drivendata_waterpump.ipynb

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  "metadata": {
    "colab": {
      "name": "drivendata waterpump.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "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/mspoorendonk/drivendata/blob/load_google_api_key_from_file/drivendata_waterpump.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pC9Hngyu5E6R"
      },
      "source": [
        "# Analysis of condition of water points in Tanzania\n",
        "\n",
        "Problem statement:\n",
        "predict the operating condition of a waterpoint for each record in the dataset: functioning, functioning but needs repair, not functioning\n",
        "\n",
        "\n",
        "Approach\n",
        "1. Download datasets\n",
        "1. Explore data and understand which features are relevant for the prediction. \n",
        "1. Clean data [Bart]\n",
        "1. Engineer some derived features\n",
        "1. decide on a method for predicting (trees or neuralnets or knn or ...)\n",
        "1. perform a train / test / validate split on the data\n",
        "1. Train model on training values and labels\n",
        "1. Predict training labels that correspond to training values\n",
        "1. Report the accuracy\n",
        "1. Tune hyperparameters with gridsearch\n",
        "1. Predict the test labels\n",
        "1. Submit CSV [Marc]\n",
        "\n",
        "\n",
        "TODO:\n",
        "here: check xgboost, pandas, bokeh (interactief)\n",
        "somewhere else: how to deploy a model in production. What software and frameworks etc.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hwqftAGR-k2J"
      },
      "source": [
        "# Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PuCGSSOKu8-h",
        "outputId": "159455c6-4a1a-4f84-8d7d-084180d739a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 906
        }
      },
      "source": [
        "# installations\n",
        "\n",
        "!pip install gmaps"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting gmaps\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4c/aa/4e659d3ab6efe55c265d8159c845e9168e4c79045aef8e5460f9511cc3a7/gmaps-0.9.0.tar.gz (1.1MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1MB 2.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: ipython>=5.3.0 in /usr/local/lib/python3.6/dist-packages (from gmaps) (5.5.0)\n",
            "Requirement already satisfied: ipywidgets>=7.0.0 in /usr/local/lib/python3.6/dist-packages (from gmaps) (7.5.1)\n",
            "Requirement already satisfied: traitlets>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from gmaps) (4.3.3)\n",
            "Collecting geojson>=2.0.0\n",
            "  Downloading https://files.pythonhosted.org/packages/e4/8d/9e28e9af95739e6d2d2f8d4bef0b3432da40b7c3588fbad4298c1be09e48/geojson-2.5.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from gmaps) (1.15.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.6/dist-packages (from ipython>=5.3.0->gmaps) (4.4.2)\n",
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            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.6/dist-packages (from ipython>=5.3.0->gmaps) (50.3.0)\n",
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            "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from ipython>=5.3.0->gmaps) (4.8.0)\n",
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            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.3.0->gmaps) (0.2.0)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython>=5.3.0->gmaps) (0.6.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=5.3.0->gmaps) (0.2.5)\n",
            "Requirement already satisfied: tornado>=4.0 in /usr/local/lib/python3.6/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.0.0->gmaps) (5.1.1)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.6/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.0.0->gmaps) (5.3.5)\n",
            "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2.0->ipywidgets>=7.0.0->gmaps) (4.6.3)\n",
            "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2.0->ipywidgets>=7.0.0->gmaps) (2.6.0)\n",
            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.6/dist-packages (from widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (5.3.1)\n",
            "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.6/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets>=7.0.0->gmaps) (19.0.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets>=7.0.0->gmaps) (2.8.1)\n",
            "Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.6/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (0.9.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.6/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (2.11.2)\n",
            "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.6/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (1.5.0)\n",
            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.6/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (5.6.1)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (1.1.1)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (1.4.2)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (3.2.1)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (0.8.4)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (0.6.0)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (0.4.4)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (0.3)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (20.4)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (0.5.1)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->gmaps) (2.4.7)\n",
            "Building wheels for collected packages: gmaps\n",
            "  Building wheel for gmaps (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gmaps: filename=gmaps-0.9.0-py2.py3-none-any.whl size=2076096 sha256=55e9a5b3396ba2af4b583afa1624a755926b8eabe7b65c8d314736c6b31a8f8f\n",
            "  Stored in directory: /root/.cache/pip/wheels/33/eb/41/5302168c36fc8ce8f48143bb58fde1015d5bc0c62bc3ddbb38\n",
            "Successfully built gmaps\n",
            "Installing collected packages: geojson, gmaps\n",
            "Successfully installed geojson-2.5.0 gmaps-0.9.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CJlE0N65YUFd"
      },
      "source": [
        "# imports\n",
        "\n",
        "import pandas as pd\n",
        "import random\n",
        "import numpy as np\n",
        "import gmaps\n",
        "import IPython\n",
        "from sklearn import tree # to create a decision tree\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn import metrics # to compute accuracy\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import train_test_split # Import train_test_split function\n",
        "from sklearn import preprocessing # for normalizing data for knn\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import pydotplus # To create our Decision Tree Graph\n",
        "from IPython.display import Image  # To Display a image of our graph\n",
        "\n",
        "from ipywidgets.embed import embed_minimal_html\n",
        "\n",
        "# Seaborn visualization library\n",
        "import seaborn as sns # for pairplots\n",
        "\n",
        "import seaborn as sn\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from google.colab import files  # for uploading files"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mOSkSXlq-gzD"
      },
      "source": [
        "# Download datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGa2jMrp23Sm",
        "outputId": "76aede41-c41e-4a87-c375-c2a385bd28ab",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 178
        }
      },
      "source": [
        "# download datasets from driven-data.org. Urls copied from data download section on website.\n",
        "# They expire after 2 days or so. Then you need to copy/paste them again.\n",
        "\n",
        "source = 'localupload'\n",
        "\n",
        "if source == 'drivendata':\n",
        "  # these urls expire after approx 2 days\n",
        "  # testvalues\n",
        "  !wget \"https://drivendata-prod.s3.amazonaws.com/data/7/public/702ddfc5-68cd-4d1d-a0de-f5f566f76d91.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIARVBOBDCY3EFSLNZR%2F20200927%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20200927T185304Z&X-Amz-Expires=86400&X-Amz-SignedHeaders=host&X-Amz-Signature=f2b7c554cb780a1facf849dc85cd18a0ce5110100690a748eaa1df42f43a12da\" -O test_values.csv\n",
        "  # training labels\n",
