NeuralNetwork.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FXE3iq5Twme-"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.model_selection import train_test_split, TimeSeriesSplit, GridSearchCV\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.metrics import mean_absolute_error as MAE\n",
    "from sklearn.decomposition import PCA\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = [16,9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pEMtdH7zxSME"
   },
   "outputs": [],
   "source": [
    "train_sj = pd.read_csv('./data/train_sj.csv')\n",
    "test_sj = pd.read_csv('./data/test_sj.csv')\n",
    "train_iq = pd.read_csv('./data/train_iq.csv')\n",
    "test_iq = pd.read_csv('./data/test_iq.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## San Juan Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 451
    },
    "colab_type": "code",
    "id": "I7ykCiVaBUJD",
    "outputId": "83a55fc0-7425-472c-bab2-012373d80eab"
   },
   "outputs": [],
   "source": [
    "X_train = train_sj.drop(labels=['total_cases'], axis=1)\n",
    "y_train = train_sj['total_cases']\n",
    "X_test = test_sj.drop(labels=['total_cases'], axis=1)\n",
    "y_test = test_sj['total_cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 451
    },
    "colab_type": "code",
    "id": "1FUeDSCzwVOt",
    "outputId": "5f4738cd-3993-4098-c1ae-f3afa7f714fe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
       "       random_state=None, shuffle=True, solver='adam', tol=0.0001,\n",
       "       validation_fraction=0.1, verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MLPRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2Jpd6ElgBUDZ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Tuning hyper-parameters for explained_variance\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'hidden_layer_sizes': (100,)}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "-0.177 (+/-0.464) for {'hidden_layer_sizes': (100,)}\n",
      "\n",
      "-0.944 (+/-3.308) for {'hidden_layer_sizes': (20, 30)}\n",
      "\n",
      "-0.583 (+/-2.120) for {'hidden_layer_sizes': (13, 13, 13)}\n",
      "\n",
      "Mean Absolute Error(MAE): 21.000000\n",
      "-------------------------------------------------------\n",
      "\n",
      "# Tuning hyper-parameters for neg_mean_absolute_error\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'hidden_layer_sizes': (20, 30)}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "-48.263 (+/-70.817) for {'hidden_layer_sizes': (100,)}\n",
      "\n",
      "-35.493 (+/-20.302) for {'hidden_layer_sizes': (20, 30)}\n",
      "\n",
      "-38.140 (+/-20.386) for {'hidden_layer_sizes': (13, 13, 13)}\n",
      "\n",
      "Mean Absolute Error(MAE): 26.132979\n",
      "-------------------------------------------------------\n",
      "\n",
      "# Tuning hyper-parameters for neg_mean_squared_error\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'hidden_layer_sizes': (100,)}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "-3357.104 (+/-6731.036) for {'hidden_layer_sizes': (100,)}\n",
      "\n",
      "-3461.746 (+/-6385.691) for {'hidden_layer_sizes': (20, 30)}\n",
      "\n",
      "-3833.532 (+/-7801.382) for {'hidden_layer_sizes': (13, 13, 13)}\n",
      "\n",
      "Mean Absolute Error(MAE): 29.170213\n",
      "-------------------------------------------------------\n",
      "\n",
      "# Tuning hyper-parameters for r2\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'hidden_layer_sizes': (20, 30)}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "-6.049 (+/-13.171) for {'hidden_layer_sizes': (100,)}\n",
      "\n",
      "-5.743 (+/-13.386) for {'hidden_layer_sizes': (20, 30)}\n",
      "\n",
      "-7.453 (+/-20.273) for {'hidden_layer_sizes': (13, 13, 13)}\n",
      "\n",
      "Mean Absolute Error(MAE): 25.404255\n",
      "-------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "scores = [\"explained_variance\" , \"neg_mean_absolute_error\" ,\"neg_mean_squared_error\" ,\"r2\"]\n",
    "model = MLPRegressor(max_iter=10000)\n",
    "parameters = {'hidden_layer_sizes': [(100, ), (20, 30), (13, 13, 13)]}\n",
    "for score in scores:\n",
    "    print(\"# Tuning hyper-parameters for %s\" % score)\n",
    "    print()\n",
    "\n",
    "    clf = GridSearchCV(model, parameters, cv=TimeSeriesSplit(n_splits=5),\n",
    "             scoring='%s' % score)\n",
    "    clf.fit(X_train, y_train)\n",
    "\n",
    "    print(\"Best parameters set found on development set:\")\n",
    "    print()\n",
    "    print(clf.best_params_)\n",
    "    print()\n",
    "    print(\"Grid scores on development set:\")\n",
    "    print()\n",
    "    means = clf.cv_results_['mean_test_score']\n",
    "    stds = clf.cv_results_['std_test_score']\n",
    "    for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n",
    "        print(\"%0.3f (+/-%0.03f) for %r\"% (mean, std * 2, params))\n",
    "        print()\n",
    "    \n",
    "    y_true, y_pred = y_test, clf.predict(X_test).astype(int)\n",
    "    \n",
    "    \n",
    "    print(\"Mean Absolute Error(MAE): %f\" %MAE(y_true, y_pred))\n",
    "    print(\"-------------------------------------------------------\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NFCM38d4P3hK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error(MAE): 26.484043\n"
     ]
    }
   ],
   "source": [
    "clf = MLPRegressor(max_iter=10000, hidden_layer_sizes=(100,))\n",
    "clf.fit(X_train, y_train)\n",
    "y_true, y_pred = y_test, clf.predict(X_test).astype(int)\n",
