>379.0</td>\n",
              "      <td>36.861544</td>\n",
              "      <td>-9.022433</td>\n",
              "      <td>Mwanza</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Ulanga</td>\n",
              "      <td>Mbuga</td>\n",
              "      <td>unknown</td>\n",
              "      <td>enough</td>\n",
              "      <td>CMo21</td>\n",
              "      <td>POLYGON ((37.00050354003906 -7.480969429016113...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53494</th>\n",
              "      <td>Netherland</td>\n",
              "      <td>0.0</td>\n",
              "      <td>33.754734</td>\n",
              "      <td>-3.157654</td>\n",
              "      <td>Nyanhumbili</td>\n",
              "      <td>17.0</td>\n",
              "      <td>Maswa</td>\n",
              "      <td>Buchambi</td>\n",
              "      <td>soft</td>\n",
              "      <td>enough</td>\n",
              "      <td>PLe7</td>\n",
              "      <td>POLYGON ((33.6700325012207 -3.178311109542847,...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  funder  ...                                           geometry\n",
              "2672               Oxfam  ...  POLYGON ((30.87990951538086 -0.997727453708648...\n",
              "49889            Mission  ...  POLYGON ((36.40432739257812 -8.525897026062012...\n",
              "52171  Watu Wa Ujerumani  ...  POLYGON ((34.09883117675781 -1.023067951202393...\n",
              "45347                Dhv  ...  POLYGON ((37.00050354003906 -7.480969429016113...\n",
              "53494         Netherland  ...  POLYGON ((33.6700325012207 -3.178311109542847,...\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yCFn-wZ9Vd18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "fe6bbd89-900e-4d66-c069-d0b6a095aff5"
      },
      "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 <= 1000000].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": [
            "['gps_height', 'longitude', 'latitude', 'region_code', 'funder', 'subvillage', 'lga', 'ward', 'water_quality', 'quantity_group', 'SOIL_TYPE', 'geometry']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zoooHXh8Vd8g"
      },
      "source": [
        "target = train['status_group']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RM7g5rFDVetR"
      },
      "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": "WJ_-BOQUVe4v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "bde8aab9-bafa-49ea-a590-34c76c2ede55"
      },
      "source": [
        "import category_encoders as ce\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "pipeline = make_pipeline(\n",
        "    ce.OneHotEncoder(use_cat_names=True), \n",
        "    SimpleImputer(strategy='mean'), \n",
        "    StandardScaler(), \n",
        "    LogisticRegression(multi_class='auto', solver='lbfgs', n_jobs=-1)\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": "error",
          "ename": "TerminatedWorkerError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTerminatedWorkerError\u001b[0m                     Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-ad464a52f00f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;31m# Fit on train\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mpipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;31m# Score on val\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    352\u001b[0m                                  self._log_message(len(self.steps) - 1)):\n\u001b[1;32m    353\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'passthrough'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    355\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   1599\u001b[0m                       \u001b[0mpenalty\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_squared_sum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_squared_sum\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1600\u001b[0m                       sample_weight=sample_weight)\n\u001b[0;32m-> 1601\u001b[0;31m             for class_, warm_start_coef_ in zip(classes_, warm_start_coef))\n\u001b[0m\u001b[1;32m   1602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1603\u001b[0m         \u001b[0mfold_coefs_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfold_coefs_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1042\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1043\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1044\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    919\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    920\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 921\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    922\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    540\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m    541\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    430\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    431\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 432\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    433\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    382\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    385\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTerminatedWorkerError\u001b[0m: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker.\n\nThe exit codes of the workers are {SIGKILL(-9)}"
          ]
        }
      ]
    }
  ]
}