Proyecto12.TripleTen.MétodosNumericos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¡ Hola Alejandro! Como te va?\n",
    "\n",
    "Mi nombre es Facundo Lozano! Ya he tenido el agrado de revisar otros proyectos tuyos, nuevamente seré tu revisor en este proyecto.\n",
    "\n",
    "Como siempre, a continuación un poco sobre la modalidad de revisión que usaremos:\n",
    "\n",
    "Cuando enccuentro un error por primera vez, simplemente lo señalaré, te dejaré encontrarlo y arreglarlo tú cuenta. Además, a lo largo del texto iré haciendo algunas observaciones sobre mejora en tu código y también haré comentarios sobre tus percepciones sobre el tema. Pero si aún no puedes realizar esta tarea, te daré una pista más precisa en la próxima iteración y también algunos ejemplos prácticos. Estaré abierto a comentarios y discusiones sobre el tema.\n",
    "\n",
    "Encontrará mis comentarios a continuación: no los mueva, modifique ni elimine.\n",
    "\n",
    "Puedes encontrar mis comentarios en cuadros verdes, amarillos o rojos como este:\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Comentario del revisor.</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Exito. Todo se ha hecho de forma exitosa.\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Comentario del revisor.</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Observación. Algunas recomendaciones.\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-danger\">\n",
    "\n",
    "<b>Comentario del revisor.</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Necesita arreglos. Este apartado necesita algunas correcciones. El trabajo no puede ser aceptado con comentarios rojos. \n",
    "</div>\n",
    "\n",
    "Puedes responder utilizando esto:\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Respuesta de estudiante.</b> <a class=\"tocSkip\"></a>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El servicio de venta de autos usados Rusty Bargain está desarrollando una aplicación para atraer nuevos clientes. Gracias a esa app, puedes averiguar rápidamente el valor de mercado de tu coche. Tienes acceso al historial: especificaciones técnicas, versiones de equipamiento y precios. Tienes que crear un modelo que determine el valor de mercado.\n",
    "A Rusty Bargain le interesa:\n",
    "- la calidad de la predicción;\n",
    "- la velocidad de la predicción;\n",
    "- el tiempo requerido para el entrenamiento"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Review General. (Iteración 1) </b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Alejandro, como siempre, me tomo este tiempo al inicio del proyecto para comentar mis apreciaciones generales de esta primera iteración de la entrega. \n",
    "\n",
    "Siempre me gusta comenzar dando la bienvenida al mundo de los datos a los estudiantes, te deseo lo mejor y espero que consigas lograr tus objetivos. Personalmente siempre me gusta brindar el siguiente consejo, \"Está bien equivocarse, es normal y es lo mejor que te puede pasar. Aprendemos de los errores y eso te hará mejor programando ya que podrás descubrir cosas a medida que avances y son estas cosas las que te darán esa experiencia para ser mejor como Data Scientist\"\n",
    "    \n",
    "Ahora si yendo a esta notebook. Felicitaciones Alejandro, has hecho un gran trabajo a lo largo del proyecto, quiero destacar tu prolijidad en los pasos y sobre todo el código implementado junto a los agregados fantásticos! El trabajo está en condiciones de ser aprobado!\n",
    "\n",
    "Felicitaciones, éxitos y saludos Alejandro!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparación de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import OneHotEncoder, MaxAbsScaler, OrdinalEncoder\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import lightgbm as lgb\n",
    "from catboost import Pool, CatBoostRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"/datasets/car_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        DateCrawled  Price VehicleType  RegistrationYear Gearbox  Power  \\\n",
      "0  24/03/2016 11:52    480         NaN              1993  manual      0   \n",
      "1  24/03/2016 10:58  18300       coupe              2011  manual    190   \n",
      "2  14/03/2016 12:52   9800         suv              2004    auto    163   \n",
      "3  17/03/2016 16:54   1500       small              2001  manual     75   \n",
      "4  31/03/2016 17:25   3600       small              2008  manual     69   \n",
      "\n",
      "   Model  Mileage  RegistrationMonth  FuelType       Brand NotRepaired  \\\n",
      "0   golf   150000                  0    petrol  volkswagen         NaN   \n",
      "1    NaN   125000                  5  gasoline        audi         yes   \n",
      "2  grand   125000                  8  gasoline        jeep         NaN   \n",
      "3   golf   150000                  6    petrol  volkswagen          no   \n",
      "4  fabia    90000                  7  gasoline       skoda          no   \n",
      "\n",
      "        DateCreated  NumberOfPictures  PostalCode          LastSeen  \n",
      "0  24/03/2016 00:00                 0       70435  07/04/2016 03:16  \n",
      "1  24/03/2016 00:00                 0       66954  07/04/2016 01:46  \n",
      "2  14/03/2016 00:00                 0       90480  05/04/2016 12:47  \n",
      "3  17/03/2016 00:00                 0       91074  17/03/2016 17:40  \n",
      "4  31/03/2016 00:00                 0       60437  06/04/2016 10:17  \n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 354369 entries, 0 to 354368\n",
      "Data columns (total 16 columns):\n",
      " #   Column             Non-Null Count   Dtype \n",
      "---  ------             --------------   ----- \n",
      " 0   DateCrawled        354369 non-null  object\n",
      " 1   Price              354369 non-null  int64 \n",
      " 2   VehicleType        316879 non-null  object\n",
      " 3   RegistrationYear   354369 non-null  int64 \n",
      " 4   Gearbox            334536 non-null  object\n",
      " 5   Power              354369 non-null  int64 \n",
      " 6   Model              334664 non-null  object\n",
      " 7   Mileage            354369 non-null  int64 \n",
      " 8   RegistrationMonth  354369 non-null  int64 \n",
      " 9   FuelType           321474 non-null  object\n",
      " 10  Brand              354369 non-null  object\n",
      " 11  NotRepaired        283215 non-null  object\n",
      " 12  DateCreated        354369 non-null  object\n",
      " 13  NumberOfPictures   354369 non-null  int64 \n",
      " 14  PostalCode         354369 non-null  int64 \n",
      " 15  LastSeen           354369 non-null  object\n",
      "dtypes: int64(7), object(9)\n",
      "memory usage: 43.3+ MB\n",
      "None\n",
      "\n",
      "DateCrawled              0\n",
      "Price                    0\n",
      "VehicleType          37490\n",
      "RegistrationYear         0\n",
      "Gearbox              19833\n",
      "Power                    0\n",
      "Model                19705\n",
      "Mileage                  0\n",
      "RegistrationMonth        0\n",
      "FuelType             32895\n",
      "Brand                    0\n",
      "NotRepaired          71154\n",
      "DateCreated              0\n",
      "NumberOfPictures         0\n",
      "PostalCode               0\n",
      "LastSeen                 0\n",
      "dtype: int64\n",
      "\n",
      "Hay 262 filas duplicadas\n",
      "\n",
      "Valores Nulos\n",
      "DateCrawled              0\n",
      "Price                    0\n",
      "VehicleType          37490\n",
      "RegistrationYear         0\n",
      "Gearbox              19833\n",
      "Power                    0\n",
      "Model                19705\n",
      "Mileage                  0\n",
      "RegistrationMonth        0\n",
      "FuelType             32895\n",
      "Brand                    0\n",
      "NotRepaired          71154\n",
      "DateCreated              0\n",
      "NumberOfPictures         0\n",
      "PostalCode               0\n",
      "LastSeen                 0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.head())\n",
    "print()\n",
    "print(df.info())\n",
    "print()\n",
    "print(df.isna().sum())\n",
    "print()\n",
    "print(f'Hay {df.duplicated().sum()} filas duplicadas')\n",
    "print()\n",
    "print(f'Valores Nulos')\n",
    "print(df.isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "\n",
    "<b>Comentario del revisor. (Iteración 1)</b> <a class=\"tocSkip\"></a>\n",
    "    \n",
    "Excelente Alejandro, hasta el momento hemos cargado correctamente los datos separandolos de las importaciones para mitigar posibles errores, a la vez has implementado métodos para visualizar y comprender la composición de nuestros datos. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= df.drop_duplicates()\n",
