GP1 5101 draft 2.ipynb

{
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  {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_seats_total</th>\n",
       "      <th>mean_net_ticket_price</th>\n",
       "      <th>Dept_Date</th>\n",
       "      <th>Purchase_Date</th>\n",
       "      <th>Train_Number_All</th>\n",
       "      <th>Culmulative_sales</th>\n",
       "      <th>isNormCabin</th>\n",
       "      <th>isReturn</th>\n",
       "      <th>isOneway</th>\n",
       "      <th>Customer_Cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>188.663422</td>\n",
       "      <td>10/9/19</td>\n",
       "      <td>10/10/18</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>358.428869</td>\n",
       "      <td>5/27/19</td>\n",
       "      <td>6/6/18</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>356.372684</td>\n",
       "      <td>5/31/20</td>\n",
       "      <td>6/17/19</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>396.370505</td>\n",
       "      <td>12/10/19</td>\n",
       "      <td>12/27/18</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>364.264552</td>\n",
       "      <td>5/31/20</td>\n",
       "      <td>6/19/19</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>318.300419</td>\n",
       "      <td>5/23/20</td>\n",
       "      <td>6/20/19</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>464.010928</td>\n",
       "      <td>5/23/19</td>\n",
       "      <td>6/20/18</td>\n",
       "      <td>E</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>355.221623</td>\n",
       "      <td>10/18/19</td>\n",
       "      <td>11/15/18</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>354.872701</td>\n",
       "      <td>10/14/19</td>\n",
       "      <td>11/15/18</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>83.177763</td>\n",
       "      <td>8/12/19</td>\n",
       "      <td>9/13/18</td>\n",
       "      <td>G</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   num_seats_total  mean_net_ticket_price Dept_Date Purchase_Date  \\\n",
       "0                1             188.663422   10/9/19      10/10/18   \n",
       "1                1             358.428869   5/27/19        6/6/18   \n",
       "2                1             356.372684   5/31/20       6/17/19   \n",
       "3                1             396.370505  12/10/19      12/27/18   \n",
       "4                1             364.264552   5/31/20       6/19/19   \n",
       "5                1             318.300419   5/23/20       6/20/19   \n",
       "6                2             464.010928   5/23/19       6/20/18   \n",
       "7                1             355.221623  10/18/19      11/15/18   \n",
       "8                1             354.872701  10/14/19      11/15/18   \n",
       "9                3              83.177763   8/12/19       9/13/18   \n",
       "\n",
       "  Train_Number_All  Culmulative_sales  isNormCabin  isReturn  isOneway  \\\n",
       "0                A                  1            1         0         0   \n",
       "1                B                  1            0         1         0   \n",
       "2                B                  1            0         1         0   \n",
       "3                C                  1            0         1         0   \n",
       "4                B                  2            0         1         0   \n",
       "5                D                  1            0         1         0   \n",
       "6                E                  2            0         1         0   \n",
       "7                C                  1            0         1         0   \n",
       "8                F                  1            0         0         0   \n",
       "9                G                 47            1         1         0   \n",
       "\n",
       "  Customer_Cat  \n",
       "0            A  \n",
       "1            A  \n",
       "2            A  \n",
       "3            A  \n",
       "4            A  \n",
       "5            A  \n",
       "6            A  \n",
       "7            A  \n",
       "8            A  \n",
       "9            A  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas_profiling import ProfileReport\n",
    "\n",
    "from statsmodels.formula.api import ols\n",
    "import scipy\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "# ln the mean net ticket price\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df_sample = pd.read_csv(\n",
    "  filepath_or_buffer='/Users/saejin123/iCloud Drive (Archive)/Desktop/Data-GP1.csv',\n",
