Data Preprocessing-Modelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Libraries "
   ]
  },
  {
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   "source": [
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import warnings\n",
    "\n",
    "# Ignore all warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\n",
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       "    }\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>loc</th>\n",
       "      <th>title</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>parking_space</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3583</td>\n",
       "      <td>Katsina</td>\n",
       "      <td>Semi-detached duplex</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1149999.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2748</td>\n",
       "      <td>Ondo</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1672416.689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9261</td>\n",
       "      <td>Ekiti</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3364799.814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2224</td>\n",
       "      <td>Anambra</td>\n",
       "      <td>Detached duplex</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2410306.756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10300</td>\n",
       "      <td>Kogi</td>\n",
       "      <td>Terrace duplex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2600700.898</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>13995</th>\n",
       "      <td>6175</td>\n",
       "      <td>Edo</td>\n",
       "      <td>Bungalow</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2367927.861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13996</th>\n",
       "      <td>9704</td>\n",
       "      <td>Kaduna</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2228516.471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13997</th>\n",
       "      <td>11190</td>\n",
       "      <td>Plateau</td>\n",
       "      <td>Bungalow</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2406812.693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13998</th>\n",
       "      <td>9256</td>\n",
       "      <td>Delta</td>\n",
       "      <td>Flat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3348918.718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13999</th>\n",
       "      <td>8787</td>\n",
       "      <td>Nasarawa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2858516.890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID       loc                 title  bedroom  bathroom  \\\n",
       "0       3583   Katsina  Semi-detached duplex      2.0       2.0   \n",
       "1       2748      Ondo             Apartment      NaN       2.0   \n",
       "2       9261     Ekiti                   NaN      7.0       5.0   \n",
       "3       2224   Anambra       Detached duplex      5.0       2.0   \n",
       "4      10300      Kogi        Terrace duplex      NaN       5.0   \n",
       "...      ...       ...                   ...      ...       ...   \n",
       "13995   6175       Edo              Bungalow      NaN       7.0   \n",
       "13996   9704    Kaduna             Apartment      NaN       7.0   \n",
       "13997  11190   Plateau              Bungalow      8.0       6.0   \n",
       "13998   9256     Delta                  Flat      NaN       6.0   \n",
       "13999   8787  Nasarawa                   NaN      9.0       7.0   \n",
       "\n",
       "       parking_space        price  \n",
       "0                1.0  1149999.565  \n",
       "1                4.0  1672416.689  \n",
       "2                NaN  3364799.814  \n",
       "3                4.0  2410306.756  \n",
       "4                6.0  2600700.898  \n",
       "...              ...          ...  \n",
       "13995            NaN  2367927.861  \n",
       "13996            5.0  2228516.471  \n",
       "13997            5.0  2406812.693  \n",
       "13998            1.0  3348918.718  \n",
       "13999            5.0  2858516.890  \n",
       "\n",
       "[14000 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"Data\\Housing_dataset_train.csv\")\n",
    "test = pd.read_csv(\"Data\\Housing_dataset_test.csv\")\n",
    "train"
   ]
  },
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>loc</th>\n",
       "      <th>title</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>parking_space</th>\n",
       "      <th>price</th>\n",
       "      <th>ind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3583</td>\n",
       "      <td>Katsina</td>\n",
       "      <td>Semi-detached duplex</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1149999.565</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2748</td>\n",
       "      <td>Ondo</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1672416.689</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9261</td>\n",
       "      <td>Ekiti</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3364799.814</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2224</td>\n",
       "      <td>Anambra</td>\n",
       "      <td>Detached duplex</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2410306.756</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10300</td>\n",
       "      <td>Kogi</td>\n",
       "      <td>Terrace duplex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2600700.898</td>\n",
       "      <td>1</td>\n",
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       "      <th>...</th>\n",
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       "      <th>5995</th>\n",
       "      <td>1594</td>\n",
       "      <td>Ekiti</td>\n",
       "      <td>Flat</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5996</th>\n",
       "      <td>2416</td>\n",
       "      <td>Adamawa</td>\n",
       "      <td>Terrace duplex</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>10195</td>\n",
       "      <td>Oyo</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>9455</td>\n",
