{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The focus of the previous examples in this course was on classification problems. However, regression problems are also quite common in practice and it will be what we will try to explore in this application.\n",
    "\n",
    "## Problem Setup\n",
    "\n",
    "The dataset that we will be using is the Kaggle dataset called [\"House Sales in King County, USA\"](https://www.kaggle.com/datasets/harlfoxem/housesalesprediction). As far as I know, this was not used in a Kaggle competition. However, it is a quite popular dataset on Kaggle. The description reads:\n",
    "\n",
    "> This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015.\n",
    ">\n",
    "> It's a great dataset for evaluating simple regression models.\n",
    "\n",
    "This means that the dataset is a snapshot of house prices in King County, USA, between May 2014 and May 2015. The task, then, is quite straightforward: given a set of features, we want to predict the price of a house.\n",
    "\n",
    "\n",
    "## Dataset\n",
    "\n",
    "Unfortunately, the dataset does not have a detailed description of the variables. However, in the comment section, some users found references with variable descriptions. The variables in the dataset should be as follows:\n",
    "\n",
    "| Variable | Description |\n",
    "|:----|:----------------|\n",
    "| id | Unique ID for each home sold |\n",
    "| date | Date of the home sale |\n",
    "| price | Price of each home sold |\n",
    "| bedrooms | Number of bedrooms |\n",
    "| bathrooms | Number of bathrooms, where .5 accounts for a room with a toilet but no shower |\n",
    "| sqft_living | Square footage of the apartments' interior living space |\n",
    "| sqft_lot | Square footage of the land space |\n",
    "| floors | Number of floors |\n",
    "| waterfront | A dummy variable for whether the apartment was overlooking the waterfront or not |\n",
    "| view | An index from 0 to 4 of how good the view of the property was |\n",
    "| condition | An index from 1 to 5 on the condition of the apartment |\n",
    "| grade | An index from 1 to 13, where 1-3 falls short of building construction and design, 7 has an average level of construction and design, and 11-13 have a high quality level of construction and design. |\n",
    "| sqft_above | The square footage of the interior housing space that is above ground level |\n",
    "| sqft_basement | The square footage of the interior housing space that is below ground level |\n",
    "| yr_built | The year the house was initially built |\n",
    "| yr_renovated | The year of the house’s last renovation |\n",
    "| zipcode | What zipcode area the house is in |\n",
    "| lat | Lattitude |\n",
    "| long | Longitude |\n",
    "| sqft_living15 | The square footage of interior housing living space for the nearest 15 neighbors |\n",
    "| sqft_lot15 | The square footage of the land lots of the nearest 15 neighbors |\n",
    "\n",
    "\n",
    "## Putting the Problem into the Context of the Course\n",
    "\n",
    "The problem of predicting house prices is a **regression problem** which belongs to the type of **supervised learning** problems. We will use the same tools that we have used in the previous examples to solve this problem. The main difference is that we will be using regression models instead of classification models.\n",
    "\n",
    "\n",
    "## Setting up the Environment\n",
    "\n",
    "We will start by setting up the environment by importing the necessary libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:44.520152Z",
     "iopub.status.busy": "2024-05-31T23:13:44.519919Z",
     "iopub.status.idle": "2024-05-31T23:13:45.546732Z",
     "shell.execute_reply": "2024-05-31T23:13:45.546242Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's download the dataset automatically, unzip it, and place it in a folder called `data` if you haven't done so already"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:45.551063Z",
     "iopub.status.busy": "2024-05-31T23:13:45.550480Z",
     "iopub.status.idle": "2024-05-31T23:13:45.557511Z",
     "shell.execute_reply": "2024-05-31T23:13:45.556455Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset already downloaded!\n"
     ]
    }
   ],
   "source": [
    "from io import BytesIO\n",
    "from urllib.request import urlopen\n",
    "from zipfile import ZipFile\n",
    "import os.path\n",
    "\n",
    "# Check if the file exists\n",
    "if not os.path.isfile('data/kc_house_data.csv'):\n",
    "\n",
    "    print('Downloading dataset...')\n",
    "\n",
    "    # Define the dataset to be downloaded\n",
    "    zipurl = 'https://www.kaggle.com/api/v1/datasets/download/harlfoxem/housesalesprediction'\n",
    "\n",
    "    # Download and unzip the dataset in the data folder\n",
    "    with urlopen(zipurl) as zipresp:\n",
    "        with ZipFile(BytesIO(zipresp.read())) as zfile:\n",
    "            zfile.extractall('data')\n",
    "\n",
    "    print('DONE!')\n",
    "\n",
    "else:\n",
    "\n",
    "    print('Dataset already downloaded!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we can load the data into a DataFrame using the `read_csv` function from the `pandas` library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:45.592677Z",
     "iopub.status.busy": "2024-05-31T23:13:45.592399Z",
     "iopub.status.idle": "2024-05-31T23:13:45.643253Z",
     "shell.execute_reply": "2024-05-31T23:13:45.642527Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/kc_house_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also download some precomputed models that we will use later on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:45.646747Z",
     "iopub.status.busy": "2024-05-31T23:13:45.646497Z",
     "iopub.status.idle": "2024-05-31T23:13:45.652700Z",
     "shell.execute_reply": "2024-05-31T23:13:45.651975Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reg_nn.joblib already downloaded!\n",
      "reg_nn_cv.joblib already downloaded!\n",
      "reg_xgb_cv.joblib already downloaded!\n",
      "reg_rf_cv.joblib.zip already downloaded!\n"
     ]
    }
   ],
   "source": [
    "for file_name in ['reg_nn.joblib', 'reg_nn_cv.joblib', 'reg_xgb_cv.joblib', 'reg_rf_cv.joblib.zip']:\n",
    "\n",
    "    if not os.path.isfile(file_name):\n",
    "\n",
    "        print(f'Downloading {file_name}...')\n",
    "\n",
    "        # Generate the download link\n",
    "        url = f'https://github.com/jmarbet/data-science-course/raw/main/notebooks/{file_name}'\n",
    "\n",
    "        if file_name.endswith('.zip'):\n",
    "\n",
    "            # Download and unzip the file\n",
    "            with urlopen(url) as zipresp:\n",
    "                with ZipFile(BytesIO(zipresp.read())) as zfile:\n",
    "                    zfile.extractall('')\n",
    "\n",
    "        else:\n",
    "\n",
    "            # Download the file\n",
    "            with urlopen(url) as response, open(file_name, 'wb') as out_file:\n",
    "                data = response.read()\n",
    "                out_file.write(data)\n",
    "\n",
    "        print('DONE!')\n",
    "\n",
    "    else:\n",
    "\n",
    "        print(f'{file_name} already downloaded!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "\n",
    "As with any new dataset, we first need to familiarize ourselves with the data. We will start by looking at the first few rows of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:45.656589Z",
     "iopub.status.busy": "2024-05-31T23:13:45.656301Z",
     "iopub.status.idle": "2024-05-31T23:13:45.669965Z",
     "shell.execute_reply": "2024-05-31T23:13:45.669342Z"
    }
   },
   "outputs": [
    {
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       "      <th>id</th>\n",
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       "      <td>2487200875</td>\n",
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       "      <th>date</th>\n",
       "      <td>20141013T000000</td>\n",
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       "      <td>20150225T000000</td>\n",
       "      <td>20141209T000000</td>\n",
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       "      <td>538000.0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>604000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>1180</td>\n",
       "      <td>2570</td>\n",
       "      <td>770</td>\n",
       "      <td>1960</td>\n",
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       "      <th>sqft_lot</th>\n",
       "      <td>5650</td>\n",
       "      <td>7242</td>\n",
       "      <td>10000</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floors</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterfront</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_above</th>\n",
       "      <td>1180</td>\n",
       "      <td>2170</td>\n",
       "      <td>770</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_basement</th>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>0</td>\n",
       "      <td>910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <td>1955</td>\n",
       "      <td>1951</td>\n",
       "      <td>1933</td>\n",
       "      <td>1965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_renovated</th>\n",
       "      <td>0</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zipcode</th>\n",
       "      <td>98178</td>\n",
       "      <td>98125</td>\n",
       "      <td>98028</td>\n",
       "      <td>98136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>47.5112</td>\n",
       "      <td>47.721</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>47.5208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long</th>\n",
       "      <td>-122.257</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>-122.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living15</th>\n",
       "      <td>1340</td>\n",
       "      <td>1690</td>\n",
       "      <td>2720</td>\n",
       "      <td>1360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot15</th>\n",
       "      <td>5650</td>\n",
       "      <td>7639</td>\n",
       "      <td>8062</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                             0                1                2  \\\n",
       "id                  7129300520       6414100192       5631500400   \n",
       "date           20141013T000000  20141209T000000  20150225T000000   \n",
       "price                 221900.0         538000.0         180000.0   \n",
       "bedrooms                     3                3                2   \n",
       "bathrooms                  1.0             2.25              1.0   \n",
       "sqft_living               1180             2570              770   \n",
       "sqft_lot                  5650             7242            10000   \n",
       "floors                     1.0              2.0              1.0   \n",
       "waterfront                   0                0                0   \n",
       "view                         0                0                0   \n",
       "condition                    3                3                3   \n",
       "grade                        7                7                6   \n",
       "sqft_above                1180             2170              770   \n",
       "sqft_basement                0              400                0   \n",
       "yr_built                  1955             1951             1933   \n",
       "yr_renovated                 0             1991                0   \n",
       "zipcode                  98178            98125            98028   \n",
       "lat                    47.5112           47.721          47.7379   \n",
       "long                  -122.257         -122.319         -122.233   \n",
       "sqft_living15             1340             1690             2720   \n",
       "sqft_lot15                5650             7639             8062   \n",
       "\n",
       "                             3  \n",
       "id                  2487200875  \n",
       "date           20141209T000000  \n",
       "price                 604000.0  \n",
       "bedrooms                     4  \n",
       "bathrooms                  3.0  \n",
       "sqft_living               1960  \n",
       "sqft_lot                  5000  \n",
       "floors                     1.0  \n",
       "waterfront                   0  \n",
       "view                         0  \n",
       "condition                    5  \n",
       "grade                        7  \n",
       "sqft_above                1050  \n",
       "sqft_basement              910  \n",
       "yr_built                  1965  \n",
       "yr_renovated                 0  \n",
       "zipcode                  98136  \n",
       "lat                    47.5208  \n",
       "long                  -122.393  \n",
       "sqft_living15             1360  \n",
       "sqft_lot15                5000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(4).T # Transpose the dataframe for readability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and for reference, we can also run `df.info()` again to see the data types of the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:45.673059Z",
     "iopub.status.busy": "2024-05-31T23:13:45.672813Z",
     "iopub.status.idle": "2024-05-31T23:13:45.682326Z",
     "shell.execute_reply": "2024-05-31T23:13:45.681794Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21613 entries, 0 to 21612\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   id             21613 non-null  int64  \n",
      " 1   date           21613 non-null  object \n",
      " 2   price          21613 non-null  float64\n",
      " 3   bedrooms       21613 non-null  int64  \n",
      " 4   bathrooms      21613 non-null  float64\n",
      " 5   sqft_living    21613 non-null  int64  \n",
      " 6   sqft_lot       21613 non-null  int64  \n",
      " 7   floors         21613 non-null  float64\n",
      " 8   waterfront     21613 non-null  int64  \n",
      " 9   view           21613 non-null  int64  \n",
      " 10  condition      21613 non-null  int64  \n",
      " 11  grade          21613 non-null  int64  \n",
      " 12  sqft_above     21613 non-null  int64  \n",
      " 13  sqft_basement  21613 non-null  int64  \n",
      " 14  yr_built       21613 non-null  int64  \n",
      " 15  yr_renovated   21613 non-null  int64  \n",
      " 16  zipcode        21613 non-null  int64  \n",
      " 17  lat            21613 non-null  float64\n",
      " 18  long           21613 non-null  float64\n",
      " 19  sqft_living15  21613 non-null  int64  \n",
      " 20  sqft_lot15     21613 non-null  int64  \n",
      "dtypes: float64(5), int64(15), object(1)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What immediately stands out is that the `date` column does not seem to be a proper datetime object. So, let's fix that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:45.685216Z",
     "iopub.status.busy": "2024-05-31T23:13:45.684976Z",
     "iopub.status.idle": "2024-05-31T23:13:45.701921Z",
