timeSeriesV2_weeklyData.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": [
    {
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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",
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       "      <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": [
    {
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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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       "      <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  cases_medellin[t]\n",
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       "..                    ...                     ...                ...\n",
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       "622             20.936353                2.785103                 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']]\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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     },
     "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>0.0</td>\n",
       "      <td>20.982925</td>\n",
       "      <td>...</td>\n",
       "      <td>43.166896</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.425911</td>\n",
       "      <td>36.598124</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>21.237032</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>12.088275</td>\n",
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       "      <td>...</td>\n",
       "      <td>36.598124</td>\n",
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       "      <td>21.425911</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>21.237032</td>\n",
       "      <td>47.196685</td>\n",
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       "    <tr>\n",
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       "      <td>20.982925</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>20.982925</td>\n",
       "      <td>1.469419</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.982925</td>\n",
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       "      <td>91.356645</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.237032</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>25.429675</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.347202</td>\n",
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       "      <td>22.0</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>20.748416</td>\n",
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       "      <td>23.602477</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.886703</td>\n",
       "      <td>70.620302</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.886703</td>\n",
       "      <td>64.636374</td>\n",
       "      <td>41.0</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>8.916642</td>\n",
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       "    <tr>\n",
       "      <th>609</th>\n",
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       "      <td>46.588027</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.347202</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>20.748416</td>\n",
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       "      <td>27.0</td>\n",
       "      <td>20.748416</td>\n",
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       "      <td>28.0</td>\n",
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       "      <td>17.006086</td>\n",
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       "    <tr>\n",
       "      <th>610</th>\n",
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       "      <td>20.748416</td>\n",
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       "      <td>27.0</td>\n",
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       "      <td>23.0</td>\n",
       "      <td>20.748416</td>\n",
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       "      <td>41.0</td>\n",
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       "      <td>20.936353</td>\n",
       "      <td>17.006086</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>5.673248</td>\n",
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       "    <tr>\n",
       "      <th>611</th>\n",
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       "      <td>20.748416</td>\n",
       "      <td>25.432126</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20.748416</td>\n",
       "      <td>50.621336</td>\n",
       "      <td>24.0</td>\n",
       "      <td>20.748416</td>\n",
       "      <td>...</td>\n",
       "      <td>8.916642</td>\n",
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       "      <td>27.0</td>\n",
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       "    <tr>\n",
       "      <th>612</th>\n",
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       "            0           1     2          3           4     5          6   \\\n",
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       "..         ...         ...   ...        ...         ...   ...        ...   \n",
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       "\n",
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       "..          ...   ...        ...  ...        ...   ...        ...        ...   \n",
       "608  107.533508  22.0  20.748416  ...  23.602477  20.0  20.886703  70.620302   \n",