       "      <td>Bauchi</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>11736</td>\n",
       "      <td>Sokoto</td>\n",
       "      <td>Mansion</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID      loc                 title  bedroom  bathroom  parking_space  \\\n",
       "0      3583  Katsina  Semi-detached duplex      2.0       2.0            1.0   \n",
       "1      2748     Ondo             Apartment      NaN       2.0            4.0   \n",
       "2      9261    Ekiti                   NaN      7.0       5.0            NaN   \n",
       "3      2224  Anambra       Detached duplex      5.0       2.0            4.0   \n",
       "4     10300     Kogi        Terrace duplex      NaN       5.0            6.0   \n",
       "...     ...      ...                   ...      ...       ...            ...   \n",
       "5995   1594    Ekiti                  Flat      4.0       5.0            2.0   \n",
       "5996   2416  Adamawa        Terrace duplex      5.0       7.0            1.0   \n",
       "5997  10195      Oyo             Townhouse      4.0       1.0            4.0   \n",
       "5998   9455   Bauchi                  Flat      3.0       7.0            5.0   \n",
       "5999  11736   Sokoto               Mansion      6.0       1.0            6.0   \n",
       "\n",
       "            price  ind  \n",
       "0     1149999.565    1  \n",
       "1     1672416.689    1  \n",
       "2     3364799.814    1  \n",
       "3     2410306.756    1  \n",
       "4     2600700.898    1  \n",
       "...           ...  ...  \n",
       "5995          NaN    0  \n",
       "5996          NaN    0  \n",
       "5997          NaN    0  \n",
       "5998          NaN    0  \n",
       "5999          NaN    0  \n",
       "\n",
       "[20000 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['ind'] = 1\n",
    "test['ind'] = 0\n",
    "\n",
    "# Concatenate train and test DataFrames\n",
    "combined_df = pd.concat([train, test])\n",
    "\n",
    "# Output the combined DataFrame\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>loc</th>\n",
       "      <th>title</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>parking_space</th>\n",
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       "      <th>ind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3583</td>\n",
       "      <td>Katsina</td>\n",
       "      <td>Semi-detached duplex</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2748</td>\n",
       "      <td>Ondo</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1672416.689</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2224</td>\n",
       "      <td>Anambra</td>\n",
       "      <td>Detached duplex</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2410306.756</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10300</td>\n",
       "      <td>Kogi</td>\n",
       "      <td>Terrace duplex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2600700.898</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1733</td>\n",
       "      <td>Borno</td>\n",
       "      <td>Mansion</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1341750.867</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5995</th>\n",
       "      <td>1594</td>\n",
       "      <td>Ekiti</td>\n",
       "      <td>Flat</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5996</th>\n",
       "      <td>2416</td>\n",
       "      <td>Adamawa</td>\n",
       "      <td>Terrace duplex</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>10195</td>\n",
       "      <td>Oyo</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>9455</td>\n",
       "      <td>Bauchi</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>11736</td>\n",
       "      <td>Sokoto</td>\n",
       "      <td>Mansion</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16531 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID      loc                 title  bedroom  bathroom  parking_space  \\\n",
       "0      3583  Katsina  Semi-detached duplex      2.0       2.0            1.0   \n",
       "1      2748     Ondo             Apartment      NaN       2.0            4.0   \n",
       "3      2224  Anambra       Detached duplex      5.0       2.0            4.0   \n",
       "4     10300     Kogi        Terrace duplex      NaN       5.0            6.0   \n",
       "5      1733    Borno               Mansion      NaN       1.0            3.0   \n",
       "...     ...      ...                   ...      ...       ...            ...   \n",
       "5995   1594    Ekiti                  Flat      4.0       5.0            2.0   \n",
       "5996   2416  Adamawa        Terrace duplex      5.0       7.0            1.0   \n",
       "5997  10195      Oyo             Townhouse      4.0       1.0            4.0   \n",
       "5998   9455   Bauchi                  Flat      3.0       7.0            5.0   \n",
       "5999  11736   Sokoto               Mansion      6.0       1.0            6.0   \n",
       "\n",
       "            price  ind  \n",
       "0     1149999.565    1  \n",
       "1     1672416.689    1  \n",
       "3     2410306.756    1  \n",
       "4     2600700.898    1  \n",
       "5     1341750.867    1  \n",
       "...           ...  ...  \n",
       "5995          NaN    0  \n",
       "5996          NaN    0  \n",
       "5997          NaN    0  \n",
       "5998          NaN    0  \n",
       "5999          NaN    0  \n",
       "\n",
       "[16531 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df.dropna(subset=['loc','title'],inplace=True)\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-03T23:24:35.138208Z",
     "iopub.status.busy": "2023-08-03T23:24:35.137638Z",
     "iopub.status.idle": "2023-08-03T23:24:35.146485Z",
     "shell.execute_reply": "2023-08-03T23:24:35.145523Z",
     "shell.execute_reply.started": "2023-08-03T23:24:35.138162Z"
    }
   },
   "outputs": [],
   "source": [
    "median_bedroom = combined_df['bedroom'].median()\n",
    "combined_df['bedroom'].fillna(median_bedroom, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-03T23:25:12.467920Z",
