Project2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research on apartment sales ads\n",
    "\n",
    "You will have the data from a real estate agency. It is an archive of sales ads for realty in St. Petersburg, Russia, and the surrounding areas collected over the past few years. You’ll need to learn how to determine the market value of real estate properties. Your task is to define the parameters. This will make it possible to build an automated system that is capable of detecting anomalies and fraudulent activity.\n",
    "\n",
    "There are two different types of data available for every apartment for sale. The first type is a user’s input. The second type is received automatically based upon the map data. For example, the distance from the city center, airport, the nearest park or body of water. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1. Open the data file and study the general information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.read_csv('/datasets/real_estate_data_us.csv', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "uploaded file and read it but it came out all together so had to separate it and used the function sep=\n",
    "Also added library to be able to create graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23699 entries, 0 to 23698\n",
      "Data columns (total 22 columns):\n",
      "date_posted          23699 non-null object\n",
      "days_listed          20518 non-null float64\n",
      "last_price           23699 non-null float64\n",
      "bedrooms             23699 non-null int64\n",
      "kitchen_area         21421 non-null float64\n",
      "living_area          21796 non-null float64\n",
      "total_area           23699 non-null float64\n",
      "balconies            12180 non-null float64\n",
      "ceiling_height       14504 non-null float64\n",
      "floors_total         23613 non-null float64\n",
      "floor                23699 non-null int64\n",
      "total_images         23699 non-null int64\n",
      "bike_parking         2775 non-null object\n",
      "is_studio            23699 non-null bool\n",
      "is_open_plan         23699 non-null bool\n",
      "locality_name        23650 non-null object\n",
      "airport_dist         18157 non-null float64\n",
      "city_center_dist     18180 non-null float64\n",
      "park_dist            8079 non-null float64\n",
      "parks_within_3000    18181 non-null float64\n",
      "pond_dist            9110 non-null float64\n",
      "ponds_within_3000    18181 non-null float64\n",
      "dtypes: bool(2), float64(14), int64(3), object(3)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 23699 entries of data. days_listed has only 20518, kitchen_area has only 21421, living_area has only 21796, balconies has only 12180, ceiling-height has only 14504, floors_total has only 23613, bike-parking has only 2775, locality_name has only 23650, airport_dist has only 18157, city_center_dist has only 18180, park_dist has only 8079, parks_within_3000 has only 18181, pond_dist has only 9110, and ponds_within_3000 has only 18181"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>date_posted</th>\n",
       "      <th>days_listed</th>\n",
       "      <th>last_price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>kitchen_area</th>\n",
       "      <th>living_area</th>\n",
       "      <th>total_area</th>\n",
       "      <th>balconies</th>\n",
       "      <th>ceiling_height</th>\n",
       "      <th>floors_total</th>\n",
       "      <th>...</th>\n",
       "      <th>bike_parking</th>\n",
       "      <th>is_studio</th>\n",
       "      <th>is_open_plan</th>\n",
       "      <th>locality_name</th>\n",
       "      <th>airport_dist</th>\n",
       "      <th>city_center_dist</th>\n",
       "      <th>park_dist</th>\n",
       "      <th>parks_within_3000</th>\n",
       "      <th>pond_dist</th>\n",
       "      <th>ponds_within_3000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-07T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>260000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.70</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>18863.0</td>\n",
       "      <td>16028.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>755.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2018-12-04T00:00:00</td>\n",
       "      <td>81.0</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>40.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Shushary village</td>\n",
       "      <td>12817.0</td>\n",
       "      <td>18603.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2015-08-20T00:00:00</td>\n",
       "      <td>558.0</td>\n",
       "      <td>103920.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.3</td>\n",
       "      <td>34.3</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>21741.0</td>\n",
       "      <td>13933.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2015-07-24T00:00:00</td>\n",
       "      <td>424.0</td>\n",
       "      <td>1298000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>28098.0</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2018-06-19T00:00:00</td>\n",
       "      <td>121.0</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>41.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.03</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>31856.0</td>\n",
       "      <td>8098.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date_posted  days_listed  last_price  bedrooms  kitchen_area  \\\n",
       "0  2019-03-07T00:00:00          NaN    260000.0         3          25.0   \n",
       "1  2018-12-04T00:00:00         81.0     67000.0         1          11.0   \n",
       "2  2015-08-20T00:00:00        558.0    103920.0         2           8.3   \n",
       "3  2015-07-24T00:00:00        424.0   1298000.0         3           NaN   \n",
       "4  2018-06-19T00:00:00        121.0    200000.0         2          41.0   \n",
       "\n",
       "   living_area  total_area  balconies  ceiling_height  floors_total  ...  \\\n",
       "0         51.0       108.0        NaN            2.70          16.0  ...   \n",
       "1         18.6        40.4        2.0             NaN          11.0  ...   \n",
       "2         34.3        56.0        0.0             NaN           5.0  ...   \n",
       "3          NaN       159.0        0.0             NaN          14.0  ...   \n",
       "4         32.0       100.0        NaN            3.03          14.0  ...   \n",
       "\n",
       "   bike_parking  is_studio is_open_plan     locality_name  airport_dist  \\\n",
       "0           NaN      False        False  Saint Petersburg       18863.0   \n",
       "1           NaN      False        False  Shushary village       12817.0   \n",
       "2           NaN      False        False  Saint Petersburg       21741.0   \n",
       "3           NaN      False        False  Saint Petersburg       28098.0   \n",
       "4           NaN      False        False  Saint Petersburg       31856.0   \n",
       "\n",
       "  city_center_dist  park_dist  parks_within_3000  pond_dist  ponds_within_3000  \n",
       "0          16028.0      482.0                1.0      755.0                2.0  \n",
       "1          18603.0        NaN                0.0        NaN                0.0  \n",
       "2          13933.0       90.0                1.0      574.0                2.0  \n",
       "3           6800.0       84.0                2.0      234.0                3.0  \n",
       "4           8098.0      112.0                2.0       48.0                1.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the data is float type and will need to change some data to integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>days_listed</th>\n",
       "      <th>last_price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>kitchen_area</th>\n",
       "      <th>living_area</th>\n",
       "      <th>total_area</th>\n",
       "      <th>balconies</th>\n",
       "      <th>ceiling_height</th>\n",
       "      <th>floors_total</th>\n",
       "      <th>floor</th>\n",
       "      <th>total_images</th>\n",
       "      <th>airport_dist</th>\n",
       "      <th>city_center_dist</th>\n",
       "      <th>park_dist</th>\n",
       "      <th>parks_within_3000</th>\n",
       "      <th>pond_dist</th>\n",
       "      <th>ponds_within_3000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>20518.000000</td>\n",
       "      <td>2.369900e+04</td>\n",
       "      <td>23699.000000</td>\n",
       "      <td>21421.000000</td>\n",
       "      <td>21796.000000</td>\n",
       "      <td>23699.000000</td>\n",
       "      <td>12180.000000</td>\n",
       "      <td>14504.000000</td>\n",
       "      <td>23613.000000</td>\n",
       "      <td>23699.000000</td>\n",
       "      <td>23699.000000</td>\n",
       "      <td>18157.000000</td>\n",
       "      <td>18180.000000</td>\n",
       "      <td>8079.000000</td>\n",
       "      <td>18181.000000</td>\n",
       "      <td>9110.000000</td>\n",
       "      <td>18181.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>180.888634</td>\n",
       "      <td>1.308310e+05</td>\n",
       "      <td>2.070636</td>\n",
       "      <td>10.569807</td>\n",
       "      <td>34.457852</td>\n",
       "      <td>60.348651</td>\n",
       "      <td>1.150082</td>\n",
       "      <td>2.771499</td>\n",
       "      <td>10.673824</td>\n",
       "      <td>5.892358</td>\n",
       "      <td>9.858475</td>\n",
       "      <td>28793.672193</td>\n",
       "      <td>14191.277833</td>\n",
       "      <td>490.804555</td>\n",
       "      <td>0.611408</td>\n",
       "      <td>517.980900</td>\n",
       "      <td>0.770255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>219.727988</td>\n",
       "      <td>2.177403e+05</td>\n",
       "      <td>1.078405</td>\n",
       "      <td>5.905438</td>\n",
       "      <td>22.030445</td>\n",
       "      <td>35.654083</td>\n",
       "      <td>1.071300</td>\n",
       "      <td>1.261056</td>\n",
       "      <td>6.597173</td>\n",
       "      <td>4.885249</td>\n",
       "      <td>5.682529</td>\n",
       "      <td>12630.880622</td>\n",
       "      <td>8608.386210</td>\n",
       "      <td>342.317995</td>\n",
       "      <td>0.802074</td>\n",
       "      <td>277.720643</td>\n",
       "      <td>0.938346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.440000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>6.800000e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.520000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>18585.000000</td>\n",
