Random_Forest_WeeklyPrecipitation_multistep.ipynb

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    },
    "id": "8da86ec5"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "jwirrXeYSq9w",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
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    },
    "id": "jwirrXeYSq9w",
    "outputId": "f6a8840a-008d-4bb1-edd3-57eb20b9bd1a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n#connect with drive\\nfrom google.colab import drive\\ndrive.mount('/content/drive')\\n\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "#connect with drive\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "FlDccZ8_SyPA",
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1625027267351,
     "user": {
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      "userId": "09638303782015555303"
     },
     "user_tz": 300
    },
    "id": "FlDccZ8_SyPA"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n# David's path\\npath = '/content/drive/MyDrive/Dengue_GIS_Visualization/DengueData/'\\n# Dana's path\\n#path='/content/drive/MyDrive/Dengue_GIS Visualization/Dengue_GIS_Visualization/DengueData/'\\n\""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "# David's path\n",
    "path = '/content/drive/MyDrive/Dengue_GIS_Visualization/DengueData/'\n",
    "# Dana's path\n",
    "#path='/content/drive/MyDrive/Dengue_GIS Visualization/Dengue_GIS_Visualization/DengueData/'\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "naq9ZkeW7pUw",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 609
    },
    "executionInfo": {
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     "status": "ok",
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     "user": {
      "displayName": "DAVID RESTREPO",
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      "userId": "09638303782015555303"
     },
     "user_tz": 300
    },
    "id": "naq9ZkeW7pUw",
    "outputId": "7ec4eb53-5cd9-40fe-e886-c2527484de20"
   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cases_medellin</th>\n",
       "      <th>DATA</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>YearWeek</th>\n",
       "      <th>LastDayWeek</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>temperature_medellin</th>\n",
       "      <th>percipitation_medellin</th>\n",
       "      <th>percipitation_medellin_rural</th>\n",
       "      <th>percipitation_medellin_urbano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>['2007', 'w01']</td>\n",
       "      <td>2007</td>\n",
       "      <td>200701</td>\n",
       "      <td>2007-01-06</td>\n",
       "      <td>1</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>13.096688</td>\n",
       "      <td>14.097693</td>\n",
       "      <td>12.628933</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>['2007', 'w02']</td>\n",
       "      <td>2007</td>\n",
       "      <td>200702</td>\n",
       "      <td>2007-01-13</td>\n",
       "      <td>1</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>8.437044</td>\n",
       "      <td>6.910390</td>\n",
       "      <td>9.067214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>['2007', 'w03']</td>\n",
       "      <td>2007</td>\n",
       "      <td>200703</td>\n",
       "      <td>2007-01-20</td>\n",
       "      <td>1</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>19.373165</td>\n",
       "      <td>21.616968</td>\n",
       "      <td>18.406665</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>['2007', 'w04']</td>\n",
       "      <td>2007</td>\n",
       "      <td>200704</td>\n",
       "      <td>2007-01-27</td>\n",
       "      <td>1</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>['2007', 'w05']</td>\n",
       "      <td>2007</td>\n",
       "      <td>200705</td>\n",
       "      <td>2007-02-03</td>\n",
       "      <td>2</td>\n",
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       "      <td>3.725206</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>28</td>\n",
       "      <td>['2018', 'w48']</td>\n",
       "      <td>2018</td>\n",
       "      <td>201848</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>8.916642</td>\n",
       "      <td>8.656351</td>\n",
       "      <td>9.114419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>12</td>\n",
       "      <td>['2018', 'w49']</td>\n",
       "      <td>2018</td>\n",
       "      <td>201849</td>\n",
       "      <td>2018-12-08</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>17.006086</td>\n",
       "      <td>16.076869</td>\n",
       "      <td>17.334870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>27</td>\n",
       "      <td>['2018', 'w50']</td>\n",
       "      <td>2018</td>\n",
       "      <td>201850</td>\n",