     "shell.execute_reply": "2024-05-31T23:13:45.701190Z"
    }
   },
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:45.705709Z",
     "iopub.status.busy": "2024-05-31T23:13:45.705408Z",
     "iopub.status.idle": "2024-05-31T23:13:45.718823Z",
     "shell.execute_reply": "2024-05-31T23:13:45.718230Z"
    }
   },
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <td>2014-10-13 00:00:00</td>\n",
       "      <td>2014-12-09 00:00:00</td>\n",
       "      <td>2015-02-25 00:00:00</td>\n",
       "      <td>2014-12-09 00:00:00</td>\n",
       "      <td>2015-02-18 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>221900.0</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>510000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>1180</td>\n",
       "      <td>2570</td>\n",
       "      <td>770</td>\n",
       "      <td>1960</td>\n",
       "      <td>1680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot</th>\n",
       "      <td>5650</td>\n",
       "      <td>7242</td>\n",
       "      <td>10000</td>\n",
       "      <td>5000</td>\n",
       "      <td>8080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floors</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterfront</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_above</th>\n",
       "      <td>1180</td>\n",
       "      <td>2170</td>\n",
       "      <td>770</td>\n",
       "      <td>1050</td>\n",
       "      <td>1680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_basement</th>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>0</td>\n",
       "      <td>910</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <td>1955</td>\n",
       "      <td>1951</td>\n",
       "      <td>1933</td>\n",
       "      <td>1965</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_renovated</th>\n",
       "      <td>0</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zipcode</th>\n",
       "      <td>98178</td>\n",
       "      <td>98125</td>\n",
       "      <td>98028</td>\n",
       "      <td>98136</td>\n",
       "      <td>98074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>47.5112</td>\n",
       "      <td>47.721</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>47.6168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long</th>\n",
       "      <td>-122.257</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>-122.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living15</th>\n",
       "      <td>1340</td>\n",
       "      <td>1690</td>\n",
       "      <td>2720</td>\n",
       "      <td>1360</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot15</th>\n",
       "      <td>5650</td>\n",
       "      <td>7639</td>\n",
       "      <td>8062</td>\n",
       "      <td>5000</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 0                    1                    2  \\\n",
       "id                      7129300520           6414100192           5631500400   \n",
       "date           2014-10-13 00:00:00  2014-12-09 00:00:00  2015-02-25 00:00:00   \n",
       "price                     221900.0             538000.0             180000.0   \n",
       "bedrooms                         3                    3                    2   \n",
       "bathrooms                      1.0                 2.25                  1.0   \n",
       "sqft_living                   1180                 2570                  770   \n",
       "sqft_lot                      5650                 7242                10000   \n",
       "floors                         1.0                  2.0                  1.0   \n",
       "waterfront                       0                    0                    0   \n",
       "view                             0                    0                    0   \n",
       "condition                        3                    3                    3   \n",
       "grade                            7                    7                    6   \n",
       "sqft_above                    1180                 2170                  770   \n",
       "sqft_basement                    0                  400                    0   \n",
       "yr_built                      1955                 1951                 1933   \n",
       "yr_renovated                     0                 1991                    0   \n",
       "zipcode                      98178                98125                98028   \n",
       "lat                        47.5112               47.721              47.7379   \n",
       "long                      -122.257             -122.319             -122.233   \n",
       "sqft_living15                 1340                 1690                 2720   \n",
       "sqft_lot15                    5650                 7639                 8062   \n",
       "\n",
       "                                 3                    4  \n",
       "id                      2487200875           1954400510  \n",
       "date           2014-12-09 00:00:00  2015-02-18 00:00:00  \n",
       "price                     604000.0             510000.0  \n",
       "bedrooms                         4                    3  \n",
       "bathrooms                      3.0                  2.0  \n",
       "sqft_living                   1960                 1680  \n",
       "sqft_lot                      5000                 8080  \n",
       "floors                         1.0                  1.0  \n",
       "waterfront                       0                    0  \n",
       "view                             0                    0  \n",
       "condition                        5                    3  \n",
       "grade                            7                    8  \n",
       "sqft_above                    1050                 1680  \n",
       "sqft_basement                  910                    0  \n",
       "yr_built                      1965                 1987  \n",
       "yr_renovated                     0                    0  \n",
       "zipcode                      98136                98074  \n",
       "lat                        47.5208              47.6168  \n",
       "long                      -122.393             -122.045  \n",
       "sqft_living15                 1360                 1800  \n",
       "sqft_lot15                    5000                 7503  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:45.721662Z",
     "iopub.status.busy": "2024-05-31T23:13:45.721442Z",
     "iopub.status.idle": "2024-05-31T23:13:45.729484Z",
     "shell.execute_reply": "2024-05-31T23:13:45.728881Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21613 entries, 0 to 21612\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   id             21613 non-null  int64         \n",
      " 1   date           21613 non-null  datetime64[ns]\n",
      " 2   price          21613 non-null  float64       \n",
      " 3   bedrooms       21613 non-null  int64         \n",
      " 4   bathrooms      21613 non-null  float64       \n",
      " 5   sqft_living    21613 non-null  int64         \n",
      " 6   sqft_lot       21613 non-null  int64         \n",
      " 7   floors         21613 non-null  float64       \n",
      " 8   waterfront     21613 non-null  int64         \n",
      " 9   view           21613 non-null  int64         \n",
      " 10  condition      21613 non-null  int64         \n",
      " 11  grade          21613 non-null  int64         \n",
      " 12  sqft_above     21613 non-null  int64         \n",
      " 13  sqft_basement  21613 non-null  int64         \n",
      " 14  yr_built       21613 non-null  int64         \n",
      " 15  yr_renovated   21613 non-null  int64         \n",
      " 16  zipcode        21613 non-null  int64         \n",
      " 17  lat            21613 non-null  float64       \n",
      " 18  long           21613 non-null  float64       \n",
      " 19  sqft_living15  21613 non-null  int64         \n",
      " 20  sqft_lot15     21613 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(5), int64(15)\n",
      "memory usage: 3.5 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much better! Note how the variable type changed for `date`. On the topic of variable types, it seems surprising that `bathrooms` and `floors` are of type `float64`. Let's check if there is anything unusual about these variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:45.732528Z",
     "iopub.status.busy": "2024-05-31T23:13:45.732287Z",
     "iopub.status.idle": "2024-05-31T23:13:45.737935Z",
     "shell.execute_reply": "2024-05-31T23:13:45.737417Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bathrooms\n",
       "2.50    5380\n",
       "1.00    3852\n",
       "1.75    3048\n",
       "2.25    2047\n",
       "2.00    1930\n",
       "1.50    1446\n",
       "2.75    1185\n",
       "3.00     753\n",
       "3.50     731\n",
       "3.25     589\n",
       "3.75     155\n",
       "4.00     136\n",
       "4.50     100\n",
       "4.25      79\n",
       "0.75      72\n",
       "4.75      23\n",
       "5.00      21\n",
       "5.25      13\n",
       "0.00      10\n",
       "5.50      10\n",
       "1.25       9\n",
       "6.00       6\n",
       "0.50       4\n",
       "5.75       4\n",
       "6.75       2\n",
       "8.00       2\n",
       "6.25       2\n",
       "6.50       2\n",
       "7.50       1\n",
       "7.75       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bathrooms'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:45.740701Z",
     "iopub.status.busy": "2024-05-31T23:13:45.740491Z",
     "iopub.status.idle": "2024-05-31T23:13:45.745098Z",
     "shell.execute_reply": "2024-05-31T23:13:45.744587Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "floors\n",
       "1.0    10680\n",
       "2.0     8241\n",
       "1.5     1910\n",
       "3.0      613\n",
       "2.5      161\n",
       "3.5        8\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['floors'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that the number of bathrooms and floors is not always an integer. This is a bit surprising, but a possible interpretation is that in the case of bathrooms, smaller bathrooms with e.g., only a toilet and a sink are counted as 0.5 bathrooms, while a full bathroom would also need a shower or a bathtub. The same logic could apply to floors, where a split-level house could have, e.g., 1.5 floors. This is just a guess, but it seems plausible.\n",
    "\n",
    "Note also that there do not seem to be any missing values, at least none were encoded as such. Now, let's look at the summary statistics of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:45.747807Z",
     "iopub.status.busy": "2024-05-31T23:13:45.747600Z",
     "iopub.status.idle": "2024-05-31T23:13:45.807969Z",
     "shell.execute_reply": "2024-05-31T23:13:45.807092Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>4580301520.864988</td>\n",
       "      <td>1000102.0</td>\n",
       "      <td>2123049194.0</td>\n",
       "      <td>3904930410.0</td>\n",
       "      <td>7308900445.0</td>\n",
       "      <td>9900000190.0</td>\n",
       "      <td>2876565571.312049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <td>21613</td>\n",
       "      <td>2014-10-29 04:38:01.959931648</td>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
       "      <td>2014-07-22 00:00:00</td>\n",
       "      <td>2014-10-16 00:00:00</td>\n",
       "      <td>2015-02-17 00:00:00</td>\n",
       "      <td>2015-05-27 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>540088.141767</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>321950.0</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>645000.0</td>\n",
       "      <td>7700000.0</td>\n",
       "      <td>367127.196483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>3.370842</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.930062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>2.114757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.770163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>2079.899736</td>\n",
       "      <td>290.0</td>\n",
       "      <td>1427.0</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>2550.0</td>\n",
       "      <td>13540.0</td>\n",
       "      <td>918.440897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>15106.967566</td>\n",
       "      <td>520.0</td>\n",
       "      <td>5040.0</td>\n",
       "      <td>7618.0</td>\n",
       "      <td>10688.0</td>\n",
       "      <td>1651359.0</td>\n",
       "      <td>41420.511515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floors</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>1.494309</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.539989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterfront</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>0.007542</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.086517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>0.234303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.766318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>3.40943</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.650743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>7.656873</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.175459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_above</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>1788.390691</td>\n",
       "      <td>290.0</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>2210.0</td>\n",
       "      <td>9410.0</td>\n",
       "      <td>828.090978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_basement</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>291.509045</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>4820.0</td>\n",
       "      <td>442.575043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>1971.005136</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>29.373411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_renovated</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>84.402258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>401.67924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zipcode</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>98077.939805</td>\n",
       "      <td>98001.0</td>\n",
       "      <td>98033.0</td>\n",
       "      <td>98065.0</td>\n",
       "      <td>98118.0</td>\n",
       "      <td>98199.0</td>\n",
       "      <td>53.505026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>47.560053</td>\n",
       "      <td>47.1559</td>\n",
       "      <td>47.471</td>\n",
       "      <td>47.5718</td>\n",
       "      <td>47.678</td>\n",
       "      <td>47.7776</td>\n",
       "      <td>0.138564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>-122.213896</td>\n",
       "      <td>-122.519</td>\n",
       "      <td>-122.328</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>-122.125</td>\n",
       "      <td>-121.315</td>\n",
       "      <td>0.140828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living15</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>1986.552492</td>\n",
