timeSeriesV2_weeklyData_Urban.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8da86ec5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "44dd90be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read Data\n",
    "data = pd.read_csv('Data/requiredMunicipalities_temperature_precipitation_elevation_DengueCasesWeek.csv')\n",
    "# Remove extra column\n",
    "data = data.drop('Unnamed: 0', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d601a8bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Municipality</th>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <th>PRECIPITATION_jun_07</th>\n",
       "      <th>PRECIPITATION_jul_07</th>\n",
       "      <th>PRECIPITATION_aug_07</th>\n",
       "      <th>...</th>\n",
       "      <th>2019/w43</th>\n",
       "      <th>2019/w44</th>\n",
       "      <th>2019/w45</th>\n",
       "      <th>2019/w46</th>\n",
       "      <th>2019/w47</th>\n",
       "      <th>2019/w48</th>\n",
       "      <th>2019/w49</th>\n",
       "      <th>2019/w50</th>\n",
       "      <th>2019/w51</th>\n",
       "      <th>2019/w52</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>antioquia</td>\n",
       "      <td>alejandria</td>\n",
       "      <td>66.638855</td>\n",
       "      <td>66.628891</td>\n",
       "      <td>199.928375</td>\n",
       "      <td>286.881470</td>\n",
       "      <td>345.874695</td>\n",
       "      <td>161.073792</td>\n",
       "      <td>339.436737</td>\n",
       "      <td>324.082489</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>antioquia</td>\n",
       "      <td>amaga</td>\n",
       "      <td>62.527489</td>\n",
       "      <td>46.315376</td>\n",
       "      <td>166.522003</td>\n",
       "      <td>288.957397</td>\n",
       "      <td>350.666260</td>\n",
       "      <td>143.757507</td>\n",
       "      <td>350.168793</td>\n",
       "      <td>210.302658</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>antioquia</td>\n",
       "      <td>amalfi</td>\n",
       "      <td>76.737572</td>\n",
       "      <td>62.784908</td>\n",
       "      <td>198.478897</td>\n",
       "      <td>322.928314</td>\n",
       "      <td>381.405701</td>\n",
       "      <td>191.377609</td>\n",
       "      <td>387.475983</td>\n",
       "      <td>408.289520</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>antioquia</td>\n",
       "      <td>andes</td>\n",
       "      <td>115.193504</td>\n",
       "      <td>61.572941</td>\n",
       "      <td>205.126846</td>\n",
       "      <td>308.268433</td>\n",
       "      <td>332.884583</td>\n",
       "      <td>116.664536</td>\n",
       "      <td>310.750671</td>\n",
       "      <td>189.581451</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>antioquia</td>\n",
       "      <td>angelopolis</td>\n",
       "      <td>69.933151</td>\n",
       "      <td>43.129227</td>\n",
       "      <td>178.370377</td>\n",
       "      <td>278.665588</td>\n",
       "      <td>326.885010</td>\n",
       "      <td>144.349655</td>\n",
       "      <td>360.914429</td>\n",
       "      <td>234.040939</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1019 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Municipality  PRECIPITATION_jan_07  PRECIPITATION_feb_07  \\\n",
       "0  antioquia   alejandria             66.638855             66.628891   \n",
       "1  antioquia        amaga             62.527489             46.315376   \n",
       "2  antioquia       amalfi             76.737572             62.784908   \n",
       "3  antioquia        andes            115.193504             61.572941   \n",
       "4  antioquia  angelopolis             69.933151             43.129227   \n",
       "\n",
       "   PRECIPITATION_mar_07  PRECIPITATION_apr_07  PRECIPITATION_may_07  \\\n",
       "0            199.928375            286.881470            345.874695   \n",
       "1            166.522003            288.957397            350.666260   \n",
       "2            198.478897            322.928314            381.405701   \n",
       "3            205.126846            308.268433            332.884583   \n",
       "4            178.370377            278.665588            326.885010   \n",
       "\n",
       "   PRECIPITATION_jun_07  PRECIPITATION_jul_07  PRECIPITATION_aug_07  ...  \\\n",
       "0            161.073792            339.436737            324.082489  ...   \n",
       "1            143.757507            350.168793            210.302658  ...   \n",
       "2            191.377609            387.475983            408.289520  ...   \n",
       "3            116.664536            310.750671            189.581451  ...   \n",
       "4            144.349655            360.914429            234.040939  ...   \n",
       "\n",
       "   2019/w43  2019/w44  2019/w45  2019/w46  2019/w47  2019/w48  2019/w49  \\\n",
       "0         0         0         0         0         0         0         0   \n",
       "1         0         0         0         0         0         1         1   \n",
       "2         0         0         0         0         0         0         0   \n",
       "3         0         0         0         0         1         0         0   \n",
       "4         0         0         0         0         0         0         0   \n",
       "\n",
       "   2019/w50  2019/w51  2019/w52  \n",
       "0         0         0         0  \n",
       "1         1         0         0  \n",
       "2         0         1         0  \n",
       "3         0         0         0  \n",
       "4         0         0         0  \n",
       "\n",
       "[5 rows x 1019 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11df9062",
   "metadata": {},
   "source": [
    "## Columns with temperature, precipitation, and Dengue Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9d039cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <th>TEMPERATURE_jun_07</th>\n",
       "      <th>TEMPERATURE_jul_07</th>\n",
       "      <th>TEMPERATURE_aug_07</th>\n",
       "      <th>TEMPERATURE_sep_07</th>\n",
       "      <th>TEMPERATURE_oct_07</th>\n",
       "      <th>...</th>\n",
       "      <th>TEMPERATURE_mar_18</th>\n",
       "      <th>TEMPERATURE_apr_18</th>\n",
       "      <th>TEMPERATURE_may_18</th>\n",
       "      <th>TEMPERATURE_jun_18</th>\n",
       "      <th>TEMPERATURE_jul_18</th>\n",
       "      <th>TEMPERATURE_aug_18</th>\n",
       "      <th>TEMPERATURE_sep_18</th>\n",
       "      <th>TEMPERATURE_oct_18</th>\n",
       "      <th>TEMPERATURE_nov_18</th>\n",
       "      <th>TEMPERATURE_dec_18</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>20.606707</td>\n",
       "      <td>20.672624</td>\n",
       "      <td>21.210392</td>\n",
       "      <td>21.113854</td>\n",
       "      <td>21.331241</td>\n",
       "      <td>21.114738</td>\n",
       "      <td>21.333450</td>\n",
       "      <td>20.634999</td>\n",
       "      <td>20.499266</td>\n",
       "      <td>19.855212</td>\n",
       "      <td>...</td>\n",
       "      <td>21.489558</td>\n",
       "      <td>20.985991</td>\n",
       "      <td>21.025772</td>\n",
       "      <td>21.377411</td>\n",
       "      <td>21.652374</td>\n",
       "      <td>21.425886</td>\n",
       "      <td>21.230342</td>\n",
       "      <td>20.725872</td>\n",
       "      <td>20.687894</td>\n",
       "      <td>20.574121</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.814870</td>\n",
       "      <td>20.851651</td>\n",
       "      <td>21.328261</td>\n",
       "      <td>21.144019</td>\n",
       "      <td>21.174519</td>\n",
       "      <td>20.900712</td>\n",
       "      <td>21.281823</td>\n",
       "      <td>20.447760</td>\n",
       "      <td>20.407709</td>\n",
       "      <td>19.706597</td>\n",
       "      <td>...</td>\n",
       "      <td>21.648314</td>\n",
       "      <td>21.020582</td>\n",
       "      <td>20.880769</td>\n",
       "      <td>21.149931</td>\n",
       "      <td>21.560988</td>\n",
       "      <td>21.380573</td>\n",
       "      <td>21.026458</td>\n",
       "      <td>20.558420</td>\n",
       "      <td>20.625113</td>\n",
       "      <td>20.717262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.520126</td>\n",
       "      <td>21.536331</td>\n",
       "      <td>22.240293</td>\n",
       "      <td>22.223073</td>\n",
       "      <td>22.306279</td>\n",
       "      <td>22.010652</td>\n",
       "      <td>22.411260</td>\n",
       "      <td>21.424171</td>\n",
       "      <td>21.347958</td>\n",
       "      <td>20.695219</td>\n",
       "      <td>...</td>\n",
       "      <td>22.440292</td>\n",
       "      <td>22.133230</td>\n",
       "      <td>21.910445</td>\n",
       "      <td>22.177232</td>\n",
       "      <td>22.709351</td>\n",
       "      <td>22.234674</td>\n",
       "      <td>22.014884</td>\n",
       "      <td>21.497476</td>\n",
       "      <td>21.550504</td>\n",
       "      <td>21.471094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.030849</td>\n",
       "      <td>16.976915</td>\n",
       "      <td>17.263741</td>\n",
       "      <td>17.063804</td>\n",
       "      <td>17.199818</td>\n",
       "      <td>17.035077</td>\n",
       "      <td>17.151788</td>\n",
       "      <td>16.501156</td>\n",
       "      <td>16.793839</td>\n",
       "      <td>15.996531</td>\n",
       "      <td>...</td>\n",
       "      <td>17.669470</td>\n",
       "      <td>16.954950</td>\n",
       "      <td>16.968828</td>\n",
       "      <td>17.341847</td>\n",
       "      <td>17.520799</td>\n",
       "      <td>17.428500</td>\n",
       "      <td>17.365973</td>\n",
       "      <td>16.974917</td>\n",
       "      <td>17.126363</td>\n",
       "      <td>17.195797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.095986</td>\n",
       "      <td>20.092206</td>\n",
       "      <td>20.527178</td>\n",
       "      <td>20.310996</td>\n",
       "      <td>20.335141</td>\n",
       "      <td>20.198780</td>\n",
       "      <td>20.571788</td>\n",
       "      <td>19.733009</td>\n",
       "      <td>19.727717</td>\n",
       "      <td>19.003105</td>\n",
       "      <td>...</td>\n",
       "      <td>20.841154</td>\n",
       "      <td>20.195543</td>\n",
       "      <td>20.037224</td>\n",
       "      <td>20.429421</td>\n",
       "      <td>20.838454</td>\n",
       "      <td>20.657140</td>\n",
       "      <td>20.333967</td>\n",
       "      <td>19.815866</td>\n",
       "      <td>19.987836</td>\n",
       "      <td>20.030714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 144 columns</p>\n",
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      ],
      "text/plain": [
       "   TEMPERATURE_jan_07  TEMPERATURE_feb_07  TEMPERATURE_mar_07  \\\n",
       "0           20.606707           20.672624           21.210392   \n",
       "1           20.814870           20.851651           21.328261   \n",
       "2           21.520126           21.536331           22.240293   \n",
       "3           17.030849           16.976915           17.263741   \n",
       "4           20.095986           20.092206           20.527178   \n",
       "\n",
       "   TEMPERATURE_apr_07  TEMPERATURE_may_07  TEMPERATURE_jun_07  \\\n",
       "0           21.113854           21.331241           21.114738   \n",
       "1           21.144019           21.174519           20.900712   \n",
       "2           22.223073           22.306279           22.010652   \n",
       "3           17.063804           17.199818           17.035077   \n",
       "4           20.310996           20.335141           20.198780   \n",
       "\n",
       "   TEMPERATURE_jul_07  TEMPERATURE_aug_07  TEMPERATURE_sep_07  \\\n",
       "0           21.333450           20.634999           20.499266   \n",
       "1           21.281823           20.447760           20.407709   \n",
       "2           22.411260           21.424171           21.347958   \n",
       "3           17.151788           16.501156           16.793839   \n",
       "4           20.571788           19.733009           19.727717   \n",
       "\n",
       "   TEMPERATURE_oct_07  ...  TEMPERATURE_mar_18  TEMPERATURE_apr_18  \\\n",
       "0           19.855212  ...           21.489558           20.985991   \n",
       "1           19.706597  ...           21.648314           21.020582   \n",
       "2           20.695219  ...           22.440292           22.133230   \n",
       "3           15.996531  ...           17.669470           16.954950   \n",
       "4           19.003105  ...           20.841154           20.195543   \n",
       "\n",
       "   TEMPERATURE_may_18  TEMPERATURE_jun_18  TEMPERATURE_jul_18  \\\n",
       "0           21.025772           21.377411           21.652374   \n",
       "1           20.880769           21.149931           21.560988   \n",
       "2           21.910445           22.177232           22.709351   \n",
       "3           16.968828           17.341847           17.520799   \n",
