notebooks/classifer_modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifer Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation Metric: __Accuracy__\n",
    "\n",
    "The DrivenData Challenge Evaluation Metric is \"Classification Rate\" (AKA, Accuracy). \n",
    "\n",
    "\"The metric used for this competition is the classification rate, which calculates the percentage of rows where the predicted class y^\n",
    "in the submission matches the actual class, y in the test set. The maximum is 1 and the minimum is 0. The goal is to maximize the classification rate.\n",
    "\n",
    "### Classes: Functional, Non Functional, and Functional Needs Repair\n",
    "\n",
    "#### Class Frequency of the classes represented in the __Training__ dataset:\n",
    "* Functional: 54.5%\n",
    "* Non Functional: 38.7%\n",
    "* Functional Needs Repair: 6.8%\n",
    "\n",
    "There is a class imbalance issue, with __Functional Needs Repair__ as the rare class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in libraries\n",
    "import warnings\n",
    "from importlib import reload\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('seaborn')\n",
    "import seaborn as sns\n",
    "\n",
    "import scipy.stats as scs\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as sms\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import KFold \n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, plot_confusion_matrix\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score # needed?\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "\n",
    "from xgboost import XGBClassifier\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define local functions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_hist(the_df, the_col):\n",
    "    plt.figure(figsize=(8,5))\n",
    "    plt.grid(linestyle='dashed', alpha=0.3, zorder=0)\n",
    "    plt.hist(the_df[the_col], alpha=0.8, zorder=2)\n",
    "    plt.title(the_col.capitalize())\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "def plot_confusion(ytrue, ypred):\n",
    "    cm_norm = confusion_matrix(ytrue, ypred, normalize='true')\n",
    "    sns.heatmap(cm_norm, cmap=sns.color_palette('Blues'), fmt='0.5g', annot=True, annot_kws={\"va\":\"bottom\"})\n",
    "    cm = confusion_matrix(ytrue, ypred)\n",
    "    sns.heatmap(cm, cmap=sns.color_palette('Blues'), fmt='0.5g', annot=True, annot_kws={\"va\":\"top\"})\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def print_accuracy(model, X_train, y_train, X_test, y_test, cm=False, cr=False, get_y_hat=False):\n",
    "    y_pred_train = model.predict(X_train)\n",
    "    y_pred_test = model.predict(X_test)\n",
    "    acc_test = round(accuracy_score(y_test, y_pred_test),3) * 100\n",
    "    acc_train = round(accuracy_score(y_train, y_pred_train),3) * 100\n",
    "    print(f'Test accuracy: {acc_test} %')\n",
    "    print(f'Train accuracy: {acc_train} %')\n",
    "    if cm == True:\n",
    "        print(confusion_matrix(y_test, y_pred_test))\n",
    "    if cr == True:\n",
    "        print(classification_report(y_test, y_pred_test))\n",
    "    if get_y_hat == True:\n",
    "        return y_pred_test\n",
    "\n",
    "\n",
    "def plot_confusion(model, X_test, y_test, normalize=None, form='.2f'):\n",
    "    plot_confusion_matrix(model, X_test, y_test, \n",
    "                          cmap=plt.cm.Blues, xticks_rotation='vertical',\n",
    "                          normalize=normalize, values_format=form)\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "def plot_feature_importance(model, ohencoder, X_encoder_input, num_features=25):\n",
    "    top_features_list = []\n",
    "    for item in zip(ohencoder.get_feature_names(X_encoder_input.columns), model.feature_importances_):\n",
    "        if item[1] > 0:\n",
    "            top_features_list.append(item)\n",
    "    top_feats = sorted(top_features_list, key=lambda x: x[1], reverse=True)\n",
    "    x_feats_importance = list(map(lambda x: x[1], top_feats[:num_features]))\n",
    "    y_feats_labels = list(map(lambda x: x[0], top_feats[:num_features]))\n",
    "    fig, ax = plt.subplots(figsize=(10,10))\n",
    "    plt.barh(y_feats_labels, x_feats_importance, align='center', color='purple', alpha=0.8)\n",
    "    ax.set_yticks(y_feats_labels)\n",
    "    ax.set_yticklabels(y_feats_labels)\n",
    "    ax.invert_yaxis()  # labels read top-to-bottom\n",
    "    ax.set_xlabel('Importance')\n",
    "    ax.set_ylabel('Features')\n",
    "    ax.set_title(f'Top {num_features} Features Ranked by Importance')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load in Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the cleand training data\n",
    "train_df = pd.read_csv('../data/train_processed_labeled.csv', index_col='id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We created the following new columns for our EDA, but we do not want to use them in modeling for our Classifier.\n",
    "* recorded_year -  Pulling out the year from date_recorded\n",
    "* waterpoint_age - Calculate as recorded_year - construction_year\n",
    "* recorded_good_quality - True if quality_group == 'good', False if  anything other than 'good'\n",
    "* recorded_good_quantity - True if quanity_group == 'sufficient', False if anythign other than 'sufficient'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop(['recorded_year', 'waterpoint_age', 'recorded_good_quality', 'recorded_good_quantity'], axis =1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection and Engineering\n",
    "Inspecting all the features and making initial choices on which ones to use for modeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Take the TRAIN dataset provided by the Challenge and make a train/test split for local model creation. We only use the TEST for Challenge submisison.\n",
    "X = train_df.drop(['status_group'], axis=1)\n",
    "y = train_df['status_group']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IiIiIBGJnjIiIiOTPjDtjLMaIiIhI/sy4GDPZYcro6GjMnz//H+9n5cqVOHPmDAAgNja22M8bO3Ys4uPj//HxiYiI6NVmssVYWRk5ciSaN2+OjIwM7N27V3QcIiIiMkarK/svmTD5YcqIiAj88MMPAIBu3bph5MiRCAwMhL29PVJSUpCZmYmFCxfC3d0dc+bMwenTp9GkSRNcvnwZixcvxvLly9GjRw9s2LABSUlJWL58OXQ6HWrUqAE/Pz9cuHABs2fPRmRkJFatWoW9e/eiTp06yMnJAQDk5OQgKCgI9+/fh0ajwbRp0+Di4iLyR0JEREQmxKSLsYyMDMTFxWHr1q0AgAEDBqBnz54AgLy8PKxevRobNmzAjh07YGFhgd9++w3btm3DxYsX0a9fP4N9jRgxAuvWrcPo0aOxbNmyAsd68OABNmzYgB9//BEqlQrdu3cH8KwY7NChAwYMGIBLly4hNDQUa9asKedXTkRE9IrRyaeTVdZMuhg7d+4c2rdvDwuLZy/D29sb58+fBwC0bNkSAFCrVi0kJSUhLS0NXl5eUCgU+Ne//gVHR8cSHSs9PR2NGzeGtbU1rK2t4e7uDgA4c+YM7ty5g127dgEAHj9+XFYvj4iIiP4/nVZ0gvJj0sWYJEnQPVcpq1QqKBTPpsEplUr99vzH5N+X/9yi9ptPrVbr9/H88/P3aWlpienTp6N58+b/5KUQERHRK8qkJ/C7ubkhISEBarUaarUaiYmJcHV1NfpYJycnpKSkQKfTIS0tDZmZmQb3KxQKfeFlY2ODrKwsAMBvv/0GAKhXrx7S0tKQl5eHnJwcJCcnAwC8vLywf/9+AMClS5c4RElERFQeOIFfnhwdHdGmTRv4+flBp9NhwIABhQ4/enh4oEGDBhgwYADc3NzQqFEjg+5Zo0aNcO7cOcydOxcBAQH4+OOPkZSUpB/urF69Ovr27QtfX1/UrVsXHh4eAAA/Pz9MnToVQ4YMgVarRXBwcPm/cCIiIjIbkk5nxjPinpOXl4cffvgBffv2xaNHj/Dvf/8bBw4c0M83e5nesTWdgu2RViU6QrH9dKeX6AglYqHsLjpCsQU2nC46Qok4VzadP2tHb5pOVgDIVWtERyi2R1q16AglYi0pX/wgGdl5Z85LPZ56zvtlvk+LaeFlvs/SMOnOWElYWVnh7NmzWLt2LRQKBcaNGyekECMiIqKS4wR+MzF9uml9uiciIiLz90oVY0RERGSiZDThvqyZ9NmURERERKaOnTEiIiKSPzOeM8bOGBEREZFA7IwRERGR7OnMeM4YizEiIiKSPw5TEhEREVF5YGeMiIiI5M98RynZGSMiIiISiZ0xIiIikj1O4CciIiISiRP4iYiIiKg8sDNGREREsqdjZ4yIiIiIygM7YwLc1uaIjlBstZRVRUcoNgtld9ERSkSt2Sc6QrFVtB4kOkKJVLS2Ex2h2NpZviM6QolYSkrREYrtX1Urio5QIpm5atER5E1QZ2zu3LlITEyEJEkICgqCp6en/r7r169j4sSJUKlUcHNzw6xZs0p1DHbGiIiISPZ02rL/epGTJ08iPT0dmzZtQmhoKEJDQw3uDwsLwwcffICtW7dCqVQiMzOzVK+NxRgRERGREXFxcfDx8QEANGrUCPfv30dOzrPRLa1Wi99++w1du3YFAMyYMQN16tQp1XFYjBEREZH8acvh6wWys7NRo0YN/W1bW1tkZWUBAO7cuYPKlStj3rx5GDx4MBYtWlTql8ZijIiIiKgYdDqdwfc3b96Ev78/oqKicO7cOfz888+l2i+LMSIiIpI9na7sv17E3t4e2dnZ+tu3bt2Cnd2zE4Rq1KiBOnXqoF69elAqlXjzzTdx8eLFUr02FmNERERERrRr1w6xsbEAgJSUFNjb28PGxgYAYGFhAScnJ1y5ckV/v7Ozc6mOw6UtiIiISPZELPrq7e0Nd3d3+Pr6QpIkzJgxA9HR0ahSpQq6d++OoKAgBAYGQqfT4fXXX9dP5i8pFmNEREQkf4LWGZs8ebLBbRcXF/339evXx4YNG/7xMThMSURERCQQO2NEREQke7w2JRERERGVC3bGiIiISPaKsxSFqWIxRkRERPKnlUQnKDccpiQiIiISiJ0xIiIikj1O4CciIiKicsHOGBEREcmeTsc5YyYnOjoa8+fPN3pfZmYmkpKSAAChoaG4evUqcnJy8MsvvxR7//3790dGRkaZZCUiIqKi6bRl/yUXZluMFeXEiRP6Yiw4OBhOTk5ISUnBsWPHBCcjIiKiV43ZD1POmzcPSUlJePr0KQYPHoxu3bph+fLlsLCwQO3atREeHo7p06dj1qxZyMnJQYMGDXDmzBn06NEDXbp0waFDhxAbG4uwsDDMmTMHZ86cgbOzM1QqFQDg5s2bCA4OhkqlglKpxJw5c1CnTh3Br5qIiMi8yKmTVdbMvjPm6OiIDRs2YP369ViyZAlsbW3Rr18/+Pv7o1u3bvrHjRgxAr169cKgQYOM7ufSpUs4ffo0tmzZgkmTJuHy5csAgCVLluCDDz5AREQEAgIC8PXXX7+U10VERETmwew7Y/fv34evry8sLS1x9+7dUu/n0qVL8PLygkKhQO3ateHk5AQAOHPmDC5fvoxvvvkGGo0Gtra2ZRWdiIiI/j9znsBv1sVYcnIytFotIiMjYWlpiebNmxfreZL01/9wtVoNANDpdFAo/mokarXP+qWWlpZYsmQJ7O3tyzA5ERERvSrMepjy2rVrqFWrFiwtLXHgwAFoNBrk5eVBkiR9kZVPoVDot1WuXBlZWVkAgN9++w0A4OzsjJSUFOh0Oly7dg3Xrl0DAHh5eWH//v0AgLi4OOzevftlvTwiIqJXhk4rlfmXXJh1Z8zHxwenT5+Gn58ffHx80LlzZ4SEhKB3796YMmWKwZCim5sbFi5ciFq1aqFPnz6YPHkyYmNj4erqCgBwcXHB66+/jkGDBqFBgwZwcXEBAIwePRpBQUHYu3cvJEnCvHnzhLxWIiIic2bOFwqXdDpzfnny1Lb6ONERiq2WsqroCMW2/U6Y6AglotbsEx2h2CpaGz+xRa4qWtuJjlBs7SzfER2hRCwlpegIxdagipXoCCWSmat+8YNkZEv2rJd6vGzf4WW+z9c2rinzfZaGWXfGiIiIyDyY8wR+s54zRkRERCR37IwRERGR7Mlpwn1ZYzFGREREsmfOM9w5TElEREQkEDtjREREJHucwE9ERERE5YKdMSIiIpI9LSfwExEREYnDCfxEREREVC7YGSMiIiLZ4wR+IiIiIioX7IwRERGR7LEzRkRERETlgp0xIiIikj2tGXfGWIwRERGR7JnzhcI5TElEREQkEDtjAljDSnSEYjus2is6QrEFNpwuOkKJVLQeJDpCsT1+ukl0hJL5Mkp0gmIb/VVF0RFK5NLDXNERiu3M/UeiI5RIBcl03htE4KKvRERERFQu2BkjIiIi2eMEfiIiIiKBuM4YEREREZULdsaIiIhI9sx5mJKdMSIiIiKB2BkjIiIi2TPnOWMsxoiIiEj2tKIDlCMOUxIREREJxM4YERERyZ45D1OyM0ZEREQkEDtjREREJHtc2oKIiIiIygU7Y0RERCR75jxnjMUYERERyZ5WJzpB+eEwJREREZFA7IwRERGR7JnzMKXJdsY+/fRTocc/dOgQAgMDhWYgIiIi02eynbFvvvlGdAQiIiJ6SbQw386Y8GIsOjoaR44cwa1bt9ChQwccPnwYCoUCPj4++OCDD3Djxg2MGzcOlpaWaNmyJX777TdERkaiTZs2iI+PR2pqKmbNmgWFQoHKlSsjLCwMqampWLduHSRJwh9//IEePXpg9OjRhWZYtWoVYmNjoVAoMHHiRLzxxhuIiIjADz/8AADo1q0bRo4cidTUVEyZMgXVqlVDvXr19M9ft24ddu/ebZCbiIiIyo6OE/jL1/Xr1zF//nwcP34cGzZswLp16/DTTz8hMzMT4eHh+Pe//42oqCjk5eUVeG5oaCj++9//IjIyEq1atcLatWsBAElJSQgLC8PGjRsRGRlZ6LGvXLmC2NhYbN68GV988QV2796Nq1evYvv27Vi3bh3WrVuHH3/8EX/++Se+/vprjB49GhEREVAonv3orl69ipiYmAK5iYiIiIpDFsWYh4cHzp49i/T0dPj7+8Pf3x+5ubm4du0a0tLS4O3tDQDo2rVrgeempaXBy8sLANCmTRucO3cOAODm5oaKFSuicuXKRR773Llz8PLygkKhQP369REaGorff/8dXl5esLCwgIWFBby9vXH+/HmDLG3atAGAQnMTERFR2dHqpDL/kgvhw5QAYGlpCUtLS3Tu3BmzZs0yuO/bb7+FJD37geX/tzAqlUrfsbKwKN5LUyqV0Gq1BtskSYLuuX5o/n51Op0+Q/5zCstNREREpm/u3LlITEyEJEkICgqCp6dngccsWrQICQkJRY7EFUUWnTEAcHd3R3x8PB4/fgydToc5c+bgyZMnqFevHpKTkwEAR44cKfC8Jk2a4MyZMwCAU6dOoWnTpiU+7unTp6FWq5GdnY1Ro0bB1dUVCQkJUKvVUKvVSExMhKurK5ydnfVZ4uPji8xNREREZUcLqcy/XuTkyZNIT0/Hpk2bEBoaitDQ0AKPuXTpEk6dOvWPXpssOmMAUKdOHfj7+2Po0KFQKpXw8fFBhQoV4O/vj/HjxyM2NlY/nPi8adOmYebMmZAkCdWqVcO8efOQkpJS7OPWrVsXffr0gZ+fH3Q6HSZMmIC6deti0KBB+m0DBgyAo6MjPv30U0ydOhVr166Fk5MTVCpVobmJiIio7IiYwB8XFwcfHx8AQKNGjXD//n3k5OTAxsZG/5iwsDBMmDABy5cvL/VxhBdj/fv3138/dOhQDB061OB+lUqF6dOno0WLFtizZw/u3LkD4K/OVOPGjQu0Bdu0aaOf0/X8YwvzwQcfFDgD0lgWd3d37Nq1q8DzjT2WiIiITFt2djbc3d31t21tbZGVlaUvxqKjo9G6dWs4Ojr+o+MIL8ZepHLlyvj8888hSRIUCgXmzZtXqv1s2rQJe/bsKbB94sSJaN68+T+NSUREROVIDhPun59Pfu/ePURHR2PNmjW4efPmP9qv7IuxOnXqYMOGDf94P4MGDcKgQYPKIBERERG9Cuzt7ZGdna2/fevWLdjZ2QEATpw4gTt37mDo0KHIy8vDn3/+iblz5yIoKKjEx5HNBH4iIiKiwugglfnXi7Rr1w6xsbEAgJSUFNjb2+uHKHv27IkffvgBmzdvxvLly+Hu7l6qQgwwgc4YERERkQje3t5wd3eHr68vJEnCjBkzEB0djSpVqqB79+5ldhwWY0RERCR7WkGXQ5o8ebLBbRcXlwKPqVu3bqnXGANYjBEREZEJkMME/vLCOWNEREREArEzRkRERLJXnAn3poqdMSIiIiKB2BkjIiIi2RM1gf9lYDFGREREssdhSiIiIiIqF+yMERERkeyZ8zAlO2NEREREArEzRkRERLJnzou+shgjIiIi2TPjUUoWY1Q0nU4rOkKxOVc2rX+qFa3tREcovi+jRCcomQl+ohMUW86Cn0VHKJEKCtN527iGDNERSsRZV190BBLEdP5VERER0SvLnIcpOYGfiIiISCB2xoiIiEj2TGfSTMmxM0ZEREQkEDtjREREJHs6M54zxmKMiIiIZI/DlERERERULtgZIyIiItnjtSmJiIiIqFywM0ZERESypwMn8BMREREJw2FKIiIiIioX7IwRERGR7JnzMCU7Y0REREQCsTNGREREsmfOc8ZYjBEREZHsmXMxxmFKIiIiIoHYGSMiIiLZ4wT+cpabm4uuXbtiwoQJePLkidHHZGZmIikpqcD26Oho7Nu3r9B9x8TElFnO57Vp06Zc9ktERESvFll1xr788stC7ztx4gQePXoET09Pg+39+/cv9Dl5eXkIDw9Hz549yywjERERvXzmPGdMWDGWk5ODMWPG4OnTp2jRogUAoGvXrti9ezfOnDmDr776ChUqVEDNmjUxY8YMLF++HBYWFqhduzbCw8PRpEkTAECNGjVQo0YN+Pn5Yc6cOUhKSoJSqcTMmTOxYcMGpKamIiQkBCEhIUZznDt3DjNnzoQkSWjevDmmTJmC1NRUzJo1CwqFApUrV0ZYWBhsbGwwadIk3LhxAx4eHvrnX7p0CbNmzYIkSfrHVq1atdx/fkRERK8SregA5UjYMOXOnTvRpEkTrF+/Hq6urgb3RUVFITAwEFFRUejduzc0Gg369esHf39/dOvWDQDQpEkTfP755/rnHD9+HDdu3MDmzZsxceJE/PDDDxgxYgScnZ0LLcQAYM6cOZg5cyY2btyI27dv49q1awgNDcV///tfREZGolWrVli7di2OHTsGtVqNTZs24Z133sG9e/cAALNnz8asWbMQERGBdu3aYd26dWX+syIiIiLzJawzlpaWhlatWgEAWrdubXBfz549MWPGDLzzzjvo3bs37OzsCjz/78OVKSkp8Pb2BgC0atUKrVq1QkZGxgtzXL58GS4uLgCABQsW6LN5eXkBeDY3bPny5ahcuTKaN28OAPDy8kKFChUAAElJSZg+fTqAZ8Oiz3fNiIiIqGzodOY7gV9YMabT6aBQPGvMabWGzce+ffuiQ4cO2L9/Pz799FMsWbKkwPMtLS0NbiuVygL7KY78DIVRqVRQKBQGeZ/PXLFiRaxduxaSZL6/JERERFR+hA1TOjs7Izk5GQAQHx9vcN+KFStgYWGBQYMGoVevXkhLS4MkSVCr1YXuz8PDQ7+f/HlgCoUCGo2myByNGjVCYmIiACAoKAhpaWlo0qQJzpw5AwA4deoUmjZtapD39OnTyMvLAwC4uLjgyJEjAIC9e/ciLi6upD8KIiIiegFtOXzJhbDOWN++fTFq1CgEBAToJ/Dnq1OnDoYPH46qVauiatWqGD58OCpXrowpU6bA1tbW6P5atWqFAwcOYMiQIQCAGTNmwM7ODiqVCmPHjsXSpUuNPi84OFg/p6xZs2Zo1KgRpk2bpp/UX61aNcybNw/W1tbYtm0b/Pz84OLiAgcHB/3zp0+fjlWrVsHa2hqLFi0qo58QERERvQoknU5nxieLylOX6p+JjlBsiZrC13CTmzDnfqIjlMhnf2wRHaHY7sx8Q3SEkpngJzpBsb1f+2fREUrknkolOkKxpeGK6Agl4qyrLzpCiey9O/elHm+1V0iZ73NEYtnvszRktc5YecnMzMSUKVMKbG/VqhXGjh0rIBERERGVhDl3jl6JYqxOnTqIjIwUHYOIiIiogFeiGCMiIiLTpjXjpS1kcW1KIiIiolcVO2NEREQke5wzRkRERCSQOV8onMOURERERAKxM0ZERESyJ6cV88saO2NEREREArEzRkRERLJnztcLYjFGREREsqcF1xkjIiIionLAzhgRERHJnjkPU7IzRkRERCQQO2NEREQke1zagoiIiIjKBTtjAlRVWIuOUGzDq/cXHaHYjt40rc9N7SzfER2h2EZ/VVF0hBLJWfCz6AjFFn69s+gIJfJOzVjREYqtuu410RFKpIKFUnQEWTPnyyGxGCMiIiLZM+NajMOURERERCKxM0ZERESyJ2qYcu7cuUhMTIQkSQgKCoKnp6f+vhMnTmDx4sVQKBRwdnZGaGgoFIqS97nYGSMiIiIy4uTJk0hPT8emTZsQGhqK0NBQg/s///xzLF26FBs3bkRubi6OHj1aquOwM0ZERESyJ2LR17i4OPj4+AAAGjVqhPv37yMnJwc2NjYAgOjoaP33tra2uHv3bqmOw84YERERyZ62HL5eJDs7GzVq1NDftrW1RVZWlv52fiF269YtHDt2DJ06dSrVa2MxRkRERFQMOiPtudu3b+OTTz7BjBkzDAq3kuAwJREREcmeiAn89vb2yM7O1t++desW7Ozs9LdzcnLw0UcfYfz48Wjfvn2pj8POGBEREZER7dq1Q2zss4WOU1JSYG9vrx+aBICwsDAEBASgY8eO/+g47IwRERGR7IlY2cLb2xvu7u7w9fWFJEmYMWMGoqOjUaVKFbRv3x47duxAeno6tm7dCgB4++23MWjQoBIfh8UYERERyZ6odcYmT55scNvFxUX/fXJycpkcg8OURERERAKxM0ZERESyp4MkOkK5YWeMiIiISCB2xoiIiEj2RM0ZexnYGSMiIiISiJ0xIiIikj12xsxQdHQ05s+fX+j9mZmZSEpKAgCEhobi6tWrLysaERER/Y2uHL7kgp2xQpw4cQKPHj2Cp6cngoODRcchIiIiM2USxVh0dDSOHj2KnJwc3LhxA++//z7q1q2LL7/8EhYWFnBwcMC8efOwZ8+eAo9799130bVrV+zevRuVK1fG/Pnz0aRJE4P9z5s3D0lJSXj69CkGDx6Mbt26Yfny5bCwsEDt2rURHh6O6dOno3bt2ggMDMSDBw+gVqsxbdo0uLu7o3v37vDx8cHp06dRpUoVrFy5EgrFK9t0JCIiKnPmPExpEsUYAFy6dAnbt2/HgwcP0KdPH1SqVAnh4eGoXbs2Zs2ahd27d0OSpAKP69evX5H7ffr0KRwdHTF16lQ8efIEPj4+GDBgAPr164caNWqgW7duCA8PBwBERETAy8sLI0eOxNmzZzFv3jxERUXh6tWr6NOnD6ZMmYKBAwciNTUVrq6uL+GnQkRERKbOZIqxVq1awcLCAra2tqhSpQp0Oh1q164NAGjTpg1OnToFNzc3g8dVq1YNd+/eLXK/1tbWuH//Pnx9fWFpaVnk45OTk/Hpp58CADw8PJCeng4AsLGx0V8eoVatWnj48GFZvGQiIiL6/3SymuVVtkymGNNqtfrvJUlCXl6e/rZKpYIkSQUep9Pp9Nuff+zzTp48iRMnTiAyMhKWlpZo3rx5oRkkSYJO99cvQ/6xlEqlweOefwwRERH9c+Y8TGkyE5sSEhKg0Whw584d5ObmwtLSEpmZmQCeFVRNmzY1+rjq1avDxsYGWVlZ0Gg0SExMNNjv3bt3UatWLVhaWuLAgQPQaDTIy8uDJElQq9UGj/Xw8EB8fLz+OH+fe0ZERERUUibTGXN0dMS4ceOQnp6O8ePHo27dupg0aRIsLCzg5OSE3r17Y9euXQUep1Ao4Ofnh08++QTOzs5o3LixwX7btm2LVatWwc/PDz4+PujcuTNCQkLQu3dvTJkyBba2tvrH+vv7IygoCP7+/tDpdPj8889f9o+BiIjolWTGjTFIOhMYU4uOjsbFixcxZcqUMnmcaH1sp4mOUGyNq1qJjlBstx5rX/wgGbmd91R0hGKrb1NRdIQSyckznd+F8OudRUcokXdqxoqOUGz3dI9ERyiR2hZVRUcokW3Zs1/q8f7bcFaZ73PBH/JoqphMZ4yIiIheXeY8Z8wkirH+/fuX6eOIiIjItMh/HK/0TGYCPxEREZE5MonOGBEREb3aTGcmaMmxM0ZEREQkEDtjREREJHvmPIGfnTEiIiIigdgZIyIiItkz57MpWYwRERGR7HECPxERERGVC3bGiIiISPbMeZiSnTEiIiIigdgZIyIiItkz5zljLMaIiIhI9nRmPE7JYUoiIiIigdgZIyIiItkz5xX4WYwJoIXp/Eb9maMWHaHYNCbWwraUlKIjFNulh7miI5RIBYXp/Gl7p2as6Aglsvt2D9ERim1CgyOiI5RIRq5KdAQSxHT+YhEREdEry7Q+bpcMizEiIiKSPXMepuQEfiIiIiKB2BkjIiIi2WNnjIiIiIjKBTtjREREJHs6M57Cz84YERERkUDsjBEREZHsmfOcMRZjREREJHsmtq53iXCYkoiIiEggdsaIiIhI9kzpUoIlxc4YERERkUDsjBEREZHsmfOcMRZjREREJHta0QHKEYcpiYiIiARiZ4yIiIhkT2fG45TsjBEREREJJKQYi46Oxvz58wts79q1K3Jzc7Fy5UqcOXNGQLK/xMTEAACOHDmC9evXC81CRET0qtPqyv5LLmQ5TDly5Eihx8/Ly0N4eDh69uyJjh07Cs1CRERE5r3OWJkWY9HR0Th69ChycnJw48YNvP/++1ixYgV2796NypUrY/78+WjSpAkAICMjAx999BFu3LiBgIAAvPfee/r9BAYGokePHmjfvj0CAwNx7do1WFtbY8GCBXBwcDB67GHDhqFNmzY4duwYFAoF+vbti+3bt0OpVCI8PByPHz9GUFAQ7t+/D41Gg2nTpsHFxQW//vorFi9eDAsLC9SuXRuzZ8/GvHnzkJqaipCQEHh6euLixYsYOnQoAgMD4eTkhNTUVLi6uiI0NBTnz59HYGAgqlSpgqZNm+Lu3bsICwsryx8rERERmbEyH6a8dOkSvvnmG0REROCrr76CVmv8ZNQrV67g66+/xtq1a7F06VKjE/N27NiB1157DRs3bsTAgQNx4MCBIo9tZ2eHDRs2QKPR4P79+1i/fj00Gg0uXLiAiIgIdOjQAREREQgJCdEPk86ZM0efo2bNmoiJicGIESPg7OyMkJAQg/2npKRg4sSJ2Lp1Kw4fPowHDx5gxYoVGDVqFCIjI5GZmVm6HxoREREVSacr+y+5KPNhylatWsHCwgK2traoVq0arl69avRx3t7esLS0RI0aNWBjY4O7d+8WeExKSgrefPNNAEDv3r1feGxPT08AgL29Pdzc3AAAr732Gh4+fIgzZ87gzp072LVrFwDg8ePHyM7ORnp6OsaMGQMAePToEWrUqFHo/uvVqwc7Ozv9MR4+fIi0tDR4e3sDeDbnLS4u7oU5iYiIiPKVeTH2fCdMp9OhZs2a+tsqlUr/vSRJBs/7+20AUCqVhXbWjFEqlUa/1+l0sLS0xPTp09G8eXP99vv378Pe3h6RkZEG+8nIyHjh/vP3q9Pp9NmNvQYiIiL658x5zliZD1MmJCRAo9Hgzp07yM3NReXKlZGVlQWNRoPExESjj3v8+DGqV69eYF8eHh44ceIEAODQoUP43//+V+pcXl5e2L9/P4BnQ6lr1qxBtWrV9LcBIDIyEufPn4dCoYBGoynWfuvVq4fk5GQAz868JCIiIiqJMu+MOTo6Yty4cUhPT8f48eORl5eHTz75BM7OzmjcuLH+cQ0bNjR4nLGuUq9evXD8+HH4+fnBwsLC6HIYxeXn54epU6diyJAh0Gq1CA4OBgCEhoZi6tSpsLS0hL29PQYNGgRJkqBSqTB27Fh07ty5yP1++umnmDZtGiIiItC4cWM8fPiw1BmJiIjIODnN8Sprkq4Ml7SNjo7GxYsXMWXKlLLapewlJCSgQoUKcHFxwbfffgudTodPPvmkyOe8Yxv8ktL9cxUUyhc/SCY0JvYv1ZTyPtKqXvwgGamgkOWqPUZpdKZ1xb3dt3uIjlBsExqY1mhFRq5p/TuLvj37pR6vPN47d98JfeFj5s6di8TEREiShKCgIP38dAA4fvw4Fi9eDKVSiY4dO2LUqFGlymE6f7EAZGZmGi30WrVqhbFjxwpIBFhZWSE4OBgVKlRAhQoVsGjRIiE5iIiIqGydPHkS6enp2LRpE9LS0hAUFIRNmzbp758zZw5Wr14NBwcH+Pn5oUePHgajgMVVpsVY//79y3J3BdSpU6fAZHvR3NzcsG3bNtExiIiIzJpWwGhCXFwcfHx8AACNGjXC/fv3kZOTAxsbG1y9ehXVqlVD7dq1AQCdOnVCXFxcqYoxXpuSiIiIyIjs7GyDJa9sbW2RlZUFAMjKyoKtra3R+0rKpIYpiYiI6NWkk8HSFmU4zd4AizEiIiKSPRGnutjb2yM7O1t/+9atWwaLvz9/382bN2Fvb1+q43CYkoiIiMiIdu3aITY2FsCzqwLZ29vDxsYGAFC3bl3k5OQgIyMDarUahw4dQrt27Up1HHbGiIiISPZErMDv7e0Nd3d3+Pr6QpIkzJgxA9HR0ahSpQq6d++OkJAQTJo0CcCztVGdnZ1LdRwWY0RERESFmDx5ssFtFxcX/fetWrUyWOqitFiMERERkeyV1+R5OWAxRkRERLLHC4UTERERUblgZ4yIiIhkj50xIiIiIioX7IwRERGR7OmELPv6crAzRkRERCQQO2NEREQke+Y8Z4zFGBEREckeizEqU0pJEh2h2GwslaIjFNv1J09ERyiRf1WtKDpCsZ25/0h0hBK5hgzREYqtuu410RFKZEKDI6IjFNuXVzqKjlAiHWy3i45AgrAYIyIiItnTcgI/EREREZUHdsaIiIhI9nSS+XbGWIwRERGR7JnzBH4OUxIREREJxM4YERERyR4n8BMRERFRuWBnjIiIiGTPnK9NyWKMiIiIZE9rxmdTcpiSiIiISCB2xoiIiEj2OIGfiIiIiMoFO2NEREQke+yMEREREVG5YGeMiIiIZI9LWxAREREJpIVGdIRyw2FKIiIiIoFYjJWB0NBQXL16FTk5Ofjll19ExyEiIjI7OmjL/EsuWIyVgeDgYDg5OSElJQXHjh0THYeIiIhMyCs1Zyw6OhpHjx5FTk4Obty4gffffx/169fH4sWLYWFhgdq1a2P27Nk4c+YMvv/+ezx69AhTpkzB+PHj0bVrV8TFxaFDhw7Q6XQ4duwYOnbsiMmTJ2PYsGGYPn06Zs2ahZycHDRo0ACDBg0S/XKJiIjMhjlfDumVKsYA4NKlS9i+fTsePHiAPn36oGbNmggPD0f16tWxYMECxMTEwMHBARcuXEBsbCysrKyQkZGBQYMGYcKECWjdujWioqIwbtw4dOnSBZMnT9bve8SIEbh48SILMSIiojJmzhP4X7lirFWrVrCwsICtrS1sbGxw+fJljBkzBgDw6NEj1KhRAw4ODvjXv/4FKysrAICNjQ0aNWoEAKhUqRLc3d1hYWEBrdZ8q3QiIiJ6OV65Yuz5AkqhUMDOzg6RkZEGj4mPj9cXYgCgVCoN7reweOV+bERERELJacJ9WXvlJvAnJCRAo9Hgzp07yM3NhUKhwKVLlwAAkZGROH/+fKn3rVAooFaryyoqERERvQJeuRaPo6Mjxo0bh/T0dIwfPx5169bF1KlTYWlpCXt7ewwaNAhnzpwp1b7d3NywcOFC1KpVCyNGjCjj5ERERK8urY5zxsxGvXr1MGXKFINtW7ZsMbjdpk0btGnTRn87Pj6+yO+fH+bkOmNERERlj8OURERERFQuXqnOWP/+/UVHICIiolLQmfHSFuyMEREREQn0SnXGiIiIyDRpzXjOGIsxIiIikj1O4CciIiKicsHOGBEREcmezozXGWNnjIiIiEggdsaIiIhI9sx5Aj87Y0REREQCsTNGREREsmfOi76yGCMiIiLZ0+k4TElERERE5YCdMSIiIpI9TuAnIiIionLBzhgRERHJnjkv+spiTACV1nRardeePBYdodgqSqb165yZqxYdodgqSFaiI5SIs66+6AjFVsFCKTpCiWTkqkRHKLYOtttFRyiRo3f6iY4ga7w2JRERERGVC9NqJRAREdEriUtbEBEREVG5YGeMiIiIZI8r8BMREREJZM7DlCzGiIiIiIpJpVIhMDAQmZmZUCqVmDdvHpycnAwe88MPP+D777+HQqHAm2++iQkTJhS5T84ZIyIiItnT6bRl/lUae/bsQdWqVbFhwwZ88sknWLRokcH9jx8/xsKFCxEeHo5Nmzbh+PHjuHTpUpH7ZDFGREREVExxcXHo3r07AKBt27Y4ffq0wf0VK1bErl27YGNjA0mSUL16ddy7d6/IfXKYkoiIiGRPLtemzM7Ohq2tLQBAoVBAkiTk5eXByuqvxbFtbGwAAKmpqbh27Rq8vLyK3CeLMSIiIiIjtmzZgi1bthhsS0xMNLit0+mMPvfKlSuYPHkyFi1aBEtLyyKPw2KMiIiIZE/E2ZQDBgzAgAEDDLYFBgYiKysLLi4uUKlU0Ol0Bl0xALhx4wZGjRqFBQsWwNXV9YXH4ZwxIiIikj2dTlPmX6XRrl07xMTEAAAOHTqENm3aFHhMcHAwQkJC4O7uXqx9sjNGREREVEy9evXC8ePHMXjwYFhZWSEsLAwAsHLlSrRq1QrVq1fHr7/+iqVLl+qf8/7776Nbt26F7lPSFTbYSeWmd40g0RGKTSWTCZPFUVEyrc8WVgrTaUzf1+SJjlAilibU9K+gVIqOUCKm9I5xQ/NAdIQSOXqnn+gIJaJUdH6px7Op1KTM95nz6GKZ77M0TOcvFhEREZEZMq1WAhEREb2SeDkkIiIiIoHMuRgTMkwZGxv7j55//vx5XL58GQAwYcIEPHnypCxiEREREb10L70Yy8jIwN69e//RPvbt24crV64AAL788ktUqFChDJIRERGRXOmgLfMvuXjhMGX+1cmvXbsGa2trzJ07F8uXL8fVq1eRl5eHsWPHon379ujevTsGDRqEQ4cOIS8vD2vWrMGDBw/w2WefQaFQQKPR4IsvvsCsWbOQlJSE5cuXQ6fT4erVq8jIyMCYMWOwYcMG/amgbdq0QXx8PM6dO4eZM2dCkiQ0b94cffv2xcaNG2Fra4uaNWti/Pjx2L17Nx4+fIigoCCoVCpIkoTQ0FBIkoTAwEA4OTkhNTUVrq6uCA0NNfo6169fj1u3bmH8+PEAgOHDh2PKlCn4888/8f3338PCwgJNmzZFYGAgcnJyMGnSJDx69AhPnjzB9OnT4enpibfeegsdO3ZEzZo18emnn5bd/yUiIiIyWy/sjO3YsQOvvfYaNm7ciIEDB2L79u2wsrJCVFQUli1bhtmzZwMANBoNGjZsiHXr1qFu3bo4ceIEYmNj0bZtW0RGRiI4OBhZWVkYMWIEWrdujdGjRwN4VuytX78eikJO858zZw5mzpyJjRs34vbt27CxsUGHDh0wceJEeHp66h+3ZMkSvPfee4iMjMSQIUOwfPlyAEBKSgomTpyIrVu34vDhw3jwwPipzr169cKBAwcAAA8fPsS9e/fg5OSEb775BmvXrkVUVBSuX7+O3377DVlZWRgwYAAiIyMxceJErFq1CgCgVqvRsWNHFmJERERlTKfTlvmXXLywGEtJSYG3tzcAoHfv3rh3755+tVkHBwdYWVnpr0besmVLAECtWrXw8OFDtGvXDjt37kRYWBjy8vLQrFmzAvt/vqAy5vLly3BxcQEALFiwAI6OjkYfl5ycjNatWwN41lU7d+4cAKBevXqws7ODQqGAvb09Hj58aPT51atXR/369ZGSkoLDhw+jZ8+euHTpEjIzMzFixAgMGzYM6enpyMzMxGuvvYbY2FgMHjwYCxcuNLga+4teDxEREZWcORdjLxymVCqV0GoNAz+/TmxeXp6+q6V8bvFCnU6H119/HTt37sSxY8ewePFivPvuu6hdu7bBvvIvnilJksF2tVoNAIV2zP5OkiR9LpVKZTTT37P/Xd++fRETE4PMzExMmDABDx48QNOmTbF69WqDxy1fvhwODg744osvcPbsWSxYsKDA6yEiIiIqjhdWOh4eHjhx4gSAZ9dgql69OuLj4wEA169fh0KhQNWqVY0+d+/evbh48SJ8fHwwbtw4JCcnQ6FQ6Aut59nY2ODWrVsAnp0tmZubCwBo1KiR/grpQUFBSEtLgyRJ0GgMrynl4eGhz3Xq1Ck0bdq0WD+A53Xs2BGnTp3CgwcPULduXTg7OyMtLQ23b98GACxduhQ3b97E3bt3Ua9ePQDA/v37oVKpSnwsIiIiKgltOXzJwws7Y/nXYPLz84OFhQVCQ0Px9ddfY9iwYVCpVJg1a1ahz23QoAFmzJiBSpUqQalUYtq0aahRowbOnTuHuXPnokqVKvrHuri4oFKlSvD19UXz5s31w5H5F9sEgGbNmqFRo0Zo2bIl5syZg8qVK+ufP3bsWAQHB2Pz5s2wtLTE3LlzS1wkWVlZoVGjRvoLe1asWBFBQUH46KOPYGVlBTc3N9jb26NPnz6YMmUKYmJiMHToUOzZswfbtm0r0bGIiIiIAF6b0sDTp08xZMgQhIeHGxSKZY3XpiwfvDZl+eG1KcsPr01ZfnhtyvL1sq9NaWXpUOb7zFPdLPN9loZpvXuVgQMHDiA8PLzA9vbt22Pv3r0YMWJEuRZiRERERM9jZ0wAdsbKBztj5YedsfLDzlj5YWesfL3szpilpV2Z71OlyirzfZaGab17ERER0StJTktRlDXT+fhIREREZIbYGSMiIiIToHnxQ0wUO2NEREREArEzRkRERLJnznPGWIwRERGRCTDfYozDlEREREQCsTNGRERE8mfGw5TsjBEREREJxM4YERERyZ4OJnT5hxJiMUZEREQmgMOURERERFQO2BkjIiIi+TOlq9SXEDtjRERERAKxM0ZERESyZ84T+CWdzoz7fkREREQyx2FKIiIiIoFYjBEREREJxGKMiIiISCAWY0REREQCsRgjIiIiEojFGBEREZFALMaIiIiIBGIxRkRERCQQV+A3cefPn8f27dvx8OFDPL9+77x58wSmMm7s2LFYunSpwbaBAwdi8+bNghKZjxs3bmDFihW4f/8+li5dir1796JZs2ZwdHQUHc2oGzduICMjAy1btkReXh6srKxERypUTk4OoqKicPv2bQQHB+PEiRNwc3ND1apVRUcz6vz588jJyTH4e9CqVSuBiQo3derUAtuUSiXq1asHX19fWf2Mt2zZggEDBhhsW7NmDYYPHy4oUeG+/fZbfPzxx6JjUAmwGDNxkydPxrBhw+Dg4CA6SqFiY2OxcuVKpKam4s0339S/Seh0Ori6ugpOZ9yFCxcQFhaG3NxcbNq0CeHh4WjVqhXc3d1FRzMqODgY/v7+WLVqFQDA1tYWgYGBiIyMFJysoPDwcMTExODRo0fYtWsXvvjiC9jZ2WHkyJGioxkVGBiItm3b4ueffwYA3LlzB5MmTdL/rOVk5MiRuH//vsHfA0mSZFuM1ahRA5mZmejatSskScKRI0dQvXp1AJDNz/jYsWP45ZdfEBMTg8uXL+u3q9Vq/Pjjj7Isxm7fvo1jx47Bw8MDlpaW+u0VK1YUmIqKwmLMxNWqVQuDBg0SHaNIPXr0QI8ePbB69WqMGDFCdJximT17NkJCQhASEgIAaN++PaZPn44NGzaIDVYIrVaLTp064bvvvgMAvPnmm1ixYoXgVMbt378fGzduxLBhwwAAQUFB8PX1lW0xlpubiyFDhuDHH38EAPTq1Uu2vwcPHjzApk2bRMcotpSUFEREROhvv/POO/jwww/x3Xff4ciRIwKT/cXLywsWFhY4evQomjRpot8uSVKBTplcHD58GPv37zfYJkkSDhw4ICgRvQiLMRN1+PBhAECTJk2wYMECtGjRAhYWf/3v7NSpk6hohWrbti3mzZtnEkOqFhYWaNSokf5248aNoVDId4qlhYUF4uLioNVqkZ2djX379sHa2lp0LKM0Gg2AZ28OAPD06VOo1WqRkYqk1Wrx559/6vMeOXIEWq1WcCrjvL29cfHiRYOiQc4ePHiAAwcOoHnz5lAoFEhOTsbNmzdx4cIFPHnyRHQ8AMD9+/fRpk0bfPnll/rfAbmLjY0tsC06OlpAEiouXijcRBmba/E8ORY4b7/9ttEh1c6dO4sJVISxY8eiY8eO2LhxI6ZPn459+/bhzz//LDDnTS5u3bqFJUuW4MyZM7CysoKnpydGjx4Ne3t70dEKWLduHWJjY5Geno7OnTsjPj4e/v7+GDJkiOhoRl26dAlz5sxBUlISKlasCBcXFwQFBRkU63Lx1ltv4erVq7CxsYFSqQTwrOiNi4sTnMy41NRUrFixAmlpadDpdKhXrx4+/fRTAICVlZUspjHMmzcPU6dO1XdynydJEtauXSsgVdHOnj2LVatW4d69ewAAlUql/5BG8sRizAw8ffoU1tbWuHfvHq5fvy6LP2DG5A8/mILc3FxEREToixsvLy8MHToUlStXFh3NQGZmZoFtOp1O/wm+Tp06LztSsWRkZCApKQlWVlZwd3dH7dq1RUciQXJycvDgwQOT+L01FYMGDcKECROwcOFChISEYN++fWjWrBm6dOkiOhoVgsWYiZs9ezaaNm2Kjh074v3330ezZs0gSRJmzZolOppe/pDqiRMnIEmSrIdUT506VeT9cpsI/e6770KSJKhUKly+fBlOTk7QaDS4du0aXF1dZXWmqql1c9944w19cXDv3j1UqFABWq0WeXl5cHBw0E/ol5Pff/8dc+fOxZ9//gmNRoPXX38dwcHBsuziAcC0adNw5MgRfQc3vyDbunWr4GQFLV68GNu2bSswRC3HrmNAQAAiIiIwZMgQrF+/HgAwYsQIrF69WnAyKgznjJm48+fPY/r06YiIiMC7776L999/X3Zn98TExBjc/vvEUjkVY/lnHz548AAXLlxA06ZNodFokJKSAk9PT9kVY9u2bQMAfPbZZ/j2229Rq1YtAMC1a9ewbNkykdEK6NGjBwDg4MGDUCgUaN26NXQ6HeLj42W5tMWJEycAAHPmzMF//vMfeHp6AgBOnz6NH374QWS0Qs2ZMwdTp05F06ZNAQAJCQmYOXOmLIfSAODcuXM4fPiwSczFOnz4MA4ePCjbuZjPq1ixIg4cOIC6deti8eLFcHJywvXr10XHoiKwGDNxeXl5uHnzJnbt2oUVK1ZArVbjwYMHomMZkFvHoyj5c8JGjRqFffv26Yclc3JyMG3aNJHRinTlyhV9IQYAjo6OuHLlirhARuTPDYyIiMCaNWv023v37i3rNZGSk5MN/t97e3vjyy+/FJiocEqlUl+IAdB3yuXKxcUFd+/eha2tregoL9S2bVtcuHAB7u7usj6ZBwAWLlyI7OxsfP755wgPD0dqairmz58vOhYVgcWYiRs6dCg++ugjvP3226hVqxa+/PJL9OzZU3Qsozp16oSsrCwolUpIkgSNRoPq1aujWrVqCAoKQvv27UVH1MvMzDTo1lSoUAFXr14VmKhoXl5eeO+99+Dl5QVJkpCcnIzXX39ddCyj7t27h0OHDqFZs2ZQKBQ4e/Ysbty4ITpWoWrVqoUxY8boz/g7e/asrBYjfV7VqlXx3XffoXXr1gCedfeqVasmOFXhrl69Ch8fH9SvXx9KpVLWw5QKhcJg3mh+VjkOU9rY2ODixYtISUnB6NGjcfPmTVmvRUmcM2byDh06ZDApU6fTYe/evXj77bcFpjIuLCwMb7zxhn5Y8pdffsHp06fh6+uLMWPGyGp+06pVq7BhwwZ9QXP58mX06dMH//d//yc4WeHS0tJw6dIlAECDBg3wr3/9S3Ai4y5cuICvv/4aaWlpAABnZ2d88skncHNzE5zMOI1Gg19++QV//PEHdDodnJ2d0aFDB4N5j3KRk5ODiIgIJCcnQ5IkeHp6YtiwYbI78STftWvXjG6X45Uj3nnnHWzZsgUVKlQQHeWF5s+fj+vXr+PPP/9EdHQ0li1bhvv378u6u/+qk99fEyqWpKQknD17FmvXrjU4o06j0eC7776TZTGWkJCAwMBA/e0OHTrgf//7H8aNGye7oZSPPvoIvr6+SE9PBwA4OTnJusNQ2OR4OQ4Rv/7665g0aRLOnz8PSZJkfzZlbm4ukpKScO7cOSgUCuTl5aF169ayKsauXbsGR0dH3LhxQ7/Icr7r16+jcePGAtMVtHHjRvj6+iIqKsrov/3//ve/AlIVrW3btrhx4wYaNGggOsoLJScnIzIyUr8cx5gxY2S7dAw9I5+/JlQidnZ2qFSpElQqFe7evavfLkkSwsLCBCYrXK1atTBq1Ch4e3vrh3sqV66Mn376SXansg8bNszom4RcJ0I//+arVqvx22+/GVwGRU5WrVqFH3/8Ed7e3sjLy8OKFSswYMAA2b5ZTJkyBa1bt8bo0aOhUqlw8uRJTJ06VVZrzq1duxZTp07FzJkzIUmSwaLKclwLK7/zZWwoXW4fzPIdPHgQa9euRZUqVQyGVOU4TKlWq6FSqfQ/yzt37uDp06eCU1FROExp4u7cuVPo5NcZM2Zg5syZLzlR4dRqNY4ePWqwwGOXLl3w+PFjVK5cWVadhosXL+q/zy9uHj58qF+Q0hR89NFHsri239/5+vpi3bp1+kVJ1Wo1/Pz8sHH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      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(X_train.corr(), square=True, cmap='magma')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial Feature Selection: NUMERIC\n",
    "Opt to INCLUDE the following (starting out):\n",
    "* __region_code__  This is the code for the Region. This feature can take the place of doing One Hot Encoding on the region feature which has the names of the regions.\n",
    "* __construction_year__\n",
    "\n",
    "Opt to EXCLUDE the following (starting out):\n",
    "* __district_code__ feature is highly (positively) correlated to region_code as Regions are divided into Districts. Looking at the value counts of district_code reveals that some districts are rarely represented. Favoring using region instead.\n",
    "*  __gps_height__  feature is somewhat correlated to region_code\n",
    "* __latitude__  feature is somewhat correlated to region_code\n",
    "* __longitude__ feature is somewhat correlated to region_code\n",
    "* __amount_tsh__ \n",
    "*  __population__ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial Feature Selection: CATEGORICAL\n",
    "\n",
    "Opt to INCLUDE the following (starting out):\n",
    "* __basin__\n",
    "* __quantity_group__ \n",
    "* __payment_type__\n",
    "* __public_meeting__ - T/F values for some public meeting status\n",
    "* __permit__ - T/F values for has permit\n",
    "* __funder__ (w/ rare category encoding)\n",
    "* __installer__ (w/ rare category encoding)\n",
    "* __scheme_management__ (w/ rare category encoding)\n",
    "* __extraction_type_class__ (w/ rare category encoding)\n",
    "* __management_group__  (w/ rare category encoding)\n",
    "* __quality_group__ (w/ rare category encoding)\n",
    "* __source_type__ (w/ rare category encoding)\n",
    "* __waterpoint_group_type__  (w/ rare category encoding)\n",
    "\n",
    "\n",
    "Opt to EXCLUDE the following:\n",
    "* __wpt_name__ - This identifier does not provide predictive information\n",
    "* __scheme_name__ - This identifier for the name of the management scheme does not provide predictive info\n",
    "* __recorded_by__ - All the same string/ does not provide predictive information\n",
    "* __date_recorded__ - This variable doesn't provide predictive information since it is the just the day the survey occured\n",
    "* __region__ - Information already captured by region_code, a numeric feature.\n",
    "* Features that are sub-types of other features (ex:  waterpoint_type is a sub-type of waterpoint_type_group)\n",
    "* Features with too high a cardinality/ where even the category with the highest percentage was less than 5% (ex: subvillage w/ 16642 unique categories)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date_recorded  cardinality: 348 , first 20 unique: ['2011-03-24' '2012-10-19' '2012-11-05' '2012-10-30' '2011-02-20'\n",
      " '2011-02-27' '2013-01-15' '2011-03-03' '2013-02-19' '2011-02-25'\n",
      " '2013-01-24' '2011-04-08' '2011-07-06' '2013-03-22' '2011-03-20'\n",
      " '2011-03-17' '2013-03-03' '2011-04-04' '2011-07-24' '2011-07-07'] \n",
      "\n",
      "funder  cardinality: 1656 , first 20 unique: ['kkkt' 'world vision' 'government of tanzania' 'unicef/ csp' 'world bank'\n",
      " 'dhv' 'kalitasi' 'mkuyu' 'kkkt church' 'kibaha independent school'\n",
      " 'danida' 'wananchi' 'lga' 'hans' 'md' 'roman' 'moravian' 'is' 'mission'\n",
      " 'water user as'] \n",
      "\n",
      "installer  cardinality: 1701 , first 20 unique: ['kkkt' 'world vision' 'government' 'rdc' 'gwasco l'\n",
      " 'china henan contractor' 'dwe' 'kalitasi' 'mkuyu' 'district council'\n",
      " 'kkkt church' 'handeni trunk main(' 'dawasco' 'danida' 'wananchi' 'hans'\n",
      " 'dw' 'commu' 'is' 'consulting engineer'] \n",
      "\n",
      "wpt_name  cardinality: 30166 , first 20 unique: ['kwa mzee amili' 'kwa mgaiwa' 'kwa mtoba hila' 'mzambalauni' 'ally juma'\n",
      " 'kwa boni' 'majengo mapya' 'idara ya maji' 'kwa mzee mpinda'\n",
      " 'kwa mzee mizia' 'kwa ntungwe' 'mzaramo' 'kwa edwaridina' 'kwa kogadi'\n",
      " 'kwa mazanga' 'kibaha independent school 2' 'kwa hashimu geo'\n",
      " 'kwa justine vahae' 'kwa john kapese' 'kwa paulo'] \n",
      "\n",
      "basin  cardinality: 9 , first 20 unique: ['pangani' 'internal' 'lake tanganyika' 'rufiji' 'wami / ruvu'\n",
      " 'ruvuma / southern coast' 'lake rukwa' 'lake victoria' 'lake nyasa'] \n",
      "\n",
      "subvillage  cardinality: 16642 , first 20 unique: ['mlola chini' 'igumo mashariki' 'ibanza' 'isadukilo' 'msafiri' 'madenko'\n",
      " 'shuleni' 'kalembwani' 'mvango' 'mbagalala' 'tandika' 'kibaoni' 'mbuga'\n",
      " 'mbuyuni b' 'sokoni' 'mkoani a' 'mauta' 'lutowelo' 'magharibi b'\n",
      " 'kabirizi mashariki'] \n",
      "\n",
      "region  cardinality: 21 , first 20 unique: ['tanga' 'tabora' 'shinyanga' 'pwani' 'morogoro' 'singida' 'kilimanjaro'\n",
      " 'lindi' 'mbeya' 'kagera' 'ruvuma' 'iringa' 'rukwa' 'arusha' 'mwanza'\n",
      " 'kigoma' 'manyara' 'mara' 'mtwara' 'dodoma'] \n",
      "\n",
      "lga  cardinality: 124 , first 20 unique: ['lushoto' 'igunga' 'shinyanga rural' 'bukombe' 'rufiji' 'mvomero'\n",
      " 'iramba' 'ulanga' 'same' 'morogoro rural' 'lindi rural' 'chunya'\n",
      " 'karagwe' 'handeni' 'kibaha' 'namtumbo' 'njombe' 'mpanda' 'bukoba rural'\n",
      " 'karatu'] \n",
      "\n",
      "ward  cardinality: 2020 , first 20 unique: ['funta' 'chabutwa' 'mwamala' 'ilolangulu' 'bungu' 'hembeti' 'kiomboi'\n",
      " 'kilosa mpepo' 'vuje' 'mkuyuni' 'nahukahuka' 'kambikatoto' 'kihanga'\n",
      " 'hedaru' 'ndolwa' 'tumbi' 'namabengo' 'wangama' 'kasokola' 'rubale'] \n",
      "\n",
      "recorded_by  cardinality: 1 , first 20 unique: ['geodata consultants ltd'] \n",
      "\n",
      "scheme_management  cardinality: 13 , first 20 unique: ['vwc' 'water authority' 'wug' 'private operator' 'unknown' 'water board'\n",
      " 'wua' 'parastatal' 'company' 'other' 'swc' 'trust' 'none'] \n",
      "\n",
      "scheme_name  cardinality: 2409 , first 20 unique: ['kong`hoi' 'adp simbo' 'nsebi ibanza' 'unknown' 'dimamba' 'rwssp'\n",
      " 'mvango water supply' 'mkuy' 'nahukahuka water supply'\n",
      " 'nyarwele water  s' 'hedaru kati water supply' 'handeni trunk main(h'\n",
      " 'water from dawasco' 'wangama water supply scheme' 'mowasu' 'mang`o'\n",
      " 'mradi wa maji wa kilagano' 'chalinze wate' 'samaria'\n",
      " 'sanza water supply'] \n",
      "\n",
      "extraction_type  cardinality: 18 , first 20 unique: ['gravity' 'nira/tanira' 'mono' 'submersible' 'swn 80' 'ksb'\n",
      " 'india mark ii' 'afridev' 'other' 'other - rope pump' 'windmill' 'cemo'\n",
      " 'other - swn 81' 'climax' 'india mark iii' 'other - play pump'\n",
      " 'other - mkulima/shinyanga' 'walimi'] \n",
      "\n",
      "extraction_type_group  cardinality: 13 , first 20 unique: ['gravity' 'nira/tanira' 'mono' 'submersible' 'swn 80' 'india mark ii'\n",
      " 'afridev' 'other' 'rope pump' 'wind-powered' 'other motorpump'\n",
      " 'other handpump' 'india mark iii'] \n",
      "\n",
      "extraction_type_class  cardinality: 7 , first 20 unique: ['gravity' 'handpump' 'motorpump' 'submersible' 'other' 'rope pump'\n",
      " 'wind-powered'] \n",
      "\n",
      "management  cardinality: 12 , first 20 unique: ['vwc' 'wug' 'private operator' 'trust' 'wua' 'unknown' 'parastatal'\n",
      " 'water board' 'company' 'water authority' 'other' 'other - school'] \n",
      "\n",
      "management_group  cardinality: 5 , first 20 unique: ['user-group' 'commercial' 'unknown' 'parastatal' 'other'] \n",
      "\n",
      "payment  cardinality: 7 , first 20 unique: ['never pay' 'unknown' 'pay monthly' 'pay per bucket'\n",
      " 'pay when scheme fails' 'pay annually' 'other'] \n",
      "\n",
      "payment_type  cardinality: 7 , first 20 unique: ['never pay' 'unknown' 'monthly' 'per bucket' 'on failure' 'annually'\n",
      " 'other'] \n",
      "\n",
      "water_quality  cardinality: 8 , first 20 unique: ['soft' 'salty' 'unknown' 'coloured' 'milky' 'salty abandoned' 'fluoride'\n",
      " 'fluoride abandoned'] \n",
      "\n",
      "quality_group  cardinality: 6 , first 20 unique: ['good' 'salty' 'unknown' 'colored' 'milky' 'fluoride'] \n",
      "\n",
      "quantity  cardinality: 5 , first 20 unique: ['enough' 'insufficient' 'dry' 'seasonal' 'unknown'] \n",
      "\n",
      "quantity_group  cardinality: 5 , first 20 unique: ['enough' 'insufficient' 'dry' 'seasonal' 'unknown'] \n",
      "\n",
      "source  cardinality: 10 , first 20 unique: ['spring' 'shallow well' 'machine dbh' 'river' 'unknown'\n",
      " 'rainwater harvesting' 'dam' 'hand dtw' 'other' 'lake'] \n",
      "\n",
      "source_type  cardinality: 7 , first 20 unique: ['spring' 'shallow well' 'borehole' 'river/lake' 'other'\n",
      " 'rainwater harvesting' 'dam'] \n",
      "\n",
      "source_class  cardinality: 3 , first 20 unique: ['groundwater' 'surface' 'unknown'] \n",