    "print(\"Mean Absolute Error(MAE): %f\" %MAE(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "s8UcLpwfjep-",
    "outputId": "7b7a31b3-6310-46d7-d14d-66928c03e625"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'San Juan Prediction')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_true, label='true')\n",
    "plt.plot(y_pred, label='predicted')\n",
    "plt.legend()\n",
    "plt.title('San Juan Prediction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_RQ1kkj2jeml"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iXxdfxeIjejF"
   },
   "source": [
    "## Iquitos Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 451
    },
    "colab_type": "code",
    "id": "I7ykCiVaBUJD",
    "outputId": "83a55fc0-7425-472c-bab2-012373d80eab"
   },
   "outputs": [],
   "source": [
    "X_train = train_iq.drop(labels=['total_cases'], axis=1)\n",
    "y_train = train_iq['total_cases']\n",
    "X_test = test_iq.drop(labels=['total_cases'], axis=1)\n",
    "y_test = test_iq['total_cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 451
    },
    "colab_type": "code",
    "id": "1FUeDSCzwVOt",
    "outputId": "5f4738cd-3993-4098-c1ae-f3afa7f714fe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
       "       random_state=None, shuffle=True, solver='adam', tol=0.0001,\n",
       "       validation_fraction=0.1, verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MLPRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2Jpd6ElgBUDZ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Tuning hyper-parameters for explained_variance\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'hidden_layer_sizes': (13, 13, 13)}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "-2.731 (+/-8.634) for {'hidden_layer_sizes': (100,)}\n",
      "\n",
      "-2.498 (+/-7.352) for {'hidden_layer_sizes': (20, 30)}\n",
      "\n",
      "-1.140 (+/-3.509) for {'hidden_layer_sizes': (13, 13, 13)}\n",
      "\n",
      "Mean Absolute Error(MAE): 19.015957\n",
      "-------------------------------------------------------\n",
      "\n",
      "# Tuning hyper-parameters for neg_mean_absolute_error\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'hidden_layer_sizes': (13, 13, 13)}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "-25.476 (+/-61.879) for {'hidden_layer_sizes': (100,)}\n",
      "\n",
      "-13.705 (+/-21.399) for {'hidden_layer_sizes': (20, 30)}\n",
      "\n",
      "-11.887 (+/-15.074) for {'hidden_layer_sizes': (13, 13, 13)}\n",
      "\n",
      "Mean Absolute Error(MAE): 21.143617\n",
      "-------------------------------------------------------\n",
      "\n",
      "# Tuning hyper-parameters for neg_mean_squared_error\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'hidden_layer_sizes': (13, 13, 13)}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "-1379.293 (+/-4935.494) for {'hidden_layer_sizes': (100,)}\n",
      "\n",
      "-274.554 (+/-353.955) for {'hidden_layer_sizes': (20, 30)}\n",
      "\n",
      "-217.155 (+/-333.186) for {'hidden_layer_sizes': (13, 13, 13)}\n",
      "\n",
      "Mean Absolute Error(MAE): 19.936170\n",
      "-------------------------------------------------------\n",
      "\n",
      "# Tuning hyper-parameters for r2\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'hidden_layer_sizes': (13, 13, 13)}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "-28.720 (+/-93.111) for {'hidden_layer_sizes': (100,)}\n",
      "\n",
      "-84.345 (+/-333.279) for {'hidden_layer_sizes': (20, 30)}\n",
      "\n",
      "-18.422 (+/-68.098) for {'hidden_layer_sizes': (13, 13, 13)}\n",
      "\n",
      "Mean Absolute Error(MAE): 18.925532\n",
      "-------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "scores = [\"explained_variance\" , \"neg_mean_absolute_error\" ,\"neg_mean_squared_error\" ,\"r2\"]\n",
    "model = MLPRegressor(max_iter=10000)\n",
    "parameters = {'hidden_layer_sizes': [(100, ), (20, 30), (13, 13, 13)]}\n",
    "for score in scores:\n",
    "    print(\"# Tuning hyper-parameters for %s\" % score)\n",
    "    print()\n",
    "\n",
    "    clf = GridSearchCV(model, parameters, cv=TimeSeriesSplit(n_splits=5),\n",
    "             scoring='%s' % score)\n",
    "    clf.fit(X_train, y_train)\n",
    "\n",
    "    print(\"Best parameters set found on development set:\")\n",
    "    print()\n",
    "    print(clf.best_params_)\n",
    "    print()\n",
    "    print(\"Grid scores on development set:\")\n",
    "    print()\n",
    "    means = clf.cv_results_['mean_test_score']\n",
    "    stds = clf.cv_results_['std_test_score']\n",
    "    for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n",
    "        print(\"%0.3f (+/-%0.03f) for %r\"% (mean, std * 2, params))\n",
    "        print()\n",
    "    \n",
    "    y_true, y_pred = y_test, clf.predict(X_test).astype(int)\n",
    "    \n",
    "    \n",
    "    print(\"Mean Absolute Error(MAE): %f\" %MAE(y_true, y_pred))\n",
    "    print(\"-------------------------------------------------------\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NFCM38d4P3hK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error(MAE): 20.925532\n"
     ]
    }
   ],
   "source": [
    "clf = MLPRegressor(max_iter=10000, hidden_layer_sizes=(13,13,13))\n",
    "clf.fit(X_train, y_train)\n",
    "y_true, y_pred = y_test, clf.predict(X_test).astype(int)\n",
    "print(\"Mean Absolute Error(MAE): %f\" %MAE(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "s8UcLpwfjep-",
