HousePrice-code_v4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploración y pre-procesamiento de la data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importo las librerías iniciales que voy a usar\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Abro los archivos de training y testing que da la competencia\n",
    "df_test = pd.read_csv('test.csv', index_col=0)\n",
    "df_train = pd.read_csv('train.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Procesamiento de merge dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# junto los dos datasets para procesarlos\n",
    "final_df = pd.concat([df_train,df_test],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tamaño del df: (2919, 80)\n",
      "Tipos de datos en las columnas:\n",
      "object     43\n",
      "int64      25\n",
      "float64    12\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Exploro el archivo\n",
    "print(f'Tamaño del df:', final_df.shape)\n",
    "print(f'Tipos de datos en las columnas:')\n",
    "print(final_df.dtypes.value_counts())\n",
    "#df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSZoning', 'LotFrontage', 'Alley', 'Utilities', 'Exterior1st',\n",
       "       'Exterior2nd', 'MasVnrType', 'MasVnrArea', 'BsmtQual', 'BsmtCond',\n",
       "       'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinType2',\n",
       "       'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Electrical', 'BsmtFullBath',\n",
       "       'BsmtHalfBath', 'KitchenQual', 'Functional', 'FireplaceQu',\n",
       "       'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea',\n",
       "       'GarageQual', 'GarageCond', 'PoolQC', 'Fence', 'MiscFeature',\n",
       "       'SaleType', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Chequeo en que columnas hay valores nan\n",
    "sns.heatmap(final_df.isnull(), yticklabels = False, cbar = False)\n",
    "final_df.columns[final_df.isna().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PoolQC          99.657417\n",
       "MiscFeature     96.402878\n",
       "Alley           93.216855\n",
       "Fence           80.438506\n",
       "SalePrice       49.982871\n",
       "FireplaceQu     48.646797\n",
       "LotFrontage     16.649538\n",
       "GarageCond       5.447071\n",
       "GarageYrBlt      5.447071\n",
       "GarageQual       5.447071\n",
       "GarageFinish     5.447071\n",
       "GarageType       5.378554\n",
       "BsmtCond         2.809181\n",
       "BsmtExposure     2.809181\n",
       "BsmtQual         2.774923\n",
       "BsmtFinType2     2.740665\n",
       "BsmtFinType1     2.706406\n",
       "MasVnrType       0.822199\n",
       "MasVnrArea       0.787941\n",
       "MSZoning         0.137033\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Me fijo el porcentaje de valores nan que hay en cada una de esas columnas\n",
    "columns_with_nan = final_df.isna().sum()/2919*100\n",
    "columns_with_nan.sort_values(ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 76)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tiro aquellas columnas que tienen mas del 60% con nan values\n",
    "final_df.drop(['PoolQC', 'MiscFeature','Alley', 'Fence',], axis = 1, inplace = True)\n",
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id\n",
       "1848    13\n",
       "2121    13\n",
       "2553    12\n",
       "2190    12\n",
       "2892    12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chequeo si hay rows que tengan muchos nan values\n",
    "final_df.isnull().sum(axis=1).sort_values(ascending=False).head()\n",
    "# las rows que tienen nan values solo tienen 13 nan de 76 values que pueden tener\n",
    "# no elimino ninguna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Llenos las 15 columnas que tienen valores nan...\n",
    "# Voy a probar imputar los nan con KNN. Sklearn tiene un imputador con KNN.\n",
    "# Para poder usarlo tengo que convertir todos las categóricas en dummies... \n",
    "# pero si hago esta codificación antes de reemplazar los missing values pierdo esos valores nan.\n",
    "\n",
    "# me quedo con las variables categóricas\n",
    "categorical_features = final_df.dtypes[final_df.dtypes == 'object'].index\n",
    "\n",
    "# Sklearn tiene un label encoder que luego se concatena con one hot encoding\n",
    "# pero necesita que los valores sean de un solo tipo, es decir que no maneja los np.nan como tal.\n",
    "# si uno los pasa a 'str' los toma como un valor en sí y les asigna un valor numérico durante el labelencoder.\n",
    "# con el siguiente código determino que valor le entregó al valor np.nan \n",
    "# y luego lo vuelvo a reemplazar para que siga siendo np.nan.\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder = LabelEncoder()\n",