    "\n",
    "df.columns = df.columns.str.lower()\n",
    "\n",
    "date_columns = ['datecrawled', 'datecreated', 'lastseen']\n",
    "for x in date_columns:\n",
    "    df[x] = pd.to_datetime(df[x], format='%d/%m/%Y %H:%M')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column datecrawled:\n",
      "['2016-03-24T11:52:00.000000000' '2016-03-24T10:58:00.000000000'\n",
      " '2016-03-14T12:52:00.000000000' ... '2016-03-27T12:42:00.000000000'\n",
      " '2016-03-08T00:49:00.000000000' '2016-03-06T21:11:00.000000000']\n",
      "-----------------------------------\n",
      "Column price:\n",
      "[  480 18300  9800 ... 12395 18429 10985]\n",
      "-----------------------------------\n",
      "Column vehicletype:\n",
      "[nan 'coupe' 'suv' 'small' 'sedan' 'convertible' 'bus' 'wagon' 'other']\n",
      "-----------------------------------\n",
      "Column registrationyear:\n",
      "[1993 2011 2004 2001 2008 1995 1980 2014 1998 2005 1910 2016 2007 2009\n",
      " 2002 2018 1997 1990 2017 1981 2003 1994 1991 1984 2006 1999 2012 2010\n",
      " 2000 1992 2013 1996 1985 1989 2015 1982 1976 1983 1973 1111 1969 1971\n",
      " 1987 1986 1988 1970 1965 1945 1925 1974 1979 1955 1978 1972 1968 1977\n",
      " 1961 1960 1966 1975 1963 1964 5000 1954 1958 1967 1959 9999 1956 3200\n",
      " 1000 1941 8888 1500 2200 4100 1962 1929 1957 1940 3000 2066 1949 2019\n",
      " 1937 1951 1800 1953 1234 8000 5300 9000 2900 6000 5900 5911 1933 1400\n",
      " 1950 4000 1948 1952 1200 8500 1932 1255 3700 3800 4800 1942 7000 1935\n",
      " 1936 6500 1923 2290 2500 1930 1001 9450 1944 1943 1934 1938 1688 2800\n",
      " 1253 1928 1919 5555 5600 1600 2222 1039 9996 1300 8455 1931 1915 4500\n",
      " 1920 1602 7800 9229 1947 1927 7100 8200 1946 7500 3500]\n",
      "-----------------------------------\n",
      "Column gearbox:\n",
      "['manual' 'auto' nan]\n",
      "-----------------------------------\n",
      "Column power:\n",
      "[    0   190   163    75    69   102   109    50   125   101   105   140\n",
      "   115   131    60   136   160   231    90   118   193    99   113   218\n",
      "   122   129    70   306    95    61   177    80   170    55   143    64\n",
      "   286   232   150   156    82   155    54   185    87   180    86    84\n",
      "   224   235   200   178   265    77   110   144   120   116   184   126\n",
      "   204    88   194   305   197   179   250    45   313    41   165    98\n",
      "   130   114   211    56   201   213    58   107    83   174   100   220\n",
      "    85    73   192    68    66   299    74    52   147    65   310    71\n",
      "    97   239   203     5   300   103   258   320    63    81   148    44\n",
      "   145   230   280   260   104   188   333   186   117   141    59   132\n",
      "   234   158    39    92    51   135    53   209    43   146    67   106\n",
      "   166   276   344    72   249   237   245   111   326   279    23   138\n",
      "   175    96   226    26    37   301   334   128   133   124    33   219\n",
      "   241   167    94   182   196   238  1398   228   112    48   272   139\n",
      "  1799    40   240    79   256   360   243   375   252   396   340   108\n",
      "   121   345   187   289   222   198   284   450   233   387   152   347\n",
      "   172    57   137   212   476   314    30     7    47   173   290   207\n",
      "   205   171   750    78    91    89   349   325    76   151   350   953\n",
      "    93   142   500   343   199   119   164    16   264   367   208   210\n",
      "    46   435  1001   225   127   176   161   603   307   354    35   206\n",
      "   771   355   273   223   255   123   154   149  7515  1400   268   620\n",
      "   169   400   275    34     2   242  1779   285   162   328  1753   329\n",
      "   134   420   247   168 10317   215    18   330   283    62    10   214\n",
      "   183   254    15   321   322   315   952   600   181   426   599   379\n",
      "   999    22   157    42   195   553     4   309   507   381   271   440\n",
      "   189   401  1111  2729    21   236  1240  1704   521     1  1900   270\n",
      "    24   159   292   808   515  1200   303    29    38  1993    14   216\n",
      "   610   504    49   267   331   363   261   385  1011   253   696 10520\n",
      "   376   227   311    27   217    20  1999    11  1056    25   394    12\n",
      "   298   296  1659   294   551  6920   246    17  1800  1625   368   390\n",
      "   221  1367   259   606   327  2017  1597   266   374   392     9   405\n",
      "    13   262   601   430   295     8   411   900   541  7512   382 10218\n",
      "  7544  1102   287    19   751   297  1090   457   282   602   388     6\n",
      "  1000  5411   335  1360   202  3750   805  1062  1024 11530   364  1796\n",
      "  5000   269   460  1002   288  1780 12012 17932   316  2005  1399   191\n",
      "   278  1300   380   257   449  1256   153  1653   520   560   281 12510\n",
      "     3  1500   386    32  1202  2016  1436  1405   671   274  1288  1055\n",
      " 10912 15016  6226  1115   277  1105   341  1223  2340  8404  1968   338\n",
      "   445  1920   370   517  4400 10710 10522  9010   431  1164 11509   371\n",
      "  1501   428 11635    36   304   348  1703  1910  1995   579 10000   580\n",
      "   408    31  1005   358  1403   489  5575 19211   585   960   365   353\n",
      "   490   352  1801   346   550  3199   800  7508  1312   357  1937   950\n",
      "  4700  1600  3454 19208   251   850   337   700  8011  2331  2789   487\n",
      "   519  2461  6006  3500  5815  6018  1781  1503   612  1596   229  1506\n",
      "  5809 15020  5867  2172  1598 17410  1363   584  1054   851  1339   318\n",
      "   510   525  1700    28  1151   685  6011   317   703  1771 15001   514\n",
      "   362   332   324  6045   530   425   308  6010   475   903   640  2000\n",
      "   248   651  2799 17011   505  1103  1521   351 16051   398  9000  1998\n",
      " 10311   409  1230 17019 12684  8259  1895   901  1317   871   909  2201\n",
      "  1595   776 17700 11011  1199   459  1162  1120 15033  1896   339   361\n",
      "   574  6512   454   650 13616   604   607 16312  1082   336 20000   416\n",
      "   678  6012  1401  1021  1870 13636  1250  9710   323  1158  2009   902\n",
      "  6062  1275   572   244   399  1016   743  1221  2402 16011  1362   544\n",
      "   645   907   702 12512  1986  1003  1432  9013  9011   442  1017  1394\n",
      " 15017 10910  1992   540  2018 11025  1433  1701  2792  1502  1239  1390\n",
      " 14009  1160  1351 11111  1783  9007   923  1149   754  1988   402  2004\n",
      "  5420  1793  1324   356  7511  1299   293 19312  1079  1689   998  1100\n",
      "  9012  1933   508 16311   682  1252  1922  7529   732  2389  2200  1707\n",
      "  1416  8500  1012   485  3000 10110   421  4507  1631  7518   377  1004\n",
      "  2007  2598  1548  1241]\n",
      "-----------------------------------\n",
      "Column model:\n",
      "['golf' nan 'grand' 'fabia' '3er' '2_reihe' 'other' 'c_max' '3_reihe'\n",
      " 'passat' 'navara' 'ka' 'polo' 'twingo' 'a_klasse' 'scirocco' '5er'\n",
      " 'meriva' 'arosa' 'c4' 'civic' 'transporter' 'punto' 'e_klasse' 'clio'\n",
      " 'kadett' 'kangoo' 'corsa' 'one' 'fortwo' '1er' 'b_klasse' 'signum'\n",
      " 'astra' 'a8' 'jetta' 'fiesta' 'c_klasse' 'micra' 'vito' 'sprinter' '156'\n",
      " 'escort' 'forester' 'xc_reihe' 'scenic' 'a4' 'a1' 'insignia' 'combo'\n",
      " 'focus' 'tt' 'a6' 'jazz' 'omega' 'slk' '7er' '80' '147' '100' 'z_reihe'\n",
      " 'sportage' 'sorento' 'v40' 'ibiza' 'mustang' 'eos' 'touran' 'getz' 'a3'\n",
      " 'almera' 'megane' 'lupo' 'r19' 'zafira' 'caddy' 'mondeo' 'cordoba' 'colt'\n",
      " 'impreza' 'vectra' 'berlingo' 'tiguan' 'i_reihe' 'espace' 'sharan'\n",
      " '6_reihe' 'panda' 'up' 'seicento' 'ceed' '5_reihe' 'yeti' 'octavia' 'mii'\n",
      " 'rx_reihe' '6er' 'modus' 'fox' 'matiz' 'beetle' 'c1' 'rio' 'touareg'\n",
      " 'logan' 'spider' 'cuore' 's_max' 'a2' 'galaxy' 'c3' 'viano' 's_klasse'\n",
      " '1_reihe' 'avensis' 'roomster' 'sl' 'kaefer' 'santa' 'cooper' 'leon'\n",
      " '4_reihe' 'a5' '500' 'laguna' 'ptcruiser' 'clk' 'primera' 'x_reihe'\n",
      " 'exeo' '159' 'transit' 'juke' 'qashqai' 'carisma' 'accord' 'corolla'\n",
      " 'lanos' 'phaeton' 'verso' 'swift' 'rav' 'picanto' 'boxster' 'kalos'\n",
      " 'superb' 'stilo' 'alhambra' 'mx_reihe' 'roadster' 'ypsilon' 'cayenne'\n",
      " 'galant' 'justy' '90' 'sirion' 'crossfire' 'agila' 'duster' 'cr_reihe'\n",
      " 'v50' 'c_reihe' 'v_klasse' 'm_klasse' 'yaris' 'c5' 'aygo' 'cc' 'carnival'\n",
      " 'fusion' '911' 'bora' 'forfour' 'm_reihe' 'cl' 'tigra' '300c' 'spark'\n",
      " 'v70' 'kuga' 'x_type' 'ducato' 's_type' 'x_trail' 'toledo' 'altea'\n",
