main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 97
    },
    "colab_type": "code",
    "id": "uTU8yGiPz5zN",
    "outputId": "754c06b0-87fc-47f0-d615-569b4051d84a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "import csv\n",
    "import warnings\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import statsmodels.api as sm\n",
    "from keras import models, layers, optimizers, regularizers\n",
    "from sklearn.model_selection import train_test_split, ShuffleSplit, GridSearchCV, cross_val_predict\n",
    "from keras import regularizers\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "giyuV4myz5za"
   },
   "source": [
    "### Import datasets\n",
    "- We will import train, test1 and test2 dataset\n",
    "- Merge test1 and train dataset, in order to expend our train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "O3bd5Le9C1yG",
    "outputId": "dcb1f8d6-5f4d-4c1a-8767-87d26ffe2d25",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['addr_unit', 'building_id', 'bedrooms', 'bathrooms', 'size_sqft',\n",
       "       'created_at', 'addr_street', 'addr_city', 'addr_zip', 'addr_lat',\n",
       "       'addr_lon', 'bin', 'bbl', 'floor_count', 'year_built', 'min_to_subway',\n",
       "       'has_doorman', 'has_elevator', 'has_fireplace', 'has_dishwasher',\n",
       "       'is_furnished', 'has_gym', 'allows_pets', 'has_washer_dryer',\n",
       "       'has_garage', 'has_roofdeck', 'has_concierge', 'has_pool', 'has_garden',\n",
       "       'has_childrens_playroom', 'rent', 'no_fee', 'description',\n",
       "       'neighborhood', 'borough', 'unit', 'floornumber', 'line'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df = pd.read_csv('https://grantmlong.com/data/SE_rents2018_train.csv', index_col=0)\n",
    "raw_test_df = pd.read_csv('https://grantmlong.com/data/SE_rents2018_test1.csv', index_col=0)\n",
    "raw_test2_df = pd.read_csv('https://grantmlong.com/data/SE_rents2018_test2.csv', index_col=0)\n",
    "raw_test3_df = pd.read_csv('https://grantmlong.com/data/SE_rents2018_test3.csv', index_col=0)\n",
    "#raw_df = raw_df.append(raw_test_df)\n",
    "raw_df.head(20)\n",
    "raw_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xxSoiIKJC1yO"
   },
   "source": [
    "## Data Explore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 304
    },
    "colab_type": "code",
    "id": "ql26Ay-HC1yI",
    "outputId": "33defc34-c066-4fc0-fd2b-f3d6438b6968"
   },
   "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>building_id</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>size_sqft</th>\n",
       "      <th>addr_zip</th>\n",
       "      <th>addr_lat</th>\n",
       "      <th>addr_lon</th>\n",
       "      <th>bin</th>\n",
       "      <th>bbl</th>\n",
       "      <th>floor_count</th>\n",
       "      <th>...</th>\n",
       "      <th>has_washer_dryer</th>\n",
       "      <th>has_garage</th>\n",
       "      <th>has_roofdeck</th>\n",
       "      <th>has_concierge</th>\n",
       "      <th>has_pool</th>\n",
       "      <th>has_garden</th>\n",
       "      <th>has_childrens_playroom</th>\n",
       "      <th>rent</th>\n",
       "      <th>no_fee</th>\n",
       "      <th>floornumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.200000e+04</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>1.199900e+04</td>\n",
       "      <td>1.200000e+04</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>10723.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.529133e+06</td>\n",
       "      <td>1.657667</td>\n",
       "      <td>1.239500</td>\n",
       "      <td>850.465750</td>\n",
       "      <td>10682.779667</td>\n",
       "      <td>40.727507</td>\n",
       "      <td>-73.956837</td>\n",
       "      <td>2.370093e+06</td>\n",
       "      <td>2.287120e+09</td>\n",
       "      <td>10.266608</td>\n",
       "      <td>...</td>\n",
       "      <td>0.271667</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.264083</td>\n",
       "      <td>0.208583</td>\n",
       "      <td>0.073167</td>\n",
       "      <td>0.132083</td>\n",
       "      <td>0.090500</td>\n",
       "      <td>3605.762917</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>6.182738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.301123e+06</td>\n",
       "      <td>1.079771</td>\n",
       "      <td>0.560208</td>\n",
       "      <td>515.773101</td>\n",
       "      <td>593.370964</td>\n",
       "      <td>0.053241</td>\n",
       "      <td>0.043024</td>\n",
       "      <td>1.242919e+06</td>\n",
       "      <td>1.202105e+09</td>\n",
       "      <td>12.019787</td>\n",
       "      <td>...</td>\n",
       "      <td>0.444837</td>\n",
       "      <td>0.375648</td>\n",
       "      <td>0.440862</td>\n",
       "      <td>0.406313</td>\n",
       "      <td>0.260421</td>\n",
       "      <td>0.338595</td>\n",
       "      <td>0.286909</td>\n",
       "      <td>2897.069446</td>\n",
       "      <td>0.499921</td>\n",
       "      <td>7.671545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.300000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10001.000000</td>\n",
       "      <td>40.573898</td>\n",
       "      <td>-74.141570</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1250.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.209200e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>610.000000</td>\n",
       "      <td>10023.000000</td>\n",
       "      <td>40.693256</td>\n",
       "      <td>-73.986076</td>\n",
       "      <td>1.052139e+06</td>\n",
       "      <td>1.011220e+09</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2250.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.495815e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>11103.000000</td>\n",
       "      <td>40.729028</td>\n",
       "      <td>-73.961700</td>\n",
       "      <td>3.019358e+06</td>\n",
       "      <td>3.006580e+09</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.225132e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>11218.000000</td>\n",
       "      <td>40.764599</td>\n",
       "      <td>-73.936663</td>\n",
       "      <td>3.326098e+06</td>\n",
       "      <td>3.050580e+09</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3900.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.877210e+07</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>11694.000000</td>\n",
       "      <td>40.909842</td>\n",
       "      <td>-73.730550</td>\n",
       "      <td>5.158986e+06</td>\n",
       "      <td>5.010640e+09</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        building_id      bedrooms     bathrooms     size_sqft      addr_zip  \\\n",
       "count  1.200000e+04  12000.000000  12000.000000  12000.000000  12000.000000   \n",
       "mean   1.529133e+06      1.657667      1.239500    850.465750  10682.779667   \n",
       "std    4.301123e+06      1.079771      0.560208    515.773101    593.370964   \n",
       "min    7.300000e+01      0.000000      0.000000      0.000000  10001.000000   \n",
       "25%    6.209200e+04      1.000000      1.000000    610.000000  10023.000000   \n",
       "50%    2.495815e+05      2.000000      1.000000    800.000000  11103.000000   \n",
       "75%    8.225132e+05      2.000000      1.000000   1000.000000  11218.000000   \n",
       "max    1.877210e+07      8.000000     20.000000  10000.000000  11694.000000   \n",
       "\n",
       "           addr_lat      addr_lon           bin           bbl   floor_count  \\\n",
       "count  12000.000000  12000.000000  1.199900e+04  1.200000e+04  12000.000000   \n",
       "mean      40.727507    -73.956837  2.370093e+06  2.287120e+09     10.266608   \n",
       "std        0.053241      0.043024  1.242919e+06  1.202105e+09     12.019787   \n",
       "min       40.573898    -74.141570  1.000000e+06  0.000000e+00      0.000000   \n",
       "25%       40.693256    -73.986076  1.052139e+06  1.011220e+09      3.000000   \n",
       "50%       40.729028    -73.961700  3.019358e+06  3.006580e+09      5.000000   \n",
       "75%       40.764599    -73.936663  3.326098e+06  3.050580e+09     12.000000   \n",
       "max       40.909842    -73.730550  5.158986e+06  5.010640e+09     90.000000   \n",
       "\n",
       "       ...  has_washer_dryer    has_garage  has_roofdeck  has_concierge  \\\n",
       "count  ...      12000.000000  12000.000000  12000.000000   12000.000000   \n",
       "mean   ...          0.271667      0.170000      0.264083       0.208583   \n",
       "std    ...          0.444837      0.375648      0.440862       0.406313   \n",
       "min    ...          0.000000      0.000000      0.000000       0.000000   \n",
       "25%    ...          0.000000      0.000000      0.000000       0.000000   \n",
       "50%    ...          0.000000      0.000000      0.000000       0.000000   \n",
       "75%    ...          1.000000      0.000000      1.000000       0.000000   \n",
       "max    ...          1.000000      1.000000      1.000000       1.000000   \n",
       "\n",
       "           has_pool    has_garden  has_childrens_playroom          rent  \\\n",
       "count  12000.000000  12000.000000            12000.000000  12000.000000   \n",
       "mean       0.073167      0.132083                0.090500   3605.762917   \n",
       "std        0.260421      0.338595                0.286909   2897.069446   \n",
       "min        0.000000      0.000000                0.000000   1250.000000   \n",
       "25%        0.000000      0.000000                0.000000   2250.000000   \n",
       "50%        0.000000      0.000000                0.000000   2900.000000   \n",
       "75%        0.000000      0.000000                0.000000   3900.000000   \n",
       "max        1.000000      1.000000                1.000000  50000.000000   \n",
       "\n",
       "             no_fee   floornumber  \n",
       "count  12000.000000  10723.000000  \n",
       "mean       0.490000      6.182738  \n",
       "std        0.499921      7.671545  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.000000      2.000000  \n",
       "50%        0.000000      3.000000  \n",
       "75%        1.000000      6.000000  \n",
       "max        1.000000     78.000000  \n",
       "\n",
       "[8 rows x 29 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "csEm7f26C1yS",
    "outputId": "d3a30c17-b4c6-45a3-c642-39b7f6069656"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12000, 38)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "bQBnI2n0FuHJ",
    "outputId": "9a62644e-70ac-4531-c6f8-96813f039d8c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ab99e73390>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw_df['rent'].hist(bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cN1cvd5g0lcL"
   },
   "source": [
    "### Seperate all features into continuous, categorical and binary features.\n",
    "\n",
    "For those none relatived features as below, we have excluded them from the features grouping: \n",
    "- addr_unit: no relationship\n",
    "- building_id: no relationship\n",
    "- addr_lat: hard to analyze latitude\n",
    "- addr_lon: hard to analyze longtitude\n",
    "- bin: need to wait for external dataset\n",
    "- bbl: need to wait for external dataset\n",
    "- description: hard to build a NLP model\n",
    "- unit: no relationship\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ce4l5HZNModF"
   },
   "outputs": [],
   "source": [
    "continuous_features =['bathrooms','bedrooms','size_sqft','floor_count','year_built','min_to_subway','floornumber' ]\n",
    "caterigal_features =['addr_street','addr_city','addr_zip','neighborhood','borough','line' ]\n",
    "binary_features = ['has_doorman', 'has_elevator', 'has_fireplace', 'has_dishwasher','is_furnished', 'has_gym', 'allows_pets', \n",
    "                   'has_washer_dryer','has_garage', 'has_roofdeck', 'has_concierge', 'has_pool', 'has_garden',\n",
    "                   'has_childrens_playroom', 'no_fee', ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DiomtTh5NqlA"
   },
   "source": [
    "## Import external dataset from Internal Revenue Service\n",
    " - We will import the 2017 individual income Tax statistic dataset from IRS website(https://www.irs.gov/pub/irs-soi/17zpallagi.csv).\n",
    " - We will expend a new feature: **average_income** based on zipcode to our raw dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "ySvUva5SC1yz",
    "outputId": "b39e242d-b80b-445c-e6ce-24592f666665"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['STATE', 'zipcode', 'agi_stub', 'N1', 'mars1', 'MARS2', 'MARS4', 'ELF',\n",
       "       'CPREP', 'PREP',\n",
       "       ...\n",
       "       'N85300', 'A85300', 'N11901', 'A11901', 'N11900', 'A11900', 'N11902',\n",
       "       'A11902', 'N12000', 'A12000'],\n",
       "      dtype='object', length=152)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_income_data=pd.read_csv('https://www.irs.gov/pub/irs-soi/17zpallagi.csv', index_col=0)\n",
    "raw_income_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4OwGK7NMC1y4",
    "outputId": "f3d9ab34-8545-461e-abad-372abd7b373a"
   },
   "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>zipcode</th>\n",
       "      <th>agi_stub</th>\n",
       "      <th>N1</th>\n",
       "      <th>mars1</th>\n",
       "      <th>MARS2</th>\n",
       "      <th>MARS4</th>\n",
       "      <th>ELF</th>\n",
       "      <th>CPREP</th>\n",
       "      <th>PREP</th>\n",
       "      <th>DIR_DEP</th>\n",
       "      <th>...</th>\n",
       "      <th>N85300</th>\n",
       "      <th>A85300</th>\n",
       "      <th>N11901</th>\n",
       "      <th>A11901</th>\n",
       "      <th>N11900</th>\n",
       "      <th>A11900</th>\n",
       "      <th>N11902</th>\n",
       "      <th>A11902</th>\n",
       "      <th>N12000</th>\n",
       "      <th>A12000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>166537.000000</td>\n",
       "      <td>166537.000000</td>\n",
       "      <td>1.665370e+05</td>\n",
       "      <td>1.665370e+05</td>\n",
       "      <td>1.665370e+05</td>\n",
       "      <td>166537.000000</td>\n",
       "      <td>1.665370e+05</td>\n",
       "      <td>166537.000000</td>\n",
       "      <td>1.665370e+05</td>\n",
       "      <td>1.665370e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>166537.000000</td>\n",
       "      <td>1.665370e+05</td>\n",
       "      <td>166537.000000</td>\n",
       "      <td>1.665370e+05</td>\n",
       "      <td>1.665370e+05</td>\n",
       "      <td>1.665370e+05</td>\n",
       "      <td>1.665370e+05</td>\n",
       "      <td>1.665370e+05</td>\n",
       "      <td>166537.000000</td>\n",
       "      <td>1.665370e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>48870.221668</td>\n",
       "      <td>3.499883</td>\n",
       "      <td>1.797506e+03</td>\n",
       "      <td>8.568076e+02</td>\n",
       "      <td>6.466075e+02</td>\n",
       "      <td>257.395354</td>\n",
       "      <td>1.595375e+03</td>\n",
       "      <td>109.165711</td>\n",
       "      <td>9.565737e+02</td>\n",
       "      <td>1.115313e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>52.247849</td>\n",
       "      <td>2.652260e+02</td>\n",
       "      <td>366.565508</td>\n",
       "      <td>2.075837e+03</td>\n",
       "      <td>1.385102e+03</td>\n",
       "      <td>4.864975e+03</td>\n",
       "      <td>1.349381e+03</td>\n",
       "      <td>3.932834e+03</td>\n",
       "      <td>43.103875</td>\n",
       "      <td>8.790301e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>27140.457939</td>\n",
       "      <td>1.707903</td>\n",
       "      <td>3.682065e+04</td>\n",
       "      <td>2.157115e+04</td>\n",
       "      <td>1.180901e+04</td>\n",
       "      <td>6579.521856</td>\n",
       "      <td>3.234179e+04</td>\n",
       "      <td>2546.536828</td>\n",
       "      <td>1.996698e+04</td>\n",
       "      <td>2.347348e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>2663.659358</td>\n",
       "      <td>1.535593e+04</td>\n",
       "      <td>6891.242958</td>\n",
       "      <td>6.608186e+04</td>\n",
       "      <td>2.947749e+04</td>\n",
       "      <td>1.012458e+05</td>\n",
       "      <td>2.909491e+04</td>\n",
       "      <td>7.693938e+04</td>\n",
       "      <td>947.324343</td>\n",
       "      <td>4.399491e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27030.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>1.580000e+02</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>1.410000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>48876.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.500000e+02</td>\n",
       "      <td>7.000000e+01</td>\n",
       "      <td>1.100000e+02</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.200000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500000e+02</td>\n",
       "      <td>1.400000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.550000e+02</td>\n",
       "      <td>1.800000e+02</td>\n",
       "      <td>6.220000e+02</td>\n",
       "      <td>1.700000e+02</td>\n",
       "      <td>5.480000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70601.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.020000e+03</td>\n",
       "      <td>3.400000e+02</td>\n",
