{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Lecture 2: Class demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports, Announcements, LOs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# import the libraries\n",
    "import os\n",
    "import sys\n",
    "sys.path.append(os.path.join(os.path.abspath(\"..\"), \"code\"))\n",
    "from plotting_functions import *\n",
    "from utils import *\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "pd.set_option(\"display.max_colwidth\", 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's bring back King County housing sale prediction data from the course introduction video. You can download the data from [here](https://www.kaggle.com/harlfoxem/housesalesprediction). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>20141013T000000</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000.0</td>\n",
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       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
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       "      <td>180000.0</td>\n",
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       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>20150218T000000</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>21608</th>\n",
       "      <td>263000018</td>\n",
       "      <td>20140521T000000</td>\n",
       "      <td>360000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1530</td>\n",
       "      <td>1131</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1530</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98103</td>\n",
       "      <td>47.6993</td>\n",
       "      <td>-122.346</td>\n",
       "      <td>1530</td>\n",
       "      <td>1509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21609</th>\n",
       "      <td>6600060120</td>\n",
       "      <td>20150223T000000</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2310</td>\n",
       "      <td>5813</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2310</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>98146</td>\n",
       "      <td>47.5107</td>\n",
       "      <td>-122.362</td>\n",
       "      <td>1830</td>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21610</th>\n",
       "      <td>1523300141</td>\n",
       "      <td>20140623T000000</td>\n",
       "      <td>402101.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1020</td>\n",
       "      <td>1350</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1020</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.5944</td>\n",
       "      <td>-122.299</td>\n",
       "      <td>1020</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21611</th>\n",
       "      <td>291310100</td>\n",
       "      <td>20150116T000000</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1600</td>\n",
       "      <td>2388</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1600</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>98027</td>\n",
       "      <td>47.5345</td>\n",
       "      <td>-122.069</td>\n",
       "      <td>1410</td>\n",
       "      <td>1287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21612</th>\n",
       "      <td>1523300157</td>\n",
       "      <td>20141015T000000</td>\n",
       "      <td>325000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1020</td>\n",
       "      <td>1076</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1020</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.5941</td>\n",
       "      <td>-122.299</td>\n",
       "      <td>1020</td>\n",
       "      <td>1357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21613 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id             date     price  bedrooms  bathrooms  \\\n",
       "0      7129300520  20141013T000000  221900.0         3       1.00   \n",
       "1      6414100192  20141209T000000  538000.0         3       2.25   \n",
       "2      5631500400  20150225T000000  180000.0         2       1.00   \n",
       "3      2487200875  20141209T000000  604000.0         4       3.00   \n",
       "4      1954400510  20150218T000000  510000.0         3       2.00   \n",
       "...           ...              ...       ...       ...        ...   \n",
       "21608   263000018  20140521T000000  360000.0         3       2.50   \n",
       "21609  6600060120  20150223T000000  400000.0         4       2.50   \n",
       "21610  1523300141  20140623T000000  402101.0         2       0.75   \n",
       "21611   291310100  20150116T000000  400000.0         3       2.50   \n",
       "21612  1523300157  20141015T000000  325000.0         2       0.75   \n",
       "\n",
       "       sqft_living  sqft_lot  floors  waterfront  view  ...  grade  \\\n",
       "0             1180      5650     1.0           0     0  ...      7   \n",
       "1             2570      7242     2.0           0     0  ...      7   \n",
       "2              770     10000     1.0           0     0  ...      6   \n",
       "3             1960      5000     1.0           0     0  ...      7   \n",
       "4             1680      8080     1.0           0     0  ...      8   \n",
       "...            ...       ...     ...         ...   ...  ...    ...   \n",
       "21608         1530      1131     3.0           0     0  ...      8   \n",
       "21609         2310      5813     2.0           0     0  ...      8   \n",
       "21610         1020      1350     2.0           0     0  ...      7   \n",
       "21611         1600      2388     2.0           0     0  ...      8   \n",
       "21612         1020      1076     2.0           0     0  ...      7   \n",
       "\n",
       "       sqft_above  sqft_basement  yr_built  yr_renovated  zipcode      lat  \\\n",
       "0            1180              0      1955             0    98178  47.5112   \n",
       "1            2170            400      1951          1991    98125  47.7210   \n",
