code/04-python-data-mining/03C-association-and-correlation.ipynb

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    "[back](./03B-classification-and-regression.ipynb)\n",
    "\n",
    "---\n",
    "## `Association and Correlation`\n",
    "\n",
    "Association, Correlation and Covariance are all important topics out of which, we have already seen how to obtain correlation from a DataFrame"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `Initial Setup`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns  # Library used to print nicer charts and visualizations\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th>id</th>\n",
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       "      <th>zipcode</th>\n",
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       "      <th>longitude</th>\n",
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       "      <th>bathrooms</th>\n",
       "      <th>rooms</th>\n",
       "      <th>squareFootage</th>\n",
       "      <th>lotSize</th>\n",
       "      <th>yearBuilt</th>\n",
       "      <th>lastSaleDate</th>\n",
       "      <th>lastSaleAmount</th>\n",
       "      <th>priorSaleDate</th>\n",
       "      <th>priorSaleAmount</th>\n",
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       "      <th>0</th>\n",
       "      <td>39525749</td>\n",
       "      <td>8171 E 84th Ave</td>\n",
       "      <td>Denver</td>\n",
       "      <td>CO</td>\n",
       "      <td>80022</td>\n",
       "      <td>39.84916</td>\n",
       "      <td>-104.893468</td>\n",
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       "      <td>6</td>\n",
       "      <td>1378</td>\n",
       "      <td>9968</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2009-12-17</td>\n",
       "      <td>75000</td>\n",
       "      <td>2004-05-13</td>\n",
       "      <td>165700.0</td>\n",
       "      <td>239753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>184578398</td>\n",
       "      <td>10556 Wheeling St</td>\n",
       "      <td>Denver</td>\n",
       "      <td>CO</td>\n",
       "      <td>80022</td>\n",
       "      <td>39.88802</td>\n",
       "      <td>-104.830930</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1653</td>\n",
       "      <td>6970</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004-09-23</td>\n",
       "      <td>216935</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>343963</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>184430015</td>\n",
       "      <td>3190 Wadsworth Blvd</td>\n",
       "      <td>Denver</td>\n",
       "      <td>CO</td>\n",
       "      <td>80033</td>\n",
       "      <td>39.76171</td>\n",
       "      <td>-105.081070</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1882</td>\n",
       "      <td>23875</td>\n",
       "      <td>1917.0</td>\n",
       "      <td>2008-04-03</td>\n",
       "      <td>330000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>488840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>155129946</td>\n",
       "      <td>3040 Wadsworth Blvd</td>\n",
       "      <td>Denver</td>\n",
       "      <td>CO</td>\n",
       "      <td>80033</td>\n",
       "      <td>39.76078</td>\n",
       "      <td>-105.081060</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2400</td>\n",
       "      <td>11500</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>185000</td>\n",
       "      <td>2008-06-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>494073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "          id              address    city state  zipcode  latitude  \\\n",
       "0   39525749      8171 E 84th Ave  Denver    CO    80022  39.84916   \n",
       "1  184578398    10556 Wheeling St  Denver    CO    80022  39.88802   \n",
       "2  184430015  3190 Wadsworth Blvd  Denver    CO    80033  39.76171   \n",
       "3  155129946  3040 Wadsworth Blvd  Denver    CO    80033  39.76078   \n",
       "\n",
       "    longitude  bedrooms  bathrooms  rooms  squareFootage  lotSize  yearBuilt  \\\n",
       "0 -104.893468         3        2.0      6           1378     9968     2003.0   \n",
       "1 -104.830930         2        2.0      6           1653     6970     2004.0   \n",
       "2 -105.081070         3        1.0      0           1882    23875     1917.0   \n",
       "3 -105.081060         4        3.0      0           2400    11500     1956.0   \n",
       "\n",
       "  lastSaleDate  lastSaleAmount priorSaleDate  priorSaleAmount  estimated_value  \n",
       "0   2009-12-17           75000    2004-05-13         165700.0           239753  \n",
       "1   2004-09-23          216935           NaN              NaN           343963  \n",
       "2   2008-04-03          330000           NaN              NaN           488840  \n",
       "3   2008-12-02          185000    2008-06-27              0.0           494073  "
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'../../assets/single_family_home_values.csv')\n",
    "df.head(4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>rooms</th>\n",
       "      <th>squareFootage</th>\n",
       "      <th>lotSize</th>\n",
       "      <th>yearBuilt</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>3</td>\n",
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       "      <td>8</td>\n",
       "      <td>2305</td>\n",
       "      <td>5600</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "   bedrooms  bathrooms  rooms  squareFootage  lotSize  yearBuilt  \\\n",
       "0         3        2.0      6           1378     9968     2003.0   \n",
       "1         2        2.0      6           1653     6970     2004.0   \n",
       "2         3        1.0      0           1882    23875     1917.0   \n",
       "3         4        3.0      0           2400    11500     1956.0   \n",
       "4         3        4.0      8           2305     5600     1998.0   \n",
       "\n",
       "   priorSaleAmount  \n",
       "0         165700.0  \n",
       "1              0.0  \n",
       "2              0.0  \n",
       "3              0.0  \n",
       "4              0.0  "
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X will be all the columns, without estimated_value\n",
    "X = df.drop('estimated_value', axis=1)\n",
    "X = X[['bedrooms', 'bathrooms', 'rooms', 'squareFootage',\n",
    "       'lotSize', 'yearBuilt', 'priorSaleAmount']]\n",