       "      <td>9238.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>294.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>9.300000e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.100000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.650000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>26726.000000</td>\n",
       "      <td>13098.500000</td>\n",
       "      <td>455.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>1.360000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>42.300000</td>\n",
       "      <td>69.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>37273.000000</td>\n",
       "      <td>16293.000000</td>\n",
       "      <td>612.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>729.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1580.000000</td>\n",
       "      <td>1.526000e+07</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>409.700000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>84869.000000</td>\n",
       "      <td>65968.000000</td>\n",
       "      <td>3190.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1344.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        days_listed    last_price      bedrooms  kitchen_area   living_area  \\\n",
       "count  20518.000000  2.369900e+04  23699.000000  21421.000000  21796.000000   \n",
       "mean     180.888634  1.308310e+05      2.070636     10.569807     34.457852   \n",
       "std      219.727988  2.177403e+05      1.078405      5.905438     22.030445   \n",
       "min        1.000000  2.440000e+02      0.000000      1.300000      2.000000   \n",
       "25%       45.000000  6.800000e+04      1.000000      7.000000     18.600000   \n",
       "50%       95.000000  9.300000e+04      2.000000      9.100000     30.000000   \n",
       "75%      232.000000  1.360000e+05      3.000000     12.000000     42.300000   \n",
       "max     1580.000000  1.526000e+07     19.000000    112.000000    409.700000   \n",
       "\n",
       "         total_area     balconies  ceiling_height  floors_total         floor  \\\n",
       "count  23699.000000  12180.000000    14504.000000  23613.000000  23699.000000   \n",
       "mean      60.348651      1.150082        2.771499     10.673824      5.892358   \n",
       "std       35.654083      1.071300        1.261056      6.597173      4.885249   \n",
       "min       12.000000      0.000000        1.000000      1.000000      1.000000   \n",
       "25%       40.000000      0.000000        2.520000      5.000000      2.000000   \n",
       "50%       52.000000      1.000000        2.650000      9.000000      4.000000   \n",
       "75%       69.900000      2.000000        2.800000     16.000000      8.000000   \n",
       "max      900.000000      5.000000      100.000000     60.000000     33.000000   \n",
       "\n",
       "       total_images  airport_dist  city_center_dist    park_dist  \\\n",
       "count  23699.000000  18157.000000      18180.000000  8079.000000   \n",
       "mean       9.858475  28793.672193      14191.277833   490.804555   \n",
       "std        5.682529  12630.880622       8608.386210   342.317995   \n",
       "min        0.000000      0.000000        181.000000     1.000000   \n",
       "25%        6.000000  18585.000000       9238.000000   288.000000   \n",
       "50%        9.000000  26726.000000      13098.500000   455.000000   \n",
       "75%       14.000000  37273.000000      16293.000000   612.000000   \n",
       "max       50.000000  84869.000000      65968.000000  3190.000000   \n",
       "\n",
       "       parks_within_3000    pond_dist  ponds_within_3000  \n",
       "count       18181.000000  9110.000000       18181.000000  \n",
       "mean            0.611408   517.980900           0.770255  \n",
       "std             0.802074   277.720643           0.938346  \n",
       "min             0.000000    13.000000           0.000000  \n",
       "25%             0.000000   294.000000           0.000000  \n",
       "50%             0.000000   502.000000           1.000000  \n",
       "75%             1.000000   729.000000           1.000000  \n",
       "max             3.000000  1344.000000           3.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0 bedrooms must be studios.  \n",
    "Weird data is ceiling_height of 1 and 100 meters\n",
    "Also total_area of 900 square meters\n",
    "That is one huge property!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_posted              0\n",
       "days_listed           3181\n",
       "last_price               0\n",
       "bedrooms                 0\n",
       "kitchen_area          2278\n",
       "living_area           1903\n",
       "total_area               0\n",
       "balconies            11519\n",
       "ceiling_height        9195\n",
       "floors_total            86\n",
       "floor                    0\n",
       "total_images             0\n",
       "bike_parking         20924\n",
       "is_studio                0\n",
       "is_open_plan             0\n",
       "locality_name           49\n",
       "airport_dist          5542\n",
       "city_center_dist      5519\n",
       "park_dist            15620\n",
       "parks_within_3000     5518\n",
       "pond_dist            14589\n",
       "ponds_within_3000     5518\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a lot of missing values here. The mapping distances must have not done the best job because it has a lot of missing data. We really only need city_center_dist for our analysis though"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I checked the data. There are no duplications. \n",
    "\n",
    "There are missing values in days_listed which I decided to leave because it may be that the properties were just not sold yet and I can still make calculations with it since it is float\n",
    "\n",
    "There are missing values in kitchen_area and living_area. I need them for ana\n",
    "\n",
    "There are missing values in balconies. I will assume that this means the property does not have a balcony and will fill in the values with 0. Same with bik_parking where there is no parking for bikes by the property.\n",
    "\n",
    "There are missing values in ceiling_height. \n",
    "\n",
    "There are missing values in floors_total. I will assumed that the missing value is 1 because the floor is probably ground level\n",
    "\n",
    "There are missing values in locality_name. I will leave this blank since it is negligible for our data since most important is St. Petersburg which already has a significant number of values for our data.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['balconies'] = data['balconies'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['bike_parking'] = data['bike_parking'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['bike_parking'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['balconies'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filled balconies and bike_parking column with 0 and came back with no missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['floors_total'] = data['floors_total'].fillna(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['floors_total'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filled floors total column with 1 for ground floor and came back with no missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['bedrooms'] = data['bedrooms'].replace(0,1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replaced all bedrooms values that had 0 to 1 because I consider 1 room to be studio and whoever filled out may have thought 0 is appropriate for a studio but I would like to consider bedrooms to only start with 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['city_center_dist']= data['city_center_dist'].fillna(data.groupby('locality_name')['city_center_dist'].transform('mean'),inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filled values for city center dist. Was not able to fill them all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4872"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['city_center_dist'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "647\n"
     ]
    }
   ],
   "source": [
    "null_diff = 5519 - 4872\n",
    "print(null_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Was able to fill 647 rows with the average for city center dist column baed on locality name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['kitchen_area'] = np.where(data['is_studio'] == True, data['kitchen_area'].fillna(value='0'), data['kitchen_area'])\n",
    "data['kitchen_area'] = data['kitchen_area'].fillna(value='0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['living_area'] = np.where(data['is_studio'] == True, data['living_area'].fillna(data['total_area']), data['living_area'])\n",
    "data['living_area'].fillna(data.groupby('bedrooms')['living_area'].transform('median'),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_posted              0\n",
       "days_listed           3181\n",
       "last_price               0\n",
       "bedrooms                 0\n",
       "kitchen_area             0\n",
       "living_area              0\n",
       "total_area               0\n",
       "balconies                0\n",
       "ceiling_height        9195\n",
       "floors_total             0\n",
       "floor                    0\n",
       "total_images             0\n",
       "bike_parking             0\n",
       "is_studio                0\n",
       "is_open_plan             0\n",
       "locality_name           49\n",
       "airport_dist          5542\n",
       "city_center_dist      4872\n",
       "park_dist            15620\n",
       "parks_within_3000     5518\n",
       "pond_dist            14589\n",