    "outputId": "7b7a31b3-6310-46d7-d14d-66928c03e625"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Iquitos Prediction')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_true, label='true')\n",
    "plt.plot(y_pred, label='predicted')\n",
    "plt.legend()\n",
    "plt.title('Iquitos Prediction')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QHnaK2QZjeeh"
   },
   "source": [
    "## Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sj_test = pd.read_csv('./data/X_sj_test.csv')\n",
    "X_iq_test = pd.read_csv('./data/X_iq_test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YspZ_aA-jea3"
   },
   "source": [
    "### San Juan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EGHRAxlXjeXO"
   },
   "outputs": [],
   "source": [
    "X_train = train_sj.append(test_sj, ignore_index=True)\n",
    "y_train = X_train['total_cases']\n",
    "X_train = X_train.drop(labels=['total_cases'], axis=1)\n",
    "X_test = X_sj_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9lsaahctjeTN"
   },
   "outputs": [],
   "source": [
    "clf = MLPRegressor(max_iter=10000, hidden_layer_sizes=(100,))\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5S5Kb_z8jeMR"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>year</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>total_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>18</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>19</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>20</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>21</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>22</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>23</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>24</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>25</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>26</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>27</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>28</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>29</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>31</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>32</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>33</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>34</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>35</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>36</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>37</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>38</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>39</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>40</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>41</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>42</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>43</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>44</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>45</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>46</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>47</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>39</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>40</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>41</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>42</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>43</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>44</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>45</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>46</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>47</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>48</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>50</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>51</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>13</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>14</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>15</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>16</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>260 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    city  year  weekofyear  total_cases\n",
       "0     sj  2008          18           41\n",
       "1     sj  2008          19           39\n",
       "2     sj  2008          20           37\n",
       "3     sj  2008          21           38\n",
       "4     sj  2008          22           41\n",
       "5     sj  2008          23           47\n",
       "6     sj  2008          24           45\n",
       "7     sj  2008          25           45\n",
       "8     sj  2008          26           46\n",
       "9     sj  2008          27           45\n",
       "10    sj  2008          28           48\n",
       "11    sj  2008          29           45\n",
       "12    sj  2008          30           48\n",
       "13    sj  2008          31           48\n",
       "14    sj  2008          32           58\n",
       "15    sj  2008          33           49\n",
       "16    sj  2008          34           63\n",
       "17    sj  2008          35           56\n",
       "18    sj  2008          36           64\n",
       "19    sj  2008          37           60\n",
       "20    sj  2008          38           61\n",
       "21    sj  2008          39           56\n",
       "22    sj  2008          40           65\n",
       "23    sj  2008          41           58\n",
       "24    sj  2008          42           66\n",
       "25    sj  2008          43           66\n",
       "26    sj  2008          44           58\n",
       "27    sj  2008          45           70\n",
       "28    sj  2008          46           62\n",