       "      <td>3.800000e+02</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>9.100000e+02</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>5.600000e+02</td>\n",
       "      <td>5.900000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>6.870000e+02</td>\n",
       "      <td>7.400000e+02</td>\n",
       "      <td>2.484000e+03</td>\n",
       "      <td>7.100000e+02</td>\n",
       "      <td>2.207000e+03</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>6.400000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99999.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.824360e+06</td>\n",
       "      <td>4.130490e+06</td>\n",
       "      <td>1.757700e+06</td>\n",
       "      <td>982390.000000</td>\n",
       "      <td>4.980210e+06</td>\n",
       "      <td>475240.000000</td>\n",
       "      <td>3.387570e+06</td>\n",
       "      <td>3.233490e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>763140.000000</td>\n",
       "      <td>4.031847e+06</td>\n",
       "      <td>890950.000000</td>\n",
       "      <td>1.750126e+07</td>\n",
       "      <td>4.598480e+06</td>\n",
       "      <td>1.667374e+07</td>\n",
       "      <td>4.561540e+06</td>\n",
       "      <td>9.443931e+06</td>\n",
       "      <td>186110.000000</td>\n",
       "      <td>1.056186e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 151 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             zipcode       agi_stub            N1         mars1         MARS2  \\\n",
       "count  166537.000000  166537.000000  1.665370e+05  1.665370e+05  1.665370e+05   \n",
       "mean    48870.221668       3.499883  1.797506e+03  8.568076e+02  6.466075e+02   \n",
       "std     27140.457939       1.707903  3.682065e+04  2.157115e+04  1.180901e+04   \n",
       "min         0.000000       1.000000  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%     27030.000000       2.000000  7.000000e+01  0.000000e+00  4.000000e+01   \n",
       "50%     48876.000000       3.000000  2.500000e+02  7.000000e+01  1.100000e+02   \n",
       "75%     70601.000000       5.000000  1.020000e+03  3.400000e+02  3.800000e+02   \n",
       "max     99999.000000       6.000000  5.824360e+06  4.130490e+06  1.757700e+06   \n",
       "\n",
       "               MARS4           ELF          CPREP          PREP       DIR_DEP  \\\n",
       "count  166537.000000  1.665370e+05  166537.000000  1.665370e+05  1.665370e+05   \n",
       "mean      257.395354  1.595375e+03     109.165711  9.565737e+02  1.115313e+03   \n",
       "std      6579.521856  3.234179e+04    2546.536828  1.996698e+04  2.347348e+04   \n",
       "min         0.000000  0.000000e+00       0.000000  0.000000e+00  0.000000e+00   \n",
       "25%         0.000000  6.000000e+01       0.000000  4.000000e+01  4.000000e+01   \n",
       "50%        20.000000  2.200000e+02       0.000000  1.500000e+02  1.400000e+02   \n",
       "75%        90.000000  9.100000e+02      60.000000  5.600000e+02  5.900000e+02   \n",
       "max    982390.000000  4.980210e+06  475240.000000  3.387570e+06  3.233490e+06   \n",
       "\n",
       "       ...         N85300        A85300         N11901        A11901  \\\n",
       "count  ...  166537.000000  1.665370e+05  166537.000000  1.665370e+05   \n",
       "mean   ...      52.247849  2.652260e+02     366.565508  2.075837e+03   \n",
       "std    ...    2663.659358  1.535593e+04    6891.242958  6.608186e+04   \n",
       "min    ...       0.000000  0.000000e+00       0.000000  0.000000e+00   \n",
       "25%    ...       0.000000  0.000000e+00      20.000000  2.100000e+01   \n",
       "50%    ...       0.000000  0.000000e+00      60.000000  1.550000e+02   \n",
       "75%    ...       0.000000  0.000000e+00     220.000000  6.870000e+02   \n",
       "max    ...  763140.000000  4.031847e+06  890950.000000  1.750126e+07   \n",
       "\n",
       "             N11900        A11900        N11902        A11902         N12000  \\\n",
       "count  1.665370e+05  1.665370e+05  1.665370e+05  1.665370e+05  166537.000000   \n",
       "mean   1.385102e+03  4.864975e+03  1.349381e+03  3.932834e+03      43.103875   \n",
       "std    2.947749e+04  1.012458e+05  2.909491e+04  7.693938e+04     947.324343   \n",
       "min    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00       0.000000   \n",
       "25%    5.000000e+01  1.580000e+02  4.000000e+01  1.410000e+02       0.000000   \n",
       "50%    1.800000e+02  6.220000e+02  1.700000e+02  5.480000e+02       0.000000   \n",
       "75%    7.400000e+02  2.484000e+03  7.100000e+02  2.207000e+03      30.000000   \n",
       "max    4.598480e+06  1.667374e+07  4.561540e+06  9.443931e+06  186110.000000   \n",
       "\n",
       "             A12000  \n",
       "count  1.665370e+05  \n",
       "mean   8.790301e+02  \n",
       "std    4.399491e+04  \n",
       "min    0.000000e+00  \n",
       "25%    0.000000e+00  \n",
       "50%    0.000000e+00  \n",
       "75%    6.400000e+01  \n",
       "max    1.056186e+07  \n",
       "\n",
       "[8 rows x 151 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_income_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dsIRl_HPz50L"
   },
   "source": [
    "### Income Dataset Description \n",
    "This dataset comes from the IRS website's 2017 ZIP Code Data (SOI) about Individual Income Tax Statistics.\n",
    "According to the documentation's overview,the Statistics of Income (SOI) Division’s ZIP code data is tabulated using individual income tax returns (Forms 1040) filed with the Internal Revenue Service (IRS) during the 12-month period, January 1, 2018 to December 31, 2018.\n",
    "The original dataset contains many income and Tax Items, we only keep the ones that are relevant: \n",
    "- STATEFIPS:The State Federal Information Processing System (FIPS) code\n",
    "- STATE: The State associated with the ZIP code\n",
    "- ZIPCODE: 5-digit Zip code\n",
    "- agi_stub: Size of adjusted gross income\n",
    "- N1: Total number of returns\n",
    "- A02650: Number of returns with total income\n",
    "\n",
    "Our goal is to find the average income of each zipcode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "npFJUlx_z50M"
   },
   "source": [
    "### Clean the raw income data and rename feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-V89w0rGz50N"
   },
   "outputs": [],
   "source": [
    "raw_income_data = raw_income_data.loc[raw_income_data['STATE']=='NY']\n",
    "raw_income_data.rename(columns = {'N1':'total_returns', 'A02650':'total_income'}, inplace = True) \n",
    "raw_income_data = raw_income_data[['STATE','zipcode','agi_stub','total_returns', 'total_income']]\n",
    "raw_income_data = raw_income_data.loc[raw_income_data['zipcode']<99999]\n",
    "raw_income_data = raw_income_data.loc[raw_income_data['zipcode']>0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-VGLOblZz50P"
   },
   "source": [
    "### Function to calculate the average income by zip code\n",
    "Each zip code has 6 different sizes of adjusted gross income which means we have 6 different number of total returns and total income for one zip code.\n",
    "By using the np.where and sum function, we can obtain the sum of income and sum of returns for each zip code. The income of the original dataset was in thousands of dollar so we need to multiply the sum of income by 1000 and then find the average. Since some zip code was not in the original set, we ingore those average that is NaN and only write the meaningful averages to csv file for future use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fYwv11cxz50Q"
   },
   "source": [
    "- Calculate average income \n",
    "- Export to ny_income_2017.csv for storage\n",
    "- For next time, no need to import the raw_income_dataset again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "frLORUzFz50R",
    "outputId": "13b8fb12-929f-4798-acf7-124cfde60616"
   },
   "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>addr_zip</th>\n",
       "      <th>zip_average_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001.0</td>\n",
       "      <td>183859.908735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002.0</td>\n",
       "      <td>59669.401750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003.0</td>\n",
       "      <td>306277.962003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004.0</td>\n",
       "      <td>385265.306122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10005.0</td>\n",
       "      <td>391201.520270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   addr_zip  zip_average_income\n",
       "0   10001.0       183859.908735\n",
       "1   10002.0        59669.401750\n",
       "2   10003.0       306277.962003\n",
       "3   10004.0       385265.306122\n",
       "4   10005.0       391201.520270"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_income = pd.DataFrame({'addr_zip':[],'zip_average_income':[]})\n",
    "\n",
    "def calculate_avg_income():\n",
    "    global average_income\n",
    "    for zipcode in range(10001, 14906):\n",
    "        current_sum=np.where(raw_income_data['zipcode']==zipcode, raw_income_data['total_income'],0).sum()\n",
    "        current_returns=np.where(raw_income_data['zipcode']==zipcode, raw_income_data['total_returns'],0).sum() \n",
    "        if(current_returns <=0 or current_sum<=0):\n",
    "            continue\n",
    "        avg_income=(current_sum*1000)/current_returns\n",
    "        new_row={'addr_zip':zipcode,'zip_average_income':avg_income}\n",
    "        average_income=average_income.append(new_row,ignore_index=True)           \n",
    "calculate_avg_income()\n",
    "average_income.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0IzznDIuz50U"
   },
   "source": [
    " - We realize that the income dataset is missing all income data between zipcode 11239 - 11354, we will take an averge of zipcode income for 11239 and 11354 to replace any zipcode income in between \n",
    " - In our training and testing dataset, only the zipcode income 11249 is missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gTUSEJ49z50U",
    "outputId": "0e334a05-9c82-48c3-ab4d-77744e611715"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11249]\n",
      "[11249]\n"
     ]
    }
   ],
   "source": [
    "print(list(set(raw_df['addr_zip']) - set(average_income['addr_zip'])))\n",
    "print(list(set(raw_test_df['addr_zip']) - set(average_income['addr_zip'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XnbmWJYxz50X"
   },
   "source": [
    "**Insert a new row for zipcode income 11249 into the average_income dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F_bdJvGyz50Y"
   },
   "outputs": [],
   "source": [
    "avg_income = (average_income.loc[(average_income['addr_zip']==11239)].iloc[0]['zip_average_income'] +\n",
    "             average_income.loc[(average_income['addr_zip']==11354)].iloc[0]['zip_average_income'])/2\n",
    "new_row = {'addr_zip':11249,'zip_average_income':avg_income}\n",
    "average_income=average_income.append(new_row,ignore_index=True)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UtLCiyTwz50a"
   },
   "source": [
    "### Merge the raw train  dataset with the income dataset by addr_zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Mkmeq1Q3z50b"
   },
   "outputs": [],
   "source": [
    "raw_test2_df=raw_test2_df.reset_index().merge(average_income, how=\"left\",on='addr_zip').set_index('rental_id')\n",
    "raw_df=raw_df.reset_index().merge(average_income, how=\"left\",on='addr_zip').set_index('rental_id')\n",
    "raw_test_df=raw_test_df.reset_index().merge(average_income, how=\"left\",on='addr_zip').set_index('rental_id')\n",
    "raw_test3_df=raw_test3_df.reset_index().merge(average_income, how=\"left\",on='addr_zip').set_index('rental_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GO3qzUewz50d"
   },
   "source": [
    "### Find zip_average_income and rent cooleration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gasWc5OYz50e"
   },
   "outputs": [],
   "source": [
    "continuous_features.append('zip_average_income')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-kqkBEJGz50h",
    "outputId": "59ca8084-dce3-452d-f506-2926ae2e526c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "zip_average_income    0.398523\n",
       "Name: rent, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continuous_df = raw_df[['zip_average_income','rent']]\n",
    "continuous_df.corr()['rent'][:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Vwee4_SPz50l"
   },
   "source": [
    "**The correlation between zip_average_income and rent is 0.393228, it is good enough to consider as a important feature that might impact the rent**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nP0OeeTjMHgJ"
   },
   "source": [
    "## Import external Unemployment dataset from Zipatlas\n",
    " - dataset source (http://zipatlas.com/us/ny/zip-code-comparison/unemployment-rate.htm)\n",
    " - The site only contains the data table, so the data were manually copied to csv file\n",
    " - People tend to stay in the areas that has more working oppurnites,which means the renting market is busy, therefore the areas with high underemployemnt rate are more likely to have lower rent, however, since we are working with zipcode, there are zipcodes that have only a few people that shift the result, so our team will need to evalue how will the overall model change.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "LoHNhTQWMHgM",
    "outputId": "dbe6ece8-5e01-4444-e847-4007f87fd913"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Zip Code', 'Location', 'City', 'Population', '% Unemployment Rate',\n",
       "       'National Rank'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment_df = pd.read_csv('./data/Unemployment_Rate_NY_by_zipcode.csv')\n",
    "unemployment_df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "colab_type": "code",
    "id": "xqB5gmnuMHgQ",
    "outputId": "a3f51809-1d3a-4711-d9d9-55c8987e92ed"
   },
   "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>addr_zip</th>\n",
       "      <th>zip_unemployment_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14614</td>\n",
       "      <td>75.47%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12970</td>\n",
       "      <td>67.58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11430</td>\n",
       "      <td>57.03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12742</td>\n",
       "      <td>48.33%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14068</td>\n",
       "      <td>38.20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12733</td>\n",
       "      <td>37.53%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12786</td>\n",
       "      <td>33.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14478</td>\n",
       "      <td>28.29%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10474</td>\n",
       "      <td>27.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13202</td>\n",
       "      <td>26.84%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   addr_zip zip_unemployment_rate\n",
       "0     14614                75.47%\n",
       "1     12970                67.58%\n",
       "2     11430                57.03%\n",
       "3     12742                48.33%\n",
       "4     14068                38.20%\n",
       "5     12733                37.53%\n",
       "6     12786                33.63%\n",
       "7     14478                28.29%\n",
       "8     10474                27.37%\n",
       "9     13202                26.84%"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We only care about zipcode and % Unemployment Rate\n",
    "unemployment_df = unemployment_df[['Zip Code','% Unemployment Rate']]\n",
    "unemployment_df.rename(columns={'Zip Code':'addr_zip','% Unemployment Rate':\"zip_unemployment_rate\"}, inplace=True)\n",
    "unemployment_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "plx7ZDYKMHgS"
   },
   "outputs": [],
   "source": [
    "# Convert the zip_unemployment_rate to float \n",
    "unemployment_df['zip_unemployment_rate'] = unemployment_df['zip_unemployment_rate'].str.replace(r'.%$', '')\n",
    "unemployment_df['zip_unemployment_rate'] = unemployment_df['zip_unemployment_rate'].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "X7hL9qx_MHgU"
   },
   "source": [
    "- We will merge the raw train dataset with the unemployment_df dataset by addr_zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WHV-4KRlMHgU"
   },
   "outputs": [],
   "source": [
    "raw_df=raw_df.reset_index().merge(unemployment_df, how=\"left\",on='addr_zip').set_index('rental_id')\n",
    "raw_test_df=raw_test_df.reset_index().merge(unemployment_df, how=\"left\",on='addr_zip').set_index('rental_id')\n",
    "raw_test2_df=raw_test2_df.reset_index().merge(unemployment_df, how=\"left\",on='addr_zip').set_index('rental_id')\n",
    "raw_test3_df=raw_test3_df.reset_index().merge(unemployment_df, how=\"left\",on='addr_zip').set_index('rental_id')\n",
    "# raw_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IDtx6MMoMHgX",
    "outputId": "ad1c76a0-7fe3-41d2-c864-f44b0c7cb222"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing data for the newly added zip_unemployment_rate column is: 338\n"
     ]
    }
   ],
   "source": [