    "X.fillna(0, inplace=True)\n",
    "X.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.estimated_value\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `Correlation`\n",
    "\n",
    "If the correlation coefficient is greater than zero, it is a positive relationship. Conversely, if the value is less than zero, it is a negative relationship.\n",
    "\n",
    "We can generate correlation for a dataset using `.corr()` function\n",
    "\n",
    "- This is a **square matrix**\n",
    "- Having all the **numerical fields** that we have in the dataset\n",
    "- Correlation is going to range from **-1 to 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <td>1.000000</td>\n",
       "      <td>-0.007972</td>\n",
       "      <td>-0.092638</td>\n",
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       "      <td>0.076366</td>\n",
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       "      <td>0.094063</td>\n",
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       "      <td>0.126193</td>\n",
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       "      <th>zipcode</th>\n",
       "      <td>-0.007972</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.461953</td>\n",
       "      <td>0.394500</td>\n",
       "      <td>-0.018420</td>\n",
       "      <td>-0.007543</td>\n",
       "      <td>0.031346</td>\n",
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       "      <td>0.033123</td>\n",
       "      <td>0.089104</td>\n",
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       "      <th>latitude</th>\n",
       "      <td>-0.092638</td>\n",
       "      <td>0.461953</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499432</td>\n",
       "      <td>-0.083083</td>\n",
       "      <td>-0.237716</td>\n",
       "      <td>-0.213634</td>\n",
       "      <td>-0.256475</td>\n",
       "      <td>-0.092881</td>\n",
       "      <td>-0.105518</td>\n",
       "      <td>-0.126800</td>\n",
       "      <td>-0.225513</td>\n",
       "      <td>-0.266116</td>\n",
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       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>0.039591</td>\n",
       "      <td>0.394500</td>\n",
       "      <td>0.499432</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.022375</td>\n",
       "      <td>0.107666</td>\n",
       "      <td>0.026108</td>\n",
       "      <td>0.067845</td>\n",
       "      <td>0.066228</td>\n",
       "      <td>0.060827</td>\n",
       "      <td>0.036747</td>\n",
       "      <td>0.097487</td>\n",
       "      <td>0.121984</td>\n",
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       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>0.076366</td>\n",
       "      <td>-0.018420</td>\n",
       "      <td>-0.083083</td>\n",
       "      <td>0.022375</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.505931</td>\n",
       "      <td>0.712343</td>\n",
       "      <td>0.620965</td>\n",
       "      <td>0.158497</td>\n",
       "      <td>-0.061681</td>\n",
       "      <td>0.135036</td>\n",
       "      <td>0.211982</td>\n",
       "      <td>0.369125</td>\n",
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       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>0.128491</td>\n",
       "      <td>-0.007543</td>\n",
       "      <td>-0.237716</td>\n",
       "      <td>0.107666</td>\n",
       "      <td>0.505931</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.686296</td>\n",
       "      <td>0.804179</td>\n",
       "      <td>0.259073</td>\n",
       "      <td>0.194597</td>\n",
       "      <td>0.296577</td>\n",
       "      <td>0.453678</td>\n",
       "      <td>0.715926</td>\n",
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       "    <tr>\n",
       "      <th>rooms</th>\n",
       "      <td>0.094063</td>\n",
       "      <td>0.031346</td>\n",
       "      <td>-0.213634</td>\n",
       "      <td>0.026108</td>\n",
       "      <td>0.712343</td>\n",
       "      <td>0.686296</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.771194</td>\n",
       "      <td>0.175949</td>\n",
       "      <td>-0.047720</td>\n",
       "      <td>0.230537</td>\n",
       "      <td>0.370929</td>\n",
       "      <td>0.581023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>squareFootage</th>\n",
       "      <td>0.143069</td>\n",
       "      <td>0.000812</td>\n",
       "      <td>-0.256475</td>\n",
       "      <td>0.067845</td>\n",
       "      <td>0.620965</td>\n",
       "      <td>0.804179</td>\n",
       "      <td>0.771194</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.369244</td>\n",
       "      <td>0.115298</td>\n",
       "      <td>0.334441</td>\n",
       "      <td>0.506272</td>\n",
       "      <td>0.824506</td>\n",
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       "    <tr>\n",
       "      <th>lotSize</th>\n",
       "      <td>0.045336</td>\n",
       "      <td>-0.037696</td>\n",
       "      <td>-0.092881</td>\n",
       "      <td>0.066228</td>\n",
       "      <td>0.158497</td>\n",
       "      <td>0.259073</td>\n",
       "      <td>0.175949</td>\n",
       "      <td>0.369244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.285517</td>\n",
       "      <td>0.193421</td>\n",
       "      <td>0.226043</td>\n",
       "      <td>0.456539</td>\n",
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       "    <tr>\n",
       "      <th>yearBuilt</th>\n",
       "      <td>0.041342</td>\n",
       "      <td>-0.174142</td>\n",
       "      <td>-0.105518</td>\n",
       "      <td>0.060827</td>\n",
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       "      <td>-0.047720</td>\n",
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       "      <td>0.285517</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.077553</td>\n",
       "      <td>0.113356</td>\n",
       "      <td>0.174430</td>\n",
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       "    <tr>\n",
       "      <th>lastSaleAmount</th>\n",
       "      <td>0.069275</td>\n",
       "      <td>0.032898</td>\n",
       "      <td>-0.126800</td>\n",
       "      <td>0.036747</td>\n",
       "      <td>0.135036</td>\n",
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       "      <td>0.381526</td>\n",