       "      <td>399.0</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>1840.0</td>\n",
       "      <td>2360.0</td>\n",
       "      <td>6210.0</td>\n",
       "      <td>685.391304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot15</th>\n",
       "      <td>21613.0</td>\n",
       "      <td>12768.455652</td>\n",
       "      <td>651.0</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>7620.0</td>\n",
       "      <td>10083.0</td>\n",
       "      <td>871200.0</td>\n",
       "      <td>27304.179631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count                           mean                  min  \\\n",
       "id             21613.0              4580301520.864988            1000102.0   \n",
       "date             21613  2014-10-29 04:38:01.959931648  2014-05-02 00:00:00   \n",
       "price          21613.0                  540088.141767              75000.0   \n",
       "bedrooms       21613.0                       3.370842                  0.0   \n",
       "bathrooms      21613.0                       2.114757                  0.0   \n",
       "sqft_living    21613.0                    2079.899736                290.0   \n",
       "sqft_lot       21613.0                   15106.967566                520.0   \n",
       "floors         21613.0                       1.494309                  1.0   \n",
       "waterfront     21613.0                       0.007542                  0.0   \n",
       "view           21613.0                       0.234303                  0.0   \n",
       "condition      21613.0                        3.40943                  1.0   \n",
       "grade          21613.0                       7.656873                  1.0   \n",
       "sqft_above     21613.0                    1788.390691                290.0   \n",
       "sqft_basement  21613.0                     291.509045                  0.0   \n",
       "yr_built       21613.0                    1971.005136               1900.0   \n",
       "yr_renovated   21613.0                      84.402258                  0.0   \n",
       "zipcode        21613.0                   98077.939805              98001.0   \n",
       "lat            21613.0                      47.560053              47.1559   \n",
       "long           21613.0                    -122.213896             -122.519   \n",
       "sqft_living15  21613.0                    1986.552492                399.0   \n",
       "sqft_lot15     21613.0                   12768.455652                651.0   \n",
       "\n",
       "                               25%                  50%                  75%  \\\n",
       "id                    2123049194.0         3904930410.0         7308900445.0   \n",
       "date           2014-07-22 00:00:00  2014-10-16 00:00:00  2015-02-17 00:00:00   \n",
       "price                     321950.0             450000.0             645000.0   \n",
       "bedrooms                       3.0                  3.0                  4.0   \n",
       "bathrooms                     1.75                 2.25                  2.5   \n",
       "sqft_living                 1427.0               1910.0               2550.0   \n",
       "sqft_lot                    5040.0               7618.0              10688.0   \n",
       "floors                         1.0                  1.5                  2.0   \n",
       "waterfront                     0.0                  0.0                  0.0   \n",
       "view                           0.0                  0.0                  0.0   \n",
       "condition                      3.0                  3.0                  4.0   \n",
       "grade                          7.0                  7.0                  8.0   \n",
       "sqft_above                  1190.0               1560.0               2210.0   \n",
       "sqft_basement                  0.0                  0.0                560.0   \n",
       "yr_built                    1951.0               1975.0               1997.0   \n",
       "yr_renovated                   0.0                  0.0                  0.0   \n",
       "zipcode                    98033.0              98065.0              98118.0   \n",
       "lat                         47.471              47.5718               47.678   \n",
       "long                      -122.328              -122.23             -122.125   \n",
       "sqft_living15               1490.0               1840.0               2360.0   \n",
       "sqft_lot15                  5100.0               7620.0              10083.0   \n",
       "\n",
       "                               max                std  \n",
       "id                    9900000190.0  2876565571.312049  \n",
       "date           2015-05-27 00:00:00                NaN  \n",
       "price                    7700000.0      367127.196483  \n",
       "bedrooms                      33.0           0.930062  \n",
       "bathrooms                      8.0           0.770163  \n",
       "sqft_living                13540.0         918.440897  \n",
       "sqft_lot                 1651359.0       41420.511515  \n",
       "floors                         3.5           0.539989  \n",
       "waterfront                     1.0           0.086517  \n",
       "view                           4.0           0.766318  \n",
       "condition                      5.0           0.650743  \n",
       "grade                         13.0           1.175459  \n",
       "sqft_above                  9410.0         828.090978  \n",
       "sqft_basement               4820.0         442.575043  \n",
       "yr_built                    2015.0          29.373411  \n",
       "yr_renovated                2015.0          401.67924  \n",
       "zipcode                    98199.0          53.505026  \n",
       "lat                        47.7776           0.138564  \n",
       "long                      -121.315           0.140828  \n",
       "sqft_living15               6210.0         685.391304  \n",
       "sqft_lot15                871200.0       27304.179631  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the pair plot of some of the quantitative variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:45.812285Z",
     "iopub.status.busy": "2024-05-31T23:13:45.811957Z",
     "iopub.status.idle": "2024-05-31T23:13:51.539356Z",
     "shell.execute_reply": "2024-05-31T23:13:51.538800Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/datascience_course_cemfi_dev/lib/python3.8/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fe6c8a45d00>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1250x1250 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df[['price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot']], diag_kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unsurprisingly, there seems to be a positive correlation between the square footage of the living area (or number of bedrooms, or number of bathrooms) and the price of a house. However, there does not seem to be such a relationship between the square footage of the lot and the price. This is more surprising given that land prices can be very high in some areas. However, if these \"houses\" include many apartments (that do not include the land they are built on), this could explain the lack of a relationship. There also seems to be one house with more than 30 bedrooms. This seems a bit unusual, so let's have a closer look"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:51.543705Z",
     "iopub.status.busy": "2024-05-31T23:13:51.543456Z",
     "iopub.status.idle": "2024-05-31T23:13:51.553325Z",
     "shell.execute_reply": "2024-05-31T23:13:51.552572Z"
    }
   },
   "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>15870</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>2402100895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <td>2014-06-25 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>640000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>1620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot</th>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floors</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterfront</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_above</th>\n",
       "      <td>1040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_basement</th>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <td>1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_renovated</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zipcode</th>\n",
       "      <td>98103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>47.6878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long</th>\n",
       "      <td>-122.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living15</th>\n",
       "      <td>1330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot15</th>\n",
       "      <td>4700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             15870\n",
       "id                      2402100895\n",
       "date           2014-06-25 00:00:00\n",
       "price                     640000.0\n",
       "bedrooms                        33\n",
       "bathrooms                     1.75\n",
       "sqft_living                   1620\n",
       "sqft_lot                      6000\n",
       "floors                         1.0\n",
       "waterfront                       0\n",
       "view                             0\n",
       "condition                        5\n",
       "grade                            7\n",
       "sqft_above                    1040\n",
       "sqft_basement                  580\n",
       "yr_built                      1947\n",
       "yr_renovated                     0\n",
       "zipcode                      98103\n",
       "lat                        47.6878\n",
       "long                      -122.331\n",
       "sqft_living15                 1330\n",
       "sqft_lot15                    4700"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('bedrooms > 30').T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What a bargain! A house with 33 bedrooms for only $640000! However, it just has 1.75 bathrooms. It's maybe not that good of a deal after all. Considering that 1040 square feet corresponds to around 96 m^2. This seems like an error in the data. We will remove this observation from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:51.557784Z",
     "iopub.status.busy": "2024-05-31T23:13:51.557496Z",
     "iopub.status.idle": "2024-05-31T23:13:51.566185Z",
     "shell.execute_reply": "2024-05-31T23:13:51.565321Z"
    }
   },
   "outputs": [],
   "source": [
    "df = df.query('bedrooms < 30')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also look at the distribution of the number of bedrooms and floors and how if affects prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:51.569531Z",
     "iopub.status.busy": "2024-05-31T23:13:51.569278Z",
     "iopub.status.idle": "2024-05-31T23:13:51.794855Z",
     "shell.execute_reply": "2024-05-31T23:13:51.794277Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='bedrooms', ylabel='price'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['bedrooms'],y=df['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:51.797912Z",
     "iopub.status.busy": "2024-05-31T23:13:51.797686Z",
     "iopub.status.idle": "2024-05-31T23:13:51.966432Z",
     "shell.execute_reply": "2024-05-31T23:13:51.965377Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='floors', ylabel='price'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['floors'],y=df['price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be great variability in the prices for a given number of bedrooms or floors.\n",
    "\n",
    "Interestingly, we also have latitudinal and longitudinal information. We can use this to plot the houses on a map. Let's do that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:51.969989Z",
     "iopub.status.busy": "2024-05-31T23:13:51.969785Z",
     "iopub.status.idle": "2024-05-31T23:13:52.577990Z",
     "shell.execute_reply": "2024-05-31T23:13:52.577460Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    \n",
       "            var heat_map_f794b751f65c19e2dcf24718303e70d5 = L.heatLayer(\n",
       "                [[47.1559, -121.646, 380000.0], [47.1593, -121.957, 687000.0], [47.1622, -121.971, 750000.0], [47.1647, -121.973, 335000.0], [47.1764, -122.026, 245000.0], [47.1775, -122.022, 360000.0], [47.1776, -122.011, 230000.0], [47.1776, -121.944, 432000.0], [47.1795, -122.036, 790000.0], [47.1803, -121.974, 265000.0], [47.1808, -122.023, 350000.0], [47.184, -121.959, 330000.0], [47.1853, -122.017, 316000.0], [47.1879, -121.973, 487000.0], [47.1895, -122.015, 350000.0], [47.1896, -122.014, 346290.0], [47.1896, -122.013, 353000.0], [47.19, -122.016, 323000.0], [47.19, -122.013, 339000.0], [47.1903, -122.013, 319950.0], [47.1913, -122.015, 245000.0], [47.1913, -122.012, 295000.0], [47.1923, -122.014, 314950.0], [47.1924, -121.988, 275000.0], [47.1927, -122.015, 285000.0], [47.1927, -122.011, 277000.0], [47.1927, -122.01, 330000.0], [47.1928, -122.01, 303000.0], [47.1932, -122.008, 308950.0], [47.1934, -121.977, 272000.0], [47.1936, -121.981, 230000.0], [47.1937, -121.99, 272950.0], [47.1937, -121.98, 285000.0], [47.1937, -121.977, 325000.0], [47.1938, -121.981, 240000.0], [47.194, -121.981, 280000.0], [47.1941, -122.038, 468000.0], [47.1941, -121.979, 275000.0], [47.1942, -121.99, 252000.0], [47.1942, -121.977, 280000.0], [47.1943, -121.991, 270000.0], [47.1944, -122.013, 297000.0], [47.1944, -121.974, 277500.0], [47.1946, -122.003, 215000.0], [47.1946, -122.0, 207200.0], [47.1947, -122.008, 315000.0], [47.1947, -121.989, 271900.0], [47.1948, -121.999, 202000.0], [47.1948, -121.975, 251826.5], [47.1948, -121.974, 249950.0], [47.1949, -122.01, 283000.0], [47.1949, -121.989, 231200.0], [47.1949, -121.982, 235000.0], [47.1951, -122.01, 344000.0], [47.1951, -122.001, 218000.0], [47.1952, -121.986, 272000.0], [47.1952, -121.976, 340000.0], [47.1955, -121.999, 180000.0], [47.1955, -121.991, 273000.0], [47.1955, -121.987, 222500.0], [47.1956, -121.986, 220000.0], [47.1956, -121.985, 279000.0], [47.1958, -122.009, 225000.0], [47.1958, -122.003, 195000.0], [47.1958, -122.002, 187000.0], [47.1958, -121.974, 293000.0], [47.1959, -122.01, 305000.0], [47.196, -122.011, 320000.0], [47.196, -122.006, 340000.0], [47.1963, -121.997, 215000.0], [47.1964, -122.009, 324360.0], [47.1967, -121.998, 237500.0], [47.1967, -121.966, 369000.0], [47.1969, -122.011, 289000.0], [47.1976, -121.999, 269950.0], [47.1981, -122.003, 225000.0], [47.1983, -122.0, 224500.0], [47.1985, -122.001, 245000.0], [47.1986, -121.967, 340000.0], [47.1991, -122.013, 380000.0], [47.1996, -122.003, 415000.0], [47.2003, -121.996, 199990.0], [47.2004, -122.003, 295000.0], [47.2005, -121.999, 219950.0], [47.2008, -121.995, 210000.0], [47.201, -121.999, 220000.0], [47.2012, -122.0, 245000.0], [47.2015, -122.005, 278500.0], [47.2015, -122.003, 265000.0], [47.2016, -122.006, 