ML_Forecast_Regression_for_User_Prediction.ipynb

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    "id": "18c07f40"
   },
   "source": [
    "#  **Dataset del sistema de alquiler de bicicletas de Citi Bike en la ciudad de New Jersey durante el año 2019**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3fc8262",
   "metadata": {
    "id": "b3fc8262"
   },
   "source": [
    "## **1. ABSTRACT:**\n",
    "\n",
    "**Citi Bike** es el programa de alquiler de bicicletas compartidas más grande de Estados Unidos. El sistema es pago y está disponible para su uso 24/24. La data del uso del sistema es pública y se encuentra disponible en su sitio web. \n",
    "\n",
    "\n",
    "El dataset seleccionado está conformado por un archivo CSV, de **15 columnas y 404.750 registros**, que contienen información del servicio de alquiler de bicicletas durante el año 2019 en la ciudad de New Jersey.\n",
    "\n",
    "El análisis tiene como **objetivo** predecir la cantidad de rentas de bicicletas en la estación de mayor uso (Grow St PATH) en el mes de Septiembre.\n",
    "\n",
    "La **audiencia** de este análisis son los directivos del servicio Citi Bike."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "RyvyYx-BZ2pr",
   "metadata": {
    "id": "RyvyYx-BZ2pr"
   },
   "source": [
    "## **2. PREGUNTAS DE INTERÉS**##\n",
    "###**Pregunta principal:**###\n",
    "¿Cuál será la cantidad de usuarios que alquilarán bicicletas del sistema Citi Bike en la estación Grow St PATH durante el mes de Septiembre?\n",
    "\n",
    "###**Preguntas secundarias:**###\n",
    "*  ¿Cuál es el top de estaciones más usadas?\n",
    "*  ¿Cuáles son los meses con mayor uso del sistema?\n",
    "*  ¿Cuáles son los horarios de mayor uso del sistema?\n",
    "*  ¿Cuál es la distribución por género y tipo de usuario del sistema?\n",
    "*  ¿Cuál es la edad promedio de los usuarios del sistema?\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94KwCDcm7gHC",
   "metadata": {
    "id": "94KwCDcm7gHC"
   },
   "source": [
    "## **3. ANÁLISIS EXPLORATORIO DE DATOS (EDA)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9efd5b61",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "%matplotlib inline\n",
    "\n",
    "import time\n",
    "from datetime import datetime as dt\n",
    "\n",
    "import warnings\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "from skforecast.ForecasterAutoreg import ForecasterAutoreg\n",
    "from skforecast.ForecasterAutoregCustom import ForecasterAutoregCustom\n",
    "from skforecast.ForecasterAutoregDirect import ForecasterAutoregDirect\n",
    "from skforecast.model_selection import grid_search_forecaster\n",
    "from skforecast.model_selection import backtesting_forecaster\n",
    "from skforecast.utils import save_forecaster\n",
    "from skforecast.utils import load_forecaster\n",
    "\n",
    "from sklearn import metrics \n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from rdr_scorer import RdR_scorer\n",
    "\n",
    "import geopy.distance\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c025e472",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 676
    },
    "executionInfo": {
     "elapsed": 3937,
     "status": "ok",
     "timestamp": 1683209535856,
     "user": {
      "displayName": "Ana Paula Sosa Pivatto",
      "userId": "02813882217908415455"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201</td>\n",
       "      <td>2019-01-01 03:09:09.7110</td>\n",
       "      <td>2019-01-01 03:12:30.8790</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3214</td>\n",
       "      <td>Essex Light Rail</td>\n",
       "      <td>40.712774</td>\n",
       "      <td>-74.036486</td>\n",
       "      <td>29612</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1993</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>505</td>\n",
       "      <td>2019-01-01 05:18:00.1060</td>\n",
       "      <td>2019-01-01 05:26:25.9050</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3638</td>\n",
       "      <td>Washington St</td>\n",
       "      <td>40.724294</td>\n",
       "      <td>-74.035483</td>\n",
       "      <td>29213</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1972</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>756</td>\n",
       "      <td>2019-01-01 10:36:33.3400</td>\n",
       "      <td>2019-01-01 10:49:10.2600</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3192</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>40.711242</td>\n",
       "      <td>-74.055701</td>\n",
       "      <td>26164</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1575</td>\n",
       "      <td>2019-01-01 12:43:38.6430</td>\n",
       "      <td>2019-01-01 13:09:54.5280</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3638</td>\n",
       "      <td>Washington St</td>\n",
       "      <td>40.724294</td>\n",
       "      <td>-74.035483</td>\n",
       "      <td>29672</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1566</td>\n",
       "      <td>2019-01-01 12:43:39.6010</td>\n",
       "      <td>2019-01-01 13:09:46.5100</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3638</td>\n",
       "      <td>Washington St</td>\n",
       "      <td>40.724294</td>\n",
       "      <td>-74.035483</td>\n",
       "      <td>29522</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
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       "      <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>404745</th>\n",
       "      <td>283</td>\n",
       "      <td>2019-12-31 23:12:18.5480</td>\n",
       "      <td>2019-12-31 23:17:01.8740</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>40.730897</td>\n",
       "      <td>-74.063913</td>\n",
       "      <td>3679</td>\n",
       "      <td>Bergen Ave</td>\n",
       "      <td>40.722104</td>\n",
       "      <td>-74.071455</td>\n",
       "      <td>29245</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1990</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404746</th>\n",
       "      <td>267</td>\n",
       "      <td>2019-12-31 23:19:03.5840</td>\n",
       "      <td>2019-12-31 23:23:30.6640</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>40.719586</td>\n",
       "      <td>-74.043117</td>\n",
       "      <td>3205</td>\n",
       "      <td>JC Medical Center</td>\n",
       "      <td>40.716540</td>\n",
       "      <td>-74.049638</td>\n",
       "      <td>26153</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404747</th>\n",
       "      <td>379</td>\n",
       "      <td>2019-12-31 23:19:06.5830</td>\n",
       "      <td>2019-12-31 23:25:25.7970</td>\n",
       "      <td>3209</td>\n",
       "      <td>Brunswick St</td>\n",
       "      <td>40.724176</td>\n",
       "      <td>-74.050656</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>40.717732</td>\n",
       "      <td>-74.043845</td>\n",
       "      <td>26276</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1984</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404748</th>\n",
       "      <td>188</td>\n",
       "      <td>2019-12-31 23:24:00.2630</td>\n",
       "      <td>2019-12-31 23:27:09.0400</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>40.719252</td>\n",
       "      <td>-74.034234</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>40.719586</td>\n",
       "      <td>-74.043117</td>\n",
       "      <td>29461</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404749</th>\n",
       "      <td>326</td>\n",
       "      <td>2019-12-31 23:45:20.9780</td>\n",
       "      <td>2019-12-31 23:50:47.7490</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>40.730897</td>\n",
       "      <td>-74.063913</td>\n",
       "      <td>3280</td>\n",
       "      <td>Astor Place</td>\n",
       "      <td>40.719282</td>\n",
       "      <td>-74.071262</td>\n",
       "      <td>29123</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1988</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>404750 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        tripduration                 starttime                  stoptime  \\\n",
       "0                201  2019-01-01 03:09:09.7110  2019-01-01 03:12:30.8790   \n",
       "1                505  2019-01-01 05:18:00.1060  2019-01-01 05:26:25.9050   \n",
       "2                756  2019-01-01 10:36:33.3400  2019-01-01 10:49:10.2600   \n",
       "3               1575  2019-01-01 12:43:38.6430  2019-01-01 13:09:54.5280   \n",
       "4               1566  2019-01-01 12:43:39.6010  2019-01-01 13:09:46.5100   \n",
       "...              ...                       ...                       ...   \n",
       "404745           283  2019-12-31 23:12:18.5480  2019-12-31 23:17:01.8740   \n",
       "404746           267  2019-12-31 23:19:03.5840  2019-12-31 23:23:30.6640   \n",
       "404747           379  2019-12-31 23:19:06.5830  2019-12-31 23:25:25.7970   \n",
       "404748           188  2019-12-31 23:24:00.2630  2019-12-31 23:27:09.0400   \n",
       "404749           326  2019-12-31 23:45:20.9780  2019-12-31 23:50:47.7490   \n",
       "\n",
       "        start station id start station name  start station latitude  \\\n",
       "0                   3183     Exchange Place               40.716247   \n",
       "1                   3183     Exchange Place               40.716247   \n",
       "2                   3183     Exchange Place               40.716247   \n",
       "3                   3183     Exchange Place               40.716247   \n",
       "4                   3183     Exchange Place               40.716247   \n",
       "...                  ...                ...                     ...   \n",
       "404745              3195            Sip Ave               40.730897   \n",
       "404746              3186      Grove St PATH               40.719586   \n",
       "404747              3209       Brunswick St               40.724176   \n",
       "404748              3639         Harborside               40.719252   \n",
       "404749              3195            Sip Ave               40.730897   \n",
       "\n",
       "        start station longitude  end station id    end station name  \\\n",
       "0                    -74.033459            3214    Essex Light Rail   \n",
       "1                    -74.033459            3638       Washington St   \n",
       "2                    -74.033459            3192  Liberty Light Rail   \n",
       "3                    -74.033459            3638       Washington St   \n",
       "4                    -74.033459            3638       Washington St   \n",
       "...                         ...             ...                 ...   \n",
       "404745               -74.063913            3679          Bergen Ave   \n",
       "404746               -74.043117            3205   JC Medical Center   \n",
       "404747               -74.050656            3185           City Hall   \n",
       "404748               -74.034234            3186       Grove St PATH   \n",
       "404749               -74.063913            3280         Astor Place   \n",
       "\n",
       "        end station latitude  end station longitude  bikeid    usertype  \\\n",
       "0                  40.712774             -74.036486   29612  Subscriber   \n",
       "1                  40.724294             -74.035483   29213  Subscriber   \n",
       "2                  40.711242             -74.055701   26164  Subscriber   \n",
       "3                  40.724294             -74.035483   29672    Customer   \n",
       "4                  40.724294             -74.035483   29522    Customer   \n",
       "...                      ...                    ...     ...         ...   \n",
       "404745             40.722104             -74.071455   29245    Customer   \n",
       "404746             40.716540             -74.049638   26153  Subscriber   \n",
       "404747             40.717732             -74.043845   26276  Subscriber   \n",
       "404748             40.719586             -74.043117   29461  Subscriber   \n",
       "404749             40.719282             -74.071262   29123  Subscriber   \n",
       "\n",
       "        birth year  gender  \n",
       "0             1993       1  \n",