       "2             770              0      1933             0    98028  47.7379   \n",
       "3            1050            910      1965             0    98136  47.5208   \n",
       "4            1680              0      1987             0    98074  47.6168   \n",
       "...           ...            ...       ...           ...      ...      ...   \n",
       "21608        1530              0      2009             0    98103  47.6993   \n",
       "21609        2310              0      2014             0    98146  47.5107   \n",
       "21610        1020              0      2009             0    98144  47.5944   \n",
       "21611        1600              0      2004             0    98027  47.5345   \n",
       "21612        1020              0      2008             0    98144  47.5941   \n",
       "\n",
       "          long  sqft_living15  sqft_lot15  \n",
       "0     -122.257           1340        5650  \n",
       "1     -122.319           1690        7639  \n",
       "2     -122.233           2720        8062  \n",
       "3     -122.393           1360        5000  \n",
       "4     -122.045           1800        7503  \n",
       "...        ...            ...         ...  \n",
       "21608 -122.346           1530        1509  \n",
       "21609 -122.362           1830        7200  \n",
       "21610 -122.299           1020        2007  \n",
       "21611 -122.069           1410        1287  \n",
       "21612 -122.299           1020        1357  \n",
       "\n",
       "[21613 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df = pd.read_csv('../data/kc_house_data.csv')\n",
    "housing_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-10-13', '2014-12-09', '2015-02-18'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.to_datetime(['20141013T000000', '20141209T000000', '20150218T000000'], format='%Y%m%dT%H%M%S')\n",
    "dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is this a classification problem or a regression problem? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21613, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many data points do we have? \n",
    "housing_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
       "       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
       "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
       "       'lat', 'long', 'sqft_living15', 'sqft_lot15'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the columns in the dataset? \n",
    "housing_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore some features. Let's try the `describe()` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.161300e+04</td>\n",
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       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.580302e+09</td>\n",
       "      <td>5.400881e+05</td>\n",
       "      <td>3.370842</td>\n",
       "      <td>2.114757</td>\n",
       "      <td>2079.899736</td>\n",
       "      <td>1.510697e+04</td>\n",
       "      <td>1.494309</td>\n",
       "      <td>0.007542</td>\n",
       "      <td>0.234303</td>\n",
       "      <td>3.409430</td>\n",
       "      <td>7.656873</td>\n",
       "      <td>1788.390691</td>\n",
       "      <td>291.509045</td>\n",
       "      <td>1971.005136</td>\n",
       "      <td>84.402258</td>\n",
       "      <td>98077.939805</td>\n",
       "      <td>47.560053</td>\n",
       "      <td>-122.213896</td>\n",
       "      <td>1986.552492</td>\n",
       "      <td>12768.455652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.876566e+09</td>\n",
       "      <td>3.671272e+05</td>\n",
       "      <td>0.930062</td>\n",
       "      <td>0.770163</td>\n",
       "      <td>918.440897</td>\n",
       "      <td>4.142051e+04</td>\n",
       "      <td>0.539989</td>\n",
       "      <td>0.086517</td>\n",
       "      <td>0.766318</td>\n",
       "      <td>0.650743</td>\n",
       "      <td>1.175459</td>\n",
       "      <td>828.090978</td>\n",
       "      <td>442.575043</td>\n",
       "      <td>29.373411</td>\n",
       "      <td>401.679240</td>\n",
       "      <td>53.505026</td>\n",
       "      <td>0.138564</td>\n",
       "      <td>0.140828</td>\n",
       "      <td>685.391304</td>\n",
       "      <td>27304.179631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000102e+06</td>\n",
       "      <td>7.500000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>5.200000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98001.000000</td>\n",
       "      <td>47.155900</td>\n",
       "      <td>-122.519000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>651.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.123049e+09</td>\n",
       "      <td>3.219500e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1427.000000</td>\n",
       "      <td>5.040000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1190.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1951.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98033.000000</td>\n",
       "      <td>47.471000</td>\n",
       "      <td>-122.328000</td>\n",
       "      <td>1490.000000</td>\n",
       "      <td>5100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.904930e+09</td>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1910.000000</td>\n",
       "      <td>7.618000e+03</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1560.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98065.000000</td>\n",
       "      <td>47.571800</td>\n",
       "      <td>-122.230000</td>\n",
       "      <td>1840.000000</td>\n",
       "      <td>7620.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.308900e+09</td>\n",
       "      <td>6.450000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2550.000000</td>\n",
       "      <td>1.068800e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2210.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98118.000000</td>\n",