    "\n",
    "dic_list = []\n",
    "for x in categorical_features:\n",
    "    final_df[x+'_cat'] = labelencoder.fit_transform(final_df[x].astype(str))\n",
    "    dic = dict(zip(final_df[x].unique(), final_df[x+'_cat'].unique())) # este es el diccionario que me permite ver como se codificó\n",
    "    dic_list.append((x, dic)) # los guardo en una lista de tuples por si los necesito luego\n",
    "    if np.nan in final_df[x].unique().tolist():\n",
    "        final_df[x+'_cat'].replace(dic[np.nan], np.nan, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nan  4.  2.  1.  0.  3.]\n",
      "[nan 'TA' 'Gd' 'Fa' 'Ex' 'Po']\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>FireplaceQu_cat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TA</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TA</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gd</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TA</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>TA</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>TA</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2919 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FireplaceQu  FireplaceQu_cat\n",
       "Id                               \n",
       "1            NaN              NaN\n",
       "2             TA              4.0\n",
       "3             TA              4.0\n",
       "4             Gd              2.0\n",
       "5             TA              4.0\n",
       "...          ...              ...\n",
       "2915         NaN              NaN\n",
       "2916         NaN              NaN\n",
       "2917          TA              4.0\n",
       "2918         NaN              NaN\n",
       "2919          TA              4.0\n",
       "\n",
       "[2919 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# chequeo que el encoding funcionó\n",
    "print(final_df.FireplaceQu_cat.unique())\n",
    "print(final_df.FireplaceQu.unique())\n",
    "final_df[['FireplaceQu', 'FireplaceQu_cat']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# para poder usar el KNNImputer necesito tirar las columnas categóricas viejas.\n",
    "final_df.drop(categorical_features, axis = 1, inplace = True)\n",
    "\n",
    "from sklearn.impute import KNNImputer\n",
    "\n",
    "imputer = KNNImputer(n_neighbors=3, weights='distance') # uso 3 vecinos y se hace un promedio ponderado según la distancia euclidiana (a los más cercanos le da más peso)\n",
    "final_df_imputed = imputer.fit_transform(final_df)\n",
    "final_df_imputed = pd.DataFrame(final_df_imputed, columns=final_df.columns)\n",
    "\n",
    "# Chequeo que efectivamente no haya más nan values\n",
    "sns.heatmap(final_df_imputed.isnull(), yticklabels = False, cbar = False)\n",
    "final_df_imputed.columns[final_df_imputed.isna().any()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como las columnas categóricas fueron codificadas con números, la predicción de las missing values se hace de forma numérica y me quedan floats muchas veces porque hace un promedio. Esto necesito que pase a ser un int así puedo mapearlo con el dictionario que tengo de antes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>...</th>\n",
       "      <th>KitchenQual_cat</th>\n",
       "      <th>Functional_cat</th>\n",
       "      <th>FireplaceQu_cat</th>\n",
       "      <th>GarageType_cat</th>\n",
       "      <th>GarageFinish_cat</th>\n",
       "      <th>GarageQual_cat</th>\n",
       "      <th>GarageCond_cat</th>\n",
       "      <th>PavedDrive_cat</th>\n",
       "      <th>SaleType_cat</th>\n",
       "      <th>SaleCondition_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1915.0</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2914</th>\n",
       "      <td>160.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1936.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>160.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1894.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>20.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>85.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>60.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>758.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2919 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0           60.0         65.0   8450.0          7.0          5.0     2003.0   \n",
       "1           20.0         80.0   9600.0          6.0          8.0     1976.0   \n",
       "2           60.0         68.0  11250.0          7.0          5.0     2001.0   \n",
       "3           70.0         60.0   9550.0          7.0          5.0     1915.0   \n",