    "  header='infer', \n",
    "  index_col=False,\n",
    "  parse_dates=['Dept_Date','Purchase_Date'],\n",
    "  infer_datetime_format=True\n",
    ")\n",
    "\n",
    "df_sample['isNormCabin'] = df_sample['isNormCabin'].astype('object')\n",
    "df_sample['isReturn'] = df_sample['isReturn'].astype('object')\n",
    "df_sample['isOneway'] = df_sample['isOneway'].astype('object')\n",
    "df_sample['is_purchase_weekend'] = df_sample['Purchase_Date'].dt.dayofweek.isin([5,6])\n",
    "df_sample['is_departure_weekend'] = df_sample['Dept_Date'].dt.dayofweek.isin([5,6])\n",
    "df_sample['days_purchase_before_departure'] = (df_sample['Dept_Date'] - df_sample['Purchase_Date']) / np.timedelta64(1, \"D\")\n",
    "df_sample['log10_num_seats_total'] = df_sample['num_seats_total'].apply(np.log10)\n",
    "df_sample['log10_mean_net_ticket_price'] = df_sample['mean_net_ticket_price'].apply(np.log10)\n",
    "\n",
    "demand_df = pd.read_csv('/Users/saejin123/iCloud Drive (Archive)/Desktop/Data-GP1.csv')\n",
    "demand_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "demand_df['D2D'] = df_sample['days_purchase_before_departure']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "demand_df['ln_price'] = np.log(demand_df['mean_net_ticket_price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_departure_date_dummy = pd.get_dummies(data= demand_df['Train_Number_All'])\n",
    "\n",
    "demand_df = pd.concat(objs=[demand_df,df_departure_date_dummy],axis=1)\n",
    "\n",
    "demand_df.rename(\n",
    "  columns={\n",
    "    0: \"train_A\",\n",
    "    1: 'train_B',\n",
    "    2: 'train_C',\n",
    "    3: 'train_D',\n",
    "    4: 'train_E',\n",
    "    5: 'train_F',\n",
    "    6: 'train_G',\n",
    "    7: 'train_H',\n",
    "    8: 'train_I',\n",
    "    9: 'train_J',\n",
    "    10: 'train_K',\n",
    "    11: 'train_L',\n",
    "    12: 'train_M',\n",
    "    13: 'train_N',\n",
    "    14: 'train_O'\n",
    "  },\n",
    "  inplace=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_seats_total</th>\n",
       "      <th>mean_net_ticket_price</th>\n",
       "      <th>Dept_Date</th>\n",
       "      <th>Purchase_Date</th>\n",
       "      <th>Train_Number_All</th>\n",
       "      <th>Culmulative_sales</th>\n",
       "      <th>isNormCabin</th>\n",
       "      <th>isReturn</th>\n",
       "      <th>isOneway</th>\n",
       "      <th>Customer_Cat</th>\n",
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       "      <th>F</th>\n",
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       "      <th>J</th>\n",
       "      <th>K</th>\n",
       "      <th>L</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>188.663422</td>\n",
       "      <td>10/9/19</td>\n",
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       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>358.428869</td>\n",
       "      <td>5/27/19</td>\n",
       "      <td>6/6/18</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>356.372684</td>\n",
       "      <td>5/31/20</td>\n",
       "      <td>6/17/19</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>A</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>396.370505</td>\n",
       "      <td>12/10/19</td>\n",
       "      <td>12/27/18</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>364.264552</td>\n",
       "      <td>5/31/20</td>\n",
       "      <td>6/19/19</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>209692</th>\n",
       "      <td>4</td>\n",
       "      <td>468.650070</td>\n",
       "      <td>3/26/19</td>\n",
       "      <td>3/26/19</td>\n",
       "      <td>L</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>209693</th>\n",
       "      <td>1</td>\n",
       "      <td>468.659177</td>\n",
       "      <td>12/21/18</td>\n",
       "      <td>12/21/18</td>\n",
       "      <td>J</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>209694</th>\n",
       "      <td>1</td>\n",
       "      <td>668.061946</td>\n",
       "      <td>6/9/19</td>\n",
       "      <td>6/9/19</td>\n",
       "      <td>J</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>209695</th>\n",
       "      <td>2</td>\n",
       "      <td>381.438513</td>\n",
       "      <td>1/17/19</td>\n",
       "      <td>1/17/19</td>\n",
       "      <td>L</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209696</th>\n",
       "      <td>3</td>\n",
       "      <td>546.201649</td>\n",
       "      <td>6/25/18</td>\n",
       "      <td>6/25/18</td>\n",