       "ponds_within_3000     5518\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm still left with many missing values but most of these I will not need for my analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Data type replacement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['date_posted'] = pd.to_datetime(data['date_posted'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Changed date_posted column to datetime which will make it easier to break it down later into columns for weekday, month, and year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['last_price'] = data['last_price'].astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Changed last price to integer datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['total_area'] = data['total_area'].astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "changed total area to integer datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['balconies'] = data['balconies'].astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "changed balconies to integer datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['floors_total'] = data['floors_total'].astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "changed floors total to integer datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['kitchen_area'] = data['kitchen_area'].astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "changed kitchen area to integer datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_posted          datetime64[ns]\n",
       "days_listed                 float64\n",
       "last_price                    int64\n",
       "bedrooms                      int64\n",
       "kitchen_area                  int64\n",
       "living_area                 float64\n",
       "total_area                    int64\n",
       "balconies                     int64\n",
       "ceiling_height              float64\n",
       "floors_total                  int64\n",
       "floor                         int64\n",
       "total_images                  int64\n",
       "bike_parking                 object\n",
       "is_studio                      bool\n",
       "is_open_plan                   bool\n",
       "locality_name                object\n",
       "airport_dist                float64\n",
       "city_center_dist            float64\n",
       "park_dist                   float64\n",
       "parks_within_3000           float64\n",
       "pond_dist                   float64\n",
       "ponds_within_3000           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Showing changes of datatypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Make calculations and add them to the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['price_per_square_meter'] = data['last_price']/data['total_area']        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Created column with price per square meter calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['weekday_posted'] = data['date_posted'].dt.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['month_posted'] = data['date_posted'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['year_posted'] = data['date_posted'].dt.year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Created columns showing specific day of the week, the month, and the year that the ad was posted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorize_floor(row):\n",
    "    floor = row['floor']\n",
    "    floors_total = row['floors_total']\n",
    "    if floor == 1:\n",
    "        return  1\n",
    "    elif floor != 1:\n",
    "        if floor == floors_total:\n",
    "            return  3\n",
    "        else:\n",
    "            return  2\n",
    "           \n",
    "data['floor_category'] = data.apply(categorize_floor, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "created a column which will show which floor the property is on in the building, either first floor(1), last floor(3), or other(2). I kept these as numbers in order to be able to use them in analysis later. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['living_area_ratio'] = data['living_area'] / data['total_area']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Created the ratio for living area of total area. The data may be a little off here in certain rows because I did not fill values for living area. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['kitchen_area_ratio'] = data['kitchen_area'] / data['total_area']\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Created the ratio for kitchen area of total area. The data may be a little off here in certain rows because I did not fill values for kitchen area."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Conduct exploratory data analysis and follow the instructions below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Carefully investigate the following parameters: square area, price, number of rooms, and ceiling height. Plot a histogram for each parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigating Square area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    23699.000000\n",
       "mean        60.120511\n",
       "std         35.665822\n",
       "min         12.000000\n",
       "25%         40.000000\n",
       "50%         52.000000\n",
       "75%         69.000000\n",
       "max        900.000000\n",
       "Name: total_area, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['total_area'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f895ec3a390>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 35,
     "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": [
    "data.hist('total_area', range= (0, 200), bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a long tail here. Most of the data looks to be between 40 square meters and 200 square meters. Above 200 square meters are my outliers. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigating price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.369900e+04\n",
       "mean     1.308310e+05\n",
       "std      2.177403e+05\n",
       "min      2.440000e+02\n",
       "25%      6.800000e+04\n",
       "50%      9.300000e+04\n",
       "75%      1.360000e+05\n",
       "max      1.526000e+07\n",
       "Name: last_price, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['last_price'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only one property is very high close to 1.6 and this one is an outlier. Probably very big and very expensive. Everything else is around the same price range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f895ec2c6d0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEICAYAAABfz4NwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAa+UlEQVR4nO3de5Bc5Xnn8e8vyFwsbF3AO6tIsiWvFVxyKGOYBVFkvQOyJQGJRW1hIlZlBiKXsractb1sbCnelCoYsnLiCoHCAWYtGeGyEQqBoAU2WkXQlbh2uVgGc1c0gLCkFQijCxkwjmU/+8d5BtrjuZye6e6Z6fl9qrr6nOe857zvozOaZ86lTysiMDMz+7XRHoCZmY0NLghmZga4IJiZWXJBMDMzwAXBzMySC4KZmQEuCNZCJO2W9LHRHkd/JP07STtHexxmg3FBMEuSbpF0dSO2HRH/GBGnNGLbZvXigmDWYJImjfYYzMpwQbCWI+lMSf9X0mFJ+yXdIOnYXCZJ10o6IOk1SU9I+k1JK4HlwJck9Uj6n0P0sVvSGklPSzok6VuSjs9lHZL2SvqypJeAb/XGqtafLelOSa9IelXSDVXLfk/SM7ndrZLe15B/KLM+XBCsFf0c+CJwMnA2sBD4bC5bBHwU+A1gCnAJ8GpEdAHfAf4sIk6MiN8p0c9yYDHwb3J7/61q2b8GpgPvA1ZWryTpGOAe4EVgDjAT2JTLlgJ/BPwH4D3APwK3lc7cbARcEKzlRMSOiHgwIo5GxG7gZuDf5+KfAe8CPggoIp6JiP3D7OqGiNgTEQeBa4BLq5b9AlgbET+NiJ/0We9M4NeBP4yI1yPizYj4Xi77T8B/z3EdBf4UOM1HCdYMLgjWciT9hqR7JL0k6TWKX6onA0TE/cANwDeAA5K6JL17mF3tqZp+keKXfK9XIuLNAdabDbyYv/D7eh9wXZ7uOgwcBERxFGHWUC4I1opuBJ4F5kXEuylOwah3YURcHxFnAPMpTvX8Ye+iGvuZXTX9XuD/Vc0Ptq09wHsHuNi8B/j9iJha9TohIv5PjWMzq5kLgrWidwGvAT2SPgh8pneBpH8r6SxJ7wBeB96kOL0D8DLw/hr6WSVplqTpwFeA20uu9zCwH1gnabKk4yWdk8tuAtZI+lCOd4qkT9YwJrNhc0GwVvRfgf8I/DPwP/jlX9TvztghitM8rwJ/nsvWA/PzdM3flujnu8D/Bp4HngNKfYYhIn4O/A7wAeBHwF7gd3PZXcDXgE15uutJ4Pwy2zUbKfkLcsxqJ2k38OmI+PvRHotZvfgIwczMAPAnKM36Iem9wNMDLJ7fzLGYNYtPGZmZGeBTRmZmlkqdMpL0ReDTFPdWPwFcAcyg+Lj9ScAO4FMR8S+SjgNuBc6guIPjd/PTokhaA6ygeLTAf46IrYP1e/LJJ8ecOXNqzyq9/vrrTJ48edjrjzcTLV9wzhOFc67Njh07fhwR76l5xYgY9EXxCckXgBNyfjNweb4vy9hNwGdy+rPATTm9DLg9p+cDPwSOA+ZS3KZ3zGB9n3HGGTESDzzwwIjWH28mWr4RznmicM61Ab4fQ/xu7+9V9pTRJOCE/GTlOyk+VHMecEcu3whclNNLc55cvlCSMr4pime7vAB0UzzTxczMxoAhC0JE7AO+TvEBmv3AEYpTRIfj7Wex7OXtZ63MJJ/xksuPUJxWeivezzpmZjbKhryGIGkaxV/3c4HDwF8DSxo1oHwu/UqAtrY2KpXKsLfV09MzovXHm4mWLzjnicI5N0eZi8ofA16IiFcAJN0JnANMlTQpjwJmAfuy/T6Kh37tzVNMUyguLvfGe1Wv85YonkvfBdDe3h4dHR3DSKtQqVQYyfrjzUTLF5zzROGcm6PMNYQfAQskvTOvBSyk+MDOA8DF2aYTuDunt+Q8ufz+vMixBVgm6ThJc4F5FA/5MjOzMWDII4SIeEjSHcAPgKPAoxR/wd9L8QCuqzO2PldZD3xbUjfFs9yX5XaekrSZopgcBVZF8ZAvMzMbA0p9DiEi1gJr+4Sfp5+7hKL4UpB+H9cbEddQfLOUmZmNMf6kspmZAS4IZmaW/LTTGsxZfW+pdrvXXdjgkZiZ1Z+PEMzMDHBBMDOz5IJgZmaAC4KZmSUXBDMzA1wQzMwsuSCYmRnggmBmZskFwczMABcEMzNLLghmZga4IJiZWXJBMDMzwAXBzMySC4KZmQElCoKkUyQ9VvV6TdIXJE2XtE3Srnyflu0l6XpJ3ZIel3R61bY6s/0uSZ2NTMzMzGozZEGIiJ0RcVpEnAacAbwB3AWsBrZHxDxge84DnA/My9dK4EYASdMpvpf5LIrvYl7bW0TMzGz01XrKaCHwXES8CCwFNmZ8I3BRTi8Fbo3Cg8BUSTOAxcC2iDgYEYeAbcCSEWdgZmZ1UetXaC4DbsvptojYn9MvAW05PRPYU7XO3owNFP8lklZSHFnQ1tZGpVKpcYhv6+npGdH6fV156tFS7erZZy3qne944JwnBufcHKULgqRjgU8Aa/oui4iQFPUYUER0AV0A7e3t0dHRMextVSoVRrJ+X5eX/U7l5fXrsxb1znc8cM4Tg3NujlpOGZ0P/CAiXs75l/NUEPl+IOP7gNlV683K2EBxMzMbA2opCJfy9ukigC1A751CncDdVfHL8m6jBcCRPLW0FVgkaVpeTF6UMTMzGwNKnTKSNBn4OPD7VeF1wGZJK4AXgUsyfh9wAdBNcUfSFQARcVDSV4FHst1VEXFwxBmYmVldlCoIEfE6cFKf2KsUdx31bRvAqgG2swHYUPswzcys0fxJZTMzA1wQzMwsuSCYmRnggmBmZskFwczMABcEMzNLLghmZga4IJiZWXJBMDMzwAXBzMySC4KZmQEuCGZmllwQzMwMcEEwM7PkgmBmZoALgpmZJRcEMzMDXBDMzCyVKgiSpkq6Q9Kzkp6RdLak6ZK2SdqV79OyrSRdL6lb0uOSTq/aTme23yWps1FJmZlZ7coeIVwH/F1EfBD4MPAMsBrYHhHzgO05D3A+MC9fK4EbASRNB9YCZwFnAmt7i4iZmY2+IQuCpCnAR4H1ABHxLxFxGFgKbMxmG4GLcnopcGsUHgSmSpoBLAa2RcTBiDgEbAOW1DUbMzMbtkkl2swFXgG+JenDwA7g80BbROzPNi8BbTk9E9hTtf7ejA0U/yWSVlIcWdDW1kalUimby6/o6ekZ0fp9XXnq0VLt6tlnLeqd73jgnCcG59wcZQrCJOB04A8i4iFJ1/H26SEAIiIkRT0GFBFdQBdAe3t7dHR0DHtblUqFkazf1+Wr7y3Vbvfy+vVZi3rnOx4454nBOTdHmWsIe4G9EfFQzt9BUSBezlNB5PuBXL4PmF21/qyMDRQ3M7MxYMiCEBEvAXsknZKhhcDTwBag906hTuDunN4CXJZ3Gy0AjuSppa3AIknT8mLyooyZmdkYUOaUEcAfAN+RdCzwPHAFRTHZLGkF8CJwSba9D7gA6AbeyLZExEFJXwUeyXZXRcTBumRhZmYjVqogRMRjQHs/ixb20zaAVQNsZwOwoZYBmplZc/iTymZmBrggmJlZckEwMzPABcHMzJILgpmZAS4IZmaWXBDMzAxwQTAzs+SCYGZmgAuCmZklFwQzMwNcEMzMLLkgmJkZ4IJgZmbJBcHMzAAXBDMzSy4IZmYGuCCYmVkqVRAk7Zb0hKTHJH0/Y9MlbZO0K9+nZVySrpfULelxSadXbacz2++S1NmYlMzMbDhqOUI4NyJOi4je71ZeDWyPiHnA9pwHOB+Yl6+VwI1QFBBgLXAWcCawtreImJnZ6BvJKaOlwMac3ghcVBW/NQoPAlMlzQAWA9si4mBEHAK2AUtG0L+ZmdWRImLoRtILwCEggJsjokvS4YiYmssFHIqIqZLuAdZFxPdy2Xbgy0AHcHxEXJ3xPwZ+EhFf79PXSoojC9ra2s7YtGnTsJPr6enhxBNPHPb6fT2x70ipdqfOnFK3PmtR73zHA+c8MTjn2px77rk7qs7mlDapZLvfioh9kv4VsE3Ss9ULIyIkDV1ZSoiILqALoL29PTo6Ooa9rUqlwkjW7+vy1feWard7ef36rEW98x0PnPPE4Jybo9Qpo4jYl+8HgLsorgG8nKeCyPcD2XwfMLtq9VkZGyhuZmZjwJAFQdJkSe/qnQYWAU8CW4DeO4U6gbtzegtwWd5ttAA4EhH7ga3AIknT8mLyooyZmdkYUOaUURtwV3GZgEnAdyPi7yQ9AmyWtAJ4Ebgk298HXAB0A28AVwBExEFJXwUeyXZXRcTBumViZmYjMmRBiIjngQ/3E38VWNhPPIBVA2xrA7Ch9mGamVmj+ZPKZmYGuCCYmVlyQTAzM8AFwczMUtkPprW0OSU/cGZm1sp8hGBmZoALgpmZJRcEMzMDXBDMzCy5IJiZGeCCYGZmybedNkDZ21h3r7uwwSMxMyvPRwhmZga4IJiZWXJBMDMzwAXBzMySC4KZmQE1FARJx0h6VNI9OT9X0kOSuiXdLunYjB+X8925fE7VNtZkfKekxfVOxszMhq+WI4TPA89UzX8NuDYiPgAcAlZkfAVwKOPXZjskzQeWAR8ClgB/JemYkQ3fzMzqpVRBkDQLuBD4Zs4LOA+4I5tsBC7K6aU5Ty5fmO2XApsi4qcR8QLQDZxZjyTMzGzkyh4h/CXwJeAXOX8ScDgijub8XmBmTs8E9gDk8iPZ/q14P+uYmdkoG/KTypJ+GzgQETskdTR6QJJWAisB2traqFQqw95WT09PqfWvPPXokG0aYSS59adsvq3EOU8Mzrk5yjy64hzgE5IuAI4H3g1cB0yVNCmPAmYB+7L9PmA2sFfSJGAK8GpVvFf1Om+JiC6gC6C9vT06OjqGkVahUqlQZv3LR+kb03Yv76jr9srm20qc88TgnJtjyFNGEbEmImZFxByKi8L3R8Ry4AHg4mzWCdyd01tynlx+f0RExpflXUhzgXnAw3XLxMzMRmQkD7f7MrBJ0tXAo8D6jK8Hvi2pGzhIUUSIiKckbQaeBo4CqyLi5yPo38zM6qimghARFaCS08/Tz11CEfEm8MkB1r8GuKbWQZqZWeP5k8pmZga4IJiZWXJBMDMzwAXBzMySC4KZmQEuCGZmllwQzMwMcEEwM7PkgmBmZoALgpmZJRcEMzMDXBDMzCy5IJiZGeCCYGZmyQXBzMwAFwQzM0suCGZmBrggmJlZGrIgSDpe0sOSfijpKUl/kvG5kh6S1C3pdknHZvy4nO/O5XOqtrUm4zslLW5UUmZmVrsyRwg/Bc6LiA8DpwFLJC0AvgZcGxEfAA4BK7L9CuBQxq/NdkiaDywDPgQsAf5K0jH1TMbMzIZvyIIQhZ6cfUe+AjgPuCPjG4GLcnppzpPLF0pSxjdFxE8j4gWgGzizLlmYmdmITSrTKP+S3wF8APgG8BxwOCKOZpO9wMycngnsAYiIo5KOACdl/MGqzVavU93XSmAlQFtbG5VKpbaMqvT09JRa/8pTjw7ZphFGklt/yubbSpzzxOCcm6NUQYiInwOnSZoK3AV8sFEDioguoAugvb09Ojo6hr2tSqVCmfUvX33vsPsYid3LO+q6vbL5thLnPDE45+ao6S6jiDgMPACcDUyV1FtQZgH7cnofMBsgl08BXq2O97OOmZmNsjJ3Gb0njwyQdALwceAZisJwcTbrBO7O6S05Ty6/PyIi48vyLqS5wDzg4XolYmZmI1PmlNEMYGNeR/g1YHNE3CPpaWCTpKuBR4H12X498G1J3cBBijuLiIinJG0GngaOAqvyVJSZmY0BQxaEiHgc+Eg/8efp5y6hiHgT+OQA27oGuKb2YZqZWaP5k8pmZga4IJiZWXJBMDMzwAXBzMySC4KZmQEuCGZmllwQzMwMcEEwM7PkgmBmZoALgpmZJRcEMzMDXBDMzCy5IJiZGeCCYGZmyQXBzMwAFwQzM0tlvjHNGmTO6ntLtdu97sIGj8TMzEcIZmaWhiwIkmZLekDS05KekvT5jE+XtE3SrnyflnFJul5St6THJZ1eta3ObL9LUmfj0jIzs1qVOUI4ClwZEfOBBcAqSfOB1cD2iJgHbM95gPOBeflaCdwIRQEB1gJnUXwX89reImJmZqNvyIIQEfsj4gc5/c/AM8BMYCmwMZttBC7K6aXArVF4EJgqaQawGNgWEQcj4hCwDVhS12zMzGzYFBHlG0tzgH8AfhP4UURMzbiAQxExVdI9wLqI+F4u2w58GegAjo+IqzP+x8BPIuLrffpYSXFkQVtb2xmbNm0adnI9PT2ceOKJQ7Z7Yt+RYffRDKfOnFKqXdl8W4lznhicc23OPffcHRHRXut6pe8yknQi8DfAFyLitaIGFCIiJJWvLIOIiC6gC6C9vT06OjqGva1KpUKZ9S8vebfPaNm9vKNUu7L5thLnPDE45+YodZeRpHdQFIPvRMSdGX45TwWR7wcyvg+YXbX6rIwNFDczszGgzF1GAtYDz0TEX1Qt2gL03inUCdxdFb8s7zZaAByJiP3AVmCRpGl5MXlRxszMbAwoc8roHOBTwBOSHsvYHwHrgM2SVgAvApfksvuAC4Bu4A3gCoCIOCjpq8Aj2e6qiDhYlyzMzGzEhiwIeXFYAyxe2E/7AFYNsK0NwIZaBmhmZs3hTyqbmRnggmBmZskFwczMABcEMzNLLghmZga4IJiZWXJBMDMzwAXBzMySC4KZmQEuCGZmllwQzMwMcEEwM7PkgmBmZoALgpmZJRcEMzMDXBDMzCy5IJiZGVDuKzRtlM1ZfW+pdrcsmdzgkZhZKxvyCEHSBkkHJD1ZFZsuaZukXfk+LeOSdL2kbkmPSzq9ap3ObL9LUmdj0jEzs+Eqc8roFmBJn9hqYHtEzAO25zzA+cC8fK0EboSigABrgbOAM4G1vUXEzMzGhiELQkT8A3CwT3gpsDGnNwIXVcVvjcKDwFRJM4DFwLaIOBgRh4Bt/GqRMTOzUTTcawhtEbE/p18C2nJ6JrCnqt3ejA0U/xWSVlIcXdDW1kalUhnmEKGnp6fU+leeenTYfYwlZfNtJc55YnDOzTHii8oREZKiHoPJ7XUBXQDt7e3R0dEx7G1VKhXKrH95yYu2Y90tSyaXyreVlN3HrcQ5TwyjkfNwbzt9OU8Fke8HMr4PmF3VblbGBoqbmdkYMdwjhC1AJ7Au3++uin9O0iaKC8hHImK/pK3An1ZdSF4ErBn+sMt5Yt+Rlvnr38ys0YYsCJJuAzqAkyXtpbhbaB2wWdIK4EXgkmx+H3AB0A28AVwBEBEHJX0VeCTbXRURfS9Um5nZKBqyIETEpQMsWthP2wBWDbCdDcCGmkZnZmZN40dXmJkZ4IJgZmbJBcHMzAA/3K6l1HJX1e51FzZ4NGY23vgIwczMABcEMzNLLghmZgb4GsKEVfZLd3ytwWzi8BGCmZkBLghmZpZcEMzMDHBBMDOz5IvKNihffDabOHyEYGZmgI8QrE58JGE2/vkIwczMAB8hWJP5SMJs7PIRgpmZAaNwhCBpCXAdcAzwzYhY1+wx2NhX9kjiylOPlnrkdy1HHD6KsYmqqQVB0jHAN4CPA3uBRyRtiYinmzkOm3jK/pJvxDZdOGy8aPYRwplAd0Q8DyBpE7AUcEGwllXvYlT2qKiVNOJIcLSU/Xm4ZcnkBo/kVykimteZdDGwJCI+nfOfAs6KiM9VtVkJrMzZU4CdI+jyZODHI1h/vJlo+YJzniicc23eFxHvqXWlMXeXUUR0AV312Jak70dEez22NR5MtHzBOU8Uzrk5mn2X0T5gdtX8rIyZmdkoa3ZBeASYJ2mupGOBZcCWJo/BzMz60dRTRhFxVNLngK0Ut51uiIinGthlXU49jSMTLV9wzhOFc26Cpl5UNjOzscufVDYzM8AFwczMUksWBElLJO2U1C1p9WiPZyiSZkt6QNLTkp6S9PmMT5e0TdKufJ+WcUm6PvN7XNLpVdvqzPa7JHVWxc+Q9ESuc70kDdZHE3M/RtKjku7J+bmSHspx3p43HyDpuJzvzuVzqraxJuM7JS2uivf7czBQH03Kd6qkOyQ9K+kZSWe3+n6W9MX8uX5S0m2Sjm+1/Sxpg6QDkp6sio3afh2sj0FFREu9KC5WPwe8HzgW+CEwf7THNcSYZwCn5/S7gH8C5gN/BqzO+Grgazl9AfC/AAELgIcyPh14Pt+n5fS0XPZwtlWue37G++2jibn/F+C7wD05vxlYltM3AZ/J6c8CN+X0MuD2nJ6f+/g4YG7u+2MG+zkYqI8m5bsR+HROHwtMbeX9DMwEXgBOqPq3v7zV9jPwUeB04Mmq2Kjt14H6GDKPZv1HaNYLOBvYWjW/Blgz2uOqMYe7KZ73tBOYkbEZwM6cvhm4tKr9zlx+KXBzVfzmjM0Anq2Kv9VuoD6alOcsYDtwHnBP/vD+GJjUd19S3Jl2dk5Pynbqu3972w30czBYH03IdwrFL0f1ibfsfqYoCHvyl9yk3M+LW3E/A3P45YIwavt1oD6GyqEVTxn1/gD22puxcSEPkT8CPAS0RcT+XPQS0JbTA+U4WHxvP3EG6aMZ/hL4EvCLnD8JOBwRR3O+epxv5ZbLj2T7Wv8tBuuj0eYCrwDfUnGa7JuSJtPC+zki9gFfB34E7KfYbzto7f3cazT367B+D7ZiQRi3JJ0I/A3whYh4rXpZFGW+ofcIN6OPXpJ+GzgQETua0d8YMYnitMKNEfER4HWKw/y3tOB+nkbxAMu5wK8Dk4Elzeh7LBkv+7UVC8K4fDyGpHdQFIPvRMSdGX5Z0oxcPgM4kPGBchwsPquf+GB9NNo5wCck7QY2UZw2ug6YKqn3A5PV43wrt1w+BXiV2v8tXh2kj0bbC+yNiIdy/g6KAtHK+/ljwAsR8UpE/Ay4k2Lft/J+7jWa+3VYvwdbsSCMu8dj5B0D64FnIuIvqhZtAXrvNOikuLbQG78s7yRYABzJw8atwCJJ0/Ivs0UU5033A69JWpB9XdZnW/310VARsSYiZkXEHIp9dH9ELAceAC7uZzzV47w420fGl+XdKXOBeRQX4Pr9Och1BuqjoSLiJWCPpFMytJDi0e8tu58pThUtkPTOHFNvzi27n6uM5n4dqI/BNfIiy2i9KK6w/xPF3QdfGe3xlBjvb1Ec6j0OPJavCyjOg24HdgF/D0zP9qL4oqHngCeA9qpt/R7Qna8rquLtwJO5zg28/Sn1fvtocv4dvH2X0fsp/qN3A38NHJfx43O+O5e/v2r9r2ReO8m7Lwb7ORiojyblehrw/dzXf0txN0lL72fgT4Bnc1zfprhTqKX2M3AbxTWSn1EcCa4Yzf06WB+DvfzoCjMzA1rzlJGZmQ2DC4KZmQEuCGZmllwQzMwMcEEwM7PkgmBmZoALgpmZpf8Peu9pQD+XoUcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist('last_price', range=(0, 0.1e+07), bins= 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is right tailed and there is a spike at around 100000. The mean is 130000 and the median is 93000.\n",
    "Most properties are within the same price range of 50000 to 100000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigating Rooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    23699.000000\n",
       "mean         2.078948\n",
       "std          1.066196\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          2.000000\n",
       "75%          3.000000\n",
       "max         19.000000\n",
       "Name: bedrooms, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['bedrooms'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f895e418410>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 39,
     "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": [
    "data.hist('bedrooms', range=(1,10), bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that most of our properties have less than 2.5 rooms. The most is 1 bedroom. The 0 bedrooms I replaced with 1 bedroom assuming are studios and therefore also considered 1 bedroom. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigating Ceiling height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    14504.000000\n",
       "mean         2.771499\n",
       "std          1.261056\n",
       "min          1.000000\n",
       "25%          2.520000\n",
       "50%          2.650000\n",
       "75%          2.800000\n",
       "max        100.000000\n",
       "Name: ceiling_height, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['ceiling_height'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the data is within 2 meters. The max is way bigger than any of the data and definitely does not fit with our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f895dfc9090>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAaBElEQVR4nO3dfZBc1X3m8e9jJEBhXJIIMJElxSKxnARQLKNZgYusawYWEDhr4ayjyMWChKFkZ6HWLmsThKu8YF525VqwEwzBqxgZEWMPKmyWsRBhFVkTwh8CIRsjCcwyARE0VlCChGAMoWrY3/7RR9lm1D39Mv0mnedT1TXd5557zq/vzDx9+947PYoIzMwsD+9rdwFmZtY6Dn0zs4w49M3MMuLQNzPLiEPfzCwjDn0zs4w49O2oImlE0m+k+/dIujnd/7eSnm/y3P86Xx3rfkvSV5o9j9mkdhdg1kgR0VWm/e+A32pxOVWLiM83aixJAcyNiKFGjWlHD+/pm5llxKFvHUvSbEk/lPRPkl6TdEdq/6yk5yQdkPSopA8WrROSPlRirF5Je4oe75b0XyQ9I+mgpPslHV+0/E8l7ZX0C0lXlRu3hOmSHpb0pqQnJP1m0Zi/LWmTpP2Snpe0pGjZew7ZVDF/yXkkPZaW/ywd6vqjKmq2jDj0rSNJOgbYALwMzAFmAv2SFgNfBv4AOBn4O+D7dU6zBFgEnAr8LrA8zb0I+BLw74APAb01jLkU+CowHRgCbkljngBsAr4HnJL6/YWk08YOUOX8JeeJiI+n5R+JiK6IuL+G2i0DDn3rVAuBDwB/EhG/jIh/iYjHgc8D/z0inouIUeC/AfOL9/ZrcHtE/CIi9gM/Auan9iXAdyJiV0S8BdxQw5gPRsSTqbb7isb8fWB3RHwnIkYj4qfAD4A/LDFGNfOXm8dsXA5961SzgZdTqBX7IPDnkl6X9DqwHxCFdwK1+sei+28Bh04CfwB4pWhZ8f16x/wgcNahulPtlwK/VmKMauYvN4/ZuHz1jnWqV4BflzRpTPC/AtwSEfc1ce69wKyix7MbMOYrwN9GxPltmt8M8J6+da4nKYTfakknSDpe0jnAt4DrJJ0OIGmqpFKHSCZiPXCFpN+R9CtAVdfPV7AB+LCkyyRNTrd/I+l3mjD/q8BvTLRgOzo59K0jRcS7wL+ncCLzH4A9wB9FxIPA1yic1H0D2Alc1OC5HwFuB7ZQOEm6NS16ZwJjvglcQOEE7C8oHJ75GnBcE+a/AViXDiMtqdTZ8iL/ExWz8aW98Z3AcSXOMRz189vRxXv6ZiVI+pSk4yRNp7BH/qNWBm6757ejl0PfrLTPAfuAvwfeBf4YQNKu9EdPY2+XtmJ+s4ny4R0zs4x4T9/MLCMdfZ3+SSedFHPmzKl7/V/+8peccMIJjSuoQVxXbVxXbVxXbY7GurZv3/7PEXFyyYUR0bG3BQsWxERs2bJlQus3i+uqjeuqjeuqzdFYF/BUlMlVH94xM8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8tIR38Mg+VnzqqHD2tbOW+U3taXYnZUqrinn/5N3ZOSfpY+Vvarqf0eSS9Jejrd5qd2Sbpd0pCkZySdWTTWMkkvpNuy5j0tMzMrpZo9/XeAcyNiRNJk4HFJj6RlfxIRD4zpfxEwN93OAu4CzpJ0InA90AMEsF3SQEQcaMQTMTOzyiru6afP7xlJDyen23gfwr8YuDettxWYJmkGcCGwKSL2p6DfBCyaWPlmZlaLqv6JiqRjgO0U/kn1nRFxraR7gI9ReCewGVgVEe9I2gCsjojH07qbgWuBXuD4iLg5tX8FeDsibh0z1wpgBUB3d/eC/v7+up/cyMgIXV1dda/fLK6rvB3DBw9r654Cp5w4tQ3VjK8Ttlcprqs2R2NdfX192yOip9Syqk7kRsS7wHxJ04AHJZ0BXAf8I3AssIZCsN9YV4XvnWtNGo+enp7o7e2te6zBwUEmsn6zuK7ylpc5kbvE26tqrqs2udVV0yWbEfE6sAVYFBF70yGcd4DvAAtTt2FgdtFqs1JbuXYzM2uRaq7eOTnt4SNpCnA+8PN0nB5JAi4BdqZVBoDL01U8ZwMHI2Iv8ChwgaTpkqYDF6Q2MzNrkWoO78wA1qXj+u8D1kfEBkk/lnQyIOBp4POp/0bgYmAIeAu4AiAi9ku6CdiW+t0YEfsb91TMzKySiqEfEc8AHy3Rfm6Z/gFcXWbZWmBtjTWamVmD+GMYzMwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjFQMfUnHS3pS0s8k7ZL01dR+qqQnJA1Jul/Ssan9uPR4KC2fUzTWdan9eUkXNutJmZlZadXs6b8DnBsRHwHmA4sknQ18DfhGRHwIOABcmfpfCRxI7d9I/ZB0GrAUOB1YBPyFpGMa+WTMzGx8FUM/CkbSw8npFsC5wAOpfR1wSbq/OD0mLT9PklJ7f0S8ExEvAUPAwoY8CzMzq4oionKnwh75duBDwJ3A/wC2pr15JM0GHomIMyTtBBZFxJ607O+Bs4Ab0jrfTe13p3UeGDPXCmAFQHd394L+/v66n9zIyAhdXV11r98srqu8HcMHD2vrngKnnDi1DdWMrxO2VymuqzZHY119fX3bI6Kn1LJJ1QwQEe8C8yVNAx4EfruuSqqbaw2wBqCnpyd6e3vrHmtwcJCJrN8srqu85asePqxt5bxRlnh7Vc111Sa3umq6eiciXge2AB8Dpkk69KIxCxhO94eB2QBp+VTgteL2EuuYmVkLVHP1zslpDx9JU4DzgecohP+nU7dlwEPp/kB6TFr+4ygcQxoAlqare04F5gJPNuqJmJlZZdUc3pkBrEvH9d8HrI+IDZKeBfol3Qz8FLg79b8b+CtJQ8B+ClfsEBG7JK0HngVGgavTYSMzM2uRiqEfEc8AHy3R/iIlrr6JiH8B/rDMWLcAt9ReppmZNYL/ItfMLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8tIVf8Y3exINKfEP1kH2L36Ey2uxKxzeE/fzCwjDn0zs4w49M3MMlIx9CXNlrRF0rOSdkn6Qmq/QdKwpKfT7eKida6TNCTpeUkXFrUvSm1DklY15ymZmVk51ZzIHQVWRsRPJL0f2C5pU1r2jYi4tbizpNOApcDpwAeAv5H04bT4TuB8YA+wTdJARDzbiCdiZmaVVQz9iNgL7E3335T0HDBznFUWA/0R8Q7wkqQhYGFaNhQRLwJI6k99HfpmZi2iiKi+szQHeAw4A/gSsBx4A3iKwruBA5LuALZGxHfTOncDj6QhFkXEVan9MuCsiLhmzBwrgBUA3d3dC/r7++t9boyMjNDV1VX3+s3iusrbMXzwsLbuKXDKiVMbMhbAvJm1j1VKJ2yvUlxXbY7Guvr6+rZHRE+pZVVfpy+pC/gB8MWIeEPSXcBNQKSvtwGfravCIhGxBlgD0NPTE729vXWPNTg4yETWbxbXVd7yEtfWr5w3ypIydZW7Fr+g9I/37ktLj1WrTthepbiu2uRWV1WhL2kyhcC/LyJ+CBARrxYt/0tgQ3o4DMwuWn1WamOcdjMza4Fqrt4RcDfwXER8vah9RlG3TwE70/0BYKmk4ySdCswFngS2AXMlnSrpWAonewca8zTMzKwa1ezpnwNcBuyQ9HRq+zLwGUnzKRze2Q18DiAidklaT+EE7ShwdUS8CyDpGuBR4BhgbUTsauBzMTOzCqq5eudxQCUWbRxnnVuAW0q0bxxvPTMzay7/Ra6ZWUYc+mZmGXHom5llxKFvZpYRh76ZWUYc+mZmGXHom5llxKFvZpYRh76ZWUYc+mZmGXHom5llxKFvZpYRh76ZWUYc+mZmGXHom5llxKFvZpYRh76ZWUYc+mZmGXHom5llxKFvZpaRiqEvabakLZKelbRL0hdS+4mSNkl6IX2dntol6XZJQ5KekXRm0VjLUv8XJC1r3tMyM7NSqtnTHwVWRsRpwNnA1ZJOA1YBmyNiLrA5PQa4CJibbiuAu6DwIgFcD5wFLASuP/RCYWZmrVEx9CNib0T8JN1/E3gOmAksBtalbuuAS9L9xcC9UbAVmCZpBnAhsCki9kfEAWATsKihz8bMzMaliKi+szQHeAw4A/iHiJiW2gUciIhpkjYAqyPi8bRsM3At0AscHxE3p/avAG9HxK1j5lhB4R0C3d3dC/r7++t+ciMjI3R1ddW9frO4rvJ2DB88rK17Cpxy4tSq+1cyb2bpsWrVCdurFNdVm6Oxrr6+vu0R0VNq2aRqB5HUBfwA+GJEvFHI+YKICEnVv3qMIyLWAGsAenp6ore3t+6xBgcHmcj6zeK6ylu+6uHD2lbOG2VJmbpK9a9k96Wlx6pVJ2yvUlxXbXKrq6qrdyRNphD490XED1Pzq+mwDenrvtQ+DMwuWn1WaivXbmZmLVLN1TsC7gaei4ivFy0aAA5dgbMMeKio/fJ0Fc/ZwMGI2As8ClwgaXo6gXtBajMzsxap5vDOOcBlwA5JT6e2LwOrgfWSrgReBpakZRuBi4Eh4C3gCoCI2C/pJmBb6ndjROxvyLMwM7OqVAz9dEJWZRafV6J/AFeXGWstsLaWAs3MrHH8F7lmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGKoa+pLWS9knaWdR2g6RhSU+n28VFy66TNCTpeUkXFrUvSm1DklY1/qmYmVklk6rocw9wB3DvmPZvRMStxQ2STgOWAqcDHwD+RtKH0+I7gfOBPcA2SQMR8ewEareMzFn1cLtLMDsqVAz9iHhM0pwqx1sM9EfEO8BLkoaAhWnZUES8CCCpP/V16JuZtZAionKnQuhviIgz0uMbgOXAG8BTwMqIOCDpDmBrRHw39bsbeCQNsygirkrtlwFnRcQ1JeZaAawA6O7uXtDf31/3kxsZGaGrq6vu9ZvFdZW3Y/jgYW3dU+DVtxs3x7yZUxsyTidsr1JcV22Oxrr6+vq2R0RPqWXVHN4p5S7gJiDS19uAz9Y51ntExBpgDUBPT0/09vbWPdbg4CATWb9ZXFd5y0scxlk5b5TbdtT7o3q43Zf2NmScTthepbiu2uRWV12/SRHx6qH7kv4S2JAeDgOzi7rOSm2M025mZi1S1yWbkmYUPfwUcOjKngFgqaTjJJ0KzAWeBLYBcyWdKulYCid7B+ov28zM6lFxT1/S94Fe4CRJe4DrgV5J8ykc3tkNfA4gInZJWk/hBO0ocHVEvJvGuQZ4FDgGWBsRuxr+bMzMbFzVXL3zmRLNd4/T/xbglhLtG4GNNVVnZmYN5b/INTPLiEPfzCwjDn0zs4w49M3MMuLQNzPLiEPfzCwjDn0zs4w49M3MMuLQNzPLiEPfzCwjjfu8WrMjRLn/wrV79SdaXIlZ6zn0zeq0Y/hgyc//94uHdTIf3jEzy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDJSMfQlrZW0T9LOorYTJW2S9EL6Oj21S9LtkoYkPSPpzKJ1lqX+L0ha1pynY2Zm46lmT/8eYNGYtlXA5oiYC2xOjwEuAuam2wrgLii8SADXA2cBC4HrD71QmJlZ61QM/Yh4DNg/pnkxsC7dXwdcUtR+bxRsBaZJmgFcCGyKiP0RcQDYxOEvJGZm1mSKiMqdpDnAhog4Iz1+PSKmpfsCDkTENEkbgNUR8Xhathm4FugFjo+Im1P7V4C3I+LWEnOtoPAuge7u7gX9/f11P7mRkRG6urrqXr9ZXFd5O4YPHtbWPQVefbv5c8+bObWm/vv2HyxZV63jNFonfB9LcV21mUhdfX192yOip9SyCX/gWkSEpMqvHNWPtwZYA9DT0xO9vb11jzU4OMhE1m8W11VeqQ8wWzlvlNt2NP+zAXdf2ltT/2/e91DJumodp9E64ftYiuuqTbPqqvfqnVfTYRvS132pfRiYXdRvVmor125mZi1Ub+gPAIeuwFkGPFTUfnm6iuds4GBE7AUeBS6QND2dwL0gtZmZWQtVfM8s6fsUjsmfJGkPhatwVgPrJV0JvAwsSd03AhcDQ8BbwBUAEbFf0k3AttTvxogYe3LYzMyarGLoR8Rnyiw6r0TfAK4uM85aYG1N1ZmZWUP5L3LNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsIw59M7OMTCj0Je2WtEPS05KeSm0nStok6YX0dXpql6TbJQ1JekbSmY14AmZmVr1G7On3RcT8iOhJj1cBmyNiLrA5PQa4CJibbiuAuxowt5mZ1aAZh3cWA+vS/XXAJUXt90bBVmCapBlNmN/MzMpQRNS/svQScAAI4H9GxBpJr0fEtLRcwIGImCZpA7A6Ih5PyzYD10bEU2PGXEHhnQDd3d0L+vv7665vZGSErq6uutdvFtdV3o7hg4e1dU+BV99u/tzzZk6tqf++/QdL1lXrOI3WCd/HUlxXbSZSV19f3/aioy/vMWlCVcHvRcSwpFOATZJ+XrwwIkJSTa8qEbEGWAPQ09MTvb29dRc3ODjIRNZvFtdV3vJVDx/WtnLeKLftmOiPamW7L+2tqf8373uoZF21jtNonfB9LMV11aZZdU3o8E5EDKev+4AHgYXAq4cO26Sv+1L3YWB20eqzUpuZmbVI3aEv6QRJ7z90H7gA2AkMAMtSt2XAQ+n+AHB5uornbOBgROytu3IzM6vZRN4zdwMPFg7bMwn4XkT8taRtwHpJVwIvA0tS/43AxcAQ8BZwxQTmNjOzOtQd+hHxIvCREu2vAeeVaA/g6nrnMzOzifNf5JqZZcShb2aWEYe+mVlGHPpmZhlp/l+8mB0h5pT4wzCA3as/0ZZxzJrBoW/WIn4xsE7gwztmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhnx1TtmbeareqyVvKdvZpYR7+mbHSUOvWNYOW/0Pf+BzO8YrJj39M3MMuLQNzPLiA/vmB1hyp34NauG9/TNzDLiPX2zCsrtWa+c1+JCzBrAe/pmZhlp+Z6+pEXAnwPHAN+OiNWtrsHsSNCoY/f1jNOoyzyL5y6+lNSXkbZPS0Nf0jHAncD5wB5gm6SBiHi2lXVY+/lkZGer9a+E/f08crR6T38hMBQRLwJI6gcWAw79NvGemNWiXeHezP9qdujnPpefeUVE6yaTPg0sioir0uPLgLMi4pqiPiuAFenhbwHPT2DKk4B/nsD6zeK6auO6auO6anM01vXBiDi51IKOu3onItYAaxoxlqSnIqKnEWM1kuuqjeuqjeuqTW51tfrqnWFgdtHjWanNzMxaoNWhvw2YK+lUSccCS4GBFtdgZpatlh7eiYhRSdcAj1K4ZHNtROxq4pQNOUzUBK6rNq6rNq6rNlnV1dITuWZm1l7+i1wzs4w49M3MMnLEh76ktZL2SdpZZrkk3S5pSNIzks7skLp6JR2U9HS6/dcW1TVb0hZJz0raJekLJfq0fJtVWVfLt5mk4yU9Kelnqa6vluhznKT70/Z6QtKcDqlruaR/KtpeVzW7rqK5j5H0U0kbSixr+faqoqZ2bqvdknakeZ8qsbyxv48RcUTfgI8DZwI7yyy/GHgEEHA28ESH1NULbGjD9poBnJnuvx/4P8Bp7d5mVdbV8m2WtkFXuj8ZeAI4e0yf/wR8K91fCtzfIXUtB+5o9c9YmvtLwPdKfb/asb2qqKmd22o3cNI4yxv6+3jE7+lHxGPA/nG6LAbujYKtwDRJMzqgrraIiL0R8ZN0/03gOWDmmG4t32ZV1tVyaRuMpIeT023s1Q+LgXXp/gPAeZLUAXW1haRZwCeAb5fp0vLtVUVNnayhv49HfOhXYSbwStHjPXRAmCQfS2/PH5F0eqsnT2+rP0phL7FYW7fZOHVBG7ZZOizwNLAP2BQRZbdXRIwCB4Ff7YC6AP5DOiTwgKTZJZY3w58Bfwr83zLL27G9KtUE7dlWUHix/t+StqvwMTRjNfT3MYfQ71Q/ofD5GB8Bvgn8r1ZOLqkL+AHwxYh4o5Vzj6dCXW3ZZhHxbkTMp/AX5AslndGKeSupoq4fAXMi4neBTfz/veumkfT7wL6I2N7suapVZU0t31ZFfi8izgQuAq6W9PFmTpZD6HfkRz9ExBuH3p5HxEZgsqSTWjG3pMkUgvW+iPhhiS5t2WaV6mrnNktzvg5sARaNWfSv20vSJGAq8Fq764qI1yLinfTw28CCFpRzDvBJSbuBfuBcSd8d06fV26tiTW3aVofmHk5f9wEPUvg04mIN/X3MIfQHgMvTGfCzgYMRsbfdRUn6tUPHMSUtpPC9aHpQpDnvBp6LiK+X6dbybVZNXe3YZpJOljQt3Z9C4X9B/HxMtwFgWbr/aeDHkc7AtbOuMcd9P0nhPElTRcR1ETErIuZQOEn744j4j2O6tXR7VVNTO7ZVmvcESe8/dB+4ABh7xV9Dfx877lM2ayXp+xSu6jhJ0h7gegontYiIbwEbKZz9HgLeAq7okLo+DfyxpFHgbWBps4MiOQe4DNiRjgcDfBn49aLa2rHNqqmrHdtsBrBOhX8A9D5gfURskHQj8FREDFB4sforSUMUTt4vbXJN1db1nyV9EhhNdS1vQV0ldcD2qlRTu7ZVN/Bg2peZBHwvIv5a0uehOb+P/hgGM7OM5HB4x8zMEoe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhn5f85MUPzHqW17AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist('ceiling_height', range= (1,5), bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can see that most of the ceiling height is around 2.5 to 3.0 meters. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After investigating square meters, price, rooms, and ceiling height I see that most of the properties are small and the price range may reflect that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examination of the time it takes to sell an apartment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    20518.000000\n",
       "mean       180.888634\n",
       "std        219.727988\n",
       "min          1.000000\n",
       "25%         45.000000\n",
       "50%         95.000000\n",
       "75%        232.000000\n",
       "max       1580.000000\n",
       "Name: days_listed, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['days_listed'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average time to sell: 180.88\n"
     ]
    }
   ],
   "source": [
    "print('Average time to sell: {:.2f}'.format(180.88)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f895de35690>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 48,
     "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": [
    "data.hist('days_listed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average time for a sale is 180 days. I believe sales are happening rather quickly if it is less than 45 days since the peak of sales is around 45 days. Sales are taking an extra long time if it is passed 232 days since the average time is about 180 days. However since the distribution of the data is abnormal and we have apartments that are taking an extra long time to sell which skews the numbers we may want to look at other numbers than the quartiles that we have. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing rare and outlying values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data['total_area'] < 400]\n",
    "data = data[data['bedrooms'] < 5]\n",
    "data = data[data['ceiling_height'] < 80]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first I tried to use the IQR method to identify and remove outliers from the data, defining the cut-off values as (Q1 - 1.5 * IQR, Q3 + 1.5 * IQR) but after having heard from other students and Anastasia herself that this might not be the best option I decided to choose my own outliers. Our data is not normal distribution so the IQR method would not work very well with that and it could remove a lot of data that we may need.\n",
    "\n",
    "I chose less than 400 for total area because I know that most of the data is within 200 total meters. I chose 5 bedrooms because as well most of the data is within 1 and 2 bedrooms. I chose ceiling height as less than 80. I could not think of a specific spot for ceiling height to choose from but I know that there is the outlier of 100 in height and so I would definitely be removing that.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4.4: Finding factors with the biggest influence on an apartment’s price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient: 0.6\n"
     ]
    }
   ],
   "source": [
    "data.plot(x='total_area', y='last_price', kind='scatter', grid=True)\n",
    "plt.show()\n",
    "print('Correlation coefficient: {:.1f}'.format(data['last_price'].corr(data['total_area'])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient: 0.3\n"
     ]
    }
   ],
   "source": [
    "data.plot(x='bedrooms', y='last_price', kind='scatter', grid=True)\n",
    "plt.show()\n",
    "print('Correlation coefficient: {:.1f}'.format(data['last_price'].corr(data['bedrooms'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient: 0.1\n"
     ]
    }
   ],
   "source": [
    "data.plot(x='floor_category', y='last_price', kind='scatter', grid=True)\n",
    "plt.show()\n",
    "print('Correlation coefficient: {:.1f}'.format(data['price_per_square_meter'].corr(data['floor'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient: -0.3\n"
     ]
    }
   ],
   "source": [
    "data.plot(x='city_center_dist', y='last_price', kind='scatter', grid=True)\n",
    "plt.show()\n",
    "print('Correlation coefficient: {:.1f}'.format(data['price_per_square_meter'].corr(data['city_center_dist'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient: -0.0\n"
     ]
    },
    {
     "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": [
    "data.plot(x='weekday_posted', y='last_price', kind='scatter', grid=True)\n",
    "print('Correlation coefficient: {:.1f}'.format(data['price_per_square_meter'].corr(data['weekday_posted'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient: -0.0\n"
     ]
    },
    {
     "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": [
    "data.plot(x='month_posted', y='last_price', kind='scatter', grid=True)\n",
    "print('Correlation coefficient: {:.1f}'.format(data['price_per_square_meter'].corr(data['month_posted'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient: 0.0\n"
     ]
    },
    {
     "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": [
    "data.plot(x='year_posted', y='last_price', kind='scatter', grid=True)\n",
    "print('Correlation coefficient: {:.1f}'.format(data['price_per_square_meter'].corr(data['year_posted'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "The price is affected most by total area of the property and next by bedrooms. The rest of the parameters don't really have a correlation with the price."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most frequent localities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Saint Petersburg',\n",
       " 'Vsevolozhsk',\n",
       " 'Pushkin',\n",
       " 'Shushary village',\n",
       " 'Murino village',\n",
       " 'Kolpino',\n",
       " 'Gatchina',\n",
       " 'Petergof',\n",
       " 'Pargolovo village',\n",