     "iopub.status.busy": "2023-08-03T23:25:12.467407Z",
     "iopub.status.idle": "2023-08-03T23:25:12.476137Z",
     "shell.execute_reply": "2023-08-03T23:25:12.474961Z",
     "shell.execute_reply.started": "2023-08-03T23:25:12.467852Z"
    }
   },
   "outputs": [],
   "source": [
    "median_bathroom = combined_df['bathroom'].median()\n",
    "combined_df['bathroom'].fillna(median_bathroom, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-03T23:25:14.878574Z",
     "iopub.status.busy": "2023-08-03T23:25:14.878211Z",
     "iopub.status.idle": "2023-08-03T23:25:14.885997Z",
     "shell.execute_reply": "2023-08-03T23:25:14.885150Z",
     "shell.execute_reply.started": "2023-08-03T23:25:14.878545Z"
    }
   },
   "outputs": [],
   "source": [
    "median_parking_space = combined_df['parking_space'].median()\n",
    "combined_df['parking_space'].fillna(median_parking_space, inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                  0\n",
       "loc                 0\n",
       "title               0\n",
       "bedroom             0\n",
       "bathroom            0\n",
       "parking_space       0\n",
       "price            6000\n",
       "ind                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_df['is_lagos'] = combined_df['loc'].apply(lambda x: 1 if x == \"Lagos\" else 0)\n",
    "combined_df['is_mansion'] = combined_df['title'].apply(lambda x: 1 if x == \"Mansion\" else 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-03T23:25:49.020552Z",
     "iopub.status.busy": "2023-08-03T23:25:49.019473Z",
     "iopub.status.idle": "2023-08-03T23:25:49.029390Z",
     "shell.execute_reply": "2023-08-03T23:25:49.028066Z",
     "shell.execute_reply.started": "2023-08-03T23:25:49.020500Z"
    }
   },
   "outputs": [],
   "source": [
    "combined_df['comfort_ind'] = combined_df['bedroom']/combined_df['bathroom']\n",
    "combined_df['size'] = combined_df['bedroom']+combined_df['bathroom']+combined_df['parking_space']\n",
    "combined_df['comfort_by_size'] = combined_df['comfort_ind']*combined_df['size']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-03T23:25:52.976067Z",
     "iopub.status.busy": "2023-08-03T23:25:52.975600Z",
     "iopub.status.idle": "2023-08-03T23:25:53.021579Z",
     "shell.execute_reply": "2023-08-03T23:25:53.019769Z",
     "shell.execute_reply.started": "2023-08-03T23:25:52.976031Z"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>loc</th>\n",
       "      <th>title</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>parking_space</th>\n",
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       "      <th>population_density_level</th>\n",
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       "  <tbody>\n",
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       "      <td>3583</td>\n",
       "      <td>Katsina</td>\n",
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       "      <td>2748</td>\n",
       "      <td>Ondo</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2224</td>\n",
       "      <td>Anambra</td>\n",
       "      <td>Detached duplex</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2410306.756</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>11.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10300</td>\n",
       "      <td>Kogi</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1733</td>\n",
       "      <td>Borno</td>\n",
       "      <td>Mansion</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1341750.867</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>5995</th>\n",
       "      <td>1594</td>\n",
       "      <td>Ekiti</td>\n",
       "      <td>Flat</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5996</th>\n",
       "      <td>2416</td>\n",
       "      <td>Adamawa</td>\n",
       "      <td>Terrace duplex</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>13.0</td>\n",
       "      <td>9.285714</td>\n",
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       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>10195</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>9455</td>\n",
       "      <td>Bauchi</td>\n",
       "      <td>Flat</td>\n",
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       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>11736</td>\n",
       "      <td>Sokoto</td>\n",
       "      <td>Mansion</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16531 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID      loc                 title  bedroom  bathroom  parking_space  \\\n",
       "0      3583  Katsina  Semi-detached duplex      2.0       2.0            1.0   \n",
       "1      2748     Ondo             Apartment      4.0       2.0            4.0   \n",
       "3      2224  Anambra       Detached duplex      5.0       2.0            4.0   \n",
       "4     10300     Kogi        Terrace duplex      4.0       5.0            6.0   \n",
       "5      1733    Borno               Mansion      4.0       1.0            3.0   \n",
       "...     ...      ...                   ...      ...       ...            ...   \n",
       "5995   1594    Ekiti                  Flat      4.0       5.0            2.0   \n",
       "5996   2416  Adamawa        Terrace duplex      5.0       7.0            1.0   \n",
       "5997  10195      Oyo             Townhouse      4.0       1.0            4.0   \n",
       "5998   9455   Bauchi                  Flat      3.0       7.0            5.0   \n",
       "5999  11736   Sokoto               Mansion      6.0       1.0            6.0   \n",
       "\n",
       "            price  ind  is_lagos  is_mansion  comfort_ind  size  \\\n",
       "0     1149999.565    1         0           0     1.000000   5.0   \n",
       "1     1672416.689    1         0           0     2.000000  10.0   \n",
       "3     2410306.756    1         0           0     2.500000  11.0   \n",
       "4     2600700.898    1         0           0     0.800000  15.0   \n",
