Dummy Variables (Real Estate).ipynb

{
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>size</th>\n",
       "      <th>year</th>\n",
       "      <th>view</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>234314.144</td>\n",
       "      <td>643.09</td>\n",
       "      <td>2015</td>\n",
       "      <td>No sea view</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>228581.528</td>\n",
       "      <td>656.22</td>\n",
       "      <td>2009</td>\n",
       "      <td>No sea view</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>281626.336</td>\n",
       "      <td>487.29</td>\n",
       "      <td>2018</td>\n",
       "      <td>Sea view</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>401255.608</td>\n",
       "      <td>1504.75</td>\n",
       "      <td>2015</td>\n",
       "      <td>No sea view</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>458674.256</td>\n",
       "      <td>1275.46</td>\n",
       "      <td>2009</td>\n",
       "      <td>Sea view</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>252460.400</td>\n",
       "      <td>549.80</td>\n",
       "      <td>2009</td>\n",
       "      <td>Sea view</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>310522.592</td>\n",
       "      <td>1037.44</td>\n",
       "      <td>2009</td>\n",
       "      <td>No sea view</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>383635.568</td>\n",
       "      <td>1504.75</td>\n",
       "      <td>2006</td>\n",
       "      <td>No sea view</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>225145.248</td>\n",
       "      <td>648.29</td>\n",
       "      <td>2015</td>\n",
       "      <td>No sea view</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>274922.856</td>\n",
       "      <td>705.29</td>\n",
       "      <td>2006</td>\n",
       "      <td>Sea view</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         price     size  year         view\n",
       "0   234314.144   643.09  2015  No sea view\n",
       "1   228581.528   656.22  2009  No sea view\n",
       "2   281626.336   487.29  2018     Sea view\n",
       "3   401255.608  1504.75  2015  No sea view\n",
       "4   458674.256  1275.46  2009     Sea view\n",
       "..         ...      ...   ...          ...\n",
       "95  252460.400   549.80  2009     Sea view\n",
       "96  310522.592  1037.44  2009  No sea view\n",
       "97  383635.568  1504.75  2006  No sea view\n",
       "98  225145.248   648.29  2015  No sea view\n",
       "99  274922.856   705.29  2006     Sea view\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "#Load the data\n",
    "raw_data = pd.read_csv(\"real_estate_price_size_year_view.csv\")\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>size</th>\n",
       "      <th>year</th>\n",
       "      <th>view</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>234314.144</td>\n",
       "      <td>643.09</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>228581.528</td>\n",
       "      <td>656.22</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>281626.336</td>\n",
       "      <td>487.29</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>401255.608</td>\n",
       "      <td>1504.75</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>458674.256</td>\n",
       "      <td>1275.46</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <th>95</th>\n",
       "      <td>252460.400</td>\n",
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       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>310522.592</td>\n",
       "      <td>1037.44</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>383635.568</td>\n",
       "      <td>1504.75</td>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>225145.248</td>\n",
       "      <td>648.29</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>274922.856</td>\n",
       "      <td>705.29</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         price     size  year  view\n",
       "0   234314.144   643.09  2015     0\n",
       "1   228581.528   656.22  2009     0\n",
       "2   281626.336   487.29  2018     1\n",
       "3   401255.608  1504.75  2015     0\n",
       "4   458674.256  1275.46  2009     1\n",
       "..         ...      ...   ...   ...\n",
       "95  252460.400   549.80  2009     1\n",
       "96  310522.592  1037.44  2009     0\n",
       "97  383635.568  1504.75  2006     0\n",
       "98  225145.248   648.29  2015     0\n",
       "99  274922.856   705.29  2006     1\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "#Creating dummy variables (same data set regarding real estate, but taking to account if there's a sky deck that can view the sea)\n",
    "#1 = Sea view, 0 = No sea view\n",
    "data = raw_data.copy()\n",
    "data[\"view\"] = data[\"view\"].map({\"Sea view\":1, \"No sea view\":0})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>size</th>\n",
       "      <th>year</th>\n",
       "      <th>view</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>292289.470160</td>\n",
       "      <td>853.024200</td>\n",