       "609   76.856266  27.0  20.748416  ...  70.620302  15.0  20.886703  64.636374   \n",
       "610   25.432126  23.0  20.748416  ...  64.636374  41.0  20.936353   8.916642   \n",
       "611   50.621336  24.0  20.748416  ...   8.916642  28.0  20.936353  17.006086   \n",
       "612   46.613645  24.0  20.886703  ...  17.006086  12.0  20.936353   5.673248   \n",
       "\n",
       "       29         30         31    32         33         34  \n",
       "0     0.0  21.425911  43.166896   0.0  21.425911  36.598124  \n",
       "1     0.0  21.425911  36.598124   0.0  21.425911  91.356645  \n",
       "2     0.0  21.425911  91.356645   0.0  21.237032  58.007603  \n",
       "3     0.0  21.237032  58.007603   1.0  21.237032  47.196685  \n",
       "4     1.0  21.237032  47.196685   0.0  21.237032  62.318700  \n",
       "..    ...        ...        ...   ...        ...        ...  \n",
       "608  15.0  20.886703  64.636374  41.0  20.936353   8.916642  \n",
       "609  41.0  20.936353   8.916642  28.0  20.936353  17.006086  \n",
       "610  28.0  20.936353  17.006086  12.0  20.936353   5.673248  \n",
       "611  12.0  20.936353   5.673248  27.0  20.936353   2.785103  \n",
       "612  27.0  20.936353   2.785103  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([29.67, 30.99, 23.75, 29.68, 21.08, 32.27, 18.14, 33.32, 17.47,\n",
       "       25.17, 17.95, 16.4 , 33.71, 19.74, 27.62, 34.41, 18.85, 24.17,\n",
       "       21.08, 19.69, 16.68, 16.6 , 17.46, 15.87, 16.93, 16.46, 15.38,\n",
       "       14.94, 16.56, 18.32, 16.64, 15.09, 14.95, 17.27, 17.21, 18.03,\n",
       "       20.04, 31.3 , 36.31, 23.39, 23.44, 17.62, 18.32, 17.39, 17.7 ,\n",
       "       28.12, 18.86, 16.59, 16.5 , 17.37, 28.75, 19.72, 17.79, 17.9 ,\n",
       "       16.84, 15.67, 16.67, 40.42, 35.74, 23.37, 30.95, 16.81])"
      ]
     },
     "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",
       "    <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: 6.558870967741935\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', 0.961272497207653),\n",
       " ('Dengue_cases Week-2', 0.015255413718807901),\n",
       " ('Dengue_cases Week-11', 0.0033173144709725396),\n",
       " ('temperature Week-10', 0.001488143974733388),\n",
       " ('precipitation Week-11', 0.0011814333374979547),\n",
       " ('precipitation Week-0', 0.0011724917732370778),\n",
       " ('Dengue_cases Week-3', 0.001015432868962657),\n",
       " ('temperature Week-9', 0.0010136168084126022),\n",
       " ('Dengue_cases Week-10', 0.0009838316535637126),\n",
       " ('precipitation Week-5', 0.0008610973967219709),\n",
       " ('precipitation Week-2', 0.0008534400171749522),\n",
       " ('precipitation Week-9', 0.000765410247754704),\n",
       " ('Dengue_cases Week-8', 0.0007378514918106166),\n",
       " ('temperature Week-4', 0.0006602107605171194),\n",
       " ('temperature Week-0', 0.0006225336485038871),\n",
       " ('precipitation Week-7', 0.0005946651245967588),\n",
       " ('precipitation Week-4', 0.0005935832609056642),\n",
       " ('precipitation Week-10', 0.0005870929043193274),\n",
       " ('temperature Week-11', 0.0005355169437407986),\n",
       " ('precipitation Week-1', 0.0005242262188290284),\n",
       " ('temperature Week-3', 0.0005111363906531887),\n",
       " ('Dengue_cases Week-5', 0.0005030953025855272),\n",
       " ('precipitation Week-3', 0.0004942057859008293),\n",
       " ('Dengue_cases Week-4', 0.00047599356868292987),\n",
       " ('temperature Week-2', 0.0004644085850768751),\n",
       " ('Dengue_cases Week-9', 0.00043306530421920815),\n",
       " ('precipitation Week-6', 0.000399399252373878),\n",
       " ('precipitation Week-8', 0.00038085573147316555),\n",
       " ('Dengue_cases Week-7', 0.0003787007533885081),\n",
       " ('temperature Week-5', 0.0003760004901665904),\n",
       " ('temperature Week-7', 0.00035652509815034566),\n",
       " ('temperature Week-8', 0.0003484192217769257),\n",
       " ('temperature Week-1', 0.0003058064235537039),\n",
       " ('temperature Week-6', 0.0002718213529295435),\n",
       " ('Dengue_cases Week-6', 0.0002647629103532553)]"
      ]
     },
     "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([26.04699006, 19.11149084, 28.69522516, 27.14911861, 16.62638542,\n",
       "       30.77167808, 23.76209746, 27.03345521, 16.06480624, 25.35873871,\n",
       "       14.83808882, 16.05544514, 37.27560625, 30.7642063 , 26.97824506,\n",
       "       37.96691483, 24.62876945, 25.27434163, 25.40376271, 29.33587925,\n",
       "       24.86132857, 16.62794958, 24.86502289, 25.02121574, 31.19985762,\n",
       "       27.65262344, 22.90917962, 23.05947058, 28.38863787, 19.19213598,\n",
       "       21.48155677, 19.48396925, 19.2681555 , 18.72147668, 18.64952671,\n",
       "       22.68213691, 24.27244369, 25.86468275, 32.08166965, 22.53942715,\n",
       "       23.27784633, 19.2616492 , 21.85848404, 22.45320452, 28.29113692,\n",
       "       28.05947288, 32.47456923, 18.94901496, 17.64682587, 20.70396421,\n",
       "       36.82749388, 27.75868189, 19.43276547, 26.36922866, 23.54711913,\n",
       "       12.19343865,  8.65287184, 44.14592435, 34.7339178 , 11.04397379,\n",
       "       23.3160553 , 20.41891538])"
      ]
     },