       "      <td>0.418460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>priorSaleAmount</th>\n",
       "      <td>0.012363</td>\n",
       "      <td>0.033123</td>\n",
       "      <td>-0.225513</td>\n",
       "      <td>0.097487</td>\n",
       "      <td>0.211982</td>\n",
       "      <td>0.453678</td>\n",
       "      <td>0.370929</td>\n",
       "      <td>0.506272</td>\n",
       "      <td>0.226043</td>\n",
       "      <td>0.113356</td>\n",
       "      <td>0.381526</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.590177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>estimated_value</th>\n",
       "      <td>0.126193</td>\n",
       "      <td>0.089104</td>\n",
       "      <td>-0.266116</td>\n",
       "      <td>0.121984</td>\n",
       "      <td>0.369125</td>\n",
       "      <td>0.715926</td>\n",
       "      <td>0.581023</td>\n",
       "      <td>0.824506</td>\n",
       "      <td>0.456539</td>\n",
       "      <td>0.174430</td>\n",
       "      <td>0.418460</td>\n",
       "      <td>0.590177</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       id   zipcode  latitude  longitude  bedrooms  bathrooms  \\\n",
       "id               1.000000 -0.007972 -0.092638   0.039591  0.076366   0.128491   \n",
       "zipcode         -0.007972  1.000000  0.461953   0.394500 -0.018420  -0.007543   \n",
       "latitude        -0.092638  0.461953  1.000000   0.499432 -0.083083  -0.237716   \n",
       "longitude        0.039591  0.394500  0.499432   1.000000  0.022375   0.107666   \n",
       "bedrooms         0.076366 -0.018420 -0.083083   0.022375  1.000000   0.505931   \n",
       "bathrooms        0.128491 -0.007543 -0.237716   0.107666  0.505931   1.000000   \n",
       "rooms            0.094063  0.031346 -0.213634   0.026108  0.712343   0.686296   \n",
       "squareFootage    0.143069  0.000812 -0.256475   0.067845  0.620965   0.804179   \n",
       "lotSize          0.045336 -0.037696 -0.092881   0.066228  0.158497   0.259073   \n",
       "yearBuilt        0.041342 -0.174142 -0.105518   0.060827 -0.061681   0.194597   \n",
       "lastSaleAmount   0.069275  0.032898 -0.126800   0.036747  0.135036   0.296577   \n",
       "priorSaleAmount  0.012363  0.033123 -0.225513   0.097487  0.211982   0.453678   \n",
       "estimated_value  0.126193  0.089104 -0.266116   0.121984  0.369125   0.715926   \n",
       "\n",
       "                    rooms  squareFootage   lotSize  yearBuilt  lastSaleAmount  \\\n",
       "id               0.094063       0.143069  0.045336   0.041342        0.069275   \n",
       "zipcode          0.031346       0.000812 -0.037696  -0.174142        0.032898   \n",
       "latitude        -0.213634      -0.256475 -0.092881  -0.105518       -0.126800   \n",
       "longitude        0.026108       0.067845  0.066228   0.060827        0.036747   \n",
       "bedrooms         0.712343       0.620965  0.158497  -0.061681        0.135036   \n",
       "bathrooms        0.686296       0.804179  0.259073   0.194597        0.296577   \n",
       "rooms            1.000000       0.771194  0.175949  -0.047720        0.230537   \n",
       "squareFootage    0.771194       1.000000  0.369244   0.115298        0.334441   \n",
       "lotSize          0.175949       0.369244  1.000000   0.285517        0.193421   \n",
       "yearBuilt       -0.047720       0.115298  0.285517   1.000000        0.077553   \n",
       "lastSaleAmount   0.230537       0.334441  0.193421   0.077553        1.000000   \n",
       "priorSaleAmount  0.370929       0.506272  0.226043   0.113356        0.381526   \n",
       "estimated_value  0.581023       0.824506  0.456539   0.174430        0.418460   \n",
       "\n",
       "                 priorSaleAmount  estimated_value  \n",
       "id                      0.012363         0.126193  \n",
       "zipcode                 0.033123         0.089104  \n",
       "latitude               -0.225513        -0.266116  \n",
       "longitude               0.097487         0.121984  \n",
       "bedrooms                0.211982         0.369125  \n",
       "bathrooms               0.453678         0.715926  \n",
       "rooms                   0.370929         0.581023  \n",
       "squareFootage           0.506272         0.824506  \n",
       "lotSize                 0.226043         0.456539  \n",
       "yearBuilt               0.113356         0.174430  \n",
       "lastSaleAmount          0.381526         0.418460  \n",
       "priorSaleAmount         1.000000         0.590177  \n",
       "estimated_value         0.590177         1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can actually convert this into a `heatmap` for visualization using `seaborn`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that in the diagonal, it is `light` where the correlation is on itself\n",
    "\n",
    "And some of the `darker` points where there is very less correlation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `Covariance`\n",
    "\n",
    "> What is covariance and what is the difference between correlation and covariance?\n",
    "\n",
    "- Covariance is the **non-standardized** form of correlation, so correlation is derived from covariance.\n",
    "- We usually get the covariance first and then **standardize** it to become -1 to 1\n",
    "- However, covariance can be any value, but it captures the `variance` of what the relationship is between one feature to another feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>zipcode</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>rooms</th>\n",
       "      <th>squareFootage</th>\n",
       "      <th>lotSize</th>\n",
       "      <th>yearBuilt</th>\n",
       "      <th>lastSaleAmount</th>\n",
       "      <th>priorSaleAmount</th>\n",
       "      <th>estimated_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>3.832695e+15</td>\n",
       "      <td>-4.794798e+06</td>\n",
       "      <td>-134755.796294</td>\n",
       "      <td>97277.793396</td>\n",
       "      <td>4.241855e+06</td>\n",
       "      <td>9.277448e+06</td>\n",
       "      <td>1.140562e+07</td>\n",
       "      <td>7.357116e+09</td>\n",
       "      <td>8.457335e+09</td>\n",
       "      <td>7.661356e+07</td>\n",
       "      <td>3.326786e+12</td>\n",
       "      <td>2.403030e+11</td>\n",
       "      <td>3.940756e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zipcode</th>\n",