       "29    sj  2008          47           71\n",
       "..   ...   ...         ...          ...\n",
       "230   sj  2012          39           55\n",
       "231   sj  2012          40           59\n",
       "232   sj  2012          41           58\n",
       "233   sj  2012          42           66\n",
       "234   sj  2012          43           61\n",
       "235   sj  2012          44           61\n",
       "236   sj  2012          45           59\n",
       "237   sj  2012          46           69\n",
       "238   sj  2012          47           62\n",
       "239   sj  2012          48           66\n",
       "240   sj  2012          49           65\n",
       "241   sj  2012          50           66\n",
       "242   sj  2012          51           63\n",
       "243   sj  2013           1           23\n",
       "244   sj  2013           2           20\n",
       "245   sj  2013           3           22\n",
       "246   sj  2013           4           22\n",
       "247   sj  2013           5           23\n",
       "248   sj  2013           6           26\n",
       "249   sj  2013           7           26\n",
       "250   sj  2013           8           25\n",
       "251   sj  2013           9           26\n",
       "252   sj  2013          10           31\n",
       "253   sj  2013          11           30\n",
       "254   sj  2013          12           30\n",
       "255   sj  2013          13           33\n",
       "256   sj  2013          14           43\n",
       "257   sj  2013          15           32\n",
       "258   sj  2013          16           33\n",
       "259   sj  2013          17           40\n",
       "\n",
       "[260 rows x 4 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df_sj = pd.DataFrame(y_pred, columns=[\"total_cases\"])\n",
    "sub_df_sj.insert(0, 'city', 'sj')\n",
    "sub_df_sj.insert(1, 'year', X_test['year'])\n",
    "sub_df_sj.insert(2, 'weekofyear', X_test['weekofyear'])\n",
    "sub_df_sj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6bvLsZ4XjeE6"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YspZ_aA-jea3"
   },
   "source": [
    "### Iquitos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EGHRAxlXjeXO"
   },
   "outputs": [],
   "source": [
    "X_train = train_iq.append(test_iq, ignore_index=True)\n",
    "y_train = X_train['total_cases']\n",
    "X_train = X_train.drop(labels=['total_cases'], axis=1)\n",
    "X_test = X_iq_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9lsaahctjeTN"
   },
   "outputs": [],
   "source": [
    "clf = MLPRegressor(max_iter=10000, hidden_layer_sizes=(13,13,13))\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5S5Kb_z8jeMR"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>year</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>total_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>iq</td>\n",
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       "      <td>26</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>iq</td>\n",
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       "      <th>3</th>\n",
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       "      <td>iq</td>\n",
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       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>33</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>34</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>36</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>37</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>38</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>39</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>40</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>41</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>43</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>46</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>47</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>48</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>49</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>50</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>51</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>iq</td>\n",
       "      <td>2011</td>\n",
       "      <td>52</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>iq</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>iq</td>\n",
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       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>iq</td>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>iq</td>\n",
       "      <td>2012</td>\n",
       "      <td>48</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>iq</td>\n",
       "      <td>2012</td>\n",
       "      <td>49</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>iq</td>\n",
       "      <td>2012</td>\n",
       "      <td>50</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>iq</td>\n",
       "      <td>2012</td>\n",
       "      <td>51</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    city  year  weekofyear  total_cases\n",
       "0     iq  2010          26           11\n",
       "1     iq  2010          27           10\n",
       "2     iq  2010          28           10\n",
       "3     iq  2010          29           10\n",
       "4     iq  2010          30           10\n",
       "5     iq  2010          31           12\n",
       "6     iq  2010          32            9\n",
       "7     iq  2010          33           10\n",
       "8     iq  2010          34            9\n",