       "      <td>2018-12-15</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>5.673248</td>\n",
       "      <td>5.388778</td>\n",
       "      <td>5.794555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>17</td>\n",
       "      <td>['2018', 'w51']</td>\n",
       "      <td>2018</td>\n",
       "      <td>201851</td>\n",
       "      <td>2018-12-22</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>2.785103</td>\n",
       "      <td>2.708024</td>\n",
       "      <td>2.810241</td>\n",
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       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>17</td>\n",
       "      <td>['2018', 'w52']</td>\n",
       "      <td>2018</td>\n",
       "      <td>201852</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "<p>624 rows × 10 columns</p>\n",
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      ],
      "text/plain": [
       "     cases_medellin             DATA  YEAR  YearWeek LastDayWeek  MONTH  \\\n",
       "0                 1  ['2007', 'w01']  2007    200701  2007-01-06      1   \n",
       "1                 0  ['2007', 'w02']  2007    200702  2007-01-13      1   \n",
       "2                 0  ['2007', 'w03']  2007    200703  2007-01-20      1   \n",
       "3                 0  ['2007', 'w04']  2007    200704  2007-01-27      1   \n",
       "4                 0  ['2007', 'w05']  2007    200705  2007-02-03      2   \n",
       "..              ...              ...   ...       ...         ...    ...   \n",
       "619              28  ['2018', 'w48']  2018    201848  2018-12-01     12   \n",
       "620              12  ['2018', 'w49']  2018    201849  2018-12-08     12   \n",
       "621              27  ['2018', 'w50']  2018    201850  2018-12-15     12   \n",
       "622              17  ['2018', 'w51']  2018    201851  2018-12-22     12   \n",
       "623              17  ['2018', 'w52']  2018    201852  2018-12-29     12   \n",
       "\n",
       "     temperature_medellin  percipitation_medellin  \\\n",
       "0               21.009283               13.096688   \n",
       "1               21.009283                8.437044   \n",
       "2               21.009283               19.373165   \n",
       "3               21.009283                0.000000   \n",
       "4               20.982925                3.725206   \n",
       "..                    ...                     ...   \n",
       "619             20.936353                8.916642   \n",
       "620             20.936353               17.006086   \n",
       "621             20.936353                5.673248   \n",
       "622             20.936353                2.785103   \n",
       "623             20.936353                0.000000   \n",
       "\n",
       "     percipitation_medellin_rural  percipitation_medellin_urbano  \n",
       "0                       14.097693                      12.628933  \n",
       "1                        6.910390                       9.067214  \n",
       "2                       21.616968                      18.406665  \n",
       "3                        0.000000                       0.000000  \n",
       "4                        3.144225                       3.901021  \n",
       "..                            ...                            ...  \n",
       "619                      8.656351                       9.114419  \n",
       "620                     16.076869                      17.334870  \n",
       "621                      5.388778                       5.794555  \n",
       "622                      2.708024                       2.810241  \n",
       "623                      0.000000                       0.000000  \n",
       "\n",
       "[624 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read Data\n",
    "merge_cases_temp_precip = pd.read_csv('Data/merge_cases_temperature_WeeklyPrecipitation_timeseries.csv')\n",
    "# Remove extra column\n",
    "merge_cases_temp_precip = merge_cases_temp_precip.drop('Unnamed: 0', 1)\n",
    "merge_cases_temp_precip.LastDayWeek = pd.to_datetime(merge_cases_temp_precip.LastDayWeek)\n",
    "merge_cases_temp_precip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a120f93f",
   "metadata": {
    "id": "a120f93f"
   },
   "source": [
    "# Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ac7ee9",
   "metadata": {
    "id": "e0ac7ee9"
   },
   "source": [
    "## Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6d55f16d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "executionInfo": {
     "elapsed": 347,
     "status": "ok",
     "timestamp": 1625027267949,
     "user": {
      "displayName": "DAVID RESTREPO",
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     },
     "user_tz": 300
    },
    "id": "6d55f16d",
