Predict House Prices Part 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# House Sales Data, ETL, Build Predictive Model(linear regression), \n",
    "# Assess Model, Deploy Model   'houseSmallData.csv'\n",
    "# R^2 = 0.75  Percentage of the Price explained by the indep. variables\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data_description.txt\n",
    "SalePrice — the property’s sale price in dollars. This is the target variable that you’re trying to predict.  \n",
    "MSSubClass — The building class  \n",
    "MSZoning — The general zoning classification \n",
    "MasVnrArea - Masonary Veneer Area in sq ft\n",
    "LotFrontage — Linear feet of street connected to property  \n",
    "LotArea — Lot size in square feet  \n",
    "Street — Type of road access  \n",
    "Alley — Type of alley access  \n",
    "LotShape — General shape of property  \n",
    "LandContour — Flatness of the property  \n",
    "Utilities — Type of utilities available  \n",
    "LotConfig — Lot configuration  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$Y = X\\beta + e $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 82)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('houseSmallData.csv')\n",
    "train = data.iloc[0:100,:]\n",
    "train.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2., 17., 35., 17., 11.,  8.,  5.,  3.,  1.,  1.]),\n",
       " array([ 40000.,  79878., 119756., 159634., 199512., 239390., 279268.,\n",
       "        319146., 359024., 398902., 438780.]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "salePrice = train['SalePrice']\n",
    "salePrice.describe()\n",
    "plt.hist(salePrice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f98dbef4af0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train['GrLivArea'],y=train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f98dc065340>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train['GarageArea'],y=train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 39)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulls = train.isnull().sum().sort_values(ascending=False)[0:20]\n",
    "type(nulls)\n",
    "data = train.select_dtypes(include=[np.number]).interpolate().dropna(axis=1)\n",
    "sum(data.isnull().sum() != 0)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SalePrice', 'OverallQual', 'GrLivArea'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric = data.select_dtypes(include=[np.number])\n",
    "numeric.head()\n",
    "corr = numeric.corr()\n",
    "cols = corr['SalePrice'].sort_values(ascending=False)[:3].index\n",
    "cols\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>OverallQual</th>\n",
       "      <th>GrLivArea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>1710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>1262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>1786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>1717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>2198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>6</td>\n",
       "      <td>1470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>7</td>\n",
       "      <td>1588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>4</td>\n",
       "      <td>960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>5</td>\n",
       "      <td>835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>4</td>\n",
       "      <td>1225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    OverallQual  GrLivArea\n",
       "0             7       1710\n",
       "1             6       1262\n",
       "2             7       1786\n",
       "3             7       1717\n",
       "4             8       2198\n",
       "..          ...        ...\n",
       "95            6       1470\n",
       "96            7       1588\n",
       "97            4        960\n",
       "98            5        835\n",
       "99            4       1225\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data[cols]\n",
    "Y = X['SalePrice']\n",
    "X = X.drop(['SalePrice'], axis = 1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 is: 0.7776370735872411\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "lr = linear_model.LinearRegression()\n",
    "model = lr.fit(X, Y)\n",
    "predictions = model.predict(X)\n",
    "print(f\"R^2 is: {model.score(X,Y)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 5.,  2.,  7., 14., 31., 20., 12.,  6.,  2.,  1.]),\n",
       " array([-90155.04211637, -71166.51961477, -52177.99711316, -33189.47461156,\n",
       "        -14200.95210996,   4787.57039164,  23776.09289324,  42764.61539484,\n",
       "         61753.13789644,  80741.66039804,  99730.18289964]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Y - predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f98dd2a7eb0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(predictions, Y, color = 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>SalePrice</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>MasVnrArea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>208500</td>\n",
       "      <td>7</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>181500</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>223500</td>\n",
       "      <td>7</td>\n",
       "      <td>162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>140000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>250000</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>185000</td>\n",
       "      <td>6</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>214000</td>\n",
       "      <td>7</td>\n",
       "      <td>183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>94750</td>\n",
       "      <td>4</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>83000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>128950</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    SalePrice  OverallQual  MasVnrArea\n",
       "0      208500            7       196.0\n",
       "1      181500            6         0.0\n",
       "2      223500            7       162.0\n",
       "3      140000            7         0.0\n",
       "4      250000            8       350.0\n",
       "..        ...          ...         ...\n",
       "95     185000            6        68.0\n",
       "96     214000            7       183.0\n",
       "97      94750            4        48.0\n",
       "98      83000            5         0.0\n",
       "99     128950            4         0.0\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['SalePrice', 'OverallQual', 'MasVnrArea']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Unnamed: 0</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>...</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>100</td>\n",
       "      <td>101</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10603</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</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>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>205000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>102</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>77.0</td>\n",