       "      <td>47.678000</td>\n",
       "      <td>-122.125000</td>\n",
       "      <td>2360.000000</td>\n",
       "      <td>10083.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.900000e+09</td>\n",
       "      <td>7.700000e+06</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13540.000000</td>\n",
       "      <td>1.651359e+06</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9410.000000</td>\n",
       "      <td>4820.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>98199.000000</td>\n",
       "      <td>47.777600</td>\n",
       "      <td>-121.315000</td>\n",
       "      <td>6210.000000</td>\n",
       "      <td>871200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id         price      bedrooms     bathrooms   sqft_living  \\\n",
       "count  2.161300e+04  2.161300e+04  21613.000000  21613.000000  21613.000000   \n",
       "mean   4.580302e+09  5.400881e+05      3.370842      2.114757   2079.899736   \n",
       "std    2.876566e+09  3.671272e+05      0.930062      0.770163    918.440897   \n",
       "min    1.000102e+06  7.500000e+04      0.000000      0.000000    290.000000   \n",
       "25%    2.123049e+09  3.219500e+05      3.000000      1.750000   1427.000000   \n",
       "50%    3.904930e+09  4.500000e+05      3.000000      2.250000   1910.000000   \n",
       "75%    7.308900e+09  6.450000e+05      4.000000      2.500000   2550.000000   \n",
       "max    9.900000e+09  7.700000e+06     33.000000      8.000000  13540.000000   \n",
       "\n",
       "           sqft_lot        floors    waterfront          view     condition  \\\n",
       "count  2.161300e+04  21613.000000  21613.000000  21613.000000  21613.000000   \n",
       "mean   1.510697e+04      1.494309      0.007542      0.234303      3.409430   \n",
       "std    4.142051e+04      0.539989      0.086517      0.766318      0.650743   \n",
       "min    5.200000e+02      1.000000      0.000000      0.000000      1.000000   \n",
       "25%    5.040000e+03      1.000000      0.000000      0.000000      3.000000   \n",
       "50%    7.618000e+03      1.500000      0.000000      0.000000      3.000000   \n",
       "75%    1.068800e+04      2.000000      0.000000      0.000000      4.000000   \n",
       "max    1.651359e+06      3.500000      1.000000      4.000000      5.000000   \n",
       "\n",
       "              grade    sqft_above  sqft_basement      yr_built  yr_renovated  \\\n",
       "count  21613.000000  21613.000000   21613.000000  21613.000000  21613.000000   \n",
       "mean       7.656873   1788.390691     291.509045   1971.005136     84.402258   \n",
       "std        1.175459    828.090978     442.575043     29.373411    401.679240   \n",
       "min        1.000000    290.000000       0.000000   1900.000000      0.000000   \n",
       "25%        7.000000   1190.000000       0.000000   1951.000000      0.000000   \n",
       "50%        7.000000   1560.000000       0.000000   1975.000000      0.000000   \n",
       "75%        8.000000   2210.000000     560.000000   1997.000000      0.000000   \n",
       "max       13.000000   9410.000000    4820.000000   2015.000000   2015.000000   \n",
       "\n",
       "            zipcode           lat          long  sqft_living15     sqft_lot15  \n",
       "count  21613.000000  21613.000000  21613.000000   21613.000000   21613.000000  \n",
       "mean   98077.939805     47.560053   -122.213896    1986.552492   12768.455652  \n",
       "std       53.505026      0.138564      0.140828     685.391304   27304.179631  \n",
       "min    98001.000000     47.155900   -122.519000     399.000000     651.000000  \n",
       "25%    98033.000000     47.471000   -122.328000    1490.000000    5100.000000  \n",
       "50%    98065.000000     47.571800   -122.230000    1840.000000    7620.000000  \n",
       "75%    98118.000000     47.678000   -122.125000    2360.000000   10083.000000  \n",
       "max    98199.000000     47.777600   -121.315000    6210.000000  871200.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do we need to keep all the columns? \n",
    "X = housing_df.drop(columns=['id', 'price', 'date','zipcode'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = housing_df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "waterfront\n",
       "0    21450\n",
       "1      163\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the value counts of the `waterfront` feature? \n",
    "X['waterfront'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yr_renovated\n",
       "0       20699\n",
       "2014       91\n",
       "2013       37\n",
       "2003       36\n",
       "2005       35\n",
       "        ...  \n",
       "1951        1\n",
       "1959        1\n",
       "1948        1\n",
       "1954        1\n",
       "1944        1\n",
       "Name: count, Length: 70, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the value_counts of `yr_renovated` feature? \n",
    "X['yr_renovated'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        221900.0\n",
       "1        538000.0\n",
       "2        180000.0\n",
       "3        604000.0\n",
       "4        510000.0\n",
       "           ...   \n",
       "21608    360000.0\n",
       "21609    400000.0\n",
       "21610    402101.0\n",
       "21611    400000.0\n",
       "21612    325000.0\n",
       "Name: price, Length: 21613, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many opportunities to clean the data but we'll stop here. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train a DummyRegressor model \n",
    "\n",
    "from sklearn.dummy import DummyRegressor # Import DummyRegressor \n",
    "\n",
    "# Create a class object for the sklearn model.\n",
    "dummy = DummyRegressor()\n",
    "\n",
    "# fit the dummy regressor\n",
    "dummy.fit(X, y)\n",
    "\n",
    "# score the model \n",
    "dummy.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([540088.14176653, 540088.14176653, 540088.14176653, ...,\n",