       "4           60.0         84.0  14260.0          8.0          5.0     2000.0   \n",
       "...          ...          ...      ...          ...          ...        ...   \n",
       "2914       160.0         21.0   1936.0          4.0          7.0     1970.0   \n",
       "2915       160.0         21.0   1894.0          4.0          5.0     1970.0   \n",
       "2916        20.0        160.0  20000.0          5.0          7.0     1960.0   \n",
       "2917        85.0         62.0  10441.0          5.0          5.0     1992.0   \n",
       "2918        60.0         74.0   9627.0          7.0          5.0     1993.0   \n",
       "\n",
       "      YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  ...  KitchenQual_cat  \\\n",
       "0           2003.0       196.0       706.0         0.0  ...                2   \n",
       "1           1976.0         0.0       978.0         0.0  ...                3   \n",
       "2           2002.0       162.0       486.0         0.0  ...                2   \n",
       "3           1970.0         0.0       216.0         0.0  ...                2   \n",
       "4           2000.0       350.0       655.0         0.0  ...                2   \n",
       "...            ...         ...         ...         ...  ...              ...   \n",
       "2914        1970.0         0.0         0.0         0.0  ...                3   \n",
       "2915        1970.0         0.0       252.0         0.0  ...                3   \n",
       "2916        1996.0         0.0      1224.0         0.0  ...                3   \n",
       "2917        1992.0         0.0       337.0         0.0  ...                3   \n",
       "2918        1994.0        94.0       758.0         0.0  ...                3   \n",
       "\n",
       "      Functional_cat  FireplaceQu_cat  GarageType_cat  GarageFinish_cat  \\\n",
       "0                  6                4               1                 1   \n",
       "1                  6                4               1                 1   \n",
       "2                  6                4               1                 1   \n",
       "3                  6                2               5                 2   \n",
       "4                  6                4               1                 1   \n",
       "...              ...              ...             ...               ...   \n",
       "2914               6                3               1                 2   \n",
       "2915               6                3               4                 2   \n",
       "2916               6                4               5                 2   \n",
       "2917               6                3               2                 1   \n",
       "2918               6                4               1                 0   \n",
       "\n",
       "      GarageQual_cat  GarageCond_cat  PavedDrive_cat  SaleType_cat  \\\n",
       "0                  4               4               2             8   \n",
       "1                  4               4               2             8   \n",
       "2                  4               4               2             8   \n",
       "3                  4               4               2             8   \n",
       "4                  4               4               2             8   \n",
       "...              ...             ...             ...           ...   \n",
       "2914               4               4               2             8   \n",
       "2915               4               4               2             8   \n",
       "2916               4               4               2             8   \n",
       "2917               3               3               2             8   \n",
       "2918               4               4               2             8   \n",
       "\n",
       "      SaleCondition_cat  \n",
       "0                     4  \n",
       "1                     4  \n",
       "2                     4  \n",
       "3                     0  \n",
       "4                     4  \n",
       "...                 ...  \n",
       "2914                  4  \n",
       "2915                  0  \n",
       "2916                  0  \n",
       "2917                  4  \n",
       "2918                  4  \n",
       "\n",
       "[2919 rows x 76 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_categorical_features = []\n",
    "for x in final_df_imputed.columns:\n",
    "    if x[-4:] == '_cat':\n",
    "        final_df_imputed[x] = final_df_imputed[x].astype(int)\n",
    "        new_categorical_features.append(x)\n",
