EDA Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f4574842-3779-4c0b-9991-815ba197d137",
   "metadata": {},
   "source": [
    "# AirBnB Optimal Price - EDA Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7c3c0b0-108d-4367-9cb6-28d75d45d330",
   "metadata": {},
   "source": [
    "## First Steps - Imports & Reading In Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f96e7ee5-a5aa-4f8a-a0ee-04432c738d27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Libraries\n",
    "import pandas as pd\n",
    "pd.options.display.max_colwidth = 750   # Need it so I can better view amenities column\n",
    "from pandas_profiling import ProfileReport\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "import pylab\n",
    "import scipy.stats as stats\n",
    "import pandarallel\n",
    "from sklearn.preprocessing import StandardScaler, Normalizer, Binarizer, OrdinalEncoder, LabelEncoder\n",
    "from category_encoders import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "import keras\n",
    "import tensorflow as tf\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "779b5696-b795-4d27-bb1d-d4686284f8d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning filepaths of our airbnb data from Kaggle to variables\n",
    "filename = \"./train.csv\"\n",
    "filename2 = \"./test.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1b871331-b1e8-44c6-978d-9bc05cffe3f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading our CSV files into Pandas DataFrames\n",
    "train = pd.read_csv(filename)\n",
    "test = pd.read_csv(filename2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "824c4185-5a45-42c7-934b-81a9fb53e27e",
   "metadata": {},
   "source": [
    "## Combining Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d23f38dc-ca69-4294-92b0-d79ecb934cdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenating the two datasets into one. It's unnecessary to have pre-split training and test data. This is only useful for the holdout method.\n",
    "# With cross validation and Keras' validation_split method, this pre-splitting of data is totally unnecessary. Besides, I want an overview of ALL\n",
    "# of the data at once.\n",
    "df = pd.concat([train, test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1522cb5c-b2ca-4333-a6e8-7d785783e73c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Observations for Pre-Split Training DataFrame:  74111\n",
      " \n",
      "Number of Observations for Pre-Split Testing DataFrame:  25458\n",
      " \n",
      "Number of Observations for Concatenated DataFrame:  99569\n",
      " \n",
      "Sanity Check -  Succesful!\n"
     ]
    }
   ],
   "source": [
    "# Sanity Check for confirming the concatenation worked out\n",
    "train_num = train.shape[0]\n",
    "print(\"Number of Observations for Pre-Split Training DataFrame: \", train_num)\n",
    "print(\" \")\n",
    "test_num = test.shape[0]\n",
    "print(\"Number of Observations for Pre-Split Testing DataFrame: \", test_num)\n",
    "print(\" \")\n",
    "complete_num = train_num + test_num\n",
    "print(\"Number of Observations for Concatenated DataFrame: \", complete_num)\n",
    "print(\" \")\n",
    "if df.shape[0] == complete_num:\n",
    "    print(\"Sanity Check -  Succesful!\")\n",
    "else:\n",
    "    (\"Sanity Check - Unsuccessful!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18da3a9c-847d-4459-a30d-6adb771634c0",
   "metadata": {},
   "source": [
    "## Initial EDA & Data Clean-Up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cef4b5fd-7fae-484f-a797-803ef934e523",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>log_price</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>amenities</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>...</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>name</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>thumbnail_url</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6901257</td>\n",
       "      <td>5.010635</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{\"Wireless Internet\",\"Air conditioning\",Kitchen,Heating,\"Family/kid friendly\",Essentials,\"Hair dryer\",Iron,\"translation missing: en.hosting_amenity_50\"}</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>strict</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>40.696524</td>\n",
       "      <td>-73.991617</td>\n",
       "      <td>Beautiful brownstone 1-bedroom</td>\n",
       "      <td>Brooklyn Heights</td>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/6d7cbbf7-c034-459c-bc82-6522c957627c.jpg?aki_policy=small</td>\n",
       "      <td>11201</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6304928</td>\n",
       "      <td>5.129899</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{\"Wireless Internet\",\"Air conditioning\",Kitchen,Heating,\"Family/kid friendly\",Washer,Dryer,\"Smoke detector\",\"Fire extinguisher\",Essentials,Shampoo,Hangers,\"Hair dryer\",Iron,\"translation missing: en.hosting_amenity_50\"}</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>strict</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>40.766115</td>\n",
       "      <td>-73.989040</td>\n",
       "      <td>Superb 3BR Apt Located Near Times Square</td>\n",
       "      <td>Hell's Kitchen</td>\n",
       "      <td>6</td>\n",
       "      <td>93.0</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/348a55fe-4b65-452a-b48a-bfecb3b58a66.jpg?aki_policy=small</td>\n",
       "      <td>10019</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7919400</td>\n",
       "      <td>4.976734</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",\"Wireless Internet\",\"Air conditioning\",Kitchen,Breakfast,\"Buzzer/wireless intercom\",Heating,\"Family/kid friendly\",\"Smoke detector\",\"Carbon monoxide detector\",\"Fire extinguisher\",Essentials,Shampoo,Hangers,\"Hair dryer\",Iron,\"Laptop friendly workspace\",\"translation missing: en.hosting_amenity_50\"}</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>moderate</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>40.808110</td>\n",
       "      <td>-73.943756</td>\n",
       "      <td>The Garden Oasis</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>10</td>\n",
       "      <td>92.0</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/6fae5362-9e3a-4fa9-aa54-bbd5ea26538d.jpg?aki_policy=small</td>\n",
       "      <td>10027</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13418779</td>\n",
       "      <td>6.620073</td>\n",
       "      <td>House</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,\"Wireless Internet\",Kitchen,\"Indoor fireplace\",\"Buzzer/wireless intercom\",Heating,Washer,Dryer,\"Smoke detector\",\"Carbon monoxide detector\",\"First aid kit\",\"Fire extinguisher\",Essentials}</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>flexible</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>37.772004</td>\n",
       "      <td>-122.431619</td>\n",
       "      <td>Beautiful Flat in the Heart of SF!</td>\n",
       "      <td>Lower Haight</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/72208dad-9c86-41ea-a735-43d933111063.jpg?aki_policy=small</td>\n",
       "      <td>94117.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3808709</td>\n",
       "      <td>4.744932</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,Internet,\"Wireless Internet\",\"Air conditioning\",Kitchen,\"Elevator in building\",Heating,\"Smoke detector\",\"Carbon monoxide detector\",\"Fire extinguisher\",Essentials,Shampoo}</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>moderate</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>38.925627</td>\n",
       "      <td>-77.034596</td>\n",
       "      <td>Great studio in midtown DC</td>\n",
       "      <td>Columbia Heights</td>\n",
       "      <td>4</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  log_price property_type        room_type  \\\n",
       "0   6901257   5.010635     Apartment  Entire home/apt   \n",
       "1   6304928   5.129899     Apartment  Entire home/apt   \n",
       "2   7919400   4.976734     Apartment  Entire home/apt   \n",
       "3  13418779   6.620073         House  Entire home/apt   \n",
       "4   3808709   4.744932     Apartment  Entire home/apt   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                 amenities  \\\n",
       "0                                                                                                                                                                 {\"Wireless Internet\",\"Air conditioning\",Kitchen,Heating,\"Family/kid friendly\",Essentials,\"Hair dryer\",Iron,\"translation missing: en.hosting_amenity_50\"}   \n",
       "1                                                                                               {\"Wireless Internet\",\"Air conditioning\",Kitchen,Heating,\"Family/kid friendly\",Washer,Dryer,\"Smoke detector\",\"Fire extinguisher\",Essentials,Shampoo,Hangers,\"Hair dryer\",Iron,\"translation missing: en.hosting_amenity_50\"}   \n",
       "2  {TV,\"Cable TV\",\"Wireless Internet\",\"Air conditioning\",Kitchen,Breakfast,\"Buzzer/wireless intercom\",Heating,\"Family/kid friendly\",\"Smoke detector\",\"Carbon monoxide detector\",\"Fire extinguisher\",Essentials,Shampoo,Hangers,\"Hair dryer\",Iron,\"Laptop friendly workspace\",\"translation missing: en.hosting_amenity_50\"}   \n",
       "3                                                                                                       {TV,\"Cable TV\",Internet,\"Wireless Internet\",Kitchen,\"Indoor fireplace\",\"Buzzer/wireless intercom\",Heating,Washer,Dryer,\"Smoke detector\",\"Carbon monoxide detector\",\"First aid kit\",\"Fire extinguisher\",Essentials}   \n",
       "4                                                                                                                                           {TV,Internet,\"Wireless Internet\",\"Air conditioning\",Kitchen,\"Elevator in building\",Heating,\"Smoke detector\",\"Carbon monoxide detector\",\"Fire extinguisher\",Essentials,Shampoo}   \n",
       "\n",
       "   accommodates  bathrooms  bed_type cancellation_policy  cleaning_fee  ...  \\\n",
       "0             3        1.0  Real Bed              strict          True  ...   \n",
       "1             7        1.0  Real Bed              strict          True  ...   \n",
       "2             5        1.0  Real Bed            moderate          True  ...   \n",
       "3             4        1.0  Real Bed            flexible          True  ...   \n",
       "4             2        1.0  Real Bed            moderate          True  ...   \n",
       "\n",
       "    latitude   longitude                                      name  \\\n",
       "0  40.696524  -73.991617            Beautiful brownstone 1-bedroom   \n",
       "1  40.766115  -73.989040  Superb 3BR Apt Located Near Times Square   \n",
       "2  40.808110  -73.943756                          The Garden Oasis   \n",
       "3  37.772004 -122.431619        Beautiful Flat in the Heart of SF!   \n",
       "4  38.925627  -77.034596                Great studio in midtown DC   \n",
       "\n",
       "      neighbourhood number_of_reviews review_scores_rating  \\\n",
       "0  Brooklyn Heights                 2                100.0   \n",
       "1    Hell's Kitchen                 6                 93.0   \n",
       "2            Harlem                10                 92.0   \n",
       "3      Lower Haight                 0                  NaN   \n",
       "4  Columbia Heights                 4                 40.0   \n",
       "\n",
       "                                                                                   thumbnail_url  \\\n",
       "0  https://a0.muscache.com/im/pictures/6d7cbbf7-c034-459c-bc82-6522c957627c.jpg?aki_policy=small   \n",
       "1  https://a0.muscache.com/im/pictures/348a55fe-4b65-452a-b48a-bfecb3b58a66.jpg?aki_policy=small   \n",
       "2  https://a0.muscache.com/im/pictures/6fae5362-9e3a-4fa9-aa54-bbd5ea26538d.jpg?aki_policy=small   \n",
       "3  https://a0.muscache.com/im/pictures/72208dad-9c86-41ea-a735-43d933111063.jpg?aki_policy=small   \n",
       "4                                                                                            NaN   \n",
       "\n",
       "   zipcode bedrooms  beds  \n",
       "0    11201      1.0   1.0  \n",
       "1    10019      3.0   3.0  \n",
       "2    10027      1.0   3.0  \n",
       "3  94117.0      2.0   2.0  \n",
       "4    20009      0.0   1.0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b2771102-98a6-4df8-9731-b5ef2f50e27a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                        99569\n",
       "log_price                   767\n",
       "property_type                35\n",
       "room_type                     3\n",
       "amenities                 88651\n",
       "accommodates                 16\n",
       "bathrooms                    18\n",
       "bed_type                      5\n",
       "cancellation_policy           6\n",
       "cleaning_fee                  2\n",
       "city                          6\n",
       "description               98683\n",
       "first_review               2643\n",
       "host_has_profile_pic          2\n",
       "host_identity_verified        2\n",
       "host_response_rate           80\n",
       "host_since                 3118\n",
       "instant_bookable              2\n",
       "last_review                1457\n",
       "latitude                  99569\n",
       "longitude                 99569\n",
       "name                      98653\n",
       "neighbourhood               627\n",
       "number_of_reviews           394\n",
       "review_scores_rating         56\n",
       "thumbnail_url             88468\n",
       "zipcode                     801\n",
       "bedrooms                     12\n",
       "beds                         19\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()\n",
    "# Every observation has its own unique ID, so I'll both set and sort the DataFrame by the 'id' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a41c637e-a27a-4549-a611-61ac8a639b25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                          int64\n",
       "log_price                 float64\n",
       "property_type              object\n",
       "room_type                  object\n",
       "amenities                  object\n",
       "accommodates                int64\n",
       "bathrooms                 float64\n",
       "bed_type                   object\n",
       "cancellation_policy        object\n",