       "4           20.037224           20.429421           20.838454   \n",
       "\n",
       "   TEMPERATURE_aug_18  TEMPERATURE_sep_18  TEMPERATURE_oct_18  \\\n",
       "0           21.425886           21.230342           20.725872   \n",
       "1           21.380573           21.026458           20.558420   \n",
       "2           22.234674           22.014884           21.497476   \n",
       "3           17.428500           17.365973           16.974917   \n",
       "4           20.657140           20.333967           19.815866   \n",
       "\n",
       "   TEMPERATURE_nov_18  TEMPERATURE_dec_18  \n",
       "0           20.687894           20.574121  \n",
       "1           20.625113           20.717262  \n",
       "2           21.550504           21.471094  \n",
       "3           17.126363           17.195797  \n",
       "4           19.987836           20.030714  \n",
       "\n",
       "[5 rows x 144 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take just temperature data\n",
    "x_temperature = data[data.columns[147:291]]\n",
    "x_temperature.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "861616e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <th>PRECIPITATION_jun_07</th>\n",
       "      <th>PRECIPITATION_jul_07</th>\n",
       "      <th>PRECIPITATION_aug_07</th>\n",
       "      <th>PRECIPITATION_sep_07</th>\n",
       "      <th>PRECIPITATION_oct_07</th>\n",
       "      <th>...</th>\n",
       "      <th>PRECIPITATION_mar_18</th>\n",
       "      <th>PRECIPITATION_apr_18</th>\n",
       "      <th>PRECIPITATION_may_18</th>\n",
       "      <th>PRECIPITATION_jun_18</th>\n",
       "      <th>PRECIPITATION_jul_18</th>\n",
       "      <th>PRECIPITATION_aug_18</th>\n",
       "      <th>PRECIPITATION_sep_18</th>\n",
       "      <th>PRECIPITATION_oct_18</th>\n",
       "      <th>PRECIPITATION_nov_18</th>\n",
       "      <th>PRECIPITATION_dec_18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>66.638855</td>\n",
       "      <td>66.628891</td>\n",
       "      <td>199.928375</td>\n",
       "      <td>286.881470</td>\n",
       "      <td>345.874695</td>\n",
       "      <td>161.073792</td>\n",
       "      <td>339.436737</td>\n",
       "      <td>324.082489</td>\n",
       "      <td>243.856186</td>\n",
       "      <td>361.769562</td>\n",
       "      <td>...</td>\n",
       "      <td>147.506775</td>\n",
       "      <td>292.958588</td>\n",
       "      <td>436.878143</td>\n",
       "      <td>225.225449</td>\n",
       "      <td>195.619019</td>\n",
       "      <td>256.421906</td>\n",
       "      <td>302.413086</td>\n",
       "      <td>353.701385</td>\n",
       "      <td>277.470184</td>\n",
       "      <td>61.464046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62.527489</td>\n",
       "      <td>46.315376</td>\n",
       "      <td>166.522003</td>\n",
       "      <td>288.957397</td>\n",
       "      <td>350.666260</td>\n",
       "      <td>143.757507</td>\n",
       "      <td>350.168793</td>\n",
       "      <td>210.302658</td>\n",
       "      <td>214.617142</td>\n",
       "      <td>283.321747</td>\n",
       "      <td>...</td>\n",
       "      <td>117.747261</td>\n",
       "      <td>256.695129</td>\n",
       "      <td>421.436859</td>\n",
       "      <td>195.391815</td>\n",
       "      <td>162.015167</td>\n",
       "      <td>182.192062</td>\n",
       "      <td>274.654327</td>\n",
       "      <td>259.078125</td>\n",
       "      <td>282.538574</td>\n",
       "      <td>68.923813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76.737572</td>\n",
       "      <td>62.784908</td>\n",
       "      <td>198.478897</td>\n",
       "      <td>322.928314</td>\n",
       "      <td>381.405701</td>\n",
       "      <td>191.377609</td>\n",
       "      <td>387.475983</td>\n",
       "      <td>408.289520</td>\n",
       "      <td>306.408356</td>\n",
       "      <td>385.371216</td>\n",
       "      <td>...</td>\n",
       "      <td>164.738312</td>\n",
       "      <td>349.761841</td>\n",
       "      <td>433.156830</td>\n",
       "      <td>264.976685</td>\n",
       "      <td>236.360321</td>\n",
       "      <td>286.805847</td>\n",
       "      <td>330.032654</td>\n",
       "      <td>388.256378</td>\n",
       "      <td>315.904602</td>\n",
       "      <td>71.508133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>115.193504</td>\n",
       "      <td>61.572941</td>\n",
       "      <td>205.126846</td>\n",
       "      <td>308.268433</td>\n",
       "      <td>332.884583</td>\n",
       "      <td>116.664536</td>\n",
       "      <td>310.750671</td>\n",
       "      <td>189.581451</td>\n",
       "      <td>194.997574</td>\n",
       "      <td>333.450806</td>\n",
       "      <td>...</td>\n",
       "      <td>144.462082</td>\n",
       "      <td>262.342285</td>\n",
       "      <td>394.904175</td>\n",
       "      <td>158.639008</td>\n",
       "      <td>142.633423</td>\n",
       "      <td>161.990524</td>\n",
       "      <td>246.869614</td>\n",
       "      <td>293.866364</td>\n",
       "      <td>298.362244</td>\n",
       "      <td>85.697762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.933151</td>\n",
       "      <td>43.129227</td>\n",
       "      <td>178.370377</td>\n",
       "      <td>278.665588</td>\n",
       "      <td>326.885010</td>\n",
       "      <td>144.349655</td>\n",
       "      <td>360.914429</td>\n",
       "      <td>234.040939</td>\n",
       "      <td>214.994553</td>\n",
       "      <td>291.776550</td>\n",
       "      <td>...</td>\n",
       "      <td>126.783783</td>\n",
       "      <td>247.130173</td>\n",
       "      <td>390.748199</td>\n",
       "      <td>197.610321</td>\n",
       "      <td>163.057632</td>\n",
       "      <td>205.655136</td>\n",
       "      <td>273.291779</td>\n",
       "      <td>268.090820</td>\n",
       "      <td>284.551331</td>\n",
       "      <td>67.718147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRECIPITATION_jan_07  PRECIPITATION_feb_07  PRECIPITATION_mar_07  \\\n",
       "0             66.638855             66.628891            199.928375   \n",
       "1             62.527489             46.315376            166.522003   \n",
       "2             76.737572             62.784908            198.478897   \n",
       "3            115.193504             61.572941            205.126846   \n",
       "4             69.933151             43.129227            178.370377   \n",
       "\n",
       "   PRECIPITATION_apr_07  PRECIPITATION_may_07  PRECIPITATION_jun_07  \\\n",
       "0            286.881470            345.874695            161.073792   \n",
       "1            288.957397            350.666260            143.757507   \n",
       "2            322.928314            381.405701            191.377609   \n",
       "3            308.268433            332.884583            116.664536   \n",
       "4            278.665588            326.885010            144.349655   \n",
       "\n",
       "   PRECIPITATION_jul_07  PRECIPITATION_aug_07  PRECIPITATION_sep_07  \\\n",
       "0            339.436737            324.082489            243.856186   \n",
       "1            350.168793            210.302658            214.617142   \n",
       "2            387.475983            408.289520            306.408356   \n",
       "3            310.750671            189.581451            194.997574   \n",
       "4            360.914429            234.040939            214.994553   \n",
       "\n",
       "   PRECIPITATION_oct_07  ...  PRECIPITATION_mar_18  PRECIPITATION_apr_18  \\\n",
       "0            361.769562  ...            147.506775            292.958588   \n",
       "1            283.321747  ...            117.747261            256.695129   \n",
       "2            385.371216  ...            164.738312            349.761841   \n",
       "3            333.450806  ...            144.462082            262.342285   \n",
       "4            291.776550  ...            126.783783            247.130173   \n",
       "\n",
       "   PRECIPITATION_may_18  PRECIPITATION_jun_18  PRECIPITATION_jul_18  \\\n",
       "0            436.878143            225.225449            195.619019   \n",
       "1            421.436859            195.391815            162.015167   \n",
       "2            433.156830            264.976685            236.360321   \n",
       "3            394.904175            158.639008            142.633423   \n",
       "4            390.748199            197.610321            163.057632   \n",
       "\n",
       "   PRECIPITATION_aug_18  PRECIPITATION_sep_18  PRECIPITATION_oct_18  \\\n",
       "0            256.421906            302.413086            353.701385   \n",
       "1            182.192062            274.654327            259.078125   \n",
       "2            286.805847            330.032654            388.256378   \n",
       "3            161.990524            246.869614            293.866364   \n",
       "4            205.655136            273.291779            268.090820   \n",
       "\n",
       "   PRECIPITATION_nov_18  PRECIPITATION_dec_18  \n",
       "0            277.470184             61.464046  \n",
       "1            282.538574             68.923813  \n",
       "2            315.904602             71.508133  \n",
       "3            298.362244             85.697762  \n",
       "4            284.551331             67.718147  \n",
       "\n",
       "[5 rows x 144 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take just precipitation data\n",
    "x_precipitation = data[data.columns[2:146]]\n",
    "x_precipitation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e6780ce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2007/w01</th>\n",
       "      <th>2007/w02</th>\n",
       "      <th>2007/w03</th>\n",
       "      <th>2007/w04</th>\n",
       "      <th>2007/w05</th>\n",
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       "      <th>2007/w09</th>\n",
       "      <th>2007/w10</th>\n",
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       "      <th>3</th>\n",
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       "      <td>0</td>\n",
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       "</table>\n",
       "<p>5 rows × 624 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   2007/w01  2007/w02  2007/w03  2007/w04  2007/w05  2007/w06  2007/w07  \\\n",
       "0         0         0         0         0         0         0         0   \n",
       "1         0         0         0         0         0         0         0   \n",
       "2         0         0         0         0         0         0         0   \n",
       "3         0         0         0         0         0         0         0   \n",
       "4         0         0         0         0         0         0         0   \n",
       "\n",
       "   2007/w08  2007/w09  2007/w10  ...  2018/w43  2018/w44  2018/w45  2018/w46  \\\n",
       "0         0         0         0  ...         0         0         0         0   \n",
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       "2         0         0         0  ...         0         0         0         0   \n",
       "3         0         0         0  ...         0         0         0         0   \n",
       "4         0         0         0  ...         0         0         0         0   \n",
       "\n",
       "   2018/w47  2018/w48  2018/w49  2018/w50  2018/w51  2018/w52  \n",
       "0         0         0         0         0         0         0  \n",
       "1         0         0         0         0         0         0  \n",
       "2         0         0         0         0         0         0  \n",
       "3         0         0         0         0         0         0  \n",
       "4         0         0         0         0         0         0  \n",
       "\n",
       "[5 rows x 624 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take Columns of weekly cases (week 1 2007 - week 52 2018)\n",
    "y_data = data[data.columns[343:-52]]\n",
    "y_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51f9e3bf",
   "metadata": {},
   "source": [
    "# Temperature, Percipitation and Dengue Cases weekly 2007-2018 for Medellin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "060b80f5",
   "metadata": {},
   "source": [
    "# Data Pre-Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ba3f94b",
   "metadata": {},
   "source": [
    "Temperature, Percipitation, Cases for Medellin from original data  (sivigila - cases) (google earth engine - precipitation and worldclim - temperature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "246419ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Municipality</th>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
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       "      <th>PRECIPITATION_aug_07</th>\n",
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       "      <th>2019/w50</th>\n",
       "      <th>2019/w51</th>\n",
