Multilayer_Perceptron_WeeklyPrecipitation_multistep.ipynb

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     "status": "ok",
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     },
     "user_tz": 300
    },
    "id": "8da86ec5"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c9112cf2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
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     "status": "ok",
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     "user_tz": 300
    },
    "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": 2,
     "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": 3,
   "id": "4c0611fb",
   "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": 3,
     "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": 4,
   "id": "45f3422d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 609
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1625027267605,
     "user": {
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      "userId": "09638303782015555303"
     },
     "user_tz": 300
    },
    "id": "naq9ZkeW7pUw",
    "outputId": "7ec4eb53-5cd9-40fe-e886-c2527484de20"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/4w/k6c16td51cv9ytcmnnvx3mpw0000gn/T/ipykernel_29013/324749813.py:4: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  merge_cases_temp_precip = merge_cases_temp_precip.drop('Unnamed: 0', 1)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
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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",
       "      <td>20.982925</td>\n",
       "      <td>3.725206</td>\n",
       "      <td>3.144225</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "</div>"
      ],
      "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": 4,
     "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": "5bd33aaa",
   "metadata": {
    "id": "a120f93f"
   },
   "source": [
    "# Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4fcbfef",
   "metadata": {
    "id": "e0ac7ee9"
   },
   "source": [
    "## Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0123326f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "executionInfo": {
     "elapsed": 347,
     "status": "ok",
     "timestamp": 1625027267949,
     "user": {
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     },
     "user_tz": 300
    },
    "id": "6d55f16d",
    "outputId": "408f4b48-7502-449e-c1d6-fcb0e150b64b"
   },
   "outputs": [
    {
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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": "2ff3187f",
   "metadata": {
    "id": "2654cf46"
   },
   "source": [
    "# Prepare data to supervised learning time series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90176cf9",
   "metadata": {
    "id": "753d15c1"
   },
   "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": 6,
   "id": "6a605175",
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1625027268238,
     "user": {
      "displayName": "DAVID RESTREPO",
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     },
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    },
    "id": "0d521f51"
   },
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d285af3d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 521
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1625027268238,
     "user": {
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    },
    "id": "edcb0422",
    "outputId": "e0d1b085-b997-480a-d78c-f3ea21d1c052"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/4w/k6c16td51cv9ytcmnnvx3mpw0000gn/T/ipykernel_29013/2368306415.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",
      "  data['cases_medellin[t]'] = merge_cases_temp_precip[['cases_medellin']]\n"
     ]
    },
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       "     temperature_medellin  percipitation_medellin  cases_medellin[t]\n",
       "0               21.009283               13.096688                  1\n",
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       "..                    ...                     ...                ...\n",
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     },
     "execution_count": 7,
     "metadata": {},
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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": 8,
   "id": "d9c499d4",
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1625027268238,
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      "userId": "09638303782015555303"
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     "user_tz": 300
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    "id": "b3934515"