    "outputId": "408f4b48-7502-449e-c1d6-fcb0e150b64b"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dengue cases in time\n",
    "def timeseries (x_axis, y_axis, x_label):\n",
    "    plt.figure(figsize = (12, 8))\n",
    "    plt.plot(x_axis, y_axis, color ='black')\n",
    "    plt.xlabel(x_label) \n",
    "    plt.ylabel('Dengue Cases')\n",
    "\n",
    "timeseries(merge_cases_temp_precip['LastDayWeek'], merge_cases_temp_precip['cases_medellin'], 'cases by Week')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2654cf46",
   "metadata": {
    "id": "2654cf46"
   },
   "source": [
    "# Prepare data to supervised learning time series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "753d15c1",
   "metadata": {
    "id": "753d15c1"
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   "source": [
    "we will use:\n",
    "* data: is the dataframe in our case (Dengue Cases, Precipitation and Temperature)\n",
    "* n_in: is the number of lag weeks in the past (length of window)\n",
    "\n",
    "The heart of this \"series_to_supervised\" function is the <a href=\"https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.shift.html\">shift</a> fuction of pandas\n",
    "\n",
    "This function gets as input the number of periods(in this case the number of weeks represented as rows up or down in the dataframe) to move the columns of a dataframe.\n",
    "E.g. \n",
    "* If we have merge_cases_temp_precip['cases_medellin'].shift(1) all the rows of column cases_medellin will move one row down\n",
    "* If we have merge_cases_temp_precip['cases_medellin'].shift(-1) all the rows of column cases_medellin will move one row up\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0d521f51",
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1625027268238,
     "user": {
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   "source": [
    "#from numpy import asarray\n",
    "from pandas import read_csv\n",
    "from pandas import DataFrame\n",
    "from pandas import concat\n",
    "from numpy import asarray\n",
    "\n",
    "\n",
    "# transform a time series dataset into a supervised learning dataset\n",
    "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
    "    n_vars = 1 if type(data) is list else data.shape[1]\n",
    "    df = DataFrame(data)\n",
    "    cols = list()\n",
    "    # input sequence (t-n, ... t-1)\n",
    "    for i in range(n_in, 0, -1):   ##\n",
    "        cols.append(df.shift(i))\n",
    "    # forecast sequence (t, t+1, ... t+n)\n",
    "    for i in range(0, n_out):\n",
    "        ######\n",
    "        ## Modify so just take Dengue Cases to predict\n",
    "        ######\n",
    "        cols.append(df['cases_medellin[t]'].shift(-i))\n",
    "    # put it all together\n",
    "    agg = concat(cols, axis=1)\n",
    "    # drop rows with NaN values\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg.values"
   ]
  },
  {
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   "execution_count": 8,
   "id": "edcb0422",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
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       "     temperature_medellin  percipitation_medellin  cases_medellin[t]\n",
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     "execution_count": 8,
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   "source": [
    "# Take Columns that we want\n",
    "data = merge_cases_temp_precip[['temperature_medellin','percipitation_medellin']]\n",
    "data['cases_medellin[t]'] = merge_cases_temp_precip[['cases_medellin']]\n",
    "data  #DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b3934515",
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    "# transform the time series data into supervised learning\n",
    "# we define 10 lag weeks\n",
    "lag_weeks = 20\n",
    "weeks_to_predict = 10\n",
    "# we have as input 3 columns (temperature, precipitation, Dengue Cases)\n",
    "# The Result will be 3 x 10 = 30 columns + 3 Columns (of actual Data)\n",
    "data = series_to_supervised(data, n_in=lag_weeks, n_out=weeks_to_predict)   ##previous six steps"
   ]
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    "# if we see the resulting dataframe is a dataframe of 33 columns\n",
    "# first 3 columns are week in t-10 and columns of last week t \n",
    "DataFrame(data).head()"
   ]
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       "      <th>58</th>\n",
       "      <th>59</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>13.096688</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>8.437044</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>19.373165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.322522</td>\n",
       "      <td>69.141318</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.322522</td>\n",