       "      <th>2019/w52</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>antioquia</td>\n",
       "      <td>medellin</td>\n",
       "      <td>65.4114</td>\n",
       "      <td>49.307404</td>\n",
       "      <td>195.452469</td>\n",
       "      <td>291.927124</td>\n",
       "      <td>345.532501</td>\n",
       "      <td>157.963028</td>\n",
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       "</table>\n",
       "<p>1 rows × 1019 columns</p>\n",
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      ],
      "text/plain": [
       "        State Municipality  PRECIPITATION_jan_07  PRECIPITATION_feb_07  \\\n",
       "62  antioquia     medellin               65.4114             49.307404   \n",
       "\n",
       "    PRECIPITATION_mar_07  PRECIPITATION_apr_07  PRECIPITATION_may_07  \\\n",
       "62            195.452469            291.927124            345.532501   \n",
       "\n",
       "    PRECIPITATION_jun_07  PRECIPITATION_jul_07  PRECIPITATION_aug_07  ...  \\\n",
       "62            157.963028            386.515533            230.007416  ...   \n",
       "\n",
       "    2019/w43  2019/w44  2019/w45  2019/w46  2019/w47  2019/w48  2019/w49  \\\n",
       "62        29        17        35        16        21        15        20   \n",
       "\n",
       "    2019/w50  2019/w51  2019/w52  \n",
       "62        30        14         5  \n",
       "\n",
       "[1 rows x 1019 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take just the row of Medellin from original dataframe\n",
    "data_medellin = data[data.Municipality=='medellin']\n",
    "data_medellin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83882d60",
   "metadata": {},
   "source": [
    "### PRECIPITATION DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f68a43c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <th>PRECIPITATION_jun_07</th>\n",
       "      <th>PRECIPITATION_jul_07</th>\n",
       "      <th>PRECIPITATION_aug_07</th>\n",
       "      <th>PRECIPITATION_sep_07</th>\n",
       "      <th>PRECIPITATION_oct_07</th>\n",
       "      <th>...</th>\n",
       "      <th>PRECIPITATION_mar_18</th>\n",
       "      <th>PRECIPITATION_apr_18</th>\n",
       "      <th>PRECIPITATION_may_18</th>\n",
       "      <th>PRECIPITATION_jun_18</th>\n",
       "      <th>PRECIPITATION_jul_18</th>\n",
       "      <th>PRECIPITATION_aug_18</th>\n",
       "      <th>PRECIPITATION_sep_18</th>\n",
       "      <th>PRECIPITATION_oct_18</th>\n",
       "      <th>PRECIPITATION_nov_18</th>\n",
       "      <th>PRECIPITATION_dec_18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>65.4114</td>\n",
       "      <td>49.307404</td>\n",
       "      <td>195.452469</td>\n",
       "      <td>291.927124</td>\n",
       "      <td>345.532501</td>\n",
       "      <td>157.963028</td>\n",
       "      <td>386.515533</td>\n",
       "      <td>230.007416</td>\n",
       "      <td>234.679382</td>\n",
       "      <td>320.92984</td>\n",
       "      <td>...</td>\n",
       "      <td>140.323318</td>\n",
       "      <td>267.799438</td>\n",
       "      <td>418.379303</td>\n",
       "      <td>219.436752</td>\n",
       "      <td>180.841248</td>\n",
       "      <td>200.803131</td>\n",
       "      <td>294.888611</td>\n",
       "      <td>302.151306</td>\n",
       "      <td>276.914551</td>\n",
       "      <td>67.528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PRECIPITATION_jan_07  PRECIPITATION_feb_07  PRECIPITATION_mar_07  \\\n",
       "62               65.4114             49.307404            195.452469   \n",
       "\n",
       "    PRECIPITATION_apr_07  PRECIPITATION_may_07  PRECIPITATION_jun_07  \\\n",
       "62            291.927124            345.532501            157.963028   \n",
       "\n",
       "    PRECIPITATION_jul_07  PRECIPITATION_aug_07  PRECIPITATION_sep_07  \\\n",
       "62            386.515533            230.007416            234.679382   \n",
       "\n",
       "    PRECIPITATION_oct_07  ...  PRECIPITATION_mar_18  PRECIPITATION_apr_18  \\\n",
       "62             320.92984  ...            140.323318            267.799438   \n",
       "\n",
       "    PRECIPITATION_may_18  PRECIPITATION_jun_18  PRECIPITATION_jul_18  \\\n",
       "62            418.379303            219.436752            180.841248   \n",
       "\n",
       "    PRECIPITATION_aug_18  PRECIPITATION_sep_18  PRECIPITATION_oct_18  \\\n",
       "62            200.803131            294.888611            302.151306   \n",
       "\n",
       "    PRECIPITATION_nov_18  PRECIPITATION_dec_18  \n",
       "62            276.914551                67.528  \n",
       "\n",
       "[1 rows x 144 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take the columns of precipitation for Medellin\n",
    "x_precipitation_medellin = data_medellin[data_medellin.columns[2:146]]\n",
    "x_precipitation_medellin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "84641f80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percipitation_medellin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <td>65.411400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <td>49.307404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <td>195.452469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <td>291.927124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <td>345.532501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_aug_18</th>\n",
       "      <td>200.803131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_sep_18</th>\n",
       "      <td>294.888611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_oct_18</th>\n",
       "      <td>302.151306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_nov_18</th>\n",
       "      <td>276.914551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_dec_18</th>\n",
       "      <td>67.528000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percipitation_medellin\n",
       "PRECIPITATION_jan_07               65.411400\n",
       "PRECIPITATION_feb_07               49.307404\n",
       "PRECIPITATION_mar_07              195.452469\n",
       "PRECIPITATION_apr_07              291.927124\n",
       "PRECIPITATION_may_07              345.532501\n",
       "...                                      ...\n",
       "PRECIPITATION_aug_18              200.803131\n",
       "PRECIPITATION_sep_18              294.888611\n",
       "PRECIPITATION_oct_18              302.151306\n",
       "PRECIPITATION_nov_18              276.914551\n",
       "PRECIPITATION_dec_18               67.528000\n",
       "\n",
       "[144 rows x 1 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose the row to a Column of precipitation for Medellin\n",
    "x_precipitation_medellin_transposed = x_precipitation_medellin.T\n",
    "x_precipitation_medellin_transposed = x_precipitation_medellin_transposed.rename(columns= {62:'percipitation_medellin'})  ##integer\n",
    "x_precipitation_medellin_transposed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb2d34a",
   "metadata": {},
   "source": [
    "## Get date of precipitation from index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "71deca79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percipitation_medellin</th>\n",
       "      <th>DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <td>49.307404</td>\n",
       "      <td>[PRECIPITATION, feb, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <td>195.452469</td>\n",
       "      <td>[PRECIPITATION, mar, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <td>291.927124</td>\n",
       "      <td>[PRECIPITATION, apr, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <td>345.532501</td>\n",
       "      <td>[PRECIPITATION, may, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_aug_18</th>\n",
       "      <td>200.803131</td>\n",
       "      <td>[PRECIPITATION, aug, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_sep_18</th>\n",
       "      <td>294.888611</td>\n",
       "      <td>[PRECIPITATION, sep, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_oct_18</th>\n",
       "      <td>302.151306</td>\n",
       "      <td>[PRECIPITATION, oct, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_nov_18</th>\n",
       "      <td>276.914551</td>\n",
       "      <td>[PRECIPITATION, nov, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_dec_18</th>\n",
       "      <td>67.528000</td>\n",
       "      <td>[PRECIPITATION, dec, 18]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percipitation_medellin                      DATE\n",
       "PRECIPITATION_jan_07               65.411400  [PRECIPITATION, jan, 07]\n",
       "PRECIPITATION_feb_07               49.307404  [PRECIPITATION, feb, 07]\n",
       "PRECIPITATION_mar_07              195.452469  [PRECIPITATION, mar, 07]\n",
       "PRECIPITATION_apr_07              291.927124  [PRECIPITATION, apr, 07]\n",
       "PRECIPITATION_may_07              345.532501  [PRECIPITATION, may, 07]\n",
       "...                                      ...                       ...\n",
       "PRECIPITATION_aug_18              200.803131  [PRECIPITATION, aug, 18]\n",
       "PRECIPITATION_sep_18              294.888611  [PRECIPITATION, sep, 18]\n",
       "PRECIPITATION_oct_18              302.151306  [PRECIPITATION, oct, 18]\n",
       "PRECIPITATION_nov_18              276.914551  [PRECIPITATION, nov, 18]\n",
       "PRECIPITATION_dec_18               67.528000  [PRECIPITATION, dec, 18]\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a new column of date\n",
    "x_precipitation_medellin_transposed['DATE']=x_precipitation_medellin_transposed.index.str.split(\"_\")\n",
    "x_precipitation_medellin_transposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b013aad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percipitation_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <td>49.307404</td>\n",
       "      <td>[PRECIPITATION, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <td>195.452469</td>\n",
       "      <td>[PRECIPITATION, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <td>291.927124</td>\n",
       "      <td>[PRECIPITATION, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <td>345.532501</td>\n",
       "      <td>[PRECIPITATION, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percipitation_medellin                      DATE  YEAR\n",
       "PRECIPITATION_jan_07               65.411400  [PRECIPITATION, jan, 07]  2007\n",
       "PRECIPITATION_feb_07               49.307404  [PRECIPITATION, feb, 07]  2007\n",
       "PRECIPITATION_mar_07              195.452469  [PRECIPITATION, mar, 07]  2007\n",
       "PRECIPITATION_apr_07              291.927124  [PRECIPITATION, apr, 07]  2007\n",
       "PRECIPITATION_may_07              345.532501  [PRECIPITATION, may, 07]  2007"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get year from date column\n",
    "x_precipitation_medellin_transposed['YEAR'] = x_precipitation_medellin_transposed['DATE'].apply(lambda x: \"20\" + x[2])  \n",
    "x_precipitation_medellin_transposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3061f76c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percipitation_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <td>49.307404</td>\n",
       "      <td>[PRECIPITATION, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <td>195.452469</td>\n",
       "      <td>[PRECIPITATION, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <td>291.927124</td>\n",
       "      <td>[PRECIPITATION, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <td>345.532501</td>\n",
       "      <td>[PRECIPITATION, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>may</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percipitation_medellin                      DATE  YEAR  \\\n",
       "PRECIPITATION_jan_07               65.411400  [PRECIPITATION, jan, 07]  2007   \n",
       "PRECIPITATION_feb_07               49.307404  [PRECIPITATION, feb, 07]  2007   \n",
       "PRECIPITATION_mar_07              195.452469  [PRECIPITATION, mar, 07]  2007   \n",
       "PRECIPITATION_apr_07              291.927124  [PRECIPITATION, apr, 07]  2007   \n",
       "PRECIPITATION_may_07              345.532501  [PRECIPITATION, may, 07]  2007   \n",
       "\n",
       "                     MONTH  \n",
       "PRECIPITATION_jan_07   jan  \n",
       "PRECIPITATION_feb_07   feb  \n",
       "PRECIPITATION_mar_07   mar  \n",
       "PRECIPITATION_apr_07   apr  \n",
       "PRECIPITATION_may_07   may  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get year from month column\n",
    "x_precipitation_medellin_transposed['MONTH'] = x_precipitation_medellin_transposed['DATE'].apply(lambda x: x[1])  \n",
    "x_precipitation_medellin_transposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7856c75e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## function to change month as int to numeric\n",
    "def month_as_int(month):\n",
    "  if month == 'jan':\n",
    "    return '01'\n",