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   "outputs": [],
   "source": [
    "# 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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       "          0          1    2          3          4    5          6          7   \\\n",
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     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "# 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>56</th>\n",
       "      <th>57</th>\n",
       "      <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",
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       "    <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": 10,
     "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": 11,
   "id": "49d03dfc",
   "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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       "      <th>592</th>\n",
       "      <td>23.0</td>\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",
       "    </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": 11,
     "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": "a339c3ac",
   "metadata": {
    "id": "1c4eee6b"
   },
   "source": [
    "### Train - test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4b977e75",
   "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": "0a8e668a",
   "metadata": {
    "id": "b1d9d2f5"
   },
   "source": [
    "#### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "28114a05",
   "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": "c41a48f2",
   "metadata": {
    "id": "7fa1fd33"
   },
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "568068ad",
   "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": "4ea381cb",
   "metadata": {
    "id": "9f6af599"
   },
   "source": [
    "## Multilayer Perceptron"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a226cb6b",
   "metadata": {
    "id": "3bbed216"
   },
   "source": [
    "### Model train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c17ba593",
   "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/ML_GPU/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\" hidden_layer_sizes=60, activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10, max_fun=15000 \""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(0)\n",
    "\n",
    "# model\n",
    "model = MLPRegressor(hidden_layer_sizes=60,random_state=1, max_iter=400)\n",
    "model.fit(features_train, targets_train)   ## fit on training data\n",
    "\n",
    "\"\"\" hidden_layer_sizes=60, activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10, max_fun=15000 \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9609e8a0",
   "metadata": {
    "id": "7fdd0199"
   },
   "source": [
    "### Model Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7f7cb9e2",
   "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([[32.94911661, 22.16329997, 32.21103076, 17.89120554, 31.01562965,\n",
       "        19.8475103 , 22.22207556, 20.79800494, 20.84838508, 26.59556109],\n",
       "       [30.53519867, 18.31362065, 23.240305  , 26.93346782, 28.79024572,\n",
       "        25.00520928, 21.77893743, 25.25244307, 23.96726314, 21.35626559],\n",
       "       [31.94606587, 32.63139948, 34.82206165, 39.52007894, 35.97594029,\n",
       "        37.56230589, 30.57844905, 28.4544212 , 31.24681846, 28.77265484],\n",
       "       [39.99572035, 41.4887197 , 41.96293119, 41.54361538, 29.34689269,\n",
       "        31.49176036, 32.42004132, 29.87776678, 26.32611278, 21.06940852],\n",
       "       [35.37023852, 44.63690453, 41.63132523, 35.22243906, 31.28355106,\n",
       "        31.03728425, 41.90098319, 46.60277469, 43.02258133, 50.99900224],\n",
       "       [28.82167418, 36.76927102, 28.48730076, 23.66768756, 23.23470391,\n",
       "        28.83400149, 25.46130889, 24.20125577, 25.12865297, 37.30811784],\n",
       "       [26.25274427, 20.47518774, 11.76355706, 10.9190481 , 14.11300897,\n",
       "        18.72077575, 19.61814281, 17.82440128, 24.7826467 , 21.84607904],\n",
       "       [23.30138029, 12.22313811, 22.26669485, 19.42599535, 17.13881233,\n",
       "        16.27084788, 17.89869113, 26.91495919, 33.48096163, 32.26455611],\n",
       "       [22.39495431, 23.3089355 , 26.22354224, 27.52215648, 27.01125271,\n",
       "        27.97836239, 30.98808028, 27.66446208, 32.94823808, 35.88872536],\n",
       "       [21.9048063 , 23.22206375, 21.61776997, 29.47540225, 27.55494686,\n",
       "        32.86005116, 41.77601742, 29.64255014, 33.99141048, 33.25148392],\n",
       "       [30.10702645, 31.00523394, 38.3784922 , 45.98114629, 48.16663722,\n",