       "cleaning_fee                 bool\n",
       "city                       object\n",
       "description                object\n",
       "first_review               object\n",
       "host_has_profile_pic       object\n",
       "host_identity_verified     object\n",
       "host_response_rate         object\n",
       "host_since                 object\n",
       "instant_bookable           object\n",
       "last_review                object\n",
       "latitude                  float64\n",
       "longitude                 float64\n",
       "name                       object\n",
       "neighbourhood              object\n",
       "number_of_reviews           int64\n",
       "review_scores_rating      float64\n",
       "thumbnail_url              object\n",
       "zipcode                    object\n",
       "bedrooms                  float64\n",
       "beds                      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirming the datatypes in our DataFrame\n",
    "df.dtypes\n",
    "# We have some booleans here. They don't go well with ML models. Going to need to convert them to binary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c98ca4cd-5b8a-4be6-aa0d-115253d773cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sorting DF by zipcode\n",
    "df.sort_values(by=['zipcode'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "642b9b3f-def2-4447-9feb-9f2e6c632389",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # Sorting DataFrame by our index before setting it as such\n",
    "# df.sort_values(by=[\"host_since\"], inplace=True, ascending=True)   # host_since is the closest thing to a date or date_time column we have\n",
    "# df.set_index(\"host_since\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d3724851-4382-4f2f-bfaa-6f120df6889c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "138076"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dealing with null values\n",
    "df.isnull().sum().sum()\n",
    "# We're going to drop a lot of rows. Luckily, we have enough.\n",
    "# If we kept these, converting the NaNs to zeroes would have given us an imbalanced dataset.\n",
    "# Not worth dealing with that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4903ae96-a542-4355-a6a6-21b1d27e6e25",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing null values. Can't interpolate them considering we lack a timeseries variable. Not every sample is worth keeping.\n",
    "df.dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9463220e-2199-41fc-b6ee-c218614ccec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting bools to binary ints\n",
    "df.loc[(df[\"cleaning_fee\"] == False), 'cleaning_fee'] = 0\n",
    "df.loc[(df[\"cleaning_fee\"] == True), 'cleaning_fee'] = 1\n",
    "\n",
    "df.loc[(df[\"instant_bookable\"] == 'f'), 'instant_bookable'] = 0\n",
    "df.loc[(df[\"instant_bookable\"] == 't'), 'instant_bookable'] = 1\n",
    "\n",
    "df.loc[(df[\"host_identity_verified\"] == 'f'), 'host_identity_verified'] = 0\n",
    "df.loc[(df[\"host_identity_verified\"] == 't'), 'host_identity_verified'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fe88924-a20a-4dc5-a2c5-b7f39a655a19",
   "metadata": {},
   "source": [
    "## Feature Engineering and Exploring our Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1c548d13-f378-41ef-90c8-b1512842b9c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inverting 'price_log' to get original price values WIP\n",
    "df[\"price\"] = np.exp(df[\"log_price\"])\n",
    "\n",
    "# rounded the decimal places to the second place\n",
    "df[\"price\"] = round(df.price, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "41ac96c9-f351-4180-8e8e-aaff6d4e79cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average AirBnB Price Listings:  146.1157082748948\n",
      " \n",
      "Sorted AirbnB Price Listings:  11632       1.0\n",
      "3898        5.0\n",
      "6515       10.0\n",
      "28693      10.0\n",
      "66762      10.0\n",
      "          ...  \n",
      "49711    1950.0\n",
      "37424    1950.0\n",
      "39465    1950.0\n",
      "61826    1995.0\n",
      "55453    1999.0\n",
      "Name: price, Length: 38502, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Confirming the average prices WIP\n",
    "price_mean = df[\"price\"].mean()\n",
    "price_sorted = df[\"price\"].sort_values()\n",
    "\n",
    "print(\"Average AirBnB Price Listings: \", price_mean)\n",
    "print(\" \")\n",
    "print(\"Sorted AirbnB Price Listings: \", price_sorted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "79014d6f-faf5-40a8-a0bf-c182feacd341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of 54511    259.0\n",
       "20313     89.0\n",
       "68172    225.0\n",
       "62506    185.0\n",
       "56709    199.0\n",
       "         ...  \n",
       "27979    149.0\n",
       "10418    250.0\n",
       "33983    197.0\n",
       "43630    148.0\n",
       "8264     105.0\n",
       "Name: price, Length: 38502, dtype: float64>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.price.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c7fe3269-c0b6-4fc9-9491-391000f78a40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price                   1.000000\n",
       "log_price               0.855574\n",
       "accommodates            0.583134\n",
       "bedrooms                0.550352\n",
       "beds                    0.488349\n",
       "bathrooms               0.459425\n",
       "longitude               0.060459\n",
       "review_scores_rating    0.054083\n",
       "number_of_reviews       0.052034\n",
       "latitude                0.012907\n",
       "id                      0.005358\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirming the absolute value of the correlations of our features with our target, 'price'.\n",
    "# Its interesting that the logarithm of 'price' is only 85% correlated\n",
    "abs(df.corr()[\"price\"]).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "016ab63d-d72a-42df-a3e5-4d88405ccfa2",
   "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"
    }
   ],
   "source": [
    "# Probability plot for price\n",
    "stats.probplot(df['price'], dist=\"norm\", plot=pylab)\n",
    "pylab.show()\n",
    "# Looks like price listings are probably around $100 - $300 per night. The probabilities of price skewing towards nothing or $2000 are very unlikely"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "278b1033-2170-4a8b-9c54-9ce713a1f279",
   "metadata": {},
   "source": [
    "## Feature Engineering: Number of Amenities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7a720875-8be0-4b53-a1db-a15569dfd7b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"Wireless Internet\",\"Air conditioning\",\"Pets allowed\",Heating,Washer,Dryer,\"Smoke detector\",\"Carbon monoxide detector\",\"First aid kit\",Essentials,Shampoo,Hangers,\"Hair dryer\",Iron,\"Private entrance\"}'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"amenities\"].iloc[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "abbec2b3-205f-4e27-a962-a5feee2b7ca6",
   "metadata": {},
   "outputs": [],
   "source": [
    "####### WIP ########\n",
    "\n",
    "# def clean_data(text):\n",
    "#     \"\"\"\n",
    "#     Accepts a single text document and performs several regex substitutions in order to clean the document. \n",
    "    \n",
    "#     Parameters\n",
    "#     ----------\n",
    "#     text: string or object \n",
    "    \n",
    "#     Returns\n",
    "#     -------\n",
    "#     text: string or object\n",
    "#     \"\"\"\n",
    "    \n",
    "#     # order of operations - apply the expression from top to bottom\n",
    "#     messy = '/[^, a-zA-Z0-9-]+/g'\n",
    "    \n",
    "#     text = re.sub(messy, \"\", text)\n",
    "    \n",
    "#     # apply case normalization \n",
    "#     return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e5fae226-c5a0-4e50-a77f-2c265dfa7322",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Going to clean up all punctuation save for commas with regex function. Before converting to the number of/len() of all tokens within that row.\n",
    "# Going to do this with a function and apply it with pandarallel\n",
    "\n",
    "#### WIP ####\n",
    "# df[\"amenities\"] = df[\"amenities\"].apply(clean_data)\n",
    "# df['amenities'] = len(df.amenities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "393896d2-d626-4e0c-9992-73948389e34a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df[\"amenities\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a10523bf-9926-47d1-b28f-28058846ffbf",
   "metadata": {},
   "source": [
    "# Dropping high cardinality and otherwise useless columns\n",
    "---\n",
    "- Will need to drop 'log_price' to avoid target/data leakage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "735c3dc8-3a2d-4115-981d-e6f404fc5c5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning high cardinal columns to a variable\n",
    "cutoff = 100\n",
    "HCC_cols = [col for col in df.select_dtypes('object').columns\n",
    "                 if df[col].nunique() > cutoff]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ad6914c7-3cea-4c8a-a29c-2710ba1feffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['amenities',\n",
       " 'description',\n",
       " 'first_review',\n",
       " 'host_since',\n",
       " 'last_review',\n",
       " 'name',\n",
       " 'neighbourhood',\n",
       " 'thumbnail_url',\n",
       " 'zipcode']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirming what the HCC columns are\n",
    "HCC_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8f9fb152-a127-48a0-870f-9d2b73c6cb29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a dataframe out of these HCC columns (excluding those that I know are important)\n",
    "# If there is enough time for a stretch goal, I'll remove description from this list and vectorize it for sentiment analysis and include it in our\n",
    "# deployed web app\n",
    "df_HCC = df[[\n",
    "             'description',\n",
    "             'first_review',\n",
    "             'last_review',\n",
    "             'name',\n",
    "             'thumbnail_url'\n",
    "            ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c393927a-d08c-438f-a095-826e3612413e",
   "metadata": {
    "tags": []
   },
   "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>description</th>\n",
       "      <th>first_review</th>\n",
       "      <th>last_review</th>\n",
       "      <th>name</th>\n",
       "      <th>thumbnail_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54511</th>\n",
       "      <td>At this Sonder, you'll love the exposed brick, modern kitchen, and expansive storage space. This apartment is located in Downtown Crossing, known for quality shopping, and close to Park T station. Every booking is instantly confirmed. Every call or message is answered within minutes. Every home is equipped with crisp linens, high-speed Wi-Fi, and kitchen and bath essentials—everything you need for an exceptional stay. Our concierge is available by phone, email, or Messenger 24/7. Don’t hesitate to contact us if you need anything, from extra towels to restaurant recommendations. Otherwise, we’ll leave you to enjoy your stay. Downtown Crossing has plenty to offer within walking distance. Try out VERTS Mediterranean Grill or Central Bistro...</td>\n",
       "      <td>2017-08-13</td>\n",
       "      <td>2017-09-27</td>\n",
       "      <td>Stunning 4BR in Downtown Crossing</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/ac7e1866-775e-4b13-acd4-ce1b0ea6a480.jpg?aki_policy=small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20313</th>\n",
       "      <td>We are a family-operated Airbnb.   This cute and cool one BR duplex  is in an amazing location in the heart of Little Italy and is close to many tourist attractions, cozy cafes and shops. Cute and cool one bedroom duplex apartment on the 2nd floor, located in the North End's most lively stretch. Bedroom with a queen size bed and bathroom with a tub and shower are on the upper floor; kitchen and living room on the lower floor with a sofa bed that can sleep one adult or two small children. Steep stairs from lower floor to bedroom.  May not be appropriate for guests who have difficulty with stairs or for very young children.   Amenities include cable, internet Wi Fi, DVD, A/C on both floors, full kitchenette, linens and towels. Guests can ...</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>2017-10-02</td>\n",
       "      <td>Boston  Best One Bdrm duplex (h2c)</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/d4b6952f-d9b2-45ef-8376-cfb88546452e.jpg?aki_policy=small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68172</th>\n",
       "      <td>Situated on the best street on Beacon Hill this ho</td>\n",
       "      <td>2017-09-18</td>\n",
       "      <td>2017-10-04</td>\n",
       "      <td>Beacon Hill PERFECT one bedroom!!!!</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/a6117c4e-90c5-42fe-8b45-ca2d63e5b384.jpg?aki_policy=small</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         description  \\\n",
       "54511  At this Sonder, you'll love the exposed brick, modern kitchen, and expansive storage space. This apartment is located in Downtown Crossing, known for quality shopping, and close to Park T station. Every booking is instantly confirmed. Every call or message is answered within minutes. Every home is equipped with crisp linens, high-speed Wi-Fi, and kitchen and bath essentials—everything you need for an exceptional stay. Our concierge is available by phone, email, or Messenger 24/7. Don’t hesitate to contact us if you need anything, from extra towels to restaurant recommendations. Otherwise, we’ll leave you to enjoy your stay. Downtown Crossing has plenty to offer within walking distance. Try out VERTS Mediterranean Grill or Central Bistro...   \n",
       "20313  We are a family-operated Airbnb.   This cute and cool one BR duplex  is in an amazing location in the heart of Little Italy and is close to many tourist attractions, cozy cafes and shops. Cute and cool one bedroom duplex apartment on the 2nd floor, located in the North End's most lively stretch. Bedroom with a queen size bed and bathroom with a tub and shower are on the upper floor; kitchen and living room on the lower floor with a sofa bed that can sleep one adult or two small children. Steep stairs from lower floor to bedroom.  May not be appropriate for guests who have difficulty with stairs or for very young children.   Amenities include cable, internet Wi Fi, DVD, A/C on both floors, full kitchenette, linens and towels. Guests can ...   \n",