    "  if month == 'feb':\n",
    "    return '02' \n",
    "  if month == 'mar':\n",
    "    return '03'\n",
    "  if month == 'apr':\n",
    "    return '04' \n",
    "  if month == 'may':\n",
    "    return '05'\n",
    "  if month == 'jun':\n",
    "    return '06' \n",
    "  if month == 'jul':\n",
    "    return '07'\n",
    "  if month == 'aug':\n",
    "    return '08' \n",
    "  if month == 'sep':\n",
    "    return '09'\n",
    "  if month == 'oct':\n",
    "    return '10' \n",
    "  if month == 'nov':\n",
    "    return '11'   \n",
    "  if month == 'dec':\n",
    "    return '12'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "137703de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percipitation_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <td>49.307404</td>\n",
       "      <td>[PRECIPITATION, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <td>195.452469</td>\n",
       "      <td>[PRECIPITATION, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <td>291.927124</td>\n",
       "      <td>[PRECIPITATION, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <td>345.532501</td>\n",
       "      <td>[PRECIPITATION, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>may</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_aug_18</th>\n",
       "      <td>200.803131</td>\n",
       "      <td>[PRECIPITATION, aug, 18]</td>\n",
       "      <td>2018</td>\n",
       "      <td>aug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_sep_18</th>\n",
       "      <td>294.888611</td>\n",
       "      <td>[PRECIPITATION, sep, 18]</td>\n",
       "      <td>2018</td>\n",
       "      <td>sep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_oct_18</th>\n",
       "      <td>302.151306</td>\n",
       "      <td>[PRECIPITATION, oct, 18]</td>\n",
       "      <td>2018</td>\n",
       "      <td>oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_nov_18</th>\n",
       "      <td>276.914551</td>\n",
       "      <td>[PRECIPITATION, nov, 18]</td>\n",
       "      <td>2018</td>\n",
       "      <td>nov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_dec_18</th>\n",
       "      <td>67.528000</td>\n",
       "      <td>[PRECIPITATION, dec, 18]</td>\n",
       "      <td>2018</td>\n",
       "      <td>dec</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percipitation_medellin                      DATE  YEAR  \\\n",
       "PRECIPITATION_jan_07               65.411400  [PRECIPITATION, jan, 07]  2007   \n",
       "PRECIPITATION_feb_07               49.307404  [PRECIPITATION, feb, 07]  2007   \n",
       "PRECIPITATION_mar_07              195.452469  [PRECIPITATION, mar, 07]  2007   \n",
       "PRECIPITATION_apr_07              291.927124  [PRECIPITATION, apr, 07]  2007   \n",
       "PRECIPITATION_may_07              345.532501  [PRECIPITATION, may, 07]  2007   \n",
       "...                                      ...                       ...   ...   \n",
       "PRECIPITATION_aug_18              200.803131  [PRECIPITATION, aug, 18]  2018   \n",
       "PRECIPITATION_sep_18              294.888611  [PRECIPITATION, sep, 18]  2018   \n",
       "PRECIPITATION_oct_18              302.151306  [PRECIPITATION, oct, 18]  2018   \n",
       "PRECIPITATION_nov_18              276.914551  [PRECIPITATION, nov, 18]  2018   \n",
       "PRECIPITATION_dec_18               67.528000  [PRECIPITATION, dec, 18]  2018   \n",
       "\n",
       "                     MONTH  \n",
       "PRECIPITATION_jan_07   jan  \n",
       "PRECIPITATION_feb_07   feb  \n",
       "PRECIPITATION_mar_07   mar  \n",
       "PRECIPITATION_apr_07   apr  \n",
       "PRECIPITATION_may_07   may  \n",
       "...                    ...  \n",
       "PRECIPITATION_aug_18   aug  \n",
       "PRECIPITATION_sep_18   sep  \n",
       "PRECIPITATION_oct_18   oct  \n",
       "PRECIPITATION_nov_18   nov  \n",
       "PRECIPITATION_dec_18   dec  \n",
       "\n",
       "[144 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_precipitation_medellin_transposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fbffca04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percipitation_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <td>49.307404</td>\n",
       "      <td>[PRECIPITATION, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <td>195.452469</td>\n",
       "      <td>[PRECIPITATION, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <td>291.927124</td>\n",
       "      <td>[PRECIPITATION, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <td>345.532501</td>\n",
       "      <td>[PRECIPITATION, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percipitation_medellin                      DATE  YEAR  \\\n",
       "PRECIPITATION_jan_07               65.411400  [PRECIPITATION, jan, 07]  2007   \n",
       "PRECIPITATION_feb_07               49.307404  [PRECIPITATION, feb, 07]  2007   \n",
       "PRECIPITATION_mar_07              195.452469  [PRECIPITATION, mar, 07]  2007   \n",
       "PRECIPITATION_apr_07              291.927124  [PRECIPITATION, apr, 07]  2007   \n",
       "PRECIPITATION_may_07              345.532501  [PRECIPITATION, may, 07]  2007   \n",
       "\n",
       "                     MONTH  \n",
       "PRECIPITATION_jan_07    01  \n",
       "PRECIPITATION_feb_07    02  \n",
       "PRECIPITATION_mar_07    03  \n",
       "PRECIPITATION_apr_07    04  \n",
       "PRECIPITATION_may_07    05  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change the month from jan to 01 and apply to every row in the df\n",
    "x_precipitation_medellin_transposed['MONTH'] = x_precipitation_medellin_transposed['MONTH'].apply(lambda x: month_as_int(month=x.strip()))\n",
    "x_precipitation_medellin_transposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "562ce574",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_precipitation_medellin_transposed['MONTH']=x_precipitation_medellin_transposed['MONTH'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90626348",
   "metadata": {},
   "source": [
    "### TEMPERATURE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fa53cc76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <th>TEMPERATURE_jun_07</th>\n",
       "      <th>TEMPERATURE_jul_07</th>\n",
       "      <th>TEMPERATURE_aug_07</th>\n",
       "      <th>TEMPERATURE_sep_07</th>\n",
       "      <th>TEMPERATURE_oct_07</th>\n",
       "      <th>...</th>\n",
       "      <th>TEMPERATURE_mar_18</th>\n",
       "      <th>TEMPERATURE_apr_18</th>\n",
       "      <th>TEMPERATURE_may_18</th>\n",
       "      <th>TEMPERATURE_jun_18</th>\n",
       "      <th>TEMPERATURE_jul_18</th>\n",
       "      <th>TEMPERATURE_aug_18</th>\n",
       "      <th>TEMPERATURE_sep_18</th>\n",
       "      <th>TEMPERATURE_oct_18</th>\n",
       "      <th>TEMPERATURE_nov_18</th>\n",
       "      <th>TEMPERATURE_dec_18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>20.982925</td>\n",
       "      <td>21.425911</td>\n",
       "      <td>21.237032</td>\n",
       "      <td>21.322522</td>\n",
       "      <td>21.243176</td>\n",
       "      <td>21.686978</td>\n",
       "      <td>20.866394</td>\n",
       "      <td>20.705536</td>\n",
       "      <td>19.954406</td>\n",
       "      <td>...</td>\n",
       "      <td>21.726171</td>\n",
       "      <td>21.124271</td>\n",
       "      <td>21.037365</td>\n",
       "      <td>21.457151</td>\n",
       "      <td>21.952168</td>\n",
       "      <td>21.719432</td>\n",
       "      <td>21.347202</td>\n",
       "      <td>20.748416</td>\n",
       "      <td>20.886703</td>\n",
       "      <td>20.936353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    TEMPERATURE_jan_07  TEMPERATURE_feb_07  TEMPERATURE_mar_07  \\\n",
       "62           21.009283           20.982925           21.425911   \n",
       "\n",
       "    TEMPERATURE_apr_07  TEMPERATURE_may_07  TEMPERATURE_jun_07  \\\n",
       "62           21.237032           21.322522           21.243176   \n",
       "\n",
       "    TEMPERATURE_jul_07  TEMPERATURE_aug_07  TEMPERATURE_sep_07  \\\n",
       "62           21.686978           20.866394           20.705536   \n",
       "\n",
       "    TEMPERATURE_oct_07  ...  TEMPERATURE_mar_18  TEMPERATURE_apr_18  \\\n",
       "62           19.954406  ...           21.726171           21.124271   \n",
       "\n",
       "    TEMPERATURE_may_18  TEMPERATURE_jun_18  TEMPERATURE_jul_18  \\\n",
       "62           21.037365           21.457151           21.952168   \n",
       "\n",
       "    TEMPERATURE_aug_18  TEMPERATURE_sep_18  TEMPERATURE_oct_18  \\\n",
       "62           21.719432           21.347202           20.748416   \n",
       "\n",
       "    TEMPERATURE_nov_18  TEMPERATURE_dec_18  \n",
       "62           20.886703           20.936353  \n",
       "\n",
       "[1 rows x 144 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take the columns of temperature for Medellin\n",
    "x_temperature_medellin = data_medellin[data_medellin.columns[147:291]]\n",
    "x_temperature_medellin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c45e3b87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature_medellin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <td>21.009283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <td>20.982925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <td>21.425911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <td>21.237032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <td>21.322522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_aug_18</th>\n",
       "      <td>21.719432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_sep_18</th>\n",
       "      <td>21.347202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_oct_18</th>\n",
       "      <td>20.748416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_nov_18</th>\n",
       "      <td>20.886703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_dec_18</th>\n",
       "      <td>20.936353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    temperature_medellin\n",
       "TEMPERATURE_jan_07             21.009283\n",
       "TEMPERATURE_feb_07             20.982925\n",
       "TEMPERATURE_mar_07             21.425911\n",
       "TEMPERATURE_apr_07             21.237032\n",
       "TEMPERATURE_may_07             21.322522\n",
       "...                                  ...\n",
       "TEMPERATURE_aug_18             21.719432\n",
       "TEMPERATURE_sep_18             21.347202\n",
       "TEMPERATURE_oct_18             20.748416\n",
       "TEMPERATURE_nov_18             20.886703\n",
       "TEMPERATURE_dec_18             20.936353\n",
       "\n",
       "[144 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose the row to a Column of temperature for Medellin\n",
    "x_temperature_medellin_transposed = x_temperature_medellin.T\n",
    "x_temperature_medellin_transposed = x_temperature_medellin_transposed.rename(columns= {62:'temperature_medellin'})  ##integer\n",
    "x_temperature_medellin_transposed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48b1fd2d",
   "metadata": {},
   "source": [
    "## Get date of temperature from index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7ca81f40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature_medellin</th>\n",
       "      <th>DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <td>20.982925</td>\n",
       "      <td>[TEMPERATURE, feb, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <td>21.425911</td>\n",
       "      <td>[TEMPERATURE, mar, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <td>21.237032</td>\n",
       "      <td>[TEMPERATURE, apr, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <td>21.322522</td>\n",
       "      <td>[TEMPERATURE, may, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_aug_18</th>\n",
       "      <td>21.719432</td>\n",
       "      <td>[TEMPERATURE, aug, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_sep_18</th>\n",
       "      <td>21.347202</td>\n",
       "      <td>[TEMPERATURE, sep, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_oct_18</th>\n",
       "      <td>20.748416</td>\n",
       "      <td>[TEMPERATURE, oct, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_nov_18</th>\n",
       "      <td>20.886703</td>\n",
       "      <td>[TEMPERATURE, nov, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_dec_18</th>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    temperature_medellin                    DATE\n",
       "TEMPERATURE_jan_07             21.009283  [TEMPERATURE, jan, 07]\n",
       "TEMPERATURE_feb_07             20.982925  [TEMPERATURE, feb, 07]\n",
       "TEMPERATURE_mar_07             21.425911  [TEMPERATURE, mar, 07]\n",
       "TEMPERATURE_apr_07             21.237032  [TEMPERATURE, apr, 07]\n",
       "TEMPERATURE_may_07             21.322522  [TEMPERATURE, may, 07]\n",
       "...                                  ...                     ...\n",
