Ames House Pricing Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# House Prices Prediction using Advanced Regression Techniques\n",
    "\n",
    "In this project, I will use Ames housing prices dataset available at **Kaggle**, and apply advanced regression techniques and compare their effectiveness on such problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load packages\n",
    "import pandas as pd # data frames\n",
    "import numpy as np # arrays and computing\n",
    "import seaborn as sns # statistical data visualization\n",
    "import matplotlib\n",
    "\n",
    "import matplotlib.pyplot as plt # plots\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data\n",
    "train = pd.read_csv(\"train.csv\")\n",
    "test = pd.read_csv(\"test.csv\")\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities    ...     PoolArea PoolQC Fence MiscFeature MiscVal  \\\n",
       "0         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "1         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "2         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "3         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "4         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "\n",
       "  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0      2   2008        WD         Normal     208500  \n",
       "1      5   2007        WD         Normal     181500  \n",
       "2      9   2008        WD         Normal     223500  \n",
       "3      2   2006        WD        Abnorml     140000  \n",
       "4     12   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the data\n",
    "train.columns\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1460.000000\n",
       "mean     180921.195890\n",
       "std       79442.502883\n",
       "min       34900.000000\n",
       "25%      129975.000000\n",
       "50%      163000.000000\n",
       "75%      214000.000000\n",
       "max      755000.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#descriptive statistics summary\n",
    "train['SalePrice'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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sXle+V0Suj9Wmu1zxdhHZ59rMnagPEVkiIgMistP9fGeqL0am6pyBs+5DLZtdTGl+wI6zGJOlYiYWEfED9wM3AHXALSJSF1btdqBTVZcDm4FNbts6vOvZrwLWAQ+IiD9Gm5uAzapaC3S6tqP24exX1dXu5wuTegWyQPfAMEV5AXL8MzNA9fmE82vKaGzrnpH+jDGpJZ5vmjVAk6o2q+owsAVYH1ZnPfCwu/0YcK14V5NaD2xR1SFVPQA0ufYitum2uca1gWvzxhh9mBi6+mdmqnGo82vKePPIKUZGx2a0X2NM8sWTWGqAQyH3W11ZxDqqGgS6gcoJto1WXgl0uTbC+4rWB8BSEXlVRJ4VkfdHehIislFEGkSkob29PY6nnTmm8zos0ayaX8rw6Bj7jtkBfGOyTTyJJdKoIHweabQ6iSqfqI8jwCJVvRC4C3hERErPqKj6oKrWq2p9VVVVhKYyk6rSNTB9V46M5vyaMgB22e4wY7JOPImlFVgYcn8B0BatjogEgDLg5ATbRivvAMpdG+F