       "5     1341750.867    1         0           1     4.000000   8.0   \n",
       "...           ...  ...       ...         ...          ...   ...   \n",
       "5995          NaN    0         0           0     0.800000  11.0   \n",
       "5996          NaN    0         0           0     0.714286  13.0   \n",
       "5997          NaN    0         0           0     4.000000   9.0   \n",
       "5998          NaN    0         0           0     0.428571  15.0   \n",
       "5999          NaN    0         0           1     6.000000  13.0   \n",
       "\n",
       "      comfort_by_size  population_density_level  \n",
       "0            5.000000                         7  \n",
       "1           20.000000                         7  \n",
       "3           27.500000                        10  \n",
       "4           12.000000                         3  \n",
       "5           32.000000                         1  \n",
       "...               ...                       ...  \n",
       "5995         8.800000                         8  \n",
       "5996         9.285714                         2  \n",
       "5997        36.000000                         6  \n",
       "5998         6.428571                         6  \n",
       "5999        78.000000                         4  \n",
       "\n",
       "[16531 rows x 14 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_level_1 = ['Anambra', 'Enugu', 'Imo', 'Lagos']\n",
    "population_level_2 = ['Abia', 'Kano', 'Rivers']\n",
    "population_level_3 = ['Akwa Ibom', 'Ebonyi', 'Ekiti', 'Osun']\n",
    "population_level_4 = ['Katsina', 'Ogun', 'Ondo']\n",
    "population_level_5 = ['Bauchi', 'Delta', 'Jigawa', 'Oyo']\n",
    "population_level_6 = ['Bayelsa', 'Edo', 'Gombe']\n",
    "population_level_7 = ['Cross River', 'Kaduna', 'Plateau', 'Sokoto']\n",
    "population_level_8 = ['Kebbi', 'Kogi', 'Zamfara']\n",
    "population_level_9 = ['Adamawa', 'Benue', 'Kwara', 'Nasarawa']\n",
    "population_level_10 = ['Borno', 'Niger', 'Taraba', 'Yobe']\n",
    "\n",
    "combined_df['population_density_level'] = combined_df['loc'].apply(lambda x: 10 if x in population_level_1\n",
    "                                                                    else 9 if x in population_level_2\n",
    "                                                                    else 8 if x in population_level_3\n",
    "                                                                    else 7 if x in population_level_4\n",
    "                                                                    else 6 if x in population_level_5\n",
    "                                                                    else 5 if x in population_level_6\n",
    "                                                                    else 4 if x in population_level_7\n",
    "                                                                    else 3 if x in population_level_8\n",
    "                                                                    else 2 if x in population_level_9\n",
    "                                                                    else 1 if x in population_level_10\n",
    "                                                                    else 0)\n",
    "\n",
    "\n",
    "  \n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-03T23:25:59.415260Z",
     "iopub.status.busy": "2023-08-03T23:25:59.414840Z",
     "iopub.status.idle": "2023-08-03T23:25:59.481376Z",
     "shell.execute_reply": "2023-08-03T23:25:59.480545Z",
     "shell.execute_reply.started": "2023-08-03T23:25:59.415226Z"
    }
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>title</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>parking_space</th>\n",
       "      <th>price</th>\n",
       "      <th>ind</th>\n",
       "      <th>is_lagos</th>\n",
       "      <th>is_mansion</th>\n",
       "      <th>comfort_ind</th>\n",
       "      <th>size</th>\n",
       "      <th>comfort_by_size</th>\n",
       "      <th>population_density_level</th>\n",
       "      <th>loc_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3583</td>\n",
       "      <td>Semi-detached duplex</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1149999.565</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2748</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1672416.689</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2224</td>\n",
       "      <td>Detached duplex</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2410306.756</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>10</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10300</td>\n",
       "      <td>Terrace duplex</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2600700.898</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1733</td>\n",
       "      <td>Mansion</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1341750.867</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.17</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",
       "      <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>5995</th>\n",
       "      <td>1594</td>\n",
       "      <td>Flat</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5996</th>\n",
       "      <td>2416</td>\n",
       "      <td>Terrace duplex</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.285714</td>\n",
       "      <td>2</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>10195</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>9455</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.428571</td>\n",
       "      <td>6</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>11736</td>\n",
       "      <td>Mansion</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16531 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID                 title  bedroom  bathroom  parking_space  \\\n",
       "0      3583  Semi-detached duplex      2.0       2.0            1.0   \n",
       "1      2748             Apartment      4.0       2.0            4.0   \n",
       "3      2224       Detached duplex      5.0       2.0            4.0   \n",
