generate_predictions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "05711d36-06ff-4a7b-ae9a-b681fb23f348",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import csv\n",
    "import geojson\n",
    "import numpy as np\n",
    "from keras.models import load_model\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "60b341fc-426b-4e69-bbc6-49be3347b812",
   "metadata": {},
   "outputs": [],
   "source": [
    "co_model = load_model('MachineLearning/Models/co_model.h5')\n",
    "dewp_model = load_model('MachineLearning/Models/dewp_model.h5')\n",
    "no2_model = load_model('MachineLearning/Models/no2_model.h5')\n",
    "o3_model = load_model('MachineLearning/Models/o3_model.h5')\n",
    "pm10_model = load_model('MachineLearning/Models/pm10_model.h5')\n",
    "pres_model = load_model('MachineLearning/Models/pres_model.h5')\n",
    "rain_model = load_model('MachineLearning/Models/rain_model.h5')\n",
    "so2_model = load_model('MachineLearning/Models/so2_model.h5')\n",
    "temp_model = load_model('MachineLearning/Models/temp_model.h5')\n",
    "\n",
    "path = os.path.join(os.getcwd(), 'MachineLearning', 'Resources', 'all_city_data.csv')\n",
    "df = pd.read_csv(path)\n",
    "df = df.drop(columns = {\"Unnamed: 0\"})\n",
    "features = [\"PM10\", \"SO2\", \"NO2\", \"CO\", \"O3\", \"TEMP\", \"PRES\", \"DEWP\", \"RAIN\"]\n",
    "scalar = StandardScaler()\n",
    "# the fit_transform ops returns a 2d numpy.array, we cast it to a pd.DataFrame\n",
    "standardized_features = pd.DataFrame(scalar.fit_transform(df[features].copy()), columns = features)\n",
    "old_shape = df.shape\n",
    "# drop the unnormalized features from the dataframe\n",
    "df.drop(features, axis = 1, inplace = True)\n",
    "# join back the normalized features\n",
    "df = pd.concat([df, standardized_features], axis= 1)\n",
    "assert old_shape == df.shape, \"something went wrong!\"\n",
    "grouped_df=df.groupby('dt').mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "6a891434-9199-419d-9957-e6e050b06d61",
   "metadata": {},
   "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>dt</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-03 00:00:00</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>-1.080358</td>\n",
       "      <td>-0.444993</td>\n",
       "      <td>-1.086331</td>\n",
       "      <td>-0.842661</td>\n",
       "      <td>0.269844</td>\n",
       "      <td>-1.325402</td>\n",
       "      <td>1.238340</td>\n",
       "      <td>-1.703139</td>\n",
       "      <td>-0.080522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-03 01:00:00</td>\n",
       "      <td>7.222222</td>\n",
       "      <td>-1.047856</td>\n",
       "      <td>-0.450193</td>\n",
       "      <td>-1.071621</td>\n",
       "      <td>-0.825829</td>\n",
       "      <td>0.245656</td>\n",
       "      <td>-1.337020</td>\n",
       "      <td>1.308589</td>\n",
       "      <td>-1.703547</td>\n",
       "      <td>-0.080522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-03 02:00:00</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>-1.105939</td>\n",
       "      <td>-0.343597</td>\n",
       "      <td>-1.113193</td>\n",
       "      <td>-0.830779</td>\n",
       "      <td>0.304674</td>\n",
       "      <td>-1.403971</td>\n",
       "      <td>1.261254</td>\n",
       "      <td>-1.765618</td>\n",
       "      <td>-0.080522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-03 03:00:00</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>-1.097041</td>\n",
       "      <td>-0.360757</td>\n",
       "      <td>-1.051794</td>\n",
       "      <td>-0.815927</td>\n",
       "      <td>0.289000</td>\n",
       "      <td>-1.410469</td>\n",
       "      <td>1.384539</td>\n",
       "      <td>-1.830302</td>\n",
       "      <td>-0.080522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-03 04:00:00</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>-1.112613</td>\n",
       "      <td>-0.313959</td>\n",
       "      <td>-1.046038</td>\n",
       "      <td>-0.798105</td>\n",
       "      <td>0.282034</td>\n",
       "      <td>-1.447685</td>\n",
       "      <td>1.456186</td>\n",
       "      <td>-1.808250</td>\n",
       "      <td>-0.080522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    dt     PM2.5      PM10       SO2       NO2        CO  \\\n",
       "0  2013-01-03 00:00:00  5.800000 -1.080358 -0.444993 -1.086331 -0.842661   \n",
       "1  2013-01-03 01:00:00  7.222222 -1.047856 -0.450193 -1.071621 -0.825829   \n",
       "2  2013-01-03 02:00:00  4.500000 -1.105939 -0.343597 -1.113193 -0.830779   \n",
       "3  2013-01-03 03:00:00  4.600000 -1.097041 -0.360757 -1.051794 -0.815927   \n",
       "4  2013-01-03 04:00:00  3.800000 -1.112613 -0.313959 -1.046038 -0.798105   \n",
       "\n",
       "         O3      TEMP      PRES      DEWP      RAIN  \n",
       "0  0.269844 -1.325402  1.238340 -1.703139 -0.080522  \n",
       "1  0.245656 -1.337020  1.308589 -1.703547 -0.080522  \n",
       "2  0.304674 -1.403971  1.261254 -1.765618 -0.080522  \n",
       "3  0.289000 -1.410469  1.384539 -1.830302 -0.080522  \n",