9RezD7WY7AaCqO4D9wIo4nldWONk3zMioUjbDu8KWVhZRlOun8bAlFmOyTTyJ5WWg1s3WysU7GL8trM424DZ3+ybgKfUuI7gN2OBmdC0FaoGXorXptnnatYFr8/GJ+hCRKjcZABFZ5vpojv8lyGxtXYMAVMzwrjCfT6ibX8qutp4Z7dcYk3wx11BX1aCI3Ak8CfiB76tqo4jcBzSo6jbgIeBHItKEN1LZ4LZtFJGtwG4gCNyhqqMAkdp0Xd4NbBGRrwKvuraJ1gdwJXCfiASBUeALqnpy6i9JZjnc1Q9M73VYolk1v4xHXz7E6Jji99k8C2OyhWTj9cnr6+u1oaEh2WHMiO/9vpmv/vse/upD51GYN33XYonklbc7eeyVVr54bS1f/KDtnTQm3YnIDlWtj1XPzrzPcG1dg+T6fRTk+me87/nlBV4M3QMz3rcxJnkssWS4w139lBfmkIxTfqpK8gj45PRxHmNMdrDEkuEOdw3M+Dks4/w+YW5ZPoe7bMRiTDaxxJLh2roGZ2zxyUjmlxfQ1jVgS+gbk0UssWSw/uEgJ/uGkzZiAagpK2AoOMahzv6kxWCMmVmWWDLY+LGNZCaW8QP4uw7b+SzGZAtLLBls/NhGMneFzSnNwye2tIsx2cQSSwY73OkSSxJHLAG/jzml+eyypV2MyRqWWDJYW9cAfp9Qkp+8xALe7rDGth6y8WRcY7KRJZYMdrhrgLml+UlfTmV+eQEn+4Y50m3nsxiTDSyxZLDDnQPUuIPnyVRTlg9gu8OMyRKWWDLY4a4BaiqSn1jmlhW4A/g2M8yYbGCJJUMFR8c42jOYEiOW3ICPc6qK7dosxmQJSywZ6mjPIKNjmhIjFvCuKGlTjo3JDpZYMlTLSe9M90WzCpMciWfV/FKO9Qxx/JQdwDcm01liyVCHUiyxnF9TBkCjnYFvTMaLK7GIyDoR2SsiTSJyT4TH80TkUff4dhFZEvLYva58r4hcH6tNd7ni7SKyz7WZG6sP9/giEekVkf8+2RchE7Wc7MfvE+a5GVnJtmp+KSKw81BXskMxxkyzmInFXU/+fuAGoA64RUTqwqrdDnSq6nJgM7DJbVuHdwnhVcA64AER8cdocxOwWVVrgU7XdtQ+QmwGfhHvE890LSe9qcYBf2oMSkvyc1g5p4RXWjqTHYoxZprF862zBmhS1WZVHQa2AOvD6qwHHna3HwOuFe/KUuuBLao6pKoHgCbXXsQ23TbXuDZwbd4Yow9E5EagGWiM/6lntkMn+1NmN9i4ixZXsLOly5bQNybDxZNYaoBDIfdbXVnEOqoaBLqBygm2jVZeCXS5NsL7itiHiBQBdwNfmehJiMhGEWkQkYb29vYYTzn9HTrZz8IUSywXL6rg1FCQfcd7kx2KMWYaxZNYIq0HEv4vZ7Q6iSqfqI+v4O06m/DbSlUfVNV6Va2vqqqaqGra6x0KcqJvOCVHLIDtDjMmw8WTWFqBhSH3FwBt0eqISAAoA05OsG208g6g3LUR3le0PtYCXxeRg8AXgb8UkTvjeF4ZK9VmhI1bUlnIrKJcdrxticWYTBZPYnkZqHWztXLxDsZvC6uzDbjN3b4JeEq9pWy3ARvcjK6lQC3wUrQ23TZPuzZwbT4+UR+q+n5VXaKqS4C/B/4fVf3WJF6DjJNq57CMExEuWlRuIxZjMlzMxOKOZ9wJPAnsAbaqaqOI3CciH3HVHsI73tEE3AXc47ZtBLYCu4FfAneo6mi0Nl1bdwN3ubYqXdtR+zBnStURC8CFiypobu+js2842aEYY6ZJIHYVUNUngCfCyr4ccnsQuDnKtl8DvhZPm668GW/WWHh51D5C6vzNRI9ni5aT/ZTmByhL4gW+ornYHWd59VAn15w7J8nRGGOmQ2qc5GASquVkP4sqU2+0AnDBgjL8PuGVt+1ESWMylSWWDNSSguewjCvMDVA3r5SXD55MdijGmGliiSXDjI0prScHUu4cllCXnVPJKy2d9A4FY1c2xqQdSywZ5tipQYZHx1J2xAJw1coqRkaV55s6kh2KMWYaWGLJMC0nUndG2Lj6xbMoyvXzzFuZvwKCMdnIEkuGSdVzWELlBnxcvnw2z+5txzt1yRiTSSyxZJi3T3jL5c9PgUsST+TqldUc7hqgydYNMybjWGLJMAc6+lg0q5CcFFkuP5qrV3rrtT2z13aHGZNpUvvbx0za/vZels4uSnYYMc0vL2DFnGKeeet4skMxxiSYJZYMMjamHDzRx7I0SCzg7Q576cBJm3ZsTIaxxJJBjvQMMjgyxtKq9Egsf3TeHEZGlV81Hk12KMaYBLLEkkEOtPcBsGx2cZIjic/7llSwaFYh/9LQmuxQjDEJZIklgzR3eDOslqXJiEVEuOniBbzQfOL0iszGmPQX1+rGJj00t/dRlOunuiQv2aGc4ZHtLRHL//TiBWz+zVs8tqOV//rBFTMclTFmOtiIJYM0d/SxtKoIkUhXcU5NNeUFXH7ObB7b0crYmJ0saUwmsBFLBmlu7+WiRRXJDmPSbq5fwF9s2cmLzSe4bPnsqKObW9cumuHIjDFTEdeIRUTWicheEWkSkTOu3OguPfyoe3y7iCwJeexeV75XRK6P1aa7XPF2Ednn2sydqA8RWSMiO93PayLy0am+GOnkke0t7/p5+PmDHO4cSJvjK6GuXzWX0vwA/7z97WSHYoxJgJiJRUT8wP3ADUAdcIuI1IVVux3oVNXlwGZgk9u2Du969quAdcADIuKP0eYmYLOq1gKdru2ofQC7gHpVXe36+K6IZN1I7ETfMAppcXJkuPwcP5+4ZDG/2HWUAx19yQ7HGHOW4hmxrAGaVLVZVYeBLcD6sDrrgYfd7ceAa8Xb0b8e2KKqQ6p6AGhy7UVs021zjWsD1+aNE/Whqv2qOn6GXT6QlTvqO04NAfDWsd4zRjPp4DOXLyXH7+PB3+1PdijGmLMUz3/2NcChkPutwNpodVQ1KCLdQKUrfzFs2xp3O1KblUBXSKIIrR+tjw4RWQt8H1gMfDJk+9NEZCOwEWDRoszbV9/R6yWW2UW5SY5kckIT34ULy9na0MriWUWUFuQkMSpjzNmIZ8QSaYpR+KggWp1ElU8Yh6puV9VVwPuAe0Uk/4yKqg+qar2q1ldVVUVoKr119A5Tmh8gL8ef7FCm7P21VYyNKX/YbxcAMyadxZNYWoGFIfcXAG3R6rjjG2XAyQm2jVbeAZSHHCMJ7StaH6ep6h6gDzg/jueVUTp6h6gsTr3zVyZjVlEu71lQxvYDJ+kftvXDjElX8SSWl4FaN1srF+9g/LawOtuA29ztm4Cn1LuC0zZgg5vRtRSoBV6K1qbb5mnXBq7Nxyfqw7URABCRxcBK4GDcr0AGUFXaTw1RlYInRk7W1SuqGQ6O8fz+E8kOxRgzRTGPsbjjGXcCTwJ+4Puq2igi9wENqroNeAj4kYg04Y0iNrhtG0VkK7AbCAJ3qOooQKQ2XZd3A1tE5KvAq65tovUBXAHcIyIjwBjw56qaVftSTg0GGRgZZU4GJJa5ZfnUzSvl+f0dXLF8NvlpvGvPmGwV17RcVX0CeCKs7MshtweBm6Ns+zXga/G06cqb8WaNhZdH7ENVfwT8KOaTyGDHegYBmFN6xqGltPSBldXsPtLDi80nuHpldbLDMcZMki3pkgGOuanG1RmSWGoqvIuAPdfUwVBwNNnhGGMmyRJLBjjeM0hRXoDivMw5L/SaldX0D4/y0oGTsSsbY1KKJZYMcKxnMCOOr4RaVFnEOVVF/H5fByOjY8kOxxgzCZZY0pyqcuzUUMbsBgv1gXOr6R0K0nDQRi3GpBNLLGmua2CE4eAYc0oza8QCsLSyiMWVhfxuXwdBG7UYkzYssaS54+Mzwkoyb8QiIlyzsprugRFeaelKdjjGmDhZYklzx3q8GWGZMtU43PLqYhZUFPDsW8dt1GJMmrDEkuaO9QxSmh+gIDczTyQUEa5eUU1n/wj//saRZIdjjImDJZY0d+zUYMaOVsadO6+E6pI8Hnh6v12+2Jg0YIkljY25NcKqM2yqcTifCFetqGLvsVM89ebxZIdjjInBEksa6+wbZmRUM37EAnDBgnJqygt44JkmvLVKjTGpyhJLGjuaYWuETcTvE75w1TJeaeliu52Nb0xKs8SSxo50DyJkR2IBuLl+IbOLc7n/6aZkh2KMmYAlljR2pGuA2SV55Aay423Mz/Hz2SuW8vt9HbzR2p3scIwxUWTHN1KGOtI9yLyy7BitjPvEJYspyQvw7Wdt1GJMqrLEkqb6h4J0DYwwv6wg2aHMqNL8HD556WJ+sesoTcd7kx2OMSaCuBKLiKwTkb0i0iQi90R4PE9EHnWPbxeRJSGP3evK94rI9bHadJca3i4i+1ybuRP1ISIfFJEdIvKG+33NVF+MdHLEHbifV55dIxaAz16xlLyAj289tS/ZoRhjIoiZWETED9wP3ADUAbeISF1YtduBTlVdDmwGNrlt6/AuIbwKWAc8ICL+GG1uAjarai3Q6dqO2gfQAXxYVd8D3EaWXE3ySNcAAPOybMQCMLs4j09ftpTHX2ujsc2OtRiTauIZsawBmlS1WVWHgS3A+rA664GH3e3HgGtFRFz5FlUdUtUDQJNrL2KbbptrXBu4Nm+cqA9VfVVV21x5I5AvIpl9xiDe8ZXS/My6uNdk/Kerz6GsIIe//cWbyQ7FGBMmnsRSAxwKud/qyiLWUdUg0A1UTrBttPJKoMu1Ed5XtD5C/SnwqqoOxfG80lpb90BWjlbGlRXkcOcHlvP7fR08t68j2eEYY0LEk1gkQln4qc/R6iSqPGYcIrIKb/fYn0Woh4hsFJEGEWlob2+PVCVtDI6M0n5qKCuPr4T65KWLqSkv4H/9Yg+jtoaYMSkjnsTSCiwMub8AaItWR0QCQBlwcoJto5V3AOWujfC+ovWBiCwAfgp8SlX3R3oSqvqgqtaran1VVVUcTzt17TvWy5hm5/GVUHkBP3ffcC6NbT088lJLssMxxjjxJJaXgVo3WysX72D8trA62/AOnAPcBDyl3oJO24ANbkbXUqAWeClam26bp10buDYfn6gPESkH/h24V1X/MJknn652H/EOWM/PsnNYIvnwBfO4fHklX//lm7Sfyvg9oMakhZiJxR3PuBN4EtgDbFXVRhG5T0Q+4qo9BFSKSBNwF3CP27YR2ArsBn4J3KGqo9HadG3dDdzl2qp0bUftw7WzHPhrEdnpfqqn+Hqkhca2HvICPiqKcpMdStL95KVDrFlSSf/QKBt/2MAj21t4ZLuNXoxJprimFKnqE8ATYWVfDrk9CNwcZduvAV+Lp01X3ow3ayy8PGIfqvpV4Ksxn0QGeb21m3ll+fgk0mGn7FNVksf7V8zmmb3tXLykgmWzi5MdkjFZLTvnqqax4eAYu9t6WLt0VrJDmXETjUSuXlHNa4e62LazjTuvWT6DURljwtmSLmlmz5EehkfHWDCrMNmhpJTcgI8PXzCf46eGeL7pRLLDMSarWWJJM6+1dgGwsCK7Z4RFcu68UurmlfLbN49x2K1MYIyZeZZY0szOli5mF+dRVpCT7FBS0h9fMA+A+37WGKOmMWYov5I7AAAV+ElEQVS6WGJJMztbu1i9sByxA/cRVRTmcs25c3iy8RhPvXks2eEYk5UssaSR7oERmtv7WL2wLNmhpLTLl1eyvLqYLz/eyMDwaLLDMSbrWGJJI6+74yurF1YkOZLUFvD5uHpFFa2dA/z5j3fYuS3GzDBLLGnktUNeYnnPAhuxxLKsqpjVC8v53b4OOyPfmBlmiSWN7DzUxTlVRXbgPk43nD+XHL/ws9fa8FYLMsbMBEssaUJV2Xmom/cuLE92KGmjJD+H6+rm0tTey+uH7YJgxswUSyxpouVkPx29Q1y4yI6vTMaapbOoKS/gideP0DM4kuxwjMkKlljSxAv7vbPJL12WfUu5nA2fCOtXz6d3KMjmX7+V7HCMyQqWWNLEC80nqCrJ45wqW2BxshZUFLJm6Swefv4gjW22S8yY6WaLUKYBVeWF/Se4ZFmlnRg5RdfVzWV/ey9/9X928a9fuAyfb+LXMdr05FvXLpqO8IzJKDZiSQPNHX0cPzXEpcsqkx1K2irI9fOXHzqPV1u62NpwKNnhGJPRLLGkgdPHV86xxHI2PnphDWuXzuJrT+zhYEdfssMxJmPFlVhEZJ2I7BWRJhG5J8LjeSLyqHt8u4gsCXnsXle+V0Suj9Wmu1zxdhHZ59rMnagPEakUkadFpFdEvjXVFyKVvdB8grml+SyptKXyz4aI8Hc3vxe/T9j4owZ6h4LJDsmYjBQzsYiIH7gfuAGoA24RkbqwarcDnaq6HNgMbHLb1uFdz34VsA54QET8MdrcBGxW1Vqg07UdtQ9gEPhr4L9P8rmnBVVle/MJLlk2y46vJMDCWYXcf+tF7G/v479t3cnYmJ04aUyixTNiWQM0qWqzqg4DW4D1YXXWAw+7248B14r3Lbge2KKqQ6p6AGhy7UVs021zjWsD1+aNE/Whqn2q+hxegsk4+4730tE7bLvBEujy5bP5yw+dx5ONx/jCP++gu9/ObzEmkeJJLDVA6NHOVlcWsY6qBoFuoHKCbaOVVwJdro3wvqL1kdH+0NQBwKXLZic5kszy2cuX8Fd/fB5PvXmcP/7m7/n9vnYbvRiTIPFMN460/yX8LzBanWjlkRLaRPXjjSMqEdkIbARYtCh9pow+2XiU5dXFLLLjKwklInzu/cu4eHEFdz7yKp986CUWzirgo6truPa8OYyp4rNdj8ZMSTyJpRVYGHJ/AdAWpU6riASAMuBkjG0jlXcA5SIScKOS0PrR+oiLqj4IPAhQX1+fFv+advQO8dKBk9z5geXJDiVjXbiogt/cdRW/bDzCv+44zDefbuIfnmqiMNfPhQvLuaK2yhb9NGaS4kksLwO1IrIUOIx3MP7WsDrbgNuAF4CbgKdUVUVkG/CIiHwDmA/UAi/hjT7OaNNt87RrY4tr8/GJ+pja004Pv2o8xpjCDe+Zl+xQMlpBrp+PXriAj164gBO9QzzX1MH3fn+AF5pP8GLzSeqXVHDD+fPIDdjsfGPiETOxqGpQRO4EngT8wPdVtVFE7gMaVHUb8BDwIxFpwhtFbHDbNorIVmA3EATuUNVRgEhtui7vBraIyFeBV13bROvDtXUQKAVyReRG4DpV3T3VFyVV/GLXEZbOLuLcuSXJDiVrVBbnsX51DX1Do3T2DfO7fe28dOAkb5/o5+N21r0xcZEM/6c/ovr6em1oaEh2GBPq7Bum/mu/YeOVy7h73blnPG5XREycSMu0hL6+e4+eYmvDIRTlh59dy5qlthCoyU4iskNV62PVs7XCUtSv9xxjdEz50Pm2G2y6xUrSK+eWcMcHlvNPzx/kMz94iR/evoaLF1tyMSYa22mcop544wgLKgo4v6Y02aEYYFZRLp+7YilVJXl8+vsvs9NdJtoYcyZLLCno7RN9PPtWO+tXz7ez7VNIaUEOj3z+EsqLcvjUQ9vZZVelNCYiSywp6PvPHSDgEz516ZJkh2LCzC8v4Cefv4SS/Bw+8dB2drf1JDskY1KOJZYU09U/zNaGVj7y3hrmlOYnOxwTwYKKQn7y+UsoyPHziYe2s/foqWSHZExKsYP3KebH21sYGBnl81cuPV1mM8BSR+h7ccuaRfzj75v5+PdeZMvGS1lebVf3NAZsxJJShoKj/NPzB3l/7WzOnWsH7VPd7OI8PnfFMkC49R9f5K1jNnIxBmzEklL+8yOv0n5qiA9fMN9GKWmiqiSPn3x+Lbf843Y+/M3nuHvduXz6siVRL31slzw22cBGLClif3svT715nFXzS22XSpqpnVPCE39xBVcsn819P9/Nf/j28zz6cgs9g9GX4x8dU7Lx5GSTHWzEkgLGxpR7/+0NAn7hI++dn+xwzBRUl+Tzvdvq+ZeGVr797H7u/tc3+NJPdzG7OI+Kolx8Ar1DQdpPDTEUHGN0TMnxCyX5OVQV5zEUHOXqldUsnV2U7KdizFmzJV1SwA9fOMiXH2/kP1xYQ/0SO6M73YTvxlJVXmvt5u9//RanBoP0DXuXF8rP8ZMX8JEX8JMb8DE4MkrP4AhtXQN09A4DUL+4gk9euph1