       "       540088.14176653, 540088.14176653, 540088.14176653])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict on X using the model\n",
    "dummy.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision tree model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9991338290544213"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train a decision tree model \n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor # Import DecisionTreeRegressor \n",
    "\n",
    "# Create a class object for the sklearn model.\n",
    "dt = DecisionTreeRegressor(random_state=123)\n",
    "\n",
    "# fit the decision tree regressor \n",
    "dt.fit(X, y)\n",
    "\n",
    "# score the model \n",
    "dt.score(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are getting a perfect accuracy. Should we be happy with this model and deploy it? Why or why not?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's the depth of this model? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.get_depth()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data splitting "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's split the data and  \n",
    "- Train on the train split \n",
    "- Score on the test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data \n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9994394006711425"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate a class object \n",
    "dt = DecisionTreeRegressor(random_state=123)\n",
    "\n",
    "# Train a decision tree on X_train, y_train\n",
    "dt.fit(X_train, y_train)\n",
    "\n",
    "# Score on the train set\n",
    "dt.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.719915905190645"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Score on the test set\n",
    "dt.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Activity: Discuss the following questions in your group\n",
    "\n",
    "- Why is there a large gap between train and test scores? \n",
    "- What would be the effect of increasing or decreasing `test_size`?\n",
    "- Why are we setting the `random_state`? Is it a good idea to try a bunch of values for the `random_state` and pick the one which gives the best scores? \n",
    "- Would it be possible to further improve the scores? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try out different depths. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=1, random_state=123)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=1, random_state=123)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=1, random_state=123)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# max_depth= 1 \n",
    "dt = DecisionTreeRegressor(max_depth=1, random_state=123) \n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize your decision stump\n",
    "from sklearn.tree import plot_tree \n",
    "plot_tree(dt, feature_names = X.columns.tolist(), impurity=False, filled=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3209427041566191"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.score(X_train, y_train) # Score on the train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.31767136668453344"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.score(X_test, y_test) # Score on the test set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- How do these scores compare to the previous scores? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try depth 10. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=10, random_state=123)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=10, random_state=123)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=10, random_state=123)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = DecisionTreeRegressor(max_depth=10, random_state=123) # max_depth= 10 \n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9108334653214172"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.score(X_train, y_train) # Score on the train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7728396574320712"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.score(X_test, y_test) # Score on the test set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any improvements? Which depth should we pick? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Single validation set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are using the test data again and again. How about creating a validation set to pick the right depth and assessing the final model on the test set?   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a validation set \n",
    "X_tr, X_valid, y_tr, y_valid = train_test_split(X_train, y_train, test_size=0.2, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train_score</th>\n",
       "      <th>valid_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.319559</td>\n",
       "      <td>0.326616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.603739</td>\n",
       "      <td>0.555180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.754938</td>\n",
       "      <td>0.677567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.833913</td>\n",
       "      <td>0.737285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.890456</td>\n",
       "      <td>0.763480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.931896</td>\n",
       "      <td>0.790521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.963024</td>\n",
       "      <td>0.769030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.981643</td>\n",
       "      <td>0.752728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.991810</td>\n",
       "      <td>0.735637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.996424</td>\n",
       "      <td>0.745925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.998370</td>\n",
       "      <td>0.734048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.999213</td>\n",