       "4  0.282034 -1.447685  1.456186 -1.808250 -0.080522  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "236bbbef-38d0-49ef-83e0-38baf9bcd8b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33423"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(grouped_df['PM2.5'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "f27ab286-671d-4690-9516-8606035a99b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z1bE4vVbKWo8LU3nwCyfSiPdT8AAB7803iV37ADSyYlgXJViFVI2ohO0CqNWYrfPWlnybceu/v3J2UIv0vVRQkdoKdH/QKhkW7UedEKLUx12CVEcoMVQKRNSLiDYT0U4i2kNET8nws4loExHlENFbRNRDhveUv3Pk+VEuv0MS7UKotiC35JVj1JQlKKyM/4APFtdgpontK4PQnTYLURiqdvtEG1cLt+bjYo01DkP69bQVt9GMl9+9uyd2bKbHYpa8Mm/3xjYyH/mB18+27V7dATljXlJTj8pRzPQUmgDcKIS4DMDlAG4joqsB/BXAdCHEeQAqAEyS108CUCHDp8vrPKHDFgr838pDSedf33QMAJJmKdwyfQ3+snQ/ckpqXJfRS5TlPUT6zCSRzF6xr0Tzip35la48uay2KXbcMeCdegLnSqXgVcVYI3s8+eXqvZy0KzIKnErjVKbMBjV9DZWCiBCdpN1d/gkANwJYKMPnAbhbHt8lf0Oev4k8MppFHyIgkC03sLeC0UpSK5kYpPnVphM/OCIbEi1RTlTGdp8NKFdWey5GykRbqn17dosLt7oGxM2vO6iNmVhdY1E+5SrxUC9eI6KuRLQDQAmAZQAOA6gUQkSNqwUAhsvj4QDyAUCerwIw2EGZ9eRE5LnqmWVUyA+ciO8pCCGwfG9xIGa4WEX5qpbXKQSskKqhHFNIpKSmMaVen9GYgvJsFxd9MLnN7sKI2/Ee3eKrAb20ZazxjVkbMWnultjvD3ef8FEac5hSCkKINiHE5QBGALgKwJhUH0xEk4koi4iySkudmX+t1N56LXWtSu/sIX3ifs9eewSPzM/C3PV5lmXxu44QiK+8zPRcwlQHrD1Upnnu2mkrcfM/1tiO2yitlB3f6JGVRXTrc8rQ0Nym/XyPMuLfGyOrcdflxKdlGBoFYWH94ZNYsb/DxNnU2pHvUX9aftcViViafSSEqASwCsB4AAOJKNrvHAGgUB4XAhgJAPL8AABJdhkhxEwhRKYQIjMjI8Oe9AnExhQg1HsKBvcr79l8pDzmBjtxYNoMActnS4ShTmho0a5UW9rcTf14ZRvBbG8yt7QW35i9Cb95Z5fjctnl9P69VMMFhCeOAPUIkhk2DlnZFFU12rYkzFgeGff8NEe7geMHZmYfZRDRQHncG8AXAexDRDncIy+bCOBdebxY/oY8v1J41LdWLs/Xe6KWeUBZAPef6NjRKxaXh+XTj4o5jCaQVGhpa8f0ZQdR32zNAV0qLelq6Vo9p8R9X0p2UX5HhR5PtQ1DgwTokHPl/hI8vyonpbiaWoM1IGWmpzAMwCoiygawBcAyIcT7AH4N4DEiykFkzGCOvH4OgMEy/DEAU5wXWwPF4KPaAKRRnWd43oJWCGMF69Rq6rCwICsf/1xxCM+tiP+orXjqtD2l0YU4nUI5i08504pRZ52OKTMRtU8raHVFN6MLhBDZAMaphOciMr6QGN4I4GuOSGeRjtlH+o16rTpP2QuMH6j1LtPcKh9W4g1aIXWLppZIC61RxxSlTkcB0vKSq4UVb7VBYOV+7Sm/XhDUoqjMbiv1g1qDI2ivmFYrmpUtHLWUNhxT0LgiWjAtVazmL3UHG19T4i1bj1Zg/NMrUOOzXdlrjAeaHXiIyUiqGlqwy8b06ii3zViD71t0waH0fdQ1Qc7VB0qwJLso4frO0bNUEpZ9H+xg2FMIEx22UHXzkRFOtkrsxlVu0bHa8coG9OnZDQN6d7f3QBWiov/94wMoqmpEdkEVrj1viGPx22Hr0QpcedYgX2WIElcdxBoM7jQDHpizydaamyj7T9Rg/wn96bnJK5rld6Ry7qFXtyCdsGsFcFIPBq03lJY9hbrmNmQdrXAs3ugH70XmNctVUGYKXVNrG66ZthKXPfVx0rn8igYckStk7cptdF9zaztumf4J1h5yfzOW6K5kqeDUTJp400E0LLVa4pypSzriVCR8KgrBLImSK6d2R9dh+IVbn1z6tvNTJ72Ugvz/o9e32brfaGpZwBQ6Dp7QnsFSXteMOXJvCauYVSKFlQ04WFybtElLUFljcj+CjbnluufdMB3EjWc5Hrs+Ws8jQpL5iIkQNy3ZQoapJacfq/L1SCulECW6UtMqWlnjd5aV1Tbhw91FSeGT/53l6HMSu9LpVh90ceiF4noKFguHmcutxvnjN7Zj1JQlxhdqkLRBlOLYTJrNXJNr+9nJwnhT6Jz8pv2uH5wmLZWCFkZ237gZR4ofHQPN1rwfOcXEVzbje//ZljTgW1RlzqSi9Z3NXpuLvcfVFKg52b2cpeREXeGUJcTuzJO4OAzOz16bi0fmmVP6i3cetyWDFtEec1FVo//mI5fLmN1en6NjCs5F5QhpNdBsNnG17L+as49sZJuTZTm/vB4A0G5zjYvWYr4/LYms2M6bdkfsOjt4M/sk9We4IadVp2bm0ljE8sZv0qyz6BiOlqWAaYW0UgpGGKW90q6r5irB0S6nEGgXHf74H//fLny8p9jBJ9gnYCZOVYb274niamsLqxwzHzmioLTPKdOfyPv8UNq4/TYhBrUoOpksQXPl0anMR1GUGVpZr5gCqvgYon6P7GKUzWdPXYpzH18a+/36pmNxq0f9mAfthn3cKRIrp1svOt1yHF0tlPaqep2ZSiqzj8xj7Q6nFJnuMxJMRGFoFPiNsqeQqokraOnduZSCSuL/5M0deqcj4TYyLdWMVms9eNWisPqUsJgYrCjay/7wseZOaPEzT+JT668f7set0409tOq6uTB5XSq0twvcc+UIAMBXxg2PPxe0WiqAqDlFTBfSRikUVzeiXscdsRJl46uoqsPhl9bHEPOH50Lup9LKsNKINKNQSmvs+bkJy0dhtdF9VI7lJMejbCXGn3tx9WEcKK5Bbmktnv1of1L+Ws1ut3oK05cfxMKtBQCS91NQiui1fkh8240GG1/5BS9eCwHXP7Oq44eJDDte2YCtR8sTZhypX2uvp2B2Bo96eG5pnSVHW04QXTgXVY5BKqx2vsFhA+JdQlt1qa31TDOyPDx3C15YdRjHTc4QUzK4T4/YsVut9v9tL9Q8F6R8X7RNW04ncGJF8x7VGXxa96n5PgpQgiONlEKc+1mtyl1x4oZnV+OrL26IbxUZPsX5zNP66L85exO+NWcTAJ3ZUi6VpdtmrNW3qSfgydwjG02zscP6x/1+a8sxi8+0Fq6kVSogO772+/bqmP/R6sH2Z4lp64dDxAuG9vP0eamWWaUpstmk6+ttxyrw4ze2p/hk9+lUs4+iECjWKlai/S2495G0CWGYCU58pFajKK1txDFpPtFyn+xl3eGE4rEqr9YYhNpq1sQru3TRf6YTUxpLahpdKZp2omxubU8yQ1lBKzmcdFfjJHay7z2N9SRB6pkBadRTsEv8IjWNMQUR/99UvKafbz5O+1h/SE5JXWzHufezk1dTxxHQkebEt3ZqIVbcmIJG2kYVipuDtlf9eQWu+ssKzfN6jQm9Sk0ps1npz//tByav1GdHfmXc76Du52BHqYfFs2paKAXlYDEAS5WUGfORmxW3VzM9rJohvqdwtxyEopzkydNGHFZ1gqb5yMazo7jh5sJp4p7voTAtKr33oGKnDKRijvSStFAK459eGR9gZcBYw7VF/CVCL1rzz0rhutQgfLDLoLWvd7dmoQ2Xm4uuFrWC5jNN+D7q2HzHMArXsLMlLZDQUAlajRUQ7DTmDharuzD3uxGQSFooBauouT4G4lc0q+FG5pkpXE7Yn416CilNjbV9p7skpm3XLtaKu/aYgtJ8pE7HWEP4Fob51VNwgwMnavD0B/scHTxvbxdx65sS2ZFfiQMqe1is9Xg2oV1CrRT2FVWre4dU+ZaX7y029Kfv6JRUk61oDyaXmELvHY3myvvxCmYUZVtC4nZ1SHu9va0gdhytbJJn8ECGq8fhRQPcbr74oQfc8p/1jVkb8fInuaiwMJvOiDaDBLr7hU9x6wzjBYxBJdRK4dMcc5r3y/+3Do/Mz8KnOckLYRoUC960HeLpn08FbwbSRCArdivYGaRL/HatDjTrJdlXX1xvWR7An+meaui9W2Gl+qK9MBKtwO2onPrmVsxYfjBUYx1OEGqlYJZdhfG7VykLyAnFjl52egqpfuMHDbZKfGFVjiPPMmqI6VVWRgolevbXC7M1/fr/auFOrNjnr8O/xA1jjJIzsaehZKucKmkUh58mebMKKFFG5YKxYKiw1LHzHjOWH8KM5Yfw9tYC44tVGPOEMzOyvKZTKAWz+NGKM3risx8diOvN2MO4na0nh1YLOzG53srK14xjQVYBJpncH0AVk5WrctvOxMpOa6BZq+L+5uxNhs/Tbkj4W53uyK9UNU2uOlCCXQVVccmZWDq8WDCXSGGFfu/kg11F+NP7ey3Hq1VsDpVEdi3Uy6b65lYAUF3TpGTQKer7oze2hLOHEWqlYNcOqXWbo5+Cycj0WqNOYtxT0D4X50lWeU8sbvP5oOyKbz5Sjs1H9Le+jGLmCX16dNWdt+/OhjH6Jkdv9pqIZ0l2Ee5+4VPMXpe8I9rDr27Bl59fp1s8b71oqHvCaVDd2Kp7/vuvbcNsm9vLqjF/w1HDa5QLE0trmmK+ohIZeEoP1XCzBK03Fmql4DTaDvHklFSfZh9FWypb8sxVoMkIw75CKhsJWan25q3Pix3f+/IG3PvyBsvP1aLOoEfl5X7Damkzae4W3DdzowzXliXVcvao3KP8mQ8PWLpv1f4SjJqyBNUNHRW0lx0eN5+l1XPT3ddCcdF35mfhF//diRMqvqyCOvvOLp1UKahno9pANBDZ8L2wssEVjW7lQ9jq4pJ/PTmGD+ytfo/BXHw1zHqyTSSxxW2nfre6TsEMZtcp1DS2YMX+ko4LfK5Jjp5MNtcskk7y7Dc+1Pnh69vwt4+sKSg9sgsqLYyX2E/oxJ4CALS2tyfledBa+qliqBSIaCQRrSKivUS0h4h+IsNPJaJlRHRI/h8kw4mIniOiHCLKJqIr3BLe6e/qiIb//LLaZlM+8pU0mXSSNVDDHqlKKi+cwr09NXzaaM3Fb2lrx0mNWVV+1oVuuKGOmYkSwxPS5tVP8xx5Xk6J/sQELeasO4IZyw+qnosmi1vjIO9nF+F5xYSJVNiYexJ3Pv8p5jhoStLGeqPH1lMCplXM9BRaAfxcCDEWwNUAHiWisQCmAFghhBgNYIX8DQC3Axgt/yYDeNFxqX2gtqnV0kfz8Nwtpq7r1b2rXZEsYTjQrPNqWrucac3Fn/L2Llz5p+WOTuVz4rtM3Hkt1UqwqbXN9AedKL/d9zlYXGvrvj++vxczlh8yda3WK7k9NdOMCbOiLjK+ZbU3oxfzyv3FquNmao0eIZLl7HTmIyFEkRBimzyuAbAPwHAAdwGYJy+bB+BueXwXgPkiwkYAA4lomNOCpwtWB5oPl9bi2mkrLW2Is3TXCcNutN4HqTn7SOOe97Mj3iDV3s2pVpeddQtFGnsb2HVU9t7OItRoLIhMfHOnNkRyswKKlpG4PUYUsqwzuS7ITaJl0ax+MkqvyvoWfHtuFr4zX3tmXGfz9GFpTIGIRgEYB2ATgKFCiKhDnRMAolMWhgNQzk0skGGOE4TM+vvHB9DYYmwnb21rxzEVO66l6X8CmL02F4WVDfh47wnTt1U1tGDP8Srda/T95OjfY8VuuyO/CusPl1lupTuR11Fvr0dP1qG2SX+2ixmW7y3GPS+pD5QnKm0nxkTU4nEStTy5ZPiA2LHR3hDfmLUR9728wdYeEpHnq4dnF1TGjo0djFgjuu/FkbLkb9NJDwefHCzVORss+5Hp/RSIqC+AtwH8VAhRnbBxtSAiS29GRJMRMS/hzDPPtHKr4rm2brP9Qar5O5m97ggG9TGekvbXD/dj1toj2DD1Rgwb0DFw28umD3qrrdvuBrvW6yWlUUWkd7a1rR0FFR1ebJfvK8byfcX45/2X68aZ/AzrmaYl9+efXY2LzuiP/yf3Jq5vbsWibdYXKGUdtT8ga7d34sas2uQoO0pDT4V506iuXy+3zmxua0evLs6ZRe98/tPYcTRPtSaFaKG9r0XsiuR7omMKlp6kzsRXNjsQizeYqpGIqDsiCuE1IcQiGVwcNQvJ/9GpFYUARipuHyHD4hBCzBRCZAohMjMyMuzKr47HitfMoHK0EJ+sjbddDrAy0Az7itCoZW7Hvh69JdHPnDKmZz46gBv+tjrp3nyN/Y/NYkax672TcgvFN7fk47EFOy3L4IczUbf2bFYS7xCv49CsZ1AhgH9vNF4HYIfo2zeY6J0DxvnSMciefM7If1W6Ymb2EQGYA2CfEOIfilOLAUyUxxMBvKsIf1DOQroaQJXCzOQotrvgzoqREhNf2azpGiIRAb3B3Wws36vtRsLoe9bvKaiHRx3D6bV6N+Y6s/F6ED9MM1VktLVppTLXNeV57EgvbsMdC+2GJ97Z7ZxACuy+v9FmSGpnRcI1WuRqzFo0S0a/XsYXeYgZ89G1AB4AsIuIdsiwxwFMA7CAiCYBOArgXnluKYAJAHIA1AN42EmBzWC0LN1xUpjFUlxtzyFeYjF9c0s+3tyi7WbCCCuv0NjShtqmVsyVC9GCWGH7gVrPZPzTK/HUnRc5lkZe9BSUxL+S/7Zv6++f+joFUHyPwukppI987mxnI0wRQ6UghFgH7ZS9SeV6AeDRFOVyFT0beUNzm+GgrJ/Y9dRqeJeFgeb7Zm7ETsW2iXqfndEgtR6rD5RonjPzqXvtYkJrJtnvF+/BlNvHxIXpifazt3Zon3RhTUGiLHFb1Dr2FKC8rhmnmhh/08VmllbVt6ChuQ1nDe4TF75B9mTV0lM5pmBkSqpubMEt/1iDGy88zbJsXq60N0OoVzS7kZS/XLhTc0aJFlqrdJ1eDKSMz+lypKdsvjVnE8Y88UFsxs7OhH10QYTrn1nV8VP+r0thhs+Gwyfx0Ksdaz2Kqxux4bA1U5SWz6YoZl2vm0VvINZKdumZIzbmnsR9L29w1GmdrgnFpvlIjUMaO485Fb8eX5y+Bp9/dnVSeLlc96CanDIsMT/UvpUV+4pxoroRr286lqqovhNqpeAGe4uqjS9KQMtRl9YKabuk0nU1HFPQOd/Y0o7GlnYcKq5R9dhKAI4pBo6jUUX9/KhhpNRKE1ZEP7ZgJ74+Szs+NbIL9Ht8qw7oTROM8OxH+01OXyXdgdikPaYpMjPrycV7UFKjvn5CjZc/ycWmI+VJExacRGOcGduOVTgWr10sG49sNp6aWttiTvpeXH0YhZUNutd7bdZzE9NTUhnrKD8Cp8qM2cEvu/Ea8cS7yQOIWu+WU2Jv9a0ZvPoGX1h1GA3N6mNUiT1BK3siEwirD5Ri7vo8WzOx3NjwKSqhstWsfKcLh/V3JH4tzLyTWybBxLz81uxN2JKnrgTV8jkVv1r+j9TEE2ql4IdbYis43R2Oi87iq+t9cEuyi9Dabjw4X1TViKMnk3s/SaI48N5+70egpLnN3PRHq5u5R3cFK7GwOj2KG8mjHmVw8gFwb+Zg4ltqKQQtuKcQcnSzL1jfgCZGO7YloleJRF0tG/GD17Zh5KnJHlPtKOcA1fmOYcV8tC6nLOY2ordH/q+00HUfrXil6OpfI+z0Ys4Z0sf4IlgfBzJdMlMoj4NO6Z6SUgiaOuExhQRSnXMcj9MDzR0fqZMbjlghvzzZtppYqN0wbURx0ySlhdmNgHQHmh1uSbqpU7VmH320x7xrFat060qmGgovr0neOMgJrKRn4rUXDx/gilt2v+iUSsGrnp47XXybU1JdbJo72XXeXVilK+vN//gE6w+XeWo6rDXYFSyKns+fKoOZUEFFmRVx+0HYQC/PDhbXIt9gS86gQkRJHnjDTBq9SvBQn+KXSnwBtbnYqJ/VHIStOlCCL/3fOizIytf1HnukrM7TMQezCmi9zpTZ51bq7CdgI/3cfH8tL6mpx6sf16S5KezhrUFi1pXWNOGBOcl7b1tJz8RrCak1jII2HNE5xxR87Cmk/Gy7U1JTfKwedl4pS2UXuWLp2nrOuiO6+wY4PfPKLso0LattMj0248izHczQFp2xAjvLIezKZsbbsBn+vGQvZq09grxpdySdm7PuCNYeSh6XMCtyQUVD0rokIh5oDgxBzwenW/apVAQ1Jk0gdtiUYHPXq2T0iNplzWwk46X5KOjlLFW6JdjDo+X2ojP6O7xy2sDbrkPpPGttx3hbYgOiqEp9vYHZ11RbK5NyTyEgjZwobD7yCKe+LbvRuKkUnCKoM5LSXil0TVAKBi4djDhRbX4xnpKmFvd9lr2747hquLIBZ3U/iC5Errg094tOqRS80syuDDTbjjSgNa6CwI6ZuMze49ZX0buJchtKO8Xtpr9/Yuu5NQ5sfOQET723x9L1RNq7E4aRUCsFu9nglRdVp5VCc1s7KhvUt3/0WhY32Ha00tR1B4trsLswuE4LrWJnFzg38zPqJI4o3IpaCJHU2+lpYlOrNzZb9TZMPNAcdtzegDyKcjGTExmfirOtVKcTesFbWeY+xqjLbq8Ims0XcLay1nu7MDQmlChdhqjJrrUhljKPraZtZKDZ0i2BJtQ9hbCQXVAZt9MXEy60FLrdvYidYPqyg0lhzSZ2ANRF5XXCphQ+p/DW65XoBDYfBYeg9bs0WKlopU9dtMtHSRgnaUy1Ek6Bd1QGTLVm1pglsRVNcLZi9fpzfX3zMU/6eJGB5nDURWYIt1Kw2Yzx2hygNFftSiNbeGdBq7T06xks62uq5Xr5voTtXIkcnZJ6srYZUxdlOxafEftsuMG3Q7qZj4JVqtOM6Pf0wqrD/grCxHBzxzK/cVoep3sK3/vPVgdjM0YIgeNV9qbHWqGgogHHK+0/J2jlKNw9BZusSGwRucSnh9U9OuaUWPNwyviL9i5nAfuabaK7qCxkYwpK9Fyl6GG13bCrsMpzhecm4VYKNlXsou2FDguizrQP9quG3/yPNZ48n0nGTkehoELdVl9Wa30fBDcptrloTA+re0R4xflD+xpesyCrwANJ0o9wK4UQ4JQ/F8YZnJwFNqB3d8ficgI1J4OpEFmnEHze3eFsIy8M7+wmrBRcZtleb0xVjDlW7HcuP6psLiR0C6eNWYTgTklVyvWTN3f4JocTBG0vhlArhWAlpTo/emO73yIwCo4bbMDeGdGywhJRYFc01za14r8mFzsGnWEDkncz9BOefcR0Kj7Y7d7uYX5jZ69nI4LaUyiqasQvF2ajsj5YvbV0INQ9BYaxShi8xdrlQLHzs9oCqhNizFrrzPacDS1tWC5NvV5u4BREDJUCEb1CRCVEtFsRdioRLSOiQ/L/IBlORPQcEeUQUTYRXeGm8EGb32uFXt1ZHzPO0r2Ls2WKAE+7Cn16dPXsWWo8Mt/5nd/CiJlSNBfAbQlhUwCsEEKMBrBC/gaA2wGMln+TAbzojJjph49uc5g0xemBb69nHw0f5L9tfdne4k7/bRoqBSHEGgDlCcF3AZgnj+cBuFsRPl9E2AhgIBENc0jW9KKTFzzGeeyaj8rrmlXD7e6nYBczO+65zXe4t2B7TGGoEKJIHp8AMFQeDwegnBJQIMOYBII6q4PpfMxYfkj9BAXfvu7G4HpnJ2UjpIiUGsslh4gmE1EWEWWVljq76CYMBPxbYxgA3KHtjNhVCsVRs5D8H/UNXQhgpOK6ETIsCSHETCFEphAiMyMjw6YY4UXbnw7DBIOcklo89d5ev8VgPMauUlgMYKI8ngjgXUX4g3IW0tUAqhRmJscJ4o5YDJMuaI01MOmN4eI1InoDwA0AhhBRAYDfA5gGYAERTQJwFMC98vKlACYAyAFQD+BhF2RmGIZhXMJQKQghvq5x6iaVawWAR1MVimEYe5S44CmV6VyEegVVmBevMYwbbD1aYes+9tHFRAm1UmAYJp5Sm3s8vLczec9npnPCSoFh0gi7u40xTBRWCgyTRvD6FyZVWCkwTJrRu7u/juWYcMNKgWHSCCKegMGkRqiVApd9homHzUdMqoRaKTAME88f3me3FExqsFJgmDSjvrnNbxGYEBNqpcC2U4ZhGGcJtVJgGIZhnIWVAsMwDBMj1EqhrJZd+zIMwzhJqJXCQZt70jIMwzDqhFopnD+0n98iMAzDpBWhVgrDBvTyWwSGYZi0ItRKgaekMgzDOEu4lQI7umAYhnGUcCsF1gkMwzCOEnKlwFqBYRjGScKtFPwWgGEYJs0ItVJgmKDRt2c3v0VwjNsuOt1vERgfCLVSaPfYefz4cwbjo59ej4GndPf0uYw+484c6LcIMWqbWi3f0y+giuSPd1/s27MnXBJ8hfTSt670WwRXCLVS6NnNHfH79FDfzpAIuOD0fnhz8tWuPDfo9OsVzMorDGbE/0z6rOY5raGxGy7IcEkac/TQ+b7OGNALlwwf4NqzH/vi+a7F7RRdwlDwbBBqpXC4tM6VeEdrrJSOfrxjTu+vee/9nxnphkiOsfTHn9M9f4VGq/vWi4bizFNPMf2cIX17WhELAHDX5WeYvvaiMzryoFuXYBfjVb+4AZeO1K5Au2rULj26Jr/Xqw99BgAw+fpznBFOh246td49mSPxipTFDc7N6Ota3E6hlW9hx5WviYhuI6IDRJRDRFPceAYAnD2kj+Nx5k27AzvyK1XPaa2LeP9H1+H3Xx6Lmy8citNdXmWt13ozw9gztBUaAMz99lVJYe//6DrMuG8czhqcrBQuHKYeX9Zvb8ZNY06zJNtTd15k+lplOvitE4x6UHlldWhv1zZ1dtHoKnzu/PiewhfHDsUXxpyG+d++Cr+89QLrgmpwjsZ3dIpGjxkA5m/Ic7VSJCJcc+5gW/c+e8+lDkujjh1TYRhw/HMioq4AXgBwO4CxAL5ORGOdfg4A/FFuPTh8YG/T92h9AABw+ciBAIDPn6/ebT+tn3rr9+LhA/DwtWdj9sRMnDHAvCx6PHD1Warhsx7MjB0/PmGM6nGUqbcnhwHA/j/epvlctcrr4uED0LtHV4w+LbkHpTcYOXXChQCg+XEnyjzwlB5J17z+nQ6zyye/vCF2/PC1Z8eOrzl3iKYMAPCZUYN0z1sh67c3J4Upe0VfvWJE0vlLRgxAS1skXQf36YHdT90aU7BPfnksbpTK8/lvjIu77x5FXHdffkYs768/PwPdVXoRWvz8i+cj58+3q57b9PhNmmMHelO+K+tbcGqf5PyyylevGIGF3xsfF3bpiEiv6rVHPosjT09A3rQ78OgXztWN50+Kd/hapr3e+j/uvczS9UblLqy4YSS+CkCOECIXAIjoTQB3AXB889iqhhYAwPT7LkdFfTOeXroPDS1t+NPdl+A787Pirp16+xjcPW44+vXqhrG/+whApHIsqGhAcXUjTh/QC0P7R1r5P7zxPHxysBQA8LUrR2DsGf3Rp0c3TLh0WJIMieaYr2WOwOC+PTC0fy9ccHo/jP7NB3Hnx58zGBtyTwIAlj92PbbkVWDqol2x88sf+zxe+fQIHp9wIf698WjS80YO6o3T+vVESU0TJl9/Lq47LwPtQuDi4QPw9avOxCVPfozLRg7EzvxKfPfz5+JLl52Boyfr8I1Zm2Jx9Ore0QJ8+YEr8a/VhzFu5EBkjhqku/H7o184D2W1TWhsacfb2wrwi1vOx/dvOA83XJCB4YN6476XN8SZ9M47rS8O/2UClu0txvrDJ/GVccPxrfFnoQsR+vXqhjMG9EZLm8CzHx2I3fPxz67H9/+zFTeOOQ3HyutxzblDsOgH14AAnDW4D9ZPuRHVjS0Yc3p//PiN7ZH8+sJ5uHTEALS2CYw9oz8+2nMCT723F688lIkbxwwFAKw6UIKHX92i+W7DB/ZGYWUDnvv6OFx33hC0tLWjXQisO1SGXy7MBgCcm9EHQ/r2xEPXjMLc9XmY9pVLMGXRLjw4/ixcc+4QDB/UG317dsPfvnYpPtx9ArsKq/Dt687GkL490dgS2SLzxzeNRt+e3TDrwUx85V/rcevFp+ObV5+Fn37xfAwf2BsvfXIYuwurcfOFp6F3j6740qXD8H52EWbcPy5J5mvPG4zRp/XD3PV5+O715+DlNbkAgNkPZuIRRfm/eezQWG/k61eNxFN3Xozzfxspl0P798Jglcr959Kmv/B747GvqBovfZKLwsqG2Pnvfj5ivsr58+1Yl1OGh1TSduH3xuOelzbg7e9fg6++uD4WvuC741FYWY/Rp/XDxXJc4pufPROvbToGAFj8w+sAxCulX9xyAR6+NpKWL39yGCU1TZh03dm4ZtpK/O8H12DcmYNwwwUZaG+PXP/MPZfiVwuzkf3kLSitaUJJdROG9u+Jb8zahMF9e2D6fZfjD+/txbqcMgDAr267AF++7Aw8tmAnAODUPj3w4U8