588lL+Cf8edizERsSZc08ds9x/jKz3Zz9coqLl5ckexwTAKICKsXlnPdqrlxb3PF8tk82XiUH29/m7/YspPKolw+9r6F3Lp2EQsq7Fo8Jr3YMZYkajh4kj//8SvUzSvlW7deZGfZZ7Hnmjooygvwufcv4zOXL6G6NJ/vPLufK7/+NJ97uIFn32pn1K5wadKEjViS5Jm9x/nPP3mVmvIC/ukz76M4z96KdJXIGXw+EWqrS6itLuGqlVX8ZHsLW15u4Td7jlFZlMvVK6u5csVszq8pY2llUdTZZ8Ykkx1jmWGqyref3c///uVe5pTm86lLF1NemJuUWEx6CI6NsefIKXa3dfPWsV4GRkYBKMr1Uze/lFXzyzhvXgm1c0qorS6mJN+ueGmmhx1jSUFNx3v58uO7eH7/Cd5TU8afXrTArkpoYgr4fLynpoz31JQxOqYcPzVIW9cAh7sGOdI1wGuHWhgeHTtdv6wghzmleVSX5POR1fNZ4RJOkY2KzQyJ65MmIuuA/w/vao/fU9W/DXs8D/ghcDFwAviYqh50j90L3A6MAv9FVZ+cqE13ueItwCzgFeCTqjo8lT5SxbGeQR567gA/+MMB8nP8/N83no8P7JiKmTS/T5hXVsC8sgIuXuyVjanS2TfM8VNDHOsZPP27uf0EzzV1nN62pryAFXOKWTGnhGM9Q5QVBCgtyKGsIIeCHD8fv2Rxkp6VyTQxE4uI+IH7gQ8CrcDLIrIt7NK/twOdqrpcRDYAm4CPiUgd3iWEV+Fd8/43IrLCbROtzU3AZlXdIiLfcW1/e7J9jF8COVmGgqO8sP8E215r42evtTE6pnz0wgXcc8O5VJXk2Zn1JmF8IlQW51FZnMd5895ZCmhMlcvOqeStY73sO3aKt457v//QdOJdIxyAHL/w0HMHmFuWz9yyfOaXFTC3LJ95IffLC3PO+p8hVWVgZJSegSBbXm7BL0LA7yPgEwI+4ROXLibHb6P4dF+hIZ4RyxqgSVWbAURkC7Ae7zr249YDf+NuPwZ8S7xP4Hpgi6oOAQfc9erXuHpntCkie4BrgFtdnYddu9+eQh8vxPkaTJqqMhQcYyg4xnBwjL6hIMdPDXG0Z5C9R3tobOuh4WAnvUNBinL9fHztYj57+VIWVdq0UTNzfCIsqypmWVUx685/Z+pzcHSMB3/XTM/ACN2DQboHRugZGKGsIIcj3QO8uP8Ex04NnTELLS/go7o0j4rCXMrcSKe80BvtjCmogqKowsCwd45Oz+AIPQNB93uEnsHghLPbvvLz3eQGfBTl+inMDVCcF6Awz09xXoD8HD8Bn+ATQcR7fgCjqoyNKaNjyph6v0eVd5Xl5/hPxxz6U1qQQ2l+gKK88R8/RXkBcv0+fCL4fYJPErN3YXTMS6oDw+5nZJTeoXdem1ODwdOv1ytvdzIwMsqg+/H7hNyAj+ea2inMDVCU6z8df3lhLuXuvRh/XnnutfK7hO33yYzuIYknsdQAh0LutwJro9VR1aCIdAOVrvzFsG1r3O1IbVYCXaoajFB/Kn0k1GuHurj5uy8wHByLWsfvE2qri/nwe+fxwbo5XHbObPJz7EQ3kxzR/vMtL8ydcNLImCq9LumM//QMjHBqKEj/cBC/TzjcOUDXwAgDw6Onv3wFQKAw109pvvfFNxwco7wgh7ml+RTk+Ml3P7kBn5cIRpXg2BjBMaVuXil9w6P0DwfpHQrSPzRK33CQt0/0MxwcY0wVxfvnrjgvgAJ+EXw+Of27Z2DkdOIRAUEYGR07/aU+ODLKZKcs+cT72/b7BEFOJ1AFcAkVOF2mp+OcZEd4o8fcgJ+CHJ/3WgX8jKnSPzzqTd4YfichTab98fj/5D3z+MbHVk8+sEmIJ7FESnPhTydanWjlkca6E9WfSh/vDlBkI7DR3e0Vkb0Rtpuq2cDpndnNwJPA30atPiPeFVMKScW4UjEmSM24UjEmSM24UjEmNsPszRumHFdcB+LiSSytwMKQ+wuAtih1WkUkAJQBJ2NsG6m8AygXkYAbtYTWn0ofp6nqg8CDcTzfSRORhnim4M2kVIwJUjOuVIwJUjOuVIwJUjOuVIwJZiaueI6SvQzUishSEcnFO1C+LazONuA2d/sm4Cn1TpDZBmwQkTw326sWeClam26bp10buDYfn2IfxhhjkiDmiMUdz7gTb++OH/i+qjaKyH1Ag6puAx4CfuQOnJ/ESxS4elvxDvQHgTvGZ2tFatN1eTewRUS+Crzq2mYqfRhjjJl5WXnmfaKJyEa3qy1lpGJMkJpxpWJMkJpxpWJMkJpxpWJMMDNxWWIxxhiTUHYmkjHGmMRSVfuZ4g+wDtgLNAH3JLDd7wPHgV0hZbOAXwP73O8KVy7AP7gYXgcuCtnmNld/H3BbSPnFwBtum3/gnZFrxD7cYwvxJlbsARqBv0h2XEA+3kSN11xMX3HlS4Htrv6jQK4rz3P3m9zjS0L6vteV7wWuj/UeR+sj5HE/3jHCn6dQTAfd67sT7/hoUt8/91g53gnPb+J9ti5NgZhWutdo/KcH+GIKxPVf8T7nu4Cf4H3+k/65ivgdNhNfwJn4g/fFsR9YBuTifbnVJajtK4GLeHdi+fr4mw3cA2xytz8E/MJ9uC8Btod8QJvd7wp3e/wP4SW8P2Bx294wUR/u/rzxPxigBHgLqEtmXK5esbud4z78lwBbgQ2u/DvAf3K3/xz4jru