       "      <td>-4.794798e+06</td>\n",
       "      <td>9.438634e+01</td>\n",
       "      <td>0.105769</td>\n",
       "      <td>0.152571</td>\n",
       "      <td>-1.605609e-01</td>\n",
       "      <td>-8.546300e-02</td>\n",
       "      <td>5.964580e-01</td>\n",
       "      <td>6.549458e+00</td>\n",
       "      <td>-1.103540e+03</td>\n",
       "      <td>-5.065034e+01</td>\n",
       "      <td>2.479246e+05</td>\n",
       "      <td>1.030079e+05</td>\n",
       "      <td>4.366604e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>-1.347558e+05</td>\n",
       "      <td>1.057691e-01</td>\n",
       "      <td>0.000555</td>\n",
       "      <td>0.000468</td>\n",
       "      <td>-1.754563e-03</td>\n",
       "      <td>-6.524595e-03</td>\n",
       "      <td>-9.848155e-03</td>\n",
       "      <td>-5.015548e+00</td>\n",
       "      <td>-6.593470e+00</td>\n",
       "      <td>-7.428908e-02</td>\n",
       "      <td>-2.317704e+03</td>\n",
       "      <td>-1.773170e+03</td>\n",
       "      <td>-3.163364e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>9.727779e+04</td>\n",
       "      <td>1.525709e-01</td>\n",
       "      <td>0.000468</td>\n",
       "      <td>0.001583</td>\n",
       "      <td>7.981594e-04</td>\n",
       "      <td>4.991539e-03</td>\n",
       "      <td>2.032934e-03</td>\n",
       "      <td>2.241057e+00</td>\n",
       "      <td>7.941290e+00</td>\n",
       "      <td>7.233661e-02</td>\n",
       "      <td>1.134553e+03</td>\n",
       "      <td>1.306180e+03</td>\n",
       "      <td>2.449320e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>4.241855e+06</td>\n",
       "      <td>-1.605609e-01</td>\n",
       "      <td>-0.001755</td>\n",
       "      <td>0.000798</td>\n",
       "      <td>8.050231e-01</td>\n",
       "      <td>5.294167e-01</td>\n",
       "      <td>1.251811e+00</td>\n",
       "      <td>4.627877e+02</td>\n",
       "      <td>4.285138e+02</td>\n",
       "      <td>-1.656479e+00</td>\n",
       "      <td>9.398271e+04</td>\n",
       "      <td>6.326439e+04</td>\n",
       "      <td>1.670586e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>9.277448e+06</td>\n",
       "      <td>-8.546300e-02</td>\n",
       "      <td>-0.006525</td>\n",
       "      <td>0.004992</td>\n",
       "      <td>5.294167e-01</td>\n",
       "      <td>1.360206e+00</td>\n",
       "      <td>1.567688e+00</td>\n",
       "      <td>7.790507e+02</td>\n",
       "      <td>9.104679e+02</td>\n",
       "      <td>6.793260e+00</td>\n",
       "      <td>2.683082e+05</td>\n",
       "      <td>1.783968e+05</td>\n",
       "      <td>4.211742e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rooms</th>\n",
       "      <td>1.140562e+07</td>\n",
       "      <td>5.964580e-01</td>\n",
       "      <td>-0.009848</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>1.251811e+00</td>\n",
       "      <td>1.567688e+00</td>\n",
       "      <td>3.836116e+00</td>\n",
       "      <td>1.254643e+03</td>\n",
       "      <td>1.038417e+03</td>\n",
       "      <td>-2.797016e+00</td>\n",
       "      <td>3.502513e+05</td>\n",
       "      <td>2.430849e+05</td>\n",
       "      <td>5.740237e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>squareFootage</th>\n",
       "      <td>7.357116e+09</td>\n",
       "      <td>6.549458e+00</td>\n",
       "      <td>-5.015548</td>\n",
       "      <td>2.241057</td>\n",
       "      <td>4.627877e+02</td>\n",
       "      <td>7.790507e+02</td>\n",
       "      <td>1.254643e+03</td>\n",
       "      <td>6.899562e+05</td>\n",
       "      <td>9.241945e+05</td>\n",
       "      <td>2.866840e+03</td>\n",
       "      <td>2.154884e+08</td>\n",
       "      <td>1.377630e+08</td>\n",
       "      <td>3.454582e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lotSize</th>\n",
       "      <td>8.457335e+09</td>\n",
       "      <td>-1.103540e+03</td>\n",
       "      <td>-6.593470</td>\n",
       "      <td>7.941290</td>\n",
       "      <td>4.285138e+02</td>\n",
       "      <td>9.104679e+02</td>\n",
       "      <td>1.038417e+03</td>\n",
       "      <td>9.241945e+05</td>\n",
       "      <td>9.079853e+06</td>\n",
       "      <td>2.575406e+04</td>\n",
       "      <td>4.521032e+08</td>\n",
       "      <td>1.919438e+08</td>\n",
       "      <td>6.939180e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearBuilt</th>\n",
       "      <td>7.661356e+07</td>\n",
       "      <td>-5.065034e+01</td>\n",
       "      <td>-0.074289</td>\n",
       "      <td>0.072337</td>\n",
       "      <td>-1.656479e+00</td>\n",
       "      <td>6.793260e+00</td>\n",
       "      <td>-2.797016e+00</td>\n",
       "      <td>2.866840e+03</td>\n",
       "      <td>2.575406e+04</td>\n",
       "      <td>8.962271e+02</td>\n",
       "      <td>1.801012e+06</td>\n",
       "      <td>1.141169e+06</td>\n",
       "      <td>2.634072e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lastSaleAmount</th>\n",
       "      <td>3.326786e+12</td>\n",
       "      <td>2.479246e+05</td>\n",
       "      <td>-2317.703724</td>\n",
       "      <td>1134.552961</td>\n",
       "      <td>9.398271e+04</td>\n",
       "      <td>2.683082e+05</td>\n",
       "      <td>3.502513e+05</td>\n",
       "      <td>2.154884e+08</td>\n",
       "      <td>4.521032e+08</td>\n",
       "      <td>1.801012e+06</td>\n",
       "      <td>6.017103e+11</td>\n",
       "      <td>7.543743e+10</td>\n",
       "      <td>1.637338e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>priorSaleAmount</th>\n",
       "      <td>2.403030e+11</td>\n",
       "      <td>1.030079e+05</td>\n",
       "      <td>-1773.169570</td>\n",
       "      <td>1306.179672</td>\n",
       "      <td>6.326439e+04</td>\n",
       "      <td>1.783968e+05</td>\n",
       "      <td>2.430849e+05</td>\n",
       "      <td>1.377630e+08</td>\n",
       "      <td>1.919438e+08</td>\n",
       "      <td>1.141169e+06</td>\n",
       "      <td>7.543743e+10</td>\n",
       "      <td>1.142026e+11</td>\n",
       "      <td>9.951301e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>estimated_value</th>\n",
       "      <td>3.940756e+12</td>\n",
       "      <td>4.366604e+05</td>\n",
       "      <td>-3163.363716</td>\n",
       "      <td>2449.320289</td>\n",
       "      <td>1.670586e+05</td>\n",
       "      <td>4.211742e+05</td>\n",