    "final_df_imputed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 276)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# una vez llenos los missing values, hago el one hot encoding de las variables categóricas\n",
    "# por comodidad lo hago con dummies\n",
    "final_df_imputed = pd.get_dummies(final_df_imputed, columns=new_categorical_features)\n",
    "final_df_imputed.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ya está listo el pre-procesamiento de los datos. Ahora tengo que separar el train y el test set y comenzar con el desarrollo del modelo de machine learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine-Learning Model\n",
    "\n",
    "Lo que se quiere predecir es un valor numérico. Para eso tengo variables numéricas y categóricas que describen cada uno de los casos (o entradas de la tabla). No todos los métodos de ML pueden usar una mezcla de tipos de variables. Uno de los más sencillos de abordar es el **RandomForest**. Otra ventaja del **RandomForest** es que no es necesario estandarizar/normalizar los datos numéricos (aunque hacerlo puede llegar a mejorar un poco la predicción)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separo los subsets\n",
    "df_train = final_df_imputed.iloc[:1460,:]\n",
    "df_test = final_df_imputed.iloc[1460:,:].drop(['SalePrice'], axis=1)\n",
    "\n",
    "# Separo las variables descriptivas de lo que quiero predecir\n",
    "# Me quedo con todo menos con SalePrice como X\n",
    "X = df_train.drop(['SalePrice'],axis=1)\n",
    "# Me quedo solo con SalePrice como y\n",
    "y = df_train['SalePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Separo el dataset de training en subsets training y testing\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) # Mantener el random state me permite replicar resultados\n",
    "\n",
    "# Comienzo con el Regressor (uso regressor y no classiffier porque la variable a predecir es numérica)\n",
    "regressor = RandomForestRegressor(random_state=0)\n",
    "# Fiteo el algoritmo a los subsets de entrenamiento\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regressor score on training subset: 0.9766280034360368\n",
      "Regressor score on testing subset: 0.8477605195085054\n",
      "RootMeanSquaredError: 31702.87656623985\n"
     ]
    }
   ],
   "source": [
    "print(f'Regressor score on training subset:', regressor.score(X_train, y_train))\n",
    "print(f'Regressor score on testing subset:', regressor.score(X_test, y_test))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "y_pred = regressor.predict(X_test)\n",
    "print(f'RootMeanSquaredError:', np.sqrt(mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ya con estos scores veo que el modelo es un poco mejor que el de la versión 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 50 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  3.7min\n",
      "[Parallel(n_jobs=-1)]: Done 150 out of 150 | elapsed: 19.0min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(random_state=0),\n",
       "                   n_iter=50, n_jobs=-1,\n",
       "                   param_distributions={'bootstrap': [True],\n",
       "                                        'max_depth': [50, 100, 150, 200, 250,\n",
       "                                                      300, 350, 400, 450, 500,\n",
       "                                                      None],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10],\n",
       "                                        'n_estimators': [1000, 2000, 3000, 4000,\n",
       "                                                         5000]},\n",
       "                   random_state=0, verbose=2)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hiperparameter tunning con Cross-Validation \n",
    "# Randomized Search\n",
    "\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Número de árboles en el bosque\n",
    "n_estimators = [int(x) for x in np.linspace(start = 1000, stop = 5000, num = 5)]\n",
    "# Número de features a considerar en cada split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Número máximo de niveles por árbol\n",
    "max_depth = [int(x) for x in np.linspace(50, 500, num = 10)]\n",
    "max_depth.append(None)\n",
    "# Número mínimo de muestras necesarias para abrir un nodo\n",
    "min_samples_split = [2, 5, 10]\n",
    "# Número mínimo de muestras por nodo\n",
    "min_samples_leaf = [2, 5, 10]\n",
    "# Método de selección de muestras para entrenar cada árbol\n",
    "bootstrap = [True]\n",
    "# Creación de la grilla random\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "\n",
    "# Se larga la búsqueda randomizada de hiperparámetros\n",
    "rf_random = RandomizedSearchCV(estimator = regressor, param_distributions = random_grid, \n",