       "68172                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Situated on the best street on Beacon Hill this ho   \n",
       "\n",
       "      first_review last_review                                 name  \\\n",
       "54511   2017-08-13  2017-09-27    Stunning 4BR in Downtown Crossing   \n",
       "20313   2014-05-16  2017-10-02   Boston  Best One Bdrm duplex (h2c)   \n",
       "68172   2017-09-18  2017-10-04  Beacon Hill PERFECT one bedroom!!!!   \n",
       "\n",
       "                                                                                       thumbnail_url  \n",
       "54511  https://a0.muscache.com/im/pictures/ac7e1866-775e-4b13-acd4-ce1b0ea6a480.jpg?aki_policy=small  \n",
       "20313  https://a0.muscache.com/im/pictures/d4b6952f-d9b2-45ef-8376-cfb88546452e.jpg?aki_policy=small  \n",
       "68172  https://a0.muscache.com/im/pictures/a6117c4e-90c5-42fe-8b45-ca2d63e5b384.jpg?aki_policy=small  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_HCC.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0534b62d-dfc5-4212-af4c-a3c7c04e4313",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38502, 30)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ad3c297a-7f3c-4e38-ae34-9387ffd5a577",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'log_price', 'property_type', 'room_type', 'amenities',\n",
       "       'accommodates', 'bathrooms', 'bed_type', 'cancellation_policy',\n",
       "       'cleaning_fee', 'city', 'description', 'first_review',\n",
       "       'host_has_profile_pic', 'host_identity_verified', 'host_response_rate',\n",
       "       'host_since', 'instant_bookable', 'last_review', 'latitude',\n",
       "       'longitude', 'name', 'neighbourhood', 'number_of_reviews',\n",
       "       'review_scores_rating', 'thumbnail_url', 'zipcode', 'bedrooms', 'beds',\n",
       "       'price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e51a66ee-f489-47bb-8e17-353f383f3661",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>log_price</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>amenities</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>...</th>\n",
       "      <th>longitude</th>\n",
       "      <th>name</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>thumbnail_url</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54511</th>\n",
       "      <td>17618928</td>\n",
       "      <td>5.556828</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",\"Wireless Internet\",\"Air conditioning\",Kitchen,Elevator,Heating,\"Family/kid friendly\",Washer,Dryer,\"Smoke detector\",\"Carbon monoxide detector\",\"Fire extinguisher\",Essentials,Shampoo,Hangers,\"Hair dryer\",Iron,\"Laptop friendly workspace\",\"Hot water\",\"Bed linens\",Microwave,\"Coffee maker\",Refrigerator,Dishwasher,\"Dishes and silverware\",\"Cooking basics\",Oven,Stove,\"Long term stays allowed\"}</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>strict</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-71.060408</td>\n",
       "      <td>Stunning 4BR in Downtown Crossing</td>\n",
       "      <td>Downtown Crossing</td>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/ac7e1866-775e-4b13-acd4-ce1b0ea6a480.jpg?aki_policy=small</td>\n",
       "      <td>02108</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20313</th>\n",
       "      <td>14884022</td>\n",
       "      <td>4.488636</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"Air conditioning\",Kitchen,\"Pets allowed\",\"Buzzer/wireless intercom\",Heating,\"Smoke detector\",\"Carbon monoxide detector\",Essentials,Shampoo,\"24-hour check-in\",Hangers,\"Hair dryer\",Iron,\"Laptop friendly workspace\",\"translation missing: en.hosting_amenity_49\",\"translation missing: en.hosting_amenity_50\",\"Self Check-In\",Lockbox,\"Bed linens\"}</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>strict</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-71.052334</td>\n",
       "      <td>Boston  Best One Bdrm duplex (h2c)</td>\n",
       "      <td>North End</td>\n",
       "      <td>94</td>\n",
       "      <td>89.0</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/d4b6952f-d9b2-45ef-8376-cfb88546452e.jpg?aki_policy=small</td>\n",
       "      <td>02108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68172</th>\n",
       "      <td>18111425</td>\n",
       "      <td>5.416100</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Wireless Internet\",\"Air conditioning\",Kitchen,\"Pets allowed\",Heating,Washer,Dryer,\"Smoke detector\",\"Carbon monoxide detector\",\"Fire extinguisher\",Essentials,Shampoo,Hangers,\"Hair dryer\",Iron,\"Laptop friendly workspace\"}</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>flexible</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-71.065911</td>\n",
       "      <td>Beacon Hill PERFECT one bedroom!!!!</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>3</td>\n",
       "      <td>87.0</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/a6117c4e-90c5-42fe-8b45-ca2d63e5b384.jpg?aki_policy=small</td>\n",
       "      <td>02108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62506</th>\n",
       "      <td>13530190</td>\n",
       "      <td>5.220356</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"Air conditioning\",Kitchen,Heating,\"Family/kid friendly\",Washer,Dryer,\"Smoke detector\",\"Carbon monoxide detector\",\"Fire extinguisher\",Essentials,Shampoo,\"24-hour check-in\",Hangers,\"Hair dryer\",Iron,\"Laptop friendly workspace\",\"Self Check-In\",Keypad}</td>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>strict</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-71.055673</td>\n",
       "      <td>Coolest tri level, BEST location by Faneuil Hall</td>\n",
       "      <td>Downtown</td>\n",
       "      <td>33</td>\n",
       "      <td>87.0</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/91091236/a24ee881_original.jpg?aki_policy=small</td>\n",
       "      <td>02108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56709</th>\n",
       "      <td>2326775</td>\n",
       "      <td>5.293305</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>{Internet,\"Wireless Internet\",Kitchen,Heating,\"Family/kid friendly\",\"Smoke detector\",\"Carbon monoxide detector\",\"First aid kit\",\"Fire extinguisher\",Essentials,Shampoo,Hangers,\"Hair dryer\",\"Laptop friendly workspace\",\"translation missing: en.hosting_amenity_50\"}</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>moderate</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-71.069026</td>\n",
       "      <td>Prime Location Beacon Hill 1 BR</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>35</td>\n",
       "      <td>91.0</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/68883907-c355-4701-b3ef-544f8e7b882b.jpg?aki_policy=small</td>\n",
       "      <td>02108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id  log_price property_type        room_type  \\\n",
       "54511  17618928   5.556828     Apartment  Entire home/apt   \n",
       "20313  14884022   4.488636     Apartment  Entire home/apt   \n",
       "68172  18111425   5.416100     Apartment  Entire home/apt   \n",
       "62506  13530190   5.220356     Apartment  Entire home/apt   \n",
       "56709   2326775   5.293305     Apartment  Entire home/apt   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                 amenities  \\\n",
       "54511  {TV,\"Cable TV\",\"Wireless Internet\",\"Air conditioning\",Kitchen,Elevator,Heating,\"Family/kid friendly\",Washer,Dryer,\"Smoke detector\",\"Carbon monoxide detector\",\"Fire extinguisher\",Essentials,Shampoo,Hangers,\"Hair dryer\",Iron,\"Laptop friendly workspace\",\"Hot water\",\"Bed linens\",Microwave,\"Coffee maker\",Refrigerator,Dishwasher,\"Dishes and silverware\",\"Cooking basics\",Oven,Stove,\"Long term stays allowed\"}   \n",
       "20313                     {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"Air conditioning\",Kitchen,\"Pets allowed\",\"Buzzer/wireless intercom\",Heating,\"Smoke detector\",\"Carbon monoxide detector\",Essentials,Shampoo,\"24-hour check-in\",Hangers,\"Hair dryer\",Iron,\"Laptop friendly workspace\",\"translation missing: en.hosting_amenity_49\",\"translation missing: en.hosting_amenity_50\",\"Self Check-In\",Lockbox,\"Bed linens\"}   \n",
       "68172                                                                                                                                                                                     {TV,\"Wireless Internet\",\"Air conditioning\",Kitchen,\"Pets allowed\",Heating,Washer,Dryer,\"Smoke detector\",\"Carbon monoxide detector\",\"Fire extinguisher\",Essentials,Shampoo,Hangers,\"Hair dryer\",Iron,\"Laptop friendly workspace\"}   \n",
       "62506                                                                                                                {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"Air conditioning\",Kitchen,Heating,\"Family/kid friendly\",Washer,Dryer,\"Smoke detector\",\"Carbon monoxide detector\",\"Fire extinguisher\",Essentials,Shampoo,\"24-hour check-in\",Hangers,\"Hair dryer\",Iron,\"Laptop friendly workspace\",\"Self Check-In\",Keypad}   \n",
       "56709                                                                                                                                                {Internet,\"Wireless Internet\",Kitchen,Heating,\"Family/kid friendly\",\"Smoke detector\",\"Carbon monoxide detector\",\"First aid kit\",\"Fire extinguisher\",Essentials,Shampoo,Hangers,\"Hair dryer\",\"Laptop friendly workspace\",\"translation missing: en.hosting_amenity_50\"}   \n",
       "\n",
       "       accommodates  bathrooms  bed_type cancellation_policy cleaning_fee  \\\n",
       "54511             8        2.0  Real Bed              strict            1   \n",
       "20313             4        1.0  Real Bed              strict            1   \n",
       "68172             2        1.0  Real Bed            flexible            1   \n",
       "62506             3        1.5  Real Bed              strict            1   \n",
       "56709             2        1.0  Real Bed            moderate            0   \n",
       "\n",
       "       ...  longitude                                              name  \\\n",
       "54511  ... -71.060408                 Stunning 4BR in Downtown Crossing   \n",
       "20313  ... -71.052334                Boston  Best One Bdrm duplex (h2c)   \n",
       "68172  ... -71.065911               Beacon Hill PERFECT one bedroom!!!!   \n",
       "62506  ... -71.055673  Coolest tri level, BEST location by Faneuil Hall   \n",
       "56709  ... -71.069026                   Prime Location Beacon Hill 1 BR   \n",
       "\n",
       "           neighbourhood number_of_reviews review_scores_rating  \\\n",
       "54511  Downtown Crossing                 2                100.0   \n",
       "20313          North End                94                 89.0   \n",
       "68172        Beacon Hill                 3                 87.0   \n",
       "62506           Downtown                33                 87.0   \n",
       "56709        Beacon Hill                35                 91.0   \n",
       "\n",
       "                                                                                       thumbnail_url  \\\n",
       "54511  https://a0.muscache.com/im/pictures/ac7e1866-775e-4b13-acd4-ce1b0ea6a480.jpg?aki_policy=small   \n",
       "20313  https://a0.muscache.com/im/pictures/d4b6952f-d9b2-45ef-8376-cfb88546452e.jpg?aki_policy=small   \n",
       "68172  https://a0.muscache.com/im/pictures/a6117c4e-90c5-42fe-8b45-ca2d63e5b384.jpg?aki_policy=small   \n",
       "62506            https://a0.muscache.com/im/pictures/91091236/a24ee881_original.jpg?aki_policy=small   \n",
       "56709  https://a0.muscache.com/im/pictures/68883907-c355-4701-b3ef-544f8e7b882b.jpg?aki_policy=small   \n",
       "\n",
       "      zipcode bedrooms beds  price  \n",
       "54511   02108      4.0  4.0  259.0  \n",
       "20313   02108      1.0  2.0   89.0  \n",
       "68172   02108      1.0  1.0  225.0  \n",
       "62506   02108      1.0  1.0  185.0  \n",
       "56709   02108      1.0  1.0  199.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4aa0499b-a59f-4b1d-9f9c-e59f10f0319d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54511                                                   02108\n",
       "25530                                                   02108\n",
       "9677                                                    02108\n",
       "2894                                                    02108\n",
       "64803                                                   02108\n",
       "                                 ...                         \n",
       "35195                                                   94158\n",
       "63010                                                   94158\n",
       "42509                                                   94158\n",
       "67215                                                   94158\n",
       "8264     95202\\r\\r\\r\\r\\r\\r\\n\\r\\r\\r\\r\\r\\r\\n\\r\\r\\r\\r\\r\\r\\n94158\n",
       "Name: zipcode, Length: 38502, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['zipcode'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b167c3ad-7f84-483d-b5f7-7254d0f3f90c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['02108', '02109', '02110', '02111', '02113', '02114', '02115',\n",
       "       '02116', '02118', '02119', '02120', '02121', '02122', '02124',\n",
       "       '02125', '02126', '02127', '02128', '02129', '02130', '02131',\n",
       "       '02132', '02134', '02135', '02136', '02139', '02143', '02145',\n",
       "       '02186', '02199', '02210', '02215', '02467', '10001', '10001.0',\n",
       "       '10002', '10002.0', '10003', '10003-8623', '10003.0', '10004',\n",
       "       '10005', '10006', '10007', '10007.0', '10009', '10009.0', '10010',\n",
       "       '10010.0', '10011', '10011.0', '10012', '10012.0', '10013',\n",
       "       '10013.0', '10014', '10014.0', '10016', '10017', '10018',\n",
       "       '10018.0', '10019', '10019.0', '10021', '10022', '10023', '10024',\n",
       "       '10025', '10026', '10026.0', '10027', '10027.0', '10028', '10029',\n",
       "       '10029.0', '10030', '10030.0', '10031', '10032', '10033', '10034',\n",
       "       '10035', '10035.0', '10036', '10036.0', '10037', '10037.0',\n",