       " 'Sestroretsk']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_locality = data.groupby('locality_name').count().sort_values(by='date_posted', ascending=False).head(10).reset_index()\n",
    "locality_list = top_locality['locality_name'].tolist()\n",
    "locality_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saint Petersburg     9758\n",
      "Vsevolozhsk           272\n",
      "Pushkin               231\n",
      "Shushary village      216\n",
      "Murino village        212\n",
      "Kolpino               201\n",
      "Gatchina              148\n",
      "Petergof              145\n",
      "Pargolovo village     137\n",
      "Sestroretsk           128\n",
      "Name: locality_name, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data['locality_name'].value_counts().head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>price_per_square_meter</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>locality_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>2308.621871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Pushkin</td>\n",
       "      <td>2086.587241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sestroretsk</td>\n",
       "      <td>2058.233834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Pargolovo village</td>\n",
       "      <td>1828.804829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Murino village</td>\n",
       "      <td>1760.578217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Petergof</td>\n",
       "      <td>1721.641261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Shushary village</td>\n",
       "      <td>1599.555570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Kolpino</td>\n",
       "      <td>1528.491819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Gatchina</td>\n",
       "      <td>1409.497173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Vsevolozhsk</td>\n",
       "      <td>1353.207804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   price_per_square_meter\n",
       "locality_name                            \n",
       "Saint Petersburg              2308.621871\n",
       "Pushkin                       2086.587241\n",
       "Sestroretsk                   2058.233834\n",
       "Pargolovo village             1828.804829\n",
       "Murino village                1760.578217\n",
       "Petergof                      1721.641261\n",
       "Shushary village              1599.555570\n",
       "Kolpino                       1528.491819\n",
       "Gatchina                      1409.497173\n",
       "Vsevolozhsk                   1353.207804"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_top = data.groupby('locality_name').count().sort_values(by='weekday_posted',\n",
    "                                                                       ascending=False).head(10).reset_index()\n",
    "top_locality_names = data_top['locality_name'].unique()\n",
    "data.query('locality_name in @top_locality_names').pivot_table(index='locality_name', values='price_per_square_meter').sort_values(by = 'price_per_square_meter', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "Saint Petersburg has the highest price per square meter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Thoroughly look at apartment offers: Each apartment has information about the distance to the city center. Select apartments in Saint Petersburg (‘locality_name’). Your task is to pinpoint which area is considered to be in the city center. In order to do that, create a column with the distance to the city center in km and round to the nearest whole number. Next, calculate the average price for each kilometer and plot a graph to display how prices are affected by the distance to the city center. Find a place on the graph where it shifts significantly. That's the city center border."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding the city center border"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f895b89bc10>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_SP = data.loc[data['locality_name'] == 'Saint Petersburg'].copy()\n",
    "#converting city_center_dist to km and rounding\n",
    "data_SP['city_center_km'] = (data_SP['city_center_dist'] / 1000).round(0)\n",
    "#calculating average price\n",
    "data_SP.pivot_table(index = 'city_center_km', values = 'last_price').plot(grid = True, figsize= (9,3), title = ('Price and distance'))\n",
    "#calculating average price per square meter\n",
    "data_SP.pivot_table(index = 'city_center_km', values = 'price_per_square_meter').plot(grid = True, figsize= (9,3), title=('Average price and distance'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "I took the data from Saint Petersburg to find the city center border. There is a significant drop in price after about 6 kilometers. That is where I will assume the border of the city center is."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Factors that affect an apartment’s price in the city center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_center = data_SP.query('city_center_km <= 6')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>date_posted</th>\n",
       "      <th>days_listed</th>\n",
       "      <th>last_price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>kitchen_area</th>\n",
       "      <th>living_area</th>\n",
       "      <th>total_area</th>\n",
       "      <th>balconies</th>\n",
       "      <th>ceiling_height</th>\n",
       "      <th>floors_total</th>\n",
       "      <th>...</th>\n",
       "      <th>pond_dist</th>\n",
       "      <th>ponds_within_3000</th>\n",
       "      <th>price_per_square_meter</th>\n",
       "      <th>weekday_posted</th>\n",
       "      <th>month_posted</th>\n",
       "      <th>year_posted</th>\n",
       "      <th>floor_category</th>\n",
       "      <th>living_area_ratio</th>\n",
       "      <th>kitchen_area_ratio</th>\n",
       "      <th>city_center_km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>2018-09-11</td>\n",
       "      <td>37.0</td>\n",
       "      <td>400000</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>68.00</td>\n",
       "      <td>118</td>\n",
       "      <td>0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>779.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3389.830508</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>0.576271</td>\n",
       "      <td>0.135593</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>2019-02-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80000</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>8.30</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2.90</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>286.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3809.523810</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>0.395238</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>2017-02-17</td>\n",
       "      <td>144.0</td>\n",
       "      <td>660000</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>86.00</td>\n",
       "      <td>180</td>\n",
       "      <td>4</td>\n",
       "      <td>2.90</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3666.666667</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>0.477778</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>163.0</td>\n",
       "      <td>130000</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>25.60</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>3.45</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>812.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2549.019608</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>0.501961</td>\n",
       "      <td>0.137255</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>340.0</td>\n",
       "      <td>266216</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>31.52</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>2.70</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>133.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3131.952941</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>0.370824</td>\n",
       "      <td>0.305882</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <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",
       "      <td>23644</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>166.0</td>\n",
       "      <td>99800</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>32.30</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>2.70</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>171.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1609.677419</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>0.520968</td>\n",
       "      <td>0.193548</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23648</td>\n",
       "      <td>2017-05-12</td>\n",
       "      <td>621.0</td>\n",
       "      <td>202000</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>48.60</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>3.27</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>117.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2589.743590</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>0.623077</td>\n",
       "      <td>0.102564</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23664</td>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>816.0</td>\n",
       "      <td>180000</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>64.60</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>3.36</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>118.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1636.363636</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>0.587273</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23691</td>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>519.0</td>\n",
       "      <td>189400</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>40.30</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>2.75</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>806.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2630.555556</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>0.559722</td>\n",
       "      <td>0.138889</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23694</td>\n",
       "      <td>2017-03-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>194000</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>73.30</td>\n",
       "      <td>133</td>\n",
       "      <td>0</td>\n",
       "      <td>3.70</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>381.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1458.646617</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>0.551128</td>\n",