       "1             1972       2  \n",
       "2             1985       1  \n",
       "3             1969       0  \n",
       "4             1969       0  \n",
       "...            ...     ...  \n",
       "404745        1990       1  \n",
       "404746        1989       1  \n",
       "404747        1984       1  \n",
       "404748        1987       1  \n",
       "404749        1988       1  \n",
       "\n",
       "[404750 rows x 15 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url ='https://github.com/raw/Andresmup/Proyecto_Data_Science_Bicicletas/master/Data_Wrangling/Dataset_2019.csv'\n",
    "df_csv = pd.read_csv(url)\n",
    "df_csv\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "HiDj0HUHkSMR",
   "metadata": {
    "id": "HiDj0HUHkSMR"
   },
   "source": [
    "##**Resumen de datos del Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "VK8F7chWkOhi",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 309,
     "status": "ok",
     "timestamp": 1683209573299,
     "user": {
      "displayName": "Ana Paula Sosa Pivatto",
      "userId": "02813882217908415455"
     },
     "user_tz": 180
    },
    "id": "VK8F7chWkOhi",
    "outputId": "191919ac-468c-4a04-b70a-4328bff0a00c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 404750 entries, 0 to 404749\n",
      "Data columns (total 15 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   tripduration             404750 non-null  int64  \n",
      " 1   starttime                404750 non-null  object \n",
      " 2   stoptime                 404750 non-null  object \n",
      " 3   start station id         404750 non-null  int64  \n",
      " 4   start station name       404750 non-null  object \n",
      " 5   start station latitude   404750 non-null  float64\n",
      " 6   start station longitude  404750 non-null  float64\n",
      " 7   end station id           404750 non-null  int64  \n",
      " 8   end station name         404750 non-null  object \n",
      " 9   end station latitude     404750 non-null  float64\n",
      " 10  end station longitude    404750 non-null  float64\n",
      " 11  bikeid                   404750 non-null  int64  \n",
      " 12  usertype                 404750 non-null  object \n",
      " 13  birth year               404750 non-null  int64  \n",
      " 14  gender                   404750 non-null  int64  \n",
      "dtypes: float64(4), int64(6), object(5)\n",
      "memory usage: 46.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_csv.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80b50b05",
   "metadata": {
    "id": "80b50b05"
   },
   "source": [
    "##**Top de estaciones de salida más utilizadas**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "asNzjV23eZWz",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 337
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1683210179214,
     "user": {
      "displayName": "Ana Paula Sosa Pivatto",
      "userId": "02813882217908415455"
     },
     "user_tz": 180
    },
    "id": "asNzjV23eZWz",
    "outputId": "4fc80bef-af23-4606-fa3d-394196323c70"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_csv['tripnumber'] = df_csv.index\n",
    "salida = pd.DataFrame(df_csv,columns=['tripnumber','start station name'])\n",
    "salida = salida.groupby(\"start station name\")[\"tripnumber\"].count()\n",
    "salida= salida.to_frame()\n",
    "salida= salida.sort_values(by = ['tripnumber'], ascending=False).head(5)  \n",
    "salida = salida.rename({'tripnumber': 'Numero_viajes'}, axis=1)\n",
    "salida.reset_index(inplace=True)\n",
    "salida = salida.rename({'start station name': 'Estación'}, axis=1)\n",
    "salida\n",
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "f, ax = plt.subplots(figsize=(10, 3))\n",
    "ax = sns.barplot(y=salida.Estación, x=salida.Numero_viajes, color= '#010358')\n",
    "#ax.bar_label(ax.containers[0])\n",
    "\n",
    "plt.title('Top 5 estaciones más utilizadas', fontsize=16, fontweight=\"bold\", color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Rq9xE5lGKzwx",
   "metadata": {
    "id": "Rq9xE5lGKzwx"
   },
   "source": [
    "La estación más utilizada es **Grove St PATH**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "-gq0Wp9JdZsp",
   "metadata": {
    "id": "-gq0Wp9JdZsp"
   },
   "source": [
    "##**Top 10 recorridos/trayectos realizados**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "t8rNQgyIeUiH",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 335
    },
    "executionInfo": {
     "elapsed": 1299,
     "status": "ok",
     "timestamp": 1683210319690,
     "user": {
      "displayName": "Ana Paula Sosa Pivatto",
      "userId": "02813882217908415455"
     },
     "user_tz": 180
    },
    "id": "t8rNQgyIeUiH",
    "outputId": "b8a3d83a-e104-4dc4-80e0-047ddcf73d3c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_by_start_station_id = pd.DataFrame(df_csv,columns=['tripnumber','start station name', 'end station name'])\n",
    "n_by_start_station_id = n_by_start_station_id.groupby([\"start station name\", \"end station name\"])[\"tripnumber\"].count()\n",
    "n_by_start_station_id = n_by_start_station_id.to_frame()\n",
    "n_by_start_station_id = n_by_start_station_id.sort_values(by = ['tripnumber'], ascending=False).head(10)\n",
    "n_by_start_station_id = n_by_start_station_id.rename({'tripnumber': 'Numero_viajes'}, axis=1)\n",
    "n_by_start_station_id.reset_index(inplace=True)\n",
    "n_by_start_station_id[\"Rutas\"] = n_by_start_station_id[\"start station name\"].astype(str)+ ' --> ' + n_by_start_station_id[\"end station name\"]\n",
    "n_by_start_station_id.drop(columns=['start station name', 'end station name'])\n",
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "f, ax = plt.subplots(figsize=(10, 3))\n",
    "ax = sns.barplot(y=n_by_start_station_id.Rutas,x=n_by_start_station_id.Numero_viajes, orient = 'h', color= '#010358')\n",
    "#ax.bar_label(ax.containers[0])\n",
    "\n",
    "plt.title('Top 10 recorridos realizados', fontsize=16, fontweight=\"bold\", color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fxoGnwDlLIRh",
   "metadata": {
    "id": "fxoGnwDlLIRh"
   },
   "source": [
    "En el gráfico anterior se puede observar que en el **90%** de los recorridos más realizados tienen como origen o destino la estación **Grove St PATH**.\n",
    "\n",
    "# A partir de los insight obtenidos en los gráficos anteriores, comenzaremos a trabajar solamente con los datos de la estación **Grove St PATH.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "XYtp04U0aU61",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 696
    },
    "executionInfo": {
     "elapsed": 388,
     "status": "ok",
     "timestamp": 1683214668659,
     "user": {
      "displayName": "Ana Paula Sosa Pivatto",
      "userId": "02813882217908415455"
     },
     "user_tz": 180
    },
    "id": "XYtp04U0aU61",
    "outputId": "598f3711-c036-4803-fec0-9fbe67111417"
   },
   "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>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "      <th>tripnumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1686</th>\n",
       "      <td>254</td>\n",
       "      <td>2019-01-01 01:37:17.2410</td>\n",
       "      <td>2019-01-01 01:41:31.4040</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>40.719586</td>\n",
       "      <td>-74.043117</td>\n",
       "      <td>3270</td>\n",
       "      <td>Jersey &amp; 6th St</td>\n",
       "      <td>40.725289</td>\n",
       "      <td>-74.045572</td>\n",
       "      <td>29521</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1984</td>\n",
       "      <td>1</td>\n",
       "      <td>1686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1687</th>\n",
       "      <td>413</td>\n",
       "      <td>2019-01-01 02:28:20.5500</td>\n",
       "      <td>2019-01-01 02:35:14.5400</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>40.719586</td>\n",
       "      <td>-74.043117</td>\n",
       "      <td>3203</td>\n",
       "      <td>Hamilton Park</td>\n",
       "      <td>40.727596</td>\n",
       "      <td>-74.044247</td>\n",
       "      <td>29551</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1984</td>\n",
       "      <td>2</td>\n",
       "      <td>1687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1688</th>\n",
       "      <td>306</td>\n",
       "      <td>2019-01-01 02:33:11.6000</td>\n",
       "      <td>2019-01-01 02:38:18.2540</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>40.719586</td>\n",
       "      <td>-74.043117</td>\n",
       "      <td>3213</td>\n",
       "      <td>Van Vorst Park</td>\n",
       "      <td>40.718489</td>\n",
       "      <td>-74.047727</td>\n",
       "      <td>29245</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>1688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689</th>\n",
       "      <td>263</td>\n",
       "      <td>2019-01-01 03:08:06.0790</td>\n",
       "      <td>2019-01-01 03:12:29.8340</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>40.719586</td>\n",
       "      <td>-74.043117</td>\n",
       "      <td>3638</td>\n",
       "      <td>Washington St</td>\n",
       "      <td>40.724294</td>\n",
       "      <td>-74.035483</td>\n",
       "      <td>29515</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990</td>\n",
       "      <td>1</td>\n",
       "      <td>1689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690</th>\n",
       "      <td>165</td>\n",
       "      <td>2019-01-01 04:08:02.0470</td>\n",
       "      <td>2019-01-01 04:10:47.6500</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>40.719586</td>\n",
       "      <td>-74.043117</td>\n",
       "      <td>3213</td>\n",
       "      <td>Van Vorst Park</td>\n",
       "      <td>40.718489</td>\n",
       "      <td>-74.047727</td>\n",
       "      <td>29220</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1952</td>\n",
       "      <td>1</td>\n",
       "      <td>1690</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404698</th>\n",
       "      <td>74</td>\n",
       "      <td>2019-12-31 19:39:08.7960</td>\n",
       "      <td>2019-12-31 19:40:23.7620</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>40.719586</td>\n",
       "      <td>-74.043117</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>40.717732</td>\n",
       "      <td>-74.043845</td>\n",
       "      <td>26286</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1988</td>\n",
       "      <td>1</td>\n",
       "      <td>404698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404727</th>\n",
       "      <td>364</td>\n",
       "      <td>2019-12-31 21:17:08.8910</td>\n",
       "      <td>2019-12-31 21:23:13.1420</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>40.719586</td>\n",
       "      <td>-74.043117</td>\n",
       "      <td>3269</td>\n",
       "      <td>Brunswick &amp; 6th</td>\n",
       "      <td>40.726012</td>\n",
       "      <td>-74.050389</td>\n",
       "      <td>26308</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1978</td>\n",
       "      <td>1</td>\n",
       "      <td>404727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404740</th>\n",
       "      <td>287</td>\n",
       "      <td>2019-12-31 22:34:34.4360</td>\n",
       "      <td>2019-12-31 22:39:22.2880</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>40.719586</td>\n",
       "      <td>-74.043117</td>\n",
       "      <td>3276</td>\n",
       "      <td>Marin Light Rail</td>\n",
       "      <td>40.714584</td>\n",
       "      <td>-74.042817</td>\n",
       "      <td>29263</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1979</td>\n",
       "      <td>1</td>\n",
       "      <td>404740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404741</th>\n",
       "      <td>279</td>\n",
       "      <td>2019-12-31 22:40:16.6780</td>\n",
       "      <td>2019-12-31 22:44:56.1790</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>40.719586</td>\n",
       "      <td>-74.043117</td>\n",
       "      <td>3638</td>\n",
       "      <td>Washington St</td>\n",
       "      <td>40.724294</td>\n",
       "      <td>-74.035483</td>\n",
       "      <td>29600</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "      <td>404741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404746</th>\n",