       "      <td>5.740237e+05</td>\n",
       "      <td>3.454582e+08</td>\n",
       "      <td>6.939180e+08</td>\n",
       "      <td>2.634072e+06</td>\n",
       "      <td>1.637338e+11</td>\n",
       "      <td>9.951301e+10</td>\n",
       "      <td>2.544380e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           id       zipcode       latitude     longitude  \\\n",
       "id               3.832695e+15 -4.794798e+06 -134755.796294  97277.793396   \n",
       "zipcode         -4.794798e+06  9.438634e+01       0.105769      0.152571   \n",
       "latitude        -1.347558e+05  1.057691e-01       0.000555      0.000468   \n",
       "longitude        9.727779e+04  1.525709e-01       0.000468      0.001583   \n",
       "bedrooms         4.241855e+06 -1.605609e-01      -0.001755      0.000798   \n",
       "bathrooms        9.277448e+06 -8.546300e-02      -0.006525      0.004992   \n",
       "rooms            1.140562e+07  5.964580e-01      -0.009848      0.002033   \n",
       "squareFootage    7.357116e+09  6.549458e+00      -5.015548      2.241057   \n",
       "lotSize          8.457335e+09 -1.103540e+03      -6.593470      7.941290   \n",
       "yearBuilt        7.661356e+07 -5.065034e+01      -0.074289      0.072337   \n",
       "lastSaleAmount   3.326786e+12  2.479246e+05   -2317.703724   1134.552961   \n",
       "priorSaleAmount  2.403030e+11  1.030079e+05   -1773.169570   1306.179672   \n",
       "estimated_value  3.940756e+12  4.366604e+05   -3163.363716   2449.320289   \n",
       "\n",
       "                     bedrooms     bathrooms         rooms  squareFootage  \\\n",
       "id               4.241855e+06  9.277448e+06  1.140562e+07   7.357116e+09   \n",
       "zipcode         -1.605609e-01 -8.546300e-02  5.964580e-01   6.549458e+00   \n",
       "latitude        -1.754563e-03 -6.524595e-03 -9.848155e-03  -5.015548e+00   \n",
       "longitude        7.981594e-04  4.991539e-03  2.032934e-03   2.241057e+00   \n",
       "bedrooms         8.050231e-01  5.294167e-01  1.251811e+00   4.627877e+02   \n",
       "bathrooms        5.294167e-01  1.360206e+00  1.567688e+00   7.790507e+02   \n",
       "rooms            1.251811e+00  1.567688e+00  3.836116e+00   1.254643e+03   \n",
       "squareFootage    4.627877e+02  7.790507e+02  1.254643e+03   6.899562e+05   \n",
       "lotSize          4.285138e+02  9.104679e+02  1.038417e+03   9.241945e+05   \n",
       "yearBuilt       -1.656479e+00  6.793260e+00 -2.797016e+00   2.866840e+03   \n",
       "lastSaleAmount   9.398271e+04  2.683082e+05  3.502513e+05   2.154884e+08   \n",
       "priorSaleAmount  6.326439e+04  1.783968e+05  2.430849e+05   1.377630e+08   \n",
       "estimated_value  1.670586e+05  4.211742e+05  5.740237e+05   3.454582e+08   \n",
       "\n",
       "                      lotSize     yearBuilt  lastSaleAmount  priorSaleAmount  \\\n",
       "id               8.457335e+09  7.661356e+07    3.326786e+12     2.403030e+11   \n",
       "zipcode         -1.103540e+03 -5.065034e+01    2.479246e+05     1.030079e+05   \n",
       "latitude        -6.593470e+00 -7.428908e-02   -2.317704e+03    -1.773170e+03   \n",
       "longitude        7.941290e+00  7.233661e-02    1.134553e+03     1.306180e+03   \n",
       "bedrooms         4.285138e+02 -1.656479e+00    9.398271e+04     6.326439e+04   \n",
       "bathrooms        9.104679e+02  6.793260e+00    2.683082e+05     1.783968e+05   \n",
       "rooms            1.038417e+03 -2.797016e+00    3.502513e+05     2.430849e+05   \n",
       "squareFootage    9.241945e+05  2.866840e+03    2.154884e+08     1.377630e+08   \n",
       "lotSize          9.079853e+06  2.575406e+04    4.521032e+08     1.919438e+08   \n",
       "yearBuilt        2.575406e+04  8.962271e+02    1.801012e+06     1.141169e+06   \n",
       "lastSaleAmount   4.521032e+08  1.801012e+06    6.017103e+11     7.543743e+10   \n",
       "priorSaleAmount  1.919438e+08  1.141169e+06    7.543743e+10     1.142026e+11   \n",
       "estimated_value  6.939180e+08  2.634072e+06    1.637338e+11     9.951301e+10   \n",
       "\n",
       "                 estimated_value  \n",
       "id                  3.940756e+12  \n",
       "zipcode             4.366604e+05  \n",
       "latitude           -3.163364e+03  \n",
       "longitude           2.449320e+03  \n",
       "bedrooms            1.670586e+05  \n",
       "bathrooms           4.211742e+05  \n",
       "rooms               5.740237e+05  \n",
       "squareFootage       3.454582e+08  \n",
       "lotSize             6.939180e+08  \n",
       "yearBuilt           2.634072e+06  \n",
       "lastSaleAmount      1.637338e+11  \n",
       "priorSaleAmount     9.951301e+10  \n",
       "estimated_value     2.544380e+11  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cov()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whenever we have to pick the features, we can use this to understand the correlation and decide the best features.\n",
    "\n",
    "For example, if we consider **estimated_value**, we can see that the best set of features would be _bathrooms, squareFootage, rooms and priorSaleAmount_\n",
    "\n",
    "Another reason for using this is for detecting the **outliers**. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `Understanding and Removing Outliers`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, when we are looking at a new dataset, we want to have a good understanding of what the outliers are.\n",
    "\n",
    "In the previous chapter, the linear regression model which could have been very distorted just because of a few outliers because we are trying to fit a line into a dataset and if there are few outliers which are very far away from majority of the data points, then it is very likely that this line we're trying to fit gets lifted by these outliers.\n",
    "\n",
    "So, it is very important to know the outliers and either drop / remove these outliers or impute them using mean or median.\n",
    "\n",
    "So now, we'll take a look at how we detect these outliers and remove them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>rooms</th>\n",
       "      <th>squareFootage</th>\n",
       "      <th>lotSize</th>\n",
       "      <th>yearBuilt</th>\n",
       "      <th>priorSaleAmount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1378</td>\n",