       "      <td>0.097744</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1756 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      date_posted  days_listed  last_price  bedrooms  kitchen_area  \\\n",
       "63     2018-09-11         37.0      400000         3            16   \n",
       "114    2019-02-07          NaN       80000         1             6   \n",
       "121    2017-02-17        144.0      660000         3            24   \n",
       "136    2018-08-01        163.0      130000         2             7   \n",
       "137    2018-01-01        340.0      266216         2            26   \n",
       "...           ...          ...         ...       ...           ...   \n",
       "23644  2018-04-26        166.0       99800         2            12   \n",
       "23648  2017-05-12        621.0      202000         3             8   \n",
       "23664  2015-12-10        816.0      180000         4            15   \n",
       "23691  2016-10-13        519.0      189400         2            10   \n",
       "23694  2017-03-21          NaN      194000         3            13   \n",
       "\n",
       "       living_area  total_area  balconies  ceiling_height  floors_total  ...  \\\n",
       "63           68.00         118          0            3.00             9  ...   \n",
       "114           8.30          21          0            2.90             8  ...   \n",
       "121          86.00         180          4            2.90             9  ...   \n",
       "136          25.60          51          1            3.45             6  ...   \n",
       "137          31.52          85          1            2.70            10  ...   \n",
       "...            ...         ...        ...             ...           ...  ...   \n",
       "23644        32.30          62          0            2.70             4  ...   \n",
       "23648        48.60          78          0            3.27             5  ...   \n",
       "23664        64.60         110          0            3.36             7  ...   \n",
       "23691        40.30          72          1            2.75            25  ...   \n",
       "23694        73.30         133          0            3.70             5  ...   \n",
       "\n",
       "       pond_dist  ponds_within_3000 price_per_square_meter  weekday_posted  \\\n",
       "63         779.0                1.0            3389.830508               1   \n",
       "114        286.0                1.0            3809.523810               3   \n",
       "121          NaN                0.0            3666.666667               4   \n",
       "136        812.0                1.0            2549.019608               2   \n",
       "137        133.0                3.0            3131.952941               0   \n",
       "...          ...                ...                    ...             ...   \n",
       "23644      171.0                1.0            1609.677419               3   \n",
       "23648      117.0                2.0            2589.743590               4   \n",
       "23664      118.0                1.0            1636.363636               3   \n",
       "23691      806.0                1.0            2630.555556               3   \n",
       "23694      381.0                3.0            1458.646617               1   \n",
       "\n",
       "       month_posted year_posted  floor_category  living_area_ratio  \\\n",
       "63                9        2018               2           0.576271   \n",
       "114               2        2019               2           0.395238   \n",
       "121               2        2017               2           0.477778   \n",
       "136               8        2018               2           0.501961   \n",
       "137               1        2018               2           0.370824   \n",
       "...             ...         ...             ...                ...   \n",
       "23644             4        2018               3           0.520968   \n",
       "23648             5        2017               3           0.623077   \n",
       "23664            12        2015               2           0.587273   \n",
       "23691            10        2016               2           0.559722   \n",
       "23694             3        2017               2           0.551128   \n",
       "\n",
       "       kitchen_area_ratio  city_center_km  \n",
       "63               0.135593             5.0  \n",
       "114              0.285714             6.0  \n",
       "121              0.133333             4.0  \n",
       "136              0.137255             4.0  \n",
       "137              0.305882             4.0  \n",
       "...                   ...             ...  \n",
       "23644            0.193548             4.0  \n",
       "23648            0.102564             1.0  \n",
       "23664            0.136364             5.0  \n",
       "23691            0.138889             4.0  \n",
       "23694            0.097744             4.0  \n",
       "\n",
       "[1756 rows x 30 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(data[['price_per_square_meter', 'total_area', 'bedrooms', 'ceiling_height']],figsize=[14,6])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(data_center[['price_per_square_meter', 'total_area', 'bedrooms', 'ceiling_height']],figsize=[14,6])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient for Price vs. Area: 0.6\n"
     ]
    }
   ],
   "source": [
    "#('last_price_vs._total_area') \n",
    "print('Correlation coefficient for Price vs. Area: {:.1f}'.format(data['last_price'].corr(data_center['total_area'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient for Price vs. ceiling_height: 0.1\n"
     ]
    }
   ],
   "source": [
    "print('Correlation coefficient for Price vs. ceiling_height: {:.1f}'.format(data['last_price'].corr(data_center['ceiling_height'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient for Price vs. Bedrooms: 0.2\n"
     ]
    }
   ],
   "source": [
    "#('last_price_vs._bedrooms') \n",
    "print('Correlation coefficient for Price vs. Bedrooms: {:.1f}'.format(data['last_price'].corr(data_center['bedrooms'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "The price with the center area has a little more density when it comes to total area but the information is pretty similar to suggest that there is no major difference between the two areas. The correlation for total area seems closer than the rest at 0.6."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Also identify the factors that affect an apartment’s price: number of rooms, floor, distance to the city center, and ad publication date."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bedrooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient: 0.3\n"
     ]
    },
    {
     "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": [
    "data_center.plot(x='bedrooms', y='last_price', kind='scatter', grid=True)\n",
    "print('Correlation coefficient: {:.1f}'.format(data['last_price'].corr(data['bedrooms'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Floors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient: 0.1\n"
     ]
    },
    {
     "data": {
      "image/png": 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+gzGvwZgTOK+8hlteR73UFhGfiYhpwF9FxG9HxLT0en1EHLHwSDolnekgaSzwFuBRYA3ZQKMAi4AVaXplek+avzoiIsWvSD3SpgEzgAeBh4AZqQfbKLIOCCvTOnnbMDOzAuQZJPQ5SRMiYrekvwXOBj5xlCeQngosTb3PRgC3RcQdkn4GLJf0CeAnwJfT8l8GviapHdhBVkiIiEck3Qb8DDgIXJMu4SHp3WRjxjUBt0bEI2lb1+Zpw8zMipGn8PxdRHxb0nnAhcD/BD4PvKHSChGxCfj9MvEnyHqk9Y/vB/6kwrY+CXyyTPxO4M5atGFmZvWXpzv1ofTzj4AlEfF/gVG1T8nMzI5neQrPVklfBP4UuFPS6JzrD0seut7MrK88haP3+TsXR8QLwEnAX9clq+OEh643M3u5PI9F2BsR/wbslPRKslEIfl63zIY4D11vZlZenpEL5kt6DHgS+H/p5131Smyo89D1Zmbl5bnU9nFgNvAf6Xs9FwLr6pLVccBD15uZlZen8HRHRAcwQtKIiFgDtNYpryHPQ9ebmZWX53s8L0gaD9wLfF3S88Ce+qR1fJg/cwpzpp/Mg/ffx9r557noWE21b9tN595u2rftZnrLhEanY1a1PGc8C4B9wAeA75ONMv22eiR1PPHQ9VYPH/veZi68+V62dO7lwpvv5WMrNjc6JbOq5enVticiDkXEwYhYGhG3pEtvZlag9m27Wbbu6T6xZfc/Tfu23Q3KyCyfoxYeSbsl7Srz2i1pVxFJmtlhG595IVfcbCDq+eX3akannhARJ5Z5TYiIE2uekZkd0czTJuaKm+VV7y+/e8gbsyFmessEFp77yj6xhee+0h0MrCaK+PJ7nl5tZjZI3LDgdSycfTqbN6zj3z8w20XHaqb3y+/7Ofw9xN4vv9eqk5TPeMyGqOktE5h0QrOLjtVUEV9+d+ExM7OXFPHld19qMzOzPur95Xef8ZiZ2cvU88vvLjxmZlYoFx4zMyuUC4+ZmRXKhcfMzApV18Ij6TRJayT9TNIjkt6X4idJWiXpsfRzUopL0i2S2iVtknR2ybYWpeUfk7SoJD5L0ua0zi2SNNA2zMys/up9xnMQ+FBEnEH29NJrJJ0BXAfcExEzgHvSe4BLgBnptRj4PGRFBLgeeANwDnB9byFJy1xdst68FM/VhpmZFaOuhScino2IH6fp3cCjwBSyZ/ssTYstBd6ephcAyyKzDpgo6VTgYmBVROyIiE5gFTAvzTsxItZFRADL+m0rTxtmZpbUc3Tqwr5AKul04PeBB4CWiHg2zXoOaEnTU4BnSlbbkmJHim8pE2cAbTxbEkPSYrIzIlpaWmhra6vq9yynq6vrmNavF+eVz2DL61BPsGdPF/esXkPTCDU6nT4G277q5byqs3NfN1s69/EbY4LlK+9m6qSxvGJsc822X0jhSY/Mvh14f0TsSrdhAIiIkBT1bH8gbUTEEmAJQGtra8ydO3fA7be1tXEs69eL88pnMOW1YuNWrr19E+/9veCWB/dz02VnMX/mlKOvWJDBtK9KOa+j6+g6wJwbV7O/u4kPve4gn97cxJjmg6y99k1DZ5BQSc1kRefrEfFvKbyt9/JW+vl8im8FTitZfWqKHSk+tUx8IG2YDQlFDFtvw1fv6NSlekenrpV692oT8GXg0Yj4x5JZK4HenmmLgBUl8YWp59lsYGe6XHY3cJGkSalTwUXA3WneLkmzU1sL+20rTxtmQ0IRHww2fB0Po1PPAd4BvFnSxvS6FPgU8BZJjwEXpvcAdwJPAO3AvwDvAoiIHcDHgYfS64YUIy3zpbTO48BdKZ6rDbOhoogPBhu+hvzo1BFxH1DprucFZZYP4JoK27oVuLVMfD1wZpl4R942zIaC3g+GD9++qW4fDDa81Xt0aj8WwWwIqvcHg5lHpzazl+nc8yL7u3vo3PNio1Mxy8WFx2wI+tj3NnPhzfeypXMvF958Lx9bsbnRKZlVzYXHbIhp37abZeue7hNbdv/TtG/b3aCMzPJx4TEbYu5r/3WuuNlg48JjNsScXOFmb6W42WDjwmM2xLzmNyfkipsNNi48ZkPMr3buzxU3G4h6jk7twmM2xPyqc2+uuFleKzZuZc6Nq3ly+x7m3LialRtrO5ylC4/ZELP/YE+uuFkeRQxC68JjNsScVmFMtkpxszyG/OjUZlZ7nXu7c8XN8jgeRqc2sxqbedrEXHGzPHoHoR09UoyQGD1SNR+E1oXHbIiZ3jKBN06f3Cf2xumTmd7i7tRWG9njmnsfLFD7x6q78JgNMR1dB3joqc4+sYee6vQTSK0mejsXHDjYQ08EBw66c4HZsFfpJq+fQGq14M4FZvYy40Y1sb+7783f/d09jBvV1KCM7HjizgVm9jJ7XjzE6Ka+191HN4k9Lx5qUEZ2PBnyj742s9qbOmksB6Nv7GBQ079IbXibP3MKZ5x6Ips3rOOOd8+ueccVn/GYDTGde17kUE/fynOoJ/wkUquZFRu38tbP3sezL+zjrZ+9z0PmmA13fh6P1dOQHzJH0q2Snpf0cEnsJEmrJD2Wfk5KcUm6RVK7pE2Szi5ZZ1Fa/jFJi0risyRtTuvcIkkDbaNe6jnCqw1Po0eW/29bKW6Wx/HQq+2rwLx+seuAeyJiBnBPeg9wCTAjvRYDn4esiADXA28AzgGu7y0kaZmrS9abN5A26qXeI7za8DRhTPlbs5XiZnlMnTSW/Qf7dlTZf/DQ0OnVFhH3Ajv6hRcAS9P0UuDtJfFlkVkHTJR0KnAxsCoidkREJ7AKmJfmnRgR6yIigGX9tpWnjZor4nTVhqeu/Qdzxc3yyj5SK78/Vqr1Bl/WgHQ6cEdEnJnevxARE9O0gM6ImCjpDuBTEXFfmncPcC0wFxgTEZ9I8b8D9gFtafkLU/yNwLUR8da8bUTE+jJ5LyY7K6KlpWXW8uXLc/3e+7oP8eT2PRyKoGUsbNsHTRLTThnH2ObB8X2Lrq4uxo8f3+g0XsZ5Hdn23Qd4blf20LfeYwvgN08cwykTBsfjrwfLvurPeR1dLT+7zj///A0R0do/3tBz84gISXWtfANtIyKWAEsAWltbY+7cubnW7+g6wAduXM3+7h4+9LqDfHrzSMY0j2Dt/PNq2h/+WLS1tZH39yqC8zqyf7n3cT699ucALx1bAB+9dDp/8qbfaWRqLxks+6o/53V0RXx2NeJu5Lbey1vp5/MpvhU4rWS5qSl2pPjUMvGBtFFzRXwJy4anFys88K1S3CyPyeNHc3nr1D6