       "      <td>267</td>\n",
       "      <td>2019-12-31 23:19:03.5840</td>\n",
       "      <td>2019-12-31 23:23:30.6640</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>40.719586</td>\n",
       "      <td>-74.043117</td>\n",
       "      <td>3205</td>\n",
       "      <td>JC Medical Center</td>\n",
       "      <td>40.716540</td>\n",
       "      <td>-74.049638</td>\n",
       "      <td>26153</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>404746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46658 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        tripduration                 starttime                  stoptime  \\\n",
       "1686             254  2019-01-01 01:37:17.2410  2019-01-01 01:41:31.4040   \n",
       "1687             413  2019-01-01 02:28:20.5500  2019-01-01 02:35:14.5400   \n",
       "1688             306  2019-01-01 02:33:11.6000  2019-01-01 02:38:18.2540   \n",
       "1689             263  2019-01-01 03:08:06.0790  2019-01-01 03:12:29.8340   \n",
       "1690             165  2019-01-01 04:08:02.0470  2019-01-01 04:10:47.6500   \n",
       "...              ...                       ...                       ...   \n",
       "404698            74  2019-12-31 19:39:08.7960  2019-12-31 19:40:23.7620   \n",
       "404727           364  2019-12-31 21:17:08.8910  2019-12-31 21:23:13.1420   \n",
       "404740           287  2019-12-31 22:34:34.4360  2019-12-31 22:39:22.2880   \n",
       "404741           279  2019-12-31 22:40:16.6780  2019-12-31 22:44:56.1790   \n",
       "404746           267  2019-12-31 23:19:03.5840  2019-12-31 23:23:30.6640   \n",
       "\n",
       "        start station id start station name  start station latitude  \\\n",
       "1686                3186      Grove St PATH               40.719586   \n",
       "1687                3186      Grove St PATH               40.719586   \n",
       "1688                3186      Grove St PATH               40.719586   \n",
       "1689                3186      Grove St PATH               40.719586   \n",
       "1690                3186      Grove St PATH               40.719586   \n",
       "...                  ...                ...                     ...   \n",
       "404698              3186      Grove St PATH               40.719586   \n",
       "404727              3186      Grove St PATH               40.719586   \n",
       "404740              3186      Grove St PATH               40.719586   \n",
       "404741              3186      Grove St PATH               40.719586   \n",
       "404746              3186      Grove St PATH               40.719586   \n",
       "\n",
       "        start station longitude  end station id   end station name  \\\n",
       "1686                 -74.043117            3270    Jersey & 6th St   \n",
       "1687                 -74.043117            3203      Hamilton Park   \n",
       "1688                 -74.043117            3213     Van Vorst Park   \n",
       "1689                 -74.043117            3638      Washington St   \n",
       "1690                 -74.043117            3213     Van Vorst Park   \n",
       "...                         ...             ...                ...   \n",
       "404698               -74.043117            3185          City Hall   \n",
       "404727               -74.043117            3269    Brunswick & 6th   \n",
       "404740               -74.043117            3276   Marin Light Rail   \n",
       "404741               -74.043117            3638      Washington St   \n",
       "404746               -74.043117            3205  JC Medical Center   \n",
       "\n",
       "        end station latitude  end station longitude  bikeid    usertype  \\\n",
       "1686               40.725289             -74.045572   29521  Subscriber   \n",
       "1687               40.727596             -74.044247   29551  Subscriber   \n",
       "1688               40.718489             -74.047727   29245  Subscriber   \n",
       "1689               40.724294             -74.035483   29515  Subscriber   \n",
       "1690               40.718489             -74.047727   29220  Subscriber   \n",
       "...                      ...                    ...     ...         ...   \n",
       "404698             40.717732             -74.043845   26286  Subscriber   \n",
       "404727             40.726012             -74.050389   26308  Subscriber   \n",
       "404740             40.714584             -74.042817   29263  Subscriber   \n",
       "404741             40.724294             -74.035483   29600  Subscriber   \n",
       "404746             40.716540             -74.049638   26153  Subscriber   \n",
       "\n",
       "        birth year  gender  tripnumber  \n",
       "1686          1984       1        1686  \n",
       "1687          1984       2        1687  \n",
       "1688          1989       1        1688  \n",
       "1689          1990       1        1689  \n",
       "1690          1952       1        1690  \n",
       "...            ...     ...         ...  \n",
       "404698        1988       1      404698  \n",
       "404727        1978       1      404727  \n",
       "404740        1979       1      404740  \n",
       "404741        1987       1      404741  \n",
       "404746        1989       1      404746  \n",
       "\n",
       "[46658 rows x 16 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grove = df_csv[(df_csv['start station name'] == 'Grove St PATH')]\n",
    "df_grove"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Eu2RdjvqajUp",
   "metadata": {
    "id": "Eu2RdjvqajUp"
   },
   "source": [
    "##**Distribución de viajes por día**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "230f6b4d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 568
    },
    "executionInfo": {
     "elapsed": 2106,
     "status": "ok",
     "timestamp": 1683215180391,
     "user": {
      "displayName": "Ana Paula Sosa Pivatto",
      "userId": "02813882217908415455"
     },
     "user_tz": 180
    },
    "id": "230f6b4d",
    "outputId": "812190f6-8d14-46a2-eb84-d70dd9e7bf7c"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1300x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "df_grove[\"starttime\"] = pd.to_datetime(df_grove[\"starttime\"])\n",
    "df_grove[\"date\"]=df_grove[\"starttime\"].dt.date\n",
    "df_grove.head(15)\n",
    "df_count=df_grove.groupby(\"date\").agg({'date':'count'}).rename(columns = {'date':'count'}).reset_index()\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.figure(figsize = (13,6))\n",
    "ax=sns.lineplot(x = \"date\", y = \"count\",data = df_count, color= '#010358')\n",
    "ax.set(xlabel=\"Fecha\", ylabel=\"Cantidad de viajes\")\n",
    "plt.title(\"Distribución de viajes por días\", fontsize=16, fontweight=\"bold\", color='black')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "171742f1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 426
    },
    "executionInfo": {
     "elapsed": 332,
     "status": "ok",
     "timestamp": 1683216762795,
     "user": {
      "displayName": "Ana Paula Sosa Pivatto",
      "userId": "02813882217908415455"
     },
     "user_tz": 180
    },
    "id": "171742f1",
    "outputId": "62886c13-27e3-457a-bf41-5d57023bd2f4"
   },
   "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>month</th>\n",
       "      <th>Count</th>\n",
       "      <th>Mes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>5463</td>\n",
       "      <td>Agosto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>5282</td>\n",
       "      <td>Septiembre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>4833</td>\n",
       "      <td>Julio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>4678</td>\n",
       "      <td>Octubre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>4499</td>\n",
       "      <td>Junio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4114</td>\n",
       "      <td>Mayo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3727</td>\n",
       "      <td>Abril</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>3585</td>\n",
       "      <td>Noviembre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3113</td>\n",
       "      <td>Marzo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2661</td>\n",
       "      <td>Enero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2447</td>\n",
       "      <td>Febrero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>2256</td>\n",
       "      <td>Diciembre</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    month  Count         Mes\n",
       "7       8   5463      Agosto\n",
       "8       9   5282  Septiembre\n",
       "6       7   4833       Julio\n",
       "9      10   4678     Octubre\n",
       "5       6   4499       Junio\n",
       "4       5   4114        Mayo\n",
       "3       4   3727       Abril\n",
       "10     11   3585   Noviembre\n",
       "2       3   3113       Marzo\n",
       "0       1   2661       Enero\n",
       "1       2   2447     Febrero\n",
       "11     12   2256   Diciembre"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "df_grove[\"month\"]=df_grove[\"starttime\"].dt.month\n",
    "df_count_M=df_grove.groupby(\"month\").agg({'month':'count'}).rename(columns = {'month':'Count'}).reset_index()\n",
    "df_count_M[\"Mes\"]=[\"Enero\", \"Febrero\",\"Marzo\",\"Abril\",\"Mayo\",\"Junio\",\"Julio\",\"Agosto\",\"Septiembre\",\n",
    "                   \"Octubre\",\"Noviembre\",\"Diciembre\"]\n",
    "df_count_M.sort_values(by=\"Count\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nmGcWSpaclca",
   "metadata": {
    "id": "nmGcWSpaclca"
   },
   "source": [
    "En los meses de verano (**julio-octubre**) es donde se visualiza la mayor cantidad de viajes, alrededor del **45%** del total anual."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "AX-s8pA2NQUJ",
   "metadata": {
    "id": "AX-s8pA2NQUJ"
   },
   "source": [
    "##**Distribución de viajes por días de la semana**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "rc5ZND8UMt3r",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "executionInfo": {
     "elapsed": 449,
     "status": "ok",
     "timestamp": 1683217056517,
     "user": {
      "displayName": "Ana Paula Sosa Pivatto",
      "userId": "02813882217908415455"
     },
     "user_tz": 180
    },
    "id": "rc5ZND8UMt3r",
    "outputId": "cce86d6d-7ef7-4bf5-ed1e-e215cda4d088"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "df_dia= df_grove\n",
    "df_dia[\"day\"]=df_grove[\"starttime\"].dt.day_name()\n",
    "df_dia=df_dia.groupby(\"day\").agg({'day':'count'}).rename(columns = {'day':'Count'}).reset_index()\n",
    "df_dia=df_dia.sort_values(by=\"Count\", ascending=False)\n",
    "\n",
    "f, ax = plt.subplots(figsize=(10, 3))\n",
    "ax = sns.barplot(y=df_dia.day, x=df_dia.Count, color= '#010358')\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.title('Viajes por día de la semana', fontsize=16, fontweight=\"bold\", color='black')\n",
    "plt.xlabel(\"Cantidad\")\n",
    "plt.ylabel(\"Días\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6Th4RxvaQ42-",
   "metadata": {
    "id": "6Th4RxvaQ42-"
   },
   "source": [
    "Como se puede visualizar en el gráfico la mayor cantidad de rentas son **durante los días de semana**, siendo los sábados y domingos las fechas con menor cantidad."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "uBZzaQIzannX",
   "metadata": {
    "id": "uBZzaQIzannX"
   },
   "source": [
    "##**Análisis de viajes por hora**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8dd58d14",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 568
    },
    "executionInfo": {
     "elapsed": 981,
     "status": "ok",
     "timestamp": 1683217062877,
     "user": {
      "displayName": "Ana Paula Sosa Pivatto",
      "userId": "02813882217908415455"
     },
     "user_tz": 180
    },
    "id": "8dd58d14",
    "outputId": "ad0aebc6-f444-445b-f377-440106caca47"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "df_grove['hour'] = df_grove['starttime'].dt.hour\n",
    "hs=df_grove.groupby('hour').size()\n",
    "hs.index\n",
    "hs.values\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.figure(figsize = (13,6))\n",