        "  !wget \"https://drivendata-prod.s3.amazonaws.com/data/7/public/0bf8bc6e-30d0-4c50-956a-603fc693d966.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIARVBOBDCY3EFSLNZR%2F20200927%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20200927T185304Z&X-Amz-Expires=86400&X-Amz-SignedHeaders=host&X-Amz-Signature=f91daa03811de5cb244f5f2d8446fb46a99eb37bedf7bd0c609d8b076bebfbe2\" -O training_labels.csv\n",
        "  # training values\n",
        "  !wget \"https://drivendata-prod.s3.amazonaws.com/data/7/public/4910797b-ee55-40a7-8668-10efd5c1b960.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIARVBOBDCY3EFSLNZR%2F20200927%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20200927T185304Z&X-Amz-Expires=86400&X-Amz-SignedHeaders=host&X-Amz-Signature=b38e27dab8fac51df99d1ec837ffd2f4a3c3e1ffd48494951a846a144f88434f\" -O training_values.csv\n",
        "\n",
        "if source == 'github':\n",
        "  pass\n",
        "  # github urls (to a private repository) expire after 10 minutes or so\n",
        "\n",
        "if source == 'localupload':\n",
        "  # save them on your local harddrive. You can upload multiple at once.\n",
        "  # make sure to give them the same names on your local machine.\n",
        "  !rm -f test_values.csv training_labels.csv training_values.csv\n",
        "  files.upload()\n",
        "\n",
        "# Read google API key for plotting\n",
        "key = ''\n",
        "try:\n",
        "  with open(\"google_API.key\") as f:\n",
        "      key = f.read().rstrip(\"\\n\")\n",
        "      print('Google API key loaded from file')\n",
        "except:\n",
        "  print('No Google API key found')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-1ec7d37f-6d54-4d0b-bd9d-7506a7f05737\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-1ec7d37f-6d54-4d0b-bd9d-7506a7f05737\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving google_API.key to google_API.key\n",
            "Saving test_values.csv to test_values.csv\n",
            "Saving training_labels.csv to training_labels.csv\n",
            "Saving training_values.csv to training_values.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s5JdImi9qyql"
      },
      "source": [
        "# Boundary coordinates of Tanzania\n",
        "# Source: https://en.wikipedia.org/wiki/List_of_countries_by_northernmost_point (and similar)\n",
        "tanzania_lat = [-11.750-0.1, -0.983+0.1]\n",
        "tanzania_lon = [29.167-0.1, 40.250+0.1]"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TlOL83NOOp9n",
        "outputId": "f2a80f99-be8f-444b-8562-1bb164d37c07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 806
        }
      },
      "source": [
        "training_values = pd.read_csv('training_values.csv', parse_dates=['date_recorded'])\n",
        "training_values"
      ],
      "execution_count": 9,
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>69572</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>2011-03-14</td>\n",
              "      <td>Roman</td>\n",
              "      <td>1390</td>\n",
              "      <td>Roman</td>\n",
              "      <td>34.938093</td>\n",
              "      <td>-9.856322</td>\n",
              "      <td>none</td>\n",
              "      <td>0</td>\n",
              "      <td>Lake Nyasa</td>\n",
              "      <td>Mnyusi B</td>\n",
              "      <td>Iringa</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>Ludewa</td>\n",
              "      <td>Mundindi</td>\n",
              "      <td>109</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>VWC</td>\n",
              "      <td>Roman</td>\n",
              "      <td>False</td>\n",
              "      <td>1999</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 annually</td>\n",
              "      <td>annually</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>enough</td>\n",
              "      <td>enough</td>\n",
              "      <td>spring</td>\n",
              "      <td>spring</td>\n",
              "      <td>groundwater</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>communal standpipe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8776</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2013-03-06</td>\n",
              "      <td>Grumeti</td>\n",
              "      <td>1399</td>\n",
              "      <td>GRUMETI</td>\n",
              "      <td>34.698766</td>\n",
              "      <td>-2.147466</td>\n",
              "      <td>Zahanati</td>\n",
              "      <td>0</td>\n",
              "      <td>Lake Victoria</td>\n",
              "      <td>Nyamara</td>\n",
              "      <td>Mara</td>\n",
              "      <td>20</td>\n",
              "      <td>2</td>\n",
              "      <td>Serengeti</td>\n",
              "      <td>Natta</td>\n",
              "      <td>280</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>Other</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>2010</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>wug</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>insufficient</td>\n",
              "      <td>insufficient</td>\n",
              "      <td>rainwater harvesting</td>\n",
              "      <td>rainwater harvesting</td>\n",
              "      <td>surface</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>communal standpipe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>34310</td>\n",
              "      <td>25.0</td>\n",
              "      <td>2013-02-25</td>\n",
              "      <td>Lottery Club</td>\n",
              "      <td>686</td>\n",
              "      <td>World vision</td>\n",
              "      <td>37.460664</td>\n",
              "      <td>-3.821329</td>\n",
              "      <td>Kwa Mahundi</td>\n",
              "      <td>0</td>\n",
              "      <td>Pangani</td>\n",
              "      <td>Majengo</td>\n",
              "      <td>Manyara</td>\n",
              "      <td>21</td>\n",
              "      <td>4</td>\n",
              "      <td>Simanjiro</td>\n",
              "      <td>Ngorika</td>\n",
              "      <td>250</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>VWC</td>\n",
              "      <td>Nyumba ya mungu pipe scheme</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>enough</td>\n",
              "      <td>enough</td>\n",
              "      <td>dam</td>\n",
              "      <td>dam</td>\n",
              "      <td>surface</td>\n",
              "      <td>communal standpipe multiple</td>\n",
              "      <td>communal standpipe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>67743</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2013-01-28</td>\n",
              "      <td>Unicef</td>\n",
              "      <td>263</td>\n",
              "      <td>UNICEF</td>\n",
              "      <td>38.486161</td>\n",
              "      <td>-11.155298</td>\n",
              "      <td>Zahanati Ya Nanyumbu</td>\n",
              "      <td>0</td>\n",
              "      <td>Ruvuma / Southern Coast</td>\n",
              "      <td>Mahakamani</td>\n",
              "      <td>Mtwara</td>\n",
              "      <td>90</td>\n",
              "      <td>63</td>\n",
              "      <td>Nanyumbu</td>\n",
              "      <td>Nanyumbu</td>\n",
              "      <td>58</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>1986</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>soft</td>\n",
              "      <td>good</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19728</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2011-07-13</td>\n",
              "      <td>Action In A</td>\n",
              "      <td>0</td>\n",
              "      <td>Artisan</td>\n",
              "      <td>31.130847</td>\n",
              "      <td>-1.825359</td>\n",
              "      <td>Shuleni</td>\n",
              "      <td>0</td>\n",
              "      <td>Lake Victoria</td>\n",
              "      <td>Kyanyamisa</td>\n",
              "      <td>Kagera</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>Karagwe</td>\n",
              "      <td>Nyakasimbi</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>other</td>\n",
              "      <td>other</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>communal standpipe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59395</th>\n",
              "      <td>60739</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2013-05-03</td>\n",
              "      <td>Germany Republi</td>\n",
              "      <td>1210</td>\n",
              "      <td>CES</td>\n",
              "      <td>37.169807</td>\n",
              "      <td>-3.253847</td>\n",
              "      <td>Area Three Namba 27</td>\n",
              "      <td>0</td>\n",
              "      <td>Pangani</td>\n",