+h71F1Zi7Pg+rD5Ri1oOZqGpoQWV9M+79zEj079UdC747Hr9auBN5J+tjsj739XGoqGvG7xfvAQBse+KL6NGtC2oaWzD+6ZW4cFh/vPjNK9CzexdLyt0zhBCO/gG4B8Bsxe8HADyvd8+VV14p7FBZ1yw+3F2keq65tc1WnFFKqhvFB7uOa54vrKgXlfXNhvEUVzeINQdL4sIamlvF7sLK2O/6plZR09iSdG9TS5toke9R3dAsth+rEEII0djSKhqaWzWf2dLaliTbsZN1Ir+8LvZ79YESse1ouer9S7OPi+LqBs34m1raxJYjJ1XPLdhyTLyzvSAurL29Xaw+UCLa2tpV79lwuEwcLatTPafHzvwKsfFwmaV7nnhnl3hne4HIKakRO45ViKLKBrEk+7hoa2sXmzXeSQghNh4uE/VN2mkeBBqaO8rF4ZIaUVHXFHe+rqlFtMo82Hq0XOSU1MTOvbXlmFi1vziuzClpb28X/96QJwor6sXbW/NT/r7UaG1rF40t3qZxdUOzyMrTznezZOWVi9c2Ho0rI8r8cAsAWcLhOpyEw9M6iegeALcJIR6Rvx8A8FkhxA8TrpsMYDIAnHnmmVcePZrcKmYYhmG0IaKtQohM4yvN40bfpRCA0qg3QobFIYSYKYTIFEJkZmT4O/WOYRiGieCGUtgCYDQRnU1EPQDcD2CxC89hGIZhHMbxgWYhRCsR/RDARwC6AnhFCLHH6ecwDMMwzuPKElUhxFIAS92Im2EYhnGPAM6HYhiGYfyClQLDMAwTg5UCwzAME4OVAsMwDBPD8cVrtoQgKgVgd/XaEABlDorjFWGUO4wyA+GUO4wyA+GUO4wyAxG5+wghHF3oFQilkApElOX0ij4vCKPcYZQZCKfcYZQZCKfcYZQZcE9uNh8xDMMwMVgpMAzDMDHSQSnM9FsAm4RR7jDKDIRT7jDKDIRT7jDKDLgkd+jHFBiGYRjnSIeeAsMwDOMQoVYKXu0FbUGePCLaRUQ7iChLhp1KRMuI6JD8P0iGExE9J2XPJqIrFPFMlNcfIqKJLsj5ChGVENFuRZhjchLRlTIdcuS9KW/mqyHzk0RUKNN7BxFNUJybKp9/gIhuVYSrlhnp1XeTDH9LevhNVeaRRLSKiPYS0R4i+okMD3paa8kd9PTuRUSbiWinlPspvWcRUU/5O0eeH2X3fVyQeS4RHVGk9eUy3P0y4vSuPV79IeKB9TCAcwD0ALATwFifZcoDMCQh7BkAU+TxFAB/lccTAHwAgABcDWCTDD8VQK78P0geD3JYzusBXAFgtxtyAtgsryV57+0uyfwkgF+oXDtWloeeAM6W5aSrXpkBsADA/fL4JQDfd0DmYQCukMf9AByUsgU9rbXkDnp6E4C+8rg7gE0ybVSfBeAHAF6Sx/cDeMvu+7gg81wA96hc73oZCXNPIbYXtBCiGUB0L+igcReAefJ4HoC7FeHzRYSNAAYS0TAAtwJYJoQoF0JUAFgG4DYnBRJCrAFQ7oac8lx/IcRGESmR8xVxOS2zFncBeFMI0SSEOAIgB5HyolpmZMvpRgAL5f3K909F5iIhxDZ5XANgH4DhCH5aa8mtRVDSWwghauXP7vJP6DxLmQ8LAdwkZbP0Pi7JrIXrZSTMSmE4gHzF7wLoF1wvEAA+JqKtFNluFACGCiGK5PEJAEPlsZb8fr2XU3IOl8eJ4W7xQ9mNfiVqhjGQTS18MIBKIUSrWzJL08Q4RFqCoUnrBLmBgKc3EXUloh0AShCpGA/rPCsmnzxfJWXz9NtMlFkIEU3rP8u0nk5EPRNlNimb5TISZqUQRK4TQlwB4HYAjxLR9cqTUlMHfrpXWOQE8CKAcwFcDqAIwN99lUYDIuoL4G0APxVCVCvPBTmtVeQOfHoLIdqEEJcjsg3wVQDG+CuRMYkyE9HFAKYiIvtnEDEJ/dorecKsFEztBe0lQohC+b8EwP8QKZTFsgsH+b9EXq4lv1/v5ZSchfI4MdxxhBDF8oNqBzALkfS2I/NJRLrh3RLCU4aIuiNSsb4mhFgkgwOf1mpyhyG9owghKgGsAjBe51kx+eT5AVI2X75Nhcy3SROeEEI0AXgV9tPaehnRG3AI8h8iu8blIjIQFB30uchHefoA6Kc4Xo/IWMCziB9UfEYe34H4AaPNomPA6Agig0WD5PGpLsg7CvGDto7JieSBrQkuyTxMcfwzROzAAHAR4gcKcxEZJNQsMwD+i/jByB84IC8hYsOdkRAe6LTWkTvo6Z0BYKA87g1gLYAvaT0LwKOIH2heYPd9XJB5mCIvZgCY5lUZcbSi8foPkZH4g4jYDX/jsyznyEKyE8CeqDyI2ChXADgEYLkiowjAC1L2XQAyFXF9G5HBrRwAD7sg6xuIdP9bELExTnJSTgCZAHbLe56HXCTpgsz/ljJlA1iM+ErrN/L5B6CYbaFVZmT+bZbv8l8APR2Q+TpETEPZAHbIvwkhSGstuYOe3pcC2C7l2w3gd3rPAtBL/s6R58+x+z4uyLxSpvVuAP9Bxwwl18sIr2hmGIZhYoR5TIFhGIZxGFYKDMMwTAxWCgzDMEwMVgoMwzBMDFYKDMMwTAxWCgzDMEwMVgoMwzBMDFYKDMMwTIz/D6Y0v+oxmegVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_df['PM2.5'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "92a28eef-6f49-4ddb-8225-348d19aa7976",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dt       0\n",
       "PM2.5    0\n",
       "PM10     0\n",
       "SO2      0\n",
       "NO2      0\n",
       "CO       0\n",
       "O3       0\n",
       "TEMP     0\n",
       "PRES     0\n",
       "DEWP     0\n",
       "RAIN     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "d5300e06-4890-4a5b-b755-a6550b3fae59",
   "metadata": {},
   "outputs": [],
   "source": [
    "days_of_data = (365)*3\n",
    "\n",
    "############### Get prediction for PM10 ####################\n",
    "predictions=[]\n",
    "input_ary=np.array(grouped_df['PM10'][-365:].values)\n",
    "for each_t in range(days_of_data): \n",
    "                input_ary = input_ary[-365:]\n",
    "                input_ary = np.reshape(input_ary, (1, 365, 1))\n",
    "                prediction = so2_model.predict(input_ary)\n",
    "                predictions.append(prediction[0][0])\n",
    "                input_ary=np.append(input_ary, prediction)\n",
    "pm10 = predictions\n",
    "            \n",
    "############### Get prediction for S02 ####################\n",
    "predictions=[]\n",
    "input_ary=np.array(grouped_df['SO2'][-365:].values)\n",
    "for each_t in range(days_of_data): \n",
    "                input_ary = input_ary[-365:]\n",
    "                input_ary = np.reshape(input_ary, (1, 365, 1))\n",
    "                prediction = so2_model.predict(input_ary)\n",
    "                predictions.append(prediction[0][0])\n",
    "                input_ary=np.append(input_ary, prediction)\n",
    "so2 = predictions\n",
    "            \n",
    "############### Get prediction for CO ####################\n",
    "predictions=[]\n",
    "input_ary=np.array(grouped_df['CO'][-365:].values)\n",
    "for each_t in range(days_of_data): \n",
    "                input_ary = input_ary[-365:]\n",
    "                input_ary = np.reshape(input_ary, (1, 365, 1))\n",
    "                prediction = co_model.predict(input_ary)\n",
    "                predictions.append(prediction[0][0])\n",
    "                input_ary=np.append(input_ary, prediction)\n",
    "co = predictions\n",
    "            \n",
    "############### Get prediction for DEWP ####################\n",
    "predictions=[]\n",
    "input_ary=np.array(grouped_df['DEWP'][-365:].values)\n",
    "for each_t in range(days_of_data): \n",
    "                input_ary = input_ary[-365:]\n",
    "                input_ary = np.reshape(input_ary, (1, 365, 1))\n",
    "                prediction = dewp_model.predict(input_ary)\n",
    "                predictions.append(prediction[0][0])\n",
    "                input_ary=np.append(input_ary, prediction)\n",
    "dewp = predictions\n",
    "            \n",
    "############### Get prediction for NO2 ####################\n",
    "predictions=[]\n",
    "input_ary=np.array(grouped_df['NO2'][-365:].values)\n",
    "for each_t in range(days_of_data): \n",
    "                input_ary = input_ary[-365:]\n",
    "                input_ary = np.reshape(input_ary, (1, 365, 1))\n",
    "                prediction = no2_model.predict(input_ary)\n",