195000.0], [47.202, -121.994, 209000.0], [47.2021, -122.002, 224950.0], [47.2021, -121.999, 219950.0], [47.2021, -121.996, 169000.0], [47.2025, -121.999, 262500.0], [47.2026, -122.0, 239000.0], [47.2027, -122.0, 264950.0], [47.2032, -121.964, 312000.0], [47.2034, -122.003, 449999.0], [47.2039, -122.003, 185000.0], [47.2041, -122.021, 379500.0], [47.2043, -122.008, 165050.0], [47.2043, -121.996, 257100.0], [47.2046, -121.996, 347500.0], [47.2048, -121.999, 265000.0], [47.205, -121.996, 231000.0], [47.205, -121.995, 195000.0], [47.2051, -122.006, 225000.0], [47.2052, -121.987, 195000.0], [47.2053, -121.993, 197500.0], [47.2056, -121.999, 259950.0], [47.2056, -121.994, 240000.0], [47.2057, -122.003, 212000.0], [47.2058, -121.997, 250000.0], [47.206, -121.998, 251000.0], [47.2062, -121.993, 295000.0], [47.2063, -122.023, 253500.0], [47.2064, -121.988, 184900.0], [47.2068, -121.992, 169900.0], [47.2069, -122.024, 425000.0], [47.2069, -121.989, 325000.0], [47.2073, -122.012, 244000.0], [47.2074, -122.001, 235000.0], [47.2077, -122.016, 299990.0], [47.2077, -122.011, 245000.0], [47.2077, -121.993, 215000.0], [47.2078, -122.003, 270000.0], [47.2079, -121.993, 215000.0], [47.2079, -121.967, 390500.0], [47.208, -121.963, 332000.0], [47.2082, -122.009, 325000.0], [47.2082, -121.995, 262000.0], [47.2083, -122.005, 235000.0], [47.2086, -121.996, 247000.0], [47.2087, -122.009, 338150.0], [47.2088, -121.992, 175000.0], [47.2089, -122.011, 329990.0], [47.2089, -122.003, 270000.0], [47.209, -122.012, 250000.0], [47.209, -122.005, 249000.0], [47.2091, -122.009, 294008.0], [47.2092, -121.997, 219000.0], [47.2093, -122.005, 188000.0], [47.2093, -122.0, 225000.0], [47.2093, -121.962, 269500.0], [47.2094, -121.996, 227000.0], [47.2098, -122.016, 495000.0], [47.2098, -122.004, 255000.0], [47.2099, -122.043, 220000.0], [47.2099, -122.001, 199950.0], [47.2103, -121.999, 283000.0], [47.2105, -121.989, 210000.0], [47.2107, -121.99, 152900.0], [47.2109, -121.987, 216500.0], [47.2109, -121.985, 122000.0], [47.2113, -121.993, 200000.0], [47.2113, -121.992, 205500.0], [47.2114, -122.009, 290000.0], [47.2114, -121.986, 200000.0], [47.2117, -121.99, 267000.0], [47.2118, -121.983, 246500.0], [47.2119, 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667000.0], [47.241, -121.979, 473000.0], [47.2411, -122.043, 243950.0], [47.2411, -121.932, 570000.0], [47.2413, -121.972, 530000.0], [47.2416, -121.979, 470000.0], [47.2458, -122.002, 705000.0], [47.2477, -121.937, 330000.0], [47.2485, -122.101, 450000.0], [47.2492, -121.975, 550000.0], [47.2495, -121.97, 370000.0], [47.2498, -122.003, 599950.0], [47.2504, -122.0, 430000.0], [47.2507, -122.039, 246000.0], [47.2527, -121.98, 495000.0], [47.2558, -122.122, 250000.0], [47.2558, -122.024, 305000.0], [47.2559, -122.007, 315000.0], [47.2568, -122.07, 267300.0], [47.2568, -121.952, 475000.0], [47.2574, -122.31, 244000.0], [47.2574, -122.072, 280000.0], [47.2575, -122.312, 320000.0], [47.2575, -122.305, 190500.0], [47.2576, -122.255, 227000.0], [47.2576, -122.214, 265500.0], [47.2576, -122.099, 323000.0], [47.2577, -122.198, 303210.0], [47.2578, -122.217, 265000.0], [47.2579, -122.312, 272000.0], [47.258, -122.305, 315000.0], [47.258, -122.252, 160000.0], [47.258, -122.208, 254500.0], [47.2581, -122.2, 328000.0], [47.2582, -122.223, 427500.0], [47.2582, -122.198, 274950.0], [47.2582, -122.196, 339990.0], [47.2582, -122.113, 293000.0], [47.2582, -121.935, 367000.0], [47.2583, -122.3, 280000.0], [47.2583, -122.203, 280000.0], [47.2583, -122.198, 309780.0], [47.2584, -122.196, 335606.0], [47.2584, -122.124, 345000.0], [47.2584, -122.093, 355000.0], [47.2585, -122.201, 279950.0], [47.2585, -122.061, 400000.0], [47.2585, -121.925, 378000.0], [47.2586, -122.221, 359800.0], [47.2586, -122.2, 279000.0], [47.2586, -122.194, 399895.0], [47.2587, -122.253, 212000.0], [47.2588, -122.21, 316000.0], [47.2589, -122.279, 220000.0], [47.2589, -122.256, 250000.0], [47.259, -122.256, 255000.0], [47.259, -122.215, 300499.0], [47.259, -122.202, 333000.0], [47.2592, -122.304, 410500.0], [47.2592, -122.291, 289000.0], [47.2592, -122.228, 400000.0], [47.2592, -122.194, 392440.0], [47.2593, -122.312, 230000.0], [47.2593, -122.222, 360000.0], [47.2595, -122.218, 268000.0], [47.2595, 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378000.0], [47.773, -122.265, 493000.0], [47.773, -122.256, 405000.0], [47.773, -122.161, 644000.0], [47.773, -122.16, 613500.0], [47.7731, -122.343, 290000.0], [47.7731, -122.282, 609000.0], [47.7731, -122.257, 419000.0], [47.7731, -122.167, 490000.0], [47.7731, -122.139, 699000.0], [47.7731, -122.086, 630000.0], [47.7731, -122.08, 699950.0], [47.7731, -121.897, 336900.0], [47.7732, -122.382, 505000.0], [47.7732, -122.172, 335000.0], [47.7732, -122.163, 360000.0], [47.7733, -122.351, 500000.0], [47.7733, -122.324, 422250.0], [47.7733, -122.318, 392400.0], [47.7733, -122.317, 385000.0], [47.7733, -122.271, 505000.0], [47.7733, -122.266, 450000.0], [47.7733, -122.226, 450000.0], [47.7733, -122.17, 435000.0], [47.7734, -122.359, 410000.0], [47.7734, -122.315, 336800.0], [47.7734, -122.238, 525000.0], [47.7734, -122.237, 494000.0], [47.7734, -122.224, 525000.0], [47.7735, -122.386, 1200000.0], [47.7735, -122.378, 442500.0], [47.7735, -122.371, 535100.0], [47.7735, -122.363, 443500.0], [47.7735, -122.281, 490000.0], [47.7735, -122.273, 470000.0], [47.7735, -122.227, 533000.0], [47.7735, -122.167, 576925.0], [47.7735, -122.158, 499950.0], [47.7735, -122.133, 499500.0], [47.7736, -122.371, 722500.0], [47.7736, -122.296, 399950.0], [47.7736, -122.214, 333500.0], [47.7736, -121.901, 330000.0], [47.7737, -122.35, 398000.0], [47.7737, -122.238, 502000.0], [47.7737, -122.235, 525000.0], [47.7738, -122.266, 470101.0], [47.7738, -122.261, 339950.0], [47.7738, -122.203, 628000.0], [47.7738, -122.158, 495000.0], [47.7739, -122.358, 345000.0], [47.7739, -122.322, 368888.0], [47.7739, -122.274, 382000.0], [47.7739, -122.227, 528000.0], [47.7739, -122.199, 485000.0], [47.7739, -122.164, 565000.0], [47.774, -122.384, 615000.0], [47.774, -122.351, 331000.0], [47.774, -122.348, 300000.0], [47.774, -122.283, 399000.0], [47.774, -122.28, 680000.0], [47.774, -122.208, 370000.0], [47.774, -122.162, 680000.0], [47.774, -122.134, 495000.0], [47.774, -122.08, 623000.0], [47.774, -122.077, 595000.0], [47.7741, -122.38, 419000.0], [47.7741, -122.379, 655500.0], [47.7741, -122.373, 494000.0], [47.7741, -122.203, 630000.0], [47.7741, -122.122, 595000.0], [47.7742, -122.287, 434975.0], [47.7742, -122.166, 587000.0], [47.7742, -122.14, 464000.0], [47.7743, -122.388, 750000.0], [47.7743, -122.286, 525000.0], [47.7743, -122.279, 665000.0], [47.7743, -122.27, 405000.0], [47.7743, -122.26, 249000.0], [47.7743, -122.235, 400000.0], [47.7744, -122.338, 377000.0], [47.7744, -122.225, 715000.0], [47.7744, -122.224, 614000.0], [47.7745, -122.368, 887000.0], [47.7745, -122.225, 650000.0], [47.7745, -122.088, 725000.0], [47.7746, -122.378, 299950.0], [47.7746, -122.323, 415000.0], [47.7746, -122.239, 335000.0], [47.7746, -122.197, 439000.0], [47.7746, -122.158, 451000.0], [47.7747, -122.366, 700000.0], [47.7748, -122.337, 360000.0], [47.7748, -122.304, 275000.0], [47.7748, -122.291, 405000.0], [47.7748, -122.244, 619990.0], [47.7749, -122.339, 415000.0], [47.7749, -122.283, 615000.0], [47.7749, -122.159, 552500.0], [47.7749, -122.048, 775000.0], [47.775, -122.139, 465425.0], [47.775, -122.125, 625000.0], [47.775, -122.124, 653000.0], [47.7751, -122.38, 465250.0], [47.7751, -122.32, 440000.0], [47.7751, -122.284, 600000.0], [47.7751, -122.268, 330000.0], [47.7751, -122.222, 395000.0], [47.7751, -122.168, 465000.0], [47.7751, -122.088, 685000.0], [47.7752, -122.285, 537250.0], [47.7752, -122.198, 480000.0], [47.7752, -122.197, 413000.0], [47.7753, -122.375, 405750.0], [47.7753, -122.365, 492650.0], [47.7753, -122.359, 437000.0], [47.7753, -122.355, 275000.0], [47.7753, -122.353, 339275.0], [47.7754, -122.296, 342000.0], [47.7754, -122.291, 385000.0], [47.7754, -122.276, 440000.0], [47.7754, -122.233, 398000.0], [47.7754, -122.173, 738515.0], [47.7754, -122.032, 515000.0], [47.7755, -122.299, 305000.0], [47.7755, -122.235, 385000.0], [47.7756, -122.372, 469000.0], [47.7756, -122.351, 339950.0], [47.7756, -122.273, 590000.0], [47.7757, -122.391, 941500.0], [47.7757, -122.353, 336500.0], [47.7757, -122.35, 290000.0], [47.7757, -122.303, 365000.0], [47.7757, -122.295, 565000.0], [47.7757, -122.173, 769995.0], [47.7757, -122.139, 440000.0], [47.7757, -122.132, 474900.0], [47.7758, -122.379, 435000.0], [47.7758, -122.301, 266500.0], [47.7758, -122.238, 196000.0], [47.7758, -122.2, 470000.0], [47.7759, -122.272, 420000.0], [47.7759, -122.214, 532500.0], [47.7759, -122.08, 665000.0], [47.776, -122.26, 425000.0], [47.776, -122.246, 515000.0], [47.776, -122.24, 427500.0], [47.776, -122.239, 382475.0], [47.776, -122.173, 749995.0], [47.776, -122.169, 445000.0], [47.7761, -122.285, 605000.0], [47.7761, -122.169, 480000.0], [47.7762, -122.384, 510000.0], [47.7762, -122.367, 485000.0], [47.7762, -122.337, 392500.0], [47.7762, -122.27, 320000.0], [47.7762, -122.257, 446000.0], [47.7762, -122.21, 400000.0], [47.7762, -122.198, 270000.0], [47.7763, -122.36, 263700.0], [47.7763, -122.305, 299000.0], [47.7763, -122.209, 390000.0], [47.7764, -122.386, 650000.0], [47.7764, -122.385, 995000.0], [47.7765, -122.324, 360000.0], [47.7765, -122.276, 445800.0], [47.7765, -122.261, 385000.0], [47.7766, -122.355, 439950.0], [47.7766, -122.307, 255000.0], [47.7766, -122.277, 469000.0], [47.7766, -122.248, 285000.0], [47.7767, -122.378, 485000.0], [47.7767, -122.369, 579000.0], [47.7767, -122.365, 417500.0], [47.7767, -122.356, 370000.0], [47.7767, -122.239, 432475.0], [47.7768, -122.361, 443000.0], [47.7768, -122.268, 374990.0], [47.7769, -122.376, 380000.0], [47.7769, -122.253, 449000.0], [47.7769, -122.25, 400000.0], [47.777, -122.362, 434900.0], [47.777, -122.307, 339000.0], [47.7771, -122.362, 525000.0], [47.7771, -122.339, 379950.0], [47.7772, -122.387, 536000.0], [47.7772, -122.357, 364000.0], [47.7772, -122.296, 350000.0], [47.7774, -122.35, 395000.0], [47.7775, -122.365, 245560.0], [47.7775, -122.337, 285000.0], [47.7775, -122.336, 270000.0], [47.7776, -122.384, 550000.0], [47.7776, -122.375, 389950.0], [47.7776, -122.352, 407500.0]],\n",
       "                {&quot;blur&quot;: 15, &quot;maxZoom&quot;: 18, &quot;minOpacity&quot;: 0.5, &quot;radius&quot;: 8}\n",
       "            ).addTo(feature_group_178e71d27f2c78094136f1cc9ec09383);\n",
       "        \n",
       "    \n",
       "            var feature_group_44d554b8c2432590d10ebddb137c4227 = L.featureGroup(\n",
       "                {}\n",
       "            ).addTo(map_d010e2fad7b017646f24dfe1a0aa6104);\n",
       "        \n",
       "    \n",
       "            var marker_bb9c2e3d4dd3560b51a71fa33c7bdb94 = L.marker(\n",
       "                [47.6298, -122.323],\n",
       "                {}\n",
       "            ).addTo(feature_group_44d554b8c2432590d10ebddb137c4227);\n",
       "        \n",
       "    \n",
       "            var icon_eb83de121a776769dd5b150851a5168b = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_bb9c2e3d4dd3560b51a71fa33c7bdb94.setIcon(icon_eb83de121a776769dd5b150851a5168b);\n",
       "        \n",
       "    \n",
       "        var popup_6bd7201c64ba318e492b7837a81e0591 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_f04bc3397c2a21ed356494dfadb3d858 = $(`&lt;div id=&quot;html_f04bc3397c2a21ed356494dfadb3d858&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Price: 7700000.0&lt;/div&gt;`)[0];\n",
       "                popup_6bd7201c64ba318e492b7837a81e0591.setContent(html_f04bc3397c2a21ed356494dfadb3d858);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_bb9c2e3d4dd3560b51a71fa33c7bdb94.bindPopup(popup_6bd7201c64ba318e492b7837a81e0591)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_f1aba45fb0d37261ee1fae18ce477813 = L.marker(\n",
       "                [47.65, -122.214],\n",
       "                {}\n",
       "            ).addTo(feature_group_44d554b8c2432590d10ebddb137c4227);\n",
       "        \n",
       "    \n",
       "            var icon_98f7ec86c19682c6a78492183cbdadd1 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_f1aba45fb0d37261ee1fae18ce477813.setIcon(icon_98f7ec86c19682c6a78492183cbdadd1);\n",
       "        \n",
       "    \n",
       "        var popup_c19b598f8d03941f7e50ac410b001feb = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_ff41deec2565d6ccd75afc4962ed100f = $(`&lt;div id=&quot;html_ff41deec2565d6ccd75afc4962ed100f&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Price: 7062500.0&lt;/div&gt;`)[0];\n",
       "                popup_c19b598f8d03941f7e50ac410b001feb.setContent(html_ff41deec2565d6ccd75afc4962ed100f);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_f1aba45fb0d37261ee1fae18ce477813.bindPopup(popup_c19b598f8d03941f7e50ac410b001feb)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_bbf9d4d4e75767cbe7572d272dcfab67 = L.marker(\n",
       "                [47.6305, -122.24],\n",
       "                {}\n",
       "            ).addTo(feature_group_44d554b8c2432590d10ebddb137c4227);\n",
       "        \n",
       "    \n",
       "            var icon_84c88f9ff99ab1dbf69db3f06d42248f = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_bbf9d4d4e75767cbe7572d272dcfab67.setIcon(icon_84c88f9ff99ab1dbf69db3f06d42248f);\n",
       "        \n",
       "    \n",
       "        var popup_4dc02536d2c55035fef70927e650ade3 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_544e282c9a69526efbb0086e04c63671 = $(`&lt;div id=&quot;html_544e282c9a69526efbb0086e04c63671&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Price: 6885000.0&lt;/div&gt;`)[0];\n",
       "                popup_4dc02536d2c55035fef70927e650ade3.setContent(html_544e282c9a69526efbb0086e04c63671);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_bbf9d4d4e75767cbe7572d272dcfab67.bindPopup(popup_4dc02536d2c55035fef70927e650ade3)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_683aa7e5c6bc8e0f20065e6b5432d870 = L.marker(\n",
       "                [47.6289, -122.233],\n",
       "                {}\n",
       "            ).addTo(feature_group_44d554b8c2432590d10ebddb137c4227);\n",
       "        \n",
       "    \n",
       "            var icon_70a5b016b7acdcf09ab89737ec34dacd = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_683aa7e5c6bc8e0f20065e6b5432d870.setIcon(icon_70a5b016b7acdcf09ab89737ec34dacd);\n",
       "        \n",
       "    \n",
       "        var popup_c8619981cac966944448e801ed5d92d6 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_0f9c58d15261299d5be7002739e4befe = $(`&lt;div id=&quot;html_0f9c58d15261299d5be7002739e4befe&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Price: 5570000.0&lt;/div&gt;`)[0];\n",
       "                popup_c8619981cac966944448e801ed5d92d6.setContent(html_0f9c58d15261299d5be7002739e4befe);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_683aa7e5c6bc8e0f20065e6b5432d870.bindPopup(popup_c8619981cac966944448e801ed5d92d6)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_00082f2d3178e9fdce1b9be746c89397 = L.marker(\n",
