{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2713953f-cc8a-4aaa-845d-8a3231c0d04a",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ca838dc6-3673-429a-8189-8414dd011b8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "63af9c7e-03ce-4b79-a85c-367288c4a50b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/Aemf1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b39929a7-378a-4587-a5f8-1cbe0503888a",
   "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>City</th>\n",
       "      <th>Price</th>\n",
       "      <th>Day</th>\n",
       "      <th>Room Type</th>\n",
       "      <th>Shared Room</th>\n",
       "      <th>Private Room</th>\n",
       "      <th>Person Capacity</th>\n",
       "      <th>Superhost</th>\n",
       "      <th>Multiple Rooms</th>\n",
       "      <th>Business</th>\n",
       "      <th>Cleanliness Rating</th>\n",
       "      <th>Guest Satisfaction</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>City Center (km)</th>\n",
       "      <th>Metro Distance (km)</th>\n",
       "      <th>Attraction Index</th>\n",
       "      <th>Normalised Attraction Index</th>\n",
       "      <th>Restraunt Index</th>\n",
       "      <th>Normalised Restraunt Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>194.033698</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>Private room</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.022964</td>\n",
       "      <td>2.539380</td>\n",
       "      <td>78.690379</td>\n",
       "      <td>4.166708</td>\n",
       "      <td>98.253896</td>\n",
       "      <td>6.846473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>344.245776</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>Private room</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.488389</td>\n",
       "      <td>0.239404</td>\n",
       "      <td>631.176378</td>\n",
       "      <td>33.421209</td>\n",
       "      <td>837.280757</td>\n",
       "      <td>58.342928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>264.101422</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>Private room</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.748312</td>\n",
       "      <td>3.651621</td>\n",
       "      <td>75.275877</td>\n",
       "      <td>3.985908</td>\n",
       "      <td>95.386955</td>\n",
       "      <td>6.646700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>433.529398</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>Private room</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.384862</td>\n",
       "      <td>0.439876</td>\n",
       "      <td>493.272534</td>\n",
       "      <td>26.119108</td>\n",
       "      <td>875.033098</td>\n",
       "      <td>60.973565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>485.552926</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>Private room</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.544738</td>\n",
       "      <td>0.318693</td>\n",
       "      <td>552.830324</td>\n",
       "      <td>29.272733</td>\n",
       "      <td>815.305740</td>\n",
       "      <td>56.811677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>552.808567</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>Private room</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.131420</td>\n",
       "      <td>1.904668</td>\n",
       "      <td>174.788957</td>\n",
       "      <td>9.255191</td>\n",
       "      <td>225.201662</td>\n",
       "      <td>15.692376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>215.124317</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>Private room</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.881092</td>\n",
       "      <td>0.729747</td>\n",
       "      <td>200.167652</td>\n",
       "      <td>10.599010</td>\n",
       "      <td>242.765524</td>\n",
       "      <td>16.916251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>2771.307384</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.686807</td>\n",
       "      <td>1.458404</td>\n",
       "      <td>208.808109</td>\n",
       "      <td>11.056528</td>\n",
       "      <td>272.313823</td>\n",
       "      <td>18.975219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>1001.804420</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.719141</td>\n",
       "      <td>1.196112</td>\n",
       "      <td>106.226456</td>\n",
       "      <td>5.624761</td>\n",
       "      <td>133.876202</td>\n",
       "      <td>9.328686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>276.521454</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>Private room</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.142361</td>\n",
       "      <td>0.924404</td>\n",
       "      <td>206.252862</td>\n",
       "      <td>10.921226</td>\n",
       "      <td>238.291258</td>\n",
       "      <td>16.604478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City        Price      Day        Room Type  Shared Room  \\\n",
       "0  Amsterdam   194.033698  Weekday     Private room        False   \n",
       "1  Amsterdam   344.245776  Weekday     Private room        False   \n",
       "2  Amsterdam   264.101422  Weekday     Private room        False   \n",
       "3  Amsterdam   433.529398  Weekday     Private room        False   \n",
       "4  Amsterdam   485.552926  Weekday     Private room        False   \n",
       "5  Amsterdam   552.808567  Weekday     Private room        False   \n",
       "6  Amsterdam   215.124317  Weekday     Private room        False   \n",
       "7  Amsterdam  2771.307384  Weekday  Entire home/apt        False   \n",
       "8  Amsterdam  1001.804420  Weekday  Entire home/apt        False   \n",
       "9  Amsterdam   276.521454  Weekday     Private room        False   \n",
       "\n",
       "   Private Room  Person Capacity  Superhost  Multiple Rooms  Business  \\\n",
       "0          True              2.0      False               1         0   \n",
       "1          True              4.0      False               0         0   \n",
       "2          True              2.0      False               0         1   \n",
       "3          True              4.0      False               0         1   \n",
       "4          True              2.0       True               0         0   \n",
       "5          True              3.0      False               0         0   \n",
       "6          True              2.0      False               0         0   \n",
       "7         False              4.0       True               0         0   \n",
       "8         False              4.0      False               0         0   \n",
       "9          True              2.0      False               1         0   \n",
       "\n",
       "   Cleanliness Rating  Guest Satisfaction  Bedrooms  City Center (km)  \\\n",
       "0                10.0                93.0         1          5.022964   \n",