       "      <td>72.831904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.322522</td>\n",
       "      <td>113.719540</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>8.437044</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>19.373165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.982925</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.322522</td>\n",
       "      <td>72.831904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.322522</td>\n",
       "      <td>113.719540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.322522</td>\n",
       "      <td>89.360562</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>19.373165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.982925</td>\n",
       "      <td>3.725206</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.982925</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.322522</td>\n",
       "      <td>113.719540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.322522</td>\n",
       "      <td>89.360562</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.243176</td>\n",
       "      <td>37.069386</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.982925</td>\n",
       "      <td>3.725206</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.982925</td>\n",
       "      <td>12.088275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.982925</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.322522</td>\n",
       "      <td>89.360562</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.243176</td>\n",
       "      <td>37.069386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.243176</td>\n",
       "      <td>10.419748</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.982925</td>\n",
       "      <td>3.725206</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.982925</td>\n",
       "      <td>12.088275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.982925</td>\n",
       "      <td>1.469419</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.982925</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.243176</td>\n",
       "      <td>37.069386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.243176</td>\n",
       "      <td>10.419748</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.243176</td>\n",
       "      <td>16.130454</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>21.037365</td>\n",
       "      <td>81.478776</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.037365</td>\n",
       "      <td>83.898868</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.037365</td>\n",
       "      <td>75.602551</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.457151</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>21.347202</td>\n",
       "      <td>55.577611</td>\n",
       "      <td>25.0</td>\n",
       "      <td>21.347202</td>\n",
       "      <td>25.429675</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.347202</td>\n",
       "      <td>46.588027</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>21.037365</td>\n",
       "      <td>83.898868</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.037365</td>\n",
       "      <td>75.602551</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.457151</td>\n",
       "      <td>29.926445</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.457151</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>21.347202</td>\n",
       "      <td>25.429675</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.347202</td>\n",
       "      <td>46.588027</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.347202</td>\n",
       "      <td>107.533508</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>21.037365</td>\n",
       "      <td>75.602551</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.457151</td>\n",
       "      <td>29.926445</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.457151</td>\n",
       "      <td>23.102764</td>\n",
       "      <td>23.0</td>\n",
       "      <td>21.457151</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.347202</td>\n",
       "      <td>46.588027</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.347202</td>\n",
       "      <td>107.533508</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.748416</td>\n",
       "      <td>76.856266</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>21.457151</td>\n",
       "      <td>29.926445</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.457151</td>\n",
       "      <td>23.102764</td>\n",
       "      <td>23.0</td>\n",
       "      <td>21.457151</td>\n",
       "      <td>9.321223</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.457151</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.347202</td>\n",