       "       '10038', '10038.0', '10039', '10040', '10044', '10048', '10065',\n",
       "       '10069', '10075', '10118', '10119.0', '10128', '10128.0',\n",
       "       '10129.0', '10162', '10270', '10279', '10280', '10282', '10301',\n",
       "       '10301.0', '10302', '10303', '10304', '10304.0', '10305',\n",
       "       '10305.0', '10306', '10307', '10308', '10309', '10310', '10312',\n",
       "       '10312.0', '10314', '10314.0', '10451', '10451.0', '10452',\n",
       "       '10452.0', '10453', '10453.0', '10454', '10455', '10456',\n",
       "       '10456.0', '10457', '10457.0', '10458', '10459', '10459.0',\n",
       "       '10460', '10460.0', '10461', '10462', '10463', '10463.0', '10464',\n",
       "       '10465', '10466', '10467', '10467.0', '10468', '10469', '10470',\n",
       "       '10471', '10471.0', '10472', '10473', '10473.0', '10474', '10475',\n",
       "       '10475.0', '10550.0', '11101', '11102', '11103', '11103-3233',\n",
       "       '11104', '11105', '11106', '11109', '11201', '11201.0', '11203',\n",
       "       '11203.0', '11204', '11205', '11205.0', '11206', '11206.0',\n",
       "       '11207', '11207.0', '11208.0', '11209', '11210', '11210.0',\n",
       "       '11211', '11211.0', '11212', '11212.0', '11213', '11213.0',\n",
       "       '11214', '11215', '11215.0', '11216', '11216.0', '11217',\n",
       "       '11217.0', '11218', '11219', '11219.0', '11220', '11220.0',\n",
       "       '11221', '11221.0', '11222', '11222.0', '11223', '11224',\n",
       "       '11224.0', '11225', '11225.0', '11226', '11226.0', '11228',\n",
       "       '11228.0', '11229', '11230', '11231', '11231.0', '11232', '11233',\n",
       "       '11233.0', '11234', '11234.0', '11235', '11236', '11236.0',\n",
       "       '11237', '11237.0', '11238', '11238.0', '11249',\n",
       "       '11249\\r\\r\\r\\r\\r\\r\\n11249', '11249.0', '11354', '11355', '11356.0',\n",
       "       '11357', '11358', '11360', '11361', '11364', '11365', '11366',\n",
       "       '11367', '11368', '11368.0', '11369.0', '11370', '11370.0',\n",
       "       '11372', '11372.0', '11373', '11373.0', '11374', '11374.0',\n",
       "       '11375', '11377', '11377.0', '11378', '11379', '11385',\n",
       "       '11385-2308', '11411.0', '11412', '11412.0', '11413', '11414',\n",
       "       '11415', '11416', '11417', '11418', '11419', '11420', '11421',\n",
       "       '11422', '11423', '11426', '11427', '11428', '11429', '11429.0',\n",
       "       '11432', '11433', '11434', '11435', '11436', '11509.0', '11691',\n",
       "       '11692', '11693', '11694', '1m', '20001', '20001-4044', '20002',\n",
       "       '20003', '20004', '20005', '20006', '20007', '20008', '20009',\n",
       "       '20010', '20011', '20012', '20015', '20016', '20017', '20018',\n",
       "       '20019', '20020', '20024', '20032', '20036', '20037', '20052',\n",
       "       '20712', '20781', '20782', '20815', '20816', '20910', '20912',\n",
       "       '60601', '60602', '60603', '60605', '60607', '60608', '60609',\n",
       "       '60610', '60611', '60612', '60613', '60614', '60615', '60616',\n",
       "       '60618', '60619', '60620', '60621', '60622', '60623', '60624',\n",
       "       '60625', '60626', '60628', '60629', '60630', '60631', '60632',\n",
       "       '60633', '60634', '60636', '60637', '60638', '60639', '60640',\n",
       "       '60641', '60642', '60643', '60644', '60645', '60646', '60647',\n",
       "       '60649', '60651', '60652', '60653', '60654', '60656', '60657',\n",
       "       '60659', '60660', '60660-1448', '60661', '60707', '90001', '90002',\n",
       "       '90003', '90004', '90005', '90006', '90007', '90008', '90010',\n",
       "       '90011', '90012', '90013', '90014', '90015', '90016', '90017',\n",
       "       '90018', '90019', '90020', '90021', '90022', '90023', '90024',\n",
       "       '90025', '90026', '90027', '90028', '90029', '90031', '90032',\n",
       "       '90033', '90034', '90035', '90036', '90036-2514', '90037', '90038',\n",
       "       '90039', '90039-2715', '90040', '90041', '90042', '90043', '90044',\n",
       "       '90045', '90046', '90047', '90048', '90049', '90056', '90057',\n",
       "       '90059', '90061', '90062', '90063', '90064', '90065', '90066',\n",
       "       '90067', '90068', '90069', '90071', '90077', '90094', '90201',\n",
       "       '90210', '90211', '90212', '90220', '90221', '90222', '90230',\n",
       "       '90232', '90240', '90241', '90242', '90245', '90247', '90248',\n",
       "       '90249', '90250', '90254', '90255', '90260', '90262', '90265',\n",
       "       '90266', '90272', '90274', '90275', '90277', '90278', '90280',\n",
       "       '90290', '90291', '90292', '90293', '90301', '90302', '90303',\n",
       "       '90304', '90305', '90401', '90402', '90403', '90404', '90405',\n",
       "       '90501', '90503', '90504', '90505', '90601', '90602', '90603',\n",
       "       '90604', '90605', '90638', '90640', '90650', '90660', '90670',\n",
       "       '90703', '90706', '90710', '90712', '90713', '90715', '90716',\n",
       "       '90717', '90723', '90731', '90732', '90745', '90746', '90755',\n",
       "       '90802', '90803', '90804', '90805', '90806', '90807', '90808',\n",
       "       '90810', '90813', '90814', '90815', '91001', '91001-2243', '91006',\n",
       "       '91007', '91008', '91010', '91011', '91016', '91020', '91024',\n",
       "       '91030', '91040', '91042', '91101', '91103', '91104', '91105',\n",
       "       '91106', '91107', '91108', '91201', '91202', '91203', '91204',\n",
       "       '91205', '91206', '91207', '91208', '91210', '91214', '91303',\n",
       "       '91304', '91306', '91307', '91311', '91316', '91324', '91325',\n",
       "       '91326', '91331', '91335', '91340', '91342', '91343', '91344',\n",
       "       '91345', '91352', '91356', '91364', '91367', '91401', '91402',\n",
       "       '91403', '91405', '91406', '91411', '91423', '91436', '91501',\n",
       "       '91502', '91504', '91505', '91506', '91523', '91601', '91602',\n",
       "       '91604', '91605', '91606', '91606-1412', '91607', '91702', '91706',\n",
       "       '91722', '91723', '91724', '91731', '91732', '91733', '91741',\n",
       "       '91744', '91746', '91754', '91755', '91770', '91775', '91776',\n",
       "       '91780', '91790', '91791', '91792', '91801', '91803', '94014',\n",
       "       '94102', '94102.0', '94103', '94104', '94104.0', '94105', '94107',\n",
       "       '94108', '94109', '94109.0', '94110', '94111', '94112', '94114',\n",
       "       '94114.0', '94115', '94115.0', '94116', '94117', '94117.0',\n",
       "       '94118', '94118.0', '94121', '94121.0', '94122', '94122.0',\n",
       "       '94123', '94124', '94127', '94131', '94132', '94133', '94134',\n",
       "       '94158', '95202\\r\\r\\r\\r\\r\\r\\n\\r\\r\\r\\r\\r\\r\\n\\r\\r\\r\\r\\r\\r\\n94158'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['zipcode'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f40f4b7c-46cf-4275-bd75-899845e40ad2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "653"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['zipcode'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ff70344-0203-4e20-9260-14551121be00",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "605cc3d8-c779-4d2b-bf08-74447d70eeba",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_data(zipcode):\n",
    "    \"\"\"\n",
    "    Accepts a single text document and performs several regex substitutions in order to clean the document. \n",
    "    Parameters\n",
    "    ----------\n",
    "    text: string or object \n",
    "    Returns\n",
    "    -------\n",
    "    text: string or object\n",
    "    \"\"\"\n",
    "    # order of operations - apply the expression from top to bottom\n",
    "    non_alpha = '[^, a-zA-Z0-9]'\n",
    "    replace_with = \" \"\n",
    "\n",
    "    re_zips = re.sub(non_alpha, replace_with, zipcode)\n",
    "    split_zips = re_zips.split(\" \", 1)[0]\n",
    "\n",
    "    return split_zips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9f39dff9-e0d3-48f7-b842-2cf35ed15dcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "oddValue = df[df['zipcode'] == '1m'].index\n",
    "df.drop(oddValue, inplace=True)\n",
    "\n",
    "badValue = df[df['zipcode'] == '95202\\r\\r\\r\\r\\r\\r\\n\\r\\r\\r\\r\\r\\r\\n\\r\\r\\r\\r\\r\\r\\n94158'].index\n",
    "df.drop(badValue, inplace=True)\n",
    "\n",
    "weirdValue = df[df['zipcode'] == '11249\\r\\r\\r\\r\\r\\r\\n11249'].index\n",
    "df.drop(weirdValue, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fa4b2a7c-4dd8-4569-a109-c261fa0e425b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['zipcode'] = df['zipcode'].apply(clean_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e165421a-358c-41e9-828e-2aaa6d037405",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['02108', '02109', '02110', '02111', '02113', '02114', '02115',\n",
       "       '02116', '02118', '02119', '02120', '02121', '02122', '02124',\n",
       "       '02125', '02126', '02127', '02128', '02129', '02130', '02131',\n",
       "       '02132', '02134', '02135', '02136', '02139', '02143', '02145',\n",
       "       '02186', '02199', '02210', '02215', '02467', '10001', '10002',\n",
       "       '10003', '10004', '10005', '10006', '10007', '10009', '10010',\n",
       "       '10011', '10012', '10013', '10014', '10016', '10017', '10018',\n",
       "       '10019', '10021', '10022', '10023', '10024', '10025', '10026',\n",
       "       '10027', '10028', '10029', '10030', '10031', '10032', '10033',\n",
       "       '10034', '10035', '10036', '10037', '10038', '10039', '10040',\n",
       "       '10044', '10048', '10065', '10069', '10075', '10118', '10119',\n",
       "       '10128', '10129', '10162', '10270', '10279', '10280', '10282',\n",
       "       '10301', '10302', '10303', '10304', '10305', '10306', '10307',\n",
       "       '10308', '10309', '10310', '10312', '10314', '10451', '10452',\n",
       "       '10453', '10454', '10455', '10456', '10457', '10458', '10459',\n",
       "       '10460', '10461', '10462', '10463', '10464', '10465', '10466',\n",
       "       '10467', '10468', '10469', '10470', '10471', '10472', '10473',\n",
       "       '10474', '10475', '10550', '11101', '11102', '11103', '11104',\n",
       "       '11105', '11106', '11109', '11201', '11203', '11204', '11205',\n",
       "       '11206', '11207', '11208', '11209', '11210', '11211', '11212',\n",
       "       '11213', '11214', '11215', '11216', '11217', '11218', '11219',\n",
       "       '11220', '11221', '11222', '11223', '11224', '11225', '11226',\n",
       "       '11228', '11229', '11230', '11231', '11232', '11233', '11234',\n",
       "       '11235', '11236', '11237', '11238', '11249', '11354', '11355',\n",
       "       '11356', '11357', '11358', '11360', '11361', '11364', '11365',\n",
       "       '11366', '11367', '11368', '11369', '11370', '11372', '11373',\n",
       "       '11374', '11375', '11377', '11378', '11379', '11385', '11411',\n",
       "       '11412', '11413', '11414', '11415', '11416', '11417', '11418',\n",
       "       '11419', '11420', '11421', '11422', '11423', '11426', '11427',\n",
       "       '11428', '11429', '11432', '11433', '11434', '11435', '11436',\n",
       "       '11509', '11691', '11692', '11693', '11694', '20001', '20002',\n",
       "       '20003', '20004', '20005', '20006', '20007', '20008', '20009',\n",
       "       '20010', '20011', '20012', '20015', '20016', '20017', '20018',\n",
       "       '20019', '20020', '20024', '20032', '20036', '20037', '20052',\n",
       "       '20712', '20781', '20782', '20815', '20816', '20910', '20912',\n",
       "       '60601', '60602', '60603', '60605', '60607', '60608', '60609',\n",
       "       '60610', '60611', '60612', '60613', '60614', '60615', '60616',\n",
       "       '60618', '60619', '60620', '60621', '60622', '60623', '60624',\n",
       "       '60625', '60626', '60628', '60629', '60630', '60631', '60632',\n",
       "       '60633', '60634', '60636', '60637', '60638', '60639', '60640',\n",
       "       '60641', '60642', '60643', '60644', '60645', '60646', '60647',\n",
       "       '60649', '60651', '60652', '60653', '60654', '60656', '60657',\n",
       "       '60659', '60660', '60661', '60707', '90001', '90002', '90003',\n",
       "       '90004', '90005', '90006', '90007', '90008', '90010', '90011',\n",
       "       '90012', '90013', '90014', '90015', '90016', '90017', '90018',\n",
       "       '90019', '90020', '90021', '90022', '90023', '90024', '90025',\n",
       "       '90026', '90027', '90028', '90029', '90031', '90032', '90033',\n",
       "       '90034', '90035', '90036', '90037', '90038', '90039', '90040',\n",
       "       '90041', '90042', '90043', '90044', '90045', '90046', '90047',\n",
       "       '90048', '90049', '90056', '90057', '90059', '90061', '90062',\n",
       "       '90063', '90064', '90065', '90066', '90067', '90068', '90069',\n",
       "       '90071', '90077', '90094', '90201', '90210', '90211', '90212',\n",
       "       '90220', '90221', '90222', '90230', '90232', '90240', '90241',\n",
       "       '90242', '90245', '90247', '90248', '90249', '90250', '90254',\n",
       "       '90255', '90260', '90262', '90265', '90266', '90272', '90274',\n",
       "       '90275', '90277', '90278', '90280', '90290', '90291', '90292',\n",
       "       '90293', '90301', '90302', '90303', '90304', '90305', '90401',\n",
       "       '90402', '90403', '90404', '90405', '90501', '90503', '90504',\n",
       "       '90505', '90601', '90602', '90603', '90604', '90605', '90638',\n",
       "       '90640', '90650', '90660', '90670', '90703', '90706', '90710',\n",
       "       '90712', '90713', '90715', '90716', '90717', '90723', '90731',\n",
       "       '90732', '90745', '90746', '90755', '90802', '90803', '90804',\n",
       "       '90805', '90806', '90807', '90808', '90810', '90813', '90814',\n",
       "       '90815', '91001', '91006', '91007', '91008', '91010', '91011',\n",
       "       '91016', '91020', '91024', '91030', '91040', '91042', '91101',\n",
       "       '91103', '91104', '91105', '91106', '91107', '91108', '91201',\n",
       "       '91202', '91203', '91204', '91205', '91206', '91207', '91208',\n",
       "       '91210', '91214', '91303', '91304', '91306', '91307', '91311',\n",
       "       '91316', '91324', '91325', '91326', '91331', '91335', '91340',\n",
       "       '91342', '91343', '91344', '91345', '91352', '91356', '91364',\n",