    "print(\"Missing data for the newly added zip_unemployment_rate column is:\",raw_df['zip_unemployment_rate'].isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PBhokT66MHgZ"
   },
   "source": [
    "We will try to replace the missing data with the mean of the unemployment rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "HgdMJKvaMHga",
    "outputId": "5fe176fb-90e7-4bc8-ed2e-3ca76b110a5a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df['zip_unemployment_rate'].fillna(raw_df['zip_unemployment_rate'].mean(), inplace=True)\n",
    "raw_test_df['zip_unemployment_rate'].fillna(raw_test_df['zip_unemployment_rate'].mean(), inplace=True)\n",
    "raw_test2_df['zip_unemployment_rate'].fillna(raw_test2_df['zip_unemployment_rate'].mean(), inplace=True)\n",
    "raw_test3_df['zip_unemployment_rate'].fillna(raw_test3_df['zip_unemployment_rate'].mean(), inplace=True)\n",
    "raw_df['zip_unemployment_rate'].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bLAmOhJ8MHgc"
   },
   "source": [
    "### Find zip_unemployment_rate and rent cooleration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DzlXjv3BMHgc"
   },
   "outputs": [],
   "source": [
    "continuous_features.append('zip_unemployment_rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "24IRNXy-MHge",
    "outputId": "f120ab04-fb0c-4370-bd2c-f89bb3db760f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "zip_unemployment_rate   -0.165366\n",
       "Name: rent, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continuous_df = raw_df[['zip_unemployment_rate','rent']]\n",
    "continuous_df.corr()['rent'][:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1NK0P27BMHgg"
   },
   "source": [
    "- The correlation between zip_unemployment_rate and rent is  -0.167267.\n",
    "- It clearly shows that the unemployment rate and rent has negative correlation, as we have stated, area with higher unemployment rate tend to have a lower rent, but the relationship is not that significant comparing to the average income"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FMxkLsSRG95a"
   },
   "source": [
    "## Import external Population dataset from NYC OpenData\n",
    " - dataset source (https://data.cityofnewyork.us/City-Government/New-York-City-Population-By-Neighborhood-Tabulatio/swpk-hqdp).\n",
    " - This population dataset from NYC OpenData is based on neighbourhood, we can append neighbourhood_population to eash row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MzkL5NE0G95b"
   },
   "outputs": [],
   "source": [
    "# import dataset\n",
    "poulation_df = pd.read_csv('./data/NYC_Population.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Xov6NdDBG95f",
    "outputId": "f0e0b76b-7306-4114-d102-5a8fe880e8f6"
   },
   "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>neighborhood</th>\n",
       "      <th>neighborhood_population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Borough</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>Claremont-Bathgate</td>\n",
       "      <td>31078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>Eastchester-Edenwald-Baychester</td>\n",
       "      <td>34517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>Bedford Park-Fordham North</td>\n",
       "      <td>54415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>Belmont</td>\n",
       "      <td>27378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>Bronxdale</td>\n",
       "      <td>35538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>West Farms-Bronx River</td>\n",
       "      <td>35011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>Soundview-Castle Hill-Clason Point-Harding Park</td>\n",
       "      <td>53686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>Pelham Bay-Country Club-City Island</td>\n",
       "      <td>26583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>Co-Op City</td>\n",
       "      <td>43752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>East Concourse-Concourse Village</td>\n",
       "      <td>62284</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            neighborhood  \\\n",
       "Borough                                                    \n",
       "Bronx                                 Claremont-Bathgate   \n",
       "Bronx                    Eastchester-Edenwald-Baychester   \n",
       "Bronx                         Bedford Park-Fordham North   \n",
       "Bronx                                            Belmont   \n",
       "Bronx                                          Bronxdale   \n",
       "Bronx                             West Farms-Bronx River   \n",
       "Bronx    Soundview-Castle Hill-Clason Point-Harding Park   \n",
       "Bronx                Pelham Bay-Country Club-City Island   \n",
       "Bronx                                         Co-Op City   \n",
       "Bronx                   East Concourse-Concourse Village   \n",
       "\n",
       "         neighborhood_population  \n",
       "Borough                           \n",
       "Bronx                      31078  \n",
       "Bronx                      34517  \n",
       "Bronx                      54415  \n",
       "Bronx                      27378  \n",
       "Bronx                      35538  \n",
       "Bronx                      35011  \n",
       "Bronx                      53686  \n",
       "Bronx                      26583  \n",
       "Bronx                      43752  \n",
       "Bronx                      62284  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataset contain 2010 and 2000 datas, we only take 2010 data\n",
    "poulation_df = poulation_df[poulation_df['Year']==2010]\n",
    "poulation_df = poulation_df[['NTA Name','Population']]\n",
    "poulation_df.rename(columns={'NTA Name':'neighborhood','Population':\"neighborhood_population\"}, inplace=True)\n",
    "poulation_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_zxXC7aRG95h"
   },
   "outputs": [],
   "source": [
    "# join the train datasets with population dataset\n",
    "raw_df=raw_df.reset_index().merge(poulation_df, how=\"left\",on='neighborhood').set_index('rental_id')\n",
    "raw_test_df=raw_test_df.reset_index().merge(poulation_df, how=\"left\",on='neighborhood').set_index('rental_id')\n",
    "raw_test2_df=raw_test2_df.reset_index().merge(poulation_df, how=\"left\",on='neighborhood').set_index('rental_id')\n",
    "raw_test3_df=raw_test3_df.reset_index().merge(poulation_df, how=\"left\",on='neighborhood').set_index('rental_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "E9P4E7UCG95k"
   },
   "source": [
    "- We join two datasets by using 'neighborhood', but some of the 'neighborhood' names are not exact the same\n",
    "- For those neighborhood name can't find in the population_df, we try to look for substring match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wYvnDL8VG95k"
   },
   "outputs": [],
   "source": [
    "\n",
    "def joinPopulation(df):\n",
    "    for neighborhood in pd.unique(df.neighborhood.values):\n",
    "        if neighborhood != neighborhood:\n",
    "            continue\n",
    "        population = df.loc[df['neighborhood'] == neighborhood,'neighborhood_population'].mean()\n",
    "        if population == population:\n",
    "            continue\n",
    "        match_rows = poulation_df['neighborhood'].str.match(neighborhood)\n",
    "        mean_population =poulation_df[match_rows]['neighborhood_population'].mean()\n",
    "        df.loc[df['neighborhood'] == neighborhood, 'neighborhood_population'] = mean_population\n",
    "joinPopulation(raw_df)\n",
    "joinPopulation(raw_test2_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BV4jp7yPG95o"
   },
   "outputs": [],
   "source": [
    "raw_df['neighborhood_population'].fillna(raw_df['neighborhood_population'].mean(), inplace=True)\n",
    "raw_test_df['neighborhood_population'].fillna(raw_test_df['neighborhood_population'].mean(), inplace=True)\n",
    "raw_test2_df['neighborhood_population'].fillna(raw_test2_df['neighborhood_population'].mean(), inplace=True)\n",
    "raw_test3_df['neighborhood_population'].fillna(raw_test3_df['neighborhood_population'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cQF3iHHBHgOm"
   },
   "source": [
    "## Import external housing sale price dataset\n",
    " - dataset source (https://www.kaggle.com/new-york-city/nyc-property-sales)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "2RBrhWfVHfCa",
    "outputId": "984041eb-abff-4cb7-edf0-dd6e7c44b028"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ave_sale_price', 'addr_zip'], dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sale_df =  pd.read_csv('./data/sale_price.csv')\n",
    "sale_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9Nqw3-mAItSE"
   },
   "outputs": [],
   "source": [
    "raw_test2_df=raw_test2_df.reset_index().merge(sale_df, how=\"left\",on='addr_zip').set_index('rental_id')\n",
    "raw_df=raw_df.reset_index().merge(sale_df, how=\"left\",on='addr_zip').set_index('rental_id')\n",
    "raw_test_df=raw_test_df.reset_index().merge(sale_df, how=\"left\",on='addr_zip').set_index('rental_id')\n",
    "raw_test3_df=raw_test3_df.reset_index().merge(sale_df, how=\"left\",on='addr_zip').set_index('rental_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NPXRtXY4MHgz"
   },
   "source": [
    "## Import external median estimated rental values dataset from Zillow\n",
    " - dataset source (https://www.zillow.com/research/data/).\n",
    " - this dataset provides a smoothed measure of the median estimated market rate rent across a given region and housing type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "vB3zACi6MHg0",
    "outputId": "889d5cb4-f97e-46fc-e8a8-cca6aec074a4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'RegionName', 'State', 'Metro', 'County', 'City', 'SizeRank',\n",
       "       'Zri', 'MoM', 'QoQ', 'YoY', 'ZriRecordCnt'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rv_df =  pd.read_csv('./data/Zip_Zri_AllHomesPlusMultifamily_Summary.csv', encoding = \"ISO-8859-1\")\n",
    "rv_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LWECqCbjMHg2"
   },
   "source": [
    "As we can see from all the columns, the only columns we will need are \n",
    "- RegionName : which in our case is the zipcode \n",
    "- Zri : which stands for Zillow Rent Index,it is a smoothed measure of the median estimated market rate rent across a given region. \n",
    "\n",
    "Therefore we can drop all other columns and also, we should frist drop all the rows that are not in the state of NY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xblFfZ3oMHg3"
   },
   "outputs": [],
   "source": [
    "rv_df = rv_df[rv_df['State']=='NY']\n",
    "rv_df = rv_df[['RegionName','Zri']]\n",
    "rv_df.rename(columns={'RegionName':'addr_zip','Zri':\"estimated_rental_value\"}, inplace=True)\n",
    "# rv_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pk64ZG4FMHg5"
   },
   "source": [
    "We can check if there is any missing data just in case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u14g8NSPMHg5",
    "outputId": "242fa3fb-8217-4601-f0c9-ef04f061145e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "addr_zip                  0\n",
       "estimated_rental_value    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rv_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dGfan4pWMHg8"
   },
   "outputs": [],
   "source": [
    "raw_df=raw_df.reset_index().merge(rv_df, how=\"left\",on='addr_zip').set_index('rental_id')\n",
    "raw_test_df=raw_test_df.reset_index().merge(rv_df, how=\"left\",on='addr_zip').set_index('rental_id')\n",
    "raw_test2_df=raw_test2_df.reset_index().merge(rv_df, how=\"left\",on='addr_zip').set_index('rental_id')\n",
    "raw_test3_df=raw_test3_df.reset_index().merge(rv_df, how=\"left\",on='addr_zip').set_index('rental_id')\n",
    "# raw_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0NQcXeLLMHg_",
    "outputId": "a53d14f7-982e-4682-da58-dc812fd674c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing data for the newly added estimated_rental_value column is: 526\n"
     ]
    }
   ],
   "source": [
    "print(\"Missing data for the newly added estimated_rental_value column is:\",raw_df['estimated_rental_value'].isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GNeT9tNSMHhC"
   },
   "source": [
    "We will use the mean of the estimate_rental_value to fill the missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C08KT9FSMHhD"
   },
   "outputs": [],
   "source": [
    "raw_df['estimated_rental_value'].fillna(raw_df['estimated_rental_value'].mean(), inplace=True)\n",
    "raw_test_df['estimated_rental_value'].fillna(raw_test_df['estimated_rental_value'].mean(), inplace=True)\n",
    "raw_test2_df['estimated_rental_value'].fillna(raw_test2_df['estimated_rental_value'].mean(), inplace=True)\n",
    "raw_test3_df['estimated_rental_value'].fillna(raw_test3_df['estimated_rental_value'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "09KcC3YeMHhE",
    "outputId": "590ebd48-13fe-4ffd-b749-0dcfe9b23487"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df['estimated_rental_value'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "b5rSxx3dMHhG"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Sq-lexQfz50m"
   },
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "g_YNLB4Lz50n"
   },
   "source": [
    "\n",
    "### Handling missing data\n",
    "In order to handle missing data in this dataset, we frist find and count all the null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IBbbVEkLz50q",
    "outputId": "05f679c4-20ad-4541-faa1-d1747e07fd0e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "addr_unit                    91\n",
       "building_id                   0\n",
       "bedrooms                      0\n",
       "bathrooms                     0\n",
       "size_sqft                     0\n",
       "created_at                    0\n",
       "addr_street                   0\n",
       "addr_city                     0\n",
       "addr_zip                      0\n",
       "addr_lat                      0\n",
       "addr_lon                      0\n",
       "bin                           1\n",
       "bbl                           0\n",
       "floor_count                   0\n",
       "year_built                  403\n",
       "min_to_subway               126\n",
       "has_doorman                   0\n",
       "has_elevator                  0\n",
       "has_fireplace                 0\n",
       "has_dishwasher                0\n",
       "is_furnished                  0\n",
       "has_gym                       0\n",
       "allows_pets                   0\n",
       "has_washer_dryer              0\n",
       "has_garage                    0\n",
       "has_roofdeck                  0\n",
       "has_concierge                 0\n",
       "has_pool                      0\n",
       "has_garden                    0\n",
       "has_childrens_playroom        0\n",
       "rent                          0\n",
       "no_fee                        0\n",
       "description                  15\n",
       "neighborhood                  3\n",
       "borough                       0\n",
       "unit                         95\n",
       "floornumber                1277\n",
       "line                       3524\n",
       "zip_average_income            0\n",
       "zip_unemployment_rate         0\n",
       "neighborhood_population       0\n",
       "ave_sale_price                0\n",
       "estimated_rental_value        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UrIsCH6xz50t"
   },
   "source": [
    "As we can see from the result,there are missing data appearing on: \n",
    "- addr_unit\n",
    "- bin \n",
    "- year_built \n",
    "- min_to_subway \n",
    "- description \n",
    "- neighborhood \n",
    "- unit \n",
    "- floornumber \n",
    "- line \n",
    "\n",
    "Continuous features floornumber, year_built and min_to_subway has impact on the rent, so we will fill with mode or mean. For catagorigious feature neighborhood, we will not use it in building model, because it has too many possible values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "TlnpEmrkz50t",
    "outputId": "efa9b222-62e0-42d9-88b4-1a52d3a7b7a6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original shape of dataset: (12000, 43)\n",
      "shape of dataset after handling missing data, should stay the same: (12000, 43)\n"
     ]
    }
   ],
   "source": [
    "train_df = raw_df\n",
    "test_df = raw_test_df\n",
    "test2_df = raw_test2_df\n",
    "test3_df = raw_test3_df\n",
    "\n",
    "train_df['floornumber'].fillna(train_df['floornumber'].mode()[0], inplace=True)\n",
    "test_df['floornumber'].fillna(test_df['floornumber'].mode()[0], inplace=True)\n",
    "test2_df['floornumber'].fillna(test2_df['floornumber'].mode()[0], inplace=True)\n",
    "test3_df['floornumber'].fillna(test3_df['floornumber'].mode()[0], inplace=True)\n",
    "\n",
    "train_df['min_to_subway'].fillna(train_df['min_to_subway'].mean(), inplace=True)\n",
    "test_df['min_to_subway'].fillna(test_df['min_to_subway'].mode()[0], inplace=True)\n",
    "test2_df['min_to_subway'].fillna(test2_df['min_to_subway'].mean(), inplace=True)\n",
    "test3_df['min_to_subway'].fillna(test3_df['min_to_subway'].mean(), inplace=True)\n",