    "plt.bar(hs.index,hs.values, color= '#010358')\n",
    "plt.title('Distribución de viajes por hora año 2019', fontsize=16, fontweight=\"bold\", color='black')\n",
    "plt.xlabel('Hora')\n",
    "plt.ylabel('Total de viajes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6l8XbcVCbl0Q",
   "metadata": {
    "id": "6l8XbcVCbl0Q"
   },
   "source": [
    "Los horarios de mayor demanda son: **entre las 17 - 19 hs**. \n",
    "Esto indica que el servicio es muy usado para retornar a sus casas desde el lugar de trabajo o estudio.\n",
    "Concordando con el gráfico por días de la semana donde hay mayor cantidad de alquileres.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03gIuLgAbMQx",
   "metadata": {
    "id": "03gIuLgAbMQx"
   },
   "source": [
    "##**Distribución de usuarios por género**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e7cc0a83",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "executionInfo": {
     "elapsed": 740,
     "status": "ok",
     "timestamp": 1683216266750,
     "user": {
      "displayName": "Ana Paula Sosa Pivatto",
      "userId": "02813882217908415455"
     },
     "user_tz": 180
    },
    "id": "e7cc0a83",
    "outputId": "99c37b65-877a-4cc7-8001-d6c9b531b8e7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prom_MF=df_grove.gender.value_counts()/df_grove[\"gender\"].count()*100\n",
    "values = [prom_MF[0],prom_MF[1],prom_MF[2]]\n",
    "labels=[\"Desconocido\", \"Masculino\", \"Femenino\"]\n",
    "colors= [\"#F2F2F2\",\"#6AB3D9\", \"#D81C1C\"]\n",
    "fig1, ax1 = plt.subplots(figsize=(8, 5))\n",
    "ax1.pie(values, labels=labels, textprops={\"fontsize\":14}, colors=colors, autopct='%1.1f%%', explode=(0.02, 0.02, 0.02))\n",
    "plt.title(\"Distribución de usuarios por género\", fontsize=16, fontweight=\"bold\", color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3l0vIFqbqVs",
   "metadata": {
    "id": "f3l0vIFqbqVs"
   },
   "source": [
    "El **73%** de los usuarios del servicio de bicicletas son **hombres**. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60a98Yn9bRnC",
   "metadata": {
    "id": "60a98Yn9bRnC"
   },
   "source": [
    "##**Distribución de usuarios por tipo de suscripción**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1b0dadd0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "executionInfo": {
     "elapsed": 451,
     "status": "ok",
     "timestamp": 1683216625893,
     "user": {
      "displayName": "Ana Paula Sosa Pivatto",
      "userId": "02813882217908415455"
     },
     "user_tz": 180
    },
    "id": "1b0dadd0",
    "outputId": "511c5189-b3f8-4c7c-c053-6e9ca4bddb91"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prom_MF=df_grove.usertype.value_counts()/df_grove[\"usertype\"].count()*100\n",
    "users = [prom_MF[\"Subscriber\"],prom_MF[\"Customer\"]]\n",
    "labels=[\"Subscriber\", \"Customer\"]\n",
    "colors= [\"#6AB3D9\", \"#D81C1C\"]\n",
    "fig2, ax2 = plt.subplots(figsize=(8, 5))\n",
    "ax2.pie(users, labels=labels, textprops={\"fontsize\":14}, colors=colors, autopct='%1.1f%%', explode=(0.02, 0.02))\n",
    "plt.title(\"Distribución de usuarios por tipo de suscripción\", fontsize=16, fontweight=\"bold\", color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rJZ1s-mKbv5M",
   "metadata": {
    "id": "rJZ1s-mKbv5M"
   },
   "source": [
    "Alrededor del **95% de los usuarios es Subscriber**, es decir pagan una membresía mensual. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wTv8EUkf9a5p",
   "metadata": {
    "id": "wTv8EUkf9a5p"
   },
   "source": [
    "##**Distribución etaria de los usuarios del sistema**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f33720d2",
   "metadata": {
    "id": "f33720d2"
   },
   "outputs": [],
   "source": [
    "df_grove['age']=2019-df_grove['birth year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "143900cf",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 589
    },
    "executionInfo": {
     "elapsed": 881,
     "status": "ok",
     "timestamp": 1683216384462,
     "user": {
      "displayName": "Ana Paula Sosa Pivatto",
      "userId": "02813882217908415455"
     },
     "user_tz": 180
    },
    "id": "143900cf",
    "outputId": "075013a9-f69e-4eb8-b917-598ee11d19f9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Grupos etarios')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usuarios_edades_grove = df_grove['age']\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "ax.hist(usuarios_edades_grove.values.flatten(), bins=8, color= '#010358')\n",
    "ax.set_title('Cantidad de usuarios por grupos etarios', fontsize=16, fontweight=\"bold\", color='black')\n",
    "ax.set_ylabel('Cantidad de usuarios', fontsize=14, color='black')\n",
    "ax.set_xlabel('Grupos etarios', fontsize=14, color='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc716f86",
   "metadata": {
    "id": "cc716f86"
   },
   "source": [
    "*   La mayoría de los usuarios tienen **35 años**\n",
    "*   El **90%** de los usuarios tienen menos de 51 años.\n",
    "*   El **50%** de los usuarios tienen entre 30 y 44 años. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dxaSIamXdDEa",
   "metadata": {
    "id": "dxaSIamXdDEa"
   },
   "source": [
    "Como observamos aquí por el grupo etario y comparando con los días de la semana más usados (lunes a viernes), queda claro que dicha estación es altamente usada por gente en edad laboral, la cual utiliza el servicio de bicicletas para regresar a su lugar de residencia."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "CvAQkDhn7ZaA",
   "metadata": {
    "id": "CvAQkDhn7ZaA"
   },
   "source": [
    "##**4. ENTRENAMIENTO Y TESTEO** \n",
    "Entrenamiento y testeo de al menos 2 modelos distintos de Machine Learning utilizando algún método de validación cruzada.\n",
    "\n",
    "Aquí se prodece a manipular los datos para obtener un dataframe el cuál contenga agrupado por día la cantidad de viajes que se iniciaron desde Grove St PATH, la cuál es la estación que queremos analizar para poder realizar una predicción mediante series de tiempo."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "W6CQ9wx8TZMz",
   "metadata": {
    "id": "W6CQ9wx8TZMz"
   },
   "source": [
    "Aquí se procede a manipular los datos para obtener un DF el cual contenga agrupado por día la cantidad de viajes que se iniciaron desde Grove St PATH, la cual es la estación que queremos analizar para poder realizar una predicción mediante series de tiempo.\n",
    "\n",
    "Acotamos el dataframe para que solo contenga viajes desde el mes de enero hasta el mes de septiembre ya que es este el cual intentaremos predecir usando una serie de tiempo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "TYhHQx8QTZM0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 676
    },
    "executionInfo": {
     "elapsed": 2321,
     "status": "ok",
     "timestamp": 1682948735520,
     "user": {
      "displayName": "Angela D'Alessio Muro",
      "userId": "04544484181130964961"
     },
     "user_tz": -120
    },
    "id": "TYhHQx8QTZM0",
    "outputId": "e095d31c-80f4-4e4e-e593-b479f6f087a6"
   },
   "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>date</th>\n",
       "      <th>viajes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>2019-09-26</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>2019-09-27</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>2019-09-28</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>2019-09-29</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>273 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  viajes\n",
       "0    2019-01-01      36\n",
       "1    2019-01-02     133\n",
       "2    2019-01-03     106\n",
       "3    2019-01-04     117\n",
       "4    2019-01-05      31\n",
       "..          ...     ...\n",
       "268  2019-09-26     238\n",
       "269  2019-09-27     222\n",
       "270  2019-09-28     124\n",
       "271  2019-09-29     141\n",
       "272  2019-09-30     214\n",
       "\n",
       "[273 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bicis = df_grove\n",
    "bicis[\"starttime\"] = pd.to_datetime(bicis[\"starttime\"])\n",
    "bicis[\"stoptime\"] = pd.to_datetime(bicis[\"stoptime\"])\n",
    "bicis=bicis[(bicis['starttime'] <'2019-10-01')]\n",
    "bicis['date'] = bicis['starttime'].dt.date\n",
    "bicis=bicis.groupby('date').agg({'starttime':'count'}).rename(columns ={'starttime': 'viajes'}).reset_index()\n",
    "bicis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "R6bnawqdTZM0",
   "metadata": {
    "id": "R6bnawqdTZM0"
   },
   "outputs": [],
   "source": [
    "databicis = bicis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "yx8RvLsqHuLQ",
   "metadata": {
    "id": "yx8RvLsqHuLQ"
   },
   "source": [
    "Realizamos una copia del data frame para ser manipulado y realizar la predicción."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "nB2zovbLTZM1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1682948753962,
     "user": {
      "displayName": "Angela D'Alessio Muro",
      "userId": "04544484181130964961"
     },
     "user_tz": -120
    },
    "id": "nB2zovbLTZM1",
    "outputId": "ad7ef12a-a0d1-4e5d-a631-02251dc115b8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "databicis = databicis.set_index('date')\n",
    "databicis = databicis.rename(columns={'viajes': 'y'})\n",
    "databicis = databicis.asfreq('d')\n",
    "databicis = databicis.sort_index()\n",
    "databicis.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "IrWdgzIpTZM1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 545,
     "status": "ok",
     "timestamp": 1682948756860,
     "user": {
      "displayName": "Angela D'Alessio Muro",
      "userId": "04544484181130964961"
     },
     "user_tz": -120
    },
    "id": "IrWdgzIpTZM1",
    "outputId": "bda646c4-4079-4ef9-9c09-87d787180ea9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(databicis.index == pd.date_range(start=databicis.index.min(),\n",
    "                             end=databicis.index.max(),\n",
    "                             freq=databicis.index.freq)).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eOK2QM4uIguq",
   "metadata": {
    "id": "eOK2QM4uIguq"
   },
   "source": [
    "Comprobamos que no tenemos falta de datos en ningun dia en nuestro periodo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "t4lS-exSTZM2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 796
    },
    "executionInfo": {
     "elapsed": 1542,
     "status": "ok",
     "timestamp": 1682948760925,
     "user": {
      "displayName": "Angela D'Alessio Muro",
      "userId": "04544484181130964961"
     },
     "user_tz": -120
    },
    "id": "t4lS-exSTZM2",
    "outputId": "5608a940-675f-4c92-e0f1-3838cdd60db8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dates : 2019-01-01 00:00:00 --- 2019-08-31 00:00:00  (n=243)\n",
      "Test dates  : 2019-09-01 00:00:00 --- 2019-09-30 00:00:00  (n=30)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "steps = 30\n",
    "databicis_train = databicis[:-steps]\n",
    "databicis_test  = databicis[-steps:]\n",
    "\n",
    "sns.set_theme(style=\"whitegrid\", rc= {\"axes.spines.right\": False, \"axes.spines.top\": False})\n",
    "\n",
    "print(f\"Train dates : {databicis_train.index.min()} --- {databicis_train.index.max()}  (n={len(databicis_train)})\")\n",
    "print(f\"Test dates  : {databicis_test.index.min()} --- {databicis_test.index.max()}  (n={len(databicis_test)})\")\n",
    "\n",
    "fig, ax=plt.subplots(figsize=(14, 8))\n",
    "databicis_train['y'].plot(ax=ax, label='train')\n",
    "databicis_test['y'].plot(ax=ax, label='test')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tKHCt4f3IqED",
   "metadata": {
    "id": "tKHCt4f3IqED"
   },
   "source": [
    "Dividimos el dataset en 2 siendo desde enero a agosto la parte usada para el entrenamiento y el mes de septiembre la parte que usaremos para testear los resultados obtenidos comparandolos con esta que seria su contraparte real.\n",
    "\n",
    "Como para asi demarcar que en este caso vamos a realizar una prediccion sobre el mes de septiembre lo graficamos en rojo."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "CTQA-xZXTZM2",
   "metadata": {
    "id": "CTQA-xZXTZM2"
   },
   "source": [