      "\n",
      "waterpoint_type  cardinality: 7 , first 20 unique: ['communal standpipe' 'hand pump' 'communal standpipe multiple' 'other'\n",
      " 'improved spring' 'cattle trough' 'dam'] \n",
      "\n",
      "waterpoint_type_group  cardinality: 6 , first 20 unique: ['communal standpipe' 'hand pump' 'other' 'improved spring'\n",
      " 'cattle trough' 'dam'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking out the cardinality in categorical columns - # of different labels/ unique string values for a feature/column\n",
    "for var in X_train.columns:\n",
    "    # print the first 20 unique values in the Object columns\n",
    "    if train_df[var].dtype == 'O':\n",
    "        unique_vals = X_train[var].unique()\n",
    "        print(var, ' cardinality:', unique_vals.size, ', first 20 unique:',  unique_vals[0:20], '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rare categories inspection\n",
    "\n",
    "Apply rare category encoding for:\n",
    " * funder\n",
    " * installer\n",
    " * scheme_management\n",
    " * extraction_type_class (has 2 out of 7 w >5%)\n",
    " * management_group (has 3 of out 5 categories w/ > 5%)\n",
    " * quality_group (has 3 out of 6 categories w/ > 5%)\n",
    " * source_type (has 2 out of 7 categories w/ > 5%)\n",
    " * waterpoint_type_group (has 3 out of 6 categories w/ >5%)\n",
    "\n",
    "\n",
    "I am not include features that have too high a cardinality/where even the category with the highest percentage was less than 5%.\n",
    "\n",
    "\n",
    "NOTE: some the features are sub-types (ex: waterpoint_type is a sub-type of waterpoint_type_group), so I'm using the highest type in these cases:\n",
    "* waterpoint_type_group\n",
    "* source_type\n",
    "* quality_group\n",
    "* management_group\n",
    "* extraction_type_class \n",
    "* quantity_group - does not need rare category encoding applied\n",
    "* payment_type (seems to be a duplicate of the payment feature) - does not need rare category encoding applied\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date_recorded\n",
      "2002-10-14    0.000022\n",
      "2004-01-07    0.000022\n",
      "2004-03-01    0.000065\n",
      "2004-03-06    0.000022\n",
      "2004-04-01    0.000022\n",
      "                ...   \n",
      "2013-11-02    0.000413\n",
      "2013-11-03    0.003214\n",
      "2013-12-01    0.000022\n",
      "2013-12-02    0.000586\n",
      "2013-12-03    0.004169\n",
      "Name: date_recorded, Length: 348, dtype: float64\n",
      "0.009793277164943976 2.1714583514288198e-05\n",
      "\n",
      "funder\n",
      "0                      0.013702\n",
      "a/co germany           0.000261\n",
      "aar                    0.000543\n",
      "abas ka                0.000022\n",
      "abasia                 0.000847\n",
      "                         ...   \n",
      "zaburi and neig        0.000022\n",
      "zao                    0.000022\n",
      "zao water spring       0.000065\n",
      "zinduka                0.000022\n",
      "zingibali secondary    0.000022\n",
      "Name: funder, Length: 1656, dtype: float64\n",
      "0.15263180752193173 2.1714583514288198e-05\n",
      "\n",
      "installer\n",
      "-                      0.000065\n",
      "0                      0.013702\n",
      "a.d.b                  0.000022\n",
      "aar                    0.000087\n",
      "aartisa                0.000022\n",
      "                         ...   \n",
      "zao                    0.000022\n",
      "zao water spring       0.000065\n",
      "zinduka                0.000022\n",
      "zingibali secondary    0.000022\n",
      "zuber mihungo          0.000022\n",
      "Name: installer, Length: 1701, dtype: float64\n",
      "0.2833318856944324 2.1714583514288198e-05\n",
      "\n",
      "wpt_name\n",
      "24                   0.000022\n",
      "a saidi              0.000022\n",
      "abass                0.000022\n",
      "abbas                0.000022\n",
      "abdala hamisi        0.000022\n",
      "                       ...   \n",
      "zumbawanu            0.000022\n",
      "zumbawanu shuleni    0.000022\n",
      "zungu                0.000022\n",
      "zunguni              0.000022\n",
      "zuwena kindo         0.000022\n",
      "Name: wpt_name, Length: 30166, dtype: float64\n",
      "0.060084252584035436 2.1714583514288198e-05\n",
      "\n",
      "basin\n",
      "internal                   0.134630\n",
      "lake nyasa                 0.087597\n",
      "lake rukwa                 0.042561\n",
      "lake tanganyika            0.110310\n",
      "lake victoria              0.148593\n",
      "pangani                    0.155368\n",
      "rufiji                     0.138886\n",
      "ruvuma / southern coast    0.078824\n",
      "wami / ruvu                0.103231\n",
      "Name: basin, dtype: float64\n",
      "0.15536784504473206 0.04256058368800487\n",
      "\n",
      "subvillage\n",
      "'a' kati             0.000022\n",
      "1                    0.002345\n",
      "18                   0.000022\n",
      "19                   0.000022\n",
      "20                   0.000022\n",
      "                       ...   \n",
      "zumbawanu            0.000043\n",
      "zumbawanu shuleni    0.000022\n",
      "zunguni              0.000022\n",
      "zunzuli              0.000022\n",
      "zuri                 0.000022\n",
      "Name: subvillage, Length: 16642, dtype: float64\n",
      "0.008685833405715278 2.1714583514288198e-05\n",
      "\n",
      "region\n",
      "arusha           0.057804\n",
      "dar es salaam    0.014245\n",
      "dodoma           0.037545\n",
      "iringa           0.091874\n",
      "kagera           0.057826\n",
      "kigoma           0.048380\n",
      "kilimanjaro      0.075958\n",
      "lindi            0.027382\n",
      "manyara          0.027382\n",
      "mara             0.034504\n",
      "mbeya            0.079997\n",
      "morogoro         0.069096\n",
      "mtwara           0.030053\n",
      "mwanza           0.039868\n",
      "pwani            0.045818\n",
      "rukwa            0.031312\n",
      "ruvuma           0.046274\n",
      "shinyanga        0.068987\n",
      "singida          0.036220\n",
      "tabora           0.034808\n",
      "tanga            0.044667\n",
      "Name: region, dtype: float64\n",
      "0.09187440284895336 0.014244766785373057\n",
      "\n",
      "lga\n",
      "arusha rural    0.021628\n",
      "arusha urban    0.001064\n",
      "babati          0.009185\n",
      "bagamoyo        0.017285\n",
      "bahi            0.003995\n",
      "                  ...   \n",
      "tunduru         0.007492\n",
      "ukerewe         0.006037\n",
      "ulanga          0.011465\n",
      "urambo          0.006818\n",
      "uyui            0.005950\n",
      "Name: lga, Length: 124, dtype: float64\n",
      "0.04390688786589073 2.1714583514288198e-05\n",
      "\n",
      "ward\n",
      "aghondi         0.000239\n",
      "akheri          0.000695\n",
      "arash           0.000195\n",
      "arri            0.000369\n",
      "arusha chini    0.001021\n",
      "                  ...   \n",
      "ziwani          0.000543\n",
      "zoissa          0.000304\n",
      "zombo           0.000413\n",
      "zongomera       0.000608\n",
      "zuzu            0.000304\n",
      "Name: ward, Length: 2020, dtype: float64\n",
      "0.005233214626943455 2.1714583514288198e-05\n",
      "\n",
      "scheme_management\n",
      "company             0.018457\n",
      "none                0.000022\n",
      "other               0.013116\n",
      "parastatal          0.027643\n",
      "private operator    0.018197\n",
      "swc                 0.001715\n",
      "trust               0.001238\n",
      "unknown             0.064818\n",
      "vwc                 0.627247\n",
      "water authority     0.054634\n",
      "water board         0.047946\n",
      "wua                 0.050638\n",
      "wug                 0.074329\n",
      "Name: scheme_management, dtype: float64\n",
      "0.6272474593937288 2.1714583514288198e-05\n",
      "\n",
      "scheme_name\n",
      "a                          0.000695\n",
      "adp                        0.000109\n",
      "adp simbo                  0.000478\n",
      "adra                       0.000195\n",
      "african barrick gold       0.000022\n",
      "                             ...   \n",
      "ziwani juu water supply    0.000065\n",
      "ziwani water supply        0.000130\n",
      "zo                         0.000152\n",
      "zois                       0.000065\n",
      "zuzu                       0.000174\n",
      "Name: scheme_name, Length: 2409, dtype: float64\n",
      "0.4635846434465387 2.1714583514288198e-05\n",
      "\n",
      "extraction_type\n",
      "afridev                      0.029402\n",
      "cemo                         0.001715\n",
      "climax                       0.000565\n",
      "gravity                      0.463476\n",
      "india mark ii                0.039108\n",
      "india mark iii               0.001629\n",
      "ksb                          0.023560\n",
      "mono                         0.048880\n",
      "nira/tanira                  0.128029\n",
      "other                        0.107400\n",
      "other - mkulima/shinyanga    0.000043\n",
      "other - play pump            0.001498\n",
      "other - rope pump            0.007600\n",
      "other - swn 81               0.003930\n",
      "submersible                  0.081082\n",
      "swn 80                       0.059715\n",
      "walimi                       0.000347\n",
      "windmill                     0.002019\n",
      "Name: extraction_type, dtype: float64\n",
      "0.4634760705289673 4.3429167028576395e-05\n",
      "\n",
      "extraction_type_group\n",
      "afridev            0.029402\n",
      "gravity            0.463476\n",
      "india mark ii      0.039108\n",
      "india mark iii     0.001629\n",
      "mono               0.048880\n",
      "nira/tanira        0.128029\n",
      "other              0.107400\n",
      "other handpump     0.005820\n",
      "other motorpump    0.002280\n",
      "rope pump          0.007600\n",
      "submersible        0.104643\n",
      "swn 80             0.059715\n",
      "wind-powered       0.002019\n",
      "Name: extraction_type_group, dtype: float64\n",
      "0.4634760705289673 0.0016285937635716148\n",
      "\n",
      "extraction_type_class\n",
      "gravity         0.463476\n",
      "handpump        0.263702\n",
      "motorpump       0.051160\n",
      "other           0.107400\n",
      "rope pump       0.007600\n",
      "submersible     0.104643\n",
      "wind-powered    0.002019\n",
      "Name: extraction_type_class, dtype: float64\n",
      "0.4634760705289673 0.0020194562668288023\n",
      "\n",
      "management\n",
      "company             0.011769\n",
      "other               0.014701\n",
      "other - school      0.001737\n",
      "parastatal          0.029163\n",
      "private operator    0.033875\n",
      "trust               0.001368\n",
      "unknown             0.008925\n",
      "vwc                 0.690372\n",
      "water authority     0.015482\n",
      "water board         0.051094\n",
      "wua                 0.044493\n",
      "wug                 0.097021\n",
      "Name: management, dtype: float64\n",
      "0.6903717536697647 0.0013680187614001564\n",
      "\n",
      "management_group\n",
      "commercial    0.062495\n",
      "other         0.016438\n",