              "      <td>Kiduruni</td>\n",
              "      <td>Kilimanjaro</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>Hai</td>\n",
              "      <td>Masama Magharibi</td>\n",
              "      <td>125</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>Water Board</td>\n",
              "      <td>Losaa Kia water supply</td>\n",
              "      <td>True</td>\n",
              "      <td>1999</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 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>spring</td>\n",
              "      <td>spring</td>\n",
              "      <td>groundwater</td>\n",
              "      <td>communal standpipe</td>\n",
              "      <td>communal standpipe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59396</th>\n",
              "      <td>27263</td>\n",
              "      <td>4700.0</td>\n",
              "      <td>2011-05-07</td>\n",
              "      <td>Cefa-njombe</td>\n",
              "      <td>1212</td>\n",
              "      <td>Cefa</td>\n",
              "      <td>35.249991</td>\n",
              "      <td>-9.070629</td>\n",
              "      <td>Kwa Yahona Kuvala</td>\n",
              "      <td>0</td>\n",
              "      <td>Rufiji</td>\n",
              "      <td>Igumbilo</td>\n",
              "      <td>Iringa</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>Njombe</td>\n",
              "      <td>Ikondo</td>\n",
              "      <td>56</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>VWC</td>\n",
              "      <td>Ikondo electrical water sch</td>\n",
              "      <td>True</td>\n",
              "      <td>1996</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 annually</td>\n",
              "      <td>annually</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</td>\n",
              "      <td>enough</td>\n",
              "      <td>enough</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59397</th>\n",
              "      <td>37057</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2011-04-11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34.017087</td>\n",
              "      <td>-8.750434</td>\n",
              "      <td>Mashine</td>\n",
              "      <td>0</td>\n",
              "      <td>Rufiji</td>\n",
              "      <td>Madungulu</td>\n",
              "      <td>Mbeya</td>\n",
              "      <td>12</td>\n",
              "      <td>7</td>\n",
              "      <td>Mbarali</td>\n",
              "      <td>Chimala</td>\n",
              "      <td>0</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>VWC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>swn 80</td>\n",
              "      <td>swn 80</td>\n",
              "      <td>handpump</td>\n",
              "      <td>vwc</td>\n",
              "      <td>user-group</td>\n",
              "      <td>pay monthly</td>\n",
              "      <td>monthly</td>\n",
              "      <td>fluoride</td>\n",
              "      <td>fluoride</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>hand pump</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59398</th>\n",
              "      <td>31282</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2011-03-08</td>\n",
              "      <td>Malec</td>\n",
              "      <td>0</td>\n",
              "      <td>Musa</td>\n",
              "      <td>35.861315</td>\n",
              "      <td>-6.378573</td>\n",
              "      <td>Mshoro</td>\n",
              "      <td>0</td>\n",
              "      <td>Rufiji</td>\n",
              "      <td>Mwinyi</td>\n",
              "      <td>Dodoma</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>Chamwino</td>\n",
              "      <td>Mvumi Makulu</td>\n",
              "      <td>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>0</td>\n",
              "      <td>nira/tanira</td>\n",
              "      <td>nira/tanira</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>insufficient</td>\n",
              "      <td>insufficient</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59399</th>\n",
              "      <td>26348</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2011-03-23</td>\n",
              "      <td>World Bank</td>\n",
              "      <td>191</td>\n",
              "      <td>World</td>\n",
              "      <td>38.104048</td>\n",
              "      <td>-6.747464</td>\n",
              "      <td>Kwa Mzee Lugawa</td>\n",
              "      <td>0</td>\n",
              "      <td>Wami / Ruvu</td>\n",
              "      <td>Kikatanyemba</td>\n",
              "      <td>Morogoro</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>Morogoro Rural</td>\n",
              "      <td>Ngerengere</td>\n",
              "      <td>150</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>2002</td>\n",
              "      <td>nira/tanira</td>\n",
              "      <td>nira/tanira</td>\n",
              "      <td>handpump</td>\n",
              "      <td>vwc</td>\n",
              "      <td>user-group</td>\n",
              "      <td>pay when scheme fails</td>\n",
              "      <td>on failure</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>59400 rows × 40 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id  amount_tsh  ...              waterpoint_type waterpoint_type_group\n",
              "0      69572      6000.0  ...           communal standpipe    communal standpipe\n",
              "1       8776         0.0  ...           communal standpipe    communal standpipe\n",
              "2      34310        25.0  ...  communal standpipe multiple    communal standpipe\n",
              "3      67743         0.0  ...  communal standpipe multiple    communal standpipe\n",
              "4      19728         0.0  ...           communal standpipe    communal standpipe\n",
              "...      ...         ...  ...                          ...                   ...\n",
              "59395  60739        10.0  ...           communal standpipe    communal standpipe\n",
              "59396  27263      4700.0  ...           communal standpipe    communal standpipe\n",
              "59397  37057         0.0  ...                    hand pump             hand pump\n",
              "59398  31282         0.0  ...                    hand pump             hand pump\n",
              "59399  26348         0.0  ...                    hand pump             hand pump\n",
              "\n",
              "[59400 rows x 40 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6dJiyxY0knjO",
        "outputId": "6173d118-93cf-4935-9993-1cb37a2033ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 852
        }
      },
      "source": [
        "training_values.info()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 59400 entries, 0 to 59399\n",
            "Data columns (total 40 columns):\n",
            " #   Column                 Non-Null Count  Dtype         \n",
            "---  ------                 --------------  -----         \n",
            " 0   id                     59400 non-null  int64         \n",
            " 1   amount_tsh             59400 non-null  float64       \n",
            " 2   date_recorded          59400 non-null  datetime64[ns]\n",
            " 3   funder                 55765 non-null  object        \n",
            " 4   gps_height             59400 non-null  int64         \n",
            " 5   installer              55745 non-null  object        \n",
            " 6   longitude              59400 non-null  float64       \n",
            " 7   latitude               59400 non-null  float64       \n",
            " 8   wpt_name               59400 non-null  object        \n",
            " 9   num_private            59400 non-null  int64         \n",
            " 10  basin                  59400 non-null  object        \n",
            " 11  subvillage             59029 non-null  object        \n",
            " 12  region                 59400 non-null  object        \n",
            " 13  region_code            59400 non-null  int64         \n",
            " 14  district_code          59400 non-null  int64         \n",
            " 15  lga                    59400 non-null  object        \n",
            " 16  ward                   59400 non-null  object        \n",
            " 17  population             59400 non-null  int64         \n",
            " 18  public_meeting         56066 non-null  object        \n",
            " 19  recorded_by            59400 non-null  object        \n",
            " 20  scheme_management      55523 non-null  object        \n",