       "       '91367', '91401', '91402', '91403', '91405', '91406', '91411',\n",
       "       '91423', '91436', '91501', '91502', '91504', '91505', '91506',\n",
       "       '91523', '91601', '91602', '91604', '91605', '91606', '91607',\n",
       "       '91702', '91706', '91722', '91723', '91724', '91731', '91732',\n",
       "       '91733', '91741', '91744', '91746', '91754', '91755', '91770',\n",
       "       '91775', '91776', '91780', '91790', '91791', '91792', '91801',\n",
       "       '91803', '94014', '94102', '94103', '94104', '94105', '94107',\n",
       "       '94108', '94109', '94110', '94111', '94112', '94114', '94115',\n",
       "       '94116', '94117', '94118', '94121', '94122', '94123', '94124',\n",
       "       '94127', '94131', '94132', '94133', '94134', '94158'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['zipcode'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f90c6cf9-9209-4b07-91e0-1bed5e824273",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting zipcode to int\n",
    "df['zipcode'] = df['zipcode'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "40ec1f44-33f1-4a28-8bef-b13abc9dfe0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54511     2108\n",
       "25530     2108\n",
       "9677      2108\n",
       "2894      2108\n",
       "64803     2108\n",
       "         ...  \n",
       "63010    94158\n",
       "42509    94158\n",
       "33983    94158\n",
       "67215    94158\n",
       "43630    94158\n",
       "Name: zipcode, Length: 38499, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['zipcode'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d337d1f9-916e-4569-a584-ad31ee96f826",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2108,  2109,  2110,  2111,  2113,  2114,  2115,  2116,  2118,\n",
       "        2119,  2120,  2121,  2122,  2124,  2125,  2126,  2127,  2128,\n",
       "        2129,  2130,  2131,  2132,  2134,  2135,  2136,  2139,  2143,\n",
       "        2145,  2186,  2199,  2210,  2215,  2467, 10001, 10002, 10003,\n",
       "       10004, 10005, 10006, 10007, 10009, 10010, 10011, 10012, 10013,\n",
       "       10014, 10016, 10017, 10018, 10019, 10021, 10022, 10023, 10024,\n",
       "       10025, 10026, 10027, 10028, 10029, 10030, 10031, 10032, 10033,\n",
       "       10034, 10035, 10036, 10037, 10038, 10039, 10040, 10044, 10048,\n",
       "       10065, 10069, 10075, 10118, 10119, 10128, 10129, 10162, 10270,\n",
       "       10279, 10280, 10282, 10301, 10302, 10303, 10304, 10305, 10306,\n",
       "       10307, 10308, 10309, 10310, 10312, 10314, 10451, 10452, 10453,\n",
       "       10454, 10455, 10456, 10457, 10458, 10459, 10460, 10461, 10462,\n",
       "       10463, 10464, 10465, 10466, 10467, 10468, 10469, 10470, 10471,\n",
       "       10472, 10473, 10474, 10475, 10550, 11101, 11102, 11103, 11104,\n",
       "       11105, 11106, 11109, 11201, 11203, 11204, 11205, 11206, 11207,\n",
       "       11208, 11209, 11210, 11211, 11212, 11213, 11214, 11215, 11216,\n",
       "       11217, 11218, 11219, 11220, 11221, 11222, 11223, 11224, 11225,\n",
       "       11226, 11228, 11229, 11230, 11231, 11232, 11233, 11234, 11235,\n",
       "       11236, 11237, 11238, 11249, 11354, 11355, 11356, 11357, 11358,\n",
       "       11360, 11361, 11364, 11365, 11366, 11367, 11368, 11369, 11370,\n",
       "       11372, 11373, 11374, 11375, 11377, 11378, 11379, 11385, 11411,\n",
       "       11412, 11413, 11414, 11415, 11416, 11417, 11418, 11419, 11420,\n",
       "       11421, 11422, 11423, 11426, 11427, 11428, 11429, 11432, 11433,\n",
       "       11434, 11435, 11436, 11509, 11691, 11692, 11693, 11694, 20001,\n",
       "       20002, 20003, 20004, 20005, 20006, 20007, 20008, 20009, 20010,\n",
       "       20011, 20012, 20015, 20016, 20017, 20018, 20019, 20020, 20024,\n",
       "       20032, 20036, 20037, 20052, 20712, 20781, 20782, 20815, 20816,\n",
       "       20910, 20912, 60601, 60602, 60603, 60605, 60607, 60608, 60609,\n",
       "       60610, 60611, 60612, 60613, 60614, 60615, 60616, 60618, 60619,\n",
       "       60620, 60621, 60622, 60623, 60624, 60625, 60626, 60628, 60629,\n",
       "       60630, 60631, 60632, 60633, 60634, 60636, 60637, 60638, 60639,\n",
       "       60640, 60641, 60642, 60643, 60644, 60645, 60646, 60647, 60649,\n",
       "       60651, 60652, 60653, 60654, 60656, 60657, 60659, 60660, 60661,\n",
       "       60707, 90001, 90002, 90003, 90004, 90005, 90006, 90007, 90008,\n",
       "       90010, 90011, 90012, 90013, 90014, 90015, 90016, 90017, 90018,\n",
       "       90019, 90020, 90021, 90022, 90023, 90024, 90025, 90026, 90027,\n",
       "       90028, 90029, 90031, 90032, 90033, 90034, 90035, 90036, 90037,\n",
       "       90038, 90039, 90040, 90041, 90042, 90043, 90044, 90045, 90046,\n",
       "       90047, 90048, 90049, 90056, 90057, 90059, 90061, 90062, 90063,\n",
       "       90064, 90065, 90066, 90067, 90068, 90069, 90071, 90077, 90094,\n",
       "       90201, 90210, 90211, 90212, 90220, 90221, 90222, 90230, 90232,\n",
       "       90240, 90241, 90242, 90245, 90247, 90248, 90249, 90250, 90254,\n",
       "       90255, 90260, 90262, 90265, 90266, 90272, 90274, 90275, 90277,\n",
       "       90278, 90280, 90290, 90291, 90292, 90293, 90301, 90302, 90303,\n",
       "       90304, 90305, 90401, 90402, 90403, 90404, 90405, 90501, 90503,\n",
       "       90504, 90505, 90601, 90602, 90603, 90604, 90605, 90638, 90640,\n",
       "       90650, 90660, 90670, 90703, 90706, 90710, 90712, 90713, 90715,\n",
       "       90716, 90717, 90723, 90731, 90732, 90745, 90746, 90755, 90802,\n",
       "       90803, 90804, 90805, 90806, 90807, 90808, 90810, 90813, 90814,\n",
       "       90815, 91001, 91006, 91007, 91008, 91010, 91011, 91016, 91020,\n",
       "       91024, 91030, 91040, 91042, 91101, 91103, 91104, 91105, 91106,\n",
       "       91107, 91108, 91201, 91202, 91203, 91204, 91205, 91206, 91207,\n",
       "       91208, 91210, 91214, 91303, 91304, 91306, 91307, 91311, 91316,\n",
       "       91324, 91325, 91326, 91331, 91335, 91340, 91342, 91343, 91344,\n",
       "       91345, 91352, 91356, 91364, 91367, 91401, 91402, 91403, 91405,\n",
       "       91406, 91411, 91423, 91436, 91501, 91502, 91504, 91505, 91506,\n",
       "       91523, 91601, 91602, 91604, 91605, 91606, 91607, 91702, 91706,\n",
       "       91722, 91723, 91724, 91731, 91732, 91733, 91741, 91744, 91746,\n",
       "       91754, 91755, 91770, 91775, 91776, 91780, 91790, 91791, 91792,\n",
       "       91801, 91803, 94014, 94102, 94103, 94104, 94105, 94107, 94108,\n",
       "       94109, 94110, 94111, 94112, 94114, 94115, 94116, 94117, 94118,\n",
       "       94121, 94122, 94123, 94124, 94127, 94131, 94132, 94133, 94134,\n",
       "       94158])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['zipcode'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e218361e-611b-4b5f-bc1a-80854e5306a7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "559"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['zipcode'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8a12e3fe-c3ac-4713-a73c-346bb4fe6314",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # cleaning 'zip_code'\n",
    "\n",
    "# df['zipcode'] = df['zipcode'].str.split('.').str[0]\n",
    "# df['zipcode'] = df['zipcode'].str.split('-').str[0]\n",
    "\n",
    "# oddValue = df[df['zipcode'] == '1m'].index\n",
    "# df.drop(oddValue, inplace=True)\n",
    "\n",
    "# badValue = df[df['zipcode'] == '95202\\r\\r\\r\\r\\r\\r\\n\\r\\r\\r\\r\\r\\r\\n\\r\\r\\r\\r\\r\\r\\n94158'].index\n",
    "# df.drop(badValue, inplace=True)\n",
    "\n",
    "# weirdValue = df[df['zipcode'] == '11249\\r\\r\\r\\r\\r\\r\\n11249'].index\n",
    "# df.drop(weirdValue, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b95c1294-5cd7-4cbd-9ab3-1f472d3910ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=\"host_has_profile_pic\", inplace=True)  # Why would a profile pic matter? lol\n",
    "df.drop(columns=\"host_response_rate\", inplace=True)   # Not important. We just need the host to respond is all. The rate at which they do is superfluous\n",
    "df.drop(columns=\"number_of_reviews\", inplace=True)   # Its not the number of reviews that matter, but the average rating of them\n",
    "df.drop(columns=\"log_price\", inplace=True)   # This will lead to data leakage\n",
    "df.drop(columns=\"description\", inplace=True)   # Literally just a written version of our lat, long, and zipcode features\n",
    "df.drop(columns=\"first_review\", inplace=True)  # Unnecessary\n",
    "df.drop(columns=\"last_review\", inplace=True)  # Useless\n",
    "df.drop(columns=\"thumbnail_url\", inplace=True)  # When do URLs ever help ML models?\n",
    "df.drop(columns=\"id\", inplace=True)   # Since we are IDs necessary? \n",
    "df.drop(columns=\"amenities\", inplace=True) # This column is a messed up dictionary. Will be dropping it for now. Will add it back in later when perfecting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "75a1638a-15f6-477b-b3b8-a0889cc68d99",
   "metadata": {
    "tags": []
   },
   "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>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>city</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>host_since</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>name</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54511</th>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>strict</td>\n",
       "      <td>1</td>\n",
       "      <td>Boston</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-02-14</td>\n",
       "      <td>1</td>\n",
       "      <td>42.356821</td>\n",
       "      <td>-71.060408</td>\n",
       "      <td>Stunning 4BR in Downtown Crossing</td>\n",
       "      <td>Downtown Crossing</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2108</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20313</th>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>strict</td>\n",
       "      <td>1</td>\n",
       "      <td>Boston</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-03-22</td>\n",
       "      <td>1</td>\n",
       "      <td>42.364847</td>\n",
       "      <td>-71.052334</td>\n",
       "      <td>Boston  Best One Bdrm duplex (h2c)</td>\n",
       "      <td>North End</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68172</th>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>flexible</td>\n",
       "      <td>1</td>\n",
       "      <td>Boston</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-10-04</td>\n",
       "      <td>1</td>\n",
       "      <td>42.357428</td>\n",
       "      <td>-71.065911</td>\n",
       "      <td>Beacon Hill PERFECT one bedroom!!!!</td>\n",
       "      <td>Beacon Hill</td>\n",
       "      <td>87.0</td>\n",
       "      <td>2108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_type        room_type  accommodates  bathrooms  bed_type  \\\n",
       "54511     Apartment  Entire home/apt             8        2.0  Real Bed   \n",
       "20313     Apartment  Entire home/apt             4        1.0  Real Bed   \n",
       "68172     Apartment  Entire home/apt             2        1.0  Real Bed   \n",
       "\n",
       "      cancellation_policy cleaning_fee    city host_identity_verified  \\\n",
       "54511              strict            1  Boston                      1   \n",
       "20313              strict            1  Boston                      0   \n",
       "68172            flexible            1  Boston                      0   \n",
       "\n",
       "       host_since instant_bookable   latitude  longitude  \\\n",
       "54511  2014-02-14                1  42.356821 -71.060408   \n",
       "20313  2013-03-22                1  42.364847 -71.052334   \n",
       "68172  2014-10-04                1  42.357428 -71.065911   \n",
       "\n",
       "                                      name      neighbourhood  \\\n",
       "54511    Stunning 4BR in Downtown Crossing  Downtown Crossing   \n",
       "20313   Boston  Best One Bdrm duplex (h2c)          North End   \n",
       "68172  Beacon Hill PERFECT one bedroom!!!!        Beacon Hill   \n",
       "\n",
       "       review_scores_rating  zipcode  bedrooms  beds  price  \n",
       "54511                 100.0     2108       4.0   4.0  259.0  \n",
       "20313                  89.0     2108       1.0   2.0   89.0  \n",
       "68172                  87.0     2108       1.0   1.0  225.0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b326ad1-a1f1-45b5-94a0-755f0427988e",
   "metadata": {},
   "source": [
    "## Plotly Express - Scatter Mapbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "70c82ec5-694b-4f18-8c18-31358b854cee",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# figure = px.scatter_mapbox(\n",
    "#                            df,\n",
    "#                            lat='latitude',\n",
    "#                            lon='longitude',\n",
    "#                            hover_name='name',\n",
    "#                            hover_data=['price',\n",
    "#                                        'review_scores_rating'],\n",
    "#                            color_discrete_sequence=[\"goldenrod\"],\n",
    "#                            zoom=3, height=350\n",
    "#                            )\n",
    "\n",
    "# figure.update_layout(mapbox_style=\"open-street-map\")\n",
    "\n",
    "# figure.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
    "\n",
    "# figure.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89dca457-b98c-48c3-9be4-8220bacfcfa5",
   "metadata": {},
   "source": [
    "# Plotly Express - Analysis: \n",
    "---\n",
    "\n",
    "We only have data on Los Angeles, San Francisco, Chicago, D.C., NYC & Boston. The Continental USA. Considering that I reversed the natural logarithm of our 'log_price' variable, it seems that those price values are all in USD.\n",
    "\n",
    "Given this, I believe training my model on just these cities alone (with ample regularization tuning) will be enough to account for other major cities around the US."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "229c1968-f3e4-4f75-bb7f-cbe8e9ac7df0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # Doing this here after the big Plotly Dash map\n",
    "df.drop(columns=\"name\", inplace=True)                      # Names are purely nominal\n",
    "df.drop(columns=\"latitude\", inplace=True)                 # Won't work with web app\n",
    "df.drop(columns=\"longitude\", inplace=True)\n",