      " 'voyager' 'calibra' 'bravo' 'antara' 'tucson' 'citigo' 'jimny' 'wrangler'\n",
      " 'lybra' 'q7' 'lancer' 'captiva' 'c2' 'discovery' 'freelander' 'sandero'\n",
      " 'note' '900' 'cherokee' 'clubman' 'samara' 'defender' '601' 'cx_reihe'\n",
      " 'legacy' 'pajero' 'auris' 'niva' 's60' 'nubira' 'vivaro' 'g_klasse'\n",
      " 'lodgy' '850' 'range_rover' 'q3' 'serie_2' 'glk' 'charade' 'croma'\n",
      " 'outlander' 'doblo' 'musa' 'move' '9000' 'v60' '145' 'aveo' '200' 'b_max'\n",
      " 'range_rover_sport' 'terios' 'rangerover' 'q5' 'range_rover_evoque'\n",
      " 'materia' 'delta' 'gl' 'kalina' 'amarok' 'elefantino' 'i3' 'kappa'\n",
      " 'serie_3' 'serie_1']\n",
      "-----------------------------------\n",
      "Column mileage:\n",
      "[150000 125000  90000  40000  30000  70000   5000 100000  60000  20000\n",
      "  80000  50000  10000]\n",
      "-----------------------------------\n",
      "Column registrationmonth:\n",
      "[ 0  5  8  6  7 10 12 11  2  3  1  4  9]\n",
      "-----------------------------------\n",
      "Column fueltype:\n",
      "['petrol' 'gasoline' nan 'lpg' 'other' 'hybrid' 'cng' 'electric']\n",
      "-----------------------------------\n",
      "Column brand:\n",
      "['volkswagen' 'audi' 'jeep' 'skoda' 'bmw' 'peugeot' 'ford' 'mazda'\n",
      " 'nissan' 'renault' 'mercedes_benz' 'opel' 'seat' 'citroen' 'honda' 'fiat'\n",
      " 'mini' 'smart' 'hyundai' 'sonstige_autos' 'alfa_romeo' 'subaru' 'volvo'\n",
      " 'mitsubishi' 'kia' 'suzuki' 'lancia' 'toyota' 'chevrolet' 'dacia'\n",
      " 'daihatsu' 'trabant' 'saab' 'chrysler' 'jaguar' 'daewoo' 'porsche'\n",
      " 'rover' 'land_rover' 'lada']\n",
      "-----------------------------------\n",
      "Column notrepaired:\n",
      "[nan 'yes' 'no']\n",
      "-----------------------------------\n",
      "Column datecreated:\n",
      "['2016-03-24T00:00:00.000000000' '2016-03-14T00:00:00.000000000'\n",
      " '2016-03-17T00:00:00.000000000' '2016-03-31T00:00:00.000000000'\n",
      " '2016-04-04T00:00:00.000000000' '2016-04-01T00:00:00.000000000'\n",
      " '2016-03-21T00:00:00.000000000' '2016-03-26T00:00:00.000000000'\n",
      " '2016-04-07T00:00:00.000000000' '2016-03-15T00:00:00.000000000'\n",
      " '2016-03-11T00:00:00.000000000' '2016-03-20T00:00:00.000000000'\n",
      " '2016-03-23T00:00:00.000000000' '2016-03-27T00:00:00.000000000'\n",
      " '2016-03-12T00:00:00.000000000' '2016-03-13T00:00:00.000000000'\n",
      " '2016-03-18T00:00:00.000000000' '2016-03-10T00:00:00.000000000'\n",
      " '2016-03-07T00:00:00.000000000' '2016-03-09T00:00:00.000000000'\n",
      " '2016-03-08T00:00:00.000000000' '2016-04-03T00:00:00.000000000'\n",
      " '2016-03-29T00:00:00.000000000' '2016-03-25T00:00:00.000000000'\n",
      " '2016-03-28T00:00:00.000000000' '2016-03-30T00:00:00.000000000'\n",
      " '2016-03-22T00:00:00.000000000' '2016-02-09T00:00:00.000000000'\n",
      " '2016-03-05T00:00:00.000000000' '2016-04-02T00:00:00.000000000'\n",
      " '2016-03-16T00:00:00.000000000' '2016-03-19T00:00:00.000000000'\n",
      " '2016-04-05T00:00:00.000000000' '2016-03-06T00:00:00.000000000'\n",
      " '2016-02-12T00:00:00.000000000' '2016-03-03T00:00:00.000000000'\n",
      " '2016-03-01T00:00:00.000000000' '2016-03-04T00:00:00.000000000'\n",
      " '2016-04-06T00:00:00.000000000' '2016-02-15T00:00:00.000000000'\n",
      " '2016-02-24T00:00:00.000000000' '2016-02-27T00:00:00.000000000'\n",
      " '2015-03-20T00:00:00.000000000' '2016-02-28T00:00:00.000000000'\n",
      " '2016-02-17T00:00:00.000000000' '2016-01-27T00:00:00.000000000'\n",
      " '2016-02-20T00:00:00.000000000' '2016-02-29T00:00:00.000000000'\n",
      " '2016-02-10T00:00:00.000000000' '2016-02-23T00:00:00.000000000'\n",
      " '2016-02-21T00:00:00.000000000' '2015-11-02T00:00:00.000000000'\n",
      " '2016-02-19T00:00:00.000000000' '2016-02-26T00:00:00.000000000'\n",
      " '2016-02-11T00:00:00.000000000' '2016-01-10T00:00:00.000000000'\n",
      " '2016-02-06T00:00:00.000000000' '2016-02-18T00:00:00.000000000'\n",
      " '2016-01-29T00:00:00.000000000' '2016-03-02T00:00:00.000000000'\n",
      " '2015-12-06T00:00:00.000000000' '2016-01-24T00:00:00.000000000'\n",
      " '2016-01-30T00:00:00.000000000' '2016-02-02T00:00:00.000000000'\n",
      " '2016-02-16T00:00:00.000000000' '2016-02-13T00:00:00.000000000'\n",
      " '2016-02-05T00:00:00.000000000' '2016-02-22T00:00:00.000000000'\n",
      " '2015-11-17T00:00:00.000000000' '2014-03-10T00:00:00.000000000'\n",
      " '2016-02-07T00:00:00.000000000' '2016-01-23T00:00:00.000000000'\n",
      " '2016-02-25T00:00:00.000000000' '2016-02-14T00:00:00.000000000'\n",
      " '2016-01-02T00:00:00.000000000' '2015-09-04T00:00:00.000000000'\n",
      " '2015-11-12T00:00:00.000000000' '2015-12-27T00:00:00.000000000'\n",
      " '2015-11-24T00:00:00.000000000' '2016-01-20T00:00:00.000000000'\n",
      " '2016-02-03T00:00:00.000000000' '2015-12-05T00:00:00.000000000'\n",
      " '2015-08-07T00:00:00.000000000' '2016-01-28T00:00:00.000000000'\n",
      " '2016-01-31T00:00:00.000000000' '2016-02-08T00:00:00.000000000'\n",
      " '2016-01-07T00:00:00.000000000' '2016-01-22T00:00:00.000000000'\n",
      " '2016-01-18T00:00:00.000000000' '2016-01-08T00:00:00.000000000'\n",
      " '2015-11-23T00:00:00.000000000' '2016-01-13T00:00:00.000000000'\n",
      " '2016-01-17T00:00:00.000000000' '2016-01-15T00:00:00.000000000'\n",
      " '2015-11-08T00:00:00.000000000' '2016-01-26T00:00:00.000000000'\n",
      " '2016-02-04T00:00:00.000000000' '2016-01-25T00:00:00.000000000'\n",
      " '2016-01-16T00:00:00.000000000' '2015-08-10T00:00:00.000000000'\n",
      " '2016-01-03T00:00:00.000000000' '2016-01-19T00:00:00.000000000'\n",
      " '2015-12-30T00:00:00.000000000' '2016-02-01T00:00:00.000000000'\n",
      " '2015-12-17T00:00:00.000000000' '2015-11-10T00:00:00.000000000'\n",
      " '2016-01-06T00:00:00.000000000' '2015-09-09T00:00:00.000000000'\n",
      " '2015-06-18T00:00:00.000000000']\n",
      "-----------------------------------\n",
      "Column numberofpictures:\n",
      "[0]\n",
      "-----------------------------------\n",
      "Column postalcode:\n",
      "[70435 66954 90480 ... 38528 38325 31096]\n",
      "-----------------------------------\n",
      "Column lastseen:\n",
      "['2016-04-07T03:16:00.000000000' '2016-04-07T01:46:00.000000000'\n",
      " '2016-04-05T12:47:00.000000000' ... '2016-03-08T20:43:00.000000000'\n",
      " '2016-03-08T21:28:00.000000000' '2016-03-18T11:30:00.000000000']\n",
      "-----------------------------------\n"
     ]
    }
   ],
   "source": [
    "for column in df.columns:\n",
    "    print(f'Column {column}:')\n",
    "    print(df[column].unique())\n",
    "    print('-----------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "\n",
    "<b>Comentario del revisor. (Iteración 1)</b> <a class=\"tocSkip\"></a>\n",
    "    \n",
    "Perfecta normalización sobre las columnas datetime y al ver los posibles valores unicos de cada feature!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "golf        29215\n",
      "other       24402\n",
      "3er         19744\n",
      "polo        13057\n",
      "corsa       12559\n",
      "            ...  \n",
      "antara        108\n",
      "90            103\n",
      "wrangler      103\n",
      "juke          102\n",
      "exeo          100\n",
      "Name: model, Length: 208, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "frecuencias = df['model'].value_counts()\n",
    "\n",
    "umbral = 100 \n",
    "\n",
    "valores_poco_frecuentes = frecuencias[frecuencias < umbral].index\n",
    "\n",
    "df['model'] = df['model'].apply(\n",
    "    lambda x: x if x not in valores_poco_frecuentes else 'otros'\n",
    ")\n",
    "\n",
    "print(df['model'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valores mínimos por columna:\n",
      "power                  0\n",
      "registrationyear    1000\n",
      "dtype: int64\n",
      "\n",
      "Valores máximos por columna:\n",
      "power               20000\n",
      "registrationyear     9999\n",
      "dtype: int64\n",
      "\n",
      "count    354107.000000\n",
      "mean        110.089651\n",
      "std         189.914972\n",
      "min           0.000000\n",
      "25%          69.000000\n",
      "50%         105.000000\n",
      "75%         143.000000\n",
      "max       20000.000000\n",
      "Name: power, dtype: float64\n",
      "\n",
      "count    354107.000000\n",
      "mean       2004.235355\n",
      "std          90.261168\n",
      "min        1000.000000\n",
      "25%        1999.000000\n",
      "50%        2003.000000\n",
      "75%        2008.000000\n",
      "max        9999.000000\n",
      "Name: registrationyear, dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "columnas_interes = ['power', 'registrationyear']\n",