    "\n",
    "train_df['year_built'].fillna(train_df['year_built'].mean(), inplace=True)\n",
    "test_df['year_built'].fillna(test_df['year_built'].mode()[0], inplace=True)\n",
    "test2_df['year_built'].fillna(test2_df['year_built'].mean(), inplace=True)\n",
    "test3_df['year_built'].fillna(test3_df['year_built'].mean(), inplace=True)\n",
    "\n",
    "print(\"original shape of dataset:\",raw_df.shape)\n",
    "print(\"shape of dataset after handling missing data, should stay the same:\",train_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TeWzSum5z50w"
   },
   "source": [
    "## Remove outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5ZlyWStHz50w",
    "outputId": "8f0581aa-c64c-423e-b272-3be0a0cc3b6f"
   },
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "for feature in continuous_features:\n",
    "    train_df.plot.scatter(feature, 'rent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nSGretTYz50z"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "addr_unit                  8\n",
       "building_id                8\n",
       "bedrooms                   8\n",
       "bathrooms                  8\n",
       "size_sqft                  8\n",
       "created_at                 8\n",
       "addr_street                8\n",
       "addr_city                  8\n",
       "addr_zip                   8\n",
       "addr_lat                   8\n",
       "addr_lon                   8\n",
       "bin                        8\n",
       "bbl                        8\n",
       "floor_count                8\n",
       "year_built                 8\n",
       "min_to_subway              8\n",
       "has_doorman                8\n",
       "has_elevator               8\n",
       "has_fireplace              8\n",
       "has_dishwasher             8\n",
       "is_furnished               8\n",
       "has_gym                    8\n",
       "allows_pets                8\n",
       "has_washer_dryer           8\n",
       "has_garage                 8\n",
       "has_roofdeck               8\n",
       "has_concierge              8\n",
       "has_pool                   8\n",
       "has_garden                 8\n",
       "has_childrens_playroom     8\n",
       "rent                       8\n",
       "no_fee                     8\n",
       "description                8\n",
       "neighborhood               8\n",
       "borough                    8\n",
       "unit                       7\n",
       "floornumber                8\n",
       "line                       3\n",
       "zip_average_income         8\n",
       "zip_unemployment_rate      8\n",
       "neighborhood_population    8\n",
       "ave_sale_price             8\n",
       "estimated_rental_value     8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[train_df['rent']>40000].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oi-VpdMNz501"
   },
   "source": [
    "**drop size_sqrt = 0 for now, since there are 713 rows, might replace with mode when creating models**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "eVa8L2Siz502",
    "outputId": "3231029c-0525-408f-c74e-4a355f8a9422"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rent :  (12000, 43)\n",
      "rent :  (11992, 43)\n",
      "bathrooms :  (11992, 43)\n",
      "bathrooms :  (11987, 43)\n",
      "size_sqft :  (11987, 43)\n",
      "size_sqft :  (11149, 43)\n",
      "year_built :  (11149, 43)\n",
      "year_built :  (11082, 43)\n",
      "min_to_subway :  (11082, 43)\n",
      "min_to_subway :  (10954, 43)\n",
      "floornumber :  (10954, 43)\n",
      "floornumber :  (10940, 43)\n"
     ]
    }
   ],
   "source": [
    "def remove_outliers(train_df, feature, low_value, high_value):\n",
    "    print(feature, ': ', train_df.shape)\n",
    "    train_df = train_df[train_df[feature]>low_value]\n",
    "    train_df = train_df[train_df[feature]<=high_value]\n",
    "    train_df.reset_index(drop=True,inplace=True)\n",
    "    print(feature, ': ', train_df.shape)\n",
    "    return train_df\n",
    "\n",
    "train_df = remove_outliers(train_df, 'rent', 0, 40000)\n",
    "train_df = remove_outliers(train_df, 'bathrooms', 0, 12)\n",
    "train_df = remove_outliers(train_df, 'size_sqft', 0, 10000)\n",
    "train_df = remove_outliers(train_df, 'year_built', 1700, 2019)\n",
    "train_df = remove_outliers(train_df, 'min_to_subway', 0, 60)\n",
    "train_df = remove_outliers(train_df, 'floornumber', 0, 60)\n",
    "\n",
    "train_df['year_built'] = 2018 - train_df['year_built'].astype(int)\n",
    "test_df['year_built'] = 2018 - test_df['year_built'].astype(int)\n",
    "test2_df['year_built'] = 2018 - test2_df['year_built'].astype(int)\n",
    "test3_df['year_built'] = 2018 - test3_df['year_built'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EdMZuLXPz504"
   },
   "source": [
    "### Encode categorical feature and drop useless features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Yk6n-wuhz504"
   },
   "outputs": [],
   "source": [
    "boroughs = np.array(train_df['borough'].unique())\n",
    "\n",
    "for borough in boroughs:\n",
    "    train_df[borough] = train_df['borough'].apply(lambda x : int(x == borough))\n",
    "    test_df[borough] = test_df['borough'].apply(lambda x : int(x == borough))\n",
    "    test2_df[borough] = test2_df['borough'].apply(lambda x : int(x == borough))\n",
    "    test3_df[borough] = test3_df['borough'].apply(lambda x : int(x == borough))\n",
    "\n",
    "features_notNeed = ['addr_unit', 'building_id', 'created_at', 'addr_street', 'addr_city', 'addr_zip', 'bin', 'bbl', 'description', \\\n",
    "                    'neighborhood', 'unit', 'borough', 'line']\n",
    "\n",
    "train_df = train_df.drop(features_notNeed, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "R8wB_RrBz507"
   },
   "source": [
    "### Use pair coorelation for continuous features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0T9tc1Sgz507"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bathrooms                0.640694\n",
       "bedrooms                 0.356034\n",
       "size_sqft                0.687094\n",
       "floor_count              0.260418\n",
       "year_built              -0.108018\n",
       "min_to_subway           -0.085295\n",
       "floornumber              0.246378\n",
       "zip_average_income       0.425890\n",
       "zip_unemployment_rate   -0.172119\n",
       "Name: rent, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continuous_df = train_df[continuous_features+['rent']]\n",
    "continuous_df.corr()['rent'][:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1oR3OgtGz50_"
   },
   "source": [
    "### Check coorelation for binary features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Hpz9vhlAz51A"
   },
   "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>Coorelation</th>\n",
       "      <th>Feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>has_doorman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>has_elevator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>has_fireplace</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>has_dishwasher</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>is_furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>has_gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>allows_pets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>has_washer_dryer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>has_garage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>has_roofdeck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>has_concierge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>has_pool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>has_garden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>has_childrens_playroom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>no_fee</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Coorelation                 Feature\n",
       "0           1.0             has_doorman\n",
       "1           1.0            has_elevator\n",
       "2           1.0           has_fireplace\n",
       "3           1.0          has_dishwasher\n",
       "4           1.0            is_furnished\n",
       "5           1.0                 has_gym\n",
       "6           1.0             allows_pets\n",
       "7           1.0        has_washer_dryer\n",
       "8           1.0              has_garage\n",
       "9           1.0            has_roofdeck\n",
       "10          1.0           has_concierge\n",
       "11          1.0                has_pool\n",
       "12          1.0              has_garden\n",
       "13          1.0  has_childrens_playroom\n",
       "14         -1.0                  no_fee"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[binary_features+['rent']].corr()['rent'][:-1]\n",
    "coor_results= []\n",
    "\n",
    "for feature in binary_features:\n",
    "  df = raw_df.groupby([feature]).aggregate(['mean'])['rent']\n",
    "  df[feature]= df.index\n",
    "  coor_results.append(df.corr().iloc[0][1])\n",
    "coor_df = pd.DataFrame({'Coorelation': coor_results,'Feature':binary_features})\n",
    "coor_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2yKBFlfwz51C"
   },
   "source": [
    "As we can see in the correlation table, all binary features highly affected the rents. When we build the models, we should include all binary features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "d7UQLvU9z51D"
   },
   "source": [
    "# Build Models\n",
    " - We will be using cross validation to evaluate the performances of all our modles,and then deciding which should be the most suitable one, thus we will first create a function called get_cv_results to obtain the cv_performance.\n",
    " - we will try 5 different basic categorical models:`multiple regression, decision tree, random froest, Gradient Boosting Regression and Neural Network`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7YFRHMFYz51G"
   },
   "outputs": [],
   "source": [
    "#md_df = shuffle(train_df).reset_index(drop=True)\n",
    "features = list(train_df.columns)\n",
    "features.remove('rent')\n",
    "k_fold = KFold(n_splits=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AknQGhA5z51I"
   },
   "outputs": [],
   "source": [
    "def get_cv_results(regressor):\n",
    "    \n",
    "    results = []\n",
    "    for train, test in k_fold.split(train_df):\n",
    "        regressor.fit(train_df.loc[train, features], train_df.loc[train, 'rent'])\n",
    "        y_predicted = regressor.predict(train_df.loc[test, features])\n",
    "        accuracy = mean_squared_error(train_df.loc[test, 'rent'], y_predicted)\n",
    "        results.append(accuracy)\n",
    "\n",
    "    return np.mean(results), np.std(results)\n",
    "\n",
    "def get_test_result(regressor):\n",
    "    regressor.fit(train_df[features], train_df['rent'])\n",
    "    y_predicted = regressor.predict(test_df[features])\n",
    "    accuracy = mean_squared_error(test_df['rent'], y_predicted)\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U6Eo0zUHz51K"
   },
   "source": [
    "### Radnom Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9FtBQ3enz51L"
   },
   "outputs": [],
   "source": [
    "rforest = RandomForestRegressor(\n",
    "    random_state=11, \n",
    "    max_depth=10,\n",
    "    n_estimators=200\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mSdqSbSez51N"
   },
   "outputs": [],
   "source": [
    "#get_cv_results(rforest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kjJtv3iQMHhp"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1763913.7963390597"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_test_result(rforest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3-raCdY4G96R"
   },
   "source": [
    "Now let's see how each features affected this model by checking the feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iOGKT8Ncz51Q"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size_sqft   0.5439052194610208\n",
      "zip_average_income   0.1594457441237323\n",
      "bathrooms   0.10752272887127175\n",
      "estimated_rental_value   0.06599815017926904\n",
      "addr_lon   0.013383038877882971\n",
      "bedrooms   0.011737695317676045\n",
      "year_built   0.011627808693078617\n",
      "floornumber   0.010331776867014163\n",
      "min_to_subway   0.009679897529332439\n",
      "ave_sale_price   0.009663151360778185\n",
      "addr_lat   0.009659036179539924\n",
      "floor_count   0.00884564437525413\n",
      "has_gym   0.006391893881072813\n",
      "zip_unemployment_rate   0.004815052357133212\n",
      "has_dishwasher   0.0033434387869756784\n",
      "has_washer_dryer   0.0030426487561268896\n",
      "has_garden   0.0030421782447075645\n",
      "is_furnished   0.0030355850860898934\n",
      "has_roofdeck   0.002179375594743553\n",
      "has_elevator   0.0018782917883513306\n",
      "neighborhood_population   0.0017588123541198708\n",
      "has_concierge   0.001683430972829461\n",
      "no_fee   0.001578351034759349\n",
      "has_doorman   0.001181398182817028\n",
      "has_pool   0.0009271799455887582\n",
      "allows_pets   0.0009182829119736733\n",
      "has_childrens_playroom   0.0008746216925613864\n",
      "has_fireplace   0.0008344401684056826\n",
      "has_garage   0.00042189341149655976\n",
      "Manhattan   0.00012060873200720578\n",
      "Brooklyn   0.00010649730424055116\n",
      "Queens   5.8024713685988764e-05\n",
      "Bronx   8.056079430954705e-06\n",
      "Staten Island   4.616503213468746e-08\n"
     ]
    }
   ],
   "source": [
    "rforest.fit(train_df[features], train_df['rent'])  \n",
    "for feature,score in sorted(zip(features,rforest.feature_importances_), key=lambda x:x[1], reverse=True):\n",
    "    print(feature, ' ', score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DoQlfyCZz51T"
   },
   "source": [
    "### Multiple Regression\n",
    " - Multiple regression is an extension of simple linear regression. It is used when we want to predict the value of a variable based on the value of two or more other variables\n",
    " - We will based on p value to choose significant variables(p<0.1)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zCx2KQlWz51U"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>rent</td>       <th>  R-squared:         </th> <td>   0.721</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.720</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   855.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 07 Dec 2019</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:46:04</td>     <th>  Log-Likelihood:    </th> <td> -95083.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 10940</td>      <th>  AIC:               </th> <td>1.902e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 10906</td>      <th>  BIC:               </th> <td>1.905e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    33</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>                <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bedrooms</th>                <td>  119.6166</td> <td>   18.760</td> <td>    6.376</td> <td> 0.000</td> <td>   82.843</td> <td>  156.390</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>               <td> 1133.7673</td> <td>   36.229</td> <td>   31.295</td> <td> 0.000</td> <td> 1062.753</td> <td> 1204.782</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>size_sqft</th>               <td>    2.8032</td> <td>    0.045</td> <td>   61.770</td> <td> 0.000</td> <td>    2.714</td> <td>    2.892</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_lat</th>                <td> 3140.3324</td> <td>  467.737</td> <td>    6.714</td> <td> 0.000</td> <td> 2223.482</td> <td> 4057.182</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_lon</th>                <td>-4105.6763</td> <td>  584.281</td> <td>   -7.027</td> <td> 0.000</td> <td>-5250.974</td> <td>-2960.379</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floor_count</th>             <td>   -9.3134</td> <td>    2.363</td> <td>   -3.942</td> <td> 0.000</td> <td>  -13.945</td> <td>   -4.682</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>year_built</th>              <td>   -0.3567</td> <td>    0.490</td> <td>   -0.727</td> <td> 0.467</td> <td>   -1.318</td> <td>    0.604</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>min_to_subway</th>           <td>  -20.4178</td> <td>    3.962</td> <td>   -5.153</td> <td> 0.000</td> <td>  -28.185</td> <td>  -12.651</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_doorman</th>             <td>  111.1803</td> <td>   51.691</td> <td>    2.151</td> <td> 0.032</td> <td>    9.856</td> <td>  212.504</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_elevator</th>            <td>  325.5354</td> <td>   42.083</td> <td>    7.735</td> <td> 0.000</td> <td>  243.044</td> <td>  408.