       "4     10300        Terrace duplex      4.0       5.0            6.0   \n",
       "5      1733               Mansion      4.0       1.0            3.0   \n",
       "...     ...                   ...      ...       ...            ...   \n",
       "5995   1594                  Flat      4.0       5.0            2.0   \n",
       "5996   2416        Terrace duplex      5.0       7.0            1.0   \n",
       "5997  10195             Townhouse      4.0       1.0            4.0   \n",
       "5998   9455                  Flat      3.0       7.0            5.0   \n",
       "5999  11736               Mansion      6.0       1.0            6.0   \n",
       "\n",
       "            price  ind  is_lagos  is_mansion  comfort_ind  size  \\\n",
       "0     1149999.565    1         0           0     1.000000   5.0   \n",
       "1     1672416.689    1         0           0     2.000000  10.0   \n",
       "3     2410306.756    1         0           0     2.500000  11.0   \n",
       "4     2600700.898    1         0           0     0.800000  15.0   \n",
       "5     1341750.867    1         0           1     4.000000   8.0   \n",
       "...           ...  ...       ...         ...          ...   ...   \n",
       "5995          NaN    0         0           0     0.800000  11.0   \n",
       "5996          NaN    0         0           0     0.714286  13.0   \n",
       "5997          NaN    0         0           0     4.000000   9.0   \n",
       "5998          NaN    0         0           0     0.428571  15.0   \n",
       "5999          NaN    0         0           1     6.000000  13.0   \n",
       "\n",
       "      comfort_by_size  population_density_level  loc_encoded  \n",
       "0            5.000000                         7         0.20  \n",
       "1           20.000000                         7         0.23  \n",
       "3           27.500000                        10         0.24  \n",
       "4           12.000000                         3         0.18  \n",
       "5           32.000000                         1         0.17  \n",
       "...               ...                       ...          ...  \n",
       "5995         8.800000                         8         0.21  \n",
       "5996         9.285714                         2         0.19  \n",
       "5997        36.000000                         6         0.23  \n",
       "5998         6.428571                         6         0.17  \n",
       "5999        78.000000                         4         0.17  \n",
       "\n",
       "[16531 rows x 14 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import category_encoders as ce\n",
    "\n",
    "# Assuming you have the combined_df DataFrame with 'loc' and 'price' columns\n",
    "\n",
    "# Create a new DataFrame to store the encoded values\n",
    "encoded_df = combined_df.copy()\n",
    "\n",
    "# Define the encoder instance\n",
    "encoder = ce.TargetEncoder(cols=['loc'])\n",
    "\n",
    "# Fit the encoder on the 'loc' column and 'price' target variable\n",
    "encoder.fit(encoded_df['loc'], encoded_df['price'])\n",
    "\n",
    "# Transform the 'loc' column with the encoded values\n",
    "encoded_df['loc_encoded'] = encoder.transform(encoded_df['loc'])\n",
    "\n",
    "\n",
    "encoded_df['loc_encoded'] = round((encoded_df['loc_encoded']/10000000),2)\n",
    "# Drop the original 'loc' column if you no longer need it\n",
    "encoded_df.drop('loc', axis=1, inplace=True)\n",
    "\n",
    "# Print the updated DataFrame with the encoded 'loc' column\n",
    "encoded_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-03T23:26:03.788543Z",
     "iopub.status.busy": "2023-08-03T23:26:03.788092Z",
     "iopub.status.idle": "2023-08-03T23:26:03.854887Z",
     "shell.execute_reply": "2023-08-03T23:26:03.853455Z",
     "shell.execute_reply.started": "2023-08-03T23:26:03.788506Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>bedroom</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>parking_space</th>\n",
       "      <th>price</th>\n",
       "      <th>ind</th>\n",
       "      <th>is_lagos</th>\n",
       "      <th>is_mansion</th>\n",
       "      <th>comfort_ind</th>\n",
       "      <th>size</th>\n",
       "      <th>comfort_by_size</th>\n",
       "      <th>population_density_level</th>\n",
       "      <th>loc_encoded</th>\n",
       "      <th>title_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3583</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1149999.565</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>7</td>\n",
       "      <td>0.20</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2748</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1672416.689</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.23</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2224</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2410306.756</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>10</td>\n",
       "      <td>0.24</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10300</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2600700.898</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.18</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1733</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1341750.867</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.17</td>\n",
       "      <td>10</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",
       "      <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>16526</th>\n",
       "      <td>1594</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.21</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16527</th>\n",
       "      <td>2416</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.285714</td>\n",
       "      <td>2</td>\n",
       "      <td>0.19</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16528</th>\n",
       "      <td>10195</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.23</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16529</th>\n",