      "parastatal    0.029163\n",
      "unknown       0.008925\n",
      "user-group    0.882980\n",
      "Name: management_group, dtype: float64\n",
      "0.882980109441501 0.008924693824372448\n",
      "\n",
      "payment\n",
      "never pay                0.422718\n",
      "other                    0.015070\n",
      "pay annually             0.063428\n",
      "pay monthly              0.143533\n",
      "pay per bucket           0.155607\n",
      "pay when scheme fails    0.066707\n",
      "unknown                  0.132937\n",
      "Name: payment, dtype: float64\n",
      "0.4227177972726483 0.015069920958916008\n",
      "\n",
      "payment_type\n",
      "annually      0.063428\n",
      "monthly       0.143533\n",
      "never pay     0.422718\n",
      "on failure    0.066707\n",
      "other         0.015070\n",
      "per bucket    0.155607\n",
      "unknown       0.132937\n",
      "Name: payment_type, dtype: float64\n",
      "0.4227177972726483 0.015069920958916008\n",
      "\n",
      "water_quality\n",
      "coloured              0.008338\n",
      "fluoride              0.003192\n",
      "fluoride abandoned    0.000195\n",
      "milky                 0.014288\n",
      "salty                 0.082950\n",
      "salty abandoned       0.004061\n",
      "soft                  0.858182\n",
      "unknown               0.028794\n",
      "Name: water_quality, dtype: float64\n",
      "0.8581820550681838 0.00019543125162859375\n",
      "\n",
      "quality_group\n",
      "colored     0.008338\n",
      "fluoride    0.003387\n",
      "good        0.858182\n",
      "milky       0.014288\n",
      "salty       0.087010\n",
      "unknown     0.028794\n",
      "Name: quality_group, dtype: float64\n",
      "0.8581820550681838 0.0033874750282289587\n",
      "\n",
      "quantity\n",
      "dry             0.104035\n",
      "enough          0.560063\n",
      "insufficient    0.252845\n",
      "seasonal        0.069726\n",
      "unknown         0.013333\n",
      "Name: quantity, dtype: float64\n",
      "0.5600625380005212 0.013332754277772953\n",
      "\n",
      "quantity_group\n",
      "dry             0.104035\n",
      "enough          0.560063\n",
      "insufficient    0.252845\n",
      "seasonal        0.069726\n",
      "unknown         0.013333\n",
      "Name: quantity_group, dtype: float64\n",
      "0.5600625380005212 0.013332754277772953\n",
      "\n",
      "source\n",
      "dam                     0.011465\n",
      "hand dtw                0.015026\n",
      "lake                    0.011118\n",
      "machine dbh             0.188092\n",
      "other                   0.003561\n",
      "rainwater harvesting    0.038370\n",
      "river                   0.165965\n",
      "shallow well            0.269956\n",
      "spring                  0.295427\n",
      "unknown                 0.001021\n",
      "Name: source, dtype: float64\n",
      "0.2954269087118909 0.0010205854251715452\n",
      "\n",
      "source_type\n",
      "borehole                0.203118\n",
      "dam                     0.011465\n",
      "other                   0.004582\n",
      "rainwater harvesting    0.038370\n",
      "river/lake              0.177082\n",
      "shallow well            0.269956\n",
      "spring                  0.295427\n",
      "Name: source_type, dtype: float64\n",
      "0.2954269087118909 0.004581777121514809\n",
      "\n",
      "waterpoint_type\n",
      "cattle trough                  0.002106\n",
      "communal standpipe             0.493225\n",
      "communal standpipe multiple    0.103427\n",
      "dam                            0.000087\n",
      "hand pump                      0.280444\n",
      "improved spring                0.013094\n",
      "other                          0.107617\n",
      "Name: waterpoint_type, dtype: float64\n",
      "0.4932250499435421 8.685833405715279e-05\n",
      "\n",
      "waterpoint_type_group\n",
      "cattle trough         0.002106\n",
      "communal standpipe    0.596652\n",
      "dam                   0.000087\n",
      "hand pump             0.280444\n",
      "improved spring       0.013094\n",
      "other                 0.107617\n",
      "Name: waterpoint_type_group, dtype: float64\n",
      "0.5966516112220968 8.685833405715279e-05\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Look for rarely occurring categories.\n",
    "\n",
    "multi_cat_cols = []\n",
    "multi_cat_series = []\n",
    "for col in X_train.columns:\n",
    "\n",
    "    if X_train[col].dtypes =='O': # if variable  is categorical\n",
    "    \n",
    "        if X_train[col].nunique() > 4: # choosing to inspect where there are more than 5 categories\n",
    "            \n",
    "            multi_cat_cols.append(col)  # add to the list\n",
    "            count_series = X_train.groupby(col)[col].count()\n",
    "            multi_cat_series.append(count_series)\n",
    "            print(count_series/ len(X_train)) #  print the percentage of observations within each category\n",
    "            print(count_series.values.max()/len(X_train), count_series.values.min()/len(X_train)) # print the max nad min percentages\n",
    "            print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Functions for rare category encoding\n",
    "def find_non_rare_labels(df, col_name, tolerance):\n",
    "    temp = df.groupby([col_name])[col_name].count() / len(df)\n",
    "    non_rare = [x for x in temp.loc[temp>tolerance].index.values]\n",
    "    return non_rare\n",
    "\n",
    "\n",
    "def rare_encoding(X_train, X_test, col_name, tolerance):\n",
    "    X_train = X_train.copy()\n",
    "    X_test = X_test.copy()\n",
    "\n",
    "    # find the most frequent category\n",
    "    frequent_cat = find_non_rare_labels(X_train, col_name, tolerance)\n",
    "\n",
    "    # re-group rare labels\n",
    "    X_train[col_name] = np.where(X_train[col_name].isin(\n",
    "        frequent_cat), X_train[col_name], 'rare')\n",
    "    \n",
    "    X_test[col_name] = np.where(X_test[col_name].isin(\n",
    "        frequent_cat), X_test[col_name], 'rare')\n",
    "\n",
    "    return X_train, X_test\n",
    "\n",
    "\n",
    "def plot_rare_catgories(df, cols):\n",
    "    for col in cols:\n",
    "\n",
    "        temp_df = pd.Series(df[col].value_counts() / len(df) )\n",
    "\n",
    "        # make plot with the above percentages\n",
    "        fig = temp_df.sort_values(ascending=False).plot.bar(rot=50, alpha = 0.80,  colormap='magma')\n",
    "        fig.set_xlabel(col)\n",
    "\n",
    "        # add a line at 5 % to flag the threshold for rare categories\n",
    "        fig.axhline(y=0.05, color='red')\n",
    "        fig.set_ylabel('Percentage of categories')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply rare encoding to TRAIN and TEST\n",
    "rare_cat_cols = ['funder', 'installer', 'scheme_management', 'extraction_type_class', 'management_group', 'quality_group', 'source_type', \n",
    "'waterpoint_type_group']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col_name in rare_cat_cols:\n",
    "    X_train, X_test = rare_encoding(X_train, X_test, col_name, 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## SELECTED FEATURES FOR MODELING\n",
    "cont_features_to_use = ['region_code', 'construction_year']\n",
    "cat_features_to_use = rare_cat_cols + ['basin', 'quantity_group', 'payment_type', 'permit', 'public_meeting']\n",
    "features_to_use = cont_features_to_use + cat_features_to_use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One Hot Encode the chosen categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneHotEncoder(handle_unknown='ignore')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Did our test train spilt before exploring features in X_Train for rare label encoding. \n",
    "# Now that I've selected some features based off of exploring X_Train ONLY, train the model on the chosen features\n",
    "X_train = X_train[features_to_use]\n",
    "X_test = X_test[features_to_use]\n",
    "\n",
    "# OHE categorical features\n",
    "ohe = OneHotEncoder(categories='auto', handle_unknown='ignore')  \n",
    "ohe.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_encoded = ohe.transform(X_train)\n",
    "X_test_encoded = ohe.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Modeling!\n",
    "\n",
    "Try out the Baseline and Tuned verions of models from a few different Classification Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    8.4s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   18.9s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_jobs=-1, random_state=42, verbose=1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Baseline RandomForest\n",
    "base_rf_clf = RandomForestClassifier(verbose=1, n_jobs=-1, random_state=42)\n",
    "base_rf_clf.fit(X_train_encoded, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.1s finished\n",
      "Test accuracy: 78.60000000000001 %\n",
      "Train accuracy: 87.4 %\n",
      "[[5531  150  592]\n",
      " [ 418  215  129]\n",
      " [1071  106 3301]]\n",
      "                         precision    recall  f1-score   support\n",
      "\n",
      "             functional       0.79      0.88      0.83      6273\n",
      "functional needs repair       0.46      0.28      0.35       762\n",
      "         non functional       0.82      0.74      0.78      4478\n",
      "\n",
      "               accuracy                           0.79     11513\n",
      "              macro avg       0.69      0.63      0.65     11513\n",
      "           weighted avg       0.78      0.79      0.78     11513\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_accuracy(base_rf_clf, X_train_encoded, y_train, X_test_encoded, y_test, cm=True, cr=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    2.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=RandomForestClassifier(random_state=42, verbose=1),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'], 'max_depth': [6, 10],\n",
       "                         'n_estimators': [50, 75, 100, 125]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Try tuning with GridSearchCV\n",
    "tuned_rf_clf = RandomForestClassifier(verbose=1, random_state=42)\n",
    "forest_param_grid = {\"n_estimators\" : [50, 75, 100, 125],\n",
    "            'criterion' : ['gini', 'entropy'],\n",
    "            'max_depth' : [6, 10],\n",
    "            }\n",
    "\n",
    "gs_forest = GridSearchCV(estimator=tuned_rf_clf, param_grid=forest_param_grid, \n",
    "                          scoring='accuracy', cv=3, n_jobs=-1)\n",
    "gs_forest.fit(X_train_encoded, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'entropy', 'max_depth': 10, 'n_estimators': 50}\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "0.7459828020498567\n",
      "RandomForestClassifier(criterion='entropy', max_depth=10, n_estimators=50,\n",
      "                       random_state=42, verbose=1)\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "Test accuracy: 74.2 %\n",
      "Train accuracy: 74.6 %\n",
      "[[6049    0  224]\n",
      " [ 679    1   82]\n",
      " [1988    0 2490]]\n",
      "                         precision    recall  f1-score   support\n",
      "\n",
      "             functional       0.69      0.96      0.81      6273\n",
      "functional needs repair       1.00      0.00      0.00       762\n",
      "         non functional       0.89      0.56      0.68      4478\n",
      "\n",
      "               accuracy                           0.74     11513\n",