       "      <td>0.741060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.999480</td>\n",
       "      <td>0.722873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.999544</td>\n",
       "      <td>0.723951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.999558</td>\n",
       "      <td>0.734986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.999562</td>\n",
       "      <td>0.724068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.999567</td>\n",
       "      <td>0.724410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    train_score  valid_score\n",
       "1      0.319559     0.326616\n",
       "3      0.603739     0.555180\n",
       "5      0.754938     0.677567\n",
       "7      0.833913     0.737285\n",
       "9      0.890456     0.763480\n",
       "11     0.931896     0.790521\n",
       "13     0.963024     0.769030\n",
       "15     0.981643     0.752728\n",
       "17     0.991810     0.735637\n",
       "19     0.996424     0.745925\n",
       "21     0.998370     0.734048\n",
       "23     0.999213     0.741060\n",
       "25     0.999480     0.722873\n",
       "27     0.999544     0.723951\n",
       "29     0.999558     0.734986\n",
       "31     0.999562     0.724068\n",
       "33     0.999567     0.724410"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr_scores = []\n",
    "valid_scores = []\n",
    "depths = np.arange(1, 35, 2)\n",
    "\n",
    "for depth in depths:  \n",
    "    # Create and fit a decision tree model for the given depth  \n",
    "    dt = DecisionTreeRegressor(max_depth=depth, random_state=123)\n",
    "    \n",
    "    # Calculate and append r2 scores on the training and validation sets\n",
    "    dt.fit(X_tr, y_tr)\n",
    "    tr_scores.append(dt.score(X_tr, y_tr))\n",
    "    valid_scores.append(dt.score(X_valid, y_valid))\n",
    "    \n",
    "results_single_valid_df = pd.DataFrame({\"train_score\": tr_scores, \n",
    "                           \"valid_score\": valid_scores},index = depths)\n",
    "results_single_valid_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_single_valid_df[['train_score', 'valid_score']].plot(ylabel='r2 scores');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What depth gives the \"best\" validation score? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_depth = results_single_valid_df.index.values[np.argmax(results_single_valid_df['valid_score'])]\n",
    "best_depth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's assess the best model on the test set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7784948928666875"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_model = DecisionTreeRegressor(max_depth=best_depth, random_state=123)\n",
    "test_model.fit(X_train, y_train)\n",
    "test_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- How do the test scores compare to the validation scores? \n",
    "- Can we have a more robust estimate of the test score? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "depths = np.arange(1, 35, 2)\n",
    "\n",
    "cv_train_scores = []\n",
    "cv_valid_scores = []\n",
    "for depth in depths: \n",
    "    # Create and fit a decision tree model for the given depth   \n",
    "    dt = DecisionTreeRegressor(max_depth = depth, random_state=123)\n",
    "\n",
    "    # Carry out cross-validation\n",
    "    scores = cross_validate(dt, X_train, y_train, return_train_score=True)\n",
    "    cv_train_scores.append(scores['train_score'].mean())\n",
    "    cv_valid_scores.append(scores['test_score'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train_score</th>\n",
       "      <th>valid_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.321050</td>\n",
       "      <td>0.322465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.603243</td>\n",
       "      <td>0.559284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.752169</td>\n",
       "      <td>0.688484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.835876</td>\n",
       "      <td>0.758259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.894960</td>\n",
       "      <td>0.768184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.938201</td>\n",
       "      <td>0.772185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.966812</td>\n",
       "      <td>0.760966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.983340</td>\n",
       "      <td>0.754620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.992220</td>\n",
       "      <td>0.730025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.996487</td>\n",
       "      <td>0.722803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.998440</td>\n",
       "      <td>0.726659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.999178</td>\n",
       "      <td>0.730704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.999438</td>\n",
       "      <td>0.711356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.999518</td>\n",
       "      <td>0.721917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.999539</td>\n",
       "      <td>0.729374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.999545</td>\n",
       "      <td>0.740319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.999546</td>\n",
       "      <td>0.706489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    train_score  valid_score\n",
       "1      0.321050     0.322465\n",
       "3      0.603243     0.559284\n",
       "5      0.752169     0.688484\n",
       "7      0.835876     0.758259\n",
       "9      0.894960     0.768184\n",
       "11     0.938201     0.772185\n",
       "13     0.966812     0.760966\n",
       "15     0.983340     0.754620\n",
       "17     0.992220     0.730025\n",
       "19     0.996487     0.722803\n",
       "21     0.998440     0.726659\n",
       "23     0.999178     0.730704\n",
       "25     0.999438     0.711356\n",
       "27     0.999518     0.721917\n",
       "29     0.999539     0.729374\n",
       "31     0.999545     0.740319\n",
       "33     0.999546     0.706489"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pd.DataFrame({\"train_score\": cv_train_scores, \n",
    "                           \"valid_score\": cv_valid_scores\n",
    "                           },\n",
    "                           index=depths\n",
    "                            )\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_df[['train_score', 'valid_score']].plot(ylabel='r2 score', title='Housing price prediction depth vs. r2 score');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's the \"best\" depth with cross-validation? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_depth = results_df.index.values[np.argmax(results_df['valid_score'])]\n",
    "best_depth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discuss the following questions in your group\n",
    "\n",
    "1. For which depth(s) we are underfitting? How about overfitting?\n",
    "2. Above we are picking the depth which gives us the best cross-validation score. Is it always a good idea to pick such a depth? What if you have a much simpler model (smaller `max_depth`), which gives us almost the same CV scores?\n",
    "3. If we care about the test scores in the end, why don't we use it in training? \n",
    "4. Do you trust our hyperparameter optimization? In other words, do you believe that we have found the best possible depth? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assessing on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9308647034083802"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_final = DecisionTreeRegressor(max_depth=best_depth, random_state=123)\n",
    "dt_final.fit(X_train, y_train)\n",
    "dt_final.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7784948928666875"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_final.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do these scores compare to the scores when we used a single validation set? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learned model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What's the depth of the model? \n",
    "dt_final.get_depth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_tree(dt_final, feature_names = X_train.columns.tolist(), impurity=False, filled=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00080741, 0.00327551, 0.25123925, 0.01808825, 0.00079645,\n",
       "       0.03213916, 0.01190633, 0.00106308, 0.36400802, 0.02313684,\n",
       "       0.00295235, 0.01209545, 0.00064647, 0.17216105, 0.06835056,\n",
       "       0.02416048, 0.01317334])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Which features are the most important ones?\n",
    "dt_final.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine feature importances. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>feature_importances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>grade</td>\n",
       "      <td>0.364008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sqft_living</td>\n",
       "      <td>0.251239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>lat</td>\n",
       "      <td>0.172161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>long</td>\n",
       "      <td>0.068351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>waterfront</td>\n",
       "      <td>0.032139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>sqft_living15</td>\n",
       "      <td>0.024160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sqft_above</td>\n",
       "      <td>0.023137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sqft_lot</td>\n",
       "      <td>0.018088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sqft_lot15</td>\n",
       "      <td>0.013173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>yr_built</td>\n",
       "      <td>0.012095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>view</td>\n",
       "      <td>0.011906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bathrooms</td>\n",
       "      <td>0.003276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sqft_basement</td>\n",
       "      <td>0.002952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>condition</td>\n",
       "      <td>0.001063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bedrooms</td>\n",
       "      <td>0.000807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>floors</td>\n",
       "      <td>0.000796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>yr_renovated</td>\n",
       "      <td>0.000646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         features  feature_importances\n",
       "8           grade             0.364008\n",
       "2     sqft_living             0.251239\n",
       "13            lat             0.172161\n",
       "14           long             0.068351\n",
       "5      waterfront             0.032139\n",
       "15  sqft_living15             0.024160\n",
       "9      sqft_above             0.023137\n",
       "3        sqft_lot             0.018088\n",
       "16     sqft_lot15             0.013173\n",
       "11       yr_built             0.012095\n",
       "6            view             0.011906\n",
       "1       bathrooms             0.003276\n",
       "10  sqft_basement             0.002952\n",
       "7       condition             0.001063\n",
       "0        bedrooms             0.000807\n",
       "4          floors             0.000796\n",
       "12   yr_renovated             0.000646"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame( \n",
    "    data = {\n",
    "        \"features\": dt_final.feature_names_in_,\n",
    "        \"feature_importances\": dt_final.feature_importances_\n",
    "    }\n",
    ")\n",
    "df.sort_values(\"feature_importances\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  }
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