new-york-airbnb.ipynb

{
 "cells": [
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "from datetime import datetime\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>last_review</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539</td>\n",
       "      <td>Clean &amp; quiet apt home by the park</td>\n",
       "      <td>2787</td>\n",
       "      <td>John</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>40.64749</td>\n",
       "      <td>-73.97237</td>\n",
       "      <td>Private room</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>0.21</td>\n",
       "      <td>6</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2595</td>\n",
       "      <td>Skylit Midtown Castle</td>\n",
       "      <td>2845</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>40.75362</td>\n",
       "      <td>-73.98377</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3647</td>\n",
       "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
       "      <td>4632</td>\n",
       "      <td>Elisabeth</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.80902</td>\n",
       "      <td>-73.94190</td>\n",
       "      <td>Private room</td>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3831</td>\n",
       "      <td>Cozy Entire Floor of Brownstone</td>\n",
       "      <td>4869</td>\n",
       "      <td>LisaRoxanne</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Clinton Hill</td>\n",
       "      <td>40.68514</td>\n",
       "      <td>-73.95976</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>270</td>\n",
       "      <td>2019-07-05</td>\n",
       "      <td>4.64</td>\n",
       "      <td>1</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5022</td>\n",
       "      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n",
       "      <td>7192</td>\n",
       "      <td>Laura</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Harlem</td>\n",
       "      <td>40.79851</td>\n",
       "      <td>-73.94399</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-11-19</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                                              name  host_id  \\\n",
       "0  2539                Clean & quiet apt home by the park     2787   \n",
       "1  2595                             Skylit Midtown Castle     2845   \n",
       "2  3647               THE VILLAGE OF HARLEM....NEW YORK !     4632   \n",
       "3  3831                   Cozy Entire Floor of Brownstone     4869   \n",
       "4  5022  Entire Apt: Spacious Studio/Loft by central park     7192   \n",
       "\n",
       "     host_name neighbourhood_group neighbourhood  latitude  longitude  \\\n",
       "0         John            Brooklyn    Kensington  40.64749  -73.97237   \n",
       "1     Jennifer           Manhattan       Midtown  40.75362  -73.98377   \n",
       "2    Elisabeth           Manhattan        Harlem  40.80902  -73.94190   \n",
       "3  LisaRoxanne            Brooklyn  Clinton Hill  40.68514  -73.95976   \n",
       "4        Laura           Manhattan   East Harlem  40.79851  -73.94399   \n",
       "\n",
       "         room_type  price  minimum_nights  number_of_reviews last_review  \\\n",
       "0     Private room    149               1                  9  2018-10-19   \n",
       "1  Entire home/apt    225               1                 45  2019-05-21   \n",
       "2     Private room    150               3                  0         NaN   \n",
       "3  Entire home/apt     89               1                270  2019-07-05   \n",
       "4  Entire home/apt     80              10                  9  2018-11-19   \n",
       "\n",
       "   reviews_per_month  calculated_host_listings_count  availability_365  \n",
       "0               0.21                               6               365  \n",
       "1               0.38                               2               355  \n",
       "2                NaN                               1               365  \n",
       "3               4.64                               1               194  \n",
       "4               0.10                               1                 0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/AB_NYC_2019.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                                    0\n",
      "name                                 16\n",
      "host_id                               0\n",
      "host_name                            21\n",
      "neighbourhood_group                   0\n",
      "neighbourhood                         0\n",
      "latitude                              0\n",
      "longitude                             0\n",
      "room_type                             0\n",
      "price                                 0\n",
      "minimum_nights                        0\n",
      "number_of_reviews                     0\n",
      "last_review                       10052\n",
      "reviews_per_month                 10052\n",
      "calculated_host_listings_count        0\n",
      "availability_365                      0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data.price\n",
    "X = data.drop(['price', 'name', 'host_name', 'id'], 1)\n",
    "\n",
    "# Change last_review to number of days since last review instead of the date of the last review\n",
    "date_format = \"%Y-%m-%d\"\n",
    "def convert_last_review(review):\n",
    "    if review == review:\n",
    "        review_date = datetime.strptime(review, date_format)\n",
    "        return (datetime(2019, 8, 12) - review_date).days\n",
    "X['last_review'] = X['last_review'].apply(convert_last_review)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "numerical_cols = [cname for cname in X.columns if X[cname].dtype in ['int64', 'float64']]\n",
    "categorical_cols = [cname for cname in X.columns if X[cname].dtype == \"object\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_transformer = SimpleImputer(strategy=\"mean\")\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
    "])\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numerical_transformer, numerical_cols),\n",
    "        ('cat', categorical_transformer, categorical_cols)\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE of Random Forest Regression model: 63.519\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestRegressor(n_estimators=150, min_samples_split=4, min_samples_leaf=6, random_state=0, n_jobs=-1)\n",
    "my_pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])\n",
    "my_pipeline.fit(X_train, y_train)\n",
    "\n",
    "pred = my_pipeline.predict(X_test)\n",
    "score = mean_absolute_error(y_test, pred)\n",
    "print(f\"MAE of Random Forest Regression model: {round(score, 3)}\")"
   ]
  }
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