       "      <td>L</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>209697 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        num_seats_total  mean_net_ticket_price Dept_Date Purchase_Date  \\\n",
       "0                     1             188.663422   10/9/19      10/10/18   \n",
       "1                     1             358.428869   5/27/19        6/6/18   \n",
       "2                     1             356.372684   5/31/20       6/17/19   \n",
       "3                     1             396.370505  12/10/19      12/27/18   \n",
       "4                     1             364.264552   5/31/20       6/19/19   \n",
       "...                 ...                    ...       ...           ...   \n",
       "209692                4             468.650070   3/26/19       3/26/19   \n",
       "209693                1             468.659177  12/21/18      12/21/18   \n",
       "209694                1             668.061946    6/9/19        6/9/19   \n",
       "209695                2             381.438513   1/17/19       1/17/19   \n",
       "209696                3             546.201649   6/25/18       6/25/18   \n",
       "\n",
       "       Train_Number_All  Culmulative_sales  isNormCabin  isReturn  isOneway  \\\n",
       "0                     A                  1            1         0         0   \n",
       "1                     B                  1            0         1         0   \n",
       "2                     B                  1            0         1         0   \n",
       "3                     C                  1            0         1         0   \n",
       "4                     B                  2            0         1         0   \n",
       "...                 ...                ...          ...       ...       ...   \n",
       "209692                L                  4            0         0         0   \n",
       "209693                J                  1            0         0         1   \n",
       "209694                J                 10            0         0         0   \n",
       "209695                L                 17            0         0         0   \n",
       "209696                L                  6            0         0         0   \n",
       "\n",
       "       Customer_Cat  ...  F  G  H  I  J  K  L  M  N  O  \n",
       "0                 A  ...  0  0  0  0  0  0  0  0  0  0  \n",
       "1                 A  ...  0  0  0  0  0  0  0  0  0  0  \n",
       "2                 A  ...  0  0  0  0  0  0  0  0  0  0  \n",
       "3                 A  ...  0  0  0  0  0  0  0  0  0  0  \n",
       "4                 A  ...  0  0  0  0  0  0  0  0  0  0  \n",
       "...             ...  ... .. .. .. .. .. .. .. .. .. ..  \n",
       "209692            B  ...  0  0  0  0  0  0  1  0  0  0  \n",
       "209693            B  ...  0  0  0  0  1  0  0  0  0  0  \n",
       "209694            B  ...  0  0  0  0  1  0  0  0  0  0  \n",
       "209695            B  ...  0  0  0  0  0  0  1  0  0  0  \n",
       "209696            B  ...  0  0  0  0  0  0  1  0  0  0  \n",
       "\n",
       "[209697 rows x 27 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demand_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  iloc._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "demand_df['Customer_Cat'].iloc[demand_df['Customer_Cat'] == 'A'] = 0\n",
    "demand_df['Customer_Cat'].iloc[demand_df['Customer_Cat'] == 'B'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Results: Ordinary least squares\n",
      "====================================================================\n",
      "Model:              OLS              Adj. R-squared:     0.574      \n",
      "Dependent Variable: ln_price         AIC:                208204.3775\n",
      "Date:               2021-09-21 18:26 BIC:                208388.9390\n",
      "No. Observations:   209697           Log-Likelihood:     -1.0408e+05\n",
      "Df Model:           17               F-statistic:        1.662e+04  \n",
      "Df Residuals:       209679           Prob (F-statistic): 0.00       \n",
      "R-squared:          0.574            Scale:              0.15801    \n",
      "--------------------------------------------------------------------\n",
      "                    Coef.  Std.Err.     t     P>|t|   [0.025  0.975]\n",
      "--------------------------------------------------------------------\n",
      "Intercept           6.0657   0.0081  745.9994 0.0000  6.0498  6.0817\n",
      "Customer_Cat[T.1]  -0.2906   0.0023 -123.7146 0.0000 -0.2952 -0.2860\n",
      "A                  -0.1583   0.0089  -17.8626 0.0000 -0.1756 -0.1409\n",