       "      <td>9455</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.428571</td>\n",
       "      <td>6</td>\n",
       "      <td>0.17</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16530</th>\n",
       "      <td>11736</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.17</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16531 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  bedroom  bathroom  parking_space        price  ind  is_lagos  \\\n",
       "0       3583      2.0       2.0            1.0  1149999.565    1         0   \n",
       "1       2748      4.0       2.0            4.0  1672416.689    1         0   \n",
       "2       2224      5.0       2.0            4.0  2410306.756    1         0   \n",
       "3      10300      4.0       5.0            6.0  2600700.898    1         0   \n",
       "4       1733      4.0       1.0            3.0  1341750.867    1         0   \n",
       "...      ...      ...       ...            ...          ...  ...       ...   \n",
       "16526   1594      4.0       5.0            2.0          NaN    0         0   \n",
       "16527   2416      5.0       7.0            1.0          NaN    0         0   \n",
       "16528  10195      4.0       1.0            4.0          NaN    0         0   \n",
       "16529   9455      3.0       7.0            5.0          NaN    0         0   \n",
       "16530  11736      6.0       1.0            6.0          NaN    0         0   \n",
       "\n",
       "       is_mansion  comfort_ind  size  comfort_by_size  \\\n",
       "0               0     1.000000   5.0         5.000000   \n",
       "1               0     2.000000  10.0        20.000000   \n",
       "2               0     2.500000  11.0        27.500000   \n",
       "3               0     0.800000  15.0        12.000000   \n",
       "4               1     4.000000   8.0        32.000000   \n",
       "...           ...          ...   ...              ...   \n",
       "16526           0     0.800000  11.0         8.800000   \n",
       "16527           0     0.714286  13.0         9.285714   \n",
       "16528           0     4.000000   9.0        36.000000   \n",
       "16529           0     0.428571  15.0         6.428571   \n",
       "16530           1     6.000000  13.0        78.000000   \n",
       "\n",
       "       population_density_level  loc_encoded  title_rank  \n",
       "0                             7         0.20           5  \n",
       "1                             7         0.23           2  \n",
       "2                            10         0.24           8  \n",
       "3                             3         0.18           6  \n",
       "4                             1         0.17          10  \n",
       "...                         ...          ...         ...  \n",
       "16526                         8         0.21           3  \n",
       "16527                         2         0.19           6  \n",
       "16528                         6         0.23           7  \n",
       "16529                         6         0.17           3  \n",
       "16530                         4         0.17          10  \n",
       "\n",
       "[16531 rows x 14 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Assuming you have the combined_df DataFrame with 'title' and 'price' columns\n",
    "\n",
    "# Calculate the average price for each title\n",
    "title_average_price = combined_df.groupby('title')['price'].mean()\n",
    "\n",
    "# Create a new DataFrame to store the title and its corresponding average price\n",
    "title_avg_price_df = pd.DataFrame({'title': title_average_price.index, 'avg_price': title_average_price.values})\n",
    "\n",
    "# Sort the DataFrame by the average price in ascending order\n",
    "title_avg_price_df.sort_values(by='avg_price', inplace=True)\n",
    "\n",
    "# Create a new column 'title_rank' with the rank based on average price\n",
    "title_avg_price_df['title_rank'] = range(1, len(title_avg_price_df) + 1)\n",
    "\n",
    "# Merge the 'title_rank' column back to the original DataFrame based on 'title' column\n",
    "encoded_df = pd.merge(encoded_df, title_avg_price_df[['title', 'title_rank']], on='title', how='left')\n",
    "\n",
    "# Sort the DataFrame by 'title_rank' column to order the titles based on average price rank\n",
    "#encoded_df.sort_values(by='title_rank', inplace=True)\n",
    "\n",
    "# Drop the 'title_rank' column if you no longer need it\n",
    "encoded_df.drop('title', axis=1, inplace=True)\n",
    "\n",
    "# Print the updated DataFrame with the 'title' column ranked by average price\n",
    "encoded_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-03T23:27:09.930185Z",
     "iopub.status.busy": "2023-08-03T23:27:09.929748Z",
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     "shell.execute_reply": "2023-08-03T23:27:09.933961Z",
     "shell.execute_reply.started": "2023-08-03T23:27:09.930152Z"
    }
   },
   "outputs": [],
   "source": [
    "combined_df = encoded_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-03T23:27:11.364537Z",
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     "shell.execute_reply": "2023-08-03T23:27:11.393099Z",
     "shell.execute_reply.started": "2023-08-03T23:27:11.364509Z"
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       "          ID  bedroom  bathroom  parking_space        price  is_lagos  \\\n",
       "0       3583      2.0       2.0            1.0  1149999.565         0   \n",
       "1       2748      4.0       2.0            4.0  1672416.689         0   \n",
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       "...      ...      ...       ...            ...          ...       ...   \n",
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       "10530   9256      4.0       6.0            1.0  3348918.718         0   \n",
       "\n",
       "       is_mansion  comfort_ind  size  comfort_by_size  \\\n",
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       "\n",