       "9     iq  2010          35            9\n",
       "10    iq  2010          36            9\n",
       "11    iq  2010          37            9\n",
       "12    iq  2010          38            5\n",
       "13    iq  2010          39            9\n",
       "14    iq  2010          40           -1\n",
       "15    iq  2010          41           10\n",
       "16    iq  2010          42            5\n",
       "17    iq  2010          43            8\n",
       "18    iq  2010          44            9\n",
       "19    iq  2010          45            1\n",
       "20    iq  2010          46            6\n",
       "21    iq  2010          47           10\n",
       "22    iq  2010          48            9\n",
       "23    iq  2010          49            8\n",
       "24    iq  2010          50            9\n",
       "25    iq  2010          51            9\n",
       "26    iq  2011          52            3\n",
       "27    iq  2011           1            9\n",
       "28    iq  2011           2            7\n",
       "29    iq  2011           3           12\n",
       "..   ...   ...         ...          ...\n",
       "126   iq  2012          48           10\n",
       "127   iq  2012          49           10\n",
       "128   iq  2012          50            9\n",
       "129   iq  2012          51            9\n",
       "130   iq  2013           1           10\n",
       "131   iq  2013           2           13\n",
       "132   iq  2013           3           13\n",
       "133   iq  2013           4           12\n",
       "134   iq  2013           5           11\n",
       "135   iq  2013           6           10\n",
       "136   iq  2013           7            5\n",
       "137   iq  2013           8            7\n",
       "138   iq  2013           9           13\n",
       "139   iq  2013          10           14\n",
       "140   iq  2013          11            9\n",
       "141   iq  2013          12            9\n",
       "142   iq  2013          13           12\n",
       "143   iq  2013          14            9\n",
       "144   iq  2013          15           12\n",
       "145   iq  2013          16            8\n",
       "146   iq  2013          17           13\n",
       "147   iq  2013          18           10\n",
       "148   iq  2013          19           10\n",
       "149   iq  2013          20            9\n",
       "150   iq  2013          21           10\n",
       "151   iq  2013          22           10\n",
       "152   iq  2013          23           13\n",
       "153   iq  2013          24           14\n",
       "154   iq  2013          25           12\n",
       "155   iq  2013          26           11\n",
       "\n",
       "[156 rows x 4 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df_iq = pd.DataFrame(y_pred, columns=[\"total_cases\"])\n",
    "sub_df_iq.insert(0, 'city', 'iq')\n",
    "sub_df_iq.insert(1, 'year', X_test['year'])\n",
    "sub_df_iq.insert(2, 'weekofyear', X_test['weekofyear'])\n",
    "sub_df_iq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = sub_df_sj.append(sub_df_iq, ignore_index=True)\n",
    "sub.to_csv('Submission.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>year</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>total_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sj</td>\n",
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       "      <td>18</td>\n",
       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sj</td>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sj</td>\n",
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       "      <td>20</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sj</td>\n",
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       "      <td>21</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>25</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>27</td>\n",
       "      <td>21</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>32</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>33</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>34</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>35</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>36</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>37</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>38</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>39</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>41</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>42</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>43</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>44</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>45</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>46</td>\n",
       "      <td>33</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>47</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>40</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>44</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>45</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>46</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>47</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>49</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>50</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>sj</td>\n",
       "      <td>2012</td>\n",
       "      <td>51</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>sj</td>\n",
       "      <td>2013</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>260 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    city  year  weekofyear  total_cases\n",