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_fireplace</th>           <td>  510.0940</td> <td>   75.165</td> <td>    6.786</td> <td> 0.000</td> <td>  362.757</td> <td>  657.431</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_dishwasher</th>          <td> -178.1552</td> <td>   31.948</td> <td>   -5.576</td> <td> 0.000</td> <td> -240.779</td> <td> -115.531</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>is_furnished</th>            <td>  672.8321</td> <td>   83.062</td> <td>    8.100</td> <td> 0.000</td> <td>  510.015</td> <td>  835.649</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_gym</th>                 <td>  307.0011</td> <td>   52.295</td> <td>    5.871</td> <td> 0.000</td> <td>  204.494</td> <td>  409.508</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>allows_pets</th>             <td>  111.1818</td> <td>   30.963</td> <td>    3.591</td> <td> 0.000</td> <td>   50.488</td> <td>  171.876</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_washer_dryer</th>        <td>  335.7516</td> <td>   36.166</td> <td>    9.284</td> <td> 0.000</td> <td>  264.860</td> <td>  406.643</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_garage</th>              <td>  -89.3494</td> <td>   43.358</td> <td>   -2.061</td> <td> 0.039</td> <td> -174.340</td> <td>   -4.359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_roofdeck</th>            <td>   57.3032</td> <td>   40.052</td> <td>    1.431</td> <td> 0.153</td> <td>  -21.207</td> <td>  135.813</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_concierge</th>           <td>  231.8750</td> <td>   53.649</td> <td>    4.322</td> <td> 0.000</td> <td>  126.714</td> <td>  337.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_pool</th>                <td>   74.9582</td> <td>   63.714</td> <td>    1.176</td> <td> 0.239</td> <td>  -49.933</td> <td>  199.849</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_garden</th>              <td>  -20.0382</td> <td>   43.418</td> <td>   -0.462</td> <td> 0.644</td> <td> -105.145</td> <td>   65.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_childrens_playroom</th>  <td>   39.5591</td> <td>   59.096</td> <td>    0.669</td> <td> 0.503</td> <td>  -76.280</td> <td>  155.398</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>no_fee</th>                  <td> -131.8493</td> <td>   31.729</td> <td>   -4.156</td> <td> 0.000</td> <td> -194.043</td> <td>  -69.655</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floornumber</th>             <td>   21.9317</td> <td>    2.935</td> <td>    7.472</td> <td> 0.000</td> <td>   16.179</td> <td>   27.685</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_average_income</th>      <td>    0.0008</td> <td>    0.000</td> <td>    4.613</td> <td> 0.000</td> <td>    0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_unemployment_rate</th>   <td>  -30.5967</td> <td>    4.344</td> <td>   -7.043</td> <td> 0.000</td> <td>  -39.112</td> <td>  -22.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>neighborhood_population</th> <td>   -0.0030</td> <td>    0.001</td> <td>   -4.749</td> <td> 0.000</td> <td>   -0.004</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ave_sale_price</th>          <td> 1.792e-05</td> <td> 9.87e-06</td> <td>    1.815</td> <td> 0.070</td> <td>-1.43e-06</td> <td> 3.73e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>estimated_rental_value</th>  <td>    0.5049</td> <td>    0.025</td> <td>   20.126</td> <td> 0.000</td> <td>    0.456</td> <td>    0.554</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Brooklyn</th>                <td>-4.337e+05</td> <td> 5.12e+04</td> <td>   -8.470</td> <td> 0.000</td> <td>-5.34e+05</td> <td>-3.33e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Manhattan</th>               <td>-4.336e+05</td> <td> 5.12e+04</td> <td>   -8.464</td> <td> 0.000</td> <td>-5.34e+05</td> <td>-3.33e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Queens</th>                  <td>-4.337e+05</td> <td> 5.12e+04</td> <td>   -8.474</td> <td> 0.000</td> <td>-5.34e+05</td> <td>-3.33e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Bronx</th>                   <td>-4.343e+05</td> <td> 5.12e+04</td> <td>   -8.479</td> <td> 0.000</td> <td>-5.35e+05</td> <td>-3.34e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Staten Island</th>           <td>-4.348e+05</td> <td> 5.13e+04</td> <td>   -8.482</td> <td> 0.000</td> <td>-5.35e+05</td> <td>-3.34e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>8526.869</td> <th>  Durbin-Watson:     </th>  <td>   2.009</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>2469863.163</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 2.741</td>  <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>76.405</td>  <th>  Cond. No.          </th>  <td>2.17e+10</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.17e+10. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   rent   R-squared:                       0.721\n",
       "Model:                            OLS   Adj. R-squared:                  0.720\n",
       "Method:                 Least Squares   F-statistic:                     855.0\n",
       "Date:                Sat, 07 Dec 2019   Prob (F-statistic):               0.00\n",
       "Time:                        00:46:04   Log-Likelihood:                -95083.\n",
       "No. Observations:               10940   AIC:                         1.902e+05\n",
       "Df Residuals:                   10906   BIC:                         1.905e+05\n",
       "Df Model:                          33                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===========================================================================================\n",
       "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------------\n",
       "bedrooms                  119.6166     18.760      6.376      0.000      82.843     156.390\n",
       "bathrooms                1133.7673     36.229     31.295      0.000    1062.753    1204.782\n",
       "size_sqft                   2.8032      0.045     61.770      0.000       2.714       2.892\n",
       "addr_lat                 3140.3324    467.737      6.714      0.000    2223.482    4057.182\n",
       "addr_lon                -4105.6763    584.281     -7.027      0.000   -5250.974   -2960.379\n",
       "floor_count                -9.3134      2.363     -3.942      0.000     -13.945      -4.682\n",
       "year_built                 -0.3567      0.490     -0.727      0.467      -1.318       0.604\n",
       "min_to_subway             -20.4178      3.962     -5.153      0.000     -28.185     -12.651\n",
       "has_doorman               111.1803     51.691      2.151      0.032       9.856     212.504\n",
       "has_elevator              325.5354     42.083      7.735      0.000     243.044     408.026\n",
       "has_fireplace             510.0940     75.165      6.786      0.000     362.757     657.431\n",
       "has_dishwasher           -178.1552     31.948     -5.576      0.000    -240.779    -115.531\n",
       "is_furnished              672.8321     83.062      8.100      0.000     510.015     835.649\n",
       "has_gym                   307.0011     52.295      5.871      0.000     204.494     409.508\n",
       "allows_pets               111.1818     30.963      3.591      0.000      50.488     171.876\n",
       "has_washer_dryer          335.7516     36.166      9.284      0.000     264.860     406.643\n",
       "has_garage                -89.3494     43.358     -2.061      0.039    -174.340      -4.359\n",
       "has_roofdeck               57.3032     40.052      1.431      0.153     -21.207     135.813\n",
       "has_concierge             231.8750     53.649      4.322      0.000     126.714     337.036\n",
       "has_pool                   74.9582     63.714      1.176      0.239     -49.933     199.849\n",
       "has_garden                -20.0382     43.418     -0.462      0.644    -105.145      65.069\n",
       "has_childrens_playroom     39.5591     59.096      0.669      0.503     -76.280     155.398\n",
       "no_fee                   -131.8493     31.729     -4.156      0.000    -194.043     -69.655\n",
       "floornumber                21.9317      2.935      7.472      0.000      16.179      27.685\n",
       "zip_average_income          0.0008      0.000      4.613      0.000       0.000       0.001\n",
       "zip_unemployment_rate     -30.5967      4.344     -7.043      0.000     -39.112     -22.081\n",
       "neighborhood_population    -0.0030      0.001     -4.749      0.000      -0.004      -0.002\n",
       "ave_sale_price           1.792e-05   9.87e-06      1.815      0.070   -1.43e-06    3.73e-05\n",
       "estimated_rental_value      0.5049      0.025     20.126      0.000       0.456       0.554\n",
       "Brooklyn                -4.337e+05   5.12e+04     -8.470      0.000   -5.34e+05   -3.33e+05\n",
       "Manhattan               -4.336e+05   5.12e+04     -8.464      0.000   -5.34e+05   -3.33e+05\n",
       "Queens                  -4.337e+05   5.12e+04     -8.474      0.000   -5.34e+05   -3.33e+05\n",
       "Bronx                   -4.343e+05   5.12e+04     -8.479      0.000   -5.35e+05   -3.34e+05\n",
       "Staten Island           -4.348e+05   5.13e+04     -8.482      0.000   -5.35e+05   -3.34e+05\n",
       "==============================================================================\n",
       "Omnibus:                     8526.869   Durbin-Watson:                   2.009\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          2469863.163\n",
       "Skew:                           2.741   Prob(JB):                         0.00\n",
       "Kurtosis:                      76.405   Cond. No.                     2.17e+10\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.17e+10. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul_reg_features = features[:]\n",
    "x = train_df[features] \n",
    "y = train_df['rent']\n",
    "est = sm.OLS(y, x).fit()\n",
    "est.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "K0JZnMWLz51X"
   },
   "source": [
    "- As we can see, variables has_garden and year_built have P value > 0.1\n",
    "- After we remove these two variables, all P values are < 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qZQRPWFEz51X"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>rent</td>       <th>  R-squared:         </th> <td>   0.721</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.720</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   910.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 07 Dec 2019</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:46:09</td>     <th>  Log-Likelihood:    </th> <td> -95084.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 10940</td>      <th>  AIC:               </th> <td>1.902e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 10908</td>      <th>  BIC:               </th> <td>1.905e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    31</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>                <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bedrooms</th>                <td>  119.3989</td> <td>   18.748</td> <td>    6.369</td> <td> 0.000</td> <td>   82.649</td> <td>  156.148</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>               <td> 1136.0321</td> <td>   36.120</td> <td>   31.452</td> <td> 0.000</td> <td> 1065.231</td> <td> 1206.833</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>size_sqft</th>               <td>    2.7986</td> <td>    0.045</td> <td>   62.184</td> <td> 0.000</td> <td>    2.710</td> <td>    2.887</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_lat</th>                <td> 3132.9238</td> <td>  467.516</td> <td>    6.701</td> <td> 0.000</td> <td> 2216.508</td> <td> 4049.340</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_lon</th>                <td>-4087.3033</td> <td>  583.624</td> <td>   -7.003</td> <td> 0.000</td> <td>-5231.312</td> <td>-2943.295</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floor_count</th>             <td>   -9.0023</td> <td>    2.330</td> <td>   -3.863</td> <td> 0.000</td> <td>  -13.570</td> <td>   -4.435</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>min_to_subway</th>           <td>  -20.3102</td> <td>    3.937</td> <td>   -5.159</td> <td> 0.000</td> <td>  -28.027</td> <td>  -12.593</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_doorman</th>             <td>  112.1323</td> <td>   51.648</td> <td>    2.171</td> <td> 0.030</td> <td>   10.892</td> <td>  213.372</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_elevator</th>            <td>  331.3493</td> <td>   41.424</td> <td>    7.999</td> <td> 0.000</td> <td>  250.151</td> <td>  412.547</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_fireplace</th>           <td>  506.2097</td> <td>   75.021</td> <td>    6.748</td> <td> 0.000</td> <td>  359.155</td> <td>  653.265</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_dishwasher</th>          <td> -177.3283</td> <td>   31.922</td> <td>   -5.555</td> <td> 0.000</td> <td> -239.900</td> <td> -114.756</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>is_furnished</th>            <td>  669.4545</td> <td>   82.958</td> <td>    8.070</td> <td> 0.000</td> <td>  506.841</td> <td>  832.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_gym</th>                 <td>  311.5164</td> <td>   51.890</td> <td>    6.003</td> <td> 0.000</td> <td>  209.802</td> <td>  413.230</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>allows_pets</th>             <td>  110.0881</td> <td>   30.911</td> <td>    3.561</td> <td> 0.000</td> <td>   49.496</td> <td>  170.680</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_washer_dryer</th>        <td>  338.7902</td> <td>   35.873</td> <td>    9.444</td> <td> 0.000</td> <td>  268.473</td> <td>  409.107</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_garage</th>              <td>  -87.1804</td> <td>   43.013</td> <td>   -2.027</td> <td> 0.043</td> <td> -171.494</td> <td>   -2.867</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_roofdeck</th>            <td>   60.4151</td> <td>   39.544</td> <td>    1.528</td> <td> 0.127</td> <td>  -17.098</td> <td>  137.928</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_concierge</th>           <td>  232.6871</td> <td>   53.458</td> <td>    4.353</td> <td> 0.000</td> <td>  127.900</td> <td>  337.474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_pool</th>                <td>   74.7362</td> <td>   63.688</td> <td>    1.173</td> <td> 0.241</td> <td>  -50.104</td> <td>  199.576</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>has_childrens_playroom</th>  <td>   34.4459</td> <td>   58.311</td> <td>    0.591</td> <td> 0.555</td> <td>  -79.855</td> <td>  148.747</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>no_fee</th>                  <td> -132.0909</td> <td>   31.725</td> <td>   -4.164</td> <td> 0.000</td> <td> -194.278</td> <td>  -69.904</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floornumber</th>             <td>   21.9857</td> <td>    2.934</td> <td>    7.494</td> <td> 0.000</td> <td>   16.235</td> <td>   27.737</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_average_income</th>      <td>    0.0008</td> <td>    0.000</td> <td>    4.619</td> <td> 0.000</td> <td>    0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zip_unemployment_rate</th>   <td>  -30.4889</td> <td>    4.341</td> <td>   -7.024</td> <td> 0.000</td> <td>  -38.997</td> <td>  -21.980</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>neighborhood_population</th> <td>   -0.0030</td> <td>    0.001</td> <td>   -4.769</td> <td> 0.000</td> <td>   -0.004</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ave_sale_price</th>          <td> 1.771e-05</td> <td> 9.84e-06</td> <td>    1.801</td> <td> 0.072</td> <td>-1.57e-06</td> <td>  3.7e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>estimated_rental_value</th>  <td>    0.5042</td> <td>    0.025</td> <td>   20.111</td> <td> 0.000</td> <td>    0.455</td> <td>    0.553</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Brooklyn</th>                <td>-4.321e+05</td> <td> 5.12e+04</td> <td>   -8.446</td> <td> 0.000</td> <td>-5.32e+05</td> <td>-3.32e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Manhattan</th>               <td> -4.32e+05</td> <td> 5.12e+04</td> <td>   -8.440</td> <td> 0.000</td> <td>-5.32e+05</td> <td>-3.32e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Queens</th>                  <td>-4.321e+05</td> <td> 5.11e+04</td> <td>   -8.450</td> <td> 0.000</td> <td>-5.32e+05</td> <td>-3.32e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Bronx</th>                   <td>-4.327e+05</td> <td> 5.12e+04</td> <td>   -8.454</td> <td> 0.000</td> <td>-5.33e+05</td> <td>-3.32e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Staten Island</th>           <td>-4.332e+05</td> <td> 5.12e+04</td> <td>   -8.458</td> <td> 0.000</td> <td>-5.34e+05</td> <td>-3.33e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>8531.420</td> <th>  Durbin-Watson:     </th>  <td>   2.009</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>2461289.872</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 2.745</td>  <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>76.276</td>  <th>  Cond. No.          </th>  <td>2.16e+10</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.16e+10. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   rent   R-squared:                       0.721\n",
       "Model:                            OLS   Adj. R-squared:                  0.720\n",
       "Method:                 Least Squares   F-statistic:                     910.2\n",
       "Date:                Sat, 07 Dec 2019   Prob (F-statistic):               0.00\n",