9AXjU3a5z71+e+yPa797fqO9xtD5CXq+7gEd4J7GkQkwHgdlhZcn+XD0MfM7dzsVLNEmNKcLf+VG88zeS+VmvAQ4ABSHv9aejvefM4Ocq4us201/ImfLjPhRPhty/F7g3ge0v4d2JZS8wz92eB+x1t78L3BJeD7gF+G5I+Xdd2TzgzZDy0/Wi9RElvsfx1npLibiAQrxFS9finQ8VCH+f8GYhXupuB1w9CX/vxutFe4/dNhH7cPcXAL/FW57o5xPVn6mYXNlBzkwsSXv/gFK8L0tJlZgifK6uA/6Q7Lh4Z+WRWe5z8nPg+mjvOTP4uYr0Y8dYpi7SUjfTspSMM0dVjwC439Ux4piovDVC+UR9vIuILAEuxBshJDUuEfGLyE68XYe/xvuvK65lgYDQZYEmE+tESw8B/D3wP4HxtX/iXqpoGmMCb0WKX4nIDrcSBST3/VsGtAM/EJFXReR7IlKU5JjCbcDb7TTRNtMel6oeBv4OaAGO4H1OdpAan6szWGKZuriWkpkBk13q5qziFpFi4F+BL6pqT7LjUtVRVV2NN0pYA5w3QTuJiilqrCLyJ8BxVd0R8lgilyo6m9fvclW9CLgBuENEroywzbiZeP8CeLt8v62qFwJ9eLt/khnTO515J29/BPiXWFWnOy4RqcBbcHcp3iruRXjvY7R2ZvJzdQZLLFMX11IyCXRMROYBuN/HY8QxUfmCCOUT9YEry8FLKj9W1X9LlbgAVLULeAZvH3e5W/YnvJ3Tfce5LFC08tNLD0Xo43LgIyJyEO+6QtfgjWCSGdP4a9Tmfh8HfoqXiJP5/rUCraq63d1/DC/RpMRnCu+L+xVVPRbH85juuP4IOKCq7ao6AvwbcBkp8LmKxBLL1MWz1E0ihS5pcxvvXurmU+K5BOh2Q+gngetEpML9t3Md3r7RI8ApEbnEXXbgU0ReNie0D1zdh4A9qvqNVIhLRKpEpNzdLsD749tD4pYFmvTSQ6p6r6ouUNUlrv5TqvrxZMbkXp8iESkZv+1e913JfP9U9ShwSERWuseuxVtBI6mf9RC38M5usIm2mYm4WoBLRKTQbTP+WiX1cxVVrIMw9jPhAfYP4c2O2g98KYHt/gRvP+oI3n8St+Pt6/wt3pS/3wKzXF3Bu2jafrzpi/Uh7XwWb+pgE/CZkPJ6vC+V/cC3eGeqY8Q+3GNX4A2BX+edaZgfSmZcwAV4U3pfd9t92ZUvc38sTXi7MfJceb673+QeXxbS95dcv3txM3Qmeo+j9RH2Pl7NO7PCkhqTe+w13pma/aUYr+1Mfa5WAw3uPfw/eLOnkhqTe7wQ70q1ZSFlyX6tvoI3LXsX8CO8mV0p8VkP/7Ez740xxiSU7QozxhiTUJZYjDHGJJQlFmOMMQllicUYY0xCWWIxxhiTUJZYjJkiEfmSiDSKyOsislNE1k5Q959E5KZoj4fUOeDaekVELo1S7wsi8qmzjd+Y6RLz0sTGmDO5L/0/wVvxeUhEZuOtCnu2/oeqPiYi1+EtWnhBWL8BVf1OAvoxZtpYYjFmauYBHao6BKCqHQAi8mXgw0AB8DzwZxp2spiIXAx8AyjGWzLj0+oWHgzxO2C5q/+Ma+tyYJs7g75XVf9ORJbjLWVeBYwCN6vqfhH5H8B/xDuJ7qeq+n8l+PkbE5XtCjNman4FLBSRt0TkARG5ypV/S1Xfp6rn4yWXPwndSLz11r4J3KSqF+Nd1O1rEdr/MN5Z3OPKVfUqVf1/w+r9GLhfVd+Lt3bUETfaqcVbC2w1cLFMvOCkMQllIxZjpkBVe93I4/3AB4BHReQevDWg/ifekiCz8JZP+VnIpiuB84Ffe0s+4cdbvmfc/xaRv8JbTv72kPJHw2NwI5caVf2pi2nQlV+Hty7Vq65qMV6i+d3ZPGdj4mWJxZgpUtVRvBWVnxGRN4A/wzsmUq+qh0Tkb/DWbAolQKOqRjwwjzvGEqG8L0JZpCXNx8v/l6p+N8ZTMGZa2K4wY6ZARFaKSG1I0Wq8Rf0AOsS7bk2kWWB7garxGV8ikiMiq6YSg3rXw2kVkRtdW3kiUoi3qu5nXQyISI2IRLuQlTEJZyMWY6amGPimW7Y/iLfy60agC+/YyEG8pcjfRVWH3bTjfxCRMry/wb/H22U2FZ8Evisi9+Gthn2zqv5KRM4DXnC723qBTxD5miPGJJytbmyMMSahbFeYMcaYhLLEYowxJqEssRhjjEkoSyzGGGMSyhKLMcaYhLLEYowxJqEssRhjjEkoSyzGGGMS6v8HU45PteWT2ScAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram\n",
    "sns.distplot(train['SalePrice']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some observations from the histogram:\n",
    "1. Not a normal distribution\n",
    "2. Left-skewed distribution\n",
    "3. Has a clear peak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = pd.concat((train.loc[:,'MSSubClass':'SaleCondition'],\n",