      "B                  -0.2121   0.0085  -25.0599 0.0000 -0.2286 -0.1955\n",
      "C                   0.0698   0.0087    8.0161 0.0000  0.0527  0.0869\n",
      "D                  -0.1481   0.0088  -16.8372 0.0000 -0.1653 -0.1308\n",
      "E                  -0.2353   0.0085  -27.7892 0.0000 -0.2519 -0.2187\n",
      "F                  -0.1865   0.0086  -21.6706 0.0000 -0.2033 -0.1696\n",
      "G                  -0.2061   0.0085  -24.2067 0.0000 -0.2228 -0.1894\n",
      "H                  -0.2196   0.0086  -25.5437 0.0000 -0.2365 -0.2028\n",
      "I                  -0.1655   0.0086  -19.1781 0.0000 -0.1824 -0.1486\n",
      "J                  -0.1707   0.0088  -19.3900 0.0000 -0.1880 -0.1535\n",
      "K                  -0.0961   0.0094  -10.2375 0.0000 -0.1144 -0.0777\n",
      "L                   0.0247   0.0095    2.5913 0.0096  0.0060  0.0435\n",
      "M                  -0.1696   0.0091  -18.7240 0.0000 -0.1873 -0.1518\n",
      "O                   0.7045   0.3976    1.7720 0.0764 -0.0747  1.4838\n",
      "isNormCabin        -0.7433   0.0020 -369.9740 0.0000 -0.7473 -0.7394\n",
      "isOneway            0.1510   0.0027   56.6512 0.0000  0.1458  0.1562\n",
      "--------------------------------------------------------------------\n",
      "Omnibus:              4778.312      Durbin-Watson:         1.289    \n",
      "Prob(Omnibus):        0.000         Jarque-Bera (JB):      11105.900\n",
      "Skew:                 -0.014        Prob(JB):              0.000    \n",
      "Kurtosis:             4.127         Condition No.:         681      \n",
      "====================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "reduced_price = ols('ln_price ~ A + B + C + D + E + F + G + H + I + J + K + L + M + O + isNormCabin + isOneway + Customer_Cat', data = demand_df).fit()\n",
    "reduced_seats = ols('num_seats_total ~ A + B + C + D + E + F + G + H + I + J + K + L + M + O + isNormCabin + isOneway + Customer_Cat', data = demand_df).fit()\n",
    "print(reduced_price.summary2())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>               <td>OLS</td>         <td>Adj. R-squared:</td>      <td>0.052</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>  <td>num_seats_total</td>        <td>AIC:</td>         <td>891771.4321</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2021-09-21 18:26</td>        <td>BIC:</td>         <td>891791.9389</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>       <td>209697</td>        <td>Log-Likelihood:</td>   <td>-4.4588e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>              <td>1</td>           <td>F-statistic:</td>      <td>1.145e+04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>         <td>209695</td>      <td>Prob (F-statistic):</td>    <td>0.00</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "      <td>R-squared:</td>            <td>0.052</td>            <td>Scale:</td>          <td>4.1156</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>Intercept</th> <td>6.4695</td>   <td>0.0384</td>  <td>168.2589</td>  <td>0.0000</td> <td>6.3941</td>  <td>6.5449</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ln_price</th>  <td>-0.7784</td>  <td>0.0073</td>  <td>-106.9939</td> <td>0.0000</td> <td>-0.7926</td> <td>-0.7641</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td>Omnibus:</td>    <td>167118.064</td>  <td>Durbin-Watson:</td>      <td>1.749</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Prob(Omnibus):</td>    <td>0.000</td>   <td>Jarque-Bera (JB):</td> <td>8347069.210</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Skew:</td>        <td>3.460</td>       <td>Prob(JB):</td>        <td>0.000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Kurtosis:</td>     <td>33.124</td>    <td>Condition No.:</td>       <td>47</td>     \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Results: Ordinary least squares\n",
       "====================================================================\n",
       "Model:              OLS              Adj. R-squared:     0.052      \n",
       "Dependent Variable: num_seats_total  AIC:                891771.4321\n",
       "Date:               2021-09-21 18:26 BIC:                891791.9389\n",
       "No. Observations:   209697           Log-Likelihood:     -4.4588e+05\n",
       "Df Model:           1                F-statistic:        1.145e+04  \n",
       "Df Residuals:       209695           Prob (F-statistic): 0.00       \n",
       "R-squared:          0.052            Scale:              4.1156     \n",