       "TEMPERATURE_aug_18             21.719432  [TEMPERATURE, aug, 18]\n",
       "TEMPERATURE_sep_18             21.347202  [TEMPERATURE, sep, 18]\n",
       "TEMPERATURE_oct_18             20.748416  [TEMPERATURE, oct, 18]\n",
       "TEMPERATURE_nov_18             20.886703  [TEMPERATURE, nov, 18]\n",
       "TEMPERATURE_dec_18             20.936353  [TEMPERATURE, dec, 18]\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column of date\n",
    "x_temperature_medellin_transposed['DATE']=x_temperature_medellin_transposed.index.str.split(\"_\")\n",
    "x_temperature_medellin_transposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f15a154",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <td>20.982925</td>\n",
       "      <td>[TEMPERATURE, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <td>21.425911</td>\n",
       "      <td>[TEMPERATURE, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <td>21.237032</td>\n",
       "      <td>[TEMPERATURE, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <td>21.322522</td>\n",
       "      <td>[TEMPERATURE, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    temperature_medellin                    DATE  YEAR\n",
       "TEMPERATURE_jan_07             21.009283  [TEMPERATURE, jan, 07]  2007\n",
       "TEMPERATURE_feb_07             20.982925  [TEMPERATURE, feb, 07]  2007\n",
       "TEMPERATURE_mar_07             21.425911  [TEMPERATURE, mar, 07]  2007\n",
       "TEMPERATURE_apr_07             21.237032  [TEMPERATURE, apr, 07]  2007\n",
       "TEMPERATURE_may_07             21.322522  [TEMPERATURE, may, 07]  2007"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get year from date column\n",
    "x_temperature_medellin_transposed['YEAR'] = x_temperature_medellin_transposed['DATE'].apply(lambda x: \"20\" + x[2])  \n",
    "x_temperature_medellin_transposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "93a97f67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <td>20.982925</td>\n",
       "      <td>[TEMPERATURE, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <td>21.425911</td>\n",
       "      <td>[TEMPERATURE, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <td>21.237032</td>\n",
       "      <td>[TEMPERATURE, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <td>21.322522</td>\n",
       "      <td>[TEMPERATURE, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>may</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    temperature_medellin                    DATE  YEAR MONTH\n",
       "TEMPERATURE_jan_07             21.009283  [TEMPERATURE, jan, 07]  2007   jan\n",
       "TEMPERATURE_feb_07             20.982925  [TEMPERATURE, feb, 07]  2007   feb\n",
       "TEMPERATURE_mar_07             21.425911  [TEMPERATURE, mar, 07]  2007   mar\n",
       "TEMPERATURE_apr_07             21.237032  [TEMPERATURE, apr, 07]  2007   apr\n",
       "TEMPERATURE_may_07             21.322522  [TEMPERATURE, may, 07]  2007   may"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get month from date column\n",
    "x_temperature_medellin_transposed['MONTH'] = x_temperature_medellin_transposed['DATE'].apply(lambda x: x[1])  \n",
    "x_temperature_medellin_transposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5a057643",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <td>20.982925</td>\n",
       "      <td>[TEMPERATURE, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <td>21.425911</td>\n",
       "      <td>[TEMPERATURE, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <td>21.237032</td>\n",
       "      <td>[TEMPERATURE, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <td>21.322522</td>\n",
       "      <td>[TEMPERATURE, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    temperature_medellin                    DATE  YEAR MONTH\n",
       "TEMPERATURE_jan_07             21.009283  [TEMPERATURE, jan, 07]  2007    01\n",
       "TEMPERATURE_feb_07             20.982925  [TEMPERATURE, feb, 07]  2007    02\n",
       "TEMPERATURE_mar_07             21.425911  [TEMPERATURE, mar, 07]  2007    03\n",
       "TEMPERATURE_apr_07             21.237032  [TEMPERATURE, apr, 07]  2007    04\n",
       "TEMPERATURE_may_07             21.322522  [TEMPERATURE, may, 07]  2007    05"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change the month from jan to 01 and apply to every row in the df\n",
    "x_temperature_medellin_transposed['MONTH'] = x_temperature_medellin_transposed['MONTH'].apply(lambda x: month_as_int(month=x.strip()))\n",
    "x_temperature_medellin_transposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cc537bef",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_temperature_medellin_transposed['MONTH'] = x_temperature_medellin_transposed['MONTH'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6278d6b",
   "metadata": {},
   "source": [
    "## Dengue Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "07225e1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cases_medellin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007/w01</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w02</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w03</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w04</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w05</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w48</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w49</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w50</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w51</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w52</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cases_medellin\n",
       "2007/w01               1\n",
       "2007/w02               0\n",
       "2007/w03               0\n",
       "2007/w04               0\n",
       "2007/w05               0\n",
       "...                  ...\n",
       "2018/w48              28\n",
       "2018/w49              12\n",
       "2018/w50              27\n",
       "2018/w51              17\n",
       "2018/w52              17\n",
       "\n",
       "[624 rows x 1 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take columns of Dengue cases from Medellin data\n",
    "x_cases_medellin = data_medellin[data_medellin.columns[343:-52]]\n",
    "# convert to Row\n",
    "x_cases_transposed_medellin = x_cases_medellin.T\n",
    "# Rename Column\n",
    "x_cases_transposed_medellin = x_cases_transposed_medellin.rename(columns= {62:'cases_medellin'})  ##integer\n",
    "x_cases_transposed_medellin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe0353f8",
   "metadata": {},
   "source": [
    "## Date of Dengue Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bbbdafe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007/w01</th>\n",
       "      <td>1</td>\n",
       "      <td>[2007, w01]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w02</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w02]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w03</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w03]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w04</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w04]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w05</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w05]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w48</th>\n",
       "      <td>28</td>\n",
       "      <td>[2018, w48]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w49</th>\n",
       "      <td>12</td>\n",
       "      <td>[2018, w49]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w50</th>\n",
       "      <td>27</td>\n",
       "      <td>[2018, w50]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w51</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w51]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w52</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w52]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cases_medellin         DATA\n",
       "2007/w01               1  [2007, w01]\n",
       "2007/w02               0  [2007, w02]\n",
       "2007/w03               0  [2007, w03]\n",
       "2007/w04               0  [2007, w04]\n",
       "2007/w05               0  [2007, w05]\n",
       "...                  ...          ...\n",
       "2018/w48              28  [2018, w48]\n",
       "2018/w49              12  [2018, w49]\n",
       "2018/w50              27  [2018, w50]\n",
       "2018/w51              17  [2018, w51]\n",
       "2018/w52              17  [2018, w52]\n",
       "\n",
       "[624 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new Column\n",
    "x_cases_transposed_medellin['DATA'] = x_cases_transposed_medellin.index.str.split('/')  #index column split function though works on string only -> yyyy week\n",
    "x_cases_transposed_medellin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9f29230d",
   "metadata": {},
   "outputs": [
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       "<p>624 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "          cases_medellin         DATA  YEAR\n",
       "2007/w01               1  [2007, w01]  2007\n",
       "2007/w02               0  [2007, w02]  2007\n",
       "2007/w03               0  [2007, w03]  2007\n",
       "2007/w04               0  [2007, w04]  2007\n",
       "2007/w05               0  [2007, w05]  2007\n",
       "...                  ...          ...   ...\n",
       "2018/w48              28  [2018, w48]  2018\n",
       "2018/w49              12  [2018, w49]  2018\n",
       "2018/w50              27  [2018, w50]  2018\n",
       "2018/w51              17  [2018, w51]  2018\n",
       "2018/w52              17  [2018, w52]  2018\n",
       "\n",
       "[624 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take year\n",
    "x_cases_transposed_medellin['YEAR'] = x_cases_transposed_medellin['DATA'].apply(lambda x: x[0])\n",
    "x_cases_transposed_medellin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6192112f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>201852</td>\n",
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      "text/plain": [
       "          cases_medellin         DATA  YEAR YearWeek\n",
       "2007/w01               1  [2007, w01]  2007   200701\n",
       "2007/w02               0  [2007, w02]  2007   200702\n",
       "2007/w03               0  [2007, w03]  2007   200703\n",
       "2007/w04               0  [2007, w04]  2007   200704\n",
       "2007/w05               0  [2007, w05]  2007   200705\n",
       "...                  ...          ...   ...      ...\n",
       "2018/w48              28  [2018, w48]  2018   201848\n",
       "2018/w49              12  [2018, w49]  2018   201849\n",
       "2018/w50              27  [2018, w50]  2018   201850\n",
       "2018/w51              17  [2018, w51]  2018   201851\n",
       "2018/w52              17  [2018, w52]  2018   201852\n",
       "\n",
       "[624 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert to format year + week \n",
    "x_cases_transposed_medellin['YearWeek'] = x_cases_transposed_medellin['DATA'].apply(lambda x: x[0] + x[1].split('w')[1])  ##concate year and week index 0 and 1 in a list and apply to all rows\n",
    "x_cases_transposed_medellin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "97b7b350",
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   "outputs": [
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      "text/plain": [
       "          cases_medellin         DATA  YEAR  YearWeek\n",
       "2007/w01               1  [2007, w01]  2007    200701\n",
       "2007/w02               0  [2007, w02]  2007    200702\n",
       "2007/w03               0  [2007, w03]  2007    200703\n",
       "2007/w04               0  [2007, w04]  2007    200704\n",
       "2007/w05               0  [2007, w05]  2007    200705"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#YearWeekInt --> changed to int\n",
    "x_cases_transposed_medellin['YearWeek'] = x_cases_transposed_medellin['YearWeek'].astype(int)\n",
    "x_cases_transposed_medellin.head()  #integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5fe0fc8c",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2018/w52</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",
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       "<p>624 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cases_medellin         DATA  YEAR  YearWeek LastDayWeek\n",
       "2007/w01               1  [2007, w01]  2007    200701  2007-01-06\n",
       "2007/w02               0  [2007, w02]  2007    200702  2007-01-13\n",
       "2007/w03               0  [2007, w03]  2007    200703  2007-01-20\n",
       "2007/w04               0  [2007, w04]  2007    200704  2007-01-27\n",
       "2007/w05               0  [2007, w05]  2007    200705  2007-02-03\n",
       "...                  ...          ...   ...       ...         ...\n",
       "2018/w48              28  [2018, w48]  2018    201848  2018-12-01\n",
       "2018/w49              12  [2018, w49]  2018    201849  2018-12-08\n",