    "df.drop(columns=\"neighbourhood\", inplace=True)          # Redudant when we already have zipcode"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fde4cc2a-bd8b-4c8c-a5f3-42ffd3cc497a",
   "metadata": {},
   "source": [
    "## Automated EDA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "46f156c7-f844-4ce5-9382-6950ada862c3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Report = ProfileReport(df, explorative=True).to_notebook_iframe()\n",
    "# Report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa76a137-8516-4d4d-9bfe-fd9c73930c1b",
   "metadata": {},
   "source": [
    "Given Spearman's p, the features most correlated with price are accommodates, beds, bedrooms, and bathrooms (in that order). Review_scores_rating barely correlates at all. Which is rather surprising.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b25adc01-32db-405c-af89-ec33069d7c6d",
   "metadata": {},
   "source": [
    "## FINAL SELECTION OF FEATURES\n",
    "---\n",
    "- save for 'price' as it is our target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "30bc9015-4836-4121-8570-0eb34bc21128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['property_type', 'room_type', 'accommodates', 'bathrooms', 'bed_type',\n",
       "       'cancellation_policy', 'cleaning_fee', 'city', 'host_identity_verified',\n",
       "       'host_since', 'instant_bookable', 'review_scores_rating', 'zipcode',\n",
       "       'bedrooms', 'beds', 'price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5c83a22c-2275-400c-84f9-ef52a217f943",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>property_type</th>\n",
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       "      <td>2012-08-02</td>\n",
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       "      <td>2108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>185.0</td>\n",
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       "      <th>56709</th>\n",
       "      <td>Apartment</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>10358</th>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>strict</td>\n",
       "      <td>1</td>\n",
       "      <td>SF</td>\n",
       "      <td>1</td>\n",
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       "      <td>96.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>179.0</td>\n",
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       "      <td>Apartment</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>98.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>10418</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2017-01-27</td>\n",
       "      <td>1</td>\n",
       "      <td>97.0</td>\n",
       "      <td>94158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>250.0</td>\n",
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       "      <th>33983</th>\n",
       "      <td>Apartment</td>\n",
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       "      <td>Real Bed</td>\n",
       "      <td>strict</td>\n",
       "      <td>0</td>\n",
       "      <td>SF</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-03-29</td>\n",
       "      <td>1</td>\n",
       "      <td>97.0</td>\n",
       "      <td>94158</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>197.0</td>\n",
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       "    <tr>\n",
       "      <th>43630</th>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>strict</td>\n",
       "      <td>1</td>\n",
       "      <td>SF</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-06-11</td>\n",
       "      <td>1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>94158</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>148.0</td>\n",
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       "<p>38499 rows × 16 columns</p>\n",
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      ],
      "text/plain": [
       "      property_type        room_type  accommodates  bathrooms  bed_type  \\\n",
       "54511     Apartment  Entire home/apt             8        2.0  Real Bed   \n",
       "20313     Apartment  Entire home/apt             4        1.0  Real Bed   \n",
       "68172     Apartment  Entire home/apt             2        1.0  Real Bed   \n",
       "62506     Apartment  Entire home/apt             3        1.5  Real Bed   \n",
       "56709     Apartment  Entire home/apt             2        1.0  Real Bed   \n",
       "...             ...              ...           ...        ...       ...   \n",
       "10358     Apartment     Private room             2        1.0  Real Bed   \n",
       "27979     Apartment     Private room             3        1.0  Real Bed   \n",
       "10418     Apartment  Entire home/apt             2        1.0  Real Bed   \n",
       "33983     Apartment     Private room             1        1.0  Real Bed   \n",
       "43630     Apartment     Private room             4        1.0  Real Bed   \n",
       "\n",
       "      cancellation_policy cleaning_fee    city host_identity_verified  \\\n",
       "54511              strict            1  Boston                      1   \n",
       "20313              strict            1  Boston                      0   \n",
       "68172            flexible            1  Boston                      0   \n",
       "62506              strict            1  Boston                      1   \n",
       "56709            moderate            0  Boston                      1   \n",
       "...                   ...          ...     ...                    ...   \n",
       "10358              strict            1      SF                      1   \n",
       "27979              strict            1      SF                      1   \n",
       "10418              strict            1      SF                      0   \n",
       "33983              strict            0      SF                      1   \n",
       "43630              strict            1      SF                      0   \n",
       "\n",
       "       host_since instant_bookable  review_scores_rating  zipcode  bedrooms  \\\n",
       "54511  2014-02-14                1                 100.0     2108       4.0   \n",
       "20313  2013-03-22                1                  89.0     2108       1.0   \n",
       "68172  2014-10-04                1                  87.0     2108       1.0   \n",
       "62506  2012-08-02                1                  87.0     2108       1.0   \n",
       "56709  2014-07-28                0                  91.0     2108       1.0   \n",
       "...           ...              ...                   ...      ...       ...   \n",
       "10358  2015-03-29                0                  96.0    94158       1.0   \n",
       "27979  2013-04-30                0                  98.0    94158       1.0   \n",
       "10418  2017-01-27                1                  97.0    94158       0.0   \n",
       "33983  2015-03-29                1                  97.0    94158       1.0   \n",
       "43630  2012-06-11                1                  98.0    94158       1.0   \n",
       "\n",
       "       beds  price  \n",
       "54511   4.0  259.0  \n",
       "20313   2.0   89.0  \n",
       "68172   1.0  225.0  \n",
       "62506   1.0  185.0  \n",
       "56709   1.0  199.0  \n",
       "...     ...    ...  \n",
       "10358   1.0  179.0  \n",
       "27979   1.0  149.0  \n",
       "10418   1.0  250.0  \n",
       "33983   1.0  197.0  \n",
       "43630   2.0  148.0  \n",
       "\n",
       "[38499 rows x 16 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "746f7de3-5039-4d77-ab41-22658b063290",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "\n",
    "Creating both standardized and normalized versions of this dataset for our predictive model. Which preprocessing technique is best for our model? We'll just have to experiment with both!\n",
    "\n",
    "- Will need to vectorize whichever string/object columns I end up using after dropping HCC and other useless columns up above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "cb48f00e-129a-44eb-97f6-02d696cea8da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Not going to use ordinal, OHE, or dummy encoding. It creates far too many features. We'll run into the curse of dimensionality if I do this.\n",
    "# Gonna need to embed our categorical columns. Might just use difference encoding for the converted binary columns though.\n",
    "# df_dummy = pd.get_dummies(df)\n",
    "# df_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e201bef8-0a82-4c96-a874-0db80ced3efe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encoded = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8e3ab3d3-9166-417c-8bf8-34591d792d93",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <td>2108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>185.0</td>\n",
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       "      <th>56709</th>\n",
       "      <td>Apartment</td>\n",
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      "text/plain": [
       "      property_type        room_type  accommodates  bathrooms  bed_type  \\\n",
       "54511     Apartment  Entire home/apt             8        2.0  Real Bed   \n",
       "20313     Apartment  Entire home/apt             4        1.0  Real Bed   \n",
       "68172     Apartment  Entire home/apt             2        1.0  Real Bed   \n",
       "62506     Apartment  Entire home/apt             3        1.5  Real Bed   \n",
       "56709     Apartment  Entire home/apt             2        1.0  Real Bed   \n",
       "\n",
       "      cancellation_policy cleaning_fee    city host_identity_verified  \\\n",
       "54511              strict            1  Boston                      1   \n",
       "20313              strict            1  Boston                      0   \n",
       "68172            flexible            1  Boston                      0   \n",
       "62506              strict            1  Boston                      1   \n",
       "56709            moderate            0  Boston                      1   \n",
       "\n",
       "       host_since instant_bookable  review_scores_rating  zipcode  bedrooms  \\\n",
       "54511  2014-02-14                1                 100.0     2108       4.0   \n",
       "20313  2013-03-22                1                  89.0     2108       1.0   \n",
       "68172  2014-10-04                1                  87.0     2108       1.0   \n",
       "62506  2012-08-02                1                  87.0     2108       1.0   \n",
       "56709  2014-07-28                0                  91.0     2108       1.0   \n",
       "\n",
       "       beds  price  \n",
       "54511   4.0  259.0  \n",
       "20313   2.0   89.0  \n",
       "68172   1.0  225.0  \n",
       "62506   1.0  185.0  \n",
       "56709   1.0  199.0  "
      ]
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   "source": [
    "df_encoded.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "64805a0a-5b2c-47a0-b4eb-a3c84f0cd31f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def unique(list1):\n",
    " \n",
    "    # intilize a null list\n",
    "    unique_list = []\n",
    "     \n",
    "    # traverse for all elements\n",
    "    for x in list1:\n",
    "        # check if exists in unique_list or not\n",
    "        if x not in unique_list:\n",
    "            unique_list.append(x)\n",
    "    # print list\n",
    "    for x in unique_list:\n",
    "        print (x,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "14733764-2c42-4cc9-b11d-3aaa6903ce6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# unique(df.city)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "306aeafa-a00f-47ae-8e78-86e00c01b2bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# unique(df.property_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "56d27c6c-8ac3-46b1-a870-98926d78480e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# unique(df.bed_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "85da5aaf-715d-4763-ba60-1560cc984baf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# unique(df.room_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cf4a03d3-1e14-4d6a-b250-b99be8f7e539",
   "metadata": {},
   "outputs": [],
   "source": [
    "# unique(df.cancellation_policy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "62eb4933-85b3-44ef-af9a-a66fd646687c",
   "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>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>city</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>host_since</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54511</th>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>strict</td>\n",
       "      <td>1</td>\n",
       "      <td>Boston</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-02-14</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2108</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20313</th>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>strict</td>\n",
       "      <td>1</td>\n",
       "      <td>Boston</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-03-22</td>\n",
       "      <td>1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68172</th>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>flexible</td>\n",
       "      <td>1</td>\n",
       "      <td>Boston</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-10-04</td>\n",
       "      <td>1</td>\n",
       "      <td>87.0</td>\n",
       "      <td>2108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62506</th>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>strict</td>\n",
       "      <td>1</td>\n",
       "      <td>Boston</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-08-02</td>\n",
       "      <td>1</td>\n",
       "      <td>87.0</td>\n",
       "      <td>2108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56709</th>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>moderate</td>\n",
       "      <td>0</td>\n",
       "      <td>Boston</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-07-28</td>\n",
       "      <td>0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10358</th>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>strict</td>\n",
       "      <td>1</td>\n",
       "      <td>SF</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-03-29</td>\n",
       "      <td>0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>94158</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27979</th>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>strict</td>\n",