       "      <td>9968</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>165700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1653</td>\n",
       "      <td>6970</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1882</td>\n",
       "      <td>23875</td>\n",
       "      <td>1917.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2400</td>\n",
       "      <td>11500</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2305</td>\n",
       "      <td>5600</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2169</td>\n",
       "      <td>4950</td>\n",
       "      <td>1922.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2937</td>\n",
       "      <td>4500</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>557500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2937</td>\n",
       "      <td>4680</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>1208214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10</td>\n",
       "      <td>3193</td>\n",
       "      <td>4970</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>405000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14999</th>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9</td>\n",
       "      <td>3193</td>\n",
       "      <td>4364</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>925000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bedrooms  bathrooms  rooms  squareFootage  lotSize  yearBuilt  \\\n",
       "0             3        2.0      6           1378     9968     2003.0   \n",
       "1             2        2.0      6           1653     6970     2004.0   \n",
       "2             3        1.0      0           1882    23875     1917.0   \n",
       "3             4        3.0      0           2400    11500     1956.0   \n",
       "4             3        4.0      8           2305     5600     1998.0   \n",
       "...         ...        ...    ...            ...      ...        ...   \n",
       "14995         4        4.0      8           2169     4950     1922.0   \n",
       "14996         3        3.0     11           2937     4500     1890.0   \n",
       "14997         3        5.0      7           2937     4680     2007.0   \n",
       "14998         3        4.0     10           3193     4970     2005.0   \n",
       "14999         3        5.0      9           3193     4364     2001.0   \n",
       "\n",
       "       priorSaleAmount  \n",
       "0             165700.0  \n",
       "1                  0.0  \n",
       "2                  0.0  \n",
       "3                  0.0  \n",
       "4                  0.0  \n",
       "...                ...  \n",
       "14995              0.0  \n",
       "14996         557500.0  \n",
       "14997        1208214.0  \n",
       "14998         405000.0  \n",
       "14999         925000.0  \n",
       "\n",
       "[15000 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We have our X, which is the feature set\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         239753\n",
       "1         343963\n",
       "2         488840\n",
       "3         494073\n",
       "4         513676\n",
       "          ...   \n",
       "14995    1080081\n",
       "14996     807306\n",
       "14997    1737156\n",
       "14998    2008794\n",
       "14999    1421401\n",
       "Name: estimated_value, Length: 15000, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y which is the estimated_value of a property\n",
    "y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bedrooms', 'bathrooms', 'rooms', 'squareFootage', 'lotSize',\n",
       "       'yearBuilt', 'priorSaleAmount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, for all of the column headers in our feature set, we'll loop through it and try to build a histogram along with that print the mean and standard deviation\n",
    "\n",
    "And based on these histograms, we can see where most of the values lie for a particular feature and try to set our limit to eliminate the outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "bedrooms\n",
      "mean:  2.7084\n",
      "std:  0.8972307998543382\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "bathrooms\n",
      "mean:  2.1950666666666665\n",
      "std:  1.1662788442928953\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "rooms\n",
      "mean:  6.164133333333333\n",
      "std:  1.958600518215801\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "squareFootage\n",
      "mean:  1514.5044\n",
      "std:  830.6359987331762\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "lotSize\n",
      "mean:  5820.7662\n",
      "std:  3013.279470369183\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "yearBuilt\n",
      "mean:  1929.3885333333333\n",
      "std:  33.82853430411162\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "priorSaleAmount\n",
      "mean:  195216.20066666667\n",
      "std:  313797.90664518543\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in X.columns:\n",
    "  print('='*80)\n",
    "  print(col)\n",
    "  X.loc[:, col].hist() # for all the rows, and current column in the iteration, get the histogram\n",
    "  print('mean: ', X.loc[:, col].mean())\n",
    "  print('std: ', X.loc[:, col].std())\n",
    "  plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we'll work towards removing the outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove or drop outliers (by filtering the records we need) based on conditions\n",
    "X2 = X[X.bedrooms < 7]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "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": [
    "X2.bedrooms.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing outliers (by filtering the records we need) from multiple features at once\n",
    "X3 = X[(X.bedrooms < 7) & (X.bathrooms < 7)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "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": [
    "X3.bathrooms.hist()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Alternate ways to identify and remove outliers`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "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": [
    "# Seaborn - Boxplot\n",
    "\n",
    "sns.boxplot(data=X)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that **priorSaleAmount** is skewing the result, so we'll remove that and check the boxplot again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "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": [