       "       population_density_level  loc_encoded  title_rank  \n",
       "0                             7         0.20           5  \n",
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       "2                            10         0.24           8  \n",
       "3                             3         0.18           6  \n",
       "4                             1         0.17          10  \n",
       "...                         ...          ...         ...  \n",
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       "10528                         4         0.19           2  \n",
       "10529                         4         0.19           4  \n",
       "10530                         6         0.27           3  \n",
       "\n",
       "[10531 rows x 13 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = combined_df[combined_df.ind == 1]\n",
    "test = combined_df[combined_df.ind == 0]\n",
    "\n",
    "train.drop('ind',axis=1,inplace=True)\n",
    "test.drop(['ind','price'],axis=1,inplace=True)\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
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       "      <td>4</td>\n",
       "      <td>0.19</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10529</th>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>10530</th>\n",
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       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>6</td>\n",
       "      <td>0.27</td>\n",
       "      <td>3</td>\n",
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       "  </tbody>\n",
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       "<p>10531 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "       bedroom  bathroom  parking_space  is_lagos  is_mansion  comfort_ind  \\\n",
       "0          2.0       2.0            1.0         0           0     1.000000   \n",
       "1          4.0       2.0            4.0         0           0     2.000000   \n",
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       "3          4.0       5.0            6.0         0           0     0.800000   \n",
       "4          4.0       1.0            3.0         0           1     4.000000   \n",
       "...        ...       ...            ...       ...         ...          ...   \n",
       "10526      8.0       1.0            6.0         0           0     8.000000   \n",
       "10527      4.0       7.0            3.0         0           0     0.571429   \n",
       "10528      4.0       7.0            5.0         0           0     0.571429   \n",
       "10529      8.0       6.0            5.0         0           0     1.333333   \n",
       "10530      4.0       6.0            1.0         0           0     0.666667   \n",
       "\n",
       "       size  comfort_by_size  population_density_level  loc_encoded  \\\n",
       "0       5.0         5.000000                         7         0.20   \n",
       "1      10.0        20.000000                         7         0.23   \n",
       "2      11.0        27.500000                        10         0.24   \n",
       "3      15.0        12.000000                         3         0.18   \n",
       "4       8.0        32.000000                         1         0.17   \n",
       "...     ...              ...                       ...          ...   \n",
       "10526  15.0       120.000000                         1         0.19   \n",
       "10527  14.0         8.000000                         5         0.23   \n",
       "10528  16.0         9.142857                         4         0.19   \n",
       "10529  19.0        25.333333                         4         0.19   \n",
       "10530  11.0         7.333333                         6         0.27   \n",
       "\n",
       "       title_rank  \n",
       "0               5  \n",
       "1               2  \n",
       "2               8  \n",
       "3               6  \n",
       "4              10  \n",
       "...           ...  \n",
       "10526           8  \n",
       "10527           4  \n",
       "10528           2  \n",
       "10529           4  \n",
       "10530           3  \n",
       "\n",
       "[10531 rows x 11 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X = train.drop(['geopolitical_zone'],axis=1)\n",
    "X = train.drop(['ID','price'],axis=1)\n",
    "y = train.price\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Baseline Model - Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=3, min_samples_leaf=2, random_state=42)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.tree import DecisionTreeRegressor, export_graphviz\n",
    "import graphviz\n",
    "\n",
    "\n",
    "\n",
    "# Specify the Graphviz executable path directly within graphviz\n",
    "#graphviz.executables[\"dot\"] = 'C:/Program Files (x86)/Graphviz2.38/bin/dot.exe'\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create a Decision Tree regressor\n",
    "dt_regressor = DecisionTreeRegressor(random_state=42, max_depth=3, min_samples_split=2, min_samples_leaf=2)\n",
    "\n",
    "# Perform cross-validation on the regressor\n",
    "cv_scores = cross_val_score(dt_regressor, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "\n",
    "# Train the decision tree on the training data\n",
    "dt_regressor.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "<graphviz.sources.Source at 0x1a1cca4b3a0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "feature_names = X.columns\n",
    "tree_graph = tree.export_graphviz(dt_regressor, out_file=None, feature_names=feature_names,)\n",
    "graphviz.Source(tree_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation RMSE scores: [719767.50220294 697484.30437678 707338.52542303 702711.6596019\n",
      " 654620.27503047]\n",
      "Mean CV RMSE: 696384.4533270254\n",
      "Test set RMSE: 674132.5679778286\n"
     ]
    }
   ],
   "source": [
    "# Display the decision tree using graphviz\n",
    "dot_data = export_graphviz(dt_regressor, out_file=None, \n",
    "                           feature_names=X.columns, \n",