    "                predictions.append(prediction[0][0])\n",
    "                input_ary=np.append(input_ary, prediction)\n",
    "no2 = predictions\n",
    "            \n",
    "############### Get prediction for O3 ####################\n",
    "predictions=[]\n",
    "input_ary=np.array(grouped_df['O3'][-365:].values)\n",
    "for each_t in range(days_of_data): \n",
    "                input_ary = input_ary[-365:]\n",
    "                input_ary = np.reshape(input_ary, (1, 365, 1))\n",
    "                prediction = o3_model.predict(input_ary)\n",
    "                predictions.append(prediction[0][0])\n",
    "                input_ary=np.append(input_ary, prediction)\n",
    "o3 = predictions\n",
    "            \n",
    "############### Get prediction for PRES ####################\n",
    "predictions=[]\n",
    "input_ary=np.array(grouped_df['PRES'][-365:].values)\n",
    "for each_t in range(days_of_data): \n",
    "                input_ary = input_ary[-365:]\n",
    "                input_ary = np.reshape(input_ary, (1, 365, 1))\n",
    "                prediction = pres_model.predict(input_ary)\n",
    "                predictions.append(prediction[0][0])\n",
    "                input_ary=np.append(input_ary, prediction)\n",
    "pres = predictions\n",
    "            \n",
    "############### Get prediction for RAIN ####################\n",
    "predictions=[]\n",
    "input_ary=np.array(grouped_df['RAIN'][-365:].values)\n",
    "for each_t in range(days_of_data): \n",
    "                input_ary = input_ary[-365:]\n",
    "                input_ary = np.reshape(input_ary, (1, 365, 1))\n",
    "                prediction = rain_model.predict(input_ary)\n",
    "                predictions.append(prediction[0][0])\n",
    "                input_ary=np.append(input_ary, prediction)\n",
    "rain = predictions\n",
    "            \n",
    "############### Get prediction for TEMP ####################\n",
    "predictions=[]\n",
    "input_ary=np.array(grouped_df['TEMP'][-365:].values)\n",
    "for each_t in range(days_of_data): \n",
    "                input_ary = input_ary[-365:]\n",
    "                input_ary = np.reshape(input_ary, (1, 365, 1))\n",
    "                prediction = temp_model.predict(input_ary)\n",
    "                predictions.append(prediction[0][0])\n",
    "                input_ary=np.append(input_ary, prediction)\n",
    "temp = predictions\n",
    "variable_array = [pm10, so2, no2, co, o3, temp, pres, dewp, rain]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "13919bf5-2658-4df6-915b-eae3bfd92174",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "596f2ef2-aa31-4d2d-a14f-9c03fc67f09e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(input_ary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "45b2d3fa-07ac-4d84-97bd-8c1a96dd84fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1095"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(variable_array[8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "352f2b9b-4986-4a74-aa49-afa0bab4236a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d1f0685160>]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(variable_array[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "ba328339-a0a4-4174-b002-97491a13f029",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d1ef6e56a0>]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(variable_array[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "6ddca27e-d5b4-4569-aef9-826013e13fe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d181c579a0>]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(variable_array[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "25ca321d-6e50-4c57-8285-0ff3f7fbd8c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d184cb0760>]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(variable_array[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "845196a8-e031-4ddf-be7a-1855973f772e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d1f0344ee0>]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(variable_array[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "41a2fa46-ef14-483b-983b-d471a9fd4cd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d1818e28b0>]"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(variable_array[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "03e5245a-ae64-43a9-b2c8-15f95ea7e54a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d1f01890a0>]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(variable_array[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "326fc278-7dfd-4150-ac93-545308f3c003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d1f05c08e0>]"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(variable_array[7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "869ee480-b0f8-43be-a472-d17dc8f41423",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d1ed43cee0>]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(variable_array[8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "4a0e8057-f150-40c2-9c36-d052c24dc196",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_df = pd.DataFrame(variable_array)\n",
    "prediction_df = prediction_df.transpose().rename(columns = {0 : 'pm10',1 : 'so2',2 : 'no2',3 : 'co',4 : 'o3',5 : 'temp',6 : 'pres',7 : 'dewp',8 : 'rain'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "2620623c-6f8b-4857-bd29-d91148ff73b1",
   "metadata": {},
   "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>pm10</th>\n",
       "      <th>so2</th>\n",
       "      <th>no2</th>\n",
       "      <th>co</th>\n",
       "      <th>o3</th>\n",
       "      <th>temp</th>\n",
       "      <th>pres</th>\n",
       "      <th>dewp</th>\n",
       "      <th>rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.487441</td>\n",
       "      <td>-0.194838</td>\n",
       "      <td>1.596573</td>\n",
       "      <td>3.798403</td>\n",
       "      <td>-0.935316</td>\n",