    "                               n_iter = 50, cv = 3, verbose=2, random_state=0, n_jobs = -1)\n",
    "rf_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regressor best params: {'n_estimators': 1000, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'auto', 'max_depth': None, 'bootstrap': True}\n",
      "Regressor score on training subset: 0.9697623776533153\n",
      "Regressor score on testing subset: 0.845083438864364\n",
      "RootMeanSquaredError: 31980.404097365466\n"
     ]
    }
   ],
   "source": [
    "print(f'Regressor best params:', rf_random.best_params_)\n",
    "print(f'Regressor score on training subset:', rf_random.best_estimator_.score(X_train, y_train))\n",
    "print(f'Regressor score on testing subset:', rf_random.best_estimator_.score(X_test, y_test))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "y_pred = rf_random.best_estimator_.predict(X_test)\n",
    "print(f'RootMeanSquaredError:', np.sqrt(mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 24 candidates, totalling 72 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   37.5s\n",
      "[Parallel(n_jobs=-1)]: Done  72 out of  72 | elapsed:  1.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=RandomForestRegressor(random_state=0), n_jobs=-1,\n",
       "             param_grid={'bootstrap': [True], 'max_depth': [10, 50, 100],\n",
       "                         'max_features': [100, 'auto'], 'min_samples_leaf': [2],\n",
       "                         'min_samples_split': [2],\n",
       "                         'n_estimators': [50, 100, 200, 500]},\n",
       "             verbose=2)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Basandome en los parámetros de la RandomizedSearch seteo parámetros de búsqueda para la GridSearch\n",
    "# De esta forma optimizo un poco más los hiperparámetros.\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {\n",
    "    'bootstrap': [True],\n",
    "    'max_depth': [10, 50, 100],\n",
    "    'max_features': [100, 'auto'], # Es el 30 y el 60% del total - evito overfitting\n",
    "    'min_samples_leaf': [2],\n",
    "    'min_samples_split': [2],\n",
    "    'n_estimators': [50, 100, 200, 500]\n",
    "}\n",
    "\n",
    "\n",
    "# Largamos la GridSearch\n",
    "grid_search = GridSearchCV(estimator = regressor, param_grid = param_grid, \n",
    "                           cv = 3, n_jobs = -1, verbose = 2)\n",
    "\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regressor best params: {'bootstrap': True, 'max_depth': 50, 'max_features': 100, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "Regressor score on training subset: 0.9658261144399173\n",
      "Regressor score on testing subset: 0.8503789644979087\n",
      "RootMeanSquaredError: 31429.0570433044\n"
     ]
    }
   ],
   "source": [
    "print(f'Regressor best params:', grid_search.best_params_)\n",
    "print(f'Regressor score on training subset:', grid_search.best_estimator_.score(X_train, y_train))\n",
    "print(f'Regressor score on testing subset:', grid_search.best_estimator_.score(X_test, y_test))\n",
    "y_pred = grid_search.best_estimator_.predict(X_test)\n",
    "print(f'RootMeanSquaredError:', np.sqrt(mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mariano/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:672: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=10.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold: 0\n",
      "RootMeanSquaredError: 21583.194423976223\n",
      "Score: 0.9103054920825143\n",
      "Fold: 1\n",
      "RootMeanSquaredError: 23353.03090309003\n",
      "Score: 0.8864709893949622\n",
      "Fold: 2\n",
      "RootMeanSquaredError: 27285.877443292065\n",
      "Score: 0.9117444962036595\n",
      "Fold: 3\n",
      "RootMeanSquaredError: 20614.1738906853\n",
      "Score: 0.9224795190237295\n",
      "Fold: 4\n",
      "RootMeanSquaredError: 22793.673059912777\n",
      "Score: 0.9006477093065081\n",
      "Fold: 5\n",
      "RootMeanSquaredError: 41276.25815424668\n",
      "Score: 0.7334438899545064\n",
      "Fold: 6\n",
      "RootMeanSquaredError: 29020.86272781764\n",
      "Score: 0.8687385463829354\n",
      "Fold: 7\n",
      "RootMeanSquaredError: 38912.31531642393\n",
      "Score: 0.7769469170767281\n",
      "Fold: 8\n",
      "RootMeanSquaredError: 22231.987574317736\n",
      "Score: 0.912898945816958\n",
      "Fold: 9\n",
      "RootMeanSquaredError: 35139.15881995021\n",
      "Score: 0.8540500537950189\n",
      "********************\n",
      "Mean Metrics:\n",
      "RootMeanSquaredError: 28221.053231371257\n",
      "Score: 0.867772655903752\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Una vez que ya tengo los parámetros seteados puedo realizar una cross-validation \n",