       "                [47.6232, -122.22],\n",
       "                {}\n",
       "            ).addTo(feature_group_44d554b8c2432590d10ebddb137c4227);\n",
       "        \n",
       "    \n",
       "            var icon_b7beb898464d784d80231d71f9722efe = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_00082f2d3178e9fdce1b9be746c89397.setIcon(icon_b7beb898464d784d80231d71f9722efe);\n",
       "        \n",
       "    \n",
       "        var popup_448887328761a9777e45ed51beb28382 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_65986f3d8bf00d9757870d6691f2c9ac = $(`&lt;div id=&quot;html_65986f3d8bf00d9757870d6691f2c9ac&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Price: 5350000.0&lt;/div&gt;`)[0];\n",
       "                popup_448887328761a9777e45ed51beb28382.setContent(html_65986f3d8bf00d9757870d6691f2c9ac);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_00082f2d3178e9fdce1b9be746c89397.bindPopup(popup_448887328761a9777e45ed51beb28382)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_e5e99d1ef4112e94cc1616d19a41b4f7 = L.marker(\n",
       "                [47.5631, -122.21],\n",
       "                {}\n",
       "            ).addTo(feature_group_44d554b8c2432590d10ebddb137c4227);\n",
       "        \n",
       "    \n",
       "            var icon_0354344549fd532f77a2d303b975c9ec = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_e5e99d1ef4112e94cc1616d19a41b4f7.setIcon(icon_0354344549fd532f77a2d303b975c9ec);\n",
       "        \n",
       "    \n",
       "        var popup_49618aa93fc156e7cd55cd1de1d7a141 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_1eb53de20d2648f60219a42c620d67f6 = $(`&lt;div id=&quot;html_1eb53de20d2648f60219a42c620d67f6&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Price: 5300000.0&lt;/div&gt;`)[0];\n",
       "                popup_49618aa93fc156e7cd55cd1de1d7a141.setContent(html_1eb53de20d2648f60219a42c620d67f6);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_e5e99d1ef4112e94cc1616d19a41b4f7.bindPopup(popup_49618aa93fc156e7cd55cd1de1d7a141)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_4a1ba9632c1b5ec85747ffc9d48c243b = L.marker(\n",
       "                [47.6767, -122.211],\n",
       "                {}\n",
       "            ).addTo(feature_group_44d554b8c2432590d10ebddb137c4227);\n",
       "        \n",
       "    \n",
       "            var icon_dcd1a2ef14be5822ff338d4ca4fed188 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_4a1ba9632c1b5ec85747ffc9d48c243b.setIcon(icon_dcd1a2ef14be5822ff338d4ca4fed188);\n",
       "        \n",
       "    \n",
       "        var popup_643c7e1db05d68f075c6b2c62e809946 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_d3705772993dbc91d956b462120eabb0 = $(`&lt;div id=&quot;html_d3705772993dbc91d956b462120eabb0&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Price: 5110800.0&lt;/div&gt;`)[0];\n",
       "                popup_643c7e1db05d68f075c6b2c62e809946.setContent(html_d3705772993dbc91d956b462120eabb0);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_4a1ba9632c1b5ec85747ffc9d48c243b.bindPopup(popup_643c7e1db05d68f075c6b2c62e809946)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_e59a14f4f1d7593ad5765d69b0608d68 = L.marker(\n",
       "                [47.557, -122.21],\n",
       "                {}\n",
       "            ).addTo(feature_group_44d554b8c2432590d10ebddb137c4227);\n",
       "        \n",
       "    \n",
       "            var icon_f26661c0a6f7a9f2b4937b7863a543bf = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_e59a14f4f1d7593ad5765d69b0608d68.setIcon(icon_f26661c0a6f7a9f2b4937b7863a543bf);\n",
       "        \n",
       "    \n",
       "        var popup_fd3422cb07e1103125e6a97162071a8b = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_647209f871ad6b7acdb0b39e4d135066 = $(`&lt;div id=&quot;html_647209f871ad6b7acdb0b39e4d135066&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Price: 4668000.0&lt;/div&gt;`)[0];\n",
       "                popup_fd3422cb07e1103125e6a97162071a8b.setContent(html_647209f871ad6b7acdb0b39e4d135066);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_e59a14f4f1d7593ad5765d69b0608d68.bindPopup(popup_fd3422cb07e1103125e6a97162071a8b)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_ef4915bfcf26deebfcd57bef3e7a670b = L.marker(\n",
       "                [47.7493, -122.28],\n",
       "                {}\n",
       "            ).addTo(feature_group_44d554b8c2432590d10ebddb137c4227);\n",
       "        \n",
       "    \n",
       "            var icon_74d47fafccfc33874c2ba019ab4bfb15 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_ef4915bfcf26deebfcd57bef3e7a670b.setIcon(icon_74d47fafccfc33874c2ba019ab4bfb15);\n",
       "        \n",
       "    \n",
       "        var popup_fa550b4ce6d2b4f235d9d1de3a566c12 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_481c03b0f669efda90269c2ff736f0fa = $(`&lt;div id=&quot;html_481c03b0f669efda90269c2ff736f0fa&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Price: 4500000.0&lt;/div&gt;`)[0];\n",
       "                popup_fa550b4ce6d2b4f235d9d1de3a566c12.setContent(html_481c03b0f669efda90269c2ff736f0fa);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_ef4915bfcf26deebfcd57bef3e7a670b.bindPopup(popup_fa550b4ce6d2b4f235d9d1de3a566c12)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var marker_88d034f4e0568ab3615ada5d040533a8 = L.marker(\n",
       "                [47.6208, -122.219],\n",
       "                {}\n",
       "            ).addTo(feature_group_44d554b8c2432590d10ebddb137c4227);\n",
       "        \n",
       "    \n",
       "            var icon_9b66f42f6256887a8c6e8c14a1ac20f9 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_88d034f4e0568ab3615ada5d040533a8.setIcon(icon_9b66f42f6256887a8c6e8c14a1ac20f9);\n",
       "        \n",
       "    \n",
       "        var popup_85601e1c3f4bb63cb138ef52af69b24e = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_e1580ebfaa50a5f2dffd1219d683a241 = $(`&lt;div id=&quot;html_e1580ebfaa50a5f2dffd1219d683a241&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Price: 4489000.0&lt;/div&gt;`)[0];\n",
       "                popup_85601e1c3f4bb63cb138ef52af69b24e.setContent(html_e1580ebfaa50a5f2dffd1219d683a241);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_88d034f4e0568ab3615ada5d040533a8.bindPopup(popup_85601e1c3f4bb63cb138ef52af69b24e)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            var layer_control_04e8b4df5f677a608d7fd21b5c5d033c = {\n",
       "                base_layers : {\n",
       "                    &quot;openstreetmap&quot; : tile_layer_194b4bd8140f275bb21eb72e962d099f,\n",
       "                },\n",
       "                overlays :  {\n",
       "                    &quot;Heat Map&quot; : feature_group_178e71d27f2c78094136f1cc9ec09383,\n",
       "                    &quot;10 Most Expensive Houses&quot; : feature_group_44d554b8c2432590d10ebddb137c4227,\n",
       "                },\n",
       "            };\n",
       "            L.control.layers(\n",
       "                layer_control_04e8b4df5f677a608d7fd21b5c5d033c.base_layers,\n",
       "                layer_control_04e8b4df5f677a608d7fd21b5c5d033c.overlays,\n",
       "                {&quot;autoZIndex&quot;: true, &quot;collapsed&quot;: true, &quot;position&quot;: &quot;topright&quot;}\n",
       "            ).addTo(map_d010e2fad7b017646f24dfe1a0aa6104);\n",
       "        \n",
       "&lt;/script&gt;\n",
       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fe6686ea910>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "from folium.plugins import HeatMap\n",
    "\n",
    "# Initalize the map\n",
    "m = folium.Map(location=[47.5112, -122.257])\n",
    "\n",
    "# Create Layers and add them to the map\n",
    "layer_heat_map = folium.FeatureGroup(name='Heat Map').add_to(m)\n",
    "layer_most_expensive = folium.FeatureGroup(name='10 Most Expensive Houses').add_to(m)\n",
    "folium.LayerControl().add_to(m)\n",
    "\n",
    "# Add a heatmap to a layer\n",
    "data = df[['lat', 'long', 'price']].groupby(['lat','long']).mean().reset_index().values.tolist() # Note for latitudes and longitudes that show up multiple times, we take the mean()\n",
    "HeatMap(data, radius=8).add_to(layer_heat_map)\n",
    "\n",
    "# Add the 10 most expensive houses to a layer\n",
    "df_most_expensive_houses = df.sort_values(by=['price'], ascending=False).head(10)\n",
    "for indice, row in df_most_expensive_houses.iterrows():\n",
    "    folium.Marker(\n",
    "        location=[row[\"lat\"], row[\"long\"]],\n",
    "        popup=f\"Price: {row['price']}\",\n",
    "        icon=folium.map.Icon(color='red')\n",
    "    ).add_to(layer_most_expensive)\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 10 most expensive houses seem to be close to the waterfront and looking at the actual data, we can see that about half of them are indeed overlooking the waterfront"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:52.586054Z",
     "iopub.status.busy": "2024-05-31T23:13:52.585751Z",
     "iopub.status.idle": "2024-05-31T23:13:52.590896Z",
     "shell.execute_reply": "2024-05-31T23:13:52.589884Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7252    0\n",
       "3914    1\n",
       "9254    0\n",
       "4411    0\n",
       "1448    0\n",
       "1315    1\n",
       "1164    1\n",
       "8092    1\n",
       "2626    1\n",
       "8638    0\n",
       "Name: waterfront, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_most_expensive_houses['waterfront']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The heatmap also shows that the most expensive houses are located in the north-western part of the county, in or near Seattle.\n",
    "\n",
    "Finally, let's look at the distribution of some of the discrete variables in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:52.594146Z",
     "iopub.status.busy": "2024-05-31T23:13:52.593866Z",
     "iopub.status.idle": "2024-05-31T23:13:53.090525Z",
     "shell.execute_reply": "2024-05-31T23:13:53.089723Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
    "variables = ['waterfront', 'view', 'condition', 'grade']\n",
    "\n",
    "for var, ax in zip(variables, axes.flatten()):\n",
    "    sns.countplot(x=var, data=df, ax=ax)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:53.093907Z",
     "iopub.status.busy": "2024-05-31T23:13:53.093687Z",
     "iopub.status.idle": "2024-05-31T23:13:54.351905Z",
     "shell.execute_reply": "2024-05-31T23:13:54.350893Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, figsize=(15, 10))\n",
    "variables = ['yr_built', 'yr_renovated']\n",
    "\n",
    "for var, ax in zip(variables, axes.flatten()):\n",
    "    sns.countplot(x=var, data=df, ax=ax)\n",
    "\n",
    "for ax in axes.flatten():\n",
    "    if ax.get_xlabel() in ('yr_built', 'yr_renovated'):\n",
    "        ax.tick_params(axis='x', labelrotation=90)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be some cyclicality in `yr_built`. We could probably infer housing booms and busts if we analyze it carefully. `yr_renovated` seems to have a lot of zeros, which could mean that many houses have never been renovated. Let's check what's going on here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:54.355430Z",
     "iopub.status.busy": "2024-05-31T23:13:54.355199Z",
     "iopub.status.idle": "2024-05-31T23:13:54.361170Z",
     "shell.execute_reply": "2024-05-31T23:13:54.360531Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yr_renovated\n",
       "0       20698\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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['yr_renovated'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed, almost all of the houses seem to have a zero. However, some houses have values different from zero, so it might indeed be the case that houses with a value of zero have never been renovated. We could also check if the year of renovation is after the year the house was built"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:54.363738Z",
     "iopub.status.busy": "2024-05-31T23:13:54.363525Z",
     "iopub.status.idle": "2024-05-31T23:13:54.374803Z",
     "shell.execute_reply": "2024-05-31T23:13:54.374342Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, date, price, bedrooms, bathrooms, sqft_living, sqft_lot, floors, waterfront, view, condition, grade, sqft_above, sqft_basement, yr_built, yr_renovated, zipcode, lat, long, sqft_living15, sqft_lot15]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 21 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('yr_renovated != 0 and yr_renovated < yr_built')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this command, we selected all observations where `yr_renovated` is different from zero and `yr_renovated < yr_built`. Since there were no rows selected, there do not seem to be any errors in the dataset in this respect.\n",
    "\n",
    "Another thing we can check is whether there are errors in the square footage variables. For example, we could check if the sum of `sqft_above` and `sqft_basement` is equal to `sqft_living`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:54.377451Z",
     "iopub.status.busy": "2024-05-31T23:13:54.377251Z",
     "iopub.status.idle": "2024-05-31T23:13:54.387595Z",
     "shell.execute_reply": "2024-05-31T23:13:54.387080Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, date, price, bedrooms, bathrooms, sqft_living, sqft_lot, floors, waterfront, view, condition, grade, sqft_above, sqft_basement, yr_built, yr_renovated, zipcode, lat, long, sqft_living15, sqft_lot15]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 21 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('sqft_above + sqft_basement != sqft_living')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This indeed seems to be correct for all observations. \n",
    "\n",
    "We haven't looked at the square footage of the 15 nearest neighbors yet. Let's check how it relates to price and the square footage of the house itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:13:54.390192Z",
     "iopub.status.busy": "2024-05-31T23:13:54.389998Z",
     "iopub.status.idle": "2024-05-31T23:14:00.007455Z",
     "shell.execute_reply": "2024-05-31T23:14:00.006486Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/datascience_course_cemfi_dev/lib/python3.8/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fe6c99b7520>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1250x1250 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df[['price', 'sqft_living', 'sqft_lot', 'sqft_living15', 'sqft_lot15']], diag_kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be a positive relationship between the square footage of the living area of the house and the square footage of the living area of the 15 nearest neighbors. There also seems to be a positive relationship with price. This likely just reflects the fact that neighborhoods tend to have houses of similar sizes and prices.\n",