       "      <td>2012.600000</td>\n",
       "      <td>0.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>77051.727525</td>\n",
       "      <td>297.941951</td>\n",
       "      <td>4.729021</td>\n",
       "      <td>0.502418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>154282.128000</td>\n",
       "      <td>479.750000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>234280.148000</td>\n",
       "      <td>643.330000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>280590.716000</td>\n",
       "      <td>696.405000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>335723.696000</td>\n",
       "      <td>1029.322500</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>500681.128000</td>\n",
       "      <td>1842.510000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               price         size         year        view\n",
       "count     100.000000   100.000000   100.000000  100.000000\n",
       "mean   292289.470160   853.024200  2012.600000    0.490000\n",
       "std     77051.727525   297.941951     4.729021    0.502418\n",
       "min    154282.128000   479.750000  2006.000000    0.000000\n",
       "25%    234280.148000   643.330000  2009.000000    0.000000\n",
       "50%    280590.716000   696.405000  2015.000000    0.000000\n",
       "75%    335723.696000  1029.322500  2018.000000    1.000000\n",
       "max    500681.128000  1842.510000  2018.000000    1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5 number summary\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.913</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.910</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   335.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 27 Mar 2020</td> <th>  Prob (F-statistic):</th> <td>1.02e-50</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:37:49</td>     <th>  Log-Likelihood:    </th> <td> -1144.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   100</td>      <th>  AIC:               </th> <td>   2297.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    96</td>      <th>  BIC:               </th> <td>   2308.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>-5.398e+06</td> <td> 9.94e+05</td> <td>   -5.431</td> <td> 0.000</td> <td>-7.37e+06</td> <td>-3.43e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>size</th>  <td>  223.0316</td> <td>    7.838</td> <td>   28.455</td> <td> 0.000</td> <td>  207.473</td> <td>  238.590</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>year</th>  <td> 2718.9489</td> <td>  493.502</td> <td>    5.510</td> <td> 0.000</td> <td> 1739.356</td> <td> 3698.542</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>view</th>  <td> 5.673e+04</td> <td> 4627.695</td> <td>   12.258</td> <td> 0.000</td> <td> 4.75e+04</td> <td> 6.59e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>29.224</td> <th>  Durbin-Watson:     </th> <td>   1.965</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  64.957</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.088</td> <th>  Prob(JB):          </th> <td>7.85e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.295</td> <th>  Cond. No.          </th> <td>9.42e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 9.42e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.913\n",
       "Model:                            OLS   Adj. R-squared:                  0.910\n",
       "Method:                 Least Squares   F-statistic:                     335.2\n",
       "Date:                Fri, 27 Mar 2020   Prob (F-statistic):           1.02e-50\n",
       "Time:                        19:37:49   Log-Likelihood:                -1144.6\n",
       "No. Observations:                 100   AIC:                             2297.\n",
       "Df Residuals:                      96   BIC:                             2308.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const      -5.398e+06   9.94e+05     -5.431      0.000   -7.37e+06   -3.43e+06\n",
       "size         223.0316      7.838     28.455      0.000     207.473     238.590\n",
       "year        2718.9489    493.502      5.510      0.000    1739.356    3698.542\n",
       "view        5.673e+04   4627.695     12.258      0.000    4.75e+04    6.59e+04\n",
       "==============================================================================\n",
       "Omnibus:                       29.224   Durbin-Watson:                   1.965\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               64.957\n",
       "Skew:                           1.088   Prob(JB):                     7.85e-15\n",
       "Kurtosis:                       6.295   Cond. No.                     9.42e+05\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 9.42e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating multi-linear regression model\n",
    "y = data[\"price\"]\n",
    "x1 = data[[\"size\", \"year\", \"view\"]]\n",
    "x = sm.add_constant(x1)\n",
    "results = sm.OLS(y,x).fit()\n",
    "results.summary()"
   ]
  }
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