    "# del modelo con todos los datos de el train set\n",
    "params = grid_search.best_params_\n",
    "\n",
    "regressor_final = RandomForestRegressor(bootstrap=params['bootstrap'], max_depth=params['max_depth'],\n",
    "                                        max_features=params['max_features'], min_samples_leaf=params['min_samples_leaf'], \n",
    "                                        min_samples_split=params['min_samples_split'], n_estimators=params['n_estimators'], \n",
    "                                        random_state=0)\n",
    "\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "def CrossValidator(X, y):\n",
    "    cv = StratifiedKFold(n_splits=10)\n",
    "    rmse = []\n",
    "    features_importances = []\n",
    "    RMSE = []\n",
    "    score = []\n",
    "    for i, (train, test) in enumerate(cv.split(X, y)):\n",
    "        regressor_final.fit(X.iloc[train], y.iloc[train])\n",
    "        feature_imp = pd.Series(regressor_final.feature_importances_,index=X.columns).sort_values(ascending=False)\n",
    "        features_importances.append(feature_imp)\n",
    "        y_pred = regressor_final.predict(X.iloc[test])\n",
    "        RMSE.append(np.sqrt(mean_squared_error(y.iloc[test], y_pred)))\n",
    "        score.append(regressor_final.score(X.iloc[test], y.iloc[test]))\n",
    "        print(f'Fold:', i)\n",
    "        print(f'RootMeanSquaredError:', np.sqrt(mean_squared_error(y.iloc[test], y_pred)))\n",
    "        print(f'Score:', regressor_final.score(X.iloc[test], y.iloc[test]))\n",
    "\n",
    "    print('*'*20)\n",
    "    print(f'Mean Metrics:')\n",
    "    print(f'RootMeanSquaredError:', np.mean(RMSE))\n",
    "    print(f'Score:', np.mean(score))\n",
    "\n",
    "    plt.figure(figsize=(8,5))\n",
    "    feat_impor_mean = pd.Series(sum(features_importances)/len(features_importances)).sort_values(ascending=False)\n",
    "    sns.barplot(x=feat_impor_mean, y=feature_imp.index, color='grey')\n",
    "    plt.xticks(rotation=40)\n",
    "    plt.xlabel('Feature Importance Score')\n",
    "    plt.ylabel('Features')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "CrossValidator(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Shape: (1460, 275)\n",
      "Train Columns: Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',\n",
      "       'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',\n",
      "       ...\n",
      "       'SaleType_cat_5', 'SaleType_cat_6', 'SaleType_cat_7', 'SaleType_cat_8',\n",
      "       'SaleCondition_cat_0', 'SaleCondition_cat_1', 'SaleCondition_cat_2',\n",
      "       'SaleCondition_cat_3', 'SaleCondition_cat_4', 'SaleCondition_cat_5'],\n",
      "      dtype='object', length=275)\n",
      "Test Shape: (1459, 275)\n",
      "Test Columns: Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',\n",
      "       'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',\n",
      "       ...\n",
      "       'SaleType_cat_5', 'SaleType_cat_6', 'SaleType_cat_7', 'SaleType_cat_8',\n",
      "       'SaleCondition_cat_0', 'SaleCondition_cat_1', 'SaleCondition_cat_2',\n",
      "       'SaleCondition_cat_3', 'SaleCondition_cat_4', 'SaleCondition_cat_5'],\n",
      "      dtype='object', length=275)\n"
     ]
    },
    {
     "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>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_cat_5</th>\n",
       "      <th>SaleType_cat_6</th>\n",
       "      <th>SaleType_cat_7</th>\n",
       "      <th>SaleType_cat_8</th>\n",
       "      <th>SaleCondition_cat_0</th>\n",
       "      <th>SaleCondition_cat_1</th>\n",
       "      <th>SaleCondition_cat_2</th>\n",
       "      <th>SaleCondition_cat_3</th>\n",
       "      <th>SaleCondition_cat_4</th>\n",
       "      <th>SaleCondition_cat_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>20.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>923.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>60.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>791.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1463</th>\n",
       "      <td>60.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1464</th>\n",
       "      <td>120.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 275 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "1460        20.0         80.0  11622.0          5.0          6.0     1961.0   \n",
       "1461        20.0         81.0  14267.0          6.0          6.0     1958.0   \n",
       "1462        60.0         74.0  13830.0          5.0          5.0     1997.0   \n",
       "1463        60.0         78.0   9978.0          6.0          6.0     1998.0   \n",