       "      <td>107.533508</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.748416</td>\n",
       "      <td>76.856266</td>\n",
       "      <td>27.0</td>\n",
       "      <td>20.748416</td>\n",
       "      <td>25.432126</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>21.457151</td>\n",
       "      <td>23.102764</td>\n",
       "      <td>23.0</td>\n",
       "      <td>21.457151</td>\n",
       "      <td>9.321223</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.457151</td>\n",
       "      <td>46.175236</td>\n",
       "      <td>23.0</td>\n",
       "      <td>21.457151</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.748416</td>\n",
       "      <td>76.856266</td>\n",
       "      <td>27.0</td>\n",
       "      <td>20.748416</td>\n",
       "      <td>25.432126</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20.748416</td>\n",
       "      <td>50.621336</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>595 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0          1     2          3          4     5          6   \\\n",
       "0    21.009283  13.096688   1.0  21.009283   8.437044   0.0  21.009283   \n",
       "1    21.009283   8.437044   0.0  21.009283  19.373165   0.0  21.009283   \n",
       "2    21.009283  19.373165   0.0  21.009283   0.000000   0.0  20.982925   \n",
       "3    21.009283   0.000000   0.0  20.982925   3.725206   0.0  20.982925   \n",
       "4    20.982925   3.725206   0.0  20.982925  12.088275   0.0  20.982925   \n",
       "..         ...        ...   ...        ...        ...   ...        ...   \n",
       "590  21.037365  81.478776  20.0  21.037365  83.898868  21.0  21.037365   \n",
       "591  21.037365  83.898868  21.0  21.037365  75.602551  17.0  21.457151   \n",
       "592  21.037365  75.602551  17.0  21.457151  29.926445  19.0  21.457151   \n",
       "593  21.457151  29.926445  19.0  21.457151  23.102764  23.0  21.457151   \n",
       "594  21.457151  23.102764  23.0  21.457151   9.321223  22.0  21.457151   \n",
       "\n",
       "            7     8          9   ...    50         51          52    53  \\\n",
       "0    19.373165   0.0  21.009283  ...   0.0  21.322522   69.141318   1.0   \n",
       "1     0.000000   0.0  20.982925  ...   1.0  21.322522   72.831904   0.0   \n",
       "2     3.725206   0.0  20.982925  ...   0.0  21.322522  113.719540   0.0   \n",
       "3    12.088275   0.0  20.982925  ...   0.0  21.322522   89.360562   0.0   \n",
       "4     1.469419   0.0  20.982925  ...   0.0  21.243176   37.069386   0.0   \n",
       "..         ...   ...        ...  ...   ...        ...         ...   ...   \n",
       "590  75.602551  17.0  21.457151  ...  27.0  21.347202   55.577611  25.0   \n",
       "591  29.926445  19.0  21.457151  ...  25.0  21.347202   25.429675  12.0   \n",
       "592  23.102764  23.0  21.457151  ...  12.0  21.347202   46.588027  22.0   \n",
       "593   9.321223  22.0  21.457151  ...  22.0  21.347202  107.533508  22.0   \n",
       "594  46.175236  23.0  21.457151  ...  22.0  20.748416   76.856266  27.0   \n",
       "\n",
       "            54          55    56         57          58    59  \n",
       "0    21.322522   72.831904   0.0  21.322522  113.719540   0.0  \n",
       "1    21.322522  113.719540   0.0  21.322522   89.360562   0.0  \n",
       "2    21.322522   89.360562   0.0  21.243176   37.069386   0.0  \n",
       "3    21.243176   37.069386   0.0  21.243176   10.419748   0.0  \n",
       "4    21.243176   10.419748   0.0  21.243176   16.130454   0.0  \n",
       "..         ...         ...   ...        ...         ...   ...  \n",
       "590  21.347202   25.429675  12.0  21.347202   46.588027  22.0  \n",
       "591  21.347202   46.588027  22.0  21.347202  107.533508  22.0  \n",
       "592  21.347202  107.533508  22.0  20.748416   76.856266  27.0  \n",
       "593  20.748416   76.856266  27.0  20.748416   25.432126  23.0  \n",
       "594  20.748416   25.432126  23.0  20.748416   50.621336  24.0  \n",
       "\n",
       "[595 rows x 60 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all the rows except last (Dengue Cases in week t) could be taken as fetures to train Machine Learning Models\n",
    "features_set = DataFrame(data[:,:-weeks_to_predict])\n",
    "features_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ef93f4d9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1625027268483,
     "user": {
      "displayName": "DAVID RESTREPO",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgqxItBQRJ0dDzIdCZM0BMuA0KeTKTlKRNGMApY=s64",
      "userId": "09638303782015555303"
     },
     "user_tz": 300
    },
    "id": "ef93f4d9",
    "outputId": "df3b7500-f1bb-4268-a322-27e5fcefc7de"
   },
   "outputs": [
    {