    "\n",
    "Finally, let's look at the distribution of the zip codes in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:00.013950Z",
     "iopub.status.busy": "2024-05-31T23:14:00.013420Z",
     "iopub.status.idle": "2024-05-31T23:14:00.416454Z",
     "shell.execute_reply": "2024-05-31T23:14:00.415967Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "sns.countplot(x='zipcode', data=df)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This likely doesn't tell us much, but it's interesting to see that some zip codes are much more common than others. Finally, we can again look at the correlation between variables in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:00.419251Z",
     "iopub.status.busy": "2024-05-31T23:14:00.419042Z",
     "iopub.status.idle": "2024-05-31T23:14:01.038521Z",
     "shell.execute_reply": "2024-05-31T23:14:01.037981Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(16, 12))\n",
    "corr = df.drop(['id', 'date'], axis=1).corr()\n",
    "cmap = sns.diverging_palette(10, 255, as_cmap=True) # Create a color map\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool)) # Create a mask to only show the lower triangle of the matrix\n",
    "sns.heatmap(corr, cmap=cmap, annot=True, vmax=1, center=0, mask=mask) # Create a heatmap of the correlation matrix (Note: vmax=1 makes sure that the color map goes up to 1 and center=0 are used to center the color map at 0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation of House Price Prediction Models\n",
    "\n",
    "We have explored our dataset and are now ready to implement machine learning algorithms for house price prediction. Let's start by importing the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.041741Z",
     "iopub.status.busy": "2024-05-31T23:14:01.041538Z",
     "iopub.status.idle": "2024-05-31T23:14:01.241877Z",
     "shell.execute_reply": "2024-05-31T23:14:01.241248Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression, Lasso, LassoCV\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from joblib import dump, load"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing\n",
    "\n",
    "The dataset seems to be pretty clean already. Let's check again the number of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.245478Z",
     "iopub.status.busy": "2024-05-31T23:14:01.245116Z",
     "iopub.status.idle": "2024-05-31T23:14:01.251262Z",
     "shell.execute_reply": "2024-05-31T23:14:01.250752Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id               0\n",
       "date             0\n",
       "price            0\n",
       "bedrooms         0\n",
       "bathrooms        0\n",
       "sqft_living      0\n",
       "sqft_lot         0\n",
       "floors           0\n",
       "waterfront       0\n",
       "view             0\n",
       "condition        0\n",
       "grade            0\n",
       "sqft_above       0\n",
       "sqft_basement    0\n",
       "yr_built         0\n",
       "yr_renovated     0\n",
       "zipcode          0\n",
       "lat              0\n",
       "long             0\n",
       "sqft_living15    0\n",
       "sqft_lot15       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.253761Z",
     "iopub.status.busy": "2024-05-31T23:14:01.253584Z",
     "iopub.status.idle": "2024-05-31T23:14:01.260463Z",
     "shell.execute_reply": "2024-05-31T23:14:01.259923Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 21612 entries, 0 to 21612\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   id             21612 non-null  int64         \n",
      " 1   date           21612 non-null  datetime64[ns]\n",
      " 2   price          21612 non-null  float64       \n",
      " 3   bedrooms       21612 non-null  int64         \n",
      " 4   bathrooms      21612 non-null  float64       \n",
      " 5   sqft_living    21612 non-null  int64         \n",
      " 6   sqft_lot       21612 non-null  int64         \n",
      " 7   floors         21612 non-null  float64       \n",
      " 8   waterfront     21612 non-null  int64         \n",
      " 9   view           21612 non-null  int64         \n",
      " 10  condition      21612 non-null  int64         \n",
      " 11  grade          21612 non-null  int64         \n",
      " 12  sqft_above     21612 non-null  int64         \n",
      " 13  sqft_basement  21612 non-null  int64         \n",
      " 14  yr_built       21612 non-null  int64         \n",
      " 15  yr_renovated   21612 non-null  int64         \n",
      " 16  zipcode        21612 non-null  int64         \n",
      " 17  lat            21612 non-null  float64       \n",
      " 18  long           21612 non-null  float64       \n",
      " 19  sqft_living15  21612 non-null  int64         \n",
      " 20  sqft_lot15     21612 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(5), int64(15)\n",
      "memory usage: 3.6 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There don't seem to be any missing values. However, we could still check for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.263019Z",
     "iopub.status.busy": "2024-05-31T23:14:01.262811Z",
     "iopub.status.idle": "2024-05-31T23:14:01.273510Z",
     "shell.execute_reply": "2024-05-31T23:14:01.272891Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There also don't seem to be any duplicates.\n",
    "\n",
    "There are some variables such as `id`, `zipcode`, `lat` and `long` which likely don't provide very useful information given the other variables in the dataset. We will drop these variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.276497Z",
     "iopub.status.busy": "2024-05-31T23:14:01.276260Z",
     "iopub.status.idle": "2024-05-31T23:14:01.287426Z",
     "shell.execute_reply": "2024-05-31T23:14:01.286831Z"
    }
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <td>2014-10-13 00:00:00</td>\n",
       "      <td>2014-12-09 00:00:00</td>\n",
       "      <td>2015-02-25 00:00:00</td>\n",
       "      <td>2014-12-09 00:00:00</td>\n",
       "      <td>2015-02-18 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>221900.0</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>510000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>1180</td>\n",
       "      <td>2570</td>\n",
       "      <td>770</td>\n",
       "      <td>1960</td>\n",
       "      <td>1680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot</th>\n",
       "      <td>5650</td>\n",
       "      <td>7242</td>\n",
       "      <td>10000</td>\n",
       "      <td>5000</td>\n",
       "      <td>8080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floors</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterfront</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_above</th>\n",
       "      <td>1180</td>\n",
       "      <td>2170</td>\n",
       "      <td>770</td>\n",
       "      <td>1050</td>\n",
       "      <td>1680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_basement</th>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>0</td>\n",
       "      <td>910</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <td>1955</td>\n",
       "      <td>1951</td>\n",
       "      <td>1933</td>\n",
       "      <td>1965</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_renovated</th>\n",
       "      <td>0</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living15</th>\n",
       "      <td>1340</td>\n",
       "      <td>1690</td>\n",
       "      <td>2720</td>\n",
       "      <td>1360</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot15</th>\n",
       "      <td>5650</td>\n",
       "      <td>7639</td>\n",
       "      <td>8062</td>\n",
       "      <td>5000</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 0                    1                    2  \\\n",
       "date           2014-10-13 00:00:00  2014-12-09 00:00:00  2015-02-25 00:00:00   \n",
       "price                     221900.0             538000.0             180000.0   \n",
       "bedrooms                         3                    3                    2   \n",
       "bathrooms                      1.0                 2.25                  1.0   \n",
       "sqft_living                   1180                 2570                  770   \n",
       "sqft_lot                      5650                 7242                10000   \n",
       "floors                         1.0                  2.0                  1.0   \n",
       "waterfront                       0                    0                    0   \n",
       "view                             0                    0                    0   \n",
       "condition                        3                    3                    3   \n",
       "grade                            7                    7                    6   \n",
       "sqft_above                    1180                 2170                  770   \n",
       "sqft_basement                    0                  400                    0   \n",
       "yr_built                      1955                 1951                 1933   \n",
       "yr_renovated                     0                 1991                    0   \n",
       "sqft_living15                 1340                 1690                 2720   \n",
       "sqft_lot15                    5650                 7639                 8062   \n",
       "\n",
       "                                 3                    4  \n",
       "date           2014-12-09 00:00:00  2015-02-18 00:00:00  \n",
       "price                     604000.0             510000.0  \n",
       "bedrooms                         4                    3  \n",
       "bathrooms                      3.0                  2.0  \n",
       "sqft_living                   1960                 1680  \n",
       "sqft_lot                      5000                 8080  \n",
       "floors                         1.0                  1.0  \n",
       "waterfront                       0                    0  \n",
       "view                             0                    0  \n",
       "condition                        5                    3  \n",
       "grade                            7                    8  \n",
       "sqft_above                    1050                 1680  \n",
       "sqft_basement                  910                    0  \n",
       "yr_built                      1965                 1987  \n",
       "yr_renovated                     0                    0  \n",
       "sqft_living15                 1360                 1800  \n",
       "sqft_lot15                    5000                 7503  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['id', 'zipcode', 'lat', 'long'], axis=1)\n",
    "df.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Binning & Encoding {-}\n",
    "\n",
    "Furthermore, we will need to convert the `date` variable into something that can be used in a machine-learning model. We will extract the year and month from the date and drop the original `date` variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.290588Z",
     "iopub.status.busy": "2024-05-31T23:14:01.290360Z",
     "iopub.status.idle": "2024-05-31T23:14:01.297166Z",
     "shell.execute_reply": "2024-05-31T23:14:01.296622Z"
    }
   },
   "outputs": [],
   "source": [
    "df['year_sale'] = pd.DatetimeIndex(df['date']).year\n",
    "df['month_sale'] = pd.DatetimeIndex(df['date']).month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Furthermore, we can convert `yr_built` and `yr_renovated` into the age of the house and the number of years since the last renovation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.299671Z",
     "iopub.status.busy": "2024-05-31T23:14:01.299475Z",
     "iopub.status.idle": "2024-05-31T23:14:01.304337Z",
     "shell.execute_reply": "2024-05-31T23:14:01.303569Z"
    }
   },
   "outputs": [],
   "source": [
    "df['age'] = df['year_sale'] - df['yr_built']\n",
    "df['years_since_renovation'] = df['year_sale'] - np.maximum(df['yr_built'], df['yr_renovated'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the house has never been renovated, `years_since_renovation` will be equal to the age of the house. We can drop the original `yr_built`, `yr_renovated`, and `date` variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.306997Z",
     "iopub.status.busy": "2024-05-31T23:14:01.306810Z",
     "iopub.status.idle": "2024-05-31T23:14:01.310812Z",
     "shell.execute_reply": "2024-05-31T23:14:01.310354Z"
    }
   },
   "outputs": [],
   "source": [
    "df = df.drop(['yr_built', 'yr_renovated', 'date'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the summary statistics of the dataset again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.313223Z",
     "iopub.status.busy": "2024-05-31T23:14:01.313040Z",
     "iopub.status.idle": "2024-05-31T23:14:01.353821Z",
     "shell.execute_reply": "2024-05-31T23:14:01.353163Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>540083.518786</td>\n",
       "      <td>367135.061269</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>321837.50</td>\n",
       "      <td>450000.00</td>\n",
       "      <td>645000.00</td>\n",
       "      <td>7700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>3.369471</td>\n",
       "      <td>0.907982</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>2.114774</td>\n",
       "      <td>0.770177</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.50</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>2079.921016</td>\n",
       "      <td>918.456818</td>\n",
       "      <td>290.0</td>\n",
       "      <td>1426.50</td>\n",
       "      <td>1910.00</td>\n",
       "      <td>2550.00</td>\n",
       "      <td>13540.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>15107.388951</td>\n",
       "      <td>41421.423497</td>\n",
       "      <td>520.0</td>\n",
       "      <td>5040.00</td>\n",
       "      <td>7619.00</td>\n",
       "      <td>10688.25</td>\n",
       "      <td>1651359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floors</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>1.494332</td>\n",
       "      <td>0.539991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterfront</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.007542</td>\n",
       "      <td>0.086519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.234314</td>\n",
       "      <td>0.766334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>3.409356</td>\n",
       "      <td>0.650668</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>7.656904</td>\n",
       "      <td>1.175477</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_above</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>1788.425319</td>\n",
       "      <td>828.094487</td>\n",
       "      <td>290.0</td>\n",
       "      <td>1190.00</td>\n",
       "      <td>1560.00</td>\n",
       "      <td>2210.00</td>\n",
       "      <td>9410.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_basement</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>291.495697</td>\n",