      "              macro avg       0.86      0.51      0.50     11513\n",
      "           weighted avg       0.79      0.74      0.71     11513\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Inspect the tuned random forest\n",
    "print(gs_forest.best_params_)\n",
    "print(gs_forest.score(X_train_encoded, y_train))\n",
    "tuned_forest = gs_forest.best_estimator_\n",
    "print(tuned_forest)\n",
    "print_accuracy(gs_forest, X_train_encoded, y_train, X_test_encoded, y_test, cm=True, cr=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest conclusion: \n",
    "* Baseline Random Forest model suffered from overfitting. \n",
    "* Tuned Random Forest model did not overfit. Accuracy was respectable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Iter       Train Loss   Remaining Time \n",
      "         1           0.8486           16.75s\n",
      "         2           0.8232           15.54s\n",
      "         3           0.8028           14.43s\n",
      "         4           0.7862           14.27s\n",
      "         5           0.7727           14.33s\n",
      "         6           0.7614           14.39s\n",
      "         7           0.7520           14.08s\n",
      "         8           0.7434           14.17s\n",
      "         9           0.7362           13.98s\n",
      "        10           0.7298           13.83s\n",
      "        20           0.6889           12.54s\n",
      "        30           0.6668           10.69s\n",
      "        40           0.6530            8.82s\n",
      "        50           0.6423            7.16s\n",
      "        60           0.6346            5.67s\n",
      "        70           0.6286            4.19s\n",
      "        80           0.6226            2.77s\n",
      "        90           0.6177            1.38s\n",
      "       100           0.6133            0.00s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(random_state=42, verbose=1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Baseline GradientBoostingClassifer\n",
    "base_gb_clf = GradientBoostingClassifier(verbose=1, random_state=42)\n",
    "base_gb_clf.fit(X_train_encoded, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 74.4 %\n",
      "Train accuracy: 74.6 %\n",
      "[[5893    7  373]\n",
      " [ 610   37  115]\n",
      " [1834   12 2632]]\n",
      "                         precision    recall  f1-score   support\n",
      "\n",
      "             functional       0.71      0.94      0.81      6273\n",
      "functional needs repair       0.66      0.05      0.09       762\n",
      "         non functional       0.84      0.59      0.69      4478\n",
      "\n",
      "               accuracy                           0.74     11513\n",
      "              macro avg       0.74      0.53      0.53     11513\n",
      "           weighted avg       0.76      0.74      0.72     11513\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_accuracy(base_gb_clf, X_train_encoded, y_train, X_test_encoded, y_test, cm=True, cr=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 27 candidates, totalling 81 fits\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  5.9min\n",
      "[Parallel(n_jobs=-1)]: Done  81 out of  81 | elapsed: 18.4min finished\n",
      "      Iter       Train Loss   Remaining Time \n",
      "         1           0.8155            2.64m\n",
      "         2           0.7672            2.61m\n",
      "         3           0.7285            2.66m\n",
      "         4           0.6970            2.66m\n",
      "         5           0.6704            2.65m\n",
      "         6           0.6482            2.62m\n",
      "         7           0.6286            2.61m\n",
      "         8           0.6119            2.56m\n",
      "         9           0.5977            2.50m\n",
      "        10           0.5853            2.46m\n",
      "        11           0.5740            2.41m\n",
      "        12           0.5640            2.37m\n",
      "        13           0.5545            2.33m\n",
      "        14           0.5452            2.29m\n",
      "        15           0.5373            2.26m\n",
      "        16           0.5305            2.23m\n",
      "        17           0.5235            2.20m\n",
      "        18           0.5173            2.17m\n",
      "        19           0.5117            2.14m\n",
      "        20           0.5062            2.11m\n",
      "        21           0.5010            2.08m\n",
      "        22           0.4961            2.04m\n",
      "        23           0.4922            2.00m\n",
      "        24           0.4878            1.97m\n",
      "        25           0.4842            1.94m\n",
      "        26           0.4804            1.91m\n",
      "        27           0.4774            1.86m\n",
      "        28           0.4743            1.82m\n",
      "        29           0.4702            1.80m\n",
      "        30           0.4672            1.76m\n",
      "        31           0.4647            1.72m\n",
      "        32           0.4624            1.68m\n",
      "        33           0.4596            1.65m\n",
      "        34           0.4572            1.61m\n",
      "        35           0.4549            1.57m\n",
      "        36           0.4534            1.53m\n",
      "        37           0.4511            1.50m\n",
      "        38           0.4492            1.46m\n",
      "        39           0.4476            1.42m\n",
      "        40           0.4454            1.39m\n",
      "        41           0.4434            1.36m\n",
      "        42           0.4421            1.32m\n",
      "        43           0.4402            1.29m\n",
      "        44           0.4388            1.26m\n",
      "        45           0.4372            1.22m\n",
      "        46           0.4358            1.19m\n",
      "        47           0.4347            1.16m\n",
      "        48           0.4333            1.13m\n",
      "        49           0.4318            1.10m\n",
      "        50           0.4305            1.07m\n",
      "        51           0.4291            1.04m\n",
      "        52           0.4277            1.02m\n",
      "        53           0.4261           59.47s\n",
      "        54           0.4251           57.73s\n",
      "        55           0.4242           56.12s\n",
      "        56           0.4229           54.54s\n",
      "        57           0.4218           52.94s\n",
      "        58           0.4206           51.44s\n",
      "        59           0.4195           49.96s\n",
      "        60           0.4186           48.45s\n",
      "        61           0.4172           47.08s\n",
      "        62           0.4159           45.76s\n",
      "        63           0.4144           44.46s\n",
      "        64           0.4138           42.99s\n",
      "        65           0.4125           41.67s\n",
      "        66           0.4118           40.26s\n",
      "        67           0.4107           38.94s\n",
      "        68           0.4093           37.68s\n",
      "        69           0.4084           36.32s\n",
      "        70           0.4073           35.02s\n",
      "        71           0.4062           33.76s\n",
      "        72           0.4053           32.49s\n",
      "        73           0.4046           31.14s\n",
      "        74           0.4034           29.95s\n",
      "        75           0.4026           28.70s\n",
      "        76           0.4015           27.51s\n",
      "        77           0.4006           26.26s\n",
      "        78           0.3997           24.99s\n",
      "        79           0.3990           23.77s\n",
      "        80           0.3982           22.55s\n",
      "        81           0.3975           21.33s\n",
      "        82           0.3969           20.13s\n",
      "        83           0.3960           18.97s\n",
      "        84           0.3954           17.77s\n",
      "        85           0.3946           16.63s\n",
      "        86           0.3940           15.47s\n",
      "        87           0.3928           14.33s\n",
      "        88           0.3919           13.21s\n",
      "        89           0.3910           12.10s\n",
      "        90           0.3901           10.99s\n",
      "        91           0.3895            9.86s\n",
      "        92           0.3891            8.73s\n",
      "        93           0.3885            7.62s\n",
      "        94           0.3874            6.53s\n",
      "        95           0.3867            5.42s\n",
      "        96           0.3861            4.33s\n",
      "        97           0.3852            3.24s\n",
      "        98           0.3846            2.16s\n",
      "        99           0.3836            1.08s\n",
      "       100           0.3830            0.00s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3,\n",
       "             estimator=GradientBoostingClassifier(random_state=42, verbose=2),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'learning_rate': [0.5, 0.1, 0.05],\n",
       "                         'max_depth': [7, 9, 11],\n",
       "                         'n_estimators': [32, 64, 100]},\n",
       "             scoring='accuracy', verbose=2)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Try tuning with GridSearchCV\n",
    "tuned_gb_clf = GradientBoostingClassifier(verbose=2, random_state=42)\n",
    "gb_param_grid = {'n_estimators':[32, 64, 100],\n",
    "                'learning_rate' : [0.5, 0.1, 0.05],\n",
    "                   'max_depth':[7,9,11]\n",
    "                   }\n",
    "\n",
    "gs_gb = GridSearchCV(estimator=tuned_gb_clf, param_grid=gb_param_grid, \n",
    "                          scoring='accuracy', cv=3, \n",
    "                          verbose=2, n_jobs=-1)\n",
    "\n",
    "gs_gb.fit(X_train_encoded, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.1, 'max_depth': 11, 'n_estimators': 100}\n",
      "0.8510162425084686\n",
      "GradientBoostingClassifier(max_depth=11, random_state=42, verbose=2)\n",
      "Test accuracy: 78.60000000000001 %\n",
      "Train accuracy: 85.1 %\n",
      "[[5657  118  498]\n",
      " [ 457  198  107]\n",
      " [1193   86 3199]]\n",
      "                         precision    recall  f1-score   support\n",
      "\n",
      "             functional       0.77      0.90      0.83      6273\n",
      "functional needs repair       0.49      0.26      0.34       762\n",
      "         non functional       0.84      0.71      0.77      4478\n",
      "\n",
      "               accuracy                           0.79     11513\n",
      "              macro avg       0.70      0.63      0.65     11513\n",
      "           weighted avg       0.78      0.79      0.78     11513\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Inspect the tuned Gradient Boosting model\n",
    "print(gs_gb.best_params_)\n",
    "print(gs_gb.score(X_train_encoded, y_train))\n",
    "tuned_gb = gs_gb.best_estimator_\n",
    "print(tuned_gb)\n",
    "print_accuracy(gs_gb, X_train_encoded, y_train, X_test_encoded, y_test, cm=True, cr=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting conclusion:\n",
    "* Baseline model showed no signs of overfitting (test and train accuracy were consistent). Accuracy of 74% was acceptable.\n",
    "* Tuned model had better accuracy at 78.6%  but showed some overfitting. Is it acceptable? \n",