       "0     sj  2008          18           19\n",
       "1     sj  2008          19            5\n",
       "2     sj  2008          20            4\n",
       "3     sj  2008          21            2\n",
       "4     sj  2008          22            3\n",
       "5     sj  2008          23           20\n",
       "6     sj  2008          24           14\n",
       "7     sj  2008          25           28\n",
       "8     sj  2008          26            6\n",
       "9     sj  2008          27           21\n",
       "10    sj  2008          28            0\n",
       "11    sj  2008          29           13\n",
       "12    sj  2008          30            4\n",
       "13    sj  2008          31           20\n",
       "14    sj  2008          32           23\n",
       "15    sj  2008          33           14\n",
       "16    sj  2008          34            7\n",
       "17    sj  2008          35           14\n",
       "18    sj  2008          36           53\n",
       "19    sj  2008          37           30\n",
       "20    sj  2008          38           30\n",
       "21    sj  2008          39           21\n",
       "22    sj  2008          40           39\n",
       "23    sj  2008          41           12\n",
       "24    sj  2008          42           38\n",
       "25    sj  2008          43           55\n",
       "26    sj  2008          44           14\n",
       "27    sj  2008          45           16\n",
       "28    sj  2008          46           33\n",
       "29    sj  2008          47           15\n",
       "..   ...   ...         ...          ...\n",
       "230   sj  2012          39           33\n",
       "231   sj  2012          40            7\n",
       "232   sj  2012          41           18\n",
       "233   sj  2012          42            4\n",
       "234   sj  2012          43           10\n",
       "235   sj  2012          44           12\n",
       "236   sj  2012          45           37\n",
       "237   sj  2012          46           38\n",
       "238   sj  2012          47           33\n",
       "239   sj  2012          48            2\n",
       "240   sj  2012          49           27\n",
       "241   sj  2012          50           34\n",
       "242   sj  2012          51            6\n",
       "243   sj  2013           1           13\n",
       "244   sj  2013           2            6\n",
       "245   sj  2013           3            0\n",
       "246   sj  2013           4            6\n",
       "247   sj  2013           5            5\n",
       "248   sj  2013           6            6\n",
       "249   sj  2013           7           16\n",
       "250   sj  2013           8            4\n",
       "251   sj  2013           9            2\n",
       "252   sj  2013          10           14\n",
       "253   sj  2013          11            4\n",
       "254   sj  2013          12            1\n",
       "255   sj  2013          13           15\n",
       "256   sj  2013          14           10\n",
       "257   sj  2013          15            3\n",
       "258   sj  2013          16            9\n",
       "259   sj  2013          17           22\n",
       "\n",
       "[260 rows x 4 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train_sj.append(test_sj, ignore_index=True)\n",
    "y_train = X_train['total_cases']\n",
    "X_train = X_train.drop(labels=['total_cases'], axis=1)\n",
    "X_test = X_sj_test\n",
    "\n",
    "pca = PCA(n_components=5)\n",
    "pca.fit(X_train)\n",
    "X_train = pca.transform(X_train)\n",
    "X_test_pca = pca.transform(X_test)\n",
    "\n",
    "clf = MLPRegressor(max_iter=10000, hidden_layer_sizes=(100,))\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = np.abs(clf.predict(X_test_pca)).astype(int)\n",
    "\n",
    "sub_df_sj = pd.DataFrame(y_pred, columns=[\"total_cases\"])\n",
    "sub_df_sj.insert(0, 'city', 'sj')\n",
    "sub_df_sj.insert(1, 'year', X_test['year'])\n",
    "sub_df_sj.insert(2, 'weekofyear', X_test['weekofyear'])\n",
    "sub_df_sj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>year</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>total_cases</th>\n",
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       "      <th>6</th>\n",
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       "      <td>32</td>\n",
       "      <td>19</td>\n",
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       "      <th>7</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>34</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>35</td>\n",
       "      <td>13</td>\n",
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       "      <th>10</th>\n",
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       "      <th>11</th>\n",
       "      <td>iq</td>\n",
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       "      <td>37</td>\n",
       "      <td>19</td>\n",
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       "      <th>12</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>38</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>39</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>40</td>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>41</td>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>42</td>\n",
       "      <td>17</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>43</td>\n",
       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>44</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>46</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>47</td>\n",