       "1                 8.0                85.0         1          0.488389   \n",
       "2                 9.0                87.0         1          5.748312   \n",
       "3                 9.0                90.0         2          0.384862   \n",
       "4                10.0                98.0         1          0.544738   \n",
       "5                 8.0               100.0         2          2.131420   \n",
       "6                10.0                94.0         1          1.881092   \n",
       "7                10.0               100.0         3          1.686807   \n",
       "8                 9.0                96.0         2          3.719141   \n",
       "9                10.0                88.0         1          3.142361   \n",
       "\n",
       "   Metro Distance (km)  Attraction Index  Normalised Attraction Index  \\\n",
       "0             2.539380         78.690379                     4.166708   \n",
       "1             0.239404        631.176378                    33.421209   \n",
       "2             3.651621         75.275877                     3.985908   \n",
       "3             0.439876        493.272534                    26.119108   \n",
       "4             0.318693        552.830324                    29.272733   \n",
       "5             1.904668        174.788957                     9.255191   \n",
       "6             0.729747        200.167652                    10.599010   \n",
       "7             1.458404        208.808109                    11.056528   \n",
       "8             1.196112        106.226456                     5.624761   \n",
       "9             0.924404        206.252862                    10.921226   \n",
       "\n",
       "   Restraunt Index  Normalised Restraunt Index  \n",
       "0        98.253896                    6.846473  \n",
       "1       837.280757                   58.342928  \n",
       "2        95.386955                    6.646700  \n",
       "3       875.033098                   60.973565  \n",
       "4       815.305740                   56.811677  \n",
       "5       225.201662                   15.692376  \n",
       "6       242.765524                   16.916251  \n",
       "7       272.313823                   18.975219  \n",
       "8       133.876202                    9.328686  \n",
       "9       238.291258                   16.604478  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26afe438-96d3-444a-b112-452344b08d44",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "## Check NAs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "68de6af6-4782-4b09-82fd-8482eced8871",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                           0\n",
       "Price                          0\n",
       "Day                            0\n",
       "Room Type                      0\n",
       "Shared Room                    0\n",
       "Private Room                   0\n",
       "Person Capacity                0\n",
       "Superhost                      0\n",
       "Multiple Rooms                 0\n",
       "Business                       0\n",
       "Cleanliness Rating             0\n",
       "Guest Satisfaction             0\n",
       "Bedrooms                       0\n",
       "City Center (km)               0\n",
       "Metro Distance (km)            0\n",
       "Attraction Index               0\n",
       "Normalised Attraction Index    0\n",
       "Restraunt Index                0\n",
       "Normalised Restraunt Index     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d761c1-ea71-4787-836f-febc62514a0a",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "The dataset we are working with is a cleaned Europe dataset that doesn't have any missing data (NA values). However, we should still check for any potential outliers that could affect our model's performance.\n",
    "\n",
    "First, let's plot a histogram to show the frequency distribution of the prices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "74943200-f525-4e5c-a9ac-158e57376f5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for price distribution\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.histplot(df['Price'], kde=True)\n",
    "plt.title('Price Distribution')\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('Frequency')\n",
    "plt.savefig('figures/price_distribution_before.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3139c51-e4fa-4dd8-8e3d-29a6d94c5069",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "## Remove the outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e026202f-c404-4a55-98e6-b9f905a6335a",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "From the histogram, we can observe that there seem to be outliers in the price distribution. To address this issue, we will remove the outliers based on the Interquartile Range (IQR) method. Here's the code to perform this operation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2bc2619c-643a-46ab-953b-d122fbb8ecc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    41714.000000\n",
      "mean       260.094423\n",
      "std        279.408493\n",
      "min         34.779339\n",
      "25%        144.016085\n",
      "50%        203.819274\n",
      "75%        297.373358\n",
      "max      18545.450285\n",
      "Name: Price, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "price_summary = df['Price'].describe()\n",
    "print(price_summary)\n",
    "price_summary.to_csv('results/price_summary.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eec60a8-8c2d-4929-b65c-5ae191e4e5da",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "After removing the outliers, we will save the filtered dataset to a filtered CSV file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76921f66-65dd-4b5c-8a75-6a816234a112",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(39365, 19)\n",
      "-132.02700727067884\n",
      "573.4164505330268\n"
     ]
    }
   ],
   "source": [
    "# Remove outliers based on Price\n",
    "Q1 = df['Price'].quantile(0.25)\n",
    "Q3 = df['Price'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "lower_bound = Q1 - 1.8 * IQR\n",
    "upper_bound = Q3 + 1.8 * IQR\n",
    "\n",
    "# Filter out the outliers\n",
    "filtered_data = df[(df['Price'] >= lower_bound) & (df['Price'] <= upper_bound)]\n",
    "filtered_data.to_csv('data/filtered_data.csv', index=False)\n",
    "print(filtered_data.shape)\n",
    "print(lower_bound)\n",
    "print(upper_bound)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4757a35f-8e27-4ce7-bc5e-0a7ebcb04658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for price distribution\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.histplot(filtered_data['Price'], kde=True)\n",
    "plt.title('Price Distribution')\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('Frequency')\n",
    "plt.savefig('figures/price_distribution.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}