    "\n",
    "valores_minimos = df[columnas_interes].min()\n",
    "valores_maximos = df[columnas_interes].max()\n",
    "\n",
    "print('Valores mínimos por columna:')\n",
    "print(valores_minimos)\n",
    "print('\\nValores máximos por columna:')\n",
    "print(valores_maximos)\n",
    "print()\n",
    "print(df[\"power\"].describe())\n",
    "print()\n",
    "print(df[\"registrationyear\"].describe())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          datecrawled  price vehicletype  registrationyear gearbox  power  \\\n",
      "0 2016-03-24 11:52:00    480         NaN              1993  manual      0   \n",
      "1 2016-03-24 10:58:00  18300       coupe              2011  manual    190   \n",
      "2 2016-03-14 12:52:00   9800         suv              2004    auto    163   \n",
      "3 2016-03-17 16:54:00   1500       small              2001  manual     75   \n",
      "4 2016-03-31 17:25:00   3600       small              2008  manual     69   \n",
      "\n",
      "   model  mileage  registrationmonth  fueltype       brand notrepaired  \\\n",
      "0   golf   150000                  0    petrol  volkswagen         NaN   \n",
      "1    NaN   125000                  5  gasoline        audi         yes   \n",
      "2  grand   125000                  8  gasoline        jeep         NaN   \n",
      "3   golf   150000                  6    petrol  volkswagen          no   \n",
      "4  fabia    90000                  7  gasoline       skoda          no   \n",
      "\n",
      "  datecreated  numberofpictures  postalcode            lastseen  \n",
      "0  2016-03-24                 0       70435 2016-04-07 03:16:00  \n",
      "1  2016-03-24                 0       66954 2016-04-07 01:46:00  \n",
      "2  2016-03-14                 0       90480 2016-04-05 12:47:00  \n",
      "3  2016-03-17                 0       91074 2016-03-17 17:40:00  \n",
      "4  2016-03-31                 0       60437 2016-04-06 10:17:00  \n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 354107 entries, 0 to 354368\n",
      "Data columns (total 16 columns):\n",
      " #   Column             Non-Null Count   Dtype         \n",
      "---  ------             --------------   -----         \n",
      " 0   datecrawled        354107 non-null  datetime64[ns]\n",
      " 1   price              354107 non-null  int64         \n",
      " 2   vehicletype        316623 non-null  object        \n",
      " 3   registrationyear   354107 non-null  int64         \n",
      " 4   gearbox            334277 non-null  object        \n",
      " 5   power              354107 non-null  int64         \n",
      " 6   model              334406 non-null  object        \n",
      " 7   mileage            354107 non-null  int64         \n",
      " 8   registrationmonth  354107 non-null  int64         \n",
      " 9   fueltype           321218 non-null  object        \n",
      " 10  brand              354107 non-null  object        \n",
      " 11  notrepaired        282962 non-null  object        \n",
      " 12  datecreated        354107 non-null  datetime64[ns]\n",
      " 13  numberofpictures   354107 non-null  int64         \n",
      " 14  postalcode         354107 non-null  int64         \n",
      " 15  lastseen           354107 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](3), int64(7), object(6)\n",
      "memory usage: 45.9+ MB\n",
      "None\n",
      "Hay 0 filas duplicadas\n",
      "\n",
      "Valores Nulos\n",
      "datecrawled              0\n",
      "price                    0\n",
      "vehicletype          37484\n",
      "registrationyear         0\n",
      "gearbox              19830\n",
      "power                    0\n",
      "model                19701\n",
      "mileage                  0\n",
      "registrationmonth        0\n",
      "fueltype             32889\n",
      "brand                    0\n",
      "notrepaired          71145\n",
      "datecreated              0\n",
      "numberofpictures         0\n",
      "postalcode               0\n",
      "lastseen                 0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.head())\n",
    "print()\n",
    "print(df.info())\n",
    "print(f'Hay {df.duplicated().sum()} filas duplicadas')\n",
    "print()\n",
    "print(f'Valores Nulos')\n",
    "print(df.isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "\n",
    "<b>Comentario del revisor. (Iteración 1)</b> <a class=\"tocSkip\"></a>\n",
    "    \n",
    "Buena profundización para en las comprensión de los valores, tanto máximos como mínimos y aquellos duplicados! Sigamos!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### En este punto podemos observar que despues de haber pasado todas las columnas a minúsculas, eliminado las filas duplicadas y observado los valores únicos, así como valores ausentes, nos enfrentamos a tres problemas principales: \n",
    "\n",
    "1.- Existen una gran cantidad de valores ausentes en las columnas \"vehicletype\", \"gearbox\", \"model\", \"fueltype\", y especialmente en \"notrepaired\".\n",
    "\n",
    "2.- Existen valores completamente fuera de rango en algunas columnas de interés (\"power\" y \"registrationyear\")\n",
    "\n",
    "3.- Existen variables categóricas que deben codificadas para poder ser usados. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1000 1001 1039 1111 1200 1234 1253 1255 1300 1400 1500 1600 1602 1688\n",
      " 1800 1910 1915 1919 1920 1923 1925 1927 1928 1929 1930 1931 1932 1933\n",
      " 1934 1935 1936 1937 1938 1940 1941 1942 1943 1944 1945 1946 1947 1948\n",
      " 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962\n",
      " 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976\n",
      " 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990\n",
      " 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004\n",
      " 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018\n",
      " 2019 2066 2200 2222 2290 2500 2800 2900 3000 3200 3500 3700 3800 4000\n",
      " 4100 4500 4800 5000 5300 5555 5600 5900 5911 6000 6500 7000 7100 7500\n",
      " 7800 8000 8200 8455 8500 8888 9000 9229 9450 9996 9999]\n",
      "\n",
      "[    0     1     2     3     4     5     6     7     8     9    10    11\n",
      "    12    13    14    15    16    17    18    19    20    21    22    23\n",
      "    24    25    26    27    28    29    30    31    32    33    34    35\n",
      "    36    37    38    39    40    41    42    43    44    45    46    47\n",
      "    48    49    50    51    52    53    54    55    56    57    58    59\n",
      "    60    61    62    63    64    65    66    67    68    69    70    71\n",
      "    72    73    74    75    76    77    78    79    80    81    82    83\n",
      "    84    85    86    87    88    89    90    91    92    93    94    95\n",
      "    96    97    98    99   100   101   102   103   104   105   106   107\n",
      "   108   109   110   111   112   113   114   115   116   117   118   119\n",
      "   120   121   122   123   124   125   126   127   128   129   130   131\n",
      "   132   133   134   135   136   137   138   139   140   141   142   143\n",
      "   144   145   146   147   148   149   150   151   152   153   154   155\n",
      "   156   157   158   159   160   161   162   163   164   165   166   167\n",
      "   168   169   170   171   172   173   174   175   176   177   178   179\n",
      "   180   181   182   183   184   185   186   187   188   189   190   191\n",
      "   192   193   194   195   196   197   198   199   200   201   202   203\n",
      "   204   205   206   207   208   209   210   211   212   213   214   215\n",
      "   216   217   218   219   220   221   222   223   224   225   226   227\n",
      "   228   229   230   231   232   233   234   235   236   237   238   239\n",
      "   240   241   242   243   244   245   246   247   248   249   250   251\n",
      "   252   253   254   255   256   257   258   259   260   261   262   264\n",
      "   265   266   267   268   269   270   271   272   273   274   275   276\n",
      "   277   278   279   280   281   282   283   284   285   286   287   288\n",
      "   289   290   292   293   294   295   296   297   298   299   300   301\n",
      "   303   304   305   306   307   308   309   310   311   313   314   315\n",
      "   316   317   318   320   321   322   323   324   325   326   327   328\n",
      "   329   330   331   332   333   334   335   336   337   338   339   340\n",
      "   341   343   344   345   346   347   348   349   350   351   352   353\n",
      "   354   355   356   357   358   360   361   362   363   364   365   367\n",
      "   368   370   371   374   375   376   377   379   380   381   382   385\n",
      "   386   387   388   390   392   394   396   398   399   400   401   402\n",
