HousePrice-code_v5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "891b4d04",
   "metadata": {},
   "source": [
    "# Exploración y pre-procesamiento de la data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9fae9042",
   "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,
   "id": "7300f5c4",
   "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",
   "id": "2baa86f1",
   "metadata": {},
   "source": [
    "## Procesamiento de merge dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "46f9c975",
   "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,
   "id": "5465434f",
   "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,
   "id": "8c38ae52",
   "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,
   "id": "0d8652d8",
   "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",
       "GarageFinish     5.447071\n",
       "GarageQual       5.447071\n",
       "GarageYrBlt      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,
   "id": "7e31c323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id\n",
       "1       NaN\n",
       "2        TA\n",
       "3        TA\n",
       "4        Gd\n",
       "5        TA\n",
       "       ... \n",
       "1456     TA\n",
       "1457     TA\n",
       "1458     Gd\n",
       "1459    NaN\n",
       "1460    NaN\n",
       "Name: FireplaceQu, Length: 1460, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['FireplaceQu']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3547edc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 76)"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "id": "375def97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id\n",
       "2121    13\n",
       "1848    13\n",
       "1594    12\n",
       "2553    12\n",
       "2892    12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "id": "352d4088",
   "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": 11,
   "id": "7698d57d",
   "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": [
       "<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>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": 11,
     "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": 12,
   "id": "44fe2f46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 12,
     "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",
   "id": "e52aa053",
   "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": 13,
   "id": "346de4af",
   "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": 13,
     "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": 14,
   "id": "44b2defd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 276)"
      ]
     },
     "execution_count": 14,
     "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",
   "id": "1757986d",
   "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",
   "id": "6b3d7098",
   "metadata": {},
   "source": [
    "# Machine-Learning Model utilizando XGboost\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7335537b",
   "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": 16,
   "id": "6bbc1cb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=100, n_jobs=8, num_parallel_tree=1, random_state=0,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import xgboost\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 = xgboost.XGBRegressor(random_state = 0)\n",
    "# Fiteo el algoritmo a los subsets de entrenamiento\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8025c7bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regressor score on training subset: 0.9997550883200308\n",
      "Regressor score on testing subset: 0.8463704201467971\n",
      "RootMeanSquaredError: 31847.287214016334\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": "code",
   "execution_count": 18,
   "id": "d48e5fd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Optimización de hiperparametros\n",
    "\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "n_estimators = [100, 500, 900, 1100, 1500]\n",
    "max_depth = [2, 3, 5, 10, 15]\n",
    "booster=['gbtree','gblinear']\n",
    "learning_rate=[0.05,0.1,0.15,0.20]\n",
    "min_child_weight=[1,2,3,4]\n",
    "booster=['gbtree','gblinear']\n",
    "base_score=[0.25,0.5,0.75,1]\n",
    "\n",
    "# defino la grilla de hiperparametros a buscar\n",
    "hyperparameter_grid = {\n",
    "    'n_estimators': n_estimators,\n",
    "    'max_depth':max_depth,\n",
    "    'learning_rate':learning_rate,\n",
    "    'min_child_weight':min_child_weight,\n",
    "    'booster':booster,\n",
    "    'base_score':base_score\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "50e2bafd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# random search con 4-fold cross validation\n",
    "random_cv = RandomizedSearchCV(estimator=regressor,\n",
    "            param_distributions=hyperparameter_grid,\n",
    "            cv=5, n_iter=50,\n",
    "            scoring = 'neg_mean_absolute_error',n_jobs = 4,\n",
    "            verbose = 5, \n",
    "            return_train_score = True,\n",
    "            random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2f4bdd04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBRegressor(base_score=0.5, booster='gbtree',\n",
       "                                          colsample_bylevel=1,\n",
       "                                          colsample_bynode=1,\n",
       "                                          colsample_bytree=1, gamma=0,\n",
       "                                          gpu_id=-1, importance_type='gain',\n",
       "                                          interaction_constraints='',\n",
       "                                          learning_rate=0.300000012,\n",
       "                                          max_delta_step=0, max_depth=6,\n",