       "1464       120.0         43.0   5005.0          8.0          5.0     1992.0   \n",
       "\n",
       "      YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  ...  SaleType_cat_5  \\\n",
       "1460        1961.0         0.0       468.0       144.0  ...               0   \n",
       "1461        1958.0       108.0       923.0         0.0  ...               0   \n",
       "1462        1998.0         0.0       791.0         0.0  ...               0   \n",
       "1463        1998.0        20.0       602.0         0.0  ...               0   \n",
       "1464        1992.0         0.0       263.0         0.0  ...               0   \n",
       "\n",
       "      SaleType_cat_6  SaleType_cat_7  SaleType_cat_8  SaleCondition_cat_0  \\\n",
       "1460               0               0               1                    0   \n",
       "1461               0               0               1                    0   \n",
       "1462               0               0               1                    0   \n",
       "1463               0               0               1                    0   \n",
       "1464               0               0               1                    0   \n",
       "\n",
       "      SaleCondition_cat_1  SaleCondition_cat_2  SaleCondition_cat_3  \\\n",
       "1460                    0                    0                    0   \n",
       "1461                    0                    0                    0   \n",
       "1462                    0                    0                    0   \n",
       "1463                    0                    0                    0   \n",
       "1464                    0                    0                    0   \n",
       "\n",
       "      SaleCondition_cat_4  SaleCondition_cat_5  \n",
       "1460                    1                    0  \n",
       "1461                    1                    0  \n",
       "1462                    1                    0  \n",
       "1463                    1                    0  \n",
       "1464                    1                    0  \n",
       "\n",
       "[5 rows x 275 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Acá me aseguro que el test set tenga las mismas features que el train\n",
    "\n",
    "features_finals = list(X.columns)\n",
    "\n",
    "df_test = df_test[features_finals]\n",
    "\n",
    "print(f'Train Shape:', X.shape)\n",
    "print(f'Train Columns:', X.columns)\n",
    "print(f'Test Shape:', df_test.shape)\n",
    "print(f'Test Columns:', df_test.columns)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Entro el regresor final con todo el data set\n",
    "regressor_final.fit(X, y)\n",
    "# y predigo lo que quiero saber\n",
    "SalePrice = regressor_final.predict(df_test)\n",
    "\n",
    "# Le doy el formato que me pide Kaggle\n",
    "Id = list(df_test.index)\n",
    "\n",
    "df = pd.DataFrame()\n",
    "df['Id'] = Id\n",
    "df['Id'] = df['Id']+1\n",
    "df['SalePrice'] = SalePrice\n",
    "df.set_index('Id', drop=True, inplace=True)\n",
    "df.to_csv('NIS_MM_submission_4.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>125950.182270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>153642.536364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>179595.749119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>188017.978968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>197444.256905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>2915</td>\n",
       "      <td>87269.558571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>2916</td>\n",
       "      <td>86686.237738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2917</td>\n",
       "      <td>155033.246000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2918</td>\n",
       "      <td>116128.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2919</td>\n",
       "      <td>216532.308206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1459 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id      SalePrice\n",
       "0     1461  125950.182270\n",
       "1     1462  153642.536364\n",
       "2     1463  179595.749119\n",
       "3     1464  188017.978968\n",
       "4     1465  197444.256905\n",
       "...    ...            ...\n",
       "1454  2915   87269.558571\n",
       "1455  2916   86686.237738\n",
       "1456  2917  155033.246000\n",
       "1457  2918  116128.583333\n",
       "1458  2919  216532.308206\n",
       "\n",
       "[1459 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('NIS_MM_submission_4.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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