       "      <td>-1.596820</td>\n",
       "      <td>1.019441</td>\n",
       "      <td>-0.744515</td>\n",
       "      <td>-0.070045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.602680</td>\n",
       "      <td>-0.193370</td>\n",
       "      <td>1.193128</td>\n",
       "      <td>3.527185</td>\n",
       "      <td>-0.934116</td>\n",
       "      <td>-1.587485</td>\n",
       "      <td>1.016677</td>\n",
       "      <td>-0.742778</td>\n",
       "      <td>-0.060898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.702716</td>\n",
       "      <td>-0.182367</td>\n",
       "      <td>0.874851</td>\n",
       "      <td>3.195804</td>\n",
       "      <td>-0.936675</td>\n",
       "      <td>-1.599675</td>\n",
       "      <td>1.015593</td>\n",
       "      <td>-0.741085</td>\n",
       "      <td>-0.056395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.985223</td>\n",
       "      <td>-0.168977</td>\n",
       "      <td>0.648952</td>\n",
       "      <td>2.892605</td>\n",
       "      <td>-0.942125</td>\n",
       "      <td>-1.636510</td>\n",
       "      <td>1.017341</td>\n",
       "      <td>-0.739223</td>\n",
       "      <td>-0.054278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.489818</td>\n",
       "      <td>-0.157559</td>\n",
       "      <td>0.497542</td>\n",
       "      <td>2.628342</td>\n",
       "      <td>-0.948926</td>\n",
       "      <td>-1.687791</td>\n",
       "      <td>1.022106</td>\n",
       "      <td>-0.737180</td>\n",
       "      <td>-0.053577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pm10       so2       no2        co        o3      temp      pres  \\\n",
       "0  3.487441 -0.194838  1.596573  3.798403 -0.935316 -1.596820  1.019441   \n",
       "1  2.602680 -0.193370  1.193128  3.527185 -0.934116 -1.587485  1.016677   \n",
       "2  1.702716 -0.182367  0.874851  3.195804 -0.936675 -1.599675  1.015593   \n",
       "3  0.985223 -0.168977  0.648952  2.892605 -0.942125 -1.636510  1.017341   \n",
       "4  0.489818 -0.157559  0.497542  2.628342 -0.948926 -1.687791  1.022106   \n",
       "\n",
       "       dewp      rain  \n",
       "0 -0.744515 -0.070045  \n",
       "1 -0.742778 -0.060898  \n",
       "2 -0.741085 -0.056395  \n",
       "3 -0.739223 -0.054278  \n",
       "4 -0.737180 -0.053577  "
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "0d8e44d0-6576-417d-a16c-0fab1c7eb366",
   "metadata": {},
   "outputs": [],
   "source": [
    "pm25_model = load_model('MachineLearning/Models/pm25_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "29c5571d-004c-40bf-bfb6-0de8cc565138",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[154.18802]], dtype=float32)"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pm25_model.predict([[ 1.109255, 1.933092, -1.025820, 1.451466, -0.709386, 0.826961, 0.994897, -0.006684, -1.312745]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "eb6b6e74-3cf4-43e9-a1bb-b2aaed38f0e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pm25_predictions = []\n",
    "for index, row in prediction_df.iterrows():\n",
    "    array_of_vals = [row[0].item(), row[1].item(), row[2].item(), row[3].item(), row[4].item(), row[5].item(), row[6].item(), row[7].item(), row[8].item()]\n",
    "    pm25_prediction = pm25_model.predict([array_of_vals])\n",
    "    pm25_predictions.append(pm25_prediction[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "7b94c437-e184-45c9-8101-ff63b1acc503",
   "metadata": {},
   "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>Unnamed: 0</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>dt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-01-03 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>-21.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-01-03 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-01-03 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>1020.3</td>\n",
       "      <td>-20.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-01-03 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>-21.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-01-03 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  PM2.5  PM10   SO2   NO2     CO    O3  TEMP    PRES  DEWP  RAIN  \\\n",
       "0           0    4.0   4.0   4.0   7.0  300.0  77.0  -0.7  1023.0 -18.8   0.0   \n",
       "1           0    9.0   9.0   3.0  17.0  300.0  89.0  -0.5  1024.5 -21.4   0.0   \n",
       "2           0    4.0   4.0  14.0  20.0  300.0  69.0  -0.7  1023.0 -18.8   0.0   \n",
       "3           0    7.0   7.0   3.0   2.0  100.0  91.0  -2.3  1020.3 -20.7   0.0   \n",
       "4           0    5.0  14.0   4.0  12.0  200.0  85.0  -0.5  1024.5 -21.4   0.0   \n",
       "\n",
       "                    dt  \n",
       "0  2013-01-03 00:00:00  \n",
       "1  2013-01-03 00:00:00  \n",
       "2  2013-01-03 00:00:00  \n",
       "3  2013-01-03 00:00:00  \n",
       "4  2013-01-03 00:00:00  "
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = os.path.join(os.getcwd(), 'MachineLearning','Resources', 'all_city_data.csv')\n",
    "df = pd.read_csv(path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "a78f98a2-13e8-4849-9318-99a83138288d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = df[df['dt'] <= '2017-01-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "2926cf98-1c14-4656-a53f-cfa474277295",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2016-12-31 23:00:00'"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dt'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "b7c7628f-ad77-452d-9c9a-079b9ca15f0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['dt']=pd.to_datetime(df['dt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "2538259e-82cb-4eb8-8a9d-833554bda694",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "cf668ab6-cfd3-4b5d-980d-c8e17b30bffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-12-31 00:00:00')"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_date = df['dt'].max()\n",