    "sns.boxplot(data=X[['bedrooms', 'bathrooms', 'rooms',\n",
    "            'squareFootage', 'lotSize', 'yearBuilt']])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that now **lotSize** is still somewhat skewing the result, so we'll remove that and see the boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "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": [
    "sns.boxplot(data=X[['bedrooms', 'bathrooms', 'rooms',\n",
    "            'squareFootage', 'yearBuilt']])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same is with **squareFootage**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "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": [
    "sns.boxplot(data=X[['bedrooms', 'bathrooms', 'rooms', 'yearBuilt']])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and with **yearBuilt** removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "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": [
    "sns.boxplot(data=X[['bedrooms', 'bathrooms', 'rooms']])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now see that in rooms, anything above 20 or 25 may be considered as outliers.\n",
    "\n",
    "Typically what people do is, they will use somewhere from 25pctl to 75pctl, so that we can include the data points that belong to that range. But we can surely be flexible at picking it.\n",
    "\n",
    "So, comparing the value we've picked for bathroom and bedroom i.e., 7 seems to be a good cutoff based on the above chart and for rooms, may be 20 will be a good cutoff value.\n",
    "\n",
    "So, now, we'll go back and see the other features we didn't include."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAD4CAYAAADy46FuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbUklEQVR4nO3df5Ac5X3n8fdHuw5B2BA0CEpIYCmWyja/MXsY22CTkwRb2CCqQs5K7NPeGZ8wxSGC7eTQ/TA+ckmgnDKHfAcVBWxGNjHI4ASBxcJKnH9VQLAyPxYQWOsDGwkBYiQ4QA5hVt/7Y56F2WXVErs709uzn1fV1HR/u5/eZ2A0n3m6e7oVEZiZme3JlLw7YGZmE5uDwszMMjkozMwsk4PCzMwyOSjMzCxTe94dGG+HHHJIzJ49O+9umJkVysaNG1+KiOkjLWu5oJg9eza9vb15d8PMrFAk/XpPy7zryczMMjkozMwsk4PCzMwyOSjMzCyTg8LMCqlSqbBs2TIqlUreXWl5DgozK6RyuUxfXx+rVq3Kuystz0FhZoVTqVTo7u4mIuju7vaoosEcFGZWOOVymd27dwMwMDDgUUWDOSjMrHDWrVtHtVoFoFqt0tPTk3OPWpuDwswKZ8GCBbS31y4s0d7ezsKFC3PuUWvba1BI+rakFyU9Vlf7hqQnJT0q6R8k/V7dsuWS+iU9JenMuvpJkvrSshWSlOr7Sbol1TdIml3XpkvS5vToGq8XbWbF1tXVxZQptY+vtrY2lixZknOPWtu+jChuBDqH1XqAYyLiOOCXwHIASUcBi4GjU5trJbWlNtcBS4F56TG4zfOBnRExF7gauCptaxpwOfBR4GTgckkHv/uXaGatplQq0dnZiSQ6OzsplUp5d6ml7TUoIuKnwI5htXsioppm7wdmpelFwM0R8UZEPA30AydLmgEcGBH3Re0m3auAc+valNP0rcD8NNo4E+iJiB0RsZNaOA0PLDObpLq6ujj22GM9mmiC8bh67BeAW9L0TGrBMWhLqr2ZpofXB9s8CxARVUmvAKX6+ghthpC0lNpohSOPPHIML8XMiqJUKrFixYq8uzEpjOlgtqT/AlSBmwZLI6wWGfXRthlajFgZER0R0TF9+oiXUzczs1EadVCkg8ufAT6XdidB7Vv/EXWrzQKeS/VZI9SHtJHUDhxEbVfXnrZlZmZNNKqgkNQJ/CfgnIjYVbdoDbA4nck0h9pB6wciYhvwqqRT0vGHJcDtdW0Gz2g6D7g3Bc/dwBmSDk4Hsc9INTMza6K9HqOQ9H3gdOAQSVuonYm0HNgP6Elnud4fEV+KiMclrQaeoLZL6qKIGEibupDaGVT7A3elB8ANwHcl9VMbSSwGiIgdkv4CeDCtd0VEDDmobmZmjae39xq1ho6OjvCtUM3M3h1JGyOiY6Rl/mW2mZllclCYmVkmB4WZmWVyUJiZWSYHhZmZZXJQmJlZJgeFmZllclCYmVkmB4WZFVKlUmHZsmVUKpW8u9LyHBRmVkjlcpm+vj5WrVqVd1danoPCzAqnUqnQ3d1NRNDd3e1RRYM5KMyscMrlMrt37wZgYGDAo4oGc1CYWeGsW7eOarV2N+ZqtUpPT0/OPWptDgozK5wFCxbQ3l67S0J7ezsLFy7MuUetzUFhZoXT1dXFlCm1j6+2tjaWLFmSc49am4PCzAqnVCrR2dmJJDo7OymVSnl3qaXt9Q53ZmYTUVdXF88884xHE03goDCzQiqVSqxYsSLvbkwK3vVkZmaZHBRmZpbJQWFmZpkcFGZmlslBYWZmmfYaFJK+LelFSY/V1aZJ6pG0OT0fXLdsuaR+SU9JOrOufpKkvrRshSSl+n6Sbkn1DZJm17XpSn9js6SucXvVZma2z/ZlRHEj0DmsdhmwPiLmAevTPJKOAhYDR6c210pqS22uA5YC89JjcJvnAzsjYi5wNXBV2tY04HLgo8DJwOX1gWRmZs2x16CIiJ8CO4aVFwHlNF0Gzq2r3xwRb0TE00A/cLKkGcCBEXFfRASwalibwW3dCsxPo40zgZ6I2BERO4Ee3hlYZmbWYKM9RnFYRGwDSM+HpvpM4Nm69bak2sw0Pbw+pE1EVIFXgFLGtt5B0lJJvZJ6t2/fPsqXZGZmIxnvg9kaoRYZ9dG2GVqMWBkRHRHRMX369H3qqJkVm2+F2jyjDYoX0u4k0vOLqb4FOKJuvVnAc6k+a4T6kDaS2oGDqO3q2tO2zMx8K9QmGm1QrAEGz0LqAm6vqy9OZzLNoXbQ+oG0e+pVSaek4w9LhrUZ3NZ5wL3pOMbdwBmSDk4Hsc9INTOb5Hwr1Obal9Njvw/cB3xQ0hZJ5wNXAgslbQYWpnki4nFgNfAE0A1cFBEDaVMXAtdTO8D9K+CuVL8BKEnqB75MOoMqInYAfwE8mB5XpJqZTXK+FWpzqfblvXV0dHREb29v3t0wswY666yz2LVr11vzU6dOZe3atTn2qPgkbYyIjpGW+ZfZZlY4vhVqczkozKxwfCvU5nJQmFnh+FaozeU73JlZIflWqM3joDCzQvKtUJvHu57MzCyTg8LMzDI5KMzMLJODwswKyRcFbB4HhZkVki8K2DwOCjMrHF8UsLkcFGZWOL4oYHM5KMyscNatW0e1WgWgWq3S09OTc49am4PCzArHFwVsLgeFmRWOLwrYXA4KMyscXxSwuXytJzMrJF8UsHkcFGZWSL4oYPN415OZmWVyUJhZIfkSHs3joDCzQvIlPJrHQWFmheNLeDSXg8LMCseX8GiuMQWFpEslPS7pMUnfl/S7kqZJ6pG0OT0fXLf+ckn9kp6SdGZd/SRJfWnZCklK9f0k3ZLqGyTNHkt/zaw1+BIezTXqoJA0E1gGdETEMUAbsBi4DFgfEfOA9WkeSUel5UcDncC1ktrS5q4DlgLz0qMz1c8HdkbEXOBq4KrR9tfMWocv4dFcY9311A7sL6kdmAo8BywCyml5GTg3TS8Cbo6INyLiaaAfOFnSDODAiLgvIgJYNazN4LZuBeYPjjbMbPLyJTyaa9RBERFbgb8BfgNsA16JiHuAwyJiW1pnG3BoajITeLZuE1tSbWaaHl4f0iYiqsArwDt+qy9pqaReSb3bt28f7Usys4LwJTyaayy7ng6m9o1/DnA4cICkz2c1GaEWGfWsNkMLESsjoiMiOqZPn57dcTNrCV1dXRx77LEeTTTBWC7hsQB4OiK2A0j6IfBx4AVJMyJiW9qt9GJafwtwRF37WdR2VW1J08Pr9W22pN1bBwE7xtBnM2sRvoRH84zlGMVvgFMkTU3HDeYDm4A1QFdapwu4PU2vARanM5nmUDto/UDaPfWqpFPSdpYMazO4rfOAe9NxDDMza5JRjygiYoOkW4FfAFXgIWAl8F5gtaTzqYXJH6X1H5e0GngirX9RRAykzV0I3AjsD9yVHgA3AN+V1E9tJLF4tP01M7PRUat9Qe/o6Ije3t68u2FmViiSNkZEx0jL/MtsMzPL5KAwM7NMDgozM8vkoDAzs0wOCjMzy+SgMDOzTA4KMzPL5KAwM7NMDgozM8vkoDAzs0wOCjMzy+SgMDOzTA4KMzPL5KAwM7NMDgozM8vkoDAzs0wOCjMzy+SgMDOzTA4KMzPL5KAws0KqVCosW7aMSqWSd1danoPCzAqpXC7T19fHqlWr8u5Ky3NQmFnhVCoVuru7iQi6u7s9qmgwB4WZFU65XGb37t0ADAwMeFTRYGMKCkm/J+lWSU9K2iTpY5KmSeqRtDk9H1y3/nJJ/ZKeknRmXf0kSX1p2QpJSvX9JN2S6hskzR5Lf82sNaxbt45qtQpAtVqlp6cn5x61trGOKK4BuiPiQ8DxwCbgMmB9RMwD1qd5JB0FLAaOBjqBayW1pe1cBywF5qVHZ6qfD+yMiLnA1cBVY+yvmbWABQsW0N7eDkB7ezsLFy7MuUetbdRBIelA4JPADQAR8S8R8TKwCCin1crAuWl6EXBzRLwREU8D/cDJkmYAB0bEfRERwKphbQa3dSswf3C0YWaTV1dXF1Om1D6+2traWLJkSc49am1jGVH8PrAd+I6khyRdL+kA4LCI2AaQng9N688Enq1rvyXVZqbp4fUhbSKiCrwClMbQZzNrAaVSic7OTiTR2dlJqeSPhUYaS1C0Ax8BrouIE4HXSbuZ9mCkkUBk1LPaDN2wtFRSr6Te7du3Z/fazFrCOeecw9SpUzn77LPz7krLG0tQbAG2RMSGNH8rteB4Ie1OIj2/WLf+EXXtZwHPpfqsEepD2khqBw4CdgzvSESsjIiOiOiYPn36GF6SmRXFmjVr2LVrF3fccUfeXWl5ow6KiHgeeFbSB1NpPvAEsAboSrUu4PY0vQZYnM5kmkPtoPUDaffUq5JOSccflgxrM7it84B703EMM5vE/DuK5hrrWU8XAzdJehQ4Afgr4EpgoaTNwMI0T0Q8DqymFibdwEURMZC2cyFwPbUD3L8C7kr1G4CSpH7gy2Tv2jKzSaJcLjMwUPv4qFar/h1Fg6nVvqB3dHREb29v3t0wswY666yz2LVr11vzU6dOZe3atTn2qPgkbYyIjpGW+ZfZZlY4p5566pD50047LaeeTA4OCjMrHP+cqrkcFGZWOD/72c8y5218OSjMrHB8CY/mclCYWeH4Eh7N5aAws8LxJTyaqz3vDpiZjUZXVxfPPPOMRxNN4BGFmZllclCYWSH5ntnN46Aws8LxtZ6ay0FhZoXje2Y3l4PCzArH98xuLgeFmRWOf3DXXA4KMyuc+h/cTZkyxafINpiDwswKp1QqcfjhhwNw+OGH+wd3DeagMLPCqVQqbN26FYDnnnvOZz01mIPCzAqnXC4zeNO13bt3+6ynBnNQmFnh+Kyn5nJQmFnhLFiwgLa2NqB29Vif9dRYDgozK5yurq63dj1FhM96ajAHhZkVUn1QWGM5KMyscFauXDkkKFauXJlzj1qbg8LMCmf9+vWZ8za+HBRmVjjDdzd591NjjTkoJLVJekjSnWl+mqQeSZvT88F16y6X1C/pKUln1tVPktSXlq2QpFTfT9Itqb5B0uyx9tfMim/+/PlD5hcsWJBTTyaH8RhRXAJsqpu/DFgfEfOA9WkeSUcBi4GjgU7gWkltqc11wFJgXnp0pvr5wM6ImAtcDVw1Dv01s4K74IILhlzraenSpTn3qLWNKSgkzQI+DVxfV14ElNN0GTi3rn5zRLwREU8D/cDJkmYAB0bEfVEbP64a1mZwW7cC8wdHG2Y2eZVKpbdGEQsXLvS1nhqsfYzt/yfw58D76mqHRcQ2gIjYJunQVJ8J3F+33pZUezNND68Ptnk2basq6RWgBLxU3wlJS6mNSDjyyCPH+JLMrAguuOACnn/+eY8mmmDUIwpJnwFejIiN+9pkhFpk1LPaDC1ErIyIjojomD59+j52x8yKrFQqsWLFCo8mmmAsu54+AZwj6RngZuBfS/oe8ELanUR6fjGtvwU4oq79LOC5VJ81Qn1IG0ntwEHAjjH02cxaRKVSYdmyZb5ybBOMOigiYnlEzIqI2dQOUt8bEZ8H1gBdabUu4PY0vQZYnM5kmkPtoPUDaTfVq5JOSccflgxrM7it89Lf8HlwZka5XKavr89Xjm2CRvyO4kpgoaTNwMI0T0Q8DqwGngC6gYsiYiC1uZDaAfF+4FfAXal+A1CS1A98mXQGlZlNbpVKhe7ubiKC7u5ujyoaTK32Bb2joyN6e3vz7oaZNdA3v/lN1q5dS7Vapb29nU9/+tNceumleXer0CRtjIiOkZb5l9lmVji+H0VzOSjMrHCG/xLb96NoLAeFmRXOOeecM2T+7LPPzqknk4ODwswKZ/Xq1UPmf/CDH+TUk8nBQWFmhbNu3boh8z5G0VgOCjMrnN27d2fO2/hyUJiZWSYHhZkVTltbW+a8jS8HhZkVzgknnDBk/sQTT8ynI5OEg8LMCufJJ58cMr9p06Y9rGnjwUFhZoXz+uuvZ87b+HJQmFnh+BhFczkozKxwBgYGMudtfDkozMwsk4PCzArHu56ay0FhZoXjXU/N5aAwM7NMDgozM8vkoDAzs0wOCjMzy+SgMDOzTA4KMzPL5KAwM7NMow4KSUdI+j+SNkl6XNIlqT5NUo+kzen54Lo2yyX1S3pK0pl19ZMk9aVlKyQp1feTdEuqb5A0ewyv1czMRmEsI4oq8JWI+DBwCnCRpKOAy4D1ETEPWJ/mScsWA0cDncC1kgZ/TnkdsBSYlx6dqX4+sDMi5gJXA1eNob9mZjYKow6KiNgWEb9I068Cm4CZwCKgnFYrA+em6UXAzRHxRkQ8DfQDJ0uaARwYEfdFRACrhrUZ3NatwPzB0YaZmTXHuByjSLuETgQ2AIdFxDaohQlwaFptJvBsXbMtqTYzTQ+vD2kTEVXgFaA0wt9fKqlXUu/27dvH4yWZmVky5qCQ9F7gNuBPI+L/Za06Qi0y6llthhYiVkZER0R0TJ8+fW9dNjOzd2FMQSHpPdRC4qaI+GEqv5B2J5GeX0z1LcARdc1nAc+l+qwR6kPaSGoHDgJ2jKXPZmb27ozlrCcBNwCbIuKbdYvWAF1pugu4va6+OJ3JNIfaQesH0u6pVyWdkra5ZFibwW2dB9ybjmOYmVmTtI+h7SeAfwv0SXo41f4zcCWwWtL5wG+APwKIiMclrQaeoHbG1EURMXht4AuBG4H9gbvSA2pB9F1J/dRGEovH0F8zMxuFUQdFRPyckY8hAMzfQ5u/BP5yhHovcMwI9X8mBY2ZmeXDv8w2M7NMDgozM8vkoDAzs0wOCjMzy+SgMDOzTA4K26NKpcKyZcuoVCp5d8XMcuSgsD0ql8v09fWxatWqvLtiZjlyUNiIKpUKa9euJSJYu3atRxVmk5iDwkZULpepVqsAvPnmmx5VmE1iDgob0T333DNk/u67786pJ2aWNweFjaitrS1z3swmj7FcFNBa2Ouvv545b5PXt771Lfr7+/PuxjtccsklufzduXPncvHFF+fyt5vFIwozM8vkEYWZvSsT4dvz6aef/o7aNddc0/yOTBIeUZiZWSaPKMwKZKIeH2i2448/nkceeWTIfF7HKCaKRh4rcVCYFchPfvITdry0nf3afEfgt++bFvzysYdy7Une3hgQW7dudVCYWc1+bcH73zew9xVt0vj1q409fd3HKMwKZObMmWhPNyCeZF7YNYUXdvkjDECqvTcaxSMKswKZO3du3l2YMHamYxQ7/6WN448/Pufe5GsejX1vOCjMCmQinJo6UdSfIutTYxvLQTFBTcSzW/I8q2Qy/PrV9t3w31Gcfvrp/PjHP86lL5OBg8LM3pWJ+CUGfAmPRipEUEjqBK4B2oDrI+LKRv2tL37xi2zbtq1Rmy+0PD8c+vv76e7uzu3vA8yYMYPrr78+1z6Y5WHCB4WkNuB/AwuBLcCDktZExBON+Hsvv/yyL4C3B5P9v8vLL7+cdxcmhInw7dmX8GiuCR8UwMlAf0T8XwBJNwOLgIYExac+9akJMazeunUrv/3tb3PtQ30wHHDAATn2BPbff/+Gnv63L3zGkU1WRQiKmcCzdfNbgI/WryBpKbAU4MgjjxzTH5sI35ZgYuwHrr9EQt4fkpNhP7DZRFWEoBjp50VDrl8QESuBlQAdHR0tcW0Dfyia7dlxxx3Ho48++tb8ZP8dRaMV4WeNW4Aj6uZnAc/l1BczmwAuv/zyIfNf+9rXcurJ5FCEoHgQmCdpjqTfARYDa3Luk5nlqFQqcdxxxwG10USpVMq5R61twgdFRFSB/wjcDWwCVkfE4/n2yszydvnll3Pcccd5NNEEimiJXfpv6ejoiN7e3ry7YWZWKJI2RkTHSMsm/IjCzMzy5aAwM7NMDgozM8vkoDAzs0wtdzBb0nbg13n3o4UcAryUdyfM9sDvz/Hz/oiYPtKClgsKG1+Sevd0JoRZ3vz+bA7vejIzs0wOCjMzy+SgsL1ZmXcHzDL4/dkEPkZhZmaZPKIwM7NMDgozM8vkoLARSXpGUp+kh9Pj46PYxrmSjmpE/6w1SHptL8tnS/qTuvmpkm5K783HJP1c0nvTsn9qdH8nqyLc4c4aRFJbRAxkrPIHETGWHzOdC9xJg+5vbpPCbOBPgL9P85cAL0TEsQCSPgi8CRAR7/rLjO0bjygmGEkHSPqRpEfSN6bPSuqU9GT69rRC0p1p3a9L+mpd28ckzU7T/yhpo6TH0z3FB9d5TdIVkjYAH5P0eUkPpFHD30pqy+jb+yWtl/Roej5yT/U0AjkH+Eba9gck/QdJD6bXdpukqan9ByTdn5ZdUf8tU9Kfpfqjkv77+P7XtolCNd9I7+E+SZ9Ni64ETkvvoUuBGcDWwXYR8VREvJG28Vp6vqJuJLxV0ndSfZ/f6zZMRPgxgR7AHwJ/Vzd/EPAsMI/a/cNXA3emZV8Hvlq37mPA7DQ9LT3vn+qlNB/Av0nTHwbuAN6T5q8FlqTpZ4A+4GFgQ6rdAXSl6S8A/7iX+o3AeXX9K9VN/w/g4jR9J/DHafpLwGtp+gxqpz+K2peaO4FP5v3/yI9xfb8P/r/+Q6AHaAMOA35DLRROH3y/p/VOAF4E7kvvoXnDt1U3fxDwKHBS1nvdj70/PKKYePqABZKuknQaMAd4OiI2R+0d/r193M4ySY8A91O75/i8VB8AbkvT86n9I3pQ0sNp/vfrtvEHEXFCRHw0zX+Mt3cBfBc4dS/14Y6R9DNJfcDngKPr2v8gTf993fpnpMdDwC+AD9W9DmstpwLfj4iBiHgB+Anwr4avFBEPU3uPfgOYRu29++Hh60kScBNwdURsZO/vdcvgYxQTTET8UtJJwFnAXwP3UBsFjKTK0N2Hvwsg6XRgAfCxiNgl6ceDy4B/jrePSwgoR8Ty0Xb3XdZvBM6NiEck/Ttq3xazCPjriPjbUfXOikT7umJEvAb8EPihpN3U/q1sGrba14EtEfGduu2P5b0+qXlEMcFIOhzYFRHfA/4G+DgwR9IH0ip/XLf6M8BHUruPUBt9QG3IvTOFxIeAU/bw59YD50k6NG1jmqT3Z3Tvn4DFafpzwM/3Un8VeF9d+/cB2yS9J6036H5qux6o2w7U7pP+hbqzWmYO9tVazk+Bz0pqkzQd+CTwAMPeQ5I+IengNP07wFEMu1q0pM8AC4FldeV3+163Oh5RTDzHUjsAvJva2RwXUruU8o8kvUTtQ/iYtO5twJI0lH4Q+GWqdwNfkvQo8BS1D+J3iIgnJP1X4B5JU9Lfu4g9X6Z9GfBtSX8GbAf+/V7qNwN/J2kZcB7w34ANaft9vP0B8KfA9yR9BfgR8Erq3z1pt8J9tT0JvAZ8nto+amst/0BtF+Qj1Eakfx4Rz0uqANW0G/VGoAJcl3YtTaH2frlt2La+AhwOPJDeN2si4mvv8r1udXwJj4JJu5W+GhGfybkr4yad/fTbiAhJi6kd2F6Ud7/MrMYjCpsITgL+V/qW+DK1M6fMbILwiMLMzDL5YLaZmWVyUJiZWSYHhZmZZXJQmJlZJgeFmZll+v+hobFXw076HwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=X[['squareFootage', 'lotSize']])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definitely there are many outliers in **lotSize**, may be anything more than 50k would be an outlier. So first, lets just look at the **squareFootage**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "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": [
    "sns.boxplot(data=X[['squareFootage']])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And seeing this, we can consider 4k as the cutoff, and now see the **lotSize**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "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": [
    "sns.boxplot(data=X[['lotSize']])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here, anything above 40k should be a good cutoff.\n",
    "\n",
    "We can definitely go on and check the features individually and decide the cutoff or we can also have a combined view of more than one variable test where to draw the line to cutoff the outliers."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `Conclusion`\n",
    "\n",
    "At some scenario, if there are too many outliers, it could be just that we need to reposition the problem into **outlier** or **anomaly detection** and perhaps we can have two models.\n",
    "\n",
    "One is the model that we will be going to use for major data points or major scenarios and another one for the outliers. So we can have two models and each will have it's own purpose.\n",
    "\n",
    "---\n",
    "[next](./03D-dimensionality-reduction.ipynb)"
   ]
  }
 ],
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