            " 21  scheme_name            31234 non-null  object        \n",
            " 22  permit                 56344 non-null  object        \n",
            " 23  construction_year      59400 non-null  int64         \n",
            " 24  extraction_type        59400 non-null  object        \n",
            " 25  extraction_type_group  59400 non-null  object        \n",
            " 26  extraction_type_class  59400 non-null  object        \n",
            " 27  management             59400 non-null  object        \n",
            " 28  management_group       59400 non-null  object        \n",
            " 29  payment                59400 non-null  object        \n",
            " 30  payment_type           59400 non-null  object        \n",
            " 31  water_quality          59400 non-null  object        \n",
            " 32  quality_group          59400 non-null  object        \n",
            " 33  quantity               59400 non-null  object        \n",
            " 34  quantity_group         59400 non-null  object        \n",
            " 35  source                 59400 non-null  object        \n",
            " 36  source_type            59400 non-null  object        \n",
            " 37  source_class           59400 non-null  object        \n",
            " 38  waterpoint_type        59400 non-null  object        \n",
            " 39  waterpoint_type_group  59400 non-null  object        \n",
            "dtypes: datetime64[ns](1), float64(3), int64(7), object(29)\n",
            "memory usage: 18.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e1LWiBzgPUyv",
        "outputId": "110bc877-d8b0-45fa-e00d-aff6124f7171",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "str=''\n",
        "for n in training_values.columns:\n",
        "  str=str+\"'\"+n+\"', \"\n",
        "print(str) # print a string from which we can copy/paste the following lists\n",
        "\n",
        "numerical = ['amount_tsh', 'gps_height', 'population', 'construction_year']\n",
        "categorical = ['funder', 'installer', 'wpt_name', 'basin']  # todo: complete this list"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'id', 'amount_tsh', 'date_recorded', 'funder', 'gps_height', 'installer', 'longitude', 'latitude', 'wpt_name', 'num_private', 'basin', 'subvillage', 'region', 'region_code', 'district_code', 'lga', 'ward', 'population', 'public_meeting', 'recorded_by', 'scheme_management', 'scheme_name', 'permit', 'construction_year', 'extraction_type', 'extraction_type_group', 'extraction_type_class', 'management', 'management_group', 'payment', 'payment_type', 'water_quality', 'quality_group', 'quantity', 'quantity_group', 'source', 'source_type', 'source_class', 'waterpoint_type', 'waterpoint_type_group', \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vlLffN6gjbFB",
        "outputId": "cfa349ee-32eb-4497-fc30-4c32337cd4f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "source": [
        "# training_values.sort_values('wpt_name').head()\n",
        "# check if ['id'] is unique\n",
        "print('Number of duplicate ids: ', training_values.duplicated(subset=['id']).sum())\n",
        "\n",
        "# check if latitude, longitude is in Tanzania\n",
        "lon_in_range = (tanzania_lon[0] <= training_values['longitude']) & \\\n",
        "               (training_values['longitude'] <= tanzania_lon[1])\n",
        "lat_in_range = (tanzania_lat[0] <= training_values['latitude']) & \\\n",
        "               (training_values['latitude'] <= tanzania_lat[1])\n",
        "pos_in_range = lon_in_range & lat_in_range\n",
        "print('Number of invalid coordinates: ', (~pos_in_range).sum())\n",
        "\n",
        "duplicate_location = training_values.duplicated(subset=['longitude', 'latitude'])\n",
        "training_values[duplicate_location].head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of duplicate ids:  0\n",
            "Number of invalid coordinates:  1812\n"
          ],
          "name": "stdout"
        },
        {
          "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>32376</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2011-08-01</td>\n",
              "      <td>Government Of Tanzania</td>\n",
              "      <td>0</td>\n",
              "      <td>Government</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.000000e-08</td>\n",
              "      <td>Polisi</td>\n",
              "      <td>0</td>\n",
              "      <td>Lake Victoria</td>\n",
              "      <td>Center</td>\n",
              "      <td>Mwanza</td>\n",
              "      <td>19</td>\n",
              "      <td>6</td>\n",
              "      <td>Geita</td>\n",
              "      <td>Nyang'hwale</td>\n",
              "      <td>0</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>VWC</td>\n",
              "      <td>Nyang'hwale</td>\n",
              "      <td>True</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>unknown</td>\n",
              "      <td>unknown</td>\n",
              "      <td>unknown</td>\n",
              "      <td>unknown</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>72678</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2013-01-30</td>\n",
              "      <td>Wvt</td>\n",
              "      <td>0</td>\n",
              "      <td>WVT</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.000000e-08</td>\n",
              "      <td>Wvt Tanzania</td>\n",
              "      <td>0</td>\n",
              "      <td>Lake Victoria</td>\n",
              "      <td>Ilula</td>\n",
              "      <td>Shinyanga</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>Bariadi</td>\n",
              "      <td>Chinamili</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>Parastatal</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>gravity</td>\n",
              "      <td>parastatal</td>\n",
              "      <td>parastatal</td>\n",
              "      <td>other</td>\n",
              "      <td>other</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>communal standpipe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>56725</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2013-01-17</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>0</td>\n",
              "      <td>DWE</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.000000e-08</td>\n",
              "      <td>Kikundi Cha Wakina Mama</td>\n",
              "      <td>0</td>\n",
              "      <td>Lake Victoria</td>\n",
              "      <td>Mahaha</td>\n",
              "      <td>Shinyanga</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>Bariadi</td>\n",
              "      <td>Bunamhala</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>WUG</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>wug</td>\n",
              "      <td>user-group</td>\n",
              "      <td>unknown</td>\n",
              "      <td>unknown</td>\n",
              "      <td>soft</td>\n",
              "      <td>good</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>other</td>\n",
              "      <td>other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253</th>\n",
              "      <td>13042</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2012-10-29</td>\n",
              "      <td>Hesawa</td>\n",
              "      <td>0</td>\n",
              "      <td>DWE</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.000000e-08</td>\n",
              "      <td>Kwakisusi</td>\n",
              "      <td>0</td>\n",
              "      <td>Lake Victoria</td>\n",
              "      <td>Nyamatala</td>\n",
              "      <td>Mwanza</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>Magu</td>\n",
              "      <td>Malili</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>nira/tanira</td>\n",
              "      <td>nira/tanira</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>insufficient</td>\n",
              "      <td>insufficient</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>256</th>\n",
              "      <td>33500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2011-07-18</td>\n",
              "      <td>Hesawa</td>\n",
              "      <td>0</td>\n",
              "      <td>DWE</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.000000e-08</td>\n",
              "      <td>Bombani</td>\n",
              "      <td>0</td>\n",
              "      <td>Lake Victoria</td>\n",
              "      <td>Ujamaa</td>\n",
              "      <td>Mwanza</td>\n",
              "      <td>19</td>\n",
              "      <td>6</td>\n",
              "      <td>Geita</td>\n",
              "      <td>Kalangalala</td>\n",