       "2018/w50              27  [2018, w50]  2018    201850  2018-12-15\n",
       "2018/w51              17  [2018, w51]  2018    201851  2018-12-22\n",
       "2018/w52              17  [2018, w52]  2018    201852  2018-12-29\n",
       "\n",
       "[624 rows x 5 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_cases_transposed_medellin['LastDayWeek'] = pd.to_datetime((x_cases_transposed_medellin['YearWeek']-1).astype(str) + \"6\" , format=\"%Y%U%w\")   # 200701 + 6 -> yearweeknumber + Lastday of week \n",
    "##-1 -> our data weeknumber e.g. Yearweek 200701 yearweek starts from 01 as week 1 and datatime library starts from 00 as week 1 -> 1 -52 weeks data -> 0-51 weeks datelibrary\n",
    "## %Uweek number 0 51 = 1 52  \n",
    "##%w last day of week    This is taken from epiweek (1st epi week of 2007 starts 31-Dec-2016 ends 6-Jan-2017) \n",
    "##Library takes in yearweeknumber + weekday  -> the date form the last day of the week \n",
    "x_cases_transposed_medellin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a0eb238f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2007/w04</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007/w05</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w48</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w49</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w50</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w51</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w52</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cases_medellin         DATA  YEAR  YearWeek LastDayWeek  MONTH\n",
       "2007/w01               1  [2007, w01]  2007    200701  2007-01-06      1\n",
       "2007/w02               0  [2007, w02]  2007    200702  2007-01-13      1\n",
       "2007/w03               0  [2007, w03]  2007    200703  2007-01-20      1\n",
       "2007/w04               0  [2007, w04]  2007    200704  2007-01-27      1\n",
       "2007/w05               0  [2007, w05]  2007    200705  2007-02-03      2\n",
       "...                  ...          ...   ...       ...         ...    ...\n",
       "2018/w48              28  [2018, w48]  2018    201848  2018-12-01     12\n",
       "2018/w49              12  [2018, w49]  2018    201849  2018-12-08     12\n",
       "2018/w50              27  [2018, w50]  2018    201850  2018-12-15     12\n",
       "2018/w51              17  [2018, w51]  2018    201851  2018-12-22     12\n",
       "2018/w52              17  [2018, w52]  2018    201852  2018-12-29     12\n",
       "\n",
       "[624 rows x 6 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take month to append and duplicate temperature and precipitation\n",
    "x_cases_transposed_medellin['MONTH'] = pd.DatetimeIndex(x_cases_transposed_medellin['LastDayWeek']).month  \n",
    "x_cases_transposed_medellin    ##w5 Jan 2days , Feb 5 days --> MonthMax --> the month for the last day of the week --> Feb wins!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3cd0749",
   "metadata": {},
   "source": [
    "### JOIN TEMPERATURE AND CASES AND PRECIPITATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "342a0e49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<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>DATE</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>[TEMPERATURE, jan, 07]</td>\n",
       "    </tr>\n",
       "    <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>[TEMPERATURE, jan, 07]</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>[TEMPERATURE, jan, 07]</td>\n",
       "    </tr>\n",
       "    <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>[TEMPERATURE, jan, 07]</td>\n",
       "    </tr>\n",
       "    <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>[TEMPERATURE, feb, 07]</td>\n",
       "    </tr>\n",
       "    <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",
       "    </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>[TEMPERATURE, dec, 18]</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>[TEMPERATURE, dec, 18]</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>[TEMPERATURE, dec, 18]</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>[TEMPERATURE, dec, 18]</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>[TEMPERATURE, dec, 18]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 8 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                    DATE  \n",
       "0               21.009283  [TEMPERATURE, jan, 07]  \n",
       "1               21.009283  [TEMPERATURE, jan, 07]  \n",
       "2               21.009283  [TEMPERATURE, jan, 07]  \n",
       "3               21.009283  [TEMPERATURE, jan, 07]  \n",
       "4               20.982925  [TEMPERATURE, feb, 07]  \n",
       "..                    ...                     ...  \n",
       "619             20.936353  [TEMPERATURE, dec, 18]  \n",
       "620             20.936353  [TEMPERATURE, dec, 18]  \n",
       "621             20.936353  [TEMPERATURE, dec, 18]  \n",
       "622             20.936353  [TEMPERATURE, dec, 18]  \n",
       "623             20.936353  [TEMPERATURE, dec, 18]  \n",
       "\n",
       "[624 rows x 8 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_cases_temp = pd.merge(x_cases_transposed_medellin,x_temperature_medellin_transposed, on=['MONTH','YEAR'],how ='left')  ##month and year pair match , to avoid duplicate for each month or year it matches with\n",
    "merge_cases_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e83e9b84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Marge and duplicate precipitaton data\n",
    "#merge_cases_temp_precip = pd.merge(merge_cases_temp,x_precipitation_medellin_transposed, on=['MONTH','YEAR'],how ='left')  ##month and year pair match , to avoid duplicate for each month or year it matches with\n",
    "#merge_cases_temp_precip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d207511",
   "metadata": {},
   "source": [
    "# Load new weekly precipitation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "80cbd5c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LastDayWeek</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>2007-01-06</td>\n",
       "      <td>13.096688</td>\n",
       "      <td>14.097693</td>\n",
       "      <td>12.628933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-01-13</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>2007-01-20</td>\n",
       "      <td>19.373165</td>\n",
       "      <td>21.616968</td>\n",
       "      <td>18.406665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007-01-27</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-02-03</td>\n",
       "      <td>3.725206</td>\n",
       "      <td>3.144225</td>\n",
       "      <td>3.901021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  LastDayWeek  percipitation_medellin  percipitation_medellin_rural  \\\n",
       "0  2007-01-06               13.096688                     14.097693   \n",
       "1  2007-01-13                8.437044                      6.910390   \n",
       "2  2007-01-20               19.373165                     21.616968   \n",
       "3  2007-01-27                0.000000                      0.000000   \n",
       "4  2007-02-03                3.725206                      3.144225   \n",
       "\n",
       "   percipitation_medellin_urbano  \n",
       "0                      12.628933  \n",
       "1                       9.067214  \n",
       "2                      18.406665  \n",
       "3                       0.000000  \n",
       "4                       3.901021  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_prec_weekly = pd.read_csv('Data/Datos_prec_semanal.csv')\n",
    "# Rename extra column\n",
    "data_prec_weekly = data_prec_weekly.rename({'Unnamed: 0': 'LastDayWeek', 'Medellin':'percipitation_medellin', 'MedellinRural':'percipitation_medellin_rural', 'MedellinUrbano':'percipitation_medellin_urbano'}, axis=1)\n",
    "data_prec_weekly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f7025722",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LastDayWeek                      datetime64[ns]\n",
       "percipitation_medellin                  float64\n",
       "percipitation_medellin_rural            float64\n",
       "percipitation_medellin_urbano           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_prec_weekly.LastDayWeek = pd.to_datetime(data_prec_weekly.LastDayWeek)\n",
    "data_prec_weekly.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4755d299",
   "metadata": {},
   "source": [
    "### merge new precipitaton data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ea699dcb",
   "metadata": {},
   "outputs": [
    {
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       "      <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>[TEMPERATURE, feb, 07]</td>\n",
       "      <td>3.725206</td>\n",
       "      <td>3.144225</td>\n",
       "      <td>3.901021</td>\n",
       "    </tr>\n",
       "    <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",
       "    </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>[TEMPERATURE, dec, 18]</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>[TEMPERATURE, dec, 18]</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>[TEMPERATURE, dec, 18]</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>[TEMPERATURE, dec, 18]</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>[TEMPERATURE, dec, 18]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 11 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                    DATE  percipitation_medellin  \\\n",
       "0               21.009283  [TEMPERATURE, jan, 07]               13.096688   \n",
       "1               21.009283  [TEMPERATURE, jan, 07]                8.437044   \n",
       "2               21.009283  [TEMPERATURE, jan, 07]               19.373165   \n",
       "3               21.009283  [TEMPERATURE, jan, 07]                0.000000   \n",
       "4               20.982925  [TEMPERATURE, feb, 07]                3.725206   \n",
       "..                    ...                     ...                     ...   \n",
       "619             20.936353  [TEMPERATURE, dec, 18]                8.916642   \n",
       "620             20.936353  [TEMPERATURE, dec, 18]               17.006086   \n",
       "621             20.936353  [TEMPERATURE, dec, 18]                5.673248   \n",
       "622             20.936353  [TEMPERATURE, dec, 18]                2.785103   \n",
       "623             20.936353  [TEMPERATURE, dec, 18]                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 11 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_cases_temp_precip = pd.merge(merge_cases_temp, data_prec_weekly, on=['LastDayWeek'],how ='left')  ##month and year pair match , to avoid duplicate for each month or year it matches with\n",
    "merge_cases_temp_precip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a120f93f",
   "metadata": {},
   "source": [
    "# Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ac7ee9",
   "metadata": {},
   "source": [
    "## Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6d55f16d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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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": {},
   "source": [
    "# Prepare data to supervised learning time series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "753d15c1",
   "metadata": {},
   "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": 37,
   "id": "0d521f51",
   "metadata": {},
   "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",
    "        cols.append(df.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": 38,
   "id": "edcb0422",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/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_urbano  cases_medellin[t]\n",
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       "..                    ...                            ...                ...\n",
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       "621             20.936353                       5.794555                 27\n",
       "622             20.936353                       2.810241                 17\n",
       "623             20.936353                       0.000000                 17\n",
       "\n",
       "[624 rows x 3 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take Columns that we want\n",
    "data = merge_cases_temp_precip[['temperature_medellin','percipitation_medellin_urbano']]\n",
    "data['cases_medellin[t]'] = merge_cases_temp_precip[['cases_medellin']]\n",
    "data  #DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b3934515",
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform the time series data into supervised learning\n",
    "# we define 11 lag weeks\n",
    "lag_weeks = 11\n",
    "\n",
    "# we have as input 3 columns (temperature, precipitation, Dengue Cases)\n",
    "# The Result will be 3 x 11 = 33 columns\n",