      "   405   408   409   411   416   420   421   425   426   428   430   431\n",
      "   435   440   442   445   449   450   454   457   459   460   475   476\n",
      "   485   487   489   490   500   504   505   507   508   510   514   515\n",
      "   517   519   520   521   525   530   540   541   544   550   551   553\n",
      "   560   572   574   579   580   584   585   599   600   601   602   603\n",
      "   604   606   607   610   612   620   640   645   650   651   671   678\n",
      "   682   685   696   700   702   703   732   743   750   751   754   771\n",
      "   776   800   805   808   850   851   871   900   901   902   903   907\n",
      "   909   923   950   952   953   960   998   999  1000  1001  1002  1003\n",
      "  1004  1005  1011  1012  1016  1017  1021  1024  1054  1055  1056  1062\n",
      "  1079  1082  1090  1100  1102  1103  1105  1111  1115  1120  1149  1151\n",
      "  1158  1160  1162  1164  1199  1200  1202  1221  1223  1230  1239  1240\n",
      "  1241  1250  1252  1256  1275  1288  1299  1300  1312  1317  1324  1339\n",
      "  1351  1360  1362  1363  1367  1390  1394  1398  1399  1400  1401  1403\n",
      "  1405  1416  1432  1433  1436  1500  1501  1502  1503  1506  1521  1548\n",
      "  1595  1596  1597  1598  1600  1625  1631  1653  1659  1689  1700  1701\n",
      "  1703  1704  1707  1753  1771  1779  1780  1781  1783  1793  1796  1799\n",
      "  1800  1801  1870  1895  1896  1900  1910  1920  1922  1933  1937  1968\n",
      "  1986  1988  1992  1993  1995  1998  1999  2000  2004  2005  2007  2009\n",
      "  2016  2017  2018  2172  2200  2201  2331  2340  2389  2402  2461  2598\n",
      "  2729  2789  2792  2799  3000  3199  3454  3500  3750  4400  4507  4700\n",
      "  5000  5411  5420  5575  5809  5815  5867  6006  6010  6011  6012  6018\n",
      "  6045  6062  6226  6512  6920  7508  7511  7512  7515  7518  7529  7544\n",
      "  8011  8259  8404  8500  9000  9007  9010  9011  9012  9013  9710 10000\n",
      " 10110 10218 10311 10317 10520 10522 10710 10910 10912 11011 11025 11111\n",
      " 11509 11530 11635 12012 12510 12512 12684 13616 13636 14009 15001 15016\n",
      " 15017 15020 15033 16011 16051 16311 16312 17011 17019 17410 17700 17932\n",
      " 19208 19211 19312 20000]\n",
      "\n",
      "2004.2353554151712\n",
      "2003.0\n",
      "\n",
      "110.0896508682404\n",
      "105.0\n"
     ]
    }
   ],
   "source": [
    "print(df['registrationyear'].sort_values(ascending=True).unique())\n",
    "print()\n",
    "print(df['power'].sort_values(ascending=True).unique())\n",
    "print()\n",
    "print(df[\"registrationyear\"].mean())\n",
    "print(df[\"registrationyear\"].median())\n",
    "print()\n",
    "print(df[\"power\"].mean())\n",
    "print(df[\"power\"].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "registration_median= df[\"registrationyear\"].median()\n",
    "df.loc[df[\"registrationyear\"] <= 1960,'registrationyear'] = registration_median\n",
    "df.loc[df[\"registrationyear\"] > 2023,'registrationyear'] = registration_median "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "power_median= df[\"power\"].median()\n",
    "df.loc[df[\"power\"] <= 30,'power'] = power_median\n",
    "df.loc[df[\"power\"] > 800,'power'] = power_median "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1961.0 2019.0\n",
      "\n",
      "31.0 800.0\n",
      "5000 150000\n"
     ]
    }
   ],
   "source": [
    "print(df[\"registrationyear\"].min(), df[\"registrationyear\"].max())\n",
    "\n",
    "df[\"registrationyear\"] = df[\"registrationyear\"].astype('int8')\n",
    "\n",
    "print()\n",
    "\n",
    "print(df[\"power\"].min(), df[\"power\"].max())\n",
    "\n",
    "df[\"power\"] = df[\"power\"].astype('int8')\n",
    "\n",
    "print(df[\"mileage\"].min(), df[\"mileage\"].max())\n",
    "\n",
    "df[\"mileage\"] = df[\"mileage\"].astype('int8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "\n",
    "<b>Comentario del revisor. (Iteración 1)</b> <a class=\"tocSkip\"></a>\n",
    "    \n",
    "Bien hecho Alejandro! Tal como se debía hemos filtrado aquellos intervalos de valores normales permitiendonos así filtrar posibles outliers! Bien hecho! Como recomendación te invito a que investigues la implementación de filtrado por percentiles, esta es una herramienta muy utilizada para estos casos de outliers!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Los valores nulos serán trabajados cuando dividamos el dataframe, ya que son demasiados como para eliminarlos, mientras que los valores atípicos fueron sustituidos por la mediana, el rango de valores de los años fueron considerando la fecha actual así como el hecho de que los autos que pudieran estar a la venta en un portal no especializado. Para la potencia en cambio se considera que existieron autos de 30 cv e incluso una tendencia en japon de autos KeyCars de muy baja potencia y cilindraje, mientras que para el máximo se consideran los principales autos de las marcas existentes en el DF donde sería extremadamente raro encontrar algo de más de 800cv."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entrenamiento del modelo "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "target= \"price\"\n",
    "categorical_features=['vehicletype', 'gearbox','model', 'fueltype', 'brand',\n",
    "       'notrepaired']\n",
    "numerical_features= [\"registrationyear\", \"power\", \"mileage\"] \n",
    "\n",
    "df[categorical_features] = df[categorical_features].fillna(\"missing\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df, test_df= train_test_split(\n",
    "    df,\n",
    "    test_size = 0.2,\n",
    "    random_state = 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.copy()\n",
    "test_df = test_df.copy()\n",
    "\n",
    "train_df_categorical = train_df[categorical_features].copy()\n",
    "test_df_categorical = test_df[categorical_features].copy()\n",
    "\n",
    "ordinal_encoder = OrdinalEncoder()\n",
    "\n",
    "train_df_categorical = ordinal_encoder.fit_transform(train_df_categorical)\n",
    "test_df_categorical = ordinal_encoder.transform(test_df_categorical)\n",
    "\n",
    "train_df.loc[:, categorical_features] = train_df_categorical\n",
    "test_df.loc[:, categorical_features] = test_df_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               datecrawled  price  vehicletype  registrationyear  gearbox  \\\n",
      "111674 2016-03-07 14:25:00  19900          7.0         -0.436782      0.0   \n",
      "289786 2016-03-16 19:25:00   1150          3.0         -0.367816      1.0   \n",
      "256013 2016-03-23 18:56:00  12500          2.0         -0.459770      1.0   \n",
      "10938  2016-03-09 12:25:00   5990          5.0         -0.471264      1.0   \n",
      "33586  2016-03-19 16:47:00   7490          6.0         -0.390805      1.0   \n",
      "\n",
      "           power  model  mileage  registrationmonth  fueltype  brand  \\\n",
      "111674 -0.195312  120.0   -0.125                 11       2.0   20.0   \n",
      "289786  0.789062  126.0   -0.125                 12       5.0   38.0   \n",
      "256013 -0.789062  126.0    0.750                  8       7.0   33.0   \n",
      "10938   0.796875  105.0   -0.125                  8       4.0   38.0   \n",
      "33586   0.468750  188.0    0.250                  1       7.0   38.0   \n",
      "\n",
      "        notrepaired datecreated  numberofpictures  postalcode  \\\n",
      "111674          1.0  2016-03-07                 0       61348   \n",
      "289786          1.0  2016-03-16                 0       35075   \n",
      "256013          1.0  2016-03-23                 0       76703   \n",
      "10938           1.0  2016-03-09                 0       55411   \n",
      "33586           1.0  2016-03-19                 0        9429   \n",
      "\n",
      "                  lastseen  \n",
      "111674 2016-03-21 02:15:00  \n",
      "289786 2016-04-05 22:17:00  \n",
      "256013 2016-04-05 23:45:00  \n",
      "10938  2016-04-07 00:17:00  \n",
      "33586  2016-04-07 02:44:00  \n",
      "               datecrawled  price  vehicletype  registrationyear  gearbox  \\\n",
      "69747  2016-03-16 17:37:00   5399          0.0         -0.482759      1.0   \n",
      "45475  2016-04-04 22:48:00   4500          3.0         -0.494253      2.0   \n",
      "65377  2016-03-15 15:58:00    600          5.0         -0.540230      0.0   \n",
      "217845 2016-03-09 20:42:00   3250          0.0         -0.540230      1.0   \n",
      "293299 2016-03-21 16:58:00  10199          5.0         -0.425287      0.0   \n",
      "\n",
      "           power  model  mileage  registrationmonth  fueltype  brand  \\\n",
      "69747   0.789062  136.0   -0.125                  6       2.0   27.0   \n",
      "45475   0.820312  151.0   -0.125                  0       5.0   36.0   \n",