    "                      test.loc[:,'MSSubClass':'SaleCondition']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 288)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data = pd.get_dummies(all_data)\n",
    "all_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filling NA's with the mean of the column:\n",
    "all_data = all_data.fillna(all_data.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating matrices for sklearn:\n",
    "X_train = all_data[:train.shape[0]]\n",
    "X_test = all_data[train.shape[0]:]\n",
    "y = np.log1p(train.SalePrice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV, LinearRegression, ElasticNetCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import xgboost as xgb\n",
    "\n",
    "def rmse_cv(model):\n",
    "    rmse= np.sqrt(-cross_val_score(model, X_train, y, scoring=\"neg_mean_squared_error\", cv = 5))\n",
    "    return(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ridge = Ridge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = [0.05, 0.1, 0.3, 1, 3, 5, 10, 15, 30, 50, 75]\n",
    "cv_ridge = [rmse_cv(Ridge(alpha = alpha)).mean() \n",
    "            for alpha in alphas]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'rmse')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_ridge = pd.Series(cv_ridge, index = alphas)\n",
    "cv_ridge.plot(title = \"Validation\")\n",
    "plt.xlabel(\"alpha\")\n",
    "plt.ylabel(\"rmse\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13976596285220735"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_ridge.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13982664627139293"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train model\n",
    "model_ridge = Ridge(alpha=10).fit(X_train, y)\n",
    "rmse_ridge = rmse_cv(model_ridge).mean()\n",
    "rmse_ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1571208776535738"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear regression\n",
    "model_LinearRegr = LinearRegression()\n",
    "model_LinearRegr.fit(X_train, y)\n",
    "rmse_LinearRegr = rmse_cv(model_LinearRegr).mean()\n",
    "rmse_LinearRegr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1398266753895153"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RidgeCV\n",
    "model_RidgeCV = RidgeCV()\n",
    "model_RidgeCV.fit(X_train, y)\n",
    "rmse_RidgeCV = rmse_cv(model_RidgeCV).mean()\n",
    "rmse_RidgeCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19829552662829658"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ElasticNetCV \n",
    "model_EN = ElasticNetCV()\n",
    "model_EN.fit(X_train, y)\n",
    "rmse_EN = rmse_cv(model_EN).mean()\n",
    "rmse_EN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1982720441517538"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LassoCV \n",