              "      <td>0</td>\n",
              "      <td>True</td>\n",
              "      <td>GeoData Consultants Ltd</td>\n",
              "      <td>VWC</td>\n",
              "      <td>Borehole</td>\n",
              "      <td>True</td>\n",
              "      <td>0</td>\n",
              "      <td>swn 80</td>\n",
              "      <td>swn 80</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>unknown</td>\n",
              "      <td>unknown</td>\n",
              "      <td>dry</td>\n",
              "      <td>dry</td>\n",
              "      <td>machine dbh</td>\n",
              "      <td>borehole</td>\n",
              "      <td>groundwater</td>\n",
              "      <td>hand pump</td>\n",
              "      <td>hand pump</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  amount_tsh  ...              waterpoint_type waterpoint_type_group\n",
              "53   32376         0.0  ...  communal standpipe multiple    communal standpipe\n",
              "168  72678         0.0  ...           communal standpipe    communal standpipe\n",
              "177  56725         0.0  ...                        other                 other\n",
              "253  13042         0.0  ...                    hand pump             hand pump\n",
              "256  33500         0.0  ...                    hand pump             hand pump\n",
              "\n",
              "[5 rows x 40 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XXko4e2zOyim",
        "outputId": "f11d8114-1a41-497b-b607-f9d7fb091208",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        }
      },
      "source": [
        "training_values.describe()"
      ],
      "execution_count": 13,
      "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>gps_height</th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>num_private</th>\n",
              "      <th>region_code</th>\n",
              "      <th>district_code</th>\n",
              "      <th>population</th>\n",
              "      <th>construction_year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>59400.000000</td>\n",
              "      <td>59400.000000</td>\n",
              "      <td>59400.000000</td>\n",
              "      <td>59400.000000</td>\n",
              "      <td>5.940000e+04</td>\n",
              "      <td>59400.000000</td>\n",
              "      <td>59400.000000</td>\n",
              "      <td>59400.000000</td>\n",
              "      <td>59400.000000</td>\n",
              "      <td>59400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>37115.131768</td>\n",
              "      <td>317.650385</td>\n",
              "      <td>668.297239</td>\n",
              "      <td>34.077427</td>\n",
              "      <td>-5.706033e+00</td>\n",
              "      <td>0.474141</td>\n",
              "      <td>15.297003</td>\n",
              "      <td>5.629747</td>\n",
              "      <td>179.909983</td>\n",
              "      <td>1300.652475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>21453.128371</td>\n",
              "      <td>2997.574558</td>\n",
              "      <td>693.116350</td>\n",
              "      <td>6.567432</td>\n",
              "      <td>2.946019e+00</td>\n",
              "      <td>12.236230</td>\n",
              "      <td>17.587406</td>\n",
              "      <td>9.633649</td>\n",
              "      <td>471.482176</td>\n",
              "      <td>951.620547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-90.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.164944e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>18519.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>33.090347</td>\n",
              "      <td>-8.540621e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>37061.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>369.000000</td>\n",
              "      <td>34.908743</td>\n",
              "      <td>-5.021597e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>1986.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>55656.500000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>1319.250000</td>\n",
              "      <td>37.178387</td>\n",
              "      <td>-3.326156e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>2004.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>74247.000000</td>\n",
              "      <td>350000.000000</td>\n",
              "      <td>2770.000000</td>\n",
              "      <td>40.345193</td>\n",
              "      <td>-2.000000e-08</td>\n",
              "      <td>1776.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>30500.000000</td>\n",
              "      <td>2013.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id     amount_tsh  ...    population  construction_year\n",
              "count  59400.000000   59400.000000  ...  59400.000000       59400.000000\n",
              "mean   37115.131768     317.650385  ...    179.909983        1300.652475\n",
              "std    21453.128371    2997.574558  ...    471.482176         951.620547\n",
              "min        0.000000       0.000000  ...      0.000000           0.000000\n",
              "25%    18519.750000       0.000000  ...      0.000000           0.000000\n",
              "50%    37061.500000       0.000000  ...     25.000000        1986.000000\n",
              "75%    55656.500000      20.000000  ...    215.000000        2004.000000\n",
              "max    74247.000000  350000.000000  ...  30500.000000        2013.000000\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENzTAPCrQeYJ"
      },
      "source": [
        "training_labels = pd.read_csv('training_labels.csv')"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u637XYufRh8f",
        "outputId": "a3a43815-d2ca-4d7f-9d53-49747c47ffbb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        }
      },
      "source": [
        "training_labels"
      ],
      "execution_count": 15,
      "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>status_group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>69572</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8776</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>34310</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>67743</td>\n",
              "      <td>non functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19728</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59395</th>\n",
              "      <td>60739</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59396</th>\n",
              "      <td>27263</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59397</th>\n",
              "      <td>37057</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59398</th>\n",
              "      <td>31282</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59399</th>\n",
              "      <td>26348</td>\n",
              "      <td>functional</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>59400 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id    status_group\n",
              "0      69572      functional\n",
              "1       8776      functional\n",
              "2      34310      functional\n",
              "3      67743  non functional\n",
              "4      19728      functional\n",
              "...      ...             ...\n",
              "59395  60739      functional\n",
              "59396  27263      functional\n",
              "59397  37057      functional\n",
              "59398  31282      functional\n",
              "59399  26348      functional\n",
              "\n",
              "[59400 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wyL52BBIFcsH",
        "outputId": "0acf59a7-939a-4d6c-ac98-a02619c3c398",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        }
      },
      "source": [
        "training_all = pd.concat([training_values, training_labels], axis=1) # get them side by side\n",
        "\n",
        "\n",
        "# Create the default pairplot\n",
        "sns.pairplot(training_all[numerical + ['status_group']], hue = 'status_group')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f8296ad7c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 871.875x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kPHHIaJI-tVR"
      },
      "source": [
        "# Engineer features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XscRcgEZRYuQ"
      },
      "source": [
        "# engineer some features\n",
        "\n",
        "# maybe days since reporting a functional pump?"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vI-9_Bsg-zaK"
      },
      "source": [