       "Time:                        00:46:09   Log-Likelihood:                -95084.\n",
       "No. Observations:               10940   AIC:                         1.902e+05\n",
       "Df Residuals:                   10908   BIC:                         1.905e+05\n",
       "Df Model:                          31                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===========================================================================================\n",
       "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------------\n",
       "bedrooms                  119.3989     18.748      6.369      0.000      82.649     156.148\n",
       "bathrooms                1136.0321     36.120     31.452      0.000    1065.231    1206.833\n",
       "size_sqft                   2.7986      0.045     62.184      0.000       2.710       2.887\n",
       "addr_lat                 3132.9238    467.516      6.701      0.000    2216.508    4049.340\n",
       "addr_lon                -4087.3033    583.624     -7.003      0.000   -5231.312   -2943.295\n",
       "floor_count                -9.0023      2.330     -3.863      0.000     -13.570      -4.435\n",
       "min_to_subway             -20.3102      3.937     -5.159      0.000     -28.027     -12.593\n",
       "has_doorman               112.1323     51.648      2.171      0.030      10.892     213.372\n",
       "has_elevator              331.3493     41.424      7.999      0.000     250.151     412.547\n",
       "has_fireplace             506.2097     75.021      6.748      0.000     359.155     653.265\n",
       "has_dishwasher           -177.3283     31.922     -5.555      0.000    -239.900    -114.756\n",
       "is_furnished              669.4545     82.958      8.070      0.000     506.841     832.068\n",
       "has_gym                   311.5164     51.890      6.003      0.000     209.802     413.230\n",
       "allows_pets               110.0881     30.911      3.561      0.000      49.496     170.680\n",
       "has_washer_dryer          338.7902     35.873      9.444      0.000     268.473     409.107\n",
       "has_garage                -87.1804     43.013     -2.027      0.043    -171.494      -2.867\n",
       "has_roofdeck               60.4151     39.544      1.528      0.127     -17.098     137.928\n",
       "has_concierge             232.6871     53.458      4.353      0.000     127.900     337.474\n",
       "has_pool                   74.7362     63.688      1.173      0.241     -50.104     199.576\n",
       "has_childrens_playroom     34.4459     58.311      0.591      0.555     -79.855     148.747\n",
       "no_fee                   -132.0909     31.725     -4.164      0.000    -194.278     -69.904\n",
       "floornumber                21.9857      2.934      7.494      0.000      16.235      27.737\n",
       "zip_average_income          0.0008      0.000      4.619      0.000       0.000       0.001\n",
       "zip_unemployment_rate     -30.4889      4.341     -7.024      0.000     -38.997     -21.980\n",
       "neighborhood_population    -0.0030      0.001     -4.769      0.000      -0.004      -0.002\n",
       "ave_sale_price           1.771e-05   9.84e-06      1.801      0.072   -1.57e-06     3.7e-05\n",
       "estimated_rental_value      0.5042      0.025     20.111      0.000       0.455       0.553\n",
       "Brooklyn                -4.321e+05   5.12e+04     -8.446      0.000   -5.32e+05   -3.32e+05\n",
       "Manhattan                -4.32e+05   5.12e+04     -8.440      0.000   -5.32e+05   -3.32e+05\n",
       "Queens                  -4.321e+05   5.11e+04     -8.450      0.000   -5.32e+05   -3.32e+05\n",
       "Bronx                   -4.327e+05   5.12e+04     -8.454      0.000   -5.33e+05   -3.32e+05\n",
       "Staten Island           -4.332e+05   5.12e+04     -8.458      0.000   -5.34e+05   -3.33e+05\n",
       "==============================================================================\n",
       "Omnibus:                     8531.420   Durbin-Watson:                   2.009\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          2461289.872\n",
       "Skew:                           2.745   Prob(JB):                         0.00\n",
       "Kurtosis:                      76.276   Cond. No.                     2.16e+10\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.16e+10. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul_reg_features.remove('has_garden')\n",
    "mul_reg_features.remove('year_built')\n",
    "x = train_df[mul_reg_features] \n",
    "y = train_df['rent']\n",
    "est = sm.OLS(y, x).fit()\n",
    "est.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nSPV3iW0z51Z"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3137774.091173475"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul_reg = LinearRegression().fit(train_df[mul_reg_features], train_df['rent'])\n",
    "get_cv_results(mul_reg)\n",
    "get_test_result(mul_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "566BNiS_z51c"
   },
   "source": [
    "## Gradient Boosting Regression\n",
    "For the gradient boosting regressor we will first set up the hyperparameter max_depth=5 to avoid overfitting, will adjust more hyperparameter as we move on to improve the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DJGUq1egz51d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(928090.3461697798, 170196.55806534263)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbrdemo = GradientBoostingRegressor(\n",
    "    max_depth=5,\n",
    "    n_estimators=100\n",
    ")\n",
    "\n",
    "get_cv_results(gbrdemo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aiHVt1vMMHh9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1738243.0681299062"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_test_result(gbrdemo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oQX1f6I5z51f"
   },
   "source": [
    "### Tuning Hyperparameters\n",
    "Now let's use GridSearchCV form sci-kit learn model_selection to tune the hyperparameters, and find the most suitable one for our Gradient Boosting Regression model.The choices of hyperparameters are obtained from the toturial code, and since this function took a long time to process, we are considering reduce the amount of hyperpraramters for testing.\n",
    "\n",
    "cited link: link: https://shankarmsy.github.io/stories/gbrt-sklearn.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zAGQpu5Jz51f"
   },
   "outputs": [],
   "source": [
    "# Tuning the hyperparameters based on a cross-validation subset (cv)\n",
    "# cited link: link: https://shankarmsy.github.io/stories/gbrt-sklearn.html\n",
    "\n",
    "def GradientBooster():\n",
    "    \n",
    "    param_grid={'n_estimators':[100, 110, 120],\n",
    "            'learning_rate': [0.15, 0.1, 0.05],\n",
    "            'max_depth':[4, 5, 6],\n",
    "            'min_samples_leaf':[4, 5, 6],\n",
    "           }\n",
    "    \n",
    "    # choose cross validation generator and use ShuffleSplit which randomly shuffles and selects Train and CV sets\n",
    "    cv = ShuffleSplit(n_splits=5, test_size=.25, random_state=0)\n",
    "    \n",
    "    regressor = GridSearchCV(estimator = GradientBoostingRegressor(), param_grid=param_grid, n_jobs=4, cv=cv)\n",
    "    \n",
    "    regressor.fit(train_df[features], train_df['rent'])  \n",
    "    return regressor.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "O3lO3Axnz51j"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Since this function takes too long to run, it will waste a lot of time during development,      \n",
      "so we will just record the result for further usage:\n",
      "\n",
      "Best Estimator Parameters:\n",
      "n_estimators:  120\n",
      "max_depth:  5\n",
      "Learning Rate:  0.15\n",
      "min_samples_leaf:  6\n"
     ]
    }
   ],
   "source": [
    "# Since this function takes too long to run, it will waste a lot of time during development,\n",
    "# so we will just record the result for further usage. -----Runmin 11/18/19\n",
    "\"\"\"\n",
    "best_est=GradientBooster()\n",
    "\n",
    "print(\"Best Estimator Parameters:\")\n",
    "print(\"n_estimators: \",best_est['n_estimators'])\n",
    "print(\"max_depth: \", best_est['max_depth'])\n",
    "print(\"Learning Rate: \", best_est['learning_rate'])\n",
    "print(\"min_samples_leaf: \", best_est['min_samples_leaf'])\n",
    "\"\"\"\n",
    "print(\"Since this function takes too long to run, it will waste a lot of time during development,\\\n",
    "      \\nso we will just record the result for further usage:\")\n",
    "print(\"\\nBest Estimator Parameters:\")\n",
    "print(\"n_estimators: \",120)\n",
    "print(\"max_depth: \", 5)\n",
    "print(\"Learning Rate: \", 0.15)\n",
    "print(\"min_samples_leaf: \", 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "j5kkgBUpz51l"
   },
   "source": [
    "As we can see from the result above, we have found the suitable hyperparameters for our model, thus we can use them to check if a better result will be obtained.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "owPU2gYbz51m"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(909861.5996442286, 192336.437169567)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bettergbr1 = GradientBoostingRegressor(\n",
    "    n_estimators=120,\n",
    "    max_depth=5,\n",
    "    learning_rate=0.15,\n",
    "    min_samples_leaf=6\n",
    ")\n",
    "\n",
    "get_cv_results(bettergbr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1718723.3425536898"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_test_result(bettergbr1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BOW7I6JPz51o"
   },
   "source": [
    "After apply the new set of hyperparameters, the result of our Gradient Boosting model indeed becomes better, we will call it bettergbr1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oln3lQr1G96s"
   },
   "source": [
    "**Feature importances for Gradient Boosting Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-lhkz-V1G96s"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size_sqft   0.5565791281018633\n",
      "zip_average_income   0.14250685135614477\n",
      "bathrooms   0.09856965736017426\n",
      "estimated_rental_value   0.0796543207948048\n",
      "addr_lat   0.01504534550571234\n",
      "addr_lon   0.011164794401557834\n",
      "floornumber   0.009865035047471259\n",
      "bedrooms   0.009558536430050998\n",
      "year_built   0.00825040016997903\n",
      "has_gym   0.00796611660324822\n",
      "ave_sale_price   0.0073603702623499965\n",
      "min_to_subway   0.0071862933578486525\n",
      "has_washer_dryer   0.005879555869991454\n",
      "has_garden   0.005752107148411113\n",
      "floor_count   0.005343099759735158\n",
      "has_concierge   0.004978589007126155\n",
      "has_dishwasher   0.004198986414506865\n",
      "no_fee   0.00337437196923941\n",
      "has_elevator   0.002980329334424056\n",
      "has_roofdeck   0.0026059199445351575\n",
      "neighborhood_population   0.0021084663829615212\n",
      "zip_unemployment_rate   0.001848104306774486\n",
      "is_furnished   0.0017418123991439327\n",
      "has_doorman   0.001578407577364625\n",
      "has_fireplace   0.0015513959083950446\n",
      "has_childrens_playroom   0.0009365824092557757\n",
      "has_pool   0.0006157451689814655\n",
      "Brooklyn   0.000295439152889654\n",
      "allows_pets   0.00025906083783788927\n",
      "has_garage   0.00012185744117181193\n",
      "Manhattan   0.00011130628937340373\n",
      "Queens   1.2013286675589173e-05\n",
      "Bronx   0.0\n",
      "Staten Island   0.0\n"
     ]
    }
   ],
   "source": [
    "bettergbr1.fit(train_df[features], train_df['rent'])  \n",
    "for feature,score in sorted(zip(features, bettergbr1.feature_importances_), key=lambda x:x[1], reverse=True):\n",
    "    print(feature, ' ', score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now we will plot the line to visualize the goodness of the fit with the predictions**\n",
    "\n",
    "Cited Link: https://acadgild.com/blog/gradient-boosting-for-regression-problems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(test_df['rent'], bettergbr1.predict(test_df[features]), edgecolors=(0, 0, 0))\n",
    "ax.plot([test_df['rent'].min(), test_df['rent'].max()], [test_df['rent'].min(), test_df['rent'].max()], 'k--', lw=4)\n",
    "ax.set_xlabel('Actual')\n",
    "ax.set_ylabel('Predicted')\n",
    "ax.set_title(\"Accuracy report\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9z858Helz51o"
   },
   "source": [
    "##  Regression Tree Model\n",
    "From definition, decision trees where the target variable can take continuous values (typically real numbers) are called regression trees.\n",
    "Thus, in our case when we do the regression tree model, we actually use the decision tree classifer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "u97chZ8pz51p"
   },
   "source": [
    "Train the regression tree model using default paramater"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IM3MAJnPz51p"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      presort=False, random_state=1, splitter='best')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regtreemo = DecisionTreeRegressor(\n",
    "    random_state=1, \n",
    "    max_depth=None,\n",
    "    min_samples_leaf=1,\n",
    "    max_features=None,\n",
    "    max_leaf_nodes=None )\n",
    "\n",
    "regtreemo.fit(train_df[features], train_df['rent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "65xtmTbNz51s"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1784304.045467068, 307724.8507966775)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_cv_results(regtreemo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DJM4nC5Zz51w"
   },
   "source": [
    "Train the regression tree model using Hyperparameters that we found, so that we can compare the results among different models better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Fc3ALfdCz51x"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=5, max_features=None,\n",
       "                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=6,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      presort=False, random_state=1, splitter='best')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newregtreemo = DecisionTreeRegressor(\n",
    "    random_state=1,\n",
    "    max_depth=5,\n",
    "    min_samples_leaf=6 )\n",
    "\n",
    "newregtreemo.fit(train_df[features], train_df['rent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TVhscdXIz510"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1560286.5382077775, 217774.82075975797)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_cv_results(newregtreemo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fv9pZ8ipz513"
   },
   "source": [
    "As we can see, the regression tree model that uses the Hyperparameters does have better result.\n",
    "But Gradient Boosting Regression still has the best result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NjrKKuhBz514"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bedrooms   0.004548823381066806\n",
      "bathrooms   0.11734825780916851\n",
      "size_sqft   0.6102817404345603\n",
      "addr_lat   0.0\n",
      "addr_lon   0.0\n",
      "floor_count   0.0011509411321644757\n",
      "year_built   0.0013179992866569205\n",
      "min_to_subway   0.0\n",
      "has_doorman   0.0\n",
      "has_elevator   0.0\n",
      "has_fireplace   0.0\n",
      "has_dishwasher   0.0\n",
      "is_furnished   0.0\n",
      "has_gym   0.004362319958019443\n",
      "allows_pets   0.0\n",
      "has_washer_dryer   0.0020619724079841794\n",
      "has_garage   0.0\n",
      "has_roofdeck   0.0\n",
      "has_concierge   0.0\n",
      "has_pool   0.0\n",
      "has_garden   0.0\n",
      "has_childrens_playroom   0.0\n",
      "no_fee   0.005122870357211396\n",
      "floornumber   0.0030736720183509878\n",
      "zip_average_income   0.17967545974968172\n",
      "zip_unemployment_rate   0.0011406691267707113\n",
      "neighborhood_population   0.0\n",
      "ave_sale_price   0.0067173262797833935\n",
      "estimated_rental_value   0.06319794805858121\n",
      "Brooklyn   0.0\n",
      "Manhattan   0.0\n",
      "Queens   0.0\n",
      "Bronx   0.0\n",
      "Staten Island   0.0\n"
     ]
    }
   ],
   "source": [
    "for feature,score in zip(features,newregtreemo.feature_importances_):\n",
    "    print(feature, ' ', score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "laDG73kzz516"
   },
   "source": [
    "### Learning Curve\n",
    "We can use the Learning Curves methods provided in lecture 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fRc0BHUGz517",
    "outputId": "ca9c6315-6f45-48a2-ddf9-91e2030e6955"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 4572785.645320359 445004.0887028914\n",
      "3 2131275.8205047417 373358.7257241667\n",
      "5 1564491.366883862 222282.10967542822\n",
      "7 1468139.6035186667 268546.29228719167\n",
      "9 1469873.7952830386 239850.41674270257\n",
      "11 1386676.248651358 237384.0628583098\n",
      "13 1402462.954857372 253339.55765092178\n",
      "15 1346029.5685074665 264143.20932462276\n",
      "17 1394834.8961980105 270527.13358506537\n",