    "* Choosing the Tuned model over the Baseline so that additional hyperparmeters could be set as part of Future Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XG Boost Classifer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(objective='multi:softprob', random_state=42)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# XGBoost\n",
    "base_xgbt_clf = XGBClassifier(random_state=42)\n",
    "base_xgbt_clf.fit(X_train_encoded, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 73.4 %\n",
      "Train accuracy: 73.6 %\n",
      "[[5921    3  349]\n",
      " [ 656    1  105]\n",
      " [1951    0 2527]]\n",
      "                         precision    recall  f1-score   support\n",
      "\n",
      "             functional       0.69      0.94      0.80      6273\n",
      "functional needs repair       0.25      0.00      0.00       762\n",
      "         non functional       0.85      0.56      0.68      4478\n",
      "\n",
      "               accuracy                           0.73     11513\n",
      "              macro avg       0.60      0.50      0.49     11513\n",
      "           weighted avg       0.72      0.73      0.70     11513\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_accuracy(base_xgbt_clf, X_train_encoded, y_train, X_test_encoded, y_test, cm=True, cr=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed:   58.2s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=XGBClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={'max_depth': [4, 6, 8], 'n_estimators': [100, 200]},\n",
       "             scoring='accuracy', verbose=2)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# try tuning the XGB\n",
    "tuned_xgb_clf = XGBClassifier(random_state=42)\n",
    "\n",
    "param_grid = {\n",
    "    'max_depth': [4, 6, 8],\n",
    "    'n_estimators': [100, 200],\n",
    "}\n",
    "\n",
    "gs_xgb = GridSearchCV(tuned_xgb_clf, param_grid, scoring='accuracy', cv=3, n_jobs=-1, verbose=2)\n",
    "gs_xgb.fit(X_train_encoded, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 8, 'n_estimators': 200}\n",
      "0.8120385651003214\n",
      "Test accuracy: 78.5 %\n",
      "Train accuracy: 81.2 %\n",
      "[[5748   62  463]\n",
      " [ 508  137  117]\n",
      " [1286   45 3147]]\n",
      "                         precision    recall  f1-score   support\n",
      "\n",
      "             functional       0.76      0.92      0.83      6273\n",
      "functional needs repair       0.56      0.18      0.27       762\n",
      "         non functional       0.84      0.70      0.77      4478\n",
      "\n",
      "               accuracy                           0.78     11513\n",
      "              macro avg       0.72      0.60      0.62     11513\n",
      "           weighted avg       0.78      0.78      0.77     11513\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Inspect the tuned XGBoost model\n",
    "print(gs_xgb.best_params_)\n",
    "print(gs_xgb.score(X_train_encoded, y_train))\n",
    "tuned_xgb = gs_xgb.best_estimator_\n",
    "print_accuracy(gs_xgb, X_train_encoded, y_train, X_test_encoded, y_test, cm=True, cr=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost conclusion:\n",
    "* The Baseline model had similar accuracy for train and test so no overfiiting. Accuracy was respectable at 73%\n",
    "* The Tuned model did showed very slight overfitting with a higher train accuracy  but acceptable. Accuracy was better at 78.5%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Face Off!\n",
    "Tuned Gradient Boosted and Tuned XGBoost both had Accuracy ~79%. Which is actually \"better\"? \n",
    "\n",
    "Inspect the Confusion Matrix of both to see how well they performed for each class.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuned Gradient Boosted scores:\n",
      "Test accuracy: 78.60000000000001 %\n",
      "Train accuracy: 85.1 %\n",
      "Tuned XGBoost scores:\n",
      "Test accuracy: 78.5 %\n",
      "Train accuracy: 81.2 %\n"
     ]
    }
   ],
   "source": [
    "print('Tuned Gradient Boosted scores:')\n",
    "print_accuracy(tuned_gb, X_train_encoded, y_train, X_test_encoded, y_test)\n",
    "print('Tuned XGBoost scores:')\n",
    "print_accuracy(tuned_xgb, X_train_encoded, y_train, X_test_encoded, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "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\" y=\"-9\"/>\n",
       "   <g id=\"patch_8\">\n",
       "    <path d=\"M 450.922813 309.193495 \n",
       "L 450.922813 308.025604 \n",
       "L 450.922813 11.381385 \n",
       "L 450.922813 10.213495 \n",
       "L 465.871813 10.213495 \n",
       "L 465.871813 11.381385 \n",
       "L 465.871813 308.025604 \n",
       "L 465.871813 309.193495 \n",
       "z\n",
       "\" style=\"fill:none;\"/>\n",
       "   </g>\n",
       "  </g>\n",
       " </g>\n",
       " <defs>\n",
       "  <clipPath id=\"p1af44696e2\">\n",
       "   <rect height=\"298.98\" width=\"298.98\" x=\"129.622813\" y=\"10.213495\"/>\n",
       "  </clipPath>\n",
       "  <clipPath id=\"p8b9ce0bc5b\">\n",
       "   <rect height=\"298.98\" width=\"14.949\" x=\"450.922813\" y=\"10.213495\"/>\n",
       "  </clipPath>\n",
       " </defs>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nLSIiYhAGymyd8iUiImIU6rSFUo8+7uoS5CoHTtlcXYL8zcPNQC2YCQT6eGTLfjU8LiIiYhAGymyFtoiImFt2d9rjx49nx44dWCwWhg4dSkhIiGPdyZMnGTBgAOnp6VSuXJnRo0ffdF86pi0iIqZmsdz5z61s3ryZI0eOsGTJEsaNG8e4ceOyrJ84cSI9evRg6dKluLm5ceLEiZvuT6EtIiKmZrFY7vjnVqKiomjWrBkA5cqVIzk5GZvt8ryVzMxMtmzZQpMmTQAYMWIExYoVu+n+FNoiImJq2dlpx8fH4+vr61j28/MjLi4OgDNnzpA/f34mTJjAc889x5QpU265P4W2iIhIDrHb7Vl+P336NF27diUiIoK9e/fyww8/3HR7hbaIiJhadg6PBwQEEB8f71iOjY3F398fAF9fX4oVK0bJkiVxc3Ojfv36HDhw4Kb7U2iLiIipZWdoN2jQgG+//RaAPXv2EBAQgLe3NwDu7u6UKFGCv/76y7G+TJkyN92fTvkSERFTy84zvmrWrEmVKlXo2LEjFouFESNGsGzZMgoUKEDz5s0ZOnQogwcPxm63U758ecektBtRaIuIiKll93nar7/+epblihUrOn4vVaoUixYtuu19KbRFRMTUdEU0ERERgzDStcc1EU1ERMQg1GmLiIipGajRVmiLiIi5WQ2U2gptERExNQNltkJbRETMzUgT0RTaIiJialbjZLZCW0REzM1InbZO+RIRETEIddoiImJqBmq0FdoiImJuFoyT2gptERExNU1EExERMQgjTURTaIuIiKkZKLMV2iIiYm654jKmmZmZN93QatXZYiIiIjnphqFduXJlxzi/3W4HLo/72+12LBYL+/bty5kKRUREspGBGu0bh/b+/ftzsg4RERGXMNJEtFuOcScnJzNp0iTeeOMNANavX8+ZM2eyvTAREZGcYLHc+U9Ou2Vov/XWWxQtWpSYmBgA0tLSGDRoULYXJiIikhOsFssd/+R4rbd6wpkzZ+jatSseHh4AtGzZkgsXLmR7YSIiIjnBchc/Oe22TvlKT093jPnHx8dz7ty5bC1KREQkpxjpmPYtQ7tLly4888wzxMXF8eKLL7Jr1y6GDRuWE7WJiIjIVW4Z2q1ataJGjRps27YNT09PRo8eTUBAQE7UJiIiku1y1bXHz507R2RkJAcPHsRisRAXF8eTTz5J3rx5c6I+ERGRbJWrhsf79euHn58fNWrUwG6389tvv/HDDz/w0Ucf5UR9IiIi2cpAmX3r0LbZbHzyySeO5U6dOtG5c+dsLUpERCSnGKnTvuUpX6VLlyY2NtaxHBcXR6lSpbK1KBERkZxitdz5T067YafdqVMnLBYLFy9epHnz5pQtWxaLxcKff/5JlSpVcrJGERGRbGOkTvuGof3aa6/dcCMjvUEREZHc4oah/cADDzh+P3v2LMnJycDly5i+/vrrLF26NPurExERyWZGakNvORFtzpw5fPzxx6SlpZEvXz4uXrzI448/nhO1iYiIZDtXXEP8Tt1yItq3337LL7/8QmhoKNHR0UyePJn7778/J2oTERHJdrnqLl/58+fH09OT9PR0AJo2bUpkZGS2FyYiIpITLBbLHf/ktFsOjxcsWJDly5dTvnx5hgwZQrly5bKcAiaSU15vVYFqJQoBdt5ZtZ89x1Mc6wIL5mHisyF4uFnZdyKFccv30rZWMI9VL+Z4TuViPjw4Rl84nWHqnBXs/j0GiwUGvvA4lcuXcKy7mJbOhFlf8efR03w27T8AnDt/kZHTPifVdp609Ax6PdeM+jXLu6r8XGfy7BXs3H8UiwXe7PMEVf71eYyduYxDR06zcEY/AH7beYg3xi+gXKlAAO4rHcTgvk+6pPZ7gYFGx2/daU+aNImaNWsyZMgQSpUqxalTp5g6depNt0lPT6d9+/ZOue+2zWZjw4YNAMyePZtt27bd9T4BmjRpwtmzZ52yrxt56qmnOHbsWLa+xvX07ds3x18zu9Uq7UvJwvnoNnsTI7/aw5ttKmVZP7BlBT7b8BddPoomM9NOUME8fL3lOL3m/kqvub/yYeRBVmw74aLqc5etu/4k5kQC/538Em/1e4bJs1dkWT9j3mrKlyma5bGVkVsoFezPh+N7M3FwF6b+axu5c7/t+pOjx+P5bOrLjHj1GSZ9tDzL+mlzV1OhbLFrtqtVrQyfTOrDJ5P6mDqwwVj3075hpx0TE5NlOT4+njZt2tzWTuPi4khLS2PSpEl3Vx2wZ88eNm7cSMOGDendu/dd788MPvzwQ1eX4HQPlCvM9/suj/AcjjuLT1538nu5cfbiJSwWqFHal8Gf7wBgwsp912zf+5FyDP1iZ47WnFv9uuMgjetVBqBMiQBSbeexnbuAd748ALwU1pLk1LOs+XG7Y5tCPvk5+NcpAFJs5ynkky/H686tNm8/yMP1L187o2zJwGs+j/90u/x5rP7eOQ2PuNYNQ7tbt25YLBbsdrvjsSvLFovlpse1J0yYwNGjRxkyZAjFihXD19eXLl268McffzBmzBjCw8Np3rw5zZo1Y+vWrRQoUIDZs2djs9l4/fXXsdlsFChQgKlTpzJ69GhsNhulS5dm27ZtPProozRs2JDhw4cTExNDWloa/fr1o2HDhjRv3pwOHTrw/fffk5aWxrx58wAYOHAg586d48KFC7z99tuEhIRct+7rbZ83b17efvttYmJiyMjIoF+/ftSvX5+DBw8yevRoLBYL+fPnZ+LEifj4+DB27Fi2bdtGmTJlHPMANmzYwHvvvUeePHkoXLgwkydPxsPDw/G6LVq04KGHHqJw4cI89dRTDBs2jPT0dNzc3Bg7dizFihWjUaNGPProo+zatYvAwEAmT57MmTNneOONNwDIyMhg0qRJlCxZkrp167Jp0ybCwsIckwaHDx9+u/9N3JOKeHuy73iyYznxbDqFvb04e/Ecvvk8OXcxg9dbV6RSMR+2/pXIzO8OOJ5bJdiH08kXSLCluaL0XCchyUbF+4Idy4UK5ichMdUREvnzeZGcmnUUq8VDoayM3MJTvd8lxXaeacOfz8mSc7WExFQqXfV5+BbMT8KZm38eAH8ejeXVUZ+SknqePp2aUs/EhytyxfD4+vXriYyMZP369Y6fK8u3mog2aNAgypQpw4QJE274nJiYGJ588kmWLFlCSkoKv//+O3PnzqVhw4YsXLiQ+vXrExUVRc+ePWndujUdOnRwbLtq1So8PT2JiIhg5syZjBkzBoBLly5RtmxZFixYQPHixYmOjiYuLo727dsTHh7OgAEDmDNnzg1rut72K1aswN/fn/DwcGbNmsX48eMBGDNmDKNHj2b+/Pk0aNCABQsWcPDgQbZu3coXX3zBwIEDOXz4MAAREREMHjyYiIgI2rRpQ1JSUpbXzcjI4KGHHqJv375Mnz6dHj16MH/+fLp168YHH3wAQGxsLI899hhLlizBbrfz008/ERsby8svv0x4eDhPP/00CxcuvOY93X///YYP7Ou5+i+ZxQIBPl4sjDpCz082U7GoD43KF3Gsb1e7OMu3HXdBlSZx1Rf7G/nm+20E+Rdi2ew3+GBcL979+P9yoDBzuo2Pg5LFitCnUzPeG96N0QOfZdT0paSnZ2R/cfeoXDURLbt4e3tTsWJFAIKCgkhNTWXv3r28+uqrADz//PMALFu27Jptd+/eTd26dQEIDAzE09PTEYS1a9fOss8iRYrwwQcfMHfuXMe55jfz7+23b9/Oli1b2Lp1KwAXL14kLS2NnTt38vbbbwOXLzhTrVo1Dh48SGhoKFarlaJFi1KixOXJIC1btmTEiBE8/vjjtGnTBn9//2te90r3v23bNg4fPsyHH37IpUuX8PPzAyBfvnxUr14dgOrVq3P48GGqVKnC2LFjmTlzJikpKde9vOyNRhWMJi71IoULeDmW/Qt4EZ96EYCkc+mcSLrAsTPnAdj8ZwLlArz5+Y94AGqX9mPidYbM5c4U8StAQqLNsRx3JpUivj433WbHvr+oV+PyqE/5MsWIP5PCpUuZuLndclqN3IK/nw8JiamO5bgzKRTxu/nnEVCkII82DgWgRNHCFPYtQGxCCsFBftla673KSP8VZntoX/1NJCPjn29ybm5uWZ5nt9txc3MjMzPztvZ79bB9WloaVqv1mv3a7Xbmz59PYGAg7777Lrt27eKdd9656X7/vb2Hhwcvvvgijz32WJbn5c2bl88++yzL+/vmm28cdQCO99K2bVsaNWrEunXrHN10uXLlsuzvynC5h4cH06dPJyAgIMv6q/9crhyimDFjBg0bNuS5555jzZo1/PDDD9e8n6uH4Y0s6kACfZuW48tfj1GxaAHiUi9yLu0SAJcy7Rw/c46ShfNxNOEclYr5sGbnSeByuJ9LyyDj0m20H3Jb6tUoz+yF3/FUq7rsP3gcf78C5M/nddNtShQtzO4/YmjSoBonYxPJm8dLge0k9Wrez0cR3/FM63rsO3gcfz+fW34eq7/fRvyZFLo+3Zj4M6kkJNoIKHzzoM/NjHRp7mwPbW9vb+Li4gDYsmXLTZ9btWpVoqOjCQkJYfHixXh5eWG1WrOEPUC1atXYtGkTbdq04eTJk1itVnx8rv8fXGJiIhUqVABg3bp1juPMtys0NJTIyEgee+wxEhISmD9/PgMGDKBixYr89NNPNG7cmFWrVuHn50eZMmWYP38+drudEydOcPz45SHZWbNm0aVLFzp06EBCQgKHDh26JrSvfr1169bRqVMnoqKiiI+P5/HHH+fChQvs3r2bqlWrsn37dp555hl+++03SpYsid1uJzIy8ra/8BjRjpgk9p5IYX7vB8i0w4QV+3iiRjFSL2Tw/b5Y3l39O6OfrorVAgdO2/jx98v/zRUp4MWZszqW7UwhlUpRsVwwPd/4AKvFwht9n2Tlut/Inz8Pj9SvyuCJC4iNT+Lo8TheHPIxbR+tS7uWdRkzfSl9Bn/MpcxMBr/U1tVvI9eoXrk0le4vTreBs7BarAx+6UmWf/cb3vnz0OTBqrwxPoLTcUkcOR5Hr0Ef83TLB2hctzJD31nED9F7SU+/xNBX2uHh4bKBV5dzxd267tQtP6Xjx48zadIkEhMTCQ8P5/PPP+eBBx6gdOnSt/UCzZs3p0+fPuzcudMx9Hwj3bp148033yQsLIz8+fMzefJkTpw4weTJkwkKCnI8r02bNmzevJmwsDDS09MZPXr0Dff55JNPMmjQINasWUPnzp1ZuXIlX3755W3VDtCqVSuio6Pp2LEjly5d4pVXXgFg2LBhvP3228yZMwcvLy+mTJlCoUKFKF++PB06dKB06dKO4f9ixYrRvXt3fHx88PHxoXv37jd8vVdeeYWhQ4eyatUqLBaLY15AoUKFWL58OePHj8ff35+GDRtit9sZM2YMwcHBhIWF8fbbbztOj8uNZqw9kGX5j1P/DAnGnDlH9zmbr9lm34kUXvlsa7bXZjavPN8qy3L5Mv+cUjRxcOfrbjPhBo/L3Xu1e9bP4+pTvN4d2uW620wf+Xx2lmQoRgpti91+82kLPXr0oHPnzsybN4+IiAh+/fVXZsyYQXh4eE7VKOCYEZ4dKg7+Nlv2K3fmi1cauLoE+ZuHm4H+NTeBikXzZ8t+Byzff8fbTn2iohMrubVbHlRKT0+nadOmjjH/OnXqZHtRIiIiOSXXzR5PSUlxFHfgwAEuXryYrUXJtbKryxYRMTsjDY/fMrRffvllnn32WeLi4nj88cdJTEzk3XffzYnaREREsp2BJo/fOrTr1avH119/zR9//IGnpydlypTBy+vmpxOIiIgYhZHup33L0J4+ffp1H79yERQREREjM9IVA25Zq5ubm+MnMzOTTZs2kZqaeqvNREREDMFiufOfnHbLTvvKeclXXLp0if/85z/ZVpCIiIhc3/98CZyMjAyOHj2aHbWIiIjkuFx1TLtx48ZZzkVLTk6mXbt22VqUiIhITjFQZt86tK++3aPFYsHb2/uG1/kWERExmlx1nva7777Le++9lwOliIiI5LxcNTxevHhxli5dSo0aNfD09HQ8fuVe0SIiIkZmoMy+dWivXr36mscsFguRkZHZUpCIiEhOyu7h8fHjx7Njxw4sFgtDhw4lJCTkmudMmTKF7du33/JmXDcM7eXLl/PEE0+wfv36u69YRETEhDZv3syRI0dYsmQJhw4dYujQoSxZsiTLcw4ePMivv/6Kh4fHLfd3w4urLF269O6rFRERucdZ7uJ/txIVFUWzZs0AKFeuHMnJydhstizPmThxIv3797+tWo109TYRERGns1ru/OdW4uPj8fX1dSz7+fkRFxfnWF62bBkPPPAAwcHBt1XrDYfHt23bxsMPP3zN43a7HYvFwg8//HBbLyAiInIvy8lTvux2u+P3pKQkli1bxrx58zh9+vRtbX/D0K5cuTJTp069+wpFRETuYZZsnD4eEBBAfHy8Yzk2NhZ/f38AoqOjOXPmDJ07dyYtLY2jR48yfvx4hg4desP93TC0PT09b7tdFxERMars7LQbNGjAzJkz6dixI3v27CEgIABvb28AWrZsScuWLQE4