       "---------------------------------------------------------------------\n",
       "                Coef.   Std.Err.      t      P>|t|    [0.025   0.975]\n",
       "---------------------------------------------------------------------\n",
       "Intercept       6.4695    0.0384   168.2589  0.0000   6.3941   6.5449\n",
       "ln_price       -0.7784    0.0073  -106.9939  0.0000  -0.7926  -0.7641\n",
       "--------------------------------------------------------------------\n",
       "Omnibus:            167118.064     Durbin-Watson:        1.749      \n",
       "Prob(Omnibus):      0.000          Jarque-Bera (JB):     8347069.210\n",
       "Skew:               3.460          Prob(JB):             0.000      \n",
       "Kurtosis:           33.124         Condition No.:        47         \n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structural_form = ols('num_seats_total ~ ln_price', demand_df).fit() # no endo problem -  biased but smaller standard error\n",
    "structural_form.summary2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_price = reduced_price.predict(demand_df[['A' , 'B', 'C', 'D' ,'E', 'F', 'G', 'H' , 'I' , 'J', 'K', 'L' , 'M' ,'O','isNormCabin', 'isOneway', 'Customer_Cat']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reduced_seats.summary2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "demand_df['predPrice'] = predicted_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>               <td>OLS</td>         <td>Adj. R-squared:</td>      <td>0.047</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>  <td>num_seats_total</td>        <td>AIC:</td>         <td>892764.0439</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2021-09-21 18:26</td>        <td>BIC:</td>         <td>892784.5507</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>       <td>209697</td>        <td>Log-Likelihood:</td>   <td>-4.4638e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>              <td>1</td>           <td>F-statistic:</td>      <td>1.040e+04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>         <td>209695</td>      <td>Prob (F-statistic):</td>    <td>0.00</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "      <td>R-squared:</td>            <td>0.047</td>            <td>Scale:</td>          <td>4.1351</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>Intercept</th> <td>7.5373</td>   <td>0.0507</td>  <td>148.5813</td>  <td>0.0000</td> <td>7.4379</td>  <td>7.6367</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>predPrice</th> <td>-0.9818</td>  <td>0.0096</td>  <td>-101.9969</td> <td>0.0000</td> <td>-1.0006</td> <td>-0.9629</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td>Omnibus:</td>    <td>166715.615</td>  <td>Durbin-Watson:</td>      <td>1.770</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Prob(Omnibus):</td>    <td>0.000</td>   <td>Jarque-Bera (JB):</td> <td>8274629.665</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Skew:</td>        <td>3.449</td>       <td>Prob(JB):</td>        <td>0.000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Kurtosis:</td>     <td>32.991</td>    <td>Condition No.:</td>       <td>62</td>     \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Results: Ordinary least squares\n",
       "====================================================================\n",
       "Model:              OLS              Adj. R-squared:     0.047      \n",
       "Dependent Variable: num_seats_total  AIC:                892764.0439\n",
       "Date:               2021-09-21 18:26 BIC:                892784.5507\n",
       "No. Observations:   209697           Log-Likelihood:     -4.4638e+05\n",
       "Df Model:           1                F-statistic:        1.040e+04  \n",
       "Df Residuals:       209695           Prob (F-statistic): 0.00       \n",
       "R-squared:          0.047            Scale:              4.1351     \n",
       "---------------------------------------------------------------------\n",
       "                Coef.   Std.Err.      t      P>|t|    [0.025   0.975]\n",
       "---------------------------------------------------------------------\n",
       "Intercept       7.5373    0.0507   148.5813  0.0000   7.4379   7.6367\n",
       "predPrice      -0.9818    0.0096  -101.9969  0.0000  -1.0006  -0.9629\n",
       "--------------------------------------------------------------------\n",
       "Omnibus:            166715.615     Durbin-Watson:        1.770      \n",
       "Prob(Omnibus):      0.000          Jarque-Bera (JB):     8274629.665\n",
       "Skew:               3.449          Prob(JB):             0.000      \n",