      "65377   0.835938  123.0   -0.125                  8       7.0   27.0   \n",
      "217845  0.906250  165.0   -0.125                 10       2.0   38.0   \n",
      "293299  0.820312  105.0   -1.000                  0       2.0   38.0   \n",
      "\n",
      "        notrepaired datecreated  numberofpictures  postalcode  \\\n",
      "69747           1.0  2016-03-16                 0       51371   \n",
      "45475           0.0  2016-04-04                 0       20537   \n",
      "65377           2.0  2016-03-15                 0       51643   \n",
      "217845          1.0  2016-03-09                 0       67112   \n",
      "293299          1.0  2016-03-21                 0       33729   \n",
      "\n",
      "                  lastseen  \n",
      "69747  2016-04-06 23:16:00  \n",
      "45475  2016-04-07 01:45:00  \n",
      "65377  2016-04-06 10:15:00  \n",
      "217845 2016-03-10 17:15:00  \n",
      "293299 2016-03-31 18:53:00  \n"
     ]
    }
   ],
   "source": [
    "scaler = MaxAbsScaler()\n",
    "\n",
    "train_df.loc[:, numerical_features] = scaler.fit_transform(train_df[numerical_features])\n",
    "test_df.loc[:, numerical_features] = scaler.transform(test_df[numerical_features])\n",
    "\n",
    "print(train_df.head())\n",
    "print(test_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "\n",
    "<b>Comentario del revisor. (Iteración 1)</b> <a class=\"tocSkip\"></a>\n",
    "    \n",
    "Y aquí nuevamente excelente! Tal como siempre se recomienda perfecta transfornación de aquellas features categóricas a numéricas y luego excelente escalamiento sobre aquellas numéricas!\n",
    "    \n",
    "Por otro lado bien hecho al implementar train_test_split para la separación de datos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regresión Lineal (Prueba de Cordura)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RECM en el conjunto de prueba: 3781.33\n",
      "CPU times: user 41 ms, sys: 35.7 ms, total: 76.7 ms\n",
      "Wall time: 61.6 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "X_train = train_df[categorical_features + numerical_features]\n",
    "y_train = train_df[\"price\"] \n",
    "\n",
    "X_test = test_df[categorical_features + numerical_features]  \n",
    "y_test = test_df[\"price\"]  \n",
    "\n",
    "model_lr = LinearRegression(random_state=8)\n",
    "\n",
    "model_lr.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model_lr.predict(X_test)\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "\n",
    "print(f\"RECM en el conjunto de prueba: {rmse:.2f}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "\n",
    "<b>Comentario del revisor. (Iteración 1)</b> <a class=\"tocSkip\"></a>\n",
    "    \n",
    "Excelente declaración del modelo en su versión básica en conjunto con con los conjuntos utilizados y la métrica elegida! A la vez excelente medición del tiempo! Esto nos permitirá comparar la viabilidad de nuestros modelos!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 27 candidates, totalling 135 fits\n",
      "[CV] END ...max_depth=3, min_samples_split=2, n_estimators=5; total time=   0.5s\n",
      "[CV] END ...max_depth=3, min_samples_split=2, n_estimators=5; total time=   0.4s\n",
      "[CV] END ...max_depth=3, min_samples_split=2, n_estimators=5; total time=   0.4s\n",
      "[CV] END ...max_depth=3, min_samples_split=2, n_estimators=5; total time=   0.4s\n",
      "[CV] END ...max_depth=3, min_samples_split=2, n_estimators=5; total time=   0.4s\n",
      "[CV] END ..max_depth=3, min_samples_split=2, n_estimators=50; total time=   4.1s\n",
      "[CV] END ..max_depth=3, min_samples_split=2, n_estimators=50; total time=   4.1s\n",
      "[CV] END ..max_depth=3, min_samples_split=2, n_estimators=50; total time=   4.1s\n",
      "[CV] END ..max_depth=3, min_samples_split=2, n_estimators=50; total time=   4.1s\n",
      "[CV] END ..max_depth=3, min_samples_split=2, n_estimators=50; total time=   4.1s\n",
      "[CV] END .max_depth=3, min_samples_split=2, n_estimators=100; total time=   8.2s\n",
      "[CV] END .max_depth=3, min_samples_split=2, n_estimators=100; total time=   8.6s\n",
      "[CV] END .max_depth=3, min_samples_split=2, n_estimators=100; total time=   8.2s\n",
      "[CV] END .max_depth=3, min_samples_split=2, n_estimators=100; total time=   8.2s\n",
      "[CV] END .max_depth=3, min_samples_split=2, n_estimators=100; total time=   8.2s\n",
      "[CV] END ...max_depth=3, min_samples_split=5, n_estimators=5; total time=   0.4s\n",
      "[CV] END ...max_depth=3, min_samples_split=5, n_estimators=5; total time=   0.4s\n",
      "[CV] END ...max_depth=3, min_samples_split=5, n_estimators=5; total time=   0.4s\n",
      "[CV] END ...max_depth=3, min_samples_split=5, n_estimators=5; total time=   0.4s\n",
      "[CV] END ...max_depth=3, min_samples_split=5, n_estimators=5; total time=   0.4s\n",
      "[CV] END ..max_depth=3, min_samples_split=5, n_estimators=50; total time=   4.1s\n",
      "[CV] END ..max_depth=3, min_samples_split=5, n_estimators=50; total time=   4.2s\n",
      "[CV] END ..max_depth=3, min_samples_split=5, n_estimators=50; total time=   4.1s\n",
      "[CV] END ..max_depth=3, min_samples_split=5, n_estimators=50; total time=   4.1s\n",
      "[CV] END ..max_depth=3, min_samples_split=5, n_estimators=50; total time=   4.3s\n",
      "[CV] END .max_depth=3, min_samples_split=5, n_estimators=100; total time=   8.2s\n",
      "[CV] END .max_depth=3, min_samples_split=5, n_estimators=100; total time=   8.2s\n",
      "[CV] END .max_depth=3, min_samples_split=5, n_estimators=100; total time=   8.2s\n",
      "[CV] END .max_depth=3, min_samples_split=5, n_estimators=100; total time=   8.2s\n",
      "[CV] END .max_depth=3, min_samples_split=5, n_estimators=100; total time=   8.2s\n",
      "[CV] END ..max_depth=3, min_samples_split=10, n_estimators=5; total time=   0.4s\n",
      "[CV] END ..max_depth=3, min_samples_split=10, n_estimators=5; total time=   0.4s\n",
      "[CV] END ..max_depth=3, min_samples_split=10, n_estimators=5; total time=   0.4s\n",
      "[CV] END ..max_depth=3, min_samples_split=10, n_estimators=5; total time=   0.4s\n",
      "[CV] END ..max_depth=3, min_samples_split=10, n_estimators=5; total time=   0.4s\n",
      "[CV] END .max_depth=3, min_samples_split=10, n_estimators=50; total time=   4.1s\n",
      "[CV] END .max_depth=3, min_samples_split=10, n_estimators=50; total time=   4.1s\n",
      "[CV] END .max_depth=3, min_samples_split=10, n_estimators=50; total time=   4.1s\n",
      "[CV] END .max_depth=3, min_samples_split=10, n_estimators=50; total time=   4.1s\n",
      "[CV] END .max_depth=3, min_samples_split=10, n_estimators=50; total time=   4.1s\n",
      "[CV] END max_depth=3, min_samples_split=10, n_estimators=100; total time=   8.2s\n",
      "[CV] END max_depth=3, min_samples_split=10, n_estimators=100; total time=   8.2s\n",
      "[CV] END max_depth=3, min_samples_split=10, n_estimators=100; total time=   8.3s\n",
      "[CV] END max_depth=3, min_samples_split=10, n_estimators=100; total time=   8.2s\n",
      "[CV] END max_depth=3, min_samples_split=10, n_estimators=100; total time=   8.2s\n",
      "[CV] END ...max_depth=5, min_samples_split=2, n_estimators=5; total time=   0.7s\n",
      "[CV] END ...max_depth=5, min_samples_split=2, n_estimators=5; total time=   0.7s\n",
      "[CV] END ...max_depth=5, min_samples_split=2, n_estimators=5; total time=   0.7s\n",
      "[CV] END ...max_depth=5, min_samples_split=2, n_estimators=5; total time=   0.7s\n",
      "[CV] END ...max_depth=5, min_samples_split=2, n_estimators=5; total time=   0.7s\n",
      "[CV] END ..max_depth=5, min_samples_split=2, n_estimators=50; total time=   6.4s\n",
      "[CV] END ..max_depth=5, min_samples_split=2, n_estimators=50; total time=   6.4s\n",
      "[CV] END ..max_depth=5, min_samples_split=2, n_estimators=50; total time=   6.5s\n",
      "[CV] END ..max_depth=5, min_samples_split=2, n_estimators=50; total time=   6.4s\n",
      "[CV] END ..max_depth=5, min_samples_split=2, n_estimators=50; total time=   6.4s\n",
      "[CV] END .max_depth=5, min_samples_split=2, n_estimators=100; total time=  12.9s\n",
      "[CV] END .max_depth=5, min_samples_split=2, n_estimators=100; total time=  12.9s\n",
      "[CV] END .max_depth=5, min_samples_split=2, n_estimators=100; total time=  12.9s\n",
      "[CV] END .max_depth=5, min_samples_split=2, n_estimators=100; total time=  13.2s\n",
      "[CV] END .max_depth=5, min_samples_split=2, n_estimators=100; total time=  13.4s\n",
      "[CV] END ...max_depth=5, min_samples_split=5, n_estimators=5; total time=   0.7s\n",
      "[CV] END ...max_depth=5, min_samples_split=5, n_estimators=5; total time=   0.6s\n",
      "[CV] END ...max_depth=5, min_samples_split=5, n_estimators=5; total time=   0.7s\n",