xy1unDvnRqVcCvT3TFgErSuILU8+z2cDOiHgWuBu4SNKk1KngIuDuNG+XpNnpctrCftvK00ZdzJ85hbXXvplpp4xj7bVvZv7MKfVqyoaR7kPlC0yluFkeHV0HuG39lj6x29Zvqen96bpeapP0TbJ7NCdL2kLWO+1TwG2SrgKeAi5Pi98JXAq0A3uBdwJExA5JHwceSsvdEBG9HRbeRdZzbixwV3qRt416mjx+NGObm3ymYzVTaWgcD5ljtdDbnXo/h/+Q6e1OXavPsboWnoi4ssKsC8osG8A1FbZzK3Brmfh64Mwy8Y68bZgNFU9t350rbpbH1Elj2X2gbw/J3QcODp3u1GZWew89/UKuuFkeT27vyhUfCBeeOvPIBVZr004q/5dnpbhZHvc+Vn7opUrxgXDhqSOPXGD1cMLo5lxxszzeNOPkXPGBcOGpE49cYPXydMfeXHGzPFqnTebVLeP6xF7dMo7WaZNr1oYLT50UMdCeDU/bu8o//qBS3CyPjq4DPL59T5/Y49v3DJ3RqYezIh4fa8PTyKbyA3FUipvl8civdtH/u8gHe7J4rbjw1Mnk8aNpfdWkPrE/eNUkf5/HjlnXgfIFplLcLI+tneUv2VaKD4QLT520b9vNfe0dfWI/bO+gfZu/a2HHplJ5cdmxWti0pXy3/ErxgXDhqRM/JdLMhqItO8rfh64UHwgXnjrxeFpmNhSNalau+EC48NTJC3vL9zCqFDczGwy27S7fe61SfCBceOrkhFHlh8GrFDczGwye69yfKz4QLjx1MmXimFxxM7PBYE93d674QLjw1Mn6pzpzxc3MBoOmCrdyKsUHwoWnTpqbyu/aSnEzs8Gg0gAYtRwYw5+CdXLwUPlvVVSKm5kNFy48ddL2i2254mZmw4ULT51seaF818NKcTOz4cKFp048rImZWXkuPGZmVigXHjMzK9SwLDyS5kn6haR2Sdc1Oh8zs+Fk2BUeSU3A54BLgDOAKyWd0diszMyGj2FXeIBzgPaIeCIiXgSWAwtq3UjL+FG54mbVOqHCcH+V4mZ5XD3nVbniA6GI4dXPStIfA/Mi4s/T+3cAb4iId/dbbjGwGKClpWXW8uXLc7Vz4GAP/5Ee+tYyFralR1n8bssERo8cHPW+q6uL8ePHNzqNl3FeR7b3xUM8vr0L6Hts/c4p4zlhVFMDMztssOyr/pxXdR7eupPg8PEl4Mwpr8i9nfPPP39DRLT2j/tvpAoiYgmwBKC1tTXmzp2bextrV2xm2f1P86HXHeTTm0ey8NxX8p4LX1fjTAeura2Ngfxe9ea8ju4dX1rHD9s7Xjq23jh9Mu+5aHaj03rJYNpXpZxXdeYCn/w/D9O850l2nTiNj77tzJpufzgWnq3AaSXvp6ZYzd2w4HUsnH06mzes498/MJvpLRPq0YwNQ1/789msf7KDxzev5zt/0UrrtMmNTsmOMx9925m0tf2auXNrW3RgeN7jeQiYIWmapFHAFcDKejU2vWUCk05odtGxmmudNpmWE0e76NiQM+zOeCLioKR3A3cDTcCtEfFIg9MyMxs2hl3hAYiIO4E7G52HmdlwNBwvtZmZWQO58JiZWaFceMzMrFDD7gukAyFpO/DUMWziZODXNUqnlpxXPoMxr8GYEzivvI7XvF4VEaf0D7rwFEDS+nLf3m0055XPYMxrMOYEziuv4ZaXL7WZmVmhXHjMzKxQLjzFWNLoBCpwXvkMxrwGY07gvPIaVnn5Ho+ZmRXKZzxmZlYoFx4zMyuUC88xkHSrpOclPVxhviTdIqld0iZJZ5fMWyTpsfRaVHBef5by2SzpR5JeXzLvlym+UdL6gvOaK2lnanujpI+VzJsn6RdpX15XcF5/XZLTw5IOSTopzavL/pJ0mqQ1kn4m6RFJ7yuzTOHHV5V5FX58VZlXocdXlTk14tgaI+lBST9Nef1DmWVGS/pW2h8PSDq9ZN5HUvwXki4eUBIR4dcAX8CbgLOBhyvMvxS4i+wBfrOBB1L8JOCJ9HNSmp5UYF5/2NsecElvXun9L4GTG7S/5gJ3lIk3AY8Dvw2MAn4KnFFUXv2WfRuwut77CzgVODtNTwD+o//v3Ijjq8q8Cj++qsyr0OOrmpwadGwJGJ+mm4EHgNn9lnkX8IU0fQXwrTR9Rto/o4Fpab815c3BZzzHICLuBXYcYZEFwLLIrAMmSjoVuBhYFRE7IqITWAXMKyqviPhRahdgHdnD8Oquiv1VyTlAe0Q8EREvAsvJ9m0j8roS+Gat2q4kIp6NiB+n6d3Ao8CUfosVfnxVk1cjjq8q91cldTm+BpBTUcdWRERXetucXv17mS0Alqbp7wAXSFKKL4+IAxHxJNBOtv9yceGprynAMyXvt6RYpXgjXEX2V3OvAH4gaYOkxQ3I59x0CeAuSa9NsUGxvySdQPYBfntJuO77K13m+H2yv0xLNfT4OkJepQo/vo6SV0OOr6Ptq6KPLUlNkjYCz5P9kVLx2IqIg8BOYDI12lfD8nk8lpF0PtkHw3kl4fMiYquk3wBWSfp5OiMowo/JxnbqknQp8D1gRkFtV+NtwNqIKD07quv+kjSe7MPo/RGxq1bbPVbV5NWI4+soeTXk+Kry37DQYysiDgEzJU0EvivpzIgoe4+zHnzGU19bgdNK3k9NsUrxwkg6C/gSsCAiOnrjEbE1/Xwe+C4DOI0eqIjY1XsJILKH9TVLOplBsL+SK+h3KaSe+0tSM9kH1tcj4t/KLNKQ46uKvBpyfB0tr0YcX9Xsq6TQY6ukjReANbz8UuxL+0TSSOAVQAe12le1vnE13F7A6VS+Wf5H9L35+2CKnwQ8SXbjd1KaPqnAvF5Jdm32D/vFxytj9wUAAARSSURBVAETSqZ/BMwrMK/f5PCXms8Bnk77biTZDfJpHL75+9qi8krzX0F2H2hcEfsr/d7LgH86wjKFH19V5lX48VVlXoUeX9Xk1KBj6xRgYpoeC/wQeGu/Za6hb+eC29L0a+nbueAJBtC5wJfajoGkb5L1lDlZ0hbgerIbdUTEF8ger30p2X/CvcA707wdkj4OPJQ2dUP0PcWud14fI7te+8/Z/UIORjYCbQvZaTdk/xm/ERHfLzCvPwb+u6SDwD7gisiO9oOS3g3cTdYD6daIeKTAvAD+M/CDiNhTsmo999cc4B3A5nQtHuBvyD7UG3l8VZNXI46vavIq+viqJico/tg6FVgqqYnsqtdtEXGHpBuA9RGxEvgy8DVJ7WRF8YqU8yOSbgN+BhwEronssl0uHjLHzMwK5Xs8ZmZWKBceMzMrlAuPmZkVyoXHzMwK5cJjZmaFcuExM7NCufCYDYCk90p6VNJWSZ9tdD69JL0/jftlNmi58JgNzLuAtwAfrcXG0rAktfB+oK6Fp4a52jDlwmOWk6QvkD275S6yIWl646dLWq3sIWj3SHrlUeJflfQFSQ8AN1Voa7ykr6QHgm2SdFmKf17S+tIHeUl6L/BbwBpJa1LsIkn3S/qxpG+nASuRdKmkn6eRj2+RdEeKnyTpe6mtdWnMNST9vaSvSVpL9o32eyXNLMnzPpU88M3sSFx4zHKKiL8EfgWcD3SWzPrfwNKIOAv4OnDLUeKQDbL4hxHxwQrN/R2wMyJel9ZfneIfTcPQnAX8J0lnRcQtvXlFxPlpAMy/BS6MiLOB9cAHJY0BvghcEhGzyMbu6vUPwE9SW39DNtZYrzPStq4kG1LlvwJI+l1gTET89Mh7zizjwmNWO+cC30jTX+Pw4wAqxQG+fZSxri4EPtf7Jg4/YO1yST8GfkI2cOMZZdadneJr01hhi4BXAa8BnojsQV7Qd1Tk81KORMRqYLKkE9O8lRGxrzdv4K1p9OX/Bnz1CL+DWR++VmvWWHuOvkhfkqYBfwX8QUR0SvoqMKbcomQP+bqy3/ozyyxbjZdyjYi9klaRPZHycmDWALdpw5DPeMxq50ekUXyBPyMbbv5I8WqsIhuiHgBJk4ATyYrATkktwCUly+8GJqTpdcAcSdPTuuPSZbFfAL+t7KmYAH9asv4PU45Imgv8Oio/vOxLZJcNHyo5EzM7Khces9p5D/BOSZvIhsN/31Hi1fgEMEnSw5J+Snb/5qdkl9h+TnYJb23J8kuA70taExHbye7DfDO1fT/wmnS57F1puQ1kxWpnWv/vgVlp+U+RXZ4rKyI2ALuAr+T4fcz8WASz4UjS+MgeAS2ye0iPRcTNObfxW0AbWTHrqUOadpzyGY/Z8HR16nDwCNkTML+YZ2VJC4EHyHrXuehYLj7jMRsEJL2Tl1+CWxsR15Rb3mwoc+ExM7NC+VKbmZkVyoXHzMwK5cJjZmaFcuExM7NC/X+iew+B9TZTKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_center.plot(x='floor_category', y='last_price', kind='scatter', grid=True)\n",
    "print('Correlation coefficient: {:.1f}'.format(data['last_price'].corr(data_center['floor_category'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distance to city center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient: -0.1\n"
     ]
    },
    {
     "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": [
    "data_center.plot(x='city_center_dist', y='last_price', kind='scatter', grid=True)\n",
    "print('Correlation coefficient: {:.1f}'.format(data_center['last_price'].corr(data_center['city_center_dist'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ad publication date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient: -0.0\n"
     ]
    },
    {
     "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": [
    "data_center.plot(x='weekday_posted', y='last_price', kind='scatter', grid=True)\n",
    "print('Correlation coefficient: {:.1f}'.format(data['last_price'].corr(data['weekday_posted'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient: -0.0\n"
     ]
    },
    {
     "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": [
    "data_center.plot(x='month_posted', y='last_price', kind='scatter', grid=True)\n",
    "print('Correlation coefficient: {:.1f}'.format(data['last_price'].corr(data['month_posted'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient: -0.0\n"
     ]
    },
    {
     "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": [
    "data_center.plot(x='year_posted', y='last_price', kind='scatter', grid=True)\n",
    "print('Correlation coefficient: {:.1f}'.format(data['last_price'].corr(data['year_posted'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "I don't really see how any of these factors have impacted the price of the property except the square meters. The highest correlation is 0.6 for total_area and 0.3 for bedrooms. The rest of the data is either 0.0 or 0.1 which has no correlation at all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Overall conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the end of checking all of the data I haven't found any correlation between the price of the properties and any of the other data about them except perhaps the size of the property with the price. Which is why bedrooms has some correlation.\n",
    "\n",
    "Corrected Conclusion: Overall after checking the data and adding in the missing values that I could I found a few outliers of prices and total_area which seemed to be bigger apartments with many rooms.  \n",
    "\n",
    "Nothing really had any correlation with the price except for the area of the property. We can see this with the total_area of the properties which had the closest correlation of 0.6. The amount of bedrooms which are connected to total_area also has a correlation of 0.3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project completion checklist\n",
    "\n",
    "Mark the completed tasks with 'x'. Then press Shift+Enter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [x]  file opened\n",
    "- [x]  files explored (first rows printed, info() method)\n",
    "- [x]  missing values determined\n",
    "- [x]  missing values filled in\n",
    "- [x]  clarification of the discovered missing values provided\n",
    "- [x]  data types converted\n",
    "- [x]  explanation of which columns had the data types changed and why\n",
    "- [x]  calculated and added to the table: the price per square meter\n",
    "- [x]  calculated and added to the table: the day of the week, month, and year that the ad was published\n",
    "- [x]  calculated and added to the table: which floor the apartment is on (first, last, or other)\n",
    "- [x]  calculated and added to the table: the ratio between the living space and the total area, as well as between the kitchen space and the total area\n",
    "- [x]  the following parameters investigated: square area, price, number of rooms, and ceiling height\n",
    "- [x]  histograms for each parameter created\n",
    "- [x]  task completed: \"Examine the time it's taken to sell the apartment and create a histogram. Calculate the mean and median and explain the average time it usually takes to complete a sale. When can a sale be considered extra quick or taken an extra slow?\"\n",
    "- [x]  task completed: \"Remove rare and outlying values and describe the specific details you've discovered.\"\n",
    "- [x]  task completed: \"Which factors have had the biggest influence on an apartment’s value? Examine whether the value depends on price per meter, number of rooms, floor (top or bottom), or the proximity to the downtown area. Also study the correlation to the ad posting date: day of the week, month, and year. \"Select the 10 places with the largest number of ads and then calculate the average price per square meter in these localities. Select the locations with the highest and lowest housing prices. You can find this data by name in the ’*locality_name’* column. \"\n",
    "- [x]  task completed: \"Thoroughly look at apartment offers: each apartment has information about the distance to the downtown area. Select apartments in Saint Petersburg (*‘locality_name’*). Your task is to pinpoint which area is considered to be downtown. Create a column with the distance to the downtown area in km and round to the nearest whole number. Next, calculate the average price for each kilometer. Build a graph to display how prices are affected by the distance to the downtown area. Define the turning point where the graph significantly changes. This will indicate downtown. \"\n",
    "- [x]  task completed: \"Select a segment of apartments in the downtown. Analyze this area and examine the following parameters: square area, price, number of rooms, ceiling height. Also identify the factors that affect an apartment’s price (number of rooms, floor, distance to the downtown area, and ad publication date). Draw your conclusions. Are they different from the overall conclusions about the entire city?\"\n",
    "- [x]  each stage has a conclusion\n",
    "- [x]  overall conclusion drawn"
   ]
  }
 ],
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