      "19 1397980.7033980575 257551.17628524895\n",
      "21 1440011.5096156546 341482.44915712834\n",
      "23 1459635.4964225094 355653.68920203485\n",
      "25 1524333.7948864761 376467.07655902556\n",
      "27 1511533.83376324 386956.5958300964\n",
      "29 1457323.0414233475 348460.98396993714\n",
      "31 1501776.5903524642 364676.7889701957\n",
      "33 1620336.3882063979 336836.3183985473\n",
      "35 1583549.2168568554 306208.7042190424\n",
      "37 1590158.7750940374 309442.9770524444\n",
      "39 1605758.6871501326 306926.54803492926\n",
      "41 1614915.5590912506 317832.15879258723\n",
      "43 1617953.1615027797 339046.36835281877\n",
      "45 1596635.1015452903 341643.09703326965\n",
      "47 1576229.1001086987 376156.6264321279\n",
      "49 1570877.3062404287 324955.28021927713\n"
     ]
    }
   ],
   "source": [
    "hp_values = range(1,50,2)\n",
    "all_mu = []\n",
    "all_sigma = []\n",
    "\n",
    "for m in hp_values:\n",
    "\n",
    "    dtree=DecisionTreeRegressor(\n",
    "        random_state=1, \n",
    "        max_depth=m,\n",
    "        min_samples_leaf=m,\n",
    "    )\n",
    "\n",
    "    mu, sigma = get_cv_results(dtree)\n",
    "    all_mu.append(mu)\n",
    "    all_sigma.append(sigma)\n",
    "    \n",
    "    print(m, mu, sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SjwVqEAMz519",
    "outputId": "2474de1f-3ad0-465f-ed70-ce7fcdd4de8d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Max Depth')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 5))\n",
    "plt.plot(hp_values, all_mu)\n",
    "plt.ylabel('Cross Validation Accuracy')\n",
    "plt.xlabel('Max Depth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LCdgYSNqz51-",
    "outputId": "79185800-8a0c-402e-b9e2-dcb185087fb3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Max Depth')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 5))\n",
    "plt.plot(hp_values, all_sigma)\n",
    "plt.ylabel('Cross Validation Std Dev.')\n",
    "plt.xlabel('Max Depth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rrr5UbB8z52B"
   },
   "source": [
    "### Neural Network\n",
    "Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input.(source:https://skymind.ai/wiki/neural-network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "U8xBcG-Sz52B",
    "outputId": "058b4d79-aab1-49a3-8c7c-a98c884a2fd5"
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(train_df[features])\n",
    "Y = train_df['rent'].values.reshape(-1,1)\n",
    "\n",
    "train_x, test_x, train_y, test_y = train_test_split(X, Y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ztLWpbWHz52D"
   },
   "outputs": [],
   "source": [
    "net = models.Sequential()\n",
    "net.add(layers.Dense(64, input_dim=train_x.shape[1], kernel_regularizer=regularizers.l1(0.1), activation='relu'))\n",
    "net.add(layers.Dropout(0.1))\n",
    "\n",
    "net.add(layers.Dense(64, kernel_regularizer=regularizers.l1(0.1), activation='relu'))\n",
    "net.add(layers.Dropout(0.1))\n",
    "\n",
    "net.add(layers.Dense(128, kernel_regularizer=regularizers.l1(0.1), activation='relu'))\n",
    "net.add(layers.Dropout(0.1))\n",
    "\n",
    "net.add(layers.Dense(256, kernel_regularizer=regularizers.l1(0.1), activation='relu'))\n",
    "net.add(layers.Dropout(0.1))\n",
    "\n",
    "net.add(layers.Dense(1, activation='linear'))\n",
    "\n",
    "net.compile(loss='mean_squared_error',\n",
    "            optimizer='adam',\n",
    "            metrics=['mean_squared_error'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "Jmei-Vcaz52E",
    "outputId": "2f9619df-7f4a-46e4-b581-ae2f887add94"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From D:\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
      "\n",
      "WARNING:tensorflow:From D:\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
      "\n",
      "WARNING:tensorflow:From D:\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:2741: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "Train on 7876 samples, validate on 876 samples\n",
      "Epoch 1/70\n",
      "WARNING:tensorflow:From D:\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From D:\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From D:\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From D:\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
      "\n",
      "WARNING:tensorflow:From D:\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n",
      "7876/7876 [==============================] - 3s 329us/step - loss: 11829207.2040 - mean_squared_error: 11828820.4126 - val_loss: 2130444.6190 - val_mean_squared_error: 2130029.5611\n",
      "Epoch 2/70\n",
      "7876/7876 [==============================] - 1s 149us/step - loss: 2350000.6582 - mean_squared_error: 2349589.2066 - val_loss: 1755715.8961 - val_mean_squared_error: 1755310.1909\n",
      "Epoch 3/70\n",
      "7876/7876 [==============================] - 1s 141us/step - loss: 2015616.7596 - mean_squared_error: 2015212.4194 - val_loss: 1442819.1824 - val_mean_squared_error: 1442414.6541\n",
      "Epoch 4/70\n",
      "7876/7876 [==============================] - 1s 116us/step - loss: 1975210.6644 - mean_squared_error: 1974811.2618 - val_loss: 1494961.3088 - val_mean_squared_error: 1494566.3393\n",
      "Epoch 5/70\n",
      "7876/7876 [==============================] - 1s 140us/step - loss: 1816561.0058 - mean_squared_error: 1816166.8983 - val_loss: 1321333.3422 - val_mean_squared_error: 1320938.1684\n",
      "Epoch 6/70\n",
      "7876/7876 [==============================] - 1s 137us/step - loss: 1808143.0494 - mean_squared_error: 1807751.9959 - val_loss: 1247216.2306 - val_mean_squared_error: 1246824.4478\n",
      "Epoch 7/70\n",
      "7876/7876 [==============================] - 1s 171us/step - loss: 1726253.6375 - mean_squared_error: 1725865.5306 - val_loss: 1400454.0745 - val_mean_squared_error: 1400070.9235\n",
      "Epoch 8/70\n",
      "7876/7876 [==============================] - 1s 118us/step - loss: 1787892.3127 - mean_squared_error: 1787508.2273 - val_loss: 1233221.1652 - val_mean_squared_error: 1232839.1196\n",
      "Epoch 9/70\n",
      "7876/7876 [==============================] - 1s 127us/step - loss: 1685850.6941 - mean_squared_error: 1685469.0247 - val_loss: 1166447.2765 - val_mean_squared_error: 1166066.2380\n",
      "Epoch 10/70\n",
      "7876/7876 [==============================] - 1s 146us/step - loss: 1657136.0840 - mean_squared_error: 1656756.3227 - val_loss: 1131310.9749 - val_mean_squared_error: 1130931.1122\n",
      "Epoch 11/70\n",
      "7876/7876 [==============================] - 1s 120us/step - loss: 1741675.5810 - mean_squared_error: 1741297.4143 - val_loss: 1148987.3029 - val_mean_squared_error: 1148609.9199\n",
      "Epoch 12/70\n",
      "7876/7876 [==============================] - 1s 132us/step - loss: 1590973.2706 - mean_squared_error: 1590595.7455 - val_loss: 1241069.7593 - val_mean_squared_error: 1240694.8423\n",
      "Epoch 13/70\n",
      "7876/7876 [==============================] - 1s 153us/step - loss: 1650548.3960 - mean_squared_error: 1650171.8164 - val_loss: 1077031.8309 - val_mean_squared_error: 1076654.5207\n",
      "Epoch 14/70\n",
      "7876/7876 [==============================] - 1s 178us/step - loss: 1602599.3197 - mean_squared_error: 1602223.5560 - val_loss: 1155608.8858 - val_mean_squared_error: 1155230.2203\n",
      "Epoch 15/70\n",
      "7876/7876 [==============================] - 1s 178us/step - loss: 1566658.1761 - mean_squared_error: 1566282.5343 - val_loss: 1085680.9853 - val_mean_squared_error: 1085305.3355\n",
      "Epoch 16/70\n",
      "7876/7876 [==============================] - 1s 137us/step - loss: 1540256.3681 - mean_squared_error: 1539878.7751 - val_loss: 1073206.6677 - val_mean_squared_error: 1072827.5614\n",
      "Epoch 17/70\n",
      "7876/7876 [==============================] - 1s 172us/step - loss: 1508923.0948 - mean_squared_error: 1508542.9638 - val_loss: 1048205.6089 - val_mean_squared_error: 1047823.1043\n",
      "Epoch 18/70\n",
      "7876/7876 [==============================] - 1s 149us/step - loss: 1418555.9420 - mean_squared_error: 1418175.1022 - val_loss: 1055497.1183 - val_mean_squared_error: 1055115.6968\n",
      "Epoch 19/70\n",
      "7876/7876 [==============================] - 1s 134us/step - loss: 1349576.9322 - mean_squared_error: 1349193.4341 - val_loss: 967613.2964 - val_mean_squared_error: 967227.9160\n",
      "Epoch 20/70\n",
      "7876/7876 [==============================] - 1s 132us/step - loss: 1398169.4978 - mean_squared_error: 1397784.0033 - val_loss: 973355.0061 - val_mean_squared_error: 972967.3864\n",
      "Epoch 21/70\n",
      "7876/7876 [==============================] - 1s 135us/step - loss: 1428435.6249 - mean_squared_error: 1428047.6923 - val_loss: 957462.2192 - val_mean_squared_error: 957072.7962\n",
      "Epoch 22/70\n",
      "7876/7876 [==============================] - 1s 127us/step - loss: 1291945.8804 - mean_squared_error: 1291555.3107 - val_loss: 960156.4368 - val_mean_squared_error: 959763.7805\n",
      "Epoch 23/70\n",
      "7876/7876 [==============================] - 1s 116us/step - loss: 1308218.0168 - mean_squared_error: 1307824.1462 - val_loss: 926650.1146 - val_mean_squared_error: 926253.8500\n",
      "Epoch 24/70\n",
      "7876/7876 [==============================] - 1s 127us/step - loss: 1428076.1867 - mean_squared_error: 1427681.3706 - val_loss: 991163.2514 - val_mean_squared_error: 990767.8803\n",
      "Epoch 25/70\n",
      "7876/7876 [==============================] - 1s 153us/step - loss: 1313324.8060 - mean_squared_error: 1312929.2691 - val_loss: 1010546.2314 - val_mean_squared_error: 1010149.9422\n",
      "Epoch 26/70\n",
      "7876/7876 [==============================] - 1s 131us/step - loss: 1214310.0943 - mean_squared_error: 1213911.7019 - val_loss: 1046210.0692 - val_mean_squared_error: 1045811.2209\n",
      "Epoch 27/70\n",
      "7876/7876 [==============================] - 1s 125us/step - loss: 1266013.6911 - mean_squared_error: 1265611.8656 - val_loss: 980975.7935 - val_mean_squared_error: 980574.9772\n",
      "Epoch 28/70\n",
      "7876/7876 [==============================] - 1s 115us/step - loss: 1218026.0287 - mean_squared_error: 1217622.1901 - val_loss: 945931.7810 - val_mean_squared_error: 945525.4361\n",
      "Epoch 29/70\n",
      "7876/7876 [==============================] - 1s 120us/step - loss: 1223504.6001 - mean_squared_error: 1223095.9458 - val_loss: 888679.9124 - val_mean_squared_error: 888270.2073\n",
      "Epoch 30/70\n",
      "7876/7876 [==============================] - 1s 142us/step - loss: 1212759.4618 - mean_squared_error: 1212349.8999 - val_loss: 921031.4765 - val_mean_squared_error: 920617.0000\n",
      "Epoch 31/70\n",
      "7876/7876 [==============================] - 2s 256us/step - loss: 1279218.1093 - mean_squared_error: 1278806.2368 - val_loss: 905600.7852 - val_mean_squared_error: 905185.2521\n",
      "Epoch 32/70\n",
      "7876/7876 [==============================] - 2s 265us/step - loss: 1225165.3451 - mean_squared_error: 1224750.3489 - val_loss: 899086.8436 - val_mean_squared_error: 898671.3600\n",
      "Epoch 33/70\n",
      "7876/7876 [==============================] - 1s 158us/step - loss: 1207542.0471 - mean_squared_error: 1207126.3089 - val_loss: 918322.7681 - val_mean_squared_error: 917903.9449\n",
      "Epoch 34/70\n",
      "7876/7876 [==============================] - 2s 203us/step - loss: 1226190.4696 - mean_squared_error: 1225771.7091 - val_loss: 881618.7991 - val_mean_squared_error: 881196.6520\n",
      "Epoch 35/70\n",
      "7876/7876 [==============================] - 1s 132us/step - loss: 1146055.1778 - mean_squared_error: 1145634.1417 - val_loss: 882890.9204 - val_mean_squared_error: 882468.5484\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 36/70\n",
      "7876/7876 [==============================] - 2s 202us/step - loss: 1146500.0708 - mean_squared_error: 1146076.5827 - val_loss: 899807.0328 - val_mean_squared_error: 899382.5611\n",
      "Epoch 37/70\n",
      "7876/7876 [==============================] - 1s 152us/step - loss: 1126617.8661 - mean_squared_error: 1126191.7033 - val_loss: 894117.4659 - val_mean_squared_error: 893687.8961\n",
      "Epoch 38/70\n",
      "7876/7876 [==============================] - 1s 139us/step - loss: 1181866.0003 - mean_squared_error: 1181436.1825 - val_loss: 944077.1691 - val_mean_squared_error: 943646.1050\n",
      "Epoch 39/70\n",
      "7876/7876 [==============================] - 1s 143us/step - loss: 1117878.8682 - mean_squared_error: 1117446.7094 - val_loss: 1028308.6694 - val_mean_squared_error: 1027877.1251\n",
      "Epoch 40/70\n",
      "7876/7876 [==============================] - 1s 119us/step - loss: 1074759.1611 - mean_squared_error: 1074323.9926 - val_loss: 961382.3729 - val_mean_squared_error: 960950.3789\n",
      "Epoch 41/70\n",
      "7876/7876 [==============================] - 1s 160us/step - loss: 1026262.4387 - mean_squared_error: 1025826.6067 - val_loss: 877589.4345 - val_mean_squared_error: 877152.9368\n",
      "Epoch 42/70\n",
      "7876/7876 [==============================] - ETA: 0s - loss: 1070447.6462 - mean_squared_error: 1070009.81 - 1s 129us/step - loss: 1057750.7345 - mean_squared_error: 1057312.5732 - val_loss: 914194.1672 - val_mean_squared_error: 913749.4001\n",
      "Epoch 43/70\n",
      "7876/7876 [==============================] - 1s 145us/step - loss: 1044708.0007 - mean_squared_error: 1044265.7991 - val_loss: 861755.3208 - val_mean_squared_error: 861309.8894\n",
      "Epoch 44/70\n",
      "7876/7876 [==============================] - 1s 187us/step - loss: 1032872.8740 - mean_squared_error: 1032426.7244 - val_loss: 895759.5980 - val_mean_squared_error: 895310.1094\n",
      "Epoch 45/70\n",
      "7876/7876 [==============================] - 1s 167us/step - loss: 1051512.4022 - mean_squared_error: 1051063.3585 - val_loss: 877160.3299 - val_mean_squared_error: 876709.9595\n",
      "Epoch 46/70\n",
      "7876/7876 [==============================] - 1s 171us/step - loss: 989232.0412 - mean_squared_error: 988780.4992 - val_loss: 879851.6266 - val_mean_squared_error: 879397.3148\n",
      "Epoch 47/70\n",
      "7876/7876 [==============================] - 2s 196us/step - loss: 1022538.7117 - mean_squared_error: 1022085.4589 - val_loss: 934323.0832 - val_mean_squared_error: 933866.1941\n",
      "Epoch 48/70\n",
      "7876/7876 [==============================] - 1s 135us/step - loss: 963438.5541 - mean_squared_error: 962983.4955 - val_loss: 907724.9706 - val_mean_squared_error: 907267.8616\n",
      "Epoch 49/70\n",
      "7876/7876 [==============================] - 1s 168us/step - loss: 974536.4608 - mean_squared_error: 974078.8389 - val_loss: 958721.8353 - val_mean_squared_error: 958262.0830\n",
      "Epoch 50/70\n",
      "7876/7876 [==============================] - 2s 274us/step - loss: 1007049.5156 - mean_squared_error: 1006589.4822 - val_loss: 913489.0004 - val_mean_squared_error: 913026.4522\n",
      "Epoch 51/70\n",
      "7876/7876 [==============================] - 1s 120us/step - loss: 958622.4523 - mean_squared_error: 958158.2483 - val_loss: 968660.5111 - val_mean_squared_error: 968196.8884\n",
      "Epoch 52/70\n",
      "7876/7876 [==============================] - 1s 184us/step - loss: 988682.5475 - mean_squared_error: 988215.3873 - val_loss: 948131.7406 - val_mean_squared_error: 947664.7451\n",
      "Epoch 53/70\n",
      "7876/7876 [==============================] - 1s 138us/step - loss: 1007368.0654 - mean_squared_error: 1006899.5086 - val_loss: 935326.4288 - val_mean_squared_error: 934858.3350\n",
      "Epoch 54/70\n",
      "7876/7876 [==============================] - 1s 133us/step - loss: 948759.6201 - mean_squared_error: 948288.7608 - val_loss: 899331.5825 - val_mean_squared_error: 898860.2997\n",
      "Epoch 55/70\n",
      "7876/7876 [==============================] - 1s 116us/step - loss: 988423.6366 - mean_squared_error: 987952.5891 - val_loss: 991813.6898 - val_mean_squared_error: 991341.7691\n",
      "Epoch 56/70\n",
      "7876/7876 [==============================] - 1s 137us/step - loss: 920717.4246 - mean_squared_error: 920242.8338 - val_loss: 926914.1926 - val_mean_squared_error: 926437.1751\n",
      "Epoch 57/70\n",
      "7876/7876 [==============================] - 1s 121us/step - loss: 878744.0493 - mean_squared_error: 878267.7042 - val_loss: 841855.3118 - val_mean_squared_error: 841378.9338\n",
      "Epoch 58/70\n",
      "7876/7876 [==============================] - 1s 142us/step - loss: 996132.9776 - mean_squared_error: 995654.4371 - val_loss: 926500.0484 - val_mean_squared_error: 926022.9879\n",
      "Epoch 59/70\n",
      "7876/7876 [==============================] - 1s 187us/step - loss: 897276.3818 - mean_squared_error: 896796.5592 - val_loss: 980804.5862 - val_mean_squared_error: 980323.4466\n",
      "Epoch 60/70\n",
      "7876/7876 [==============================] - 1s 142us/step - loss: 923120.1582 - mean_squared_error: 922637.5965 - val_loss: 938296.5031 - val_mean_squared_error: 937812.3930\n",
      "Epoch 61/70\n",
      "7876/7876 [==============================] - 1s 129us/step - loss: 906062.3905 - mean_squared_error: 905577.9144 - val_loss: 924148.8215 - val_mean_squared_error: 923660.6874\n",