       "        43.48445089, 47.99471864, 42.65502946, 41.93574061, 34.39870325],\n",
       "       [27.17966392, 28.44996671, 37.15795479, 37.89236949, 33.64188014,\n",
       "        34.47336578, 33.3293108 , 34.89427427, 22.05683163, 21.95798985],\n",
       "       [23.95767269, 30.6705458 , 31.98931611, 27.33186188, 36.49518423,\n",
       "        33.53563815, 43.92339208, 34.55032199, 24.05961381, 31.26713277],\n",
       "       [22.94231151, 26.57401235, 31.10560626, 28.61851219, 25.91777661,\n",
       "        27.21188467, 28.87629251, 19.47178011, 17.5869891 , 10.05251926],\n",
       "       [23.33930031, 31.03014949, 24.16502043, 30.12591514, 32.95462971,\n",
       "        27.65551555, 34.33944523, 34.57306372, 24.18938172, 26.44862869],\n",
       "       [18.51071556, 14.79658394, 13.64858218,  8.80107805, 10.66183163,\n",
       "        13.11522773, 18.89852009, 17.44660725, 13.96581296, 28.16299409],\n",
       "       [19.68885137, 22.95338803, 18.50218793, 21.04469219, 21.54682455,\n",
       "        26.57993761, 24.82226504, 16.80387171, 23.00169019, 26.59407942],\n",
       "       [20.36262891, 20.22537357, 16.33315156, 19.52694828, 21.33219776,\n",
       "        19.66136565, 29.72374626, 25.86496353, 32.55509548, 33.01551444],\n",
       "       [20.8941336 , 15.86264444, 13.32851007, 17.4192011 , 20.14427789,\n",
       "        17.69119718, 25.54754859, 23.03350733, 16.17480702, 26.51168437],\n",
       "       [26.04941334, 27.68561279, 30.84857463, 37.44798349, 34.4244483 ,\n",
       "        40.19689434, 44.05006946, 37.697057  , 45.03141502, 48.44232464],\n",
       "       [27.79390896, 29.279519  , 32.95722991, 28.80677265, 33.81113742,\n",
       "        31.04187403, 35.51456904, 32.1902984 , 34.18447744, 41.75441888],\n",
       "       [28.54909871, 28.34039423, 29.05968282, 29.09494359, 37.82143995,\n",
       "        29.1562813 , 37.5121645 , 41.31500005, 38.89113362, 41.11824854],\n",
       "       [30.32800099, 31.32817879, 37.01237129, 38.83969178, 35.82700543,\n",
       "        43.36621866, 44.34373901, 43.89931731, 45.33877787, 51.22008935],\n",
       "       [33.31500578, 34.71240472, 28.72204768, 30.36375849, 32.98184787,\n",
       "        29.45028547, 36.36999658, 29.6725926 , 33.41455636, 41.88249072],\n",
       "       [24.01569735, 22.50956046, 30.28214995, 21.63267362, 30.39094254,\n",
       "        23.20544975, 38.57530206, 42.16010451, 45.93124046, 54.63053858],\n",
       "       [28.27314731, 29.00583238, 26.79516535, 29.10452731, 30.42679498,\n",
       "        35.7726749 , 43.04219796, 39.26024164, 45.86452615, 60.93411334],\n",
       "       [29.3199727 , 31.81658039, 29.92834544, 29.22019687, 26.92811506,\n",
       "        32.3562016 , 35.68395208, 32.44704392, 43.57400102, 49.83072327],\n",
       "       [23.09996708, 27.3093855 , 31.25482557, 21.30651264, 39.47496579,\n",
       "        28.51457781, 45.8876505 , 50.56354752, 51.74087055, 66.63736774],\n",
       "       [22.1670448 , 23.03586182, 23.00844212, 28.75426498, 30.8165066 ,\n",
       "        37.44449487, 32.38784872, 40.83047756, 41.86994125, 51.20584365],\n",
       "       [21.7412878 , 22.8900024 , 27.44103479, 29.33762723, 35.4740996 ,\n",
       "        37.41834766, 34.5820267 , 40.21117047, 43.80827783, 41.76771305],\n",
       "       [21.85005735, 19.91887766, 32.13618905, 25.96389047, 27.50206111,\n",
       "        21.32914371, 16.6027828 , 23.33487961, 26.33645158, 23.97296355],\n",
       "       [28.18036857, 26.74926531, 27.09759007, 32.59977236, 21.50097532,\n",
       "        25.02335018, 21.60976593, 24.80684217, 23.69087654, 25.59480035],\n",
       "       [18.30291189, 21.45357498, 22.84838626, 23.83105354, 15.20683177,\n",
       "        20.38536833, 27.27687652, 25.91715601, 27.63911762, 29.05386446],\n",
       "       [19.181652  , 21.44435402, 26.2412344 , 22.3033811 , 18.06433181,\n",
       "        18.29716596, 14.5854873 , 22.10620314, 24.2320112 , 23.91506838],\n",
       "       [18.88200237, 15.43988762, 19.18838435, 15.36124806, 13.82507146,\n",
       "        12.12794587, 11.61341614, 20.91020286, 24.8868698 , 21.42813346],\n",
       "       [16.57492594, 11.68634712, 22.87644946, 15.25713683, 11.36274804,\n",
       "        19.78097348, 11.80952125, 16.01069297, 26.56864041, 29.69061741],\n",
       "       [17.3481624 , 14.65103829, 12.85307388, 14.08447291,  4.79407149,\n",
       "        15.67095072,  6.13636972, 10.20669427, 20.20202146, 19.96331158],\n",
       "       [13.1213519 , 18.17543639, 15.19747589, 19.25483456,  8.36219689,\n",
       "        10.34891751, 11.07013791, 17.80013781, 12.20584226, 12.21668865],\n",