     "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: 6.6945450744384924\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([27.88298845, 20.56727275, 29.16221378, 31.12566993, 23.13877811,\n",
       "       27.50936515, 29.98310002, 27.47795852, 21.0676995 , 24.72346311,\n",
       "       19.16769114, 17.38886964, 32.0222614 , 31.03959711, 25.62359618,\n",
       "       36.62906522, 25.06946786, 27.26915084, 26.63449038, 27.10335928,\n",
       "       24.88240523, 19.06993466, 21.66066567, 27.48818557, 31.4415993 ,\n",
       "       31.60198311, 15.62166808, 20.92561852, 27.44799647, 21.75731037,\n",
       "       20.93375681, 20.5529564 , 19.07995418, 20.58504131, 26.90739264,\n",
       "       24.71745329, 20.80761735, 21.0768075 , 32.4949751 , 23.28042423,\n",
       "       22.43174122, 20.00021493, 21.03148513, 22.70697001, 26.34168574,\n",
       "       27.74482949, 32.37366849, 20.17947917, 16.20722988, 22.9328554 ,\n",
       "       35.59277186, 30.66507546, 18.7356465 , 25.38164587, 24.63354527,\n",
       "       17.76174021, 18.78674438, 33.25753773, 36.4150207 , 18.37422475,\n",
       "       27.06721783, 19.71069193])"
      ]
     },
     "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: 6.110480790300203\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": "code",
   "execution_count": null,
   "id": "9a677952",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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: 5.010163934426229\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: 4.340163934426229\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: 49.4372131147541\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: 9.053934426229505\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: 4.926393442622951\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: 8.075737704918035\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: 12.28590163934426\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: 26.54475409836066\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: 14.778196721311476\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: 6.558870967741935\n",
      "the scores are [5.010163934426229, 4.340163934426229, 49.4372131147541, 9.053934426229505, 4.926393442622951, 8.075737704918035, 12.28590163934426, 26.54475409836066, 14.778196721311476, 6.558870967741935]\n",
      "the mean mae is 14.101132998413538\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: 4.180544446060817\n",
      "the mean absolute error in 2 iteration is: 4.138569877507312\n",
      "the mean absolute error in 3 iteration is: 28.911396236342263\n",
      "the mean absolute error in 4 iteration is: 7.590813535096968\n",
      "the mean absolute error in 5 iteration is: 5.716469307454309\n",
      "the mean absolute error in 6 iteration is: 9.156534659022833\n",
      "the mean absolute error in 7 iteration is: 13.102213853719189\n",
      "the mean absolute error in 8 iteration is: 24.459456338799548\n",
      "the mean absolute error in 9 iteration is: 15.752329854855988\n",
      "the mean absolute error in 10 iteration is: 6.6945450744384924\n",
      "the scores are [4.180544446060817, 4.138569877507312, 28.911396236342263, 7.590813535096968, 5.716469307454309, 9.156534659022833, 13.102213853719189, 24.459456338799548, 15.752329854855988, 6.6945450744384924]\n",
      "the mean mae is 11.97028731832977\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: 5.662103192058785\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: 5.709596135236483\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: 41.897149499938244\n",
      "the mean absolute error in 4 iteration is: 7.797268118478156\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: 5.635914200930365\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: 9.39288380321356\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: 13.165933367452034\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 8 iteration is: 28.862788621966864\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 9 iteration is: 15.89511181422597\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: 6.110480790300203\n",
      "the scores are [5.662103192058785, 5.709596135236483, 41.897149499938244, 7.797268118478156, 5.635914200930365, 9.39288380321356, 13.165933367452034, 28.862788621966864, 15.89511181422597, 6.110480790300203]\n",
      "the mean mae is 14.01292295438007\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: [ 5.08790323  4.66854839 50.73564516  7.42688525  5.2247541   8.38639344\n",
      " 12.9995082  26.74655738 13.07508197  6.61147541]\n",
      "the mean mae is: 14.096275251189848\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: [ 4.12983649  4.09692576 31.00550723  6.2961501   5.87737394  9.00043611\n",
      " 13.62836893 26.09142816 12.77388901  6.78837776]\n",
      "the mean mae is: 11.968829348167285\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/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/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the scores are: [ 5.73345036  5.46669535 43.89454381  6.32038097  5.79355096  9.39848511\n",
      " 13.79914087 32.24974662 13.44076696  5.7560209 ]\n",
      "the mean mae is: 14.18527819111029\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": "39c889ac",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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