    "##**IMPLEMENTACIÓN DEL LINEAR REGRESSOR PARA SERIES DE TIEMPO**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "yixXYedFTZM2",
   "metadata": {
    "id": "yixXYedFTZM2"
   },
   "source": [
    "Procedemos a implementar el modelo, para ello decidimos asignar a steps el valor 30 ya que con la cantidad de dias del mes septiembre, que a su vez es el ultimo mes del dataset.\n",
    "\n",
    "Aplicamos como regressor en modelo Forecaster una regresion lineal, y aplicamos un intervalo superior e inferior respecto al valor a predecir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "UtAXhCjkTZM3",
   "metadata": {
    "id": "UtAXhCjkTZM3"
   },
   "outputs": [],
   "source": [
    "steps = 30\n",
    "datos_train = databicis[:-steps]\n",
    "datos_test  = databicis[-steps:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "FOHjEQCYstZK",
   "metadata": {
    "id": "FOHjEQCYstZK"
   },
   "outputs": [],
   "source": [
    "inicio_linear = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4rLOS-iLTZM3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 2828,
     "status": "ok",
     "timestamp": 1682948770098,
     "user": {
      "displayName": "Angela D'Alessio Muro",
      "userId": "04544484181130964961"
     },
     "user_tz": -120
    },
    "id": "4rLOS-iLTZM3",
    "outputId": "d82c3a22-3f84-486c-a68f-8181eadab5e6"
   },
   "outputs": [],
   "source": [
    "forecaster_linear = ForecasterAutoreg(\n",
    "                    regressor = LinearRegression(),\n",
    "                    lags = 15\n",
    "             )\n",
    "\n",
    "forecaster_linear.fit(y=datos_train['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "hFuBFjuMs6VC",
   "metadata": {
    "id": "hFuBFjuMs6VC"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>lower_bound</th>\n",
       "      <th>upper_bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-01</th>\n",
       "      <td>97.290421</td>\n",
       "      <td>53.630497</td>\n",
       "      <td>131.118063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02</th>\n",
       "      <td>186.075883</td>\n",
       "      <td>138.190255</td>\n",
       "      <td>223.464990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-03</th>\n",
       "      <td>215.176386</td>\n",
       "      <td>171.332066</td>\n",
       "      <td>254.964400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-04</th>\n",
       "      <td>204.284970</td>\n",
       "      <td>164.646162</td>\n",
       "      <td>246.516434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-05</th>\n",
       "      <td>224.040514</td>\n",
       "      <td>183.317187</td>\n",
       "      <td>264.759063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  pred  lower_bound  upper_bound\n",
       "2019-09-01   97.290421    53.630497   131.118063\n",
       "2019-09-02  186.075883   138.190255   223.464990\n",
       "2019-09-03  215.176386   171.332066   254.964400\n",
       "2019-09-04  204.284970   164.646162   246.516434\n",
       "2019-09-05  224.040514   183.317187   264.759063"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicciones_linear = forecaster_linear.predict_interval(\n",
    "                    steps    = steps,\n",
    "                    interval = [10, 90],\n",
    "                    n_boot   = 500\n",
    "               )\n",
    "\n",
    "predicciones_linear.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f656b9cd",
   "metadata": {},
   "source": [
    "Obtenemos un dataframe con los dias del mes de septiembre como indice, y tres columnas.\n",
    "\n",
    "La primera indica un valor tentativo de nuestra predicción. La segunda y tercera representan los valores mínimo y maximo de nuestro intervalor de confianza."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "VvOS8K7ItAwy",
   "metadata": {
    "id": "VvOS8K7ItAwy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El tiempo que tardo en ejecutarse el modelo usando regresion lineal fue:  1.5155208110809326\n"
     ]
    }
   ],
   "source": [
    "fin_linear = time.time()\n",
    "tiempo_ejecucion_linear = fin_linear - inicio_linear\n",
    "print(\"El tiempo que tardo en ejecutarse el modelo usando regresion lineal fue: \", tiempo_ejecucion_linear)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Mduh8_DXqitx",
   "metadata": {
    "id": "Mduh8_DXqitx"
   },
   "source": [
    "##**IMPLEMENTACIÓN DE RANDOM FOREST REGRESSOR PARA SERIES DE TIEMPO**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gSrZgXy7q41r",
   "metadata": {
    "id": "gSrZgXy7q41r"
   },
   "source": [
    "Aplicamos como regressor en modelo Forecaster un ramdom forest regressor, y aplicamos un intervalo superior e inferior respecto al valor a predecir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "oUt1VoyLsyYz",
   "metadata": {
    "id": "oUt1VoyLsyYz"
   },
   "outputs": [],
   "source": [
    "inicio_random = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "jij1WP3Brbj5",
   "metadata": {
    "id": "jij1WP3Brbj5"
   },
   "outputs": [],
   "source": [
    "forecaster_random = ForecasterAutoreg(\n",
    "                regressor = RandomForestRegressor(),\n",
    "                lags      = 15\n",
    "             )\n",
    "\n",
    "forecaster_random.fit(y=datos_train['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "KHPDf9KRs3KK",
   "metadata": {
    "id": "KHPDf9KRs3KK"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>lower_bound</th>\n",
       "      <th>upper_bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-01</th>\n",
       "      <td>88.26</td>\n",
       "      <td>72.330</td>\n",
       "      <td>102.270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02</th>\n",
       "      <td>197.40</td>\n",
       "      <td>179.392</td>\n",
       "      <td>211.229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-03</th>\n",
       "      <td>206.29</td>\n",
       "      <td>177.272</td>\n",
       "      <td>219.563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-04</th>\n",
       "      <td>199.61</td>\n",
       "      <td>184.883</td>\n",
       "      <td>215.754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-05</th>\n",
       "      <td>212.63</td>\n",
       "      <td>196.907</td>\n",
       "      <td>225.137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              pred  lower_bound  upper_bound\n",
       "2019-09-01   88.26       72.330      102.270\n",
       "2019-09-02  197.40      179.392      211.229\n",
       "2019-09-03  206.29      177.272      219.563\n",
       "2019-09-04  199.61      184.883      215.754\n",
       "2019-09-05  212.63      196.907      225.137"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicciones_random = forecaster_random.predict_interval(\n",
    "                    steps    = steps,\n",
    "                    interval = [10, 90],\n",
    "                    n_boot   = 500\n",
    "               )\n",
    "\n",
    "predicciones_random.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36a5b375",
   "metadata": {},
   "source": [
    "Obtenemos un dataframe con los dias del mes de septiembre como indice, y tres columnas.\n",
    "\n",
    "La primera indica un valor tentativo de nuestra predicción. La segunda y tercera representan los valores mínimo y mximo de nuestro intervalor de confianza."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "IEbP_owttcnK",
   "metadata": {
    "id": "IEbP_owttcnK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El tiempo que tardo en ejecutarse el modelo usando random forest fue:  94.09268617630005\n"
     ]
    }
   ],
   "source": [
    "fin_random = time.time()\n",
    "tiempo_ejecucion_random = fin_random - inicio_random\n",
    "print(\"El tiempo que tardo en ejecutarse el modelo usando random forest fue: \", tiempo_ejecucion_random)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "LWHEaoWq7sCc",
   "metadata": {
    "id": "LWHEaoWq7sCc"
   },
   "source": [
    "### **7.OPTIMIZACIÓN**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73a02fae",
   "metadata": {},
   "source": [
    "UTILIZACION DE GRIDSEARCH PARA BUSQUEDA DE HIPERPARAMETROS PARA LINEAR REGRESSOR\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cad30414",
   "metadata": {},
   "source": [
    "Ahora utilizaremos grid search para allar entre diferentes convinaciones de hiperparametros cual es la que nos da mejores resultados para ello se utilizara la metrica mean squared error como comparador."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "12229e2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "inicio_linear_ajuste = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "411dd77d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of models compared: 64.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loop lags_grid:   0%|                                               | 0/4 [00:00<?, ?it/s]\n",
      "loop param_grid:   0%|                                             | 0/16 [00:00<?, ?it/s]\u001b[A\n",
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      "loop param_grid: 100%|████████████████████████████████████| 16/16 [00:00<00:00, 35.17it/s]\u001b[A\n",
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      "loop param_grid: 100%|████████████████████████████████████| 16/16 [00:00<00:00, 34.22it/s]\u001b[A\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "`Forecaster` refitted using the best-found lags and parameters, and the whole data set: \n",
      "  Lags: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20] \n",
      "  Parameters: {'copy_X': False, 'fit_intercept': False, 'n_jobs': None, 'positive': False}\n",
      "  Backtesting metric: 1655.034115998275\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lags</th>\n",
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       "      <th>55</th>\n",
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       "      <th>61</th>\n",
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       "      <td>{'copy_X': True, 'fit_intercept': False, 'n_jo...</td>\n",
       "      <td>1685.993730</td>\n",
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       "      <td>4599.231995</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "      <td>{'copy_X': False, 'fit_intercept': True, 'n_jo...</td>\n",
       "      <td>4786.301912</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "      <td>{'copy_X': False, 'fit_intercept': True, 'n_jo...</td>\n",
       "      <td>4786.301912</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "      <td>{'copy_X': True, 'fit_intercept': True, 'n_job...</td>\n",
       "      <td>4786.301912</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "      <td>{'copy_X': True, 'fit_intercept': True, 'n_job...</td>\n",
       "      <td>4786.301912</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 lags  \\\n",
       "63  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "55  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "53  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "61  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "37  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "..                                                ...   \n",
       "0                                     [1, 2, 3, 4, 5]   \n",
       "11                                    [1, 2, 3, 4, 5]   \n",
       "9                                     [1, 2, 3, 4, 5]   \n",
       "3                                     [1, 2, 3, 4, 5]   \n",
       "1                                     [1, 2, 3, 4, 5]   \n",
       "\n",
       "                                               params  mean_squared_error  \\\n",
       "63  {'copy_X': False, 'fit_intercept': False, 'n_j...         1655.034116   \n",
       "55  {'copy_X': True, 'fit_intercept': False, 'n_jo...         1655.034116   \n",
       "53  {'copy_X': True, 'fit_intercept': False, 'n_jo...         1655.034116   \n",
       "61  {'copy_X': False, 'fit_intercept': False, 'n_j...         1655.034116   \n",
       "37  {'copy_X': True, 'fit_intercept': False, 'n_jo...         1685.993730   \n",
       "..                                                ...                 ...   \n",
       "0   {'copy_X': True, 'fit_intercept': True, 'n_job...         4599.231995   \n",