duwYQ4YMuWlgw01C+3pT0kVERHKb7DxPu2bNmlSpUoWOHTtisVgYMWIEy5Yto0CBAjRv3vx/3p/FfvUAu5hSxcHfuroEucoXrzRwdQnyNw83A111wwQqFs2fLfud+tOfd7ztgIfKOrGSW/uf7/IlIiKSm+Sqy5iKiIjkZrnqhiEiIiK5mYEabYW2iIiYm/U2rmx2r1Boi4iIqanTFhERMQgjHdPWtcdFREQMQp22iIiYmk75EhERMQgDZbZCW0REzE2dtoiIiEEYKLMV2iIiYm5GmpGt0BYREVPLzvtpO5uRvmCIiIiYmjptERExNeP02QptERExOc0eFxERMQjjRLZCW0RETM5AjbZCW0REzM1Is8cV2iIiYmpGOo3KSLWKiIiYmjptERExNQ2Pi4iIGIRxIluhLcCULtVdXYJc5f4gb1eXIH+rMGCFq0uQqxyZ8Xi27FedtoiIiEEYaXKXQltERExNnbaIiIhBGCeyjTUqICIiYmrqtEVExNQMNDqu0BYREXOzGmiAXKEtIiKmpk5bRETEICzqtEVERIxBnbaIiIhBGOmYtk75EhERMQh12iIiYmoaHhcRETEIhbaIiIhBaPa4iIiIQViNk9kKbRERMTcjddqaPS4iImIQ6rRFRMTUNBFNRETEIIw0PK7QFhERU9NENBEREYNQpy0iImIQOqYtIiJiEAbKbJ3yJSIiYhTqtEVExNSsBhofV2iLiIipGSeyFdoiImJ2BkpthbaIiJiaTvkSERExCAMd0lZoi4iIuRkos3XKl4iIiFGo0xYREXMzUKut0BYREVPL7olo48ePZ8eOHVgsFoYOHUpISIhjXXR0NFOnTsVqtVKmTBnGjRuH1XrjQXANj4uIiKlZLHf+cyubN2/myJEjLFmyhHHjxjFu3Lgs64cPH86MGTNYvHgxZ8+e5eeff77p/tRpi4iIqWVnnx0VFUWzZs0AKFeuHMnJydhsNry9vQFYtmyZ43c/Pz8SExNvuj912iIiYm6Wu/i5hfj4eHx9fR3Lfn5+xMXFOZavBHZsbCwbN26kcePGN92fOm0RETG1nLy4it1uv+axhIQEXnzxRUaMGJEl4K9HnbaIiEg2CQgIID4+3rEcGxuLv7+/Y9lms/HCCy/w2muv0bBhw1vuT6EtIiKmlp0T0Ro0aMC3334LwJ49ewgICHAMiQNMnDiRbt268dBDD91WrRoeFxERU8vOwfGaNWtSpUoVOnbsiMViYcSIESxbtowCBQrQsGFDvv76a44cOcLSpUsBeOyxx+jQocMN96fQFhERc8vmQ9qvv/56luWKFSs6ft+9e/f/tC+FtoiImJru8iUiImIQusuXiIiIQRgosxXaYgzzItZy4NAxwEKPsEe5r2wxx7rde/9iwefrsVotFCtamL49Hwdg9rxVHD0Wh7u7G326tya4WBEXVZ/7DJ36Jb/tPgxYmDjwGWpWKeVY98Om/Yz5YAVubhaaP1iFN3q1AmDvwRN0fn02fTs9Qu9nb34BCblzQ56sQmipQtjtMP7r3eyKSQYgoGAeJneu4XheicL5mbJqHyu3HndVqXIHdMrXXRo9ejTt2rXDZrPd9b7WrFkDwE8//cTChQvven8AgwcP5vvvv3fKvlxlz74jnDx9hvEjevBSr8f4b/iaLOs/+u8qXu/3DOOGd+f8+TS27zzIr1t/59z5i4wf0Z2Xej3G/EXrXFR97rNxywEOxcSy9r+vM/Ptzgye8kWW9YOnLGX+pF6s+WQA6zftZ/+fJzl7/iKDJn9B4zrlXVS1OdQpV5hSRfLTccZGhi3ZwbB2VR3rYpMv0PWDKLp+EEX3j6I5mXie9btPubDae0g2XhHN2RTad+nHH39k/vz5Wc67uxNpaWl8+umnADz00EN06tTJCdXlDrv2HuaBWhUAKB7sj+3sBc6dv+hY/86YXhT28wGgoE8+Um3nOXnqjKMbDwr0Iz4+iUuZmTlffC7046+/06ZxKAAVygSRlHKeFNt5AP46Fk8hn3wUD/LFarXS/MHK/PTr73h5uPP5e30J8i/oytJzvfr3F2Hd30H8Z6yNgnk9ye917YBquzol+HbnSc6lXcrpEu9Jlrv4X04z3fD4smXL2LJlC2fOnOHw4cP07NmT9u3bs2nTJqZNm4a7uzuBgYFMmDCBlStXXve5V3zyySfExsby4osv0qNHD5YvX86MGTMAqFu3Lps2bSIsLIwHH3yQ6OhoEhMT+eijjyhWrBhjx45l586duLm5MWrUKBYtWsTvv//OyJEjCQkJ4cCBAwwaNIj58+ezevVqAJo2bUrv3r0ZPHgwAQEB7NmzhxMnTjB58mSqVKnChAkT2LlzJxcvXuS5557LUquRJSXZKFu6qGPZp0A+kpJs5MvrBeD4/8SkVHbs+pOOTz/MgT9PsHLNJtq0rMup02c4HZtEauo5ChW8uy9XArEJKVSvVNKxXNjXm9iEFHy883I6IYUivv/8Gfv7FeDwsXjc3d1wd3dzRbmmUqSAF3tikhzLZ85exN/Hi7NxGVme175eSXp8FJ3D1d27jDQRzZSd9h9//MH777/PrFmziIiIAGDEiBFMmzaNiIgIChYsyIoVK2743Ct69eqFv78/c+bMoUCBAjd8PW9vb+bPn89DDz3E2rVr+eWXXzh16hSff/45AwYMYPXq1fTs2ZMyZcowcuRIx3YxMTF89dVXLFiwgAULFvDNN99w9OhR4HJnPnfuXLp27crXX3/NxYsXCQ4OZtGiRSxcuJDp06c7+U/t3pacfJYJU5fQ6/lWFCiQj5qh93F/2WIMHzufVWs2ExxchOtc8lec4HrXUv5nXQ4WIte4XidYvZQvf562cfZixnW2MCcDjY6br9MGqF69Om5ubgQFBZGamkpSUhIWi4WiRS93c3Xr1uXXX3+lcuXK1zz3TtSuXRuAoKAgkpKS2LNnDzVr1gSgTp061KlTh2PHjl2z3b59+wgNDcXd/fLHVLNmTfbv33/NPnfu3ImXlxfJycl07NgRDw+PW97ezUh8fQuQlPzPnIEzian4Fvqnmzt3/iLjJi/kufaPUL1aOcfjz7V/xPH7ywPfp6BP/pwpOJcL8i/I6YQUx/KpuGQCi1we9i7qX5DYq9adjEvSkHgOik25QBGfPI7lgIJexKVcyPKchysHEnUg7t+bmps67XvblRC8wmKxZOkW0tPTsfw9XvLv596I5V/jKxkZ/3yLdXP7Z1jQbrfj5uZG5m0cX71eXVar9br73Lx5M9HR0YSHhxMeHo6np+dt1W0EoVXLEr15HwB//nUSP98C5P17SBxg/sLveKxlXWqE3Od47K8jp5g1ZzkA23YepEypIKxWA/3NvIc9UrcSyyO3AbBjfwxB/gUpkP9yUJQsVphU2wWOnkggI+MS3/68myZ1K95sd+JEG3+P49GQy81H5eCCxCZf5OzFrMetq5UsxP4TKdfb3LR0TNtgChYsiMVi4cSJExQrVozNmzdTq1YtLl26/Uka3t7exMbGArB//37Onj17w+dWq1aN2bNn06tXL/bu3csXX3zBCy+8cM3rVapUiZkzZzq+AOzYsYM+ffqwbt21M6ETExMJCgrCw8ODyMhILl26RFpa2m3Xfy+rWL4EZcsUZeioeVitFnp1bcn3P+0gXz4vqlcrx48bdnLy1BnW/bAdgEb1q9L04RrYM+0MHjEXDw93Xu3b1qXvITepG1qW0IoladFjClarhXfffJaFK6Lx8c7LY4+EMnlwR3q9NQ+Ads1rcl+pQLbvO8pb7y3j6MkzeLi7sTxyG+HvvIBvQY1+ONO2vxLZcyyJRf9pgN0Oo5btol2d4qReyGDdrssT1Px9vEhIvXiLPcm9SqH9tzFjxjBw4EDc3d0pUaIEbdq0Yfny5be9fcWKFcmXLx8dO3akRo0aBAcH3/C5derUITIy0jFDfMSIEfj7+5Oenk6/fv14+OGHAShevDgdOnSgS5cu2O122rdvf8P9Pvjgg8yZM4cuXbrQrFkzHn744SzHx42uS4emWZZLlwpy/L543tDrbvNKnyeztSYzG/mfrH+21coXd/zeoOZ9rP1v1mstV69UkpUfv5YTpZnelFX7syz//q+u+ol3f8zJcgzBSBPRLPabzSIRU1i1+7SrS5CrNK0Y6OoS5G8VBqxwdQlylSMzHs+W/f5x6twdb1s+KJ8TK7k1ddoiImJuBuq0FdoiImJqusuXiIiIQRjpmLZCW0RETM1AmW3O87RFRESMSJ22iIiYm4FabYW2iIiYmiaiiYiIGIQmoomIiBiEgTJboS0iIiZnoNRWaIuIiKkZ6Zi2TvkSERExCHXaIiJiapqIJiIiYhAGymyFtoiImJs6bREREcMwTmortEVExNTUaYuIiBiEgTJbp3yJiIgYhTptERExNQ2Pi4iIGISRroim0BYREXMzTmYrtEVExNwMlNkKbRERMTcd0xYRETEIIx3T1ilfIiIiBqFOW0REzM04jbZCW0REzM1Ama3QFhERc9NENBEREYMw0kQ0hbaIiJiakTptzR4XERExCIW2iIiIQWh4XERETM1Iw+MKbRERMTVNRBMRETEIddoiIiIGYaDMVmiLiIjJGSi1FdoiImJqRjqmrVO+REREDEKdtoiImJomoomIiBiEgTJboS0iIiZnoNRWaIuIiKkZaSKaQltEREzNSMe0LXa73e7qIkREROTWdMqXiIiIQSi0RUREDEKhLSIiYhAKbREREYNQaIuIiBiEQltERMQgFNoiIiIGoYuriIhTjB07lrfeesvVZZjajz/+eNP1jRs3zqFKJLsotMVwzp8/f9P1efPmzaFK5Gp2u50lS5YQEhKCh4eH4/H77rvPhVWZy5o1a266XqFtfLoimhhOkyZNsFgsXO8/XYvFQmRkpAuqkrCwsGses1gsfPbZZy6oRq6Wnp7OqFGjGDt2rKtLkbukTlsMZ/369Tdct2zZshysRK4WHh7u6hLkb0uXLmX69OkkJibi6elJZmYmDz/8sKvLEidQaIth7dq1izlz5pCUlARc7ibi4+N56qmnXFuYybz88svMmjWLevXqYbnqzgt2ux2LxUJUVJQLqzOnxYsXs27dOnr16kV4eDiRkZEcO3bM1WWJEyi0xbDGjh1L//79mTx5MiNHjuS7776jevXqri7LdGbNmgVAdHT0Nes2btyY0+UI4OXlhZeXF+np6WRmZtK0aVPCwsLo1q2bq0uTu6TQFsPKkycP9erVw9PTk6pVq1K1alV69uzJI4884urSTCkmJoaFCxdmGfn49ddfbzmjWZyvWrVqRERE0LBhQ7p160ZQUBAXLlxwdVniBAptMay8efMSGRlJ8eLFmTp1KiVKlODkyZOuLsu0Bg8ezFNPPcX8+fN5+eWXiYyMZPTo0a4uy5QGDx5MWloanp6e1K1bl6SkJOrXr+/qssQJNHtcDMtmsxEfH0+RIkX49NNPSUpKom3btlStWtXVpZlSt27dmD9/Pl26dCEiIgKAF154gTlz5ri4MvPZt28fX3/9NampqVnOspgwYYILqxJnUKcthmW1Wjl06BDbt2+nePHiBAcHc/DgQYW2i9jtdjZv3kyhQoVYsmQJJUuW1OQnF3n99dcJCwsjKCjI1aWIk6nTFsPq0KEDwcHBBAYGOh6zWCy8+eabLqzKvE6fPk1cXBxFihRh+vTpJCUl0aFDB51q5AI9e/Zk7ty5ri5DsoFCWwzr6mFYcb2MjAx++uknDh8+jMVioVy5cjRq1AirVbc4yGnTpk0jLS2N2rVr4+7+z4CqrohmfAptMay5c+dy3333UatWLdzc3ByP6zKmrvHqq69it9upXr06drud7du34+7uzrRp01xdmukMGTLkuo/rmLbx6Zi2GNaSJUvIyMjI8pguY+o6p0+fZvHixVke69y5s4uqMbcJEyYQExPD/v37sVqtVK5cmaJFi7q6LHEChbYY1tq1awFITk7GarVSoEABF1dkbtWqVWPnzp2EhIQAsHfvXqpVq+biqszpk08+YfXq1dSsWZO0tDTef/992rdvT6dOnVxdmtwlDY+LYf3yyy+MGjXKceUnq9XK6NGjqVWrlqtLM6WHHnqI2NhY8ubNi91u58KFCxQqVAhAlzPNYR07dmTBggWOw0YZGRl06dLlmpEQMR512mJYM2bMIDw8nICAAABOnjzJwIEDWbhwoYsrM6effvrJ1SXIVa6eAGi1WrNcF16MS6EthuXh4eEIbICiRYtmmSkrOevUqVPMmjWL5ORkZsyYwapVq6hevTrBwcGuLs10WrVqxVNPPUX16tXJzMxkx44dPPvss64uS5xAw+NiWEOGDCFPnjw88MAD2O12oqOjyczM1D2DXaRnz5507dqVOXPmEBERQVRUFB988IFu2ekix44dY9++fVgsFipVqqQvT7mE2hIxrDFjxrBy5Uq2bNmCxWKhTp06tG7d2tVlmVZmZiaNGzfmk08+AaB+/fqOO4BJzli8eDEdO3Zk0qRJWYbDt27dCqALD+UCCm0xnB07dhAaGsrGjRvx9fWlUaNGjnUbNmzQBSRcxN3dnaioKDIzM4mPj+e7777Dy8vL1WWZypVuunz58tes0zHt3EGhLYazadMmQkNDWbNmzXXXK7RdY9y4cUyfPp3ExER69epFSEiILuaRw658gd21axfDhw/Psu61116jbdu2LqhKnEmhLYbTu3dvAGrWrEn79u2zrJs3b54rShLgq6++Yty4ca4uw9S+/fZb5s2bx4EDB9i5c6fj8YyMjGsuRCTGpIloYjgbN25kw4YNrFmzhlatWjkez8jI4JtvvuHnn392YXXmNX78eBo3bky1atXw8PBwPK7LyuastLQ0Jk6cSK9evRy35bRarfj7++vsilxAn6AYTmhoKO7u7vz888+UL1/e8Q+TxWK5pvOWnPPjjz+ybt26LI/psrI5z9PTk+eee46lS5fSr18/4PKkzeeee4777rvPxdXJ3VKnLYZ17tw5oqKiaNq0KQBff/01LVq0IF++fC6uTMS1OnfuTP/+/alduzZw+ZKy48eP113xcgHdM08Ma8CAARw7dsyxfPHiRQYOHOjCikTuDRkZGY7ABqhcuTLqz3IHDY+LYaWmptKtWzfHcocOHVi5cqULKxK5N4SEhNCvXz9q1qxJZmYmmzZtctzIRYxNoS2G5e3tTUREhOMfpqioKN3py4UuXbpEUlIShQsX5vDhwxw6dIhGjRrpXG0XGDZsGFFRUezZswd3d3deeOGFLJ23GJeOaYthpaamMnfuXPbu3YubmxvVqlWja9eueHt7u7o0U+rfvz9t2rShYsWK9O3bl9atW/P777/z3nvvubo00zl16hRr164lNTU1y7D4K6+84sKqxBnUaYthFShQgJYtW9KgQQPsdjsWi4V9+/ZRp04dV5dmSvHx8TRr1ozZs2cTFhbGs88+S48ePVxdlim9+OKLNGrUiKCgIFeXIk6m0BbD6t27NykpKQQGBmY57Uuh7RoXLlxgy5YtLF++nM8++4yUlBSSkpJcXZYpFSpUSJMycymFthhWSkoKixcvdnUZ8rfXXnuNTz75hBdeeAE/Pz8++OADunbt6uqyTKlevXosWLCAWrVqZbmgis7TNj4d0xbDeuedd2jXrh3333+/q0sxtRMnTtx0fbFixXKoErkiLCzsmscsFgufffaZC6oRZ1Joi2G1aNGCmJgYvL29cXNzAy7/wxQVFeXiyszl6aefxmKxkJ6ezuHDhylRogSXLl3i+PHjVKpUic8//9zVJYrkGgptEXGKN954g4EDBzomPx0/fpyZM2cyceJEF1dmPvXq1XPcijMjI4OzZ89SvHhx1q5d6+LK5G7pmLYYVlhY2HXvEawhQNf466+/ssxWDg4O5q+//nJdQSYWHR2dZXn//v0sX77cRdWIMym0xbCuvl9wRkYGW7ZsITU11YUVmVtoaCjPPPMMoaGhWCwW9uzZQ/ny5V1dlgAVK1Zk1KhRri5DnEDD45Kr9OzZk7lz57q6DNM6dOgQBw8exG63U6ZMGSpUqODqkkypX79+WUah4uLiyJs3r/5u5ALqtMWwFixYkGU5NjaW2NhYF1UjNpuN7777joSEBIYNG0Z0dDQpKSn4+Pi4ujTTOHv2LPnz5+fJJ590XNLXYrHg7e1NxYoVXVydOIPu8iWGM2TIEAB2795NYmIiiYmJJCUl4efnx+zZs11cnXkNHjwYHx8fdu3aBcCZM2d0gY8cFhYWhs1mY968eYSEhFCtWjWqVq1K6dKluXDhgqvLEydQpy2Gc+jQIdq1a8fRo0cpXbp0lnUrVqxg6dKlrinM5M6ePUunTp345ptvAGjdujWLFi1ycVXmUr16ddq2bUtsbCxt2rRxPH7lMr+RkZEurE6cQaEthrNw4UJiY2OZOHEigwYNcnU58rfMzEyOHj3qOJb6008/kZmZ6eKqzOXK5My5c+fSs2dPF1c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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare tuned Gradient Boosted to Tuned XGBoost\n",
    "plot_confusion_matrix(tuned_gb, X_test_encoded, y_test, normalize='true', cmap=plt.cm.Blues, xticks_rotation='vertical')\n",
    "plt.grid(b=None)\n",
    "plt.show()\n",
    "\n",
    "plot_confusion_matrix(tuned_xgb, X_test_encoded, y_test, normalize='true', cmap=plt.cm.Blues, xticks_rotation='vertical')\n",
    "plt.grid(b=None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Model Selection: Tuned Gradient Boosted Model\n",
    "* Pros:\n",
    "    * Best accuracy score of all attempted models\n",
    "    * Best performance on classifying the rare class, functional needs repair (but still crummy at .26%)\n",
    "* Cons:\n",
    "    * Slight overfitting. Test accuracy: 78.6% Train accuracy: 85.1 %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = tuned_gb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Important features\n",
    " Intuition tells us that the feature quantity_group_dry would be a good indicator of operational status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "plot_feature_importance(final_model, ohe, X_train, num_features=20)"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict on Pump it Up Challenge Test/Validation dataset\n",
    "The challenge submission process involved making predictions on their supplied Test/Validation dataset.  I used my final model (trained on 80% of the Challenge Training data) to make predictions for the Challenge.  \n",
    "\n",
    "![water](../images/ChallengeScore.png)\n",
    "\n",
    "Maybe later on I'll retain this tuned model on all of the Test data and resubmit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14850, 38)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in the cleaned test data\n",
    "validation_df = pd.read_csv('../data/test_processed.csv', index_col='id')\n",
    "validation_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_validate = validation_df\n",
    "\n",
    "# Did our test train spilt before exploring features in X_Train for rare label encoding. \n",
    "# Now that I've selected some features based off of exploring X_Train ONLY, train the model on the chosen features\n",
    "X_validate_selected_features = X_validate[features_to_use]\n",
    "\n",
    "X_validate_encoded = ohe.transform(X_validate_selected_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'final_model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
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      "\u001b[0;31mNameError\u001b[0m: name 'final_model' is not defined"
     ]
    }
   ],
   "source": [
    "y_validation = final_model.predict(X_validate_encoded)\n",
    "predictions_df = pd.DataFrame(y_validation, index=X_validate_selected_features.index, columns=['status_group'])\n",
    "print(predictions_df.head())\n",
    "print(predictions_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write the predictions out to file to submit to the Pump it Up challenge\n",
    "predictions_df.to_csv('../data/challenge_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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