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       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>24.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>595 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0     1     2     3     4     5     6     7     8     9\n",
       "0     0.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0   0.0   0.0\n",
       "1     0.0   0.0   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0\n",
       "2     0.0   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0\n",
       "3     0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0   0.0\n",
       "4     0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
       "..    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...\n",
       "590  22.0  27.0  23.0  24.0  24.0  17.0  20.0  15.0  41.0  28.0\n",
       "591  27.0  23.0  24.0  24.0  17.0  20.0  15.0  41.0  28.0  12.0\n",
       "592  23.0  24.0  24.0  17.0  20.0  15.0  41.0  28.0  12.0  27.0\n",
       "593  24.0  24.0  17.0  20.0  15.0  41.0  28.0  12.0  27.0  17.0\n",
       "594  24.0  17.0  20.0  15.0  41.0  28.0  12.0  27.0  17.0  17.0\n",
       "\n",
       "[595 rows x 10 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Last column (Dengue Cases in week t) is our target variable, so last column will be our labels\n",
    "labels_set = DataFrame(data[:,-weeks_to_predict:])\n",
    "labels_set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c4eee6b",
   "metadata": {
    "id": "1c4eee6b"
   },
   "source": [
    "### Train - test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cbe710fd",
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1625027268484,
     "user": {
      "displayName": "DAVID RESTREPO",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgqxItBQRJ0dDzIdCZM0BMuA0KeTKTlKRNGMApY=s64",
      "userId": "09638303782015555303"
     },
     "user_tz": 300
    },
    "id": "cbe710fd"
   },
   "outputs": [],
   "source": [
    "# We need a sequence so we can't split randomly\n",
    "# To divide into Train (90%) and test (10%) to do that we need to know the 90% of the total dataframe\n",
    "size = features_set.shape[0]\n",
    "split = int(size*(9/10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1d9d2f5",
   "metadata": {
    "id": "b1d9d2f5"
   },
   "source": [
    "#### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a35769ba",
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1625027268719,
     "user": {
      "displayName": "DAVID RESTREPO",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgqxItBQRJ0dDzIdCZM0BMuA0KeTKTlKRNGMApY=s64",
      "userId": "09638303782015555303"
     },
     "user_tz": 300
    },
    "id": "a35769ba"
   },
   "outputs": [],
   "source": [
    "# We will train with 1st 90% of data and test with last 10%\n",
    "features_train = features_set[:split]  ##90% train of 618  ##556  up to 90% -> first 90%\n",
    "targets_train = labels_set[:split]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fa1fd33",
   "metadata": {
    "id": "7fa1fd33"
   },
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ccbd435c",
   "metadata": {
    "executionInfo": {
     "elapsed": 265,
     "status": "ok",
     "timestamp": 1625027268983,
     "user": {
      "displayName": "DAVID RESTREPO",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgqxItBQRJ0dDzIdCZM0BMuA0KeTKTlKRNGMApY=s64",
      "userId": "09638303782015555303"
     },
     "user_tz": 300
    },
    "id": "ccbd435c"
   },
   "outputs": [],
   "source": [
    "features_test = features_set[split:]  ##10% test\n",
    "targets_test = labels_set[split:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f6af599",
   "metadata": {
    "id": "9f6af599"
   },
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bbed216",
   "metadata": {
    "id": "3bbed216"
   },
   "source": [
    "### Model train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2f78b556",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 123
    },
    "executionInfo": {
     "elapsed": 1115,
     "status": "ok",
     "timestamp": 1625027270098,
     "user": {
      "displayName": "DAVID RESTREPO",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgqxItBQRJ0dDzIdCZM0BMuA0KeTKTlKRNGMApY=s64",
      "userId": "09638303782015555303"
     },
     "user_tz": 300
    },
    "id": "2f78b556",
    "outputId": "1dadf194-9883-4e77-cdd9-e7c4ee05b05e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# model\n",
    "model = RandomForestRegressor(random_state=0,n_estimators=100)\n",
    "model.fit(features_train, targets_train)   ## fit on training data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fdd0199",
   "metadata": {
    "id": "7fdd0199"
   },
   "source": [
    "### Model Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fd8779b7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1625027270099,
     "user": {