       "11  {'copy_X': False, 'fit_intercept': True, 'n_jo...         4786.301912   \n",
       "9   {'copy_X': False, 'fit_intercept': True, 'n_jo...         4786.301912   \n",
       "3   {'copy_X': True, 'fit_intercept': True, 'n_job...         4786.301912   \n",
       "1   {'copy_X': True, 'fit_intercept': True, 'n_job...         4786.301912   \n",
       "\n",
       "    copy_X  fit_intercept  n_jobs  positive  \n",
       "63   False          False     NaN     False  \n",
       "55    True          False     NaN     False  \n",
       "53    True          False    -1.0     False  \n",
       "61   False          False    -1.0     False  \n",
       "37    True          False    -1.0     False  \n",
       "..     ...            ...     ...       ...  \n",
       "0     True           True    -1.0      True  \n",
       "11   False           True     NaN     False  \n",
       "9    False           True    -1.0     False  \n",
       "3     True           True     NaN     False  \n",
       "1     True           True    -1.0     False  \n",
       "\n",
       "[64 rows x 7 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecaster_linear_ajuste = ForecasterAutoreg(\n",
    "                regressor = LinearRegression(),\n",
    "                lags      = 12 \n",
    "             )\n",
    "\n",
    "lags_grid_linear_ajuste = [5,10,15,20]\n",
    "\n",
    "param_grid_linear_ajuste = {\n",
    "    'fit_intercept': [True, False],\n",
    "    'copy_X': [True, False],\n",
    "    'n_jobs': [-1, None],\n",
    "    'positive': [True,False]\n",
    "}\n",
    "\n",
    "resultados_grid_linear_ajuste = grid_search_forecaster(\n",
    "                        forecaster         = forecaster_linear_ajuste,\n",
    "                        y                  = datos_train['y'],\n",
    "                        param_grid         = param_grid_linear_ajuste,\n",
    "                        lags_grid          = lags_grid_linear_ajuste,\n",
    "                        steps              = steps,\n",
    "                        refit              = True,\n",
    "                        metric             = 'mean_squared_error',\n",
    "                        initial_train_size = int(len(datos_train)*0.5),\n",
    "                        fixed_train_size   = False,\n",
    "                        return_best        = True,\n",
    "                        verbose            = False\n",
    "                  )\n",
    "resultados_grid_linear_ajuste"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82de6707",
   "metadata": {},
   "source": [
    "Procedemos a entrerar nuestro modelo seleccionando la convinacion de hiperparametros que nos arrojo nuestro grid search forecaster y con ellos correremos el modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0d53b62d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El tiempo que tardo en buscar el mejor ajuste de hiperparametros usando linear regressor fue:  1.9930438995361328\n"
     ]
    }
   ],
   "source": [
    "fin_linear_ajuste = time.time()\n",
    "tiempo_ejecucion_linear_ajuste = fin_linear_ajuste - inicio_linear_ajuste\n",
    "print(\"El tiempo que tardo en buscar el mejor ajuste de hiperparametros usando linear regressor fue: \", tiempo_ejecucion_linear_ajuste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d1dcc833",
   "metadata": {},
   "outputs": [],
   "source": [
    "inicio_linear_tune = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d57ab420",
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor_linear_ajuste = LinearRegression(fit_intercept = False, copy_X = False,n_jobs = None, positive = False)\n",
    "forecaster_linear_ajuste = ForecasterAutoreg(\n",
    "                regressor = regressor_linear_ajuste,\n",
    "                lags = 20\n",
    "             )\n",
    "\n",
    "forecaster_linear_ajuste.fit(y=datos_train['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8c5522c2",
   "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>pred</th>\n",
       "      <th>lower_bound</th>\n",
       "      <th>upper_bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-01</th>\n",
       "      <td>114.519859</td>\n",
       "      <td>77.186569</td>\n",
       "      <td>152.433188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02</th>\n",
       "      <td>197.270362</td>\n",
       "      <td>153.886242</td>\n",
       "      <td>235.992558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-03</th>\n",
       "      <td>229.587928</td>\n",
       "      <td>184.919626</td>\n",
       "      <td>270.174635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-04</th>\n",
       "      <td>215.839931</td>\n",
       "      <td>173.181401</td>\n",
       "      <td>254.966473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-05</th>\n",
       "      <td>238.878815</td>\n",
       "      <td>196.055072</td>\n",
       "      <td>278.357480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  pred  lower_bound  upper_bound\n",
       "2019-09-01  114.519859    77.186569   152.433188\n",
       "2019-09-02  197.270362   153.886242   235.992558\n",
       "2019-09-03  229.587928   184.919626   270.174635\n",
       "2019-09-04  215.839931   173.181401   254.966473\n",
       "2019-09-05  238.878815   196.055072   278.357480"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicciones_linear_ajuste = forecaster_linear_ajuste.predict_interval(\n",
    "                    steps    = steps,\n",
    "                    interval = [10, 90],\n",
    "                    n_boot   = 500\n",
    "               )\n",
    "\n",
    "predicciones_linear_ajuste.head(5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50a7204b",
   "metadata": {},
   "source": [
    "Utilizaremos diferentes medidas para evaluar nuestro modelo y conocer mas sobre su efectividad como asi tambien poder llegar a compararlo con otros modelos. Ademas de medir los tiempos de ejecución y entrenamiento mas el tiempo de busqueda de grid search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "70c53042",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El tiempo que tardo en ejecutarse el modelo usando linear regressor con hiperparametros ajustados fue:  1.347033977508545\n"
     ]
    }
   ],
   "source": [
    "fin_linear_tune = time.time()\n",
    "tiempo_ejecucion_linear_tune= fin_linear_tune - inicio_linear_tune\n",
    "print(\"El tiempo que tardo en ejecutarse el modelo usando linear regressor con hiperparametros ajustados fue: \", tiempo_ejecucion_linear_tune)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dfc0d121",
   "metadata": {},
   "outputs": [],
   "source": [
    "MSE_linear_ajuste = metrics.mean_squared_error(datos_test['y'], predicciones_linear_ajuste['pred'])\n",
    "r2_linear_ajuste = r2_score(datos_test['y'], predicciones_linear_ajuste['pred'])\n",
    "rdr_linear_ajuste = RdR_scorer()\n",
    "rdr_linear_ajuste.fit(ts = datos_train, \n",
    "         y_colname = 'y', \n",
    "         n_step = len(datos_test), \n",
    "         y_true = datos_test, \n",
    "         y_pred = predicciones_linear_ajuste['pred'], \n",
    "         model_name = 'LINEAR CON AJUSTE',\n",
    "         freq = 12)\n",
    "RDR_linear_ajuste = rdr_linear_ajuste.score()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "26bed23d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE - Mean Squared Error: 1243.0084641237445\n",
      "El r^2 es: 0.6051846843223461\n",
      "El valor rdr es:  0.6820265033527184\n"
     ]
    }
   ],
   "source": [
    "print('MSE - Mean Squared Error:',MSE_linear_ajuste) # MSE\n",
    "print('El r^2 es:',r2_linear_ajuste) #R2\n",
    "print('El valor rdr es: ', RDR_linear_ajuste) #RDR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82750706",
   "metadata": {},
   "source": [
    "UTILIZACION DE GRIDSEARCH PARA BUSQUEDA DE HIPERPARAMETROS PARA RANDOM FOREST REGRESSOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1f1d3590",
   "metadata": {},
   "outputs": [],
   "source": [
    "inicio_random_ajuste = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "24735161",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of models compared: 24.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "`Forecaster` refitted using the best-found lags and parameters, and the whole data set: \n",
      "  Lags: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20] \n",
      "  Parameters: {'max_depth': 10, 'n_estimators': 500}\n",
      "  Backtesting metric: 2002.1471499941706\n",
      "\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lags</th>\n",
       "      <th>params</th>\n",
       "      <th>mean_squared_error</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>n_estimators</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{'max_depth': 10, 'n_estimators': 500}</td>\n",
       "      <td>2002.147150</td>\n",
       "      <td>10</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 100}</td>\n",
       "      <td>2043.120858</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 500}</td>\n",
       "      <td>2072.267821</td>\n",
       "      <td>5</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{'max_depth': 10, 'n_estimators': 100}</td>\n",
       "      <td>2115.438138</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{'max_depth': 10, 'n_estimators': 100}</td>\n",
       "      <td>2124.453291</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 500}</td>\n",
       "      <td>2126.237826</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 500}</td>\n",
       "      <td>2129.549682</td>\n",
       "      <td>5</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{'max_depth': 10, 'n_estimators': 500}</td>\n",
       "      <td>2151.629677</td>\n",
       "      <td>10</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 100}</td>\n",
       "      <td>2160.463524</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 100}</td>\n",
       "      <td>2161.371837</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 500}</td>\n",
       "      <td>2179.261456</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 100}</td>\n",
       "      <td>2300.330943</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
       "      <td>{'max_depth': 10, 'n_estimators': 100}</td>\n",
       "      <td>2593.402690</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
       "      <td>{'max_depth': 10, 'n_estimators': 500}</td>\n",
       "      <td>2603.195312</td>\n",
       "      <td>10</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 100}</td>\n",
       "      <td>2710.995160</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 500}</td>\n",
       "      <td>2737.433934</td>\n",
       "      <td>5</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 100}</td>\n",
       "      <td>2915.663027</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 500}</td>\n",
       "      <td>2930.842592</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 100}</td>\n",
       "      <td>6425.176464</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 500}</td>\n",
       "      <td>6448.212700</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 500}</td>\n",
       "      <td>6698.202038</td>\n",
       "      <td>5</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 100}</td>\n",
       "      <td>6795.610014</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "      <td>{'max_depth': 10, 'n_estimators': 100}</td>\n",
       "      <td>7573.964054</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "      <td>{'max_depth': 10, 'n_estimators': 500}</td>\n",
       "      <td>7621.165996</td>\n",
       "      <td>10</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 lags  \\\n",
       "23  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "14  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "21  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "16  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "22  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "19  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "15  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "17  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "18  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "20  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "13  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "12  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "10                    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]   \n",