       "      <td>1</td>\n",
       "      <td>SF</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-04-30</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>94158</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10418</th>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>strict</td>\n",
       "      <td>1</td>\n",
       "      <td>SF</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-01-27</td>\n",
       "      <td>1</td>\n",
       "      <td>97.0</td>\n",
       "      <td>94158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33983</th>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>strict</td>\n",
       "      <td>0</td>\n",
       "      <td>SF</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-03-29</td>\n",
       "      <td>1</td>\n",
       "      <td>97.0</td>\n",
       "      <td>94158</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43630</th>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>strict</td>\n",
       "      <td>1</td>\n",
       "      <td>SF</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-06-11</td>\n",
       "      <td>1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>94158</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38499 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_type        room_type  accommodates  bathrooms  bed_type  \\\n",
       "54511     Apartment  Entire home/apt             8        2.0  Real Bed   \n",
       "20313     Apartment  Entire home/apt             4        1.0  Real Bed   \n",
       "68172     Apartment  Entire home/apt             2        1.0  Real Bed   \n",
       "62506     Apartment  Entire home/apt             3        1.5  Real Bed   \n",
       "56709     Apartment  Entire home/apt             2        1.0  Real Bed   \n",
       "...             ...              ...           ...        ...       ...   \n",
       "10358     Apartment     Private room             2        1.0  Real Bed   \n",
       "27979     Apartment     Private room             3        1.0  Real Bed   \n",
       "10418     Apartment  Entire home/apt             2        1.0  Real Bed   \n",
       "33983     Apartment     Private room             1        1.0  Real Bed   \n",
       "43630     Apartment     Private room             4        1.0  Real Bed   \n",
       "\n",
       "      cancellation_policy cleaning_fee    city host_identity_verified  \\\n",
       "54511              strict            1  Boston                      1   \n",
       "20313              strict            1  Boston                      0   \n",
       "68172            flexible            1  Boston                      0   \n",
       "62506              strict            1  Boston                      1   \n",
       "56709            moderate            0  Boston                      1   \n",
       "...                   ...          ...     ...                    ...   \n",
       "10358              strict            1      SF                      1   \n",
       "27979              strict            1      SF                      1   \n",
       "10418              strict            1      SF                      0   \n",
       "33983              strict            0      SF                      1   \n",
       "43630              strict            1      SF                      0   \n",
       "\n",
       "       host_since instant_bookable  review_scores_rating  zipcode  bedrooms  \\\n",
       "54511  2014-02-14                1                 100.0     2108       4.0   \n",
       "20313  2013-03-22                1                  89.0     2108       1.0   \n",
       "68172  2014-10-04                1                  87.0     2108       1.0   \n",
       "62506  2012-08-02                1                  87.0     2108       1.0   \n",
       "56709  2014-07-28                0                  91.0     2108       1.0   \n",
       "...           ...              ...                   ...      ...       ...   \n",
       "10358  2015-03-29                0                  96.0    94158       1.0   \n",
       "27979  2013-04-30                0                  98.0    94158       1.0   \n",
       "10418  2017-01-27                1                  97.0    94158       0.0   \n",
       "33983  2015-03-29                1                  97.0    94158       1.0   \n",
       "43630  2012-06-11                1                  98.0    94158       1.0   \n",
       "\n",
       "       beds  price  \n",
       "54511   4.0  259.0  \n",
       "20313   2.0   89.0  \n",
       "68172   1.0  225.0  \n",
       "62506   1.0  185.0  \n",
       "56709   1.0  199.0  \n",
       "...     ...    ...  \n",
       "10358   1.0  179.0  \n",
       "27979   1.0  149.0  \n",
       "10418   1.0  250.0  \n",
       "33983   1.0  197.0  \n",
       "43630   2.0  148.0  \n",
       "\n",
       "[38499 rows x 16 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "90356605-296f-46a2-a402-9ae4013cc37d",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "unique(df.zipcode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4238acd6-8d2a-4099-bbff-139627d7d6b6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# List comprehension version that returns numpy arrays \n",
    "# label_encoder = LabelEncoder()\n",
    "\n",
    "# objects = ['property_type', 'room_type', 'bed_type', 'cancellation_policy', 'city', 'name', 'neighbourhood']\n",
    "\n",
    "# [label_encoder.fit_transform(df[obj]).astype(int) for obj in objects]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "80357cc2-473a-4b04-a45e-8153d547c48b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Want to refactor code to grab all object dtypes and place them in this empty list\n",
    "# dtype_obj = []\n",
    "\n",
    "# for obj in df.dtypes:\n",
    "#     return dtype_obj.append()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1579145d-07ff-4c45-88d6-4fb7728166d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# USING LABEL ENCODING IN A FOR LOOP! THIS IS SO MUCH EASIER!! WE KEEP THE SAME NUMBER OF FEATURES! \n",
    "# OHE is better used to binarize categories within a feature, turning them into their own individual features\n",
    "# Attempting this for loop with a OrdinalEncoder doesn't work. \n",
    "# Got this error back - ValueError: Expected 2D array, got 1D array instead:\n",
    "#                       array=['Condominium' 'Other' 'Apartment' ... 'Apartment' 'Condominium' 'Guesthouse']. Reshape your data either using\n",
    "# array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "objects = ['property_type', 'room_type', 'bed_type', 'cancellation_policy', 'city']\n",
    "\n",
    "for obj in objects:\n",
    "    df_encoded[obj] = label_encoder.fit_transform(df_encoded[obj]).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7ee7f7d5-09dd-4474-8bc6-c011367403f7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>city</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>host_since</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>2014-02-14</td>\n",
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       "      <td>100.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>259.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-08-02</td>\n",
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       "      <td>87.0</td>\n",
       "      <td>2108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>185.0</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2014-07-28</td>\n",
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       "      <td>2108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>199.0</td>\n",
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       "      <th>10358</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-03-29</td>\n",
       "      <td>0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>94158</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>179.0</td>\n",
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       "    <tr>\n",
       "      <th>27979</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-04-30</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>94158</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10418</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-01-27</td>\n",
       "      <td>1</td>\n",
       "      <td>97.0</td>\n",
       "      <td>94158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33983</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-03-29</td>\n",
       "      <td>1</td>\n",
       "      <td>97.0</td>\n",
       "      <td>94158</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43630</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-06-11</td>\n",
       "      <td>1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>94158</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38499 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       property_type  room_type  accommodates  bathrooms  bed_type  \\\n",
       "54511              0          0             8        2.0         4   \n",
       "20313              0          0             4        1.0         4   \n",
       "68172              0          0             2        1.0         4   \n",
       "62506              0          0             3        1.5         4   \n",
       "56709              0          0             2        1.0         4   \n",
       "...              ...        ...           ...        ...       ...   \n",
       "10358              0          1             2        1.0         4   \n",
       "27979              0          1             3        1.0         4   \n",
       "10418              0          0             2        1.0         4   \n",
       "33983              0          1             1        1.0         4   \n",
       "43630              0          1             4        1.0         4   \n",
       "\n",
       "       cancellation_policy cleaning_fee  city host_identity_verified  \\\n",
       "54511                    2            1     0                      1   \n",
       "20313                    2            1     0                      0   \n",
       "68172                    0            1     0                      0   \n",
       "62506                    2            1     0                      1   \n",
       "56709                    1            0     0                      1   \n",
       "...                    ...          ...   ...                    ...   \n",
       "10358                    2            1     5                      1   \n",
       "27979                    2            1     5                      1   \n",
       "10418                    2            1     5                      0   \n",
       "33983                    2            0     5                      1   \n",
       "43630                    2            1     5                      0   \n",
       "\n",
       "       host_since instant_bookable  review_scores_rating  zipcode  bedrooms  \\\n",
       "54511  2014-02-14                1                 100.0     2108       4.0   \n",
       "20313  2013-03-22                1                  89.0     2108       1.0   \n",
       "68172  2014-10-04                1                  87.0     2108       1.0   \n",
       "62506  2012-08-02                1                  87.0     2108       1.0   \n",
       "56709  2014-07-28                0                  91.0     2108       1.0   \n",
       "...           ...              ...                   ...      ...       ...   \n",
       "10358  2015-03-29                0                  96.0    94158       1.0   \n",
       "27979  2013-04-30                0                  98.0    94158       1.0   \n",
       "10418  2017-01-27                1                  97.0    94158       0.0   \n",
       "33983  2015-03-29                1                  97.0    94158       1.0   \n",
       "43630  2012-06-11                1                  98.0    94158       1.0   \n",
       "\n",
       "       beds  price  \n",
       "54511   4.0  259.0  \n",
       "20313   2.0   89.0  \n",
       "68172   1.0  225.0  \n",
       "62506   1.0  185.0  \n",
       "56709   1.0  199.0  \n",
       "...     ...    ...  \n",
       "10358   1.0  179.0  \n",
       "27979   1.0  149.0  \n",
       "10418   1.0  250.0  \n",
       "33983   1.0  197.0  \n",
       "43630   2.0  148.0  \n",
       "\n",
       "[38499 rows x 16 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ab0faa24-c1ac-4da7-b9c3-46a1c14bc560",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting 'host_since' to datetime\n",
    "# df_encoded['host_since'] = pd.to_datetime(df_encoded['host_since'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4a471f4e-c630-4d96-b651-f0336bd7c819",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I want to refactor this into a conditional statement or for loop\n",
    "df_encoded['cleaning_fee'] = df_encoded['cleaning_fee'].astype(int)\n",
    "df_encoded['host_identity_verified'] = df_encoded['host_identity_verified'].astype(int)\n",
    "df_encoded['instant_bookable'] = df_encoded['instant_bookable'].astype(int)\n",
    "df_encoded['bathrooms'] = df_encoded['bathrooms'].astype(int)\n",
    "df_encoded['review_scores_rating'] = df_encoded['review_scores_rating'].astype(int)\n",
    "df_encoded['bedrooms'] = df_encoded['bedrooms'].astype(int)\n",
    "df_encoded['beds'] = df_encoded['beds'].astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b411c09e-36f1-461b-b475-e826f0eb1a6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_type               int64\n",
       "room_type                   int64\n",
       "accommodates                int64\n",
       "bathrooms                   int64\n",
       "bed_type                    int64\n",
       "cancellation_policy         int64\n",
       "cleaning_fee                int64\n",
       "city                        int64\n",
       "host_identity_verified      int64\n",
       "host_since                 object\n",
       "instant_bookable            int64\n",
       "review_scores_rating        int64\n",
       "zipcode                     int64\n",
       "bedrooms                    int64\n",
       "beds                        int64\n",
       "price                     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8e6ed2a9-1593-4f5d-8472-bbe458f32609",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "unique(df_encoded.zipcode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b2705124-e06e-41d3-9054-ff442044e3ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, date, timezone\n",