      "[CV] END ...max_depth=5, min_samples_split=5, n_estimators=5; total time=   0.7s\n",
      "[CV] END ...max_depth=5, min_samples_split=5, n_estimators=5; total time=   0.7s\n",
      "[CV] END ..max_depth=5, min_samples_split=5, n_estimators=50; total time=   6.4s\n",
      "[CV] END ..max_depth=5, min_samples_split=5, n_estimators=50; total time=   6.5s\n",
      "[CV] END ..max_depth=5, min_samples_split=5, n_estimators=50; total time=   6.7s\n",
      "[CV] END ..max_depth=5, min_samples_split=5, n_estimators=50; total time=   6.4s\n",
      "[CV] END ..max_depth=5, min_samples_split=5, n_estimators=50; total time=   6.4s\n",
      "[CV] END .max_depth=5, min_samples_split=5, n_estimators=100; total time=  12.7s\n",
      "[CV] END .max_depth=5, min_samples_split=5, n_estimators=100; total time=  12.8s\n",
      "[CV] END .max_depth=5, min_samples_split=5, n_estimators=100; total time=  12.8s\n",
      "[CV] END .max_depth=5, min_samples_split=5, n_estimators=100; total time=  12.8s\n",
      "[CV] END .max_depth=5, min_samples_split=5, n_estimators=100; total time=  12.8s\n",
      "[CV] END ..max_depth=5, min_samples_split=10, n_estimators=5; total time=   0.7s\n",
      "[CV] END ..max_depth=5, min_samples_split=10, n_estimators=5; total time=   0.6s\n",
      "[CV] END ..max_depth=5, min_samples_split=10, n_estimators=5; total time=   0.7s\n",
      "[CV] END ..max_depth=5, min_samples_split=10, n_estimators=5; total time=   0.7s\n",
      "[CV] END ..max_depth=5, min_samples_split=10, n_estimators=5; total time=   0.7s\n",
      "[CV] END .max_depth=5, min_samples_split=10, n_estimators=50; total time=   6.4s\n",
      "[CV] END .max_depth=5, min_samples_split=10, n_estimators=50; total time=   6.4s\n",
      "[CV] END .max_depth=5, min_samples_split=10, n_estimators=50; total time=   6.4s\n",
      "[CV] END .max_depth=5, min_samples_split=10, n_estimators=50; total time=   6.4s\n",
      "[CV] END .max_depth=5, min_samples_split=10, n_estimators=50; total time=   6.4s\n",
      "[CV] END max_depth=5, min_samples_split=10, n_estimators=100; total time=  12.8s\n",
      "[CV] END max_depth=5, min_samples_split=10, n_estimators=100; total time=  12.8s\n",
      "[CV] END max_depth=5, min_samples_split=10, n_estimators=100; total time=  12.9s\n",
      "[CV] END max_depth=5, min_samples_split=10, n_estimators=100; total time=  12.9s\n",
      "[CV] END max_depth=5, min_samples_split=10, n_estimators=100; total time=  12.7s\n",
      "[CV] END ..max_depth=10, min_samples_split=2, n_estimators=5; total time=   1.2s\n",
      "[CV] END ..max_depth=10, min_samples_split=2, n_estimators=5; total time=   1.2s\n",
      "[CV] END ..max_depth=10, min_samples_split=2, n_estimators=5; total time=   1.2s\n",
      "[CV] END ..max_depth=10, min_samples_split=2, n_estimators=5; total time=   1.2s\n",
      "[CV] END ..max_depth=10, min_samples_split=2, n_estimators=5; total time=   1.2s\n",
      "[CV] END .max_depth=10, min_samples_split=2, n_estimators=50; total time=  11.5s\n",
      "[CV] END .max_depth=10, min_samples_split=2, n_estimators=50; total time=  11.5s\n",
      "[CV] END .max_depth=10, min_samples_split=2, n_estimators=50; total time=  11.8s\n",
      "[CV] END .max_depth=10, min_samples_split=2, n_estimators=50; total time=  11.7s\n",
      "[CV] END .max_depth=10, min_samples_split=2, n_estimators=50; total time=  11.6s\n",
      "[CV] END max_depth=10, min_samples_split=2, n_estimators=100; total time=  23.3s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=10, min_samples_split=2, n_estimators=100; total time=  23.1s\n",
      "[CV] END max_depth=10, min_samples_split=2, n_estimators=100; total time=  23.1s\n",
      "[CV] END max_depth=10, min_samples_split=2, n_estimators=100; total time=  23.1s\n",
      "[CV] END max_depth=10, min_samples_split=2, n_estimators=100; total time=  23.1s\n",
      "[CV] END ..max_depth=10, min_samples_split=5, n_estimators=5; total time=   1.2s\n",
      "[CV] END ..max_depth=10, min_samples_split=5, n_estimators=5; total time=   1.2s\n",
      "[CV] END ..max_depth=10, min_samples_split=5, n_estimators=5; total time=   1.2s\n",
      "[CV] END ..max_depth=10, min_samples_split=5, n_estimators=5; total time=   1.2s\n",
      "[CV] END ..max_depth=10, min_samples_split=5, n_estimators=5; total time=   1.2s\n",
      "[CV] END .max_depth=10, min_samples_split=5, n_estimators=50; total time=  11.6s\n",
      "[CV] END .max_depth=10, min_samples_split=5, n_estimators=50; total time=  11.5s\n",
      "[CV] END .max_depth=10, min_samples_split=5, n_estimators=50; total time=  11.6s\n",
      "[CV] END .max_depth=10, min_samples_split=5, n_estimators=50; total time=  11.6s\n",
      "[CV] END .max_depth=10, min_samples_split=5, n_estimators=50; total time=  11.5s\n",
      "[CV] END max_depth=10, min_samples_split=5, n_estimators=100; total time=  23.0s\n",
      "[CV] END max_depth=10, min_samples_split=5, n_estimators=100; total time=  23.1s\n",
      "[CV] END max_depth=10, min_samples_split=5, n_estimators=100; total time=  23.1s\n",
      "[CV] END max_depth=10, min_samples_split=5, n_estimators=100; total time=  23.1s\n",
      "[CV] END max_depth=10, min_samples_split=5, n_estimators=100; total time=  23.0s\n",
      "[CV] END .max_depth=10, min_samples_split=10, n_estimators=5; total time=   1.2s\n",
      "[CV] END .max_depth=10, min_samples_split=10, n_estimators=5; total time=   1.2s\n",
      "[CV] END .max_depth=10, min_samples_split=10, n_estimators=5; total time=   1.2s\n",
      "[CV] END .max_depth=10, min_samples_split=10, n_estimators=5; total time=   1.2s\n",
      "[CV] END .max_depth=10, min_samples_split=10, n_estimators=5; total time=   1.2s\n",
      "[CV] END max_depth=10, min_samples_split=10, n_estimators=50; total time=  11.5s\n",
      "[CV] END max_depth=10, min_samples_split=10, n_estimators=50; total time=  11.5s\n",
      "[CV] END max_depth=10, min_samples_split=10, n_estimators=50; total time=  11.6s\n",
      "[CV] END max_depth=10, min_samples_split=10, n_estimators=50; total time=  11.6s\n",
      "[CV] END max_depth=10, min_samples_split=10, n_estimators=50; total time=  11.5s\n",
      "[CV] END max_depth=10, min_samples_split=10, n_estimators=100; total time=  23.1s\n",
      "[CV] END max_depth=10, min_samples_split=10, n_estimators=100; total time=  23.1s\n",
      "[CV] END max_depth=10, min_samples_split=10, n_estimators=100; total time=  23.1s\n",
      "[CV] END max_depth=10, min_samples_split=10, n_estimators=100; total time=  23.1s\n",
      "[CV] END max_depth=10, min_samples_split=10, n_estimators=100; total time=  23.1s\n",
      "Mejores parámetros encontrados:\n",
      "{'max_depth': 10, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "CPU times: user 17min 37s, sys: 231 ms, total: 17min 37s\n",
      "Wall time: 17min 38s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "rf = RandomForestRegressor(random_state=8)\n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators': [5, 50, 100],\n",
    "    'max_depth': [3, 5, 10], \n",
    "    'min_samples_split': [2, 5, 10],\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(estimator=rf, param_grid=param_grid, cv=5, n_jobs=-1, verbose=2, scoring='neg_mean_squared_error', random_state=8)\n",
    "\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "best_rf = grid_search.best_estimator_\n",
    "\n",
    "print(\"Mejores parámetros encontrados:\")\n",
    "print(grid_search.best_params_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RECM en el conjunto de prueba con Random Forest: 2144.34\n",
      "CPU times: user 439 ms, sys: 0 ns, total: 439 ms\n",
      "Wall time: 441 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "y_pred = best_rf.predict(X_test)\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "\n",
    "print(f\"RECM en el conjunto de prueba con Random Forest: {rmse:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "\n",
    "<b>Comentario del revisor. (Iteración 1)</b> <a class=\"tocSkip\"></a>\n",
    "    \n",
    "Impresionante Alejandro! Felicitaciones por profunidzar utilizar GridSearchCV y la grilla de hiperparametros, lo has hecho excelentemente ya que esta técnica siempre es recomendada, buenos resultados!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002219 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 537\n",
      "[LightGBM] [Info] Number of data points in the train set: 283285, number of used features: 9\n",
      "[LightGBM] [Info] Start training from score 4414.933678\n",
      "Training until validation scores don't improve for 10 rounds\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[100]\tvalid_0's l2: 4.16889e+06\n",
      "RECM en el conjunto de prueba: 2041.79\n",
      "CPU times: user 4.04 s, sys: 8 ms, total: 4.05 s\n",
      "Wall time: 4.03 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "train_data = lgb.Dataset(X_train, label=y_train)\n",