       "11                    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]   \n",
       "8                     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]   \n",
       "9                     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]   \n",
       "6                     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]   \n",
       "7                     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]   \n",
       "0                                     [1, 2, 3, 4, 5]   \n",
       "1                                     [1, 2, 3, 4, 5]   \n",
       "3                                     [1, 2, 3, 4, 5]   \n",
       "2                                     [1, 2, 3, 4, 5]   \n",
       "4                                     [1, 2, 3, 4, 5]   \n",
       "5                                     [1, 2, 3, 4, 5]   \n",
       "\n",
       "                                    params  mean_squared_error  max_depth  \\\n",
       "23  {'max_depth': 10, 'n_estimators': 500}         2002.147150         10   \n",
       "14   {'max_depth': 5, 'n_estimators': 100}         2043.120858          5   \n",
       "21   {'max_depth': 5, 'n_estimators': 500}         2072.267821          5   \n",
       "16  {'max_depth': 10, 'n_estimators': 100}         2115.438138         10   \n",
       "22  {'max_depth': 10, 'n_estimators': 100}         2124.453291         10   \n",
       "19   {'max_depth': 3, 'n_estimators': 500}         2126.237826          3   \n",
       "15   {'max_depth': 5, 'n_estimators': 500}         2129.549682          5   \n",
       "17  {'max_depth': 10, 'n_estimators': 500}         2151.629677         10   \n",
       "18   {'max_depth': 3, 'n_estimators': 100}         2160.463524          3   \n",
       "20   {'max_depth': 5, 'n_estimators': 100}         2161.371837          5   \n",
       "13   {'max_depth': 3, 'n_estimators': 500}         2179.261456          3   \n",
       "12   {'max_depth': 3, 'n_estimators': 100}         2300.330943          3   \n",
       "10  {'max_depth': 10, 'n_estimators': 100}         2593.402690         10   \n",
       "11  {'max_depth': 10, 'n_estimators': 500}         2603.195312         10   \n",
       "8    {'max_depth': 5, 'n_estimators': 100}         2710.995160          5   \n",
       "9    {'max_depth': 5, 'n_estimators': 500}         2737.433934          5   \n",
       "6    {'max_depth': 3, 'n_estimators': 100}         2915.663027          3   \n",
       "7    {'max_depth': 3, 'n_estimators': 500}         2930.842592          3   \n",
       "0    {'max_depth': 3, 'n_estimators': 100}         6425.176464          3   \n",
       "1    {'max_depth': 3, 'n_estimators': 500}         6448.212700          3   \n",
       "3    {'max_depth': 5, 'n_estimators': 500}         6698.202038          5   \n",
       "2    {'max_depth': 5, 'n_estimators': 100}         6795.610014          5   \n",
       "4   {'max_depth': 10, 'n_estimators': 100}         7573.964054         10   \n",
       "5   {'max_depth': 10, 'n_estimators': 500}         7621.165996         10   \n",
       "\n",
       "    n_estimators  \n",
       "23           500  \n",
       "14           100  \n",
       "21           500  \n",
       "16           100  \n",
       "22           100  \n",
       "19           500  \n",
       "15           500  \n",
       "17           500  \n",
       "18           100  \n",
       "20           100  \n",
       "13           500  \n",
       "12           100  \n",
       "10           100  \n",
       "11           500  \n",
       "8            100  \n",
       "9            500  \n",
       "6            100  \n",
       "7            500  \n",
       "0            100  \n",
       "1            500  \n",
       "3            500  \n",
       "2            100  \n",
       "4            100  \n",
       "5            500  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecaster_random_ajuste = ForecasterAutoreg(\n",
    "                regressor = RandomForestRegressor(random_state=123),\n",
    "                lags      = 12 \n",
    "             )\n",
    "\n",
    "\n",
    "lags_grid_random_ajuste = [5,10,15,20]\n",
    "\n",
    "\n",
    "param_grid_random_ajuste = {'n_estimators': [100, 500],\n",
    "              'max_depth': [3, 5, 10]}\n",
    "\n",
    "resultados_grid_random_ajuste = grid_search_forecaster(\n",
    "                        forecaster         = forecaster_random_ajuste,\n",
    "                        y                  = datos_train['y'],\n",
    "                        param_grid         = param_grid_random_ajuste,\n",
    "                        lags_grid          = lags_grid_random_ajuste,\n",
    "                        steps              = steps,\n",
    "                        refit              = True,\n",
    "                        metric             = 'mean_squared_error',\n",
    "                        initial_train_size = int(len(datos_train)*0.5),\n",
    "                        fixed_train_size   = False,\n",
    "                        return_best        = True,\n",
    "                        verbose            = False\n",
    "                  )\n",
    "resultados_grid_random_ajuste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ca53a832",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El tiempo que tardo en buscar el mejor ajuste de hiperparametros usando random forest fue:  114.13482284545898\n"
     ]
    }
   ],
   "source": [
    "fin_random_ajuste = time.time()\n",
    "tiempo_ejecucion_random_ajuste = fin_random_ajuste - inicio_random_ajuste\n",
    "print(\"El tiempo que tardo en buscar el mejor ajuste de hiperparametros usando random forest fue: \", tiempo_ejecucion_random_ajuste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "da377fb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "inicio_random_tune = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "651c4a7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor_random_ajuste = RandomForestRegressor(max_depth=10, n_estimators=500, random_state=123)\n",
    "forecaster_random_ajuste = ForecasterAutoreg(\n",
    "                regressor = regressor_random_ajuste,\n",
    "                lags      = 20\n",
    "             )\n",
    "\n",
    "forecaster_random_ajuste.fit(y=datos_train['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b0db4ad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicciones_random_ajuste = forecaster_random_ajuste.predict_interval(\n",
    "                    steps    = steps,\n",
    "                    interval = [10, 90],\n",
    "                    n_boot   = 500\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "beb0403e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El tiempo que tardo en ejecutarse el modelo usando random forest con hiperparametros ajustados fue:  453.477477312088\n"
     ]
    }
   ],
   "source": [
    "fin_random_tune = time.time()\n",
    "tiempo_ejecucion_random_tune = fin_random_tune - inicio_random_tune\n",
    "print(\"El tiempo que tardo en ejecutarse el modelo usando random forest con hiperparametros ajustados fue: \", tiempo_ejecucion_random_tune)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3c79af8",
   "metadata": {},
   "source": [
    "Utilizaremos diferentes medidas para evaluar nuestro modelo y conocer mas sobre su efectividad como asi tambien poder llegar a compararlo con otros modelos. Ademas de medir los tiempos de ejecución y entrenamiento mas el tiempo de busqueda de grid search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "145e9cd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "MSE_random_tune = metrics.mean_squared_error(datos_test['y'], predicciones_random_ajuste['pred'])\n",
    "r2_random_tune = r2_score(datos_test['y'], predicciones_random_ajuste['pred'])\n",
    "rdr_random_tune = RdR_scorer()\n",
    "rdr_random_tune.fit(ts = datos_train, \n",
    "         y_colname = 'y', \n",
    "         n_step = len(datos_test), \n",
    "         y_true = datos_test, \n",
    "         y_pred = predicciones_random_ajuste['pred'], \n",
    "         model_name = 'RANDOM FOREST CON AJUSTE',\n",
    "         freq = 12)\n",
    "RDR_random_tune = rdr_random_tune.score()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9a10df72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE - Mean Squared Error: 1141.0801647894446\n",
      "El r^2 es: 0.637560050089889\n",
      "El valor rdr es:  0.7085172618302273\n"
     ]
    }
   ],
   "source": [
    "print('MSE - Mean Squared Error:',MSE_random_tune) # MSE\n",
    "print('El r^2 es:',r2_random_tune) #R2\n",
    "print('El valor rdr es: ', RDR_random_tune) #RDR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "GRF1onTm7ukI",
   "metadata": {
    "id": "GRF1onTm7ukI"
   },
   "source": [
    "\n",
    "### **8. SELECCIÓN DE MODELO FINAL**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fd49c67",
   "metadata": {},
   "source": [
    "Si comparamos los modelos de la sección anteior podemos sacar las siguientes conclusiones:\n",
    "El modelo de forecast que utiliza como regressor un random forest en el mejor de los casos propuestos (el que daba mejores metricas para unos hiperparametros en especifico) tiene un leves mejoras en los valores de error cuadradico medio, r cuadrado y RdR frente al modelo que utiliza como regressor un linear regressor.\n",
    "Aunque esta ligera mejoria la cual es despreciable en fines practicos es a costa de un gran costo computacional ya que tanto en la etapa de grid search para la obtención de la mejor combinación de hiperparametros como en la etapa de entrenamiento y ejecución del modelo el modelo que usaba linear regresor era casi instantaneo mientras que el que usa random forest necesitaba una cantidad desproporcionadamente mayor para la leve mejoria obtenida."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06e3bf4e",
   "metadata": {},
   "source": [
    "A continuación se graficaran y mostraran los resultados obtenidos para nuestro modelo para tener una mejor comprensión de la cantidad de bicicletas que se alquilaran teoricamente en nuestra estacion grove st path y cual es el nivel de exactitud en esto cuando lo comparamos con los datos de test."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d58dd15",
   "metadata": {},
   "source": [
    "Si analizamos los resultados veremos que un 87% de los dias el valor real estuvo en el intervalo de prección."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "97e052e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 4))\n",
    "datos_test['y'].plot(ax=ax, label='test')\n",
    "predicciones_linear_ajuste['pred'].plot(ax=ax, label='predicciones')\n",
    "sns.set_theme(style=\"whitegrid\", rc= {\"axes.spines.right\": False, \"axes.spines.top\": False})\n",
    "\n",
    "ax.fill_between(\n",
    "    predicciones_linear_ajuste.index,\n",
    "    predicciones_linear_ajuste['lower_bound'],\n",
    "    predicciones_linear_ajuste['upper_bound'],\n",
    "    color = 'red',\n",
    "    alpha = 0.2\n",
    ")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "562fbe43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cobertura del intervalo predicho: 86.67 %\n"
     ]
    }
   ],
   "source": [
    "dentro_intervalo_linear_ajuste = np.where(\n",
    "                     (databicis.loc[predicciones_linear_ajuste.index, 'y'] >= predicciones_linear_ajuste['lower_bound']) & \\\n",
    "                     (databicis.loc[predicciones_linear_ajuste.index, 'y'] <= predicciones_linear_ajuste['upper_bound']),\n",
    "                     True,\n",
    "                     False\n",
    "                   )\n",
    "\n",
    "cobertura_linear_ajuste = dentro_intervalo_linear_ajuste.mean()\n",
    "print(f\"Cobertura del intervalo predicho: {round(100*cobertura_linear_ajuste, 2)} %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cec182db",
   "metadata": {},
   "source": [