       "      <td>9206</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</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>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>178000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102</td>\n",
       "      <td>103</td>\n",
       "      <td>90</td>\n",
       "      <td>RL</td>\n",
       "      <td>64.0</td>\n",
       "      <td>7018</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Bnk</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>6</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Alloca</td>\n",
       "      <td>118964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103</td>\n",
       "      <td>104</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>94.0</td>\n",
       "      <td>10402</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</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>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>198900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104</td>\n",
       "      <td>105</td>\n",
       "      <td>50</td>\n",
       "      <td>RM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7758</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</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>6</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>169500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley  \\\n",
       "0         100  101          20       RL          NaN    10603   Pave   NaN   \n",
       "1         101  102          60       RL         77.0     9206   Pave   NaN   \n",
       "2         102  103          90       RL         64.0     7018   Pave   NaN   \n",
       "3         103  104          20       RL         94.0    10402   Pave   NaN   \n",
       "4         104  105          50       RM          NaN     7758   Pave   NaN   \n",
       "\n",
       "  LotShape LandContour  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0      IR1         Lvl  ...        0    NaN   NaN         NaN       0      2   \n",
       "1      Reg         Lvl  ...        0    NaN   NaN         NaN       0      6   \n",
       "2      Reg         Bnk  ...        0    NaN   NaN         NaN       0      6   \n",
       "3      IR1         Lvl  ...        0    NaN   NaN         NaN       0      5   \n",
       "4      Reg         Lvl  ...        0    NaN   NaN         NaN       0      6   \n",
       "\n",
       "  YrSold SaleType  SaleCondition  SalePrice  \n",
       "0   2010       WD         Normal     205000  \n",
       "1   2010       WD         Normal     178000  \n",
       "2   2009       WD         Alloca     118964  \n",
       "3   2010       WD         Normal     198900  \n",
       "4   2007       WD         Normal     169500  \n",
       "\n",
       "[5 rows x 82 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('testData.csv')\n",
    "test.shape\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>OverallQual</th>\n",
       "      <th>GrLivArea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>1610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>1732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>1535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>1226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>1818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>6</td>\n",
       "      <td>1456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>7</td>\n",
       "      <td>1726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>8</td>\n",
       "      <td>3112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>6</td>\n",
       "      <td>2229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>8</td>\n",
       "      <td>1713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    OverallQual  GrLivArea\n",
       "0             6       1610\n",
       "1             6       1732\n",
       "2             5       1535\n",
       "3             7       1226\n",
       "4             7       1818\n",
       "..          ...        ...\n",
       "95            6       1456\n",
       "96            7       1726\n",
       "97            8       3112\n",
       "98            6       2229\n",
       "99            8       1713\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = test[cols]\n",
    "Y = X['SalePrice']\n",
    "X = X.drop(['SalePrice'], axis = 1)\n",
    "X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 is: 0.6927421490147458\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([181628.33396303, 187089.54258367, 147185.62226246, 195524.36732857,\n",
       "       222024.6583402 , 260899.00654441,  94255.37646087, 113791.67446739,\n",
       "       146379.87017089, 192103.11115344, 192595.5152094 , 204656.22436636,\n",
       "       261327.45480679, 210680.17326464, 213410.77757496, 174824.20518977,\n",
       "       127355.16800882, 128832.3801767 , 284873.32148267, 174734.6771796 ,\n",
       "       153785.12279878,  97657.4408475 , 157903.4112668 , 163230.32787218,\n",
       "       180553.99784094, 143310.34560837, 152442.20264616, 116074.63872684,\n",
       "       169900.16463018, 125609.37181042, 237199.65606477, 201503.55222176,\n",
       "       137874.70920432, 229743.2590037 , 155511.72720868, 215936.75364834,\n",
       "       132816.37662946, 228336.3830575 , 254632.0458322 , 188521.99074646,\n",
       "        86063.56352991, 218264.48191288, 140471.02149938, 207834.46872755,\n",
       "       155825.0752443 , 186059.97046667, 117641.37890489, 231738.44744413,\n",
       "       188988.82258583, 138635.6972908 , 121849.19538309, 248275.55710981,\n",
       "       198772.94791144, 165602.82014181, 163275.09187727, 158619.6353482 ,\n",
       "       125027.43974429, 259824.67042232, 227799.21499646, 250852.67761638,\n",
       "       164707.54004006, 322244.8853024 , 209625.02893104,  86869.31562148,\n",
       "       181896.91799356, 139128.10134676, 162047.27195142, 268464.12340415,\n",
       "       217637.78584166, 248141.26509455, 140336.72948412, 183687.47819704,\n",
       "       219741.69408076, 170526.8607014 , 183463.65817161, 206159.00875083,\n",
       "       201772.13625228, 164419.76422104, 302817.30709455, 121804.43137801,\n",
       "       177823.39353062, 221308.43425881, 138456.64127045, 233842.35568323,\n",
       "       134517.40882278, 395408.46140342, 195121.49128278, 152602.06687802,\n",
       "       133264.01668034, 243038.16851461, 293800.55028352, 206849.66061564,\n",
       "       204700.98837145, 217145.3817857 , 117148.97484893, 174734.6771796 ,\n",
       "       217906.36987218, 311034.6922421 , 209337.25311202, 248409.84912507])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn as sklearn\n",
    "predictions = model.predict(X)\n",
    "print(f\"R^2 is: {model.score(X,Y)}\")\n",
    "sklearn.metrics.mean_squared_error(predictions, Y)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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