      "Epoch 62/70\n",
      "7876/7876 [==============================] - 1s 165us/step - loss: 866290.4596 - mean_squared_error: 865804.2701 - val_loss: 876234.5327 - val_mean_squared_error: 875746.7697\n",
      "Epoch 63/70\n",
      "7876/7876 [==============================] - 1s 151us/step - loss: 847620.7489 - mean_squared_error: 847132.0889 - val_loss: 854740.6641 - val_mean_squared_error: 854247.4675\n",
      "Epoch 64/70\n",
      "7876/7876 [==============================] - 1s 133us/step - loss: 817740.4798 - mean_squared_error: 817246.3449 - val_loss: 940990.2041 - val_mean_squared_error: 940498.5051\n",
      "Epoch 65/70\n",
      "7876/7876 [==============================] - 1s 180us/step - loss: 896497.5394 - mean_squared_error: 896003.2148 - val_loss: 916619.7941 - val_mean_squared_error: 916126.2747\n",
      "Epoch 66/70\n",
      "7876/7876 [==============================] - 1s 167us/step - loss: 891349.4377 - mean_squared_error: 890854.4936 - val_loss: 978159.6074 - val_mean_squared_error: 977664.9038\n",
      "Epoch 67/70\n",
      "7876/7876 [==============================] - 1s 149us/step - loss: 888901.3098 - mean_squared_error: 888404.3529 - val_loss: 1093114.9789 - val_mean_squared_error: 1092619.4541\n",
      "Epoch 68/70\n",
      "7876/7876 [==============================] - 1s 129us/step - loss: 867622.4054 - mean_squared_error: 867124.6967 - val_loss: 989713.7951 - val_mean_squared_error: 989213.2265\n",
      "Epoch 69/70\n",
      "7876/7876 [==============================] - 1s 160us/step - loss: 896160.9919 - mean_squared_error: 895660.4532 - val_loss: 1052636.4035 - val_mean_squared_error: 1052138.5853\n",
      "Epoch 70/70\n",
      "7876/7876 [==============================] - 1s 139us/step - loss: 800204.2958 - mean_squared_error: 799703.6296 - val_loss: 870478.6067 - val_mean_squared_error: 869976.2539\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1abb555f908>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.fit(train_x, train_y, epochs=70, batch_size=64, validation_split = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "jkKi2IVoz52G",
    "outputId": "a58e9e9a-cd9d-44d1-9607-16d43937c355"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1591015.5925490716"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(test_y, net.predict(test_x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yy3GBRAdz52K"
   },
   "source": [
    "**Although the DL Neural Network seems to provide a better result on mean squared errors but it is really unstable** \n",
    "\n",
    "we have ran few test run on it, and each time it gives different results range from 953411.6171418771 to around 2,000,000.\n",
    "Therefore, we might still choose Gradient Boosting Regression as the most suitable model, since it provides a overall better result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HTEApwmu4s8_"
   },
   "source": [
    "# Model summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1LIjA5NBz52L"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Regression:  (1053460.6931887527, 228018.6363507963)\n",
      "Gradient Boosting Regression:  (927079.4423014012, 164981.1004248631)\n",
      "Multiple Regression:  (2124417.9780780375, 424060.54235046916)\n",
      "Decision Tree Regression:  (1560286.5382077775, 217774.82075975797)\n",
      "Neural Network Regression:  20128963.897158097\n"
     ]
    }
   ],
   "source": [
    "print('Random Forest Regression: ', get_cv_results(rforest))\n",
    "print('Gradient Boosting Regression: ', get_cv_results(gbrdemo))\n",
    "print('Multiple Regression: ', get_cv_results(mul_reg))\n",
    "print('Decision Tree Regression: ', get_cv_results(newregtreemo))\n",
    "print('Neural Network Regression: ', mean_squared_error(test_y, net.predict(test_x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we move on to preduce our final predicted output for test2 and test3, we can also check how well is the performance of our model by creating a new column in our test1 set to store the predictions for test1, and then calcuate the error percentage with the actual rent value, thus we can see in what cases our model is doing great or bad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "                          learning_rate=0.15, loss='ls', max_depth=5,\n",
       "                          max_features=None, max_leaf_nodes=None,\n",
       "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                          min_samples_leaf=6, min_samples_split=2,\n",
       "                          min_weight_fraction_leaf=0.0, n_estimators=120,\n",
       "                          n_iter_no_change=None, presort='auto',\n",
       "                          random_state=None, subsample=1.0, tol=0.0001,\n",
       "                          validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbrfortesting = GradientBoostingRegressor(\n",
    "        n_estimators=120,\n",
    "        max_depth=5,\n",
    "        learning_rate=0.15,\n",
    "        min_samples_leaf=6\n",
    "    )\n",
    "gbrfortesting.fit(test_df[features], test_df['rent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_performance_df = test_df\n",
    "test_performance_df['predictions'] = gbrfortesting.predict(test_df[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_performance_df = test_performance_df.loc[:, ['rent','predictions']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we got the predictions line next to the actual rent, we can move on to calculate the error percentage of them, and sort it by the error to check what is the best case, then we sort it reversely to check the worst case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>rent</th>\n",
       "      <th>predictions</th>\n",
       "      <th>predicted_error</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rental_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7430833</th>\n",
       "      <td>5565</td>\n",
       "      <td>5565.232661</td>\n",
       "      <td>0.000042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7484308</th>\n",
       "      <td>1900</td>\n",
       "      <td>1899.702275</td>\n",
       "      <td>0.000157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7465147</th>\n",
       "      <td>3799</td>\n",
       "      <td>3799.712141</td>\n",
       "      <td>0.000187</td>\n",
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       "      <th>7440091</th>\n",
       "      <td>2300</td>\n",
       "      <td>2299.514594</td>\n",
       "      <td>0.000211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7523548</th>\n",
       "      <td>3100</td>\n",
       "      <td>3100.681049</td>\n",
       "      <td>0.000220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7486420</th>\n",
       "      <td>2575</td>\n",
       "      <td>2575.636067</td>\n",
       "      <td>0.000247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7524397</th>\n",
       "      <td>2500</td>\n",
       "      <td>2499.350195</td>\n",
       "      <td>0.000260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7473694</th>\n",
       "      <td>1780</td>\n",
       "      <td>1779.510036</td>\n",
       "      <td>0.000275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7507138</th>\n",
       "      <td>3208</td>\n",
       "      <td>3208.946266</td>\n",
       "      <td>0.000295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7503067</th>\n",
       "      <td>2750</td>\n",
       "      <td>2749.162165</td>\n",
       "      <td>0.000305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           rent  predictions  predicted_error\n",
       "rental_id                                    \n",
       "7430833    5565  5565.232661         0.000042\n",
       "7484308    1900  1899.702275         0.000157\n",
       "7465147    3799  3799.712141         0.000187\n",
       "7440091    2300  2299.514594         0.000211\n",
       "7523548    3100  3100.681049         0.000220\n",
       "7486420    2575  2575.636067         0.000247\n",
       "7524397    2500  2499.350195         0.000260\n",
       "7473694    1780  1779.510036         0.000275\n",
       "7507138    3208  3208.946266         0.000295\n",
       "7503067    2750  2749.162165         0.000305"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_performance_df['predicted_error'] = abs( test_performance_df['rent'] - test_performance_df['predictions']) / test_performance_df['rent']\n",
    "test_performance_df.sort_values(by=['predicted_error']).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rent</th>\n",
       "      <th>predictions</th>\n",
       "      <th>predicted_error</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rental_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7464925</th>\n",
       "      <td>6897</td>\n",
       "      <td>13933.520479</td>\n",
       "      <td>1.020229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7445302</th>\n",
       "      <td>2000</td>\n",
       "      <td>3548.947047</td>\n",
       "      <td>0.774474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7474246</th>\n",
       "      <td>2000</td>\n",
       "      <td>3336.906624</td>\n",
       "      <td>0.668453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7509964</th>\n",
       "      <td>1700</td>\n",
       "      <td>2559.137765</td>\n",
       "      <td>0.505375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7433743</th>\n",
       "      <td>3691</td>\n",
       "      <td>5387.367547</td>\n",
       "      <td>0.459596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7439857</th>\n",
       "      <td>2600</td>\n",
       "      <td>3789.012131</td>\n",
       "      <td>0.457312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7446871</th>\n",
       "      <td>2800</td>\n",
       "      <td>3974.044889</td>\n",
       "      <td>0.419302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7454614</th>\n",
       "      <td>1580</td>\n",
       "      <td>2240.749067</td>\n",
       "      <td>0.418196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7460626</th>\n",
       "      <td>2400</td>\n",
       "      <td>3364.392473</td>\n",
       "      <td>0.401830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7446883</th>\n",
       "      <td>5000</td>\n",
       "      <td>3014.274773</td>\n",
       "      <td>0.397145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           rent   predictions  predicted_error\n",
       "rental_id                                     \n",
       "7464925    6897  13933.520479         1.020229\n",
       "7445302    2000   3548.947047         0.774474\n",
       "7474246    2000   3336.906624         0.668453\n",
       "7509964    1700   2559.137765         0.505375\n",
       "7433743    3691   5387.367547         0.459596\n",
       "7439857    2600   3789.012131         0.457312\n",
       "7446871    2800   3974.044889         0.419302\n",
       "7454614    1580   2240.749067         0.418196\n",
       "7460626    2400   3364.392473         0.401830\n",
       "7446883    5000   3014.274773         0.397145"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_performance_df.sort_values(by=['predicted_error'], ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see that rental_id of 7430833 has the best predicted result and rental_id of 7464925 has a really bad result\n",
    "\n",
    "The two places are\n",
    "- 7430833: #7C 240 EAST 27 STREET, NEW YORK, 10016 (https://streeteasy.com/property/7815532-parc-east-7c)\n",
    "- 7464925: 1204 120 WEST 21 STREET, NEW YORK, 10011 (https://streeteasy.com/property/1321506-21-chelsea-1204)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BhxefAnV7QEB"
   },
   "source": [
    "### After explored 5 different regression models, we decided to use gradient boosting regression model to predict test2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uWbnzxXI7O8M"
   },
   "outputs": [],
   "source": [
    "output = pd.DataFrame(np.array(test2_df.index), columns=['rental_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FBuye75t7sH8"
   },
   "outputs": [],
   "source": [
    "output['predictions'] = bettergbr1.predict(test2_df[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zBZcLRq59Ghu"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rental_id</th>\n",
       "      <th>predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7428577</td>\n",
       "      <td>2018.136263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7454944</td>\n",
       "      <td>2156.497120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7473595</td>\n",
       "      <td>2563.137531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7490488</td>\n",
       "      <td>2659.592657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7460896</td>\n",
       "      <td>3582.309121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7479988</td>\n",
       "      <td>2622.059261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7496308</td>\n",
       "      <td>3492.898431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7468621</td>\n",
       "      <td>2976.873219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7430797</td>\n",
       "      <td>3422.830981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7440397</td>\n",
       "      <td>4162.271001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rental_id  predictions\n",
       "0    7428577  2018.136263\n",
       "1    7454944  2156.497120\n",
       "2    7473595  2563.137531\n",
       "3    7490488  2659.592657\n",
       "4    7460896  3582.309121\n",
       "5    7479988  2622.059261\n",
       "6    7496308  3492.898431\n",
       "7    7468621  2976.873219\n",
       "8    7430797  3422.830981\n",
       "9    7440397  4162.271001"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JuXhGtQU_Pv9"
   },
   "outputs": [],
   "source": [
    "output.to_csv('test2_prediction.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "a2WMvf7XUbrR"
   },
   "source": [
    "# Prediction for test3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "colab_type": "code",
    "id": "4ebwRUiYU7Sd",
    "outputId": "98c1385e-a389-40a0-a915-44b6a848dd09"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "                          learning_rate=0.15, loss='ls', max_depth=5,\n",
       "                          max_features=None, max_leaf_nodes=None,\n",
       "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                          min_samples_leaf=6, min_samples_split=2,\n",
       "                          min_weight_fraction_leaf=0.0, n_estimators=120,\n",
       "                          n_iter_no_change=None, presort='auto',\n",
       "                          random_state=None, subsample=1.0, tol=0.0001,\n",
       "                          validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "train_x = scaler.fit_transform(train_df[features])\n",
    "train_y = train_df['rent'].values.reshape(-1,1)\n",
    "\n",
    "bettergbr1 = GradientBoostingRegressor(\n",
    "        n_estimators=120,\n",
    "        max_depth=5,\n",
    "        learning_rate=0.15,\n",
    "        min_samples_leaf=6\n",
    "    )\n",
    "bettergbr1.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z1hjhpxwVCmL"
   },
   "outputs": [],
   "source": [
    "test_x = scaler.fit_transform(test3_df[features])\n",
    "\n",
    "output = pd.DataFrame(np.array(test3_df.index), columns=['rental_id'])\n",
    "output['predictions'] = bettergbr1.predict(test_x)\n",
    "output.to_csv('test3_prediction.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VhUncoe5XkW6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rental_id</th>\n",
       "      <th>predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7455565</td>\n",
       "      <td>4212.052640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7473295</td>\n",
       "      <td>2133.588111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7459804</td>\n",
       "      <td>3295.365104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7456330</td>\n",
       "      <td>2277.347136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7443595</td>\n",
       "      <td>3442.757603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7502623</td>\n",
       "      <td>2066.332923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7459171</td>\n",
       "      <td>4220.040506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7447600</td>\n",
       "      <td>1642.313233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7505254</td>\n",
       "      <td>3516.369924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7454620</td>\n",
       "      <td>2503.861059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rental_id  predictions\n",
       "0    7455565  4212.052640\n",
       "1    7473295  2133.588111\n",
       "2    7459804  3295.365104\n",
       "3    7456330  2277.347136\n",
       "4    7443595  3442.757603\n",
       "5    7502623  2066.332923\n",
       "6    7459171  4220.040506\n",
       "7    7447600  1642.313233\n",
       "8    7505254  3516.369924\n",
       "9    7454620  2503.861059"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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