    "Utilizaremos diferentes medidas para evaluar nuestro modelo y conocer mas sobre su efectividad. Ademas de que podremos obtener insights relevantes que serviran para tomar decisiones a posteor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "01b87877",
   "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>prediccion</th>\n",
       "      <th>valor minimo</th>\n",
       "      <th>valor maximo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-01</th>\n",
       "      <td>115.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02</th>\n",
       "      <td>197.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-03</th>\n",
       "      <td>230.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-04</th>\n",
       "      <td>216.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-05</th>\n",
       "      <td>239.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-06</th>\n",
       "      <td>192.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>234.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-07</th>\n",
       "      <td>114.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>155.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-08</th>\n",
       "      <td>119.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-09</th>\n",
       "      <td>198.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>239.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-10</th>\n",
       "      <td>214.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>257.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-11</th>\n",
       "      <td>212.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-12</th>\n",
       "      <td>229.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-13</th>\n",
       "      <td>184.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-14</th>\n",
       "      <td>113.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>158.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-15</th>\n",
       "      <td>127.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-16</th>\n",
       "      <td>197.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-17</th>\n",
       "      <td>218.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-18</th>\n",
       "      <td>222.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>272.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-19</th>\n",
       "      <td>235.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>284.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-20</th>\n",
       "      <td>191.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-21</th>\n",
       "      <td>127.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-22</th>\n",
       "      <td>139.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-23</th>\n",
       "      <td>203.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>251.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-24</th>\n",
       "      <td>219.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>271.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-25</th>\n",
       "      <td>226.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-26</th>\n",
       "      <td>234.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>289.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-27</th>\n",
       "      <td>190.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-28</th>\n",
       "      <td>129.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-29</th>\n",
       "      <td>145.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>200.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            prediccion  valor minimo  valor maximo\n",
       "2019-09-01       115.0          77.0         152.0\n",
       "2019-09-02       197.0         154.0         236.0\n",
       "2019-09-03       230.0         185.0         270.0\n",
       "2019-09-04       216.0         173.0         255.0\n",
       "2019-09-05       239.0         196.0         278.0\n",
       "2019-09-06       192.0         144.0         234.0\n",
       "2019-09-07       114.0          76.0         155.0\n",
       "2019-09-08       119.0          73.0         161.0\n",
       "2019-09-09       198.0         157.0         239.0\n",
       "2019-09-10       214.0         168.0         257.0\n",
       "2019-09-11       212.0         169.0         253.0\n",
       "2019-09-12       229.0         182.0         273.0\n",
       "2019-09-13       184.0         132.0         228.0\n",
       "2019-09-14       113.0          73.0         158.0\n",
       "2019-09-15       127.0          78.0         176.0\n",
       "2019-09-16       197.0         146.0         245.0\n",
       "2019-09-17       218.0         169.0         268.0\n",
       "2019-09-18       222.0         177.0         272.0\n",
       "2019-09-19       235.0         185.0         284.0\n",
       "2019-09-20       191.0         144.0         238.0\n",
       "2019-09-21       127.0          82.0         183.0\n",
       "2019-09-22       139.0          84.0         191.0\n",
       "2019-09-23       203.0         150.0         251.0\n",
       "2019-09-24       219.0         161.0         271.0\n",
       "2019-09-25       226.0         168.0         281.0\n",
       "2019-09-26       234.0         179.0         289.0\n",
       "2019-09-27       190.0         138.0         240.0\n",
       "2019-09-28       129.0          78.0         188.0\n",
       "2019-09-29       145.0          88.0         207.0\n",
       "2019-09-30       200.0         134.0         255.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tabla_dia = predicciones_linear_ajuste\n",
    "tabla_dia.rename(columns = {'pred':'prediccion','lower_bound':'valor minimo','upper_bound':'valor maximo'}, inplace = True)\n",
    "tabla_dia = tabla_dia.round()\n",
    "tabla_dia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eba2de4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# hide axes\n",
    "fig.patch.set_visible(False)\n",
    "ax.axis('off')\n",
    "ax.axis('tight')\n",
    "\n",
    "df = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD'))\n",
    "\n",
    "ax.table(cellText=df.values, colLabels=df.columns, loc='center')\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "66f7a9e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El promedio de la cantidad de viajes en septiembre en la estacion grove st path sera:  185.8069654595803\n"
     ]
    }
   ],
   "source": [
    "prediccion_promedio_viajes = np.mean(predicciones_linear_ajuste['prediccion'])\n",
    "print('El promedio de la cantidad de viajes en septiembre en la estacion grove st path sera: ', prediccion_promedio_viajes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6ba773f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El promedio  real de la cantidad de viajes en septiembre en la estacion grove st path fue:  176.06666666666666\n"
     ]
    }
   ],
   "source": [
    "real_promedio_viajes = np.mean(datos_test['y'])\n",
    "print('El promedio  real de la cantidad de viajes en septiembre en la estacion grove st path fue: ', real_promedio_viajes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "74806d23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE - Mean Absolute Error: 23.09730807193955\n",
      "MAPE - Mean Absolute Percentage Error: 21.661397573631607\n",
      "MSE - Mean Squared Error: 1243.0084641237445\n",
      "RMSE - Root Mean Squared Error: 35.25632516476646\n",
      "MedAE - Median Absolute Error: 15.699216158797682\n"
     ]
    }
   ],
   "source": [
    "print('MAE - Mean Absolute Error:', metrics.mean_absolute_error(datos_test['y'], predicciones_linear_ajuste['prediccion'])) # MAE\n",
    "print('MAPE - Mean Absolute Percentage Error:', np.mean(np.abs((datos_test['y'] - predicciones_linear_ajuste['prediccion']) / datos_test['y']))*100)\n",
    "print('MSE - Mean Squared Error:', metrics.mean_squared_error(datos_test['y'], predicciones_linear_ajuste['prediccion'])) # MSE\n",
    "print('RMSE - Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(datos_test['y'], predicciones_linear_ajuste['prediccion']))) # RMSE\n",
    "print('MedAE - Median Absolute Error:',metrics.median_absolute_error(datos_test['y'], predicciones_linear_ajuste['prediccion']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "021aec8f",
   "metadata": {},
   "source": [
    "Si realizamos un analisis de cada una de las metricas detalladas podemos decir que:\n",
    "\n",
    "\n",
    "*   Un error medio absoluto (MAE) nos indica que nuestros valores predichos difieren en promedio un 23.1 pasajeros respecto al valor real lo cual es un valor aceptable teniendo en cuenta el promedio de viajes por dia real fue de 185.81.\n",
    "*   El error absoluto porcentual nos da una impresion de que porcentaje de error tuvimos respecto al valor real.\n",
    "*   El error cuadratico medio (MSE) mide el error cuadrado promedio de nuestras predicciones, cuanto peor es nuestra prediccion mayor sera este valor.\n",
    "*   La Raíz de la Desviación Cuadrática Media (RMSE) es un valor el cual cuanto menor es más cercanos son los valores predichos y observados.\n",
    "*   Incluimos tambian el error mediano absoluto (MedAE) el cual es otro punto de vista parecido al MAE pero usando la mediana en vez de la media, si bien son parecidos al usar la mediana lo hace menos susceptible a que valores outliers.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c236fdd7",
   "metadata": {},
   "source": [
    "Aqui utilizamos r cuadrado como parametro de medicion siendo este un valor entre 0 y 1, cuanto mas cerca de un 1 se interpreta que la prediccion se comporta como ideal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "28a5e6ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El r^2 es: 0.6051846843223461\n"
     ]
    }
   ],
   "source": [
    "r2_linear_ajuste = r2_score(datos_test['y'], predicciones_linear_ajuste['prediccion'])\n",
    "print('El r^2 es:',r2_linear_ajuste)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "320f19f9",
   "metadata": {},
   "source": [
    "Por ultimo utilizamos otra tecnica de medición y evaluacion llamada RdR el cual es un parametro que utiliza de otras medidas de evaluacion para su calculo. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c7187e53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El valor rdr es:  0.6820265033527184\n"
     ]
    }
   ],
   "source": [
    "print('El valor rdr es: ', RDR_linear_ajuste) #RDR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "05d7b3c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x243c4a12820>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(datos_train)\n",
    "plt.plot(figsize=(9, 4))\n",
    "plt.rc('xtick', labelsize=9)\n",
    "sns.set_theme(style=\"whitegrid\", rc= {\"axes.spines.right\": False, \"axes.spines.top\": False})\n",
    "plt.plot(rdr_linear_ajuste.get_randomwalk_pred()['Y_PRED'])\n",
    "plt.fill_between(rdr_linear_ajuste.get_randomwalk_pred().index, \n",
    "                 rdr_linear_ajuste.get_randomwalk_pred()['Y_PRED_UPPER'],\n",
    "                 alpha = 0.25)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "225172da",
   "metadata": {},
   "source": [
    "El intervalo sombreado en azul se interpreta como una semi campana, intervalo el cual tiene los valores posibles que tendriamos en nuestro periodo de tiempo si hicieramos un ramdom walk, por lo que nuestra prediccion debe estar en ese intervalo. Caso contrario el uso de nuestro modelo seria contraproducente ya que daria resultados peores que una serie aleatoria."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13d65835",
   "metadata": {},
   "source": [
    "Obteniendo un valor de 0.682 o un 68.2% podemos realizar la siguiente interpretación. Nuestro modelo de prediccion es un 68.2% mejor que una serie ramdom para el intervalo a predecir."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab5a7efa",
   "metadata": {},
   "source": [
    "Como conclusión podemos podemos decir que nuestro modelo cumple con las expectativas de nuestro alcance y podria aplicarse en meses siguientes para tener con los datos de entrenamiento adecuados una idea bastante acertada de cuantas personas alquilaran una bicicleta en nuestra estación de estudio."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}