       "       [13.92718041, 23.27303942, 18.95676092, 18.72391704, 11.64156544,\n",
       "        17.44857212, 17.05767596, 14.66099884, 10.07818518,  7.76905579],\n",
       "       [13.4355563 , 21.20383609, 14.43328591, 19.80911534, 21.10276611,\n",
       "        25.14973951, 27.25305858, 19.60456558, 17.08720759, 20.82891042],\n",
       "       [14.69018377, 19.0323388 , 23.8965079 , 22.17712491, 31.97167895,\n",
       "        29.99407018, 31.65462725, 30.94744633, 30.46264962, 35.80063409],\n",
       "       [28.22434138, 21.4348455 , 26.97483728, 24.37943407, 29.56595589,\n",
       "        23.72431776, 27.12438799, 26.91989704, 21.89958382, 22.7508294 ],\n",
       "       [27.75781905, 32.14006339, 34.49756565, 29.66289555, 30.96994012,\n",
       "        25.64622371, 24.91040341, 22.26098115, 18.96229961, 20.77042056],\n",
       "       [35.36236362, 42.33907215, 38.71462485, 35.08939495, 36.77954277,\n",
       "        33.92390379, 38.08475937, 29.85999768, 22.61528669, 24.53148795],\n",
       "       [29.83283796, 36.85440095, 35.41085905, 32.51045898, 32.72833237,\n",
       "        35.33677746, 40.79570619, 31.76667328, 25.75999273, 30.46935812],\n",
       "       [22.36524868, 29.2118469 , 36.456788  , 26.92389694, 31.73989574,\n",
       "        29.27367167, 32.74663801, 33.52358695, 41.03031641, 34.79089445],\n",
       "       [22.12817972, 19.80636081, 25.77115034, 16.69068657, 24.00702717,\n",
       "        17.91855557, 18.85702829, 21.89134606, 29.62750637, 27.27290824],\n",
       "       [16.31195046, 12.60731568, 15.98453964, 11.34371311, 15.65546334,\n",
       "        15.09809216, 11.60378186, 14.96456036, 20.71179232, 24.64444416],\n",
       "       [15.63823104, 13.22335646, 17.36778813, 11.38440186, 14.12045616,\n",
       "        15.6306136 , 13.37031542,  8.42834985, 20.41916492, 20.39289365],\n",
       "       [22.52069448, 18.97481215, 18.74927971, 22.08882836, 19.04195201,\n",
       "        20.41623642, 23.16468398, 18.88351978, 25.76380624, 25.51417332],\n",
       "       [20.17103442, 17.48686171, 17.12761901, 11.68616349, 12.99954973,\n",
       "         8.10113031, 24.62803216, 18.63177069, 23.79397874, 30.56140445],\n",
       "       [28.3097315 , 26.85037755, 27.16840362, 23.93329089, 25.0726479 ,\n",
       "        20.15760735, 29.07642675, 24.12832166, 24.36554375, 43.00782702],\n",
       "       [28.4651622 , 24.28859777, 21.79642126, 24.13617641, 27.84990032,\n",
       "        22.936095  , 22.29228152, 24.09284264, 23.22440259, 33.74902144],\n",
       "       [24.73934091, 12.33608433, 20.51431036, 18.13087085, 19.4976234 ,\n",
       "        18.18247773,  9.85266384, 17.10483483, 20.00882302, 22.73822798],\n",
       "       [15.64460686,  6.82902407, 10.86302388, 11.627845  ,  7.19724907,\n",
       "         7.79891912,  2.14973976,  7.21958735, 10.06634599,  3.04397672],\n",
       "       [15.29197614, 18.97619895, 14.93796954, 19.84743387, 13.68018928,\n",
       "        11.37739229, 13.07483557, 17.84549307, 17.59642521, 12.65617554],\n",
       "       [18.88375895, 21.27550757, 26.42717588, 20.09930854, 12.69941994,\n",
       "        13.95787226, 12.25806371, 15.30544861, 13.00660687, 15.12780428],\n",
       "       [23.29367059, 21.91772227, 20.12341706, 20.41734004, 13.89726973,\n",
       "        19.97059694, 15.19227072, 12.77754758, 18.87098076, 20.64846342],\n",
       "       [15.47319163, 15.86083633, 14.27757505, 13.98614134, 15.20229162,\n",
       "        18.34063259, 16.64909321, 16.8325801 , 25.15437947, 27.533094  ],\n",
       "       [18.84325783, 19.43479932, 20.49689516, 24.5031803 , 19.91128329,\n",
       "        19.25248786, 18.3843515 , 27.32751837, 26.55825109, 23.19933978]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = model.predict(features_test)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "86b05efd",
   "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: 8.622348697110283\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": 18,
   "id": "201a27d1",
   "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[-1]  ## predicted target  cases\n",
    "dataY_plot = targets_test.to_numpy()[-1]  ##  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": "299dabfa",
   "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": "8bbf31be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54a79255",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Multilayer_Perceptron_WeeklyPrecipitation.ipynb",
   "provenance": [
    {
     "file_id": "1kbOQzG0CZQ8AsG1uaLJZpy_mNhCpPGK7",
     "timestamp": 1625022677240
    }
   ]
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}