        "# Explore data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uLzANEKyRuS9",
        "outputId": "d7e806e6-b440-468e-afc9-855ab80444cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# plot n pumps on a map. Everything above 200 gets slow\n",
        "\n",
        "n = 200\n",
        "\n",
        "\n",
        "gmaps.configure(api_key=key)  \n",
        "\n",
        "\n",
        "\n",
        "colors = []\n",
        "labels = []\n",
        "\n",
        "\n",
        "sampled_pumps = training_values.sample(n)\n",
        "\n",
        "for i in range(len(sampled_pumps)):\n",
        "  id = sampled_pumps.iloc[i]['id']\n",
        "  #print(id)\n",
        "  state = training_labels[training_labels['id']==id]['status_group'].iloc[0]\n",
        "  if state=='functional':\n",
        "    colors.append('green')\n",
        "  elif state=='non functional':\n",
        "    colors.append('red') \n",
        "  else:\n",
        "    colors.append('yellow') # needs repair\n",
        "\n",
        "  labels.append('source %s' % sampled_pumps[sampled_pumps['id']==id].iloc[0]['source'])\n",
        "\n",
        "\n",
        "pump_locations = sampled_pumps[['latitude' , 'longitude']]\n",
        "info_box_template = \"\"\"\n",
        "<dl>\n",
        "\n",
        "<td>Name</td><dd>{scheme_name}</dd>\n",
        "</dl>\n",
        "\"\"\"\n",
        "\n",
        "pump_info = training_values['scheme_name'][:2]\n",
        "\n",
        "#marker_layer = gmaps.marker_layer(pump_locations, hover_text=pump_info, info_box_content=pump_info)\n",
        "marker_layer = gmaps.symbol_layer(pump_locations, fill_color=colors, stroke_color=colors, scale=3, hover_text=labels)\n",
        "figure_layout = {\n",
        "    'width': '1400px',\n",
        "    'height': '1200px',\n",
        "    'border': '1px solid black',\n",
        "    'padding': '1px'\n",
        "}\n",
        "\n",
        "fig = gmaps.figure(layout=figure_layout)\n",
        "fig.add_layer(marker_layer)\n",
        "#fig\n",
        "embed_minimal_html('export.html', views=[fig])\n",
        "IPython.display.HTML(filename='export.html')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<!DOCTYPE html>\n",
              "<html lang=\"en\">\n",
              "<head>\n",
              "    <meta charset=\"UTF-8\">\n",
              "    <title>IPyWidget export</title>\n",
              "</head>\n",
              "<body>\n",
              "\n",
              "\n",
              "<!-- Load require.js. Delete this if your page already loads require.js -->\n",
              "<script src=\"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js\" integrity=\"sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=\" crossorigin=\"anonymous\"></script>\n",
              "<script src=\"https://unpkg.com/@jupyter-widgets/html-manager@^0.18.0/dist/embed-amd.js\" crossorigin=\"anonymous\"></script>\n",
              "\n",
              "<script type=\"application/vnd.jupyter.widget-state+json\">\n",
              "{\n",
              "  \"version_major\": 2,\n",
              "  \"version_minor\": 0,\n",
              "  \"state\": {\n",
              "    \"8c6ed258ce2e43a6bc75dee67a1c2e71\": {\n",
              "      \"model_name\": \"SymbolModel\",\n",
              "      \"model_module\": \"jupyter-gmaps\",\n",
              "      \"model_module_version\": \"0.9.0\",\n",
              "      \"state\": {\n",
              "        \"fill_color\": \"red\",\n",
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          "metadata": {
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2oP1BmwhaoD9"
      },
      "source": [
        "training_values[['longitude', 'latitude']].head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eFoRLkhd-5Ca"
      },
      "source": [
        "# Prepare for training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rd8zedFQX7JF"
      },
      "source": [
        "\n",
        "# set n to low number for faster runs and to len(training_values) for max accuracy\n",
        "n = 5000\n",
        "n = len(training_values)\n",
        "# select the describing variables\n",
        "X = pd.get_dummies(training_values[['date_recorded', 'amount_tsh',\t'gps_height',\t'longitude',\t'latitude',\t'region_code',\t'district_code',\t'population',\t'construction_year', 'source', 'source_class', 'management_group', 'payment_type', 'extraction_type_group', 'waterpoint_type_group', 'quality_group', 'quantity_group', 'extraction_type_group'\t]][:n])\n",
        "#X = pd.get_dummies(training_values[:n])\n",
        "# X=X.drop(X[X['construction_year']< 1900].index) # drop all lines with missing construction year (but thenalso drop the y!!)\n",
        "X['lifetime']=pd.DatetimeIndex(X['date_recorded']).year-X['construction_year']  # engineer a feature but don't do it for rows where construction_year is empty\n",
        "X.loc[X['lifetime']> 1000, 'lifetime']=30\n",
        "X['date_recorded']=pd.to_numeric(X['date_recorded']) # otherwise dates get ignored in the correlation and the tree\n",
        "\n",
        "\n",
        "Y = pd.get_dummies(training_labels[['status_group']][:n])\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=1)\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(X)\n",
        "X_train_normalized = scaler.transform(X_train)\n",
        "X_test_normalized  = scaler.transform(X_test)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ux1_vI5d2jbe"
      },
      "source": [
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wu8gomyui65T"
      },
      "source": [
        "Y_train"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qPhexPmsgZ0-"
      },
      "source": [
        "np.array(X_train['lifetime'][:100])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KqlNFS1OUrAN"
      },
      "source": [
        "# figure out which variables correlate with Y\n",
        "\n",
        "\n",
        "sn.set(rc={'figure.facecolor':'#a0a0a0'})\n",
        "\n",
        "XY=pd.concat([X, Y], axis=1) # get them side by side\n",
        "\n",
        "corrMatrix = XY.corr()\n",
        "plt.figure(figsize=(60,25))\n",
        "# for tips on formatting the heatmap:\n",
        "# https://heartbeat.fritz.ai/seaborn-heatmaps-13-ways-to-customize-correlation-matrix-visualizations-f1c49c816f07\n",
        "sn.heatmap(corrMatrix, annot=True,  fmt='.2f', vmin=-1, vmax=1, center= 0, cmap= 'coolwarm')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FnfLpQ-IRJuI"
      },
      "source": [
        "#Forecast"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21pkGct572-T"
      },
      "source": [
        "def calc_accuracy(y_pred, Y_test):\n",
        "  correct = 0\n",
        "  for i in range(len(y_pred)):\n",
        "    y_vals = Y_test.iloc[i].values\n",
        "    y_pred_vals = y_pred[i]\n",
        "    #print(y_vals, y_pred_vals)\n",
        "    if (y_vals == y_pred_vals).all():\n",
        "      #print(\"correct\")\n",
        "      correct += 1\n",
        "    #else:\n",
        "      #print('incorrect')\n",
        "    #if correct>10: break\n",
        "  return correct/len(y_pred), correct\n",
        "\n",
        "results = {}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SyNH4rTsRlO0"
      },
      "source": [
        "##Decision tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OZDyxJP1lXAp"
      },
      "source": [
        "print(\"Train on %d samples. Test on %d samples.\" % (len(X_train), len(X_test)))\n",
        "\n",
        "results['tree'] = 0\n",
        "for d in [1, 5, 10, 15, 20, 25, 5]: # end with 5 so it can be plotted in next cell\n",
        "  model = tree.DecisionTreeClassifier(criterion='gini',max_depth=d)\n",
        "  model = model.fit(X_train, Y_train)\n",
        "\n",
        "  #Predict the response for test dataset\n",
        "  y_pred = model.predict(X_test)\n",
        " \n",
        "  accuracy, correct=calc_accuracy(y_pred, Y_test)\n",
        "  print(\"Max depth: %d   Accuracy on test set: %.2f   #correct: %d\" % (d, accuracy, correct))\n",
        "  if accuracy > results['tree']: results['tree']=accuracy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQp0frzjlYo_"
      },
      "source": [
        "# Export/Print a decision tree in DOT format. Only do this when max_depth is small (<=6) otherwise it gets too slow.\n",
        "#print(tree.export_graphviz(clf, None))\n",
        "\n",
        "if d < 6:\n",
        "  print('extracting dot')\n",
        "  #Create Dot Data\n",
        "  dot_data = tree.export_graphviz(model, out_file=None, feature_names=list(X_train.columns.values), \n",
        "                                  class_names=['func', 'repair', 'nonfunc'], rounded=True, filled=True) #Gini decides which attribute/feature should be placed at the root node, which features will act as internal nodes or leaf nodes\n",