       "      <td>442.580931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>560.00</td>\n",
       "      <td>4820.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living15</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>1986.582871</td>\n",
       "      <td>685.392610</td>\n",
       "      <td>399.0</td>\n",
       "      <td>1490.00</td>\n",
       "      <td>1840.00</td>\n",
       "      <td>2360.00</td>\n",
       "      <td>6210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot15</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>12768.828984</td>\n",
       "      <td>27304.756179</td>\n",
       "      <td>651.0</td>\n",
       "      <td>5100.00</td>\n",
       "      <td>7620.00</td>\n",
       "      <td>10083.25</td>\n",
       "      <td>871200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_sale</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>2014.322969</td>\n",
       "      <td>0.467622</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>2014.00</td>\n",
       "      <td>2014.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_sale</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>6.574449</td>\n",
       "      <td>3.115377</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>43.316722</td>\n",
       "      <td>29.375731</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>63.00</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>years_since_renovation</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>40.935730</td>\n",
       "      <td>28.813764</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count           mean            std      min  \\\n",
       "price                   21612.0  540083.518786  367135.061269  75000.0   \n",
       "bedrooms                21612.0       3.369471       0.907982      0.0   \n",
       "bathrooms               21612.0       2.114774       0.770177      0.0   \n",
       "sqft_living             21612.0    2079.921016     918.456818    290.0   \n",
       "sqft_lot                21612.0   15107.388951   41421.423497    520.0   \n",
       "floors                  21612.0       1.494332       0.539991      1.0   \n",
       "waterfront              21612.0       0.007542       0.086519      0.0   \n",
       "view                    21612.0       0.234314       0.766334      0.0   \n",
       "condition               21612.0       3.409356       0.650668      1.0   \n",
       "grade                   21612.0       7.656904       1.175477      1.0   \n",
       "sqft_above              21612.0    1788.425319     828.094487    290.0   \n",
       "sqft_basement           21612.0     291.495697     442.580931      0.0   \n",
       "sqft_living15           21612.0    1986.582871     685.392610    399.0   \n",
       "sqft_lot15              21612.0   12768.828984   27304.756179    651.0   \n",
       "year_sale               21612.0    2014.322969       0.467622   2014.0   \n",
       "month_sale              21612.0       6.574449       3.115377      1.0   \n",
       "age                     21612.0      43.316722      29.375731     -1.0   \n",
       "years_since_renovation  21612.0      40.935730      28.813764     -1.0   \n",
       "\n",
       "                              25%        50%        75%        max  \n",
       "price                   321837.50  450000.00  645000.00  7700000.0  \n",
       "bedrooms                     3.00       3.00       4.00       11.0  \n",
       "bathrooms                    1.75       2.25       2.50        8.0  \n",
       "sqft_living               1426.50    1910.00    2550.00    13540.0  \n",
       "sqft_lot                  5040.00    7619.00   10688.25  1651359.0  \n",
       "floors                       1.00       1.50       2.00        3.5  \n",
       "waterfront                   0.00       0.00       0.00        1.0  \n",
       "view                         0.00       0.00       0.00        4.0  \n",
       "condition                    3.00       3.00       4.00        5.0  \n",
       "grade                        7.00       7.00       8.00       13.0  \n",
       "sqft_above                1190.00    1560.00    2210.00     9410.0  \n",
       "sqft_basement                0.00       0.00     560.00     4820.0  \n",
       "sqft_living15             1490.00    1840.00    2360.00     6210.0  \n",
       "sqft_lot15                5100.00    7620.00   10083.25   871200.0  \n",
       "year_sale                 2014.00    2014.00    2015.00     2015.0  \n",
       "month_sale                   4.00       6.00       9.00       12.0  \n",
       "age                         18.00      40.00      63.00      115.0  \n",
       "years_since_renovation      15.00      37.00      60.00      115.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we need to take care of the categorical variables in the dataset. We will use one-hot (aka ‘one-of-K’ or ‘dummy’) encoding for this purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.357296Z",
     "iopub.status.busy": "2024-05-31T23:14:01.357039Z",
     "iopub.status.idle": "2024-05-31T23:14:01.373581Z",
     "shell.execute_reply": "2024-05-31T23:14:01.372926Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define for which variables to do the one-hot encoding\n",
    "categorical_variables = ['view', 'condition', 'grade']\n",
    "\n",
    "# Initialize the encoder\n",
    "encoder = OneHotEncoder(sparse_output=False)\n",
    "\n",
    "# Apply the one-hot encoding to the desired columns\n",
    "one_hot_encoded = encoder.fit_transform(df[categorical_variables])\n",
    "\n",
    "# Convert the results to a DataFrame\n",
    "df_one_hot_encoded = pd.DataFrame(one_hot_encoded, columns=encoder.get_feature_names_out(['view', 'condition', 'grade']), index=df.index)\n",
    "\n",
    "# Concatenate the one-hot encoded columns with the original DataFrame\n",
    "df_encoded = pd.concat([df, df_one_hot_encoded], axis=1)\n",
    "\n",
    "# Drop the old, unencoded columns from the old Dataframe\n",
    "df_encoded = df_encoded.drop(categorical_variables, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that now we have many more dummy variables taking values zero or one in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.376845Z",
     "iopub.status.busy": "2024-05-31T23:14:01.376550Z",
     "iopub.status.idle": "2024-05-31T23:14:01.448144Z",
     "shell.execute_reply": "2024-05-31T23:14:01.447684Z"
    }
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>540083.518786</td>\n",
       "      <td>367135.061269</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>321837.50</td>\n",
       "      <td>450000.00</td>\n",
       "      <td>645000.00</td>\n",
       "      <td>7700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>3.369471</td>\n",
       "      <td>0.907982</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>2.114774</td>\n",
       "      <td>0.770177</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.50</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>2079.921016</td>\n",
       "      <td>918.456818</td>\n",
       "      <td>290.0</td>\n",
       "      <td>1426.50</td>\n",
       "      <td>1910.00</td>\n",
       "      <td>2550.00</td>\n",
       "      <td>13540.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>15107.388951</td>\n",
       "      <td>41421.423497</td>\n",
       "      <td>520.0</td>\n",
       "      <td>5040.00</td>\n",
       "      <td>7619.00</td>\n",
       "      <td>10688.25</td>\n",
       "      <td>1651359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floors</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>1.494332</td>\n",
       "      <td>0.539991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterfront</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.007542</td>\n",
       "      <td>0.086519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_above</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>1788.425319</td>\n",
       "      <td>828.094487</td>\n",
       "      <td>290.0</td>\n",
       "      <td>1190.00</td>\n",
       "      <td>1560.00</td>\n",
       "      <td>2210.00</td>\n",
       "      <td>9410.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_basement</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>291.495697</td>\n",
       "      <td>442.580931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>560.00</td>\n",
       "      <td>4820.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living15</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>1986.582871</td>\n",
       "      <td>685.392610</td>\n",
       "      <td>399.0</td>\n",
       "      <td>1490.00</td>\n",
       "      <td>1840.00</td>\n",
       "      <td>2360.00</td>\n",
       "      <td>6210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot15</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>12768.828984</td>\n",
       "      <td>27304.756179</td>\n",
       "      <td>651.0</td>\n",
       "      <td>5100.00</td>\n",
       "      <td>7620.00</td>\n",
       "      <td>10083.25</td>\n",
       "      <td>871200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_sale</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>2014.322969</td>\n",
       "      <td>0.467622</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>2014.00</td>\n",
       "      <td>2014.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_sale</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>6.574449</td>\n",
       "      <td>3.115377</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>43.316722</td>\n",
       "      <td>29.375731</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>63.00</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>years_since_renovation</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>40.935730</td>\n",
       "      <td>28.813764</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view_0</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.901721</td>\n",
       "      <td>0.297698</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view_1</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.015362</td>\n",
       "      <td>0.122990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view_2</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.044559</td>\n",
       "      <td>0.206337</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view_3</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.023598</td>\n",
       "      <td>0.151797</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view_4</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.014760</td>\n",
       "      <td>0.120595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition_1</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.001388</td>\n",
       "      <td>0.037232</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition_2</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.007959</td>\n",
       "      <td>0.088857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition_3</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.649223</td>\n",
       "      <td>0.477224</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition_4</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.262771</td>\n",
       "      <td>0.440149</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition_5</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.078660</td>\n",
       "      <td>0.269214</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade_1</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.006802</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade_3</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.011781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade_4</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.001342</td>\n",
       "      <td>0.036607</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade_5</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.011197</td>\n",
       "      <td>0.105226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade_6</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.094299</td>\n",
       "      <td>0.292252</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade_7</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.415510</td>\n",
       "      <td>0.492821</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade_8</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.280770</td>\n",
       "      <td>0.449386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade_9</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.120998</td>\n",
       "      <td>0.326132</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade_10</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.052471</td>\n",
       "      <td>0.222980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade_11</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.018462</td>\n",
       "      <td>0.134618</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade_12</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.004164</td>\n",
       "      <td>0.064399</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade_13</th>\n",
       "      <td>21612.0</td>\n",
       "      <td>0.000602</td>\n",
       "      <td>0.024519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count           mean            std      min  \\\n",
       "price                   21612.0  540083.518786  367135.061269  75000.0   \n",
       "bedrooms                21612.0       3.369471       0.907982      0.0   \n",
       "bathrooms               21612.0       2.114774       0.770177      0.0   \n",
       "sqft_living             21612.0    2079.921016     918.456818    290.0   \n",
       "sqft_lot                21612.0   15107.388951   41421.423497    520.0   \n",
       "floors                  21612.0       1.494332       0.539991      1.0   \n",
       "waterfront              21612.0       0.007542       0.086519      0.0   \n",
       "sqft_above              21612.0    1788.425319     828.094487    290.0   \n",
       "sqft_basement           21612.0     291.495697     442.580931      0.0   \n",
       "sqft_living15           21612.0    1986.582871     685.392610    399.0   \n",
       "sqft_lot15              21612.0   12768.828984   27304.756179    651.0   \n",
       "year_sale               21612.0    2014.322969       0.467622   2014.0   \n",
       "month_sale              21612.0       6.574449       3.115377      1.0   \n",
       "age                     21612.0      43.316722      29.375731     -1.0   \n",
       "years_since_renovation  21612.0      40.935730      28.813764     -1.0   \n",
       "view_0                  21612.0       0.901721       0.297698      0.0   \n",
       "view_1                  21612.0       0.015362       0.122990      0.0   \n",
       "view_2                  21612.0       0.044559       0.206337      0.0   \n",
       "view_3                  21612.0       0.023598       0.151797      0.0   \n",
       "view_4                  21612.0       0.014760       0.120595      0.0   \n",