    "                           filled=True, rounded=True, special_characters=True)\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph.view(filename=\"decision_tree_diag\")  # Save the tree as a PDF or PNG image\n",
    "\n",
    "# Convert the negative MSE scores to positive and take the square root to get RMSE\n",
    "cv_rmse_scores = (-cv_scores) ** 0.5\n",
    "\n",
    "# Print the cross-validation scores\n",
    "print(\"Cross-validation RMSE scores:\", cv_rmse_scores)\n",
    "print(\"Mean CV RMSE:\", cv_rmse_scores.mean())\n",
    "\n",
    "# Evaluate the model on the test set\n",
    "y_pred = dt_regressor.predict(X_test)\n",
    "test_rmse = ((y_pred - y_test) ** 2).mean() ** 0.5\n",
    "print(\"Test set RMSE:\", test_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-03T23:27:20.203319Z",
     "iopub.status.busy": "2023-08-03T23:27:20.202912Z",
     "iopub.status.idle": "2023-08-03T23:27:34.006483Z",
     "shell.execute_reply": "2023-08-03T23:27:34.005515Z",
     "shell.execute_reply.started": "2023-08-03T23:27:20.203285Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation RMSE: [420219.33079842 459504.72911971 395578.51878319 414523.45637395\n",
      " 394107.35725026]\n",
      "416786.67846510716\n"
     ]
    }
   ],
   "source": [
    "from catboost import CatBoostRegressor\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "\n",
    "# Assuming you have the features in X and the target variable in y\n",
    "\n",
    "# Define the CatBoost model\n",
    "model = CatBoostRegressor(loss_function='RMSE', random_seed=42,silent=True)\n",
    "\n",
    "# Define the cross-validation strategy\n",
    "cv = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "# Perform cross-validation and calculate RMSE\n",
    "rmse_scores = np.sqrt(-cross_val_score(model, X, y, cv=cv, scoring='neg_mean_squared_error'))\n",
    "\n",
    "# Train the model on the entire dataset\n",
    "model.fit(X, y)\n",
    "\n",
    "# Print the cross-validation RMSE scores\n",
    "print('Cross-Validation RMSE:', rmse_scores)\n",
    "print(rmse_scores.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-03T23:19:14.813231Z",
     "iopub.status.busy": "2023-08-03T23:19:14.812835Z",
     "iopub.status.idle": "2023-08-03T23:19:15.113665Z",
     "shell.execute_reply": "2023-08-03T23:19:15.112509Z",
     "shell.execute_reply.started": "2023-08-03T23:19:14.813190Z"
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "feature_importance = model.get_feature_importance()\n",
    "\n",
    "# Get the names of the features\n",
    "feature_names = X.columns\n",
    "\n",
    "# Create a DataFrame to store the feature importance scores with corresponding feature names\n",
    "feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importance})\n",
    "\n",
    "# Sort the DataFrame by importance in descending order\n",
    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Plot the feature importance\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(feature_importance_df['Feature'], feature_importance_df['Importance'],color='#5e17eb')\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Feature')\n",
    "plt.ylabel('Importance')\n",
    "plt.title('Feature Importance')\n",
    "plt.savefig('plots/FE_importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10531</th>\n",
       "      <td>845</td>\n",
       "      <td>2.442882e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10532</th>\n",
       "      <td>1924</td>\n",
       "      <td>1.051200e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10533</th>\n",
       "      <td>10718</td>\n",
       "      <td>1.268340e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10534</th>\n",
       "      <td>12076</td>\n",
       "      <td>8.487365e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10535</th>\n",
       "      <td>12254</td>\n",
       "      <td>1.917632e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16526</th>\n",
       "      <td>1594</td>\n",
       "      <td>1.844463e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16527</th>\n",
       "      <td>2416</td>\n",
       "      <td>1.992104e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16528</th>\n",
       "      <td>10195</td>\n",
       "      <td>1.962001e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16529</th>\n",
       "      <td>9455</td>\n",
       "      <td>1.341037e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16530</th>\n",
       "      <td>11736</td>\n",
       "      <td>3.421216e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID         price\n",
       "10531    845  2.442882e+06\n",
       "10532   1924  1.051200e+06\n",
       "10533  10718  1.268340e+06\n",
       "10534  12076  8.487365e+06\n",
       "10535  12254  1.917632e+06\n",
       "...      ...           ...\n",
       "16526   1594  1.844463e+06\n",
       "16527   2416  1.992104e+06\n",
       "16528  10195  1.962001e+06\n",
       "16529   9455  1.341037e+06\n",
       "16530  11736  3.421216e+06\n",
       "\n",
       "[6000 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = test.drop(['ID'], axis=1)\n",
    "\n",
    "# Use the trained model to predict prices for the test dataset\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Reshape the predicted values to be a 1-dimensional array\n",
    "y_pred = y_pred.reshape(-1)\n",
    "\n",
    "# Create a DataFrame with ID and predicted price columns\n",
    "predictions_df = pd.DataFrame({'ID': test['ID'], 'price': y_pred})\n",
    "\n",
    "# Display the predictions DataFrame\n",
    "predictions_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-03T23:20:11.839456Z",
     "iopub.status.busy": "2023-08-03T23:20:11.838112Z",
     "iopub.status.idle": "2023-08-03T23:20:11.862994Z",
     "shell.execute_reply": "2023-08-03T23:20:11.861894Z",
     "shell.execute_reply.started": "2023-08-03T23:20:11.839407Z"
    }
   },
   "outputs": [],
   "source": [
    "predictions_df.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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