        "  #print(dot_data)\n",
        "  print('Create graph image from DOT data')\n",
        "  graph = pydotplus.graph_from_dot_data(dot_data)\n",
        "\n",
        "  print('Show graph')\n",
        "  Image(graph.create_png())\n",
        "else:\n",
        "  print('graph to deep to fit in image')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TitZ_1pRbHIu"
      },
      "source": [
        "##Random forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwKWxHMmasjX"
      },
      "source": [
        "print(\"Train on %d samples, %d variables. Test on %d samples.\" % (X_train.shape[0], X_train.shape[1], len(X_test)))\n",
        "\n",
        "d=20\n",
        "model = RandomForestClassifier(n_jobs=None,random_state=27,verbose=0, max_depth=d, criterion='gini')\n",
        "model = model.fit(X_train, Y_train)\n",
        "\n",
        "#Predict the response for test dataset\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "accuracy, correct=calc_accuracy(y_pred, Y_test)\n",
        "print(\"Max depth: %d   Accuracy on test set: %.2f   #correct: %d\" % (d, accuracy, correct))\n",
        "results['forest']=accuracy\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9FnQqh1FABWE",
        "outputId": "1f001ef9-2481-489d-d94f-e8071f23bebe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "# feature importances\n",
        "#inspiration: https://github.com/ernestng11/touchpoint-prediction/blob/master/model-building.ipynb\n",
        "\n",
        "print(len(model.feature_importances_))\n",
        "combined = zip(model.feature_importances_, X_train.columns)\n",
        "combined = sorted(combined, reverse=True)[:50]\n",
        "#print(combined)\n",
        "#for i in len(combined):\n",
        "#  print('%s\\t%.3f' % (combined[i][1], combined[i][0]))\n",
        "\n",
        "importance, features = list(zip(*combined))\n",
        "\n",
        "f, ax = plt.subplots(figsize=(35,5))\n",
        "plot = sns.barplot(x=np.array(features), y=np.array(importance))\n",
        "ax.set_title('Feature Importance')\n",
        "plot.set_xticklabels(plot.get_xticklabels(),rotation='vertical')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "78\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 2520x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aZ0aE1t8RRTO"
      },
      "source": [
        "##KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBkoB37GCayu",
        "outputId": "337b4e6e-c637-42c3-b1cb-4c1c962e4c04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177
        }
      },
      "source": [
        "print(\"Train on %d samples. Test on %d samples.\" % (len(X_train), len(X_test)))\n",
        "\n",
        "\n",
        "results['knn']=-1\n",
        "for d in [1, 2, 3, 5, 10, 15, 20, 30]:\n",
        "  model = KNeighborsClassifier(n_neighbors=d)\n",
        "  model = model.fit(X_train_normalized, Y_train)\n",
        "\n",
        "  #Predict the response for test dataset\n",
        "  y_pred = model.predict(X_test_normalized)\n",
        "\n",
        "  accuracy, correct=calc_accuracy(y_pred, Y_test)\n",
        "  print(\"n_neighbors: %d   Accuracy on test set: %.2f   #correct: %d\" % (d, accuracy, correct))\n",
        "  if accuracy > results['knn']: results['knn']=accuracy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 47520 samples. Test on 11880 samples.\n",
            "n_neighbors: 1   Accuracy on test set: 0.71   #correct: 8446\n",
            "n_neighbors: 2   Accuracy on test set: 0.56   #correct: 6705\n",
            "n_neighbors: 3   Accuracy on test set: 0.72   #correct: 8541\n",
            "n_neighbors: 5   Accuracy on test set: 0.72   #correct: 8536\n",
            "n_neighbors: 10   Accuracy on test set: 0.68   #correct: 8079\n",
            "n_neighbors: 15   Accuracy on test set: 0.72   #correct: 8509\n",
            "n_neighbors: 20   Accuracy on test set: 0.69   #correct: 8227\n",
            "n_neighbors: 30   Accuracy on test set: 0.69   #correct: 8234\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L-cFwS5CHzMB",
        "outputId": "7c71f624-fdb3-4ebf-fc2e-975889b2a920",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        }
      },
      "source": [
        "pd.DataFrame( Y_train)"
      ],
      "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_functional</th>\n",
              "      <th>status_group_functional needs repair</th>\n",
              "      <th>status_group_non functional</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1159</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26556</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50388</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40428</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12301</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50057</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32511</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5192</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12172</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33003</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>47520 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       status_group_functional  ...  status_group_non functional\n",
              "1159                         1  ...                            0\n",
              "26556                        1  ...                            0\n",
              "50388                        1  ...                            0\n",
              "40428                        0  ...                            1\n",
              "12301                        1  ...                            0\n",
              "...                        ...  ...                          ...\n",
              "50057                        1  ...                            0\n",
              "32511                        0  ...                            1\n",
              "5192                         1  ...                            0\n",
              "12172                        0  ...                            1\n",
              "33003                        0  ...                            1\n",
              "\n",
              "[47520 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1gM_orokRXhO"
      },
      "source": [
        "##Neuralnet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KPk1W5P-E4iv",
        "outputId": "a505866d-3612-4af7-b94b-fb537c7bca23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(\"Train on %d samples. Test on %d samples.\" % (len(X_train), len(X_test)))\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "model = keras.Sequential()\n",
        "#model.add(layers.Dense(2, activation=\"relu\"))\n",
        "model.add(layers.Dense(20,  activation=\"relu\", input_shape = (X_test_normalized.shape[1],)))\n",
        "model.add(layers.Dense(10,  activation=\"relu\"))\n",
        "model.add(layers.Dense(5,  activation=\"relu\"))\n",
        "model.add(layers.Dense(3,   activation='sigmoid'))\n",
        "model.compile('adam', \"binary_crossentropy\", metrics=[\"accuracy\"])\n",
        "model.fit(x=X_train_normalized, y=Y_train, epochs=35)\n",
        "model.summary()\n",
        "\n",
        "y_pred = model.predict(X_test_normalized)\n",
        "print(len(y_pred))\n",
        "y_pred = (y_pred > 0.5).astype(\"int32\")\n",
        "\n",
        "accuracy, correct=calc_accuracy(y_pred, Y_test)\n",
        "print(\"Accuracy on test set: %.2f   #correct: %d\" % (accuracy, correct))\n",
        "results['neural net']=accuracy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 47520 samples. Test on 11880 samples.\n",
            "Epoch 1/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.4603 - accuracy: 0.6717\n",
            "Epoch 2/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.4052 - accuracy: 0.7267\n",
            "Epoch 3/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3969 - accuracy: 0.7322\n",
            "Epoch 4/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3922 - accuracy: 0.7353\n",
            "Epoch 5/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3887 - accuracy: 0.7387\n",
            "Epoch 6/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3865 - accuracy: 0.7393\n",
            "Epoch 7/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3848 - accuracy: 0.7412\n",
            "Epoch 8/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3832 - accuracy: 0.7413\n",
            "Epoch 9/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3817 - accuracy: 0.7422\n",