      "displayName": "DAVID RESTREPO",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgqxItBQRJ0dDzIdCZM0BMuA0KeTKTlKRNGMApY=s64",
      "userId": "09638303782015555303"
     },
     "user_tz": 300
    },
    "id": "fd8779b7",
    "outputId": "38935b1e-3378-449d-daf3-cb1fd48707fa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[41.69, 42.25, 46.43, 46.2 , 49.15, 51.68, 55.54, 56.99, 59.96,\n",
       "        59.22],\n",
       "       [45.2 , 47.11, 48.09, 49.17, 53.41, 55.48, 57.39, 58.64, 59.1 ,\n",
       "        63.04],\n",
       "       [44.37, 45.27, 48.06, 51.62, 50.22, 54.22, 57.8 , 56.77, 60.36,\n",
       "        57.73],\n",
       "       [44.77, 46.36, 48.85, 48.94, 50.8 , 55.92, 54.23, 58.15, 57.63,\n",
       "        62.23],\n",
       "       [42.8 , 45.54, 47.78, 48.1 , 49.52, 48.38, 52.8 , 50.6 , 53.16,\n",
       "        53.31],\n",
       "       [41.19, 42.1 , 45.95, 45.06, 46.79, 52.54, 53.18, 55.27, 56.22,\n",
       "        58.3 ],\n",
       "       [40.4 , 41.54, 42.39, 41.58, 44.53, 45.36, 45.23, 47.54, 47.41,\n",
       "        48.66],\n",
       "       [36.6 , 38.77, 37.86, 40.55, 41.24, 43.25, 45.57, 44.23, 47.96,\n",
       "        51.25],\n",
       "       [36.15, 38.07, 39.57, 41.99, 43.9 , 44.46, 47.8 , 45.32, 50.95,\n",
       "        53.32],\n",
       "       [27.8 , 31.34, 31.58, 35.33, 39.73, 39.93, 44.54, 47.05, 50.47,\n",
       "        53.24],\n",
       "       [35.71, 38.08, 40.53, 42.27, 45.94, 47.23, 51.46, 54.43, 60.65,\n",
       "        63.94],\n",
       "       [26.51, 28.19, 28.93, 31.64, 34.85, 38.85, 43.27, 47.11, 49.88,\n",
       "        55.1 ],\n",
       "       [43.62, 47.38, 46.06, 49.09, 53.03, 51.93, 58.06, 59.1 , 61.99,\n",
       "        66.73],\n",
       "       [21.48, 21.87, 24.17, 26.77, 31.59, 36.02, 39.45, 39.74, 43.2 ,\n",
       "        45.51],\n",
       "       [41.  , 44.54, 43.96, 46.79, 49.24, 51.51, 52.95, 56.99, 57.66,\n",
       "        60.03],\n",
       "       [19.72, 21.3 , 24.83, 27.29, 28.89, 30.17, 31.98, 33.14, 33.04,\n",
       "        35.31],\n",
       "       [19.74, 22.98, 24.26, 25.58, 26.98, 28.37, 29.38, 29.04, 31.75,\n",
       "        34.58],\n",
       "       [20.39, 23.46, 23.9 , 26.3 , 28.74, 29.93, 30.85, 32.43, 34.03,\n",
       "        36.86],\n",
       "       [18.97, 22.53, 21.61, 24.24, 25.74, 27.58, 30.16, 30.81, 32.82,\n",
       "        36.81],\n",
       "       [42.86, 43.78, 47.04, 52.21, 55.38, 59.25, 60.46, 63.5 , 68.39,\n",
       "        74.28],\n",
       "       [30.89, 33.98, 36.59, 39.71, 43.68, 46.19, 48.38, 50.45, 55.05,\n",
       "        58.69],\n",
       "       [33.44, 37.8 , 37.57, 42.69, 44.73, 45.12, 49.43, 51.27, 54.18,\n",
       "        58.99],\n",
       "       [42.94, 44.21, 49.55, 54.2 , 57.02, 59.96, 63.14, 67.06, 70.  ,\n",
       "        75.68],\n",
       "       [22.48, 26.49, 26.64, 26.32, 25.89, 26.75, 27.72, 26.56, 27.76,\n",
       "        30.63],\n",
       "       [20.17, 23.41, 22.12, 23.09, 22.83, 23.24, 23.81, 22.63, 23.32,\n",
       "        24.07],\n",
       "       [18.03, 19.12, 18.56, 18.61, 17.67, 18.6 , 19.18, 18.07, 18.71,\n",
       "        17.76],\n",
       "       [20.77, 20.23, 20.27, 21.95, 22.32, 22.42, 23.04, 24.35, 23.24,\n",
       "        23.78],\n",
       "       [16.46, 15.53, 16.1 , 15.76, 16.11, 15.36, 16.69, 16.85, 16.77,\n",
       "        17.53],\n",
       "       [14.8 , 15.49, 15.95, 15.92, 15.66, 16.28, 15.87, 15.99, 17.  ,\n",
       "        16.83],\n",
       "       [16.92, 17.45, 17.99, 17.95, 18.31, 18.74, 18.01, 19.03, 18.15,\n",
       "        18.21],\n",
       "       [15.  , 15.21, 14.85, 15.29, 15.18, 14.81, 16.19, 16.4 , 15.58,\n",
       "        16.1 ],\n",
       "       [18.3 , 18.54, 18.37, 18.33, 18.98, 19.9 , 20.1 , 20.17, 20.14,\n",
       "        20.3 ],\n",
       "       [14.3 , 15.12, 14.43, 14.28, 15.35, 15.64, 15.51, 15.77, 15.79,\n",
       "        16.23],\n",
       "       [14.57, 14.51, 14.33, 15.29, 15.41, 14.93, 15.96, 15.18, 14.71,\n",
       "        15.95],\n",
       "       [14.99, 14.06, 14.14, 14.02, 14.74, 13.86, 14.26, 14.3 , 14.18,\n",
       "        15.59],\n",
       "       [18.63, 18.96, 18.9 , 18.04, 19.59, 20.29, 18.62, 19.5 , 19.09,\n",
       "        19.48],\n",
       "       [16.09, 15.45, 15.03, 15.71, 16.22, 17.58, 16.4 , 18.41, 17.64,\n",
       "        18.82],\n",
       "       [14.81, 14.37, 14.98, 15.13, 15.27, 15.79, 15.87, 16.48, 16.75,\n",
       "        15.81],\n",
       "       [14.39, 14.6 , 14.81, 15.  , 15.82, 16.4 , 15.6 , 16.34, 17.07,\n",
       "        16.82],\n",
       "       [14.79, 15.83, 14.97, 15.78, 16.07, 16.53, 16.49, 17.24, 17.77,\n",
       "        18.32],\n",