       "                                          min_child_weight=1, missing=nan,\n",
       "                                          monotone_constraints='()',\n",
       "                                          n_estimators=100, n_jobs=8,\n",
       "                                          num_par...\n",
       "                                          validate_parameters=1,\n",
       "                                          verbosity=None),\n",
       "                   n_iter=50, n_jobs=4,\n",
       "                   param_distributions={'base_score': [0.25, 0.5, 0.75, 1],\n",
       "                                        'booster': ['gbtree', 'gblinear'],\n",
       "                                        'learning_rate': [0.05, 0.1, 0.15, 0.2],\n",
       "                                        'max_depth': [2, 3, 5, 10, 15],\n",
       "                                        'min_child_weight': [1, 2, 3, 4],\n",
       "                                        'n_estimators': [100, 500, 900, 1100,\n",
       "                                                         1500]},\n",
       "                   random_state=42, return_train_score=True,\n",
       "                   scoring='neg_mean_absolute_error', verbose=5)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Esta es la parte de código que más tarda\n",
    "random_cv.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "acc694f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RootMeanSquaredError: 24333.590524420284\n"
     ]
    }
   ],
   "source": [
    "print(f'RootMeanSquaredError:', np.sqrt(mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26652685",
   "metadata": {},
   "source": [
    "Pareciera que el RootMeanSquaredError da mucho mejor que las versiones anteriores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f3724c53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.25, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.1, max_delta_step=0, max_depth=2,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=900, n_jobs=8, num_parallel_tree=1, random_state=0,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bd4141cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defino el regresor final con los parámetros pasados por el best_estimator del RandomizedSearch\n",
    "\n",
    "regresor_final = xgboost.XGBRegressor(base_score=0.25, booster='gbtree', colsample_bylevel=1,\n",
    "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
    "       max_depth=2, min_child_weight=1, missing=None, n_estimators=900,\n",
    "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
    "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
    "       silent=True, subsample=1)\n",
    "\n",
    "#aparentemente hay un problema con el parametro silent (que no estamos usando). Es una bool True/False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5b1e0b2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:14:30] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[18:14:30] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.25, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.1, max_delta_step=0, max_depth=2,\n",
       "             min_child_weight=1, missing=None, monotone_constraints='()',\n",
       "             n_estimators=900, n_jobs=1, nthread=1, num_parallel_tree=1,\n",
       "             objective='reg:linear', random_state=0, reg_alpha=0, reg_lambda=1,\n",
       "             scale_pos_weight=1, seed=0, silent=True, subsample=1,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fiteo el regresor final con los datos X_train, y_train\n",
    "\n",
    "regresor_final.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "01007db2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Esto sirve para guardar el modelo. Es importante más que nada cuando se tarda mucho tiempo en entrenarlo\n",
    "import pickle\n",
    "filename = 'regresor_final_xgboost.pkl'\n",
    "pickle.dump(regresor_final, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c95168c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predigo lo que quiero saber\n",
    "SalePrice = regresor_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_PD_submission_6.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "af3328d4",
   "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>110939.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>170345.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>189415.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>195344.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>196113.900</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>79717.570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>2916</td>\n",
       "      <td>77866.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2917</td>\n",
       "      <td>170345.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2918</td>\n",
       "      <td>109283.336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2919</td>\n",
       "      <td>232452.520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1459 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id   SalePrice\n",
       "0     1461  110939.970\n",
       "1     1462  170345.970\n",
       "2     1463  189415.080\n",
       "3     1464  195344.400\n",
       "4     1465  196113.900\n",
       "...    ...         ...\n",
       "1454  2915   79717.570\n",
       "1455  2916   77866.840\n",
       "1456  2917  170345.940\n",
       "1457  2918  109283.336\n",
       "1458  2919  232452.520\n",
       "\n",
       "[1459 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('NIS_PD_submission_6.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa50e18d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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