outliers_problem.ipynb

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  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "# Load library\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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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>Bathrooms</th>\n",
       "      <th>Square_Feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>534433</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>392333</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>293222</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4322032</td>\n",
       "      <td>116.0</td>\n",
       "      <td>48000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Price  Bathrooms  Square_Feet\n",
       "0   534433        2.0         1500\n",
       "1   392333        3.5         2500\n",
       "2   293222        2.0         1500\n",
       "3  4322032      116.0        48000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create DataFrame\n",
    "houses = pd.DataFrame()\n",
    "houses['Price'] = [534433, 392333, 293222, 4322032]\n",
    "houses['Bathrooms'] = [2, 3.5, 2, 116]\n",
    "houses['Square_Feet'] = [1500, 2500, 1500, 48000]\n",
    "\n",
    "houses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Method 1: Drop\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Price</th>\n",
       "      <th>Bathrooms</th>\n",
       "      <th>Square_Feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>534433</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>392333</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>293222</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Price  Bathrooms  Square_Feet\n",
       "0  534433        2.0         1500\n",
       "1  392333        3.5         2500\n",
       "2  293222        2.0         1500"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop observations greater than some value\n",
    "houses[houses['Bathrooms'] < 20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Method 2: Mark\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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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>Bathrooms</th>\n",
       "      <th>Square_Feet</th>\n",
       "      <th>Outlier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>534433</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>392333</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>293222</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4322032</td>\n",
       "      <td>116.0</td>\n",
       "      <td>48000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Price  Bathrooms  Square_Feet  Outlier\n",
       "0   534433        2.0         1500        0\n",
       "1   392333        3.5         2500        0\n",
       "2   293222        2.0         1500        0\n",
       "3  4322032      116.0        48000        1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load library\n",
    "import numpy as np\n",
    "\n",
    "# Create feature based on boolean condition\n",
    "houses['Outlier'] = np.where(houses['Bathrooms'] < 20, 0, 1)\n",
    "\n",
    "# Show data\n",
    "houses\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Method 3: Rescale\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Bathrooms</th>\n",
       "      <th>Square_Feet</th>\n",
       "      <th>Outlier</th>\n",
       "      <th>Log_Of_Square_Feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>534433</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>7.313220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>392333</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "      <td>7.824046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>293222</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>7.313220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4322032</td>\n",
       "      <td>116.0</td>\n",
       "      <td>48000</td>\n",
       "      <td>1</td>\n",
       "      <td>10.778956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Price  Bathrooms  Square_Feet  Outlier  Log_Of_Square_Feet\n",
       "0   534433        2.0         1500        0            7.313220\n",
       "1   392333        3.5         2500        0            7.824046\n",
       "2   293222        2.0         1500        0            7.313220\n",
       "3  4322032      116.0        48000        1           10.778956"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Log feature\n",
    "houses['Log_Of_Square_Feet'] = [np.log(x) for x in houses['Square_Feet']]\n",
    "\n",
    "# Show data\n",
    "houses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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