       "       [15.45, 16.42, 16.67, 17.01, 16.84, 16.17, 17.04, 17.5 , 19.39,\n",
       "        20.  ],\n",
       "       [15.89, 18.17, 18.7 , 19.56, 18.49, 19.79, 19.63, 20.3 , 21.6 ,\n",
       "        22.74],\n",
       "       [17.12, 18.12, 17.41, 17.44, 18.23, 18.69, 18.82, 19.74, 20.28,\n",
       "        21.69],\n",
       "       [16.48, 17.5 , 17.98, 17.39, 17.94, 18.49, 17.83, 18.46, 19.35,\n",
       "        19.04],\n",
       "       [30.07, 30.11, 31.87, 35.07, 36.9 , 37.57, 39.37, 44.16, 43.63,\n",
       "        45.49],\n",
       "       [41.14, 42.79, 47.44, 54.58, 55.35, 60.04, 63.02, 66.96, 67.58,\n",
       "        71.5 ],\n",
       "       [21.73, 24.15, 24.22, 24.84, 24.19, 26.15, 27.27, 28.34, 28.68,\n",
       "        29.67],\n",
       "       [22.72, 26.56, 26.68, 26.2 , 25.48, 26.76, 28.49, 27.16, 28.66,\n",
       "        30.61],\n",
       "       [18.32, 19.31, 20.95, 20.49, 20.6 , 22.27, 23.23, 21.85, 23.38,\n",
       "        24.63],\n",
       "       [17.82, 18.39, 18.48, 18.97, 19.23, 18.65, 19.45, 19.74, 20.51,\n",
       "        21.91],\n",
       "       [15.84, 16.39, 16.86, 16.26, 15.05, 16.38, 15.81, 15.09, 17.38,\n",
       "        16.92],\n",
       "       [19.17, 19.72, 18.89, 18.34, 19.24, 19.48, 19.81, 19.72, 20.59,\n",
       "        21.22],\n",
       "       [29.19, 27.52, 28.31, 29.26, 29.65, 30.5 , 32.78, 31.64, 33.55,\n",
       "        33.56],\n",
       "       [19.81, 20.03, 20.45, 20.68, 22.3 , 23.27, 22.84, 23.31, 22.96,\n",
       "        23.84],\n",
       "       [16.03, 16.16, 17.3 , 16.6 , 17.15, 17.76, 18.17, 18.97, 18.92,\n",
       "        20.2 ],\n",
       "       [14.6 , 15.76, 16.68, 15.83, 16.4 , 15.65, 16.86, 16.64, 17.48,\n",
       "        18.47],\n",
       "       [14.8 , 15.94, 15.6 , 15.44, 16.37, 16.04, 16.7 , 16.19, 15.68,\n",
       "        17.49],\n",
       "       [29.09, 31.34, 31.35, 31.75, 34.38, 36.9 , 38.05, 40.42, 41.16,\n",
       "        42.68],\n",
       "       [17.53, 18.61, 18.62, 19.74, 22.68, 21.73, 21.95, 25.24, 25.65,\n",
       "        27.77],\n",
       "       [20.59, 22.99, 24.12, 25.43, 29.47, 26.33, 27.99, 29.22, 34.05,\n",
       "        37.68]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = model.predict(features_test)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "12f11bf1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1625027270099,
     "user": {
      "displayName": "DAVID RESTREPO",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgqxItBQRJ0dDzIdCZM0BMuA0KeTKTlKRNGMApY=s64",
      "userId": "09638303782015555303"
     },
     "user_tz": 300
    },
    "id": "12f11bf1",
    "outputId": "eae51807-5224-4416-ec99-44bfc952421e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error is: 12.107633333333332\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "print(f\"the mean absolute error is: {mean_absolute_error(targets_test, prediction)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8c07ad31",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "executionInfo": {
     "elapsed": 389,
     "status": "ok",
     "timestamp": 1625027270483,
     "user": {
      "displayName": "DAVID RESTREPO",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgqxItBQRJ0dDzIdCZM0BMuA0KeTKTlKRNGMApY=s64",
      "userId": "09638303782015555303"
     },
     "user_tz": 300
    },
    "id": "8c07ad31",
    "outputId": "c57f01f1-0c57-40eb-e9e5-c159482001f9"
   },
   "outputs": [
    {
     "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": [
    "data_predict = prediction[-2]  ## predicted target  cases\n",
    "dataY_plot = targets_test.to_numpy()[-2]  ##  real test-target cases\n",
    "\n",
    "data_predict = data_predict.reshape(len(data_predict), 1)\n",
    "dataY_plot = dataY_plot.reshape(len(dataY_plot), 1)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(dataY_plot, label = 'actual')\n",
    "plt.plot(data_predict, label = 'predicted')\n",
    "plt.legend(loc=\"upper left\")\n",
    "\n",
    "plt.suptitle('Time-Series Prediction')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a677952",
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1625027270483,
     "user": {
      "displayName": "DAVID RESTREPO",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgqxItBQRJ0dDzIdCZM0BMuA0KeTKTlKRNGMApY=s64",
      "userId": "09638303782015555303"
     },
     "user_tz": 300
    },
    "id": "9a677952"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aee28713",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7a82cdd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Multilayer_Perceptron_WeeklyPrecipitation.ipynb",
   "provenance": [
    {
     "file_id": "1kbOQzG0CZQ8AsG1uaLJZpy_mNhCpPGK7",
     "timestamp": 1625022677240
    }
   ]
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}