    "data = series_to_supervised(data, n_in=lag_weeks)   ##previous six steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0af53a5f",
   "metadata": {},
   "outputs": [
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       "\n",
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       "\n",
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     },
     "execution_count": 40,
     "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-11 and columns of last week t \n",
    "DataFrame(data).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "06a54173",
   "metadata": {},
   "outputs": [
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       "      <td>42.654390</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>609</th>\n",
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       "      <th>611</th>\n",
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       "            0           1     2          3           4     5          6   \\\n",
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       "609   77.064631  27.0  20.748416  ...  68.935231  15.0  20.886703  65.702391   \n",
       "610   23.356459  23.0  20.748416  ...  65.702391  41.0  20.936353   9.114419   \n",
       "611   49.511026  24.0  20.748416  ...   9.114419  28.0  20.936353  17.334870   \n",
       "612   45.549292  24.0  20.886703  ...  17.334870  12.0  20.936353   5.794555   \n",
       "\n",
       "       29         30         31    32         33         34  \n",
       "0     0.0  21.425911  42.654390   0.0  21.425911  35.786352  \n",
       "1     0.0  21.425911  35.786352   0.0  21.425911  89.281597  \n",
       "2     0.0  21.425911  89.281597   0.0  21.237032  57.390467  \n",
       "3     0.0  21.237032  57.390467   1.0  21.237032  48.076857  \n",
       "4     1.0  21.237032  48.076857   0.0  21.237032  63.139842  \n",
       "..    ...        ...        ...   ...        ...        ...  \n",
       "608  15.0  20.886703  65.702391  41.0  20.936353   9.114419  \n",
       "609  41.0  20.936353   9.114419  28.0  20.936353  17.334870  \n",
       "610  28.0  20.936353  17.334870  12.0  20.936353   5.794555  \n",
       "611  12.0  20.936353   5.794555  27.0  20.936353   2.810241  \n",
       "612  27.0  20.936353   2.810241  17.0  20.936353   0.000000  \n",
       "\n",
       "[613 rows x 35 columns]"
      ]
     },
     "execution_count": 41,
     "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[:,:-1])\n",
    "features_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ef93f4d9",
   "metadata": {},
   "outputs": [
    {
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       "        0\n",
       "0     0.0\n",
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       "[613 rows x 1 columns]"
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     },
     "execution_count": 42,
     "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[:,-1])\n",
    "labels_set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c4eee6b",
   "metadata": {},
   "source": [
    "### Train - test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cbe710fd",
   "metadata": {},
   "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": {},
   "source": [
    "#### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a35769ba",
   "metadata": {},
   "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": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ccbd435c",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_test = features_set[split:]  ##10% test\n",
    "targets_test = labels_set[split:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "656be139",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29deb4d0",
   "metadata": {},
   "source": [
    "### Model train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a963eb4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/ipykernel_launcher.py:5: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=0)"
      ]
     },
     "execution_count": 46,
     "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": "b9eb720e",
   "metadata": {},
   "source": [
    "### Model Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4ef6fcc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([29.34, 28.62, 23.24, 29.18, 19.84, 30.78, 18.41, 33.72, 18.18,\n",
       "       24.62, 17.61, 15.77, 34.25, 19.7 , 28.4 , 34.13, 20.2 , 23.89,\n",
       "       20.89, 19.37, 17.33, 16.65, 17.23, 16.9 , 16.87, 15.57, 15.44,\n",
       "       14.79, 16.45, 17.14, 17.29, 15.77, 15.46, 17.15, 17.34, 18.96,\n",
       "       19.79, 30.66, 35.53, 23.72, 24.1 , 18.94, 19.27, 17.93, 17.8 ,\n",
       "       27.51, 19.46, 16.78, 16.59, 17.38, 28.58, 19.17, 17.41, 17.08,\n",
       "       17.34, 15.04, 16.5 , 39.85, 36.12, 23.85, 31.07, 17.97])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the model to predict with test data\n",
    "prediction = model.predict(features_test)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a0f44937",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0\n",
       "551  27.0\n",
       "552  24.0\n",
       "553  27.0\n",
       "554  22.0\n",
       "555  28.0\n",
       "..    ...\n",
       "608  28.0\n",
       "609  12.0\n",
       "610  27.0\n",
       "611  17.0\n",
       "612  17.0\n",
       "\n",
       "[62 rows x 1 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6233ae10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error is: 6.454838709677419\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "# Calculate MAE\n",
    "print(f\"the mean absolute error is: {mean_absolute_error(targets_test, prediction)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ea3918b5",
   "metadata": {},
   "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": [
    "# Plot Actual data vs Predicted\n",
    "data_predict = prediction  ## predicted target  cases\n",
    "dataY_plot = targets_test.to_numpy()  ##  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": "markdown",
   "id": "2c335c17",
   "metadata": {},
   "source": [
    "## Features importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f83e5de8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# See feature importances of Random Forest\n",
    "features_importance = model.feature_importances_\n",
    "\n",
    "features_dic = {}\n",
    "features=['temperature', 'precipitation', 'Dengue_cases']\n",
    "t = 11\n",
    "feature = 0\n",
    "\n",
    "for i in range(0,len(features_importance)):\n",
    "    if feature == 3:\n",
    "        feature = 0\n",
    "        t = t - 1 \n",
    "    features_dic[features[feature]+\" Week-\"+str(t)] = features_importance[i]\n",
    "    feature += 1\n",
    "#features_dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "96176dfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Dengue_cases Week-1', 0.960981996040789),\n",
       " ('Dengue_cases Week-2', 0.015160461299259222),\n",
       " ('Dengue_cases Week-11', 0.0037227651969995593),\n",
       " ('temperature Week-10', 0.0014270479098686837),\n",
       " ('precipitation Week-11', 0.001274363802155448),\n",
       " ('temperature Week-9', 0.0012580977246303506),\n",
       " ('precipitation Week-0', 0.001200272396000648),\n",
       " ('precipitation Week-5', 0.0009501778353040129),\n",
       " ('Dengue_cases Week-3', 0.0008533675102585993),\n",
       " ('Dengue_cases Week-9', 0.0008370702058243295),\n",
       " ('precipitation Week-2', 0.000785491803257189),\n",
       " ('precipitation Week-9', 0.0007020688202788412),\n",
       " ('Dengue_cases Week-10', 0.0006586740630136341),\n",
       " ('temperature Week-11', 0.0006550610577740598),\n",
       " ('precipitation Week-10', 0.0006524735659595491),\n",
       " ('temperature Week-4', 0.0005819076146635306),\n",
       " ('precipitation Week-4', 0.0005749354958479797),\n",
       " ('Dengue_cases Week-8', 0.0005731925856117379),\n",
       " ('Dengue_cases Week-5', 0.0005559298658255213),\n",
       " ('precipitation Week-7', 0.0005323650802803879),\n",
       " ('precipitation Week-1', 0.0005161395510295808),\n",
       " ('precipitation Week-3', 0.0005092047528248635),\n",
       " ('temperature Week-3', 0.0004846908225647403),\n",
       " ('temperature Week-0', 0.0004789988213208455),\n",
       " ('temperature Week-8', 0.0004774333346681733),\n",
       " ('temperature Week-2', 0.00044080992635297725),\n",
       " ('Dengue_cases Week-7', 0.0004280500373832875),\n",
       " ('precipitation Week-8', 0.00041965852342732427),\n",
       " ('Dengue_cases Week-4', 0.00041726180105393595),\n",
       " ('precipitation Week-6', 0.00039792455993334407),\n",
       " ('temperature Week-1', 0.0003440966500785812),\n",
       " ('temperature Week-6', 0.00032695885386887407),\n",
       " ('temperature Week-5', 0.0003168291872564823),\n",
       " ('Dengue_cases Week-6', 0.00025703865707296886),\n",
       " ('temperature Week-7', 0.00024718464756169167)]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sort_orders = sorted(features_dic.items(), key=lambda x: x[1], reverse=True)\n",
    "sort_orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e93daa02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nimport matplotlib.pyplot as plt\\nplt.pie(range(len(features_dic)), list(features_dic.values()))\\nplt.xticks(range(len(features_dic)), list(features_dic.keys()))\\nplt.show()\\n'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(range(len(features_dic)), list(features_dic.values()))\n",
    "plt.xticks(range(len(features_dic)), list(features_dic.keys()))\n",
    "plt.show()\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6076516",
   "metadata": {},
   "source": [
    "## Bayesian Ridge Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "971435d3",
   "metadata": {},
   "source": [
    "### Model train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d2f86009",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BayesianRidge()"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import BayesianRidge\n",
    "\n",
    "# model\n",
    "model = BayesianRidge()\n",
    "model.fit(features_train, targets_train)   ## fit on training data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "266ceeff",
   "metadata": {},
   "source": [
    "### Model Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "79243d77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25.9574155 , 19.42498886, 28.57265489, 26.78181273, 16.95884897,\n",
       "       30.75854697, 23.74028498, 27.20366009, 16.20875092, 25.28994365,\n",
       "       14.81599288, 16.21491794, 37.03610333, 30.54381541, 27.10234431,\n",
       "       38.16228176, 24.56591789, 25.38180715, 25.5725265 , 29.37079642,\n",
       "       24.89092911, 16.53666298, 24.84087555, 24.95378369, 31.19858913,\n",
       "       28.02615136, 23.35059285, 22.95827043, 28.22707592, 19.39769132,\n",
       "       21.43933244, 19.68858191, 19.48797113, 18.68682676, 18.41397223,\n",
       "       22.82226798, 23.93341304, 25.91659654, 32.02020047, 22.29780062,\n",
       "       22.82562124, 19.17977826, 21.9161626 , 22.37354393, 28.31258326,\n",
       "       27.92895591, 32.30217734, 18.96644106, 17.81458679, 20.73966962,\n",
       "       36.47745986, 28.02338565, 19.85895996, 26.47065342, 23.33980964,\n",
       "       12.32764795,  8.60106722, 44.09829344, 34.79916931, 11.4744127 ,\n",
       "       23.3789231 , 20.43890807])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = model.predict(features_test)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c066fdf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error is: 6.654396103227382\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": 57,
   "id": "6e33eb18",
   "metadata": {},
   "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  ## predicted target  cases\n",
    "dataY_plot = targets_test.to_numpy()  ##  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": 58,
   "id": "8573519f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f6af599",
   "metadata": {},
   "source": [
    "## Multilayer Perceptron"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bbed216",
   "metadata": {},
   "source": [
    "### Model train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "2f78b556",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "#import random\n",
    "#random.seed(4)\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": "7fdd0199",
   "metadata": {},
   "source": [
    "### Model Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fd8779b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27.17220173, 20.99548557, 28.9852835 , 30.13430878, 23.39998878,\n",
       "       27.63416463, 29.2447822 , 27.51001636, 20.34726149, 24.19182839,\n",
       "       19.16577776, 16.84448282, 32.25200289, 30.57541144, 25.520114  ,\n",
       "       36.68306308, 24.93528227, 27.00727096, 26.39855054, 26.72423656,\n",
       "       25.1522262 , 19.09800631, 21.86651182, 27.24955267, 31.03456367,\n",
       "       31.91074138, 16.23311044, 20.46078217, 26.75404593, 21.57002432,\n",