            "Epoch 10/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3804 - accuracy: 0.7451\n",
            "Epoch 11/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3796 - accuracy: 0.7448\n",
            "Epoch 12/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3791 - accuracy: 0.7438\n",
            "Epoch 13/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3780 - accuracy: 0.7460\n",
            "Epoch 14/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3773 - accuracy: 0.7455\n",
            "Epoch 15/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3764 - accuracy: 0.7456\n",
            "Epoch 16/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3756 - accuracy: 0.7456\n",
            "Epoch 17/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3753 - accuracy: 0.7467\n",
            "Epoch 18/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3747 - accuracy: 0.7463\n",
            "Epoch 19/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3741 - accuracy: 0.7472\n",
            "Epoch 20/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3736 - accuracy: 0.7490\n",
            "Epoch 21/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3730 - accuracy: 0.7476\n",
            "Epoch 22/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3729 - accuracy: 0.7485\n",
            "Epoch 23/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3725 - accuracy: 0.7484\n",
            "Epoch 24/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3722 - accuracy: 0.7483\n",
            "Epoch 25/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3715 - accuracy: 0.7488\n",
            "Epoch 26/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3706 - accuracy: 0.7511\n",
            "Epoch 27/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3708 - accuracy: 0.7487\n",
            "Epoch 28/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3699 - accuracy: 0.7501\n",
            "Epoch 29/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3700 - accuracy: 0.7500\n",
            "Epoch 30/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3696 - accuracy: 0.7513\n",
            "Epoch 31/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3691 - accuracy: 0.7521\n",
            "Epoch 32/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3689 - accuracy: 0.7516\n",
            "Epoch 33/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3690 - accuracy: 0.7505\n",
            "Epoch 34/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3683 - accuracy: 0.7524\n",
            "Epoch 35/35\n",
            "1485/1485 [==============================] - 3s 2ms/step - loss: 0.3682 - accuracy: 0.7526\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 20)                1600      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                210       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 5)                 55        \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 3)                 18        \n",
            "=================================================================\n",
            "Total params: 1,883\n",
            "Trainable params: 1,883\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "11880\n",
            "Accuracy on test set: 0.70   #correct: 8270\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GSrcI0Gae9cE"
      },
      "source": [
        "##XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YK24eHPVfFQw",
        "outputId": "22dba33c-d6ce-49c8-a341-556ae4bbb141",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# inspiration: https://www.kaggle.com/stuarthallows/using-xgboost-with-scikit-learn\n",
        "\n",
        "from sklearn.multiclass import OneVsRestClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.preprocessing import MultiLabelBinarizer\n",
        "\n",
        "\n",
        "#for d in range(1,35):\n",
        "results['xgboost']=-1\n",
        "#for d in [2, 15, 30, 50]:\n",
        "for d in [30]:\n",
        "  model = OneVsRestClassifier(XGBClassifier(n_jobs=-1, max_depth=d, objective=\"multi:softprob\", num_class=3))\n",
        "  model = model.fit(X_train_normalized, Y_train)\n",
        "\n",
        "  #Predict the response for test dataset\n",
        "  y_pred = model.predict(X_test_normalized)\n",
        "\n",
        "  accuracy, correct=calc_accuracy(y_pred, Y_test)\n",
        "  print(\"XGBoost: max_depth: %d   Accuracy on test set: %.2f   #correct: %d\" % (d, accuracy, correct))\n",
        "  if accuracy>results['xgboost']: results['xgboost']=accuracy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "XGBoost: max_depth: 30   Accuracy on test set: 0.71   #correct: 713\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wtt6Yw9AZMoc"
      },
      "source": [
        "#print(confusion_matrix(Y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OKXg_c92EjZZ"
      },
      "source": [
        "#Evaluation\n",
        "- randomforest: .72 \n",
        "- tree: .70\n",
        "- xgboost: .70\n",
        "- nn: .65\n",
        "- knn: .48"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eiN5HwCD9cEC"
      },
      "source": [
        "for k in results.keys():\n",
        "  print('%s: %.2f' % (k.capitalize(), results[k]))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q6LPzRASj4M5"
      },
      "source": [
        "import requests\n",
        "gcloud_token = !gcloud auth print-access-token\n",
        "gcloud_tokeninfo = requests.get('https://www.googleapis.com/oauth2/v3/tokeninfo?access_token=' + gcloud_token[0]).json()\n",
        "gcloud_tokeninfo\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wHrNq7Z1PuQt"
      },
      "source": [
        "#Submit result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G0ag_iZe-aE3"
      },
      "source": [
        "print('train model')\n",
        "\n",
        "model = RandomForestClassifier(n_jobs=None,random_state=27,verbose=0, max_depth=20, criterion='gini')\n",
        "model = model.fit(X, Y)\n",
        "\n",
        "print('predict')\n",
        "test_values = pd.read_csv('test_values.csv', parse_dates=['date_recorded'])\n",
        "X_submission = pd.get_dummies(test_values[['id', 'date_recorded', 'amount_tsh',\t'gps_height',\t'longitude',\t'latitude',\t'num_private',\t'region_code',\t'district_code',\t'population',\t'construction_year', 'source', 'quality_group', 'quantity_group', 'extraction_type_group'\t]])\n",
        "X_submission['date_recorded']=pd.to_numeric(X_submission['date_recorded']) # otherwise dates get ignored in the correlation and the tree\n",
        "\n",
        "#Predict the response for test dataset\n",
        "y_pred = model.predict(X_submission)\n",
        "\n",
        "print('create submission')\n",
        "# create a dataframe for submission\n",
        "# TODO: For better performance write this without a loop with a zip() or map()\n",
        "submission = pd.DataFrame(columns=['id', 'status_group'])\n",
        "for i in range(len(y_pred)):\n",
        "  if y_pred[i][0]: status='functional'\n",
        "  if y_pred[i][1]: status='functional needs repair'\n",
        "  if y_pred[i][2]: status='non functional'\n",
        "  submission=submission.append({'id': test_values.iloc[i]['id'], 'status_group': status}, ignore_index=True)\n",
        "\n",
        "# save as csv\n",
        "submission.to_csv('submission.csv', index=False)\n",
        "submission"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Qv6DQ-SS5Gi"
      },
      "source": [
        "test_values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Vdfr1-xWIJD"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ww0xXVHe201e"
      },
      "source": [
        "# Graveyard\n",
        "Snippets that are incomplete but interesting nonetheless"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KwCo_6lJCtV1"
      },
      "source": [
        "# inspired by: https://medium.com/@gabrielziegler3/multiclass-multilabel-classification-with-xgboost-66195e4d9f2d\n",
        "\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score, KFold\n",
        "\n",
        "model = XGBClassifier(max_depth=5, objective='multi:softprob', n_estimators=1000, \n",
        "                        num_classes=3)\n",
        "model = model.fit(X_train_normalized, Y_train)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#Predict the response for test dataset\n",
        "y_pred = model.predict(X_test_normalized)\n",
        "\n",
        "accuracy=calc_accuracy(y_pred, Y_test)\n",
        "print(\"n_neighbors: %d   Accuracy on test set: %.2f   #correct: %d\" % (d, accuracy, correct))\n",
        "accuracy_xgboost=accuracy"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}