    "test_data = lgb.Dataset(X_test, label=y_test, reference=train_data)\n",
    "\n",
    "params = {\n",
    "    'objective': 'regression',\n",
    "    'metric': 'l2',\n",
    "    'boosting_type': 'gbdt',\n",
    "    'num_leaves': 31,\n",
    "    'learning_rate': 0.05,\n",
    "    'feature_fraction': 0.9\n",
    "}\n",
    "\n",
    "callbacks = [lgb.early_stopping(stopping_rounds=10)]\n",
    "\n",
    "bst = lgb.train(params,\n",
    "                train_data,\n",
    "                valid_sets=[test_data],\n",
    "                callbacks= callbacks)\n",
    "\n",
    "\n",
    "y_pred = bst.predict(X_test, num_iteration=bst.best_iteration)\n",
    "\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "\n",
    "print(f\"RECM en el conjunto de prueba: {rmse:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 81 candidates, totalling 243 fits\n",
      "Mejores parámetros: {'learning_rate': 0.1, 'max_depth': -1, 'n_estimators': 150, 'num_leaves': 70}\n",
      "Mejor puntaje: 3327131.61\n",
      "CPU times: user 12min 13s, sys: 7.45 s, total: 12min 20s\n",
      "Wall time: 12min 21s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "lgbm = lgb.LGBMRegressor()\n",
    "\n",
    "param_grid = {\n",
    "    'num_leaves': [31, 50, 70],\n",
    "    'learning_rate': [0.01, 0.05, 0.1],\n",
    "    'n_estimators': [50, 100, 150],\n",
    "    'max_depth': [10, 20, -1]\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(estimator=lgbm,\n",
    "                           param_grid=param_grid,\n",
    "                           scoring='neg_mean_squared_error',\n",
    "                           cv=3,\n",
    "                           verbose=1,\n",
    "                           n_jobs=-1\n",
    "                          random_state=8)\n",
    "\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(f\"Mejores parámetros: {grid_search.best_params_}\")\n",
    "print(f\"Mejor puntaje: {-grid_search.best_score_:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "\n",
    "<b>Comentario del revisor. (Iteración 1)</b> <a class=\"tocSkip\"></a>\n",
    "    \n",
    "Implementación perfecta Alejandro, en este caso un modelo más complejo y que se adapta a muchos tipos de problemas, nuevamente excelente uso de GridSearch!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CatBoostRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vehicletype    object\n",
      "gearbox        object\n",
      "model          object\n",
      "fueltype       object\n",
      "brand          object\n",
      "notrepaired    object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(X_train[categorical_features].dtypes)\n",
    "\n",
    "for col in categorical_features:\n",
    "    X_train[col] = X_train[col].astype(str)\n",
    "    X_test[col] = X_test[col].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Custom logger is already specified. Specify more than one logger at same time is not thread safe."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 4075.8527314\ttest: 4078.3316636\tbest: 4078.3316636 (0)\ttotal: 306ms\tremaining: 5m 5s\n",
      "200:\tlearn: 42.9990358\ttest: 42.9845264\tbest: 42.9845264 (200)\ttotal: 1m 3s\tremaining: 4m 11s\n",
      "400:\tlearn: 34.0722200\ttest: 34.2333762\tbest: 34.2333762 (400)\ttotal: 2m 4s\tremaining: 3m 5s\n",
      "600:\tlearn: 31.8896308\ttest: 32.3352755\tbest: 32.3352755 (600)\ttotal: 3m 3s\tremaining: 2m 2s\n",
      "800:\tlearn: 30.9748879\ttest: 31.7695255\tbest: 31.7695255 (800)\ttotal: 4m 1s\tremaining: 59.9s\n",
      "999:\tlearn: 30.2579719\ttest: 31.5073930\tbest: 31.5064122 (998)\ttotal: 4m 57s\tremaining: 0us\n",
      "\n",
      "bestTest = 31.50641225\n",
      "bestIteration = 998\n",
      "\n",
      "Shrink model to first 999 iterations.\n",
      "RECM en el conjunto de prueba con CatBoost: 31.51\n",
      "CPU times: user 4min 58s, sys: 334 ms, total: 4min 58s\n",
      "Wall time: 4min 59s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "train_data = Pool(data=X_train, label=y_train, cat_features=categorical_features)\n",
    "test_data = Pool(data=X_test, label=y_test, cat_features=categorical_features)\n",
    "\n",
    "model_catboost = CatBoostRegressor(\n",
    "    iterations=1000,\n",
    "    depth=6,\n",
    "    learning_rate=0.1,\n",
    "    loss_function='RMSE',\n",
    "    random_state=8\n",
    ")\n",
    "\n",
    "model_catboost.fit(train_data, eval_set=test_data, verbose=200)\n",
    "\n",
    "y_pred_catboost = model_catboost.predict(X_test)\n",
    "\n",
    "rmse_catboost = np.sqrt(mean_squared_error(y_test, y_pred_catboost))\n",
    "print(f\"RECM en el conjunto de prueba con CatBoost: {rmse_catboost:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Custom logger is already specified. Specify more than one logger at same time is not thread safe."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
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     "output_type": "stream",
     "text": [
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      "199:\tlearn: 40.0467008\ttotal: 7.46s\tremaining: 0us\n",
      "Mejores parámetros encontrados:\n",
      "{'depth': 6, 'iterations': 200, 'l2_leaf_reg': 3, 'learning_rate': 0.2, 'rsm': 0.8, 'subsample': 0.8}\n",
      "Mejor puntuación (neg_mean_squared_error):\n",
      "-1538.2844078318267\n",
      "RECM en el conjunto de prueba: 40.02\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    'iterations': [100, 200],\n",
    "    'depth': [4, 6],\n",
    "    'learning_rate': [0.01, 0.1, 0.2],\n",
    "    'l2_leaf_reg': [3],\n",
    "    'subsample': [0.8],\n",
    "    'rsm': [0.8] \n",
    "}\n",
    "    \n",
    "model = CatBoostRegressor(loss_function='RMSE', random_state=8, early_stopping_rounds=50)\n",
    "\n",
    "grid_search = GridSearchCV(estimator=model,\n",
    "                           param_grid=param_grid,\n",
    "                           scoring='neg_mean_squared_error',  \n",
    "                           cv=3,\n",
    "                           n_jobs=-1,\n",
    "                           verbose=1)\n",
    "\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Mejores parámetros encontrados:\")\n",
    "print(grid_search.best_params_)\n",
    "print(\"Mejor puntuación (neg_mean_squared_error):\")\n",
    "print(grid_search.best_score_)\n",
    "\n",
    "best_model = grid_search.best_estimator_\n",
    "y_pred = best_model.predict(X_test)\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(f\"RECM en el conjunto de prueba: {rmse:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "\n",
    "<b>Comentario del revisor. (Iteración 1)</b> <a class=\"tocSkip\"></a>\n",
    "    \n",
    "Similar a los casos anteriores, nuevamente bien hecho y buenos resultados Alejandro! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Análisis del modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De los 3 modelos analizados podemos observar que el mejor desempeño tenia, destaca tanto LGBMRegressor como CatBoostRegressor que superan ampliamente la puntuación de Random Forest Regressor y de la prueba de cordura definida por la Regresión Lineal. Llama mucho la atención que para encontrar los hiperparámetros se uso GridSearchCV y sus resultados no fueron mejores que los que encontramos en la prueba simple. \n",
    "\n",
    "Respecto a los tiempos es necesario mencionar que tanto LGBMRegressor como CatBoostRegressor fueron los más rápidos a comparación de Random Forest, lo cual habla de que no solo son más precisos sino también más veloces. \n",
    "\n",
    "Vale la pena añadir que no se utilizó el modelo XGBoost ya que requería OneHotEcoder, y fue imposible utilizarlo debido a que el kernel moria repetidamente despues de usarlo, por ello tomé la decisión de usar OrdinalEncoder con el cual pude desarrollar el resto de modelos. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "\n",
    "<b>Comentario del revisor. (Iteración 1)</b> <a class=\"tocSkip\"></a>\n",
    "    \n",
    "Un gran trabajo Alejandro!  Destaco no solo tu capacidad de código sino el analisis de lo implementado, demuestra una gran comprensión de lo hecho! Felicitaciones!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lista de control"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Escribe 'x' para verificar. Luego presiona Shift+Enter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [x]  Jupyter Notebook está abierto\n",
    "- [x]  El código no tiene errores- [ ]  Las celdas con el código han sido colocadas en orden de ejecución- [ ]  Los datos han sido descargados y preparados- [ ]  Los modelos han sido entrenados\n",
    "- [x]  Se realizó el análisis de velocidad y calidad de los modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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