    "last_date = last_date - timedelta(hours= 23)\n",
    "last_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "871b0f73-fad0-4b9e-a89c-1e2bc4f24750",
   "metadata": {},
   "outputs": [],
   "source": [
    "i = len(pm25_predictions) \n",
    "j = 0\n",
    "time_array = []\n",
    "while j < i:\n",
    "    the_day = last_date + timedelta(days= j+1)\n",
    "    time_array.append(the_day)\n",
    "    j = j + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "2669c5f5-a60b-4ac2-9024-63270548fc7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1095"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(time_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "576855de-399c-4f1c-8b84-f6464717db59",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_predictions_df = pd.DataFrame({'dt': time_array, 'pm2.5': pm25_predictions})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "1c3fb594-b9f5-4dbb-be1e-a31772e19829",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .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>dt</th>\n",
       "      <th>pm2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>381.369690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>310.970917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>235.956589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>176.285309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>135.356613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          dt       pm2.5\n",
       "0 2017-01-01  381.369690\n",
       "1 2017-01-02  310.970917\n",
       "2 2017-01-03  235.956589\n",
       "3 2017-01-04  176.285309\n",
       "4 2017-01-05  135.356613"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_predictions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "1d1ed98d-f2b2-43ec-b5f1-140d9ee2f991",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_predictions_df.to_csv(\"Resources/predictions.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "d233cde8-470a-4479-b91a-4d88e03fc642",
   "metadata": {},
   "outputs": [],
   "source": [
    "days_2016_17 = df[df['dt'] >= '2016-10-01']\n",
    "days_2016 = days_2016_17[days_2016_17['dt'] <= '2017-01-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "1602470a-a467-44dc-a0e0-277ae3e162d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "days_2016 = days_2016[['dt', 'PM2.5']].reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "29b6d36c-ef68-4d9f-88ae-47c57540297f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dt</th>\n",
       "      <th>PM2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>161.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          dt  PM2.5\n",
       "0 2016-10-01   56.0\n",
       "1 2016-10-01   80.0\n",
       "2 2016-10-01   74.0\n",
       "3 2016-10-01  143.0\n",
       "4 2016-10-01  161.0"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days_2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "fb01ee87-29a6-4ce4-bf72-19dc8bbb37a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "days_2016 = days_2016.groupby(pd.Grouper(key='dt', axis=0, freq='d')).mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "c1995e64-f710-41ba-aad9-88f0bcd54fc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>dt</th>\n",
       "      <th>PM2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>50.073684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>92.569930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>10.568702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-04</td>\n",
       "      <td>53.384937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-05</td>\n",
       "      <td>39.781690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          dt      PM2.5\n",
       "0 2016-10-01  50.073684\n",
       "1 2016-10-02  92.569930\n",
       "2 2016-10-03  10.568702\n",
       "3 2016-10-04  53.384937\n",
       "4 2016-10-05  39.781690"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2016 = days_2016\n",
    "df_2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "f8de539e-fab9-4a42-ac8e-3dcf7098a512",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2016.to_csv(\"Resources/df_2016.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "7795ed5d-31a9-49a9-8e66-e84edb628df3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d1823ddbe0>]"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_2016['PM2.5'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "e9635bde-beae-46b4-9839-1ffde58cbac4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d182439f40>]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(final_predictions_df['pm2.5'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "9f0a25c4-771d-492c-bae2-0c350aee9963",
   "metadata": {},
   "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>dt</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-03 00:00:00</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>-1.080358</td>\n",
       "      <td>-0.444993</td>\n",
       "      <td>-1.086331</td>\n",
       "      <td>-0.842661</td>\n",
       "      <td>0.269844</td>\n",
       "      <td>-1.325402</td>\n",
       "      <td>1.238340</td>\n",
       "      <td>-1.703139</td>\n",
       "      <td>-0.080522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-03 01:00:00</td>\n",
       "      <td>7.222222</td>\n",
       "      <td>-1.047856</td>\n",
       "      <td>-0.450193</td>\n",
       "      <td>-1.071621</td>\n",
       "      <td>-0.825829</td>\n",
       "      <td>0.245656</td>\n",
       "      <td>-1.337020</td>\n",
       "      <td>1.308589</td>\n",
       "      <td>-1.703547</td>\n",
       "      <td>-0.080522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-03 02:00:00</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>-1.105939</td>\n",
       "      <td>-0.343597</td>\n",
       "      <td>-1.113193</td>\n",
       "      <td>-0.830779</td>\n",
       "      <td>0.304674</td>\n",