       "      <td>17</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>iq</td>\n",
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       "      <td>48</td>\n",
       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>50</td>\n",
       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>51</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>iq</td>\n",
       "      <td>2011</td>\n",
       "      <td>52</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>iq</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>iq</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>iq</td>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>iq</td>\n",
       "      <td>2012</td>\n",
       "      <td>48</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>iq</td>\n",
       "      <td>2012</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>iq</td>\n",
       "      <td>2012</td>\n",
       "      <td>50</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>iq</td>\n",
       "      <td>2012</td>\n",
       "      <td>51</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>iq</td>\n",
       "      <td>2013</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    city  year  weekofyear  total_cases\n",
       "0     iq  2010          26           14\n",
       "1     iq  2010          27            6\n",
       "2     iq  2010          28            4\n",
       "3     iq  2010          29            6\n",
       "4     iq  2010          30           15\n",
       "5     iq  2010          31            5\n",
       "6     iq  2010          32           19\n",
       "7     iq  2010          33            7\n",
       "8     iq  2010          34           19\n",
       "9     iq  2010          35           13\n",
       "10    iq  2010          36           15\n",
       "11    iq  2010          37           19\n",
       "12    iq  2010          38            7\n",
       "13    iq  2010          39           26\n",
       "14    iq  2010          40           15\n",
       "15    iq  2010          41           15\n",
       "16    iq  2010          42           17\n",
       "17    iq  2010          43           13\n",
       "18    iq  2010          44           10\n",
       "19    iq  2010          45            9\n",
       "20    iq  2010          46           11\n",
       "21    iq  2010          47           17\n",
       "22    iq  2010          48           14\n",
       "23    iq  2010          49           17\n",
       "24    iq  2010          50           19\n",
       "25    iq  2010          51           14\n",
       "26    iq  2011          52            9\n",
       "27    iq  2011           1           11\n",
       "28    iq  2011           2            6\n",
       "29    iq  2011           3            4\n",
       "..   ...   ...         ...          ...\n",
       "126   iq  2012          48           14\n",
       "127   iq  2012          49           12\n",
       "128   iq  2012          50           20\n",
       "129   iq  2012          51           17\n",
       "130   iq  2013           1           11\n",
       "131   iq  2013           2            8\n",
       "132   iq  2013           3            6\n",
       "133   iq  2013           4            1\n",
       "134   iq  2013           5            9\n",
       "135   iq  2013           6            1\n",
       "136   iq  2013           7            2\n",
       "137   iq  2013           8            5\n",
       "138   iq  2013           9            8\n",
       "139   iq  2013          10            2\n",
       "140   iq  2013          11           16\n",
       "141   iq  2013          12            6\n",
       "142   iq  2013          13            4\n",
       "143   iq  2013          14            7\n",
       "144   iq  2013          15            2\n",
       "145   iq  2013          16            4\n",
       "146   iq  2013          17            3\n",
       "147   iq  2013          18            4\n",
       "148   iq  2013          19            8\n",
       "149   iq  2013          20            5\n",
       "150   iq  2013          21            4\n",
       "151   iq  2013          22           10\n",
       "152   iq  2013          23            2\n",
       "153   iq  2013          24            3\n",
       "154   iq  2013          25            0\n",
       "155   iq  2013          26            4\n",
       "\n",
       "[156 rows x 4 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train_iq.append(test_iq, ignore_index=True)\n",
    "y_train = X_train['total_cases']\n",
    "X_train = X_train.drop(labels=['total_cases'], axis=1)\n",
    "X_test = X_iq_test\n",
    "\n",
    "pca = PCA(n_components=5)\n",
    "pca.fit(X_train)\n",
    "X_train = pca.transform(X_train)\n",
    "X_test_pca = pca.transform(X_test)\n",
    "\n",
    "clf = MLPRegressor(max_iter=10000, hidden_layer_sizes=(13,13,13))\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = np.abs(clf.predict(X_test_pca)).astype(int)\n",
    "\n",
    "sub_df_iq = pd.DataFrame(y_pred, columns=[\"total_cases\"])\n",
    "sub_df_iq.insert(0, 'city', 'iq')\n",
    "sub_df_iq.insert(1, 'year', X_test['year'])\n",
    "sub_df_iq.insert(2, 'weekofyear', X_test['weekofyear'])\n",
    "sub_df_iq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = sub_df_sj.append(sub_df_iq, ignore_index=True)\n",
    "sub.to_csv('Submission_pca.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
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