       "       21.28127263, 19.99650082, 18.8654537 , 19.61908487, 26.17921584,\n",
       "       24.03499721, 20.38286279, 20.69467298, 32.89837026, 23.64862094,\n",
       "       21.89998318, 19.74622269, 20.82267814, 22.67707853, 26.17878953,\n",
       "       27.47729058, 32.29709452, 19.9956113 , 16.50376858, 23.07014744,\n",
       "       35.24658971, 30.77958302, 18.95318691, 25.02925536, 24.07220823,\n",
       "       17.54171596, 18.21274444, 33.49468722, 36.44020166, 17.88912663,\n",
       "       26.55710789, 18.70531289])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = model.predict(features_test)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "12f11bf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error is: 6.038338496625193\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": 62,
   "id": "8c07ad31",
   "metadata": {},
   "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  ## predicted target  cases\n",
    "dataY_plot = targets_test.to_numpy()  ##  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": "markdown",
   "id": "032b4d34",
   "metadata": {},
   "source": [
    "# Validation Function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53d09838",
   "metadata": {},
   "source": [
    "The problem with last implementation is that we are testing with last 10% of data, so we don't know how the model will behave under other conditions, such as peaks.\n",
    "\n",
    "To solve this problem this problem, This Validantion funtion moves the position of test data and calculates the MAE\n",
    "\n",
    "Iteration 1: 0-10% test && 10%-100% train and calculate the MAE\n",
    "Iteration 2: 10-20% test && 0%-10% and 20%-100% train and calculate the MAE\n",
    "Iteration 3: 20-30% test && 0%-20% and 30%-100% train and calculate the MAE\n",
    "Iteration 4: 30-40% test && 0%-30% and 40%-100% train and calculate the MAE\n",
    "Iteration 5: 40-50% test && 0%-40% and 50%-100% train and calculate the MAE\n",
    "...\n",
    "Iteration 10: 90-100% test && 00%-90% train and calculate the MAE\n",
    "\n",
    "At the end we return a list of all the MAEs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bad5465a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def validation(features_set,labels_set, model):\n",
    "    size = features_set.shape[0]\n",
    "    scores = list()\n",
    "    for i in range(1,11):\n",
    "        if (i == 1):\n",
    "            split = int(size*(1/10))\n",
    "            # Train\n",
    "            features_train = features_set[split:]  \n",
    "            targets_train = labels_set[split:]   \n",
    "            # Test\n",
    "            features_test = features_set[:split]  \n",
    "            targets_test = labels_set[:split]\n",
    "        elif (i != 10 and i != 1):\n",
    "            split = int(size*(1/10))\n",
    "            # Train\n",
    "            features_train = pd.concat([features_set[:split*(i-1)], features_set[split*(i):]])\n",
    "            targets_train = pd.concat([labels_set[:split*(i-1)], labels_set[split*(i):]])\n",
    "            # Test\n",
    "            features_test = features_set[split*(i-1):split*(i)]  \n",
    "            targets_test = labels_set[split*(i-1):split*(i)]\n",
    "        else:\n",
    "            split = int(size*(9/10))\n",
    "            # Train\n",
    "            features_train = features_set[:split]  ## 90% train of 618  ##556  up to 90% -> first 90%\n",
    "            targets_train = labels_set[:split]        \n",
    "            # Test\n",
    "            features_test = features_set[split:]  ## 90% train of 618  ##556  up to 90% -> first 90%\n",
    "            targets_test = labels_set[split:]\n",
    "            ## Define the model\n",
    "        '''\n",
    "        from sklearn.ensemble import RandomForestRegressor\n",
    "        # model\n",
    "        model = RandomForestRegressor(random_state=0,n_estimators=100)\n",
    "        '''\n",
    "        # Model train\n",
    "        model.fit(features_train, targets_train)   ## fit on training data\n",
    "        # Model test\n",
    "        prediction = model.predict(features_test)\n",
    "        from sklearn.metrics import mean_absolute_error\n",
    "        print(f\"the mean absolute error in {i} iteration is: {mean_absolute_error(targets_test, prediction)}\")\n",
    "        scores.append(mean_absolute_error(targets_test, prediction))\n",
    "    return scores\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eef2702",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "cc0f44e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 1 iteration is: 4.9642622950819675\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 2 iteration is: 4.328852459016393\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 3 iteration is: 49.487377049180324\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 4 iteration is: 8.892622950819671\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 5 iteration is: 4.934098360655738\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 6 iteration is: 8.33016393442623\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 7 iteration is: 12.352622950819674\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 8 iteration is: 26.045081967213115\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 9 iteration is: 14.351639344262296\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/ipykernel_launcher.py:36: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 10 iteration is: 6.454838709677419\n",
      "the scores are [4.9642622950819675, 4.328852459016393, 49.487377049180324, 8.892622950819671, 4.934098360655738, 8.33016393442623, 12.352622950819674, 26.045081967213115, 14.351639344262296, 6.454838709677419]\n",
      "the mean mae is 14.014156002115286\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "# model\n",
    "model = RandomForestRegressor(random_state=0,n_estimators=100)\n",
    "\n",
    "\n",
    "scores = validation(features_set = features_set,labels_set = labels_set, model = model)\n",
    "print(f'the scores are {scores}')\n",
    "print(f'the mean mae is {sum(scores) / len(scores)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "429faa6b",
   "metadata": {},
   "source": [
    "## Bayesian Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cdfb5e35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 1 iteration is: 4.199242644399132\n",
      "the mean absolute error in 2 iteration is: 4.166137765420418\n",
      "the mean absolute error in 3 iteration is: 28.907314424008362\n",
      "the mean absolute error in 4 iteration is: 7.613803513141248\n",
      "the mean absolute error in 5 iteration is: 5.70903931674633\n",
      "the mean absolute error in 6 iteration is: 9.164286370336107\n",
      "the mean absolute error in 7 iteration is: 13.10201209783692\n",
      "the mean absolute error in 8 iteration is: 24.43741201311295\n",
      "the mean absolute error in 9 iteration is: 15.759875401047445\n",
      "the mean absolute error in 10 iteration is: 6.654396103227382\n",
      "the scores are [4.199242644399132, 4.166137765420418, 28.907314424008362, 7.613803513141248, 5.70903931674633, 9.164286370336107, 13.10201209783692, 24.43741201311295, 15.759875401047445, 6.654396103227382]\n",
      "the mean mae is 11.97135196492763\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import BayesianRidge\n",
    "\n",
    "# model\n",
    "model = BayesianRidge()\n",
    "\n",
    "scores = validation(features_set = features_set,labels_set = labels_set, model = model)\n",
    "print(f'the scores are {scores}')\n",
    "print(f'the mean mae is {sum(scores) / len(scores)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "290e1d1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 1 iteration is: 5.498888799936589\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 2 iteration is: 5.586642745329773\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 3 iteration is: 41.90925940570004\n",
      "the mean absolute error in 4 iteration is: 7.587183188211263\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 5 iteration is: 5.654253516050406\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 6 iteration is: 9.45300541381023\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 7 iteration is: 13.303896111071511\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 8 iteration is: 28.333949676282007\n",
      "the mean absolute error in 9 iteration is: 16.01811159023292\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean absolute error in 10 iteration is: 6.038338496625193\n",
      "the scores are [5.498888799936589, 5.586642745329773, 41.90925940570004, 7.587183188211263, 5.654253516050406, 9.45300541381023, 13.303896111071511, 28.333949676282007, 16.01811159023292, 6.038338496625193]\n",
      "the mean mae is 13.938352894324993\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "#import random\n",
    "#random.seed(4)\n",
    "\n",
    "# model\n",
    "model = MLPRegressor(hidden_layer_sizes=60,random_state=1, max_iter=400)\n",
    "\n",
    "\n",
    "scores = validation(features_set = features_set,labels_set = labels_set, model = model)\n",
    "print(f'the scores are {scores}')\n",
    "print(f'the mean mae is {sum(scores) / len(scores)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6874a63",
   "metadata": {},
   "source": [
    "# Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2922293",
   "metadata": {},
   "source": [
    "Similar to the previous function we can use cross validation function of sklearn (The problem is that this approach could be more difficult to compare when we see other types of models like LSTM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31ddf60e",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9ab11d76",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:598: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:598: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:598: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:598: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:598: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:598: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:598: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:598: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:598: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:598: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the scores are: [ 5.08387097  4.68758065 52.00854839  7.21704918  5.17016393  8.47852459\n",
      " 13.04639344 27.73934426 13.47393443  6.54131148]\n",
      "the mean mae is: 14.344672131147538\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "model = RandomForestRegressor(random_state=0,n_estimators=100)\n",
    "\n",
    "scores = cross_val_score(model, features_set, labels_set, cv=10, scoring='neg_mean_absolute_error')\n",
    "scores = abs(scores)\n",
    "\n",
    "print(f'the scores are: {abs(scores)}')\n",
    "print(f'the mean mae is: {sum(scores) / len(scores)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65f4dcf6",
   "metadata": {},
   "source": [
    "## Bayesian Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b2ee21e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the scores are: [ 4.15207384  4.1151302  31.00386363  6.32754132  5.877842    9.00853441\n",
      " 13.62491326 26.07577555 12.76928262  6.74597103]\n",
      "the mean mae is: 11.970092785275048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# model\n",
    "model = BayesianRidge()\n",
    "\n",
    "scores = cross_val_score(model, features_set, labels_set, cv=10, scoring='neg_mean_absolute_error')\n",
    "scores = abs(scores)\n",
    "\n",
    "print(f'the scores are: {abs(scores)}')\n",
    "print(f'the mean mae is: {sum(scores) / len(scores)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eac4519f",
   "metadata": {},
   "source": [
    "## Multilayer Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3546300d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "/Users/davidrestrepo/opt/anaconda3/envs/dengue_ML_py36/lib/python3.6/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the scores are: [ 5.51887144  5.56363511 43.0141653   6.28872216  5.78138004  9.3947092\n",
      " 13.90628713 30.73786172 13.88489012  5.81195314]\n",
      "the mean mae is: 13.990247535961462\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "model = MLPRegressor(hidden_layer_sizes=60,random_state=1, max_iter=400)\n",
    "\n",
    "scores = cross_val_score(model, features_set, labels_set, cv=10, scoring='neg_mean_absolute_error')\n",
    "scores = abs(scores)\n",
    "\n",
    "print(f'the scores are: {abs(scores)}')\n",
    "print(f'the mean mae is: {sum(scores) / len(scores)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79b7926f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a55aded6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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