       "      <td>-1.403971</td>\n",
       "      <td>1.261254</td>\n",
       "      <td>-1.765618</td>\n",
       "      <td>-0.080522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-03 03:00:00</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>-1.097041</td>\n",
       "      <td>-0.360757</td>\n",
       "      <td>-1.051794</td>\n",
       "      <td>-0.815927</td>\n",
       "      <td>0.289000</td>\n",
       "      <td>-1.410469</td>\n",
       "      <td>1.384539</td>\n",
       "      <td>-1.830302</td>\n",
       "      <td>-0.080522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-03 04:00:00</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>-1.112613</td>\n",
       "      <td>-0.313959</td>\n",
       "      <td>-1.046038</td>\n",
       "      <td>-0.798105</td>\n",
       "      <td>0.282034</td>\n",
       "      <td>-1.447685</td>\n",
       "      <td>1.456186</td>\n",
       "      <td>-1.808250</td>\n",
       "      <td>-0.080522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    dt     PM2.5      PM10       SO2       NO2        CO  \\\n",
       "0  2013-01-03 00:00:00  5.800000 -1.080358 -0.444993 -1.086331 -0.842661   \n",
       "1  2013-01-03 01:00:00  7.222222 -1.047856 -0.450193 -1.071621 -0.825829   \n",
       "2  2013-01-03 02:00:00  4.500000 -1.105939 -0.343597 -1.113193 -0.830779   \n",
       "3  2013-01-03 03:00:00  4.600000 -1.097041 -0.360757 -1.051794 -0.815927   \n",
       "4  2013-01-03 04:00:00  3.800000 -1.112613 -0.313959 -1.046038 -0.798105   \n",
       "\n",
       "         O3      TEMP      PRES      DEWP      RAIN  \n",
       "0  0.269844 -1.325402  1.238340 -1.703139 -0.080522  \n",
       "1  0.245656 -1.337020  1.308589 -1.703547 -0.080522  \n",
       "2  0.304674 -1.403971  1.261254 -1.765618 -0.080522  \n",
       "3  0.289000 -1.410469  1.384539 -1.830302 -0.080522  \n",
       "4  0.282034 -1.447685  1.456186 -1.808250 -0.080522  "
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "de203a4f-5d76-40ed-a3cf-0b7179352c5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# grouped_df['dt']=pd.to_datetime(grouped_df['dt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "92a2c07e-5efa-4e14-82f0-79409488d2c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# group2_df = grouped_df.groupby(pd.Grouper(key='dt', axis=0, freq='D')).mean().dropna().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c9452b5d-817c-4a63-9c71-0c319ef9b226",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>dt</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>7.171065</td>\n",
       "      <td>-1.026632</td>\n",
       "      <td>-0.262020</td>\n",
       "      <td>-0.847045</td>\n",
       "      <td>-0.738237</td>\n",
       "      <td>0.166777</td>\n",
       "      <td>-1.145105</td>\n",
       "      <td>1.604968</td>\n",
       "      <td>-1.711925</td>\n",
       "      <td>-0.080522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>99.499369</td>\n",
       "      <td>0.161768</td>\n",
       "      <td>0.422609</td>\n",
       "      <td>-0.179143</td>\n",
       "      <td>0.118764</td>\n",
       "      <td>-0.078354</td>\n",
       "      <td>-0.317197</td>\n",
       "      <td>0.102064</td>\n",
       "      <td>-0.589099</td>\n",
       "      <td>-0.080522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-05</td>\n",
       "      <td>39.200758</td>\n",
       "      <td>-0.146804</td>\n",
       "      <td>0.012281</td>\n",
       "      <td>-0.304348</td>\n",
       "      <td>-0.521801</td>\n",
       "      <td>0.480318</td>\n",
       "      <td>0.308715</td>\n",
       "      <td>-0.108922</td>\n",
       "      <td>-0.192782</td>\n",
       "      <td>-0.080522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-06</td>\n",
       "      <td>71.583481</td>\n",
       "      <td>0.089834</td>\n",
       "      <td>-0.183119</td>\n",
       "      <td>-0.157487</td>\n",
       "      <td>-0.480521</td>\n",
       "      <td>0.462338</td>\n",
       "      <td>0.972671</td>\n",
       "      <td>-0.941084</td>\n",
       "      <td>0.379518</td>\n",
       "      <td>-0.080522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-07</td>\n",
       "      <td>161.726342</td>\n",
       "      <td>0.326210</td>\n",
       "      <td>-0.303098</td>\n",
       "      <td>0.158802</td>\n",
       "      <td>0.726373</td>\n",
       "      <td>-0.734289</td>\n",
       "      <td>0.933175</td>\n",
       "      <td>-1.639040</td>\n",
       "      <td>1.486071</td>\n",
       "      <td>1.788501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          dt       PM2.5      PM10       SO2       NO2        CO        O3  \\\n",
       "0 2013-01-03    7.171065 -1.026632 -0.262020 -0.847045 -0.738237  0.166777   \n",
       "1 2013-01-04   99.499369  0.161768  0.422609 -0.179143  0.118764 -0.078354   \n",
       "2 2013-01-05   39.200758 -0.146804  0.012281 -0.304348 -0.521801  0.480318   \n",
       "3 2013-01-06   71.583481  0.089834 -0.183119 -0.157487 -0.480521  0.462338   \n",
       "4 2013-01-07  161.726342  0.326210 -0.303098  0.158802  0.726373 -0.734289   \n",
       "\n",
       "       TEMP      PRES      DEWP      RAIN  \n",
       "0 -1.145105  1.604968 -1.711925 -0.080522  \n",
       "1 -0.317197  0.102064 -0.589099 -0.080522  \n",
       "2  0.308715 -0.108922 -0.192782 -0.080522  \n",
       "3  0.972671 -0.941084  0.379518 -0.080522  \n",
       "4  0.933175 -1.639040  1.486071  1.788501  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group2_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8685840f-9c2b-4fb2-b34f-baaa789bdb67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1402"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# len(group2_df['dt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d50cfef8-db7a-40d3-acc4-bf0a69b7104f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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