       "Kurtosis:           32.991         Condition No.:        62         \n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "struc_2sls_form = ols('num_seats_total ~ predPrice', demand_df).fit() \n",
    "struc_2sls_form.summary2()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "demand_df['res1stage'] = reduced_price.resid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "hausman_model = ols('num_seats_total ~ ln_price + res1stage', data = demand_df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>               <td>OLS</td>         <td>Adj. R-squared:</td>      <td>0.057</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>  <td>num_seats_total</td>        <td>AIC:</td>         <td>890717.7879</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2021-09-21 18:26</td>        <td>BIC:</td>         <td>890748.5481</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>       <td>209697</td>        <td>Log-Likelihood:</td>   <td>-4.4536e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>              <td>2</td>           <td>F-statistic:</td>        <td>6282.</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>         <td>209694</td>      <td>Prob (F-statistic):</td>    <td>0.00</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "      <td>R-squared:</td>            <td>0.057</td>            <td>Scale:</td>          <td>4.0949</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>Intercept</th> <td>7.5373</td>   <td>0.0505</td>  <td>149.3083</td>  <td>0.0000</td> <td>7.4384</td>  <td>7.6363</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ln_price</th>  <td>-0.9818</td>  <td>0.0096</td>  <td>-102.4960</td> <td>0.0000</td> <td>-1.0005</td> <td>-0.9630</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>res1stage</th> <td>0.4774</td>   <td>0.0147</td>   <td>32.5314</td>  <td>0.0000</td> <td>0.4486</td>  <td>0.5061</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td>Omnibus:</td>    <td>167238.072</td>  <td>Durbin-Watson:</td>      <td>1.763</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Prob(Omnibus):</td>    <td>0.000</td>   <td>Jarque-Bera (JB):</td> <td>8445097.716</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Skew:</td>        <td>3.460</td>       <td>Prob(JB):</td>        <td>0.000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Kurtosis:</td>     <td>33.310</td>    <td>Condition No.:</td>       <td>64</td>     \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Results: Ordinary least squares\n",
       "====================================================================\n",
       "Model:              OLS              Adj. R-squared:     0.057      \n",
       "Dependent Variable: num_seats_total  AIC:                890717.7879\n",
       "Date:               2021-09-21 18:26 BIC:                890748.5481\n",
       "No. Observations:   209697           Log-Likelihood:     -4.4536e+05\n",
       "Df Model:           2                F-statistic:        6282.      \n",
       "Df Residuals:       209694           Prob (F-statistic): 0.00       \n",
       "R-squared:          0.057            Scale:              4.0949     \n",
       "---------------------------------------------------------------------\n",
       "                Coef.   Std.Err.      t      P>|t|    [0.025   0.975]\n",
       "---------------------------------------------------------------------\n",
       "Intercept       7.5373    0.0505   149.3083  0.0000   7.4384   7.6363\n",
       "ln_price       -0.9818    0.0096  -102.4960  0.0000  -1.0005  -0.9630\n",
       "res1stage       0.4774    0.0147    32.5314  0.0000   0.4486   0.5061\n",
       "--------------------------------------------------------------------\n",
       "Omnibus:            167238.072     Durbin-Watson:        1.763      \n",
       "Prob(Omnibus):      0.000          Jarque-Bera (JB):     8445097.716\n",
       "Skew:               3.460          Prob(JB):             0.000      \n",
       "Kurtosis:           33.310         Condition No.:        64         \n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hausman_model.summary2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='predPrice', ylabel='num_seats_total'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='predPrice', y='num_seats_total', data=demand_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ln_price', ylabel='num_seats_total'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='ln_price', y='num_seats_total', data=demand_df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}