    "model_LassoCV= LassoCV()\n",
    "model_LassoCV.fit(X_train, y)\n",
    "rmse_LassoCV = rmse_cv(model_LassoCV).mean()\n",
    "rmse_LassoCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# xgboost\n",
    "dtrain = xgb.DMatrix(X_train, label = y)\n",
    "dtest = xgb.DMatrix(X_test)\n",
    "\n",
    "params = {\"max_depth\":2, \"eta\":0.1}\n",
    "model = xgb.cv(params, dtrain,  num_boost_round=500, early_stopping_rounds=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, 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=360,\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)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_xgb = xgb.XGBRegressor(n_estimators=360, max_depth=2, learning_rate=0.1) #the params were tuned using xgb.cv\n",
    "model_xgb.fit(X_train, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06289133333333334"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_xgb = model.values[-1,0]\n",
    "rmse_xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e0fb276a0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rmse_dict = {'data':[rmse_ridge, rmse_RidgeCV, rmse_LinearRegr, rmse_EN,rmse_LassoCV,rmse_xgb]}\n",
    "rmse_df = pd.DataFrame(data = rmse_dict, index = ['Ridge','RidgeCV','Linear regression','ElasticNetCV','LassoCV','xgboost'])\n",
    "rmse_df.plot.bar(legend = False, title = 'RMSE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict\n",
    "ridge_preds = model_ridge.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>11.641908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>11.914709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>12.051385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>12.183015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>12.192227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  SalePrice\n",
       "0  1461  11.641908\n",
       "1  1462  11.914709\n",
       "2  1463  12.051385\n",
       "3  1464  12.183015\n",
       "4  1465  12.192227"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prepare data for submission\n",
    "# solution = pd.DataFrame({\"id\":test.Id, \"SalePrice\":ridge_preds})\n",
    "solution = pd.DataFrame({\"id\":test.Id})\n",
    "solution = solution.assign(SalePrice = ridge_preds)\n",
    "solution.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save file\n",
    "solution.to_csv(\"xgboost_pred.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# So xgBoost is definitely a great algorithm for this kind of prediction.\n",
    "\n",
    "but it can sometimes result in overfitted models."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}