    "import calendar\n",
    "datetime_obj = datetime.strptime(\"2021-06-22\", \"%Y-%m-%d\")\n",
    "# timestamp = calendar.timegm(datetime_obj.timetuple())\n",
    "# dt = datetime.utcfromtimestamp(timestamp)\n",
    "utc_timestamp_seconds = datetime_obj.replace(tzinfo=timezone.utc).timestamp()\n",
    "utc_timestamp_days = int(utc_timestamp_seconds / 86400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b4f4fb67-496e-4cf4-aa8a-33b077e2579a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2021, 6, 22, 0, 0)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime_obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "99a3f3dc-2e4d-4b98-9992-336f054258ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18800"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utc_timestamp_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2732007a-20be-4fc7-bd3f-51c2a6c3f2de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54511    2014-02-14\n",
       "20313    2013-03-22\n",
       "68172    2014-10-04\n",
       "62506    2012-08-02\n",
       "56709    2014-07-28\n",
       "            ...    \n",
       "10358    2015-03-29\n",
       "27979    2013-04-30\n",
       "10418    2017-01-27\n",
       "33983    2015-03-29\n",
       "43630    2012-06-11\n",
       "Name: host_since, Length: 38499, dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded.host_since"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "65d4e07f-5ae5-4b4a-9145-bdb9dc040630",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_type               int64\n",
       "room_type                   int64\n",
       "accommodates                int64\n",
       "bathrooms                   int64\n",
       "bed_type                    int64\n",
       "cancellation_policy         int64\n",
       "cleaning_fee                int64\n",
       "city                        int64\n",
       "host_identity_verified      int64\n",
       "host_since                 object\n",
       "instant_bookable            int64\n",
       "review_scores_rating        int64\n",
       "zipcode                     int64\n",
       "bedrooms                    int64\n",
       "beds                        int64\n",
       "price                     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "49227165-44d7-4eed-94d5-fc2dfe66078a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting datetime to UTC\n",
    "from datetime import datetime, date, timezone\n",
    "import calendar\n",
    "\n",
    "\n",
    "def datetime_to_UTC_days(host_since):\n",
    "    \"\"\"\"\n",
    "    Converts datetime object of dates since becoming an AirBnB host to the number of days its been since then.\n",
    "    \"\"\"\n",
    "    datetime_obj = datetime.strptime(host_since, \"%Y-%m-%d\")\n",
    "    utc_timestamp_seconds = datetime_obj.replace(tzinfo=timezone.utc).timestamp()\n",
    "    utc_timestamp_days = int(utc_timestamp_seconds / 86400)\n",
    "    \n",
    "    return utc_timestamp_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "34f91e5e-78bb-472a-93b3-539832b9bcb0",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-02-14\n",
      "2013-03-22\n",
      "2014-10-04\n",
      "2012-08-02\n",
      "2014-07-28\n",
      "2012-10-01\n",
      "2014-09-02\n",
      "2014-08-26\n",
      "2010-03-14\n",
      "2015-02-21\n",
      "2014-12-18\n",
      "2014-08-12\n",
      "2017-07-17\n",
      "2013-12-27\n",
      "2009-07-22\n",
      "2011-11-08\n",
      "2015-03-03\n",
      "2013-09-18\n",
      "2014-10-26\n",
      "2016-06-22\n",
      "2015-04-23\n",
      "2015-11-08\n",
      "2015-12-07\n",
      "2013-11-14\n",
      "2016-03-09\n",
      "2016-09-27\n",
      "2015-02-01\n",
      "2012-05-12\n",
      "2015-08-15\n",
      "2015-03-17\n",
      "2014-10-13\n",
      "2016-08-23\n",
      "2010-11-07\n",
      "2016-11-10\n",
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      "2010-12-29\n",
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      "2011-03-21\n",
      "2011-01-22\n",
      "2008-03-03\n",
      "2009-05-22\n",
      "2010-04-15\n",
      "2009-04-27\n",
      "2010-02-16\n",
      "2010-07-12\n",
      "2010-04-03\n",
      "2011-07-22\n",
      "2010-06-18\n",
      "2009-07-20\n",
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      "2009-12-24\n",
      "2010-06-09\n",
      "2009-09-20\n",
      "2010-03-25\n",
      "2010-07-10\n",
      "2008-12-08\n",
      "2010-04-04\n",
      "2009-11-29\n",
      "2009-02-20\n"
     ]
    }
   ],
   "source": [
    "unique(df_encoded.host_since)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a154d990-21e1-46cc-a22a-b38a5b25d333",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encoded['host_since'] = df_encoded['host_since'].apply(datetime_to_UTC_days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "66c255c3-7d3c-445a-b4fb-62993de27de4",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "unique(df_encoded.host_since)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "6ff83024-d85a-4071-86c5-5a14fd0ba4c6",
   "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>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>city</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>host_since</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54511</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "      <td>16115</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>2108</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20313</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15786</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>2108</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68172</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16347</td>\n",
       "      <td>1</td>\n",
       "      <td>87</td>\n",
       "      <td>2108</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62506</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15554</td>\n",
       "      <td>1</td>\n",
       "      <td>87</td>\n",
       "      <td>2108</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56709</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16279</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>2108</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>10358</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>16523</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>94158</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27979</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>15825</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>94158</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10418</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>17193</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>94158</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33983</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>16523</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>94158</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43630</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>15502</td>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>94158</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38499 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       property_type  room_type  accommodates  bathrooms  bed_type  \\\n",
       "54511              0          0             8          2         4   \n",
       "20313              0          0             4          1         4   \n",
       "68172              0          0             2          1         4   \n",
       "62506              0          0             3          1         4   \n",
       "56709              0          0             2          1         4   \n",
       "...              ...        ...           ...        ...       ...   \n",
       "10358              0          1             2          1         4   \n",
       "27979              0          1             3          1         4   \n",
       "10418              0          0             2          1         4   \n",
       "33983              0          1             1          1         4   \n",
       "43630              0          1             4          1         4   \n",
       "\n",
       "       cancellation_policy  cleaning_fee  city  host_identity_verified  \\\n",
       "54511                    2             1     0                       1   \n",
       "20313                    2             1     0                       0   \n",
       "68172                    0             1     0                       0   \n",
       "62506                    2             1     0                       1   \n",
       "56709                    1             0     0                       1   \n",
       "...                    ...           ...   ...                     ...   \n",
       "10358                    2             1     5                       1   \n",
       "27979                    2             1     5                       1   \n",
       "10418                    2             1     5                       0   \n",
       "33983                    2             0     5                       1   \n",
       "43630                    2             1     5                       0   \n",
       "\n",
       "       host_since  instant_bookable  review_scores_rating  zipcode  bedrooms  \\\n",
       "54511       16115                 1                   100     2108         4   \n",
       "20313       15786                 1                    89     2108         1   \n",
       "68172       16347                 1                    87     2108         1   \n",
       "62506       15554                 1                    87     2108         1   \n",
       "56709       16279                 0                    91     2108         1   \n",
       "...           ...               ...                   ...      ...       ...   \n",
       "10358       16523                 0                    96    94158         1   \n",
       "27979       15825                 0                    98    94158         1   \n",
       "10418       17193                 1                    97    94158         0   \n",
       "33983       16523                 1                    97    94158         1   \n",
       "43630       15502                 1                    98    94158         1   \n",
       "\n",
       "       beds  price  \n",
       "54511     4  259.0  \n",
       "20313     2   89.0  \n",
       "68172     1  225.0  \n",
       "62506     1  185.0  \n",
       "56709     1  199.0  \n",
       "...     ...    ...  \n",
       "10358     1  179.0  \n",
       "27979     1  149.0  \n",
       "10418     1  250.0  \n",
       "33983     1  197.0  \n",
       "43630     2  148.0  \n",
       "\n",
       "[38499 rows x 16 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "35dde2d5-1111-4fca-80de-fd424150e05e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Profile = ProfileReport(df_encoded, explorative=True).to_notebook_iframe()\n",
    "# Profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "5575b4e3-4f7e-4f99-a59c-26dcf11f637e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "59ce8f32-3d66-4e2d-92b4-ab1b16dbed49",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# list(df_encoded.city)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "45060c8e-94d2-4118-844d-c789f64913bc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# unique(df_encoded.city)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "62d72b51-c156-411d-b5a7-b7511febbf2a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# unique(df_encoded.property_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "a5cf3bf2-0829-480f-8390-f6b6542e2a97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# unique(df_encoded.bed_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "33e38b16-53e7-441a-92d1-c4b7987c3b4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# unique(df_encoded.room_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "419e9b7f-08ac-4716-a5c6-3a1a029cd3c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# unique(df_encoded.cancellation_policy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "47def736-ccb5-43af-98d4-4a3cdf6451ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_type               int64\n",
       "room_type                   int64\n",
       "accommodates                int64\n",
       "bathrooms                   int64\n",
       "bed_type                    int64\n",
       "cancellation_policy         int64\n",
       "cleaning_fee                int64\n",
       "city                        int64\n",
       "host_identity_verified      int64\n",
       "host_since                  int64\n",
       "instant_bookable            int64\n",
       "review_scores_rating        int64\n",
       "zipcode                     int64\n",
       "bedrooms                    int64\n",
       "beds                        int64\n",
       "price                     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "7570f9a2-68e5-4279-b3ae-f8ec858fa924",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exporting for use in our Model Notebook\n",
    "df_encoded.to_csv('Final_EDA.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "144f488e-35f3-4615-939c-318f3e671a17",
   "metadata": {},
   "source": [
    "# Scaling our Code with Functions\n",
    "\n",
    "## WIP\n",
    "---\n",
    "\n",
    "Run all code cells prior to this wrangle function, then comment out the code that I pass in to this in order to perserve the history of my EDA before I cleaned it all up with a scalable function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "39c9318f-4e1d-47c8-b24a-99f94e918f9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def wrangle_clean_data():\n",
    "#     \"\"\"\n",
    "#     Wrangling our AirBnB historical data that has been collected over ____ period for ____ features.\n",
    "#     Cleans & feature engineers data into a format that will allow for easy FCFF NN modeling.\n",
    "#     -----\n",
    "#     Links to source of data: https://www.kaggle.com/rudymizrahi/airbnb-listings-in-major-us-cities-deloitte-ml\n",
    "#     Parameters\n",
    "#     -----\n",
    "#     None\n",
    "    \n",
    "#     Returns\n",
    "#     -----\n",
    "#     train: pandas dataframe for fitting our model\n",
    "#            Contains ____\n",
    "#     test: pandas dataframe for testing against our trained/fitted model\n",
    "#            Contains ____\n",
    "#     \"\"\"\n",
    "    "
   ]
  }
 ],
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