       "condition_1             21612.0       0.001388       0.037232      0.0   \n",
       "condition_2             21612.0       0.007959       0.088857      0.0   \n",
       "condition_3             21612.0       0.649223       0.477224      0.0   \n",
       "condition_4             21612.0       0.262771       0.440149      0.0   \n",
       "condition_5             21612.0       0.078660       0.269214      0.0   \n",
       "grade_1                 21612.0       0.000046       0.006802      0.0   \n",
       "grade_3                 21612.0       0.000139       0.011781      0.0   \n",
       "grade_4                 21612.0       0.001342       0.036607      0.0   \n",
       "grade_5                 21612.0       0.011197       0.105226      0.0   \n",
       "grade_6                 21612.0       0.094299       0.292252      0.0   \n",
       "grade_7                 21612.0       0.415510       0.492821      0.0   \n",
       "grade_8                 21612.0       0.280770       0.449386      0.0   \n",
       "grade_9                 21612.0       0.120998       0.326132      0.0   \n",
       "grade_10                21612.0       0.052471       0.222980      0.0   \n",
       "grade_11                21612.0       0.018462       0.134618      0.0   \n",
       "grade_12                21612.0       0.004164       0.064399      0.0   \n",
       "grade_13                21612.0       0.000602       0.024519      0.0   \n",
       "\n",
       "                              25%        50%        75%        max  \n",
       "price                   321837.50  450000.00  645000.00  7700000.0  \n",
       "bedrooms                     3.00       3.00       4.00       11.0  \n",
       "bathrooms                    1.75       2.25       2.50        8.0  \n",
       "sqft_living               1426.50    1910.00    2550.00    13540.0  \n",
       "sqft_lot                  5040.00    7619.00   10688.25  1651359.0  \n",
       "floors                       1.00       1.50       2.00        3.5  \n",
       "waterfront                   0.00       0.00       0.00        1.0  \n",
       "sqft_above                1190.00    1560.00    2210.00     9410.0  \n",
       "sqft_basement                0.00       0.00     560.00     4820.0  \n",
       "sqft_living15             1490.00    1840.00    2360.00     6210.0  \n",
       "sqft_lot15                5100.00    7620.00   10083.25   871200.0  \n",
       "year_sale                 2014.00    2014.00    2015.00     2015.0  \n",
       "month_sale                   4.00       6.00       9.00       12.0  \n",
       "age                         18.00      40.00      63.00      115.0  \n",
       "years_since_renovation      15.00      37.00      60.00      115.0  \n",
       "view_0                       1.00       1.00       1.00        1.0  \n",
       "view_1                       0.00       0.00       0.00        1.0  \n",
       "view_2                       0.00       0.00       0.00        1.0  \n",
       "view_3                       0.00       0.00       0.00        1.0  \n",
       "view_4                       0.00       0.00       0.00        1.0  \n",
       "condition_1                  0.00       0.00       0.00        1.0  \n",
       "condition_2                  0.00       0.00       0.00        1.0  \n",
       "condition_3                  0.00       1.00       1.00        1.0  \n",
       "condition_4                  0.00       0.00       1.00        1.0  \n",
       "condition_5                  0.00       0.00       0.00        1.0  \n",
       "grade_1                      0.00       0.00       0.00        1.0  \n",
       "grade_3                      0.00       0.00       0.00        1.0  \n",
       "grade_4                      0.00       0.00       0.00        1.0  \n",
       "grade_5                      0.00       0.00       0.00        1.0  \n",
       "grade_6                      0.00       0.00       0.00        1.0  \n",
       "grade_7                      0.00       0.00       1.00        1.0  \n",
       "grade_8                      0.00       0.00       1.00        1.0  \n",
       "grade_9                      0.00       0.00       0.00        1.0  \n",
       "grade_10                     0.00       0.00       0.00        1.0  \n",
       "grade_11                     0.00       0.00       0.00        1.0  \n",
       "grade_12                     0.00       0.00       0.00        1.0  \n",
       "grade_13                     0.00       0.00       0.00        1.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given that these categorical variables are ordinal, this might have not been strictly necessary. However, is required if you have data that is not ordinal.\n",
    "\n",
    "\n",
    "#### Splitting the Data into Training and Test Sets {-}\n",
    "\n",
    "Before we can train a machine learning model, we need to split our dataset into a training set and a test set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.451104Z",
     "iopub.status.busy": "2024-05-31T23:14:01.450843Z",
     "iopub.status.idle": "2024-05-31T23:14:01.455716Z",
     "shell.execute_reply": "2024-05-31T23:14:01.455253Z"
    }
   },
   "outputs": [],
   "source": [
    "X = df_encoded.drop('price', axis=1) # All variables except `SeriousDlqin2yrs`\n",
    "y = df_encoded[['price']] # Only SeriousDlqin2yrs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use 80% of the data for training and 20% for testing. Note that since our target variable is continuous, we don't need to stratify the split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.458630Z",
     "iopub.status.busy": "2024-05-31T23:14:01.458378Z",
     "iopub.status.idle": "2024-05-31T23:14:01.466595Z",
     "shell.execute_reply": "2024-05-31T23:14:01.466055Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scaling Features {-}\n",
    "\n",
    "To improve the performance of our machine learning model, we should scale the features. We will use the `StandardScaler` and `MinMaxScaler`class from the `sklearn.preprocessing` module to scale the features. The `StandardScaler` scales each feature to have a mean of 0 and a standard deviation of 1. The `MinMaxScaler` scales each feature to a given range, usually 0 to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.469273Z",
     "iopub.status.busy": "2024-05-31T23:14:01.469068Z",
     "iopub.status.idle": "2024-05-31T23:14:01.484951Z",
     "shell.execute_reply": "2024-05-31T23:14:01.484295Z"
    }
   },
   "outputs": [],
   "source": [
    "def scale_features(scaler, df, col_names, only_transform=False):\n",
    "\n",
    "    # Extract the features we want to scale\n",
    "    features = df[col_names] \n",
    "\n",
    "    # Fit the scaler to the features and transform them\n",
    "    if only_transform:\n",
    "        features = scaler.transform(features.values)\n",
    "    else:\n",
    "        features = scaler.fit_transform(features.values)\n",
    "\n",
    "    # Replace the original features with the scaled features\n",
    "    df[col_names] = features\n",
    "\n",
    "\n",
    "# Define which features to scale with the StandardScaler and MinMaxScaler\n",
    "for_standard_scaler = [\n",
    "    'bedrooms', \n",
    "    'bathrooms', \n",
    "    'sqft_living', \n",
    "    'sqft_lot', \n",
    "    'floors', \n",
    "    'sqft_above', \n",
    "    'sqft_basement', \n",
    "    'sqft_living15', \n",
    "    'sqft_lot15', \n",
    "    'age', \n",
    "    'years_since_renovation'\n",
    "]\n",
    "\n",
    "for_min_max_scaler = [\n",
    "    'year_sale', \n",
    "    'month_sale'\n",
    "]\n",
    "\n",
    "# Apply the standard scaler (Note: we use the same mean and std for scaling the test set)\n",
    "standard_scaler = StandardScaler() \n",
    "scale_features(standard_scaler, X_train, for_standard_scaler)\n",
    "scale_features(standard_scaler, X_test, for_standard_scaler, only_transform=True)\n",
    "\n",
    "# Apply the minmax scaler (Note: we use the same min and max for scaling the test set)\n",
    "minmax_scaler = MinMaxScaler()\n",
    "scale_features(minmax_scaler, X_train, for_min_max_scaler)\n",
    "scale_features(minmax_scaler, X_test, for_min_max_scaler, only_transform=True)\n",
    "\n",
    "# Apply standard scaler to the target variable\n",
    "target_scaler = StandardScaler()\n",
    "y_train = pd.DataFrame(target_scaler.fit_transform(y_train), columns=['price'])\n",
    "y_test = pd.DataFrame(target_scaler.transform(y_test), columns=['price']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation Criertia\n",
    "\n",
    "We will evaluate our models based on the following criteria\n",
    "\n",
    "- **Root Mean Squared Error (MSE)**: Square root of the mean of the squared differences between the predicted and the actual values\n",
    "- **Mean Absolute Error (MAE)**: Mean of the absolute differences between the predicted and the actual values\n",
    "- **R-squared (R2)**: Proportion of the variance in the dependent variable that is predictable from the independent variables\n",
    "\n",
    "We define a function that a function that will calculate these metrics for us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.489674Z",
     "iopub.status.busy": "2024-05-31T23:14:01.489417Z",
     "iopub.status.idle": "2024-05-31T23:14:01.495597Z",
     "shell.execute_reply": "2024-05-31T23:14:01.494961Z"
    }
   },
   "outputs": [],
   "source": [
    "def evaluate_model(model, X_train, y_train, X_test, y_test, label='', print_results=True):\n",
    "\n",
    "    # Predict the target variable\n",
    "    y_pred_train = model.predict(X_train)\n",
    "    y_pred_test = model.predict(X_test)\n",
    "\n",
    "    # Transform the target variable back to the original scale \n",
    "    # (This makes it easier to interpret the RMSE and MAE)\n",
    "    y_train_inv = target_scaler.inverse_transform(y_train)\n",
    "    y_test_inv = target_scaler.inverse_transform(y_test)\n",
    "    y_pred_train_inv = target_scaler.inverse_transform(y_pred_train.reshape(-1, 1))\n",
    "    y_pred_test_inv = target_scaler.inverse_transform(y_pred_test.reshape(-1, 1))\n",
    "\n",
    "    # Calculate the evaluation metrics\n",
    "    rmse_train = mean_squared_error(y_train_inv, y_pred_train_inv, squared=False)\n",
    "    rmse_test = mean_squared_error(y_test_inv, y_pred_test_inv, squared=False)\n",
    "    mae_train = mean_absolute_error(y_train_inv, y_pred_train_inv)\n",
    "    mae_test = mean_absolute_error(y_test_inv, y_pred_test_inv)\n",
    "    r2_train = r2_score(y_train_inv, y_pred_train_inv)\n",
    "    r2_test = r2_score(y_test_inv, y_pred_test_inv)\n",
    "\n",
    "    # Print the evaluation metrics\n",
    "    if print_results:\n",
    "        print(f\"--------------------------------------------------------------\")\n",
    "        print(f\"Metrics: {label}\")\n",
    "        print(f\"--------------------------------------------------------------\")\n",
    "        print(f\"RMSE (Train): {rmse_train}\")\n",
    "        print(f\"MAE (Train): {mae_train}\")\n",
    "        print(f\"R2 (Train): {r2_train}\")\n",
    "        print(f\"--------------------------------------------------------------\")\n",
    "        print(f\"RMSE (Test): {rmse_test}\")\n",
    "        print(f\"MAE (Test): {mae_test}\")\n",
    "        print(f\"R2 (Test): {r2_test}\")\n",
    "        print(f\"--------------------------------------------------------------\")\n",
    "\n",
    "    return rmse_train, rmse_test, mae_train, mae_test, r2_train, r2_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression\n",
    "\n",
    "We will start by training a simple linear regression model using only a few basic features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.498558Z",
     "iopub.status.busy": "2024-05-31T23:14:01.498302Z",
     "iopub.status.idle": "2024-05-31T23:14:01.506822Z",
     "shell.execute_reply": "2024-05-31T23:14:01.506131Z"
    }
   },
   "outputs": [],
   "source": [
    "basic_features = ['bedrooms', 'bathrooms', 'sqft_living']\n",
    "reg_lin_basic = LinearRegression().fit(X_train[basic_features], y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can evaluate the model using the function we defined earlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.509746Z",
     "iopub.status.busy": "2024-05-31T23:14:01.509510Z",
     "iopub.status.idle": "2024-05-31T23:14:01.520389Z",
     "shell.execute_reply": "2024-05-31T23:14:01.519769Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "Metrics: Linear Regression (Basic Features)\n",
      "--------------------------------------------------------------\n",
      "RMSE (Train): 253683.12629128053\n",
      "MAE (Train): 168994.9071419931\n",
      "R2 (Train): 0.5085183567620137\n",
      "--------------------------------------------------------------\n",
      "RMSE (Test): 271925.6970016718\n",
      "MAE (Test): 174452.3090166579\n",
      "R2 (Test): 0.5073277848405491\n",
      "--------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(reg_lin_basic, X_train[basic_features], y_train, X_test[basic_features], y_test, label = 'Linear Regression (Basic Features)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are not doing that badly with a RMSE of around $250000 if we take into account the minimum and maximum prices in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.523462Z",
     "iopub.status.busy": "2024-05-31T23:14:01.523243Z",
     "iopub.status.idle": "2024-05-31T23:14:01.526676Z",
     "shell.execute_reply": "2024-05-31T23:14:01.526041Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min Price: 75000.0, Max Price: 7700000.0\n"
     ]
    }
   ],
   "source": [
    "print(f'Min Price: {df[\"price\"].min()}, Max Price: {df[\"price\"].max()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and the distribution of prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.529854Z",
     "iopub.status.busy": "2024-05-31T23:14:01.529634Z",
     "iopub.status.idle": "2024-05-31T23:14:01.760605Z",
     "shell.execute_reply": "2024-05-31T23:14:01.759729Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 3000000.0)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df['price'].plot.hist(bins=100)\n",
    "ax.ticklabel_format(useOffset=False,style='plain')\n",
    "ax.tick_params(axis='x', labelrotation=45)\n",
    "ax.set_xlim(0,3000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now try a linear regression but with all the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.763650Z",
     "iopub.status.busy": "2024-05-31T23:14:01.763440Z",
     "iopub.status.idle": "2024-05-31T23:14:01.781038Z",
     "shell.execute_reply": "2024-05-31T23:14:01.780382Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_lin = LinearRegression().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can evaluate the model using the function we defined earlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.784500Z",
     "iopub.status.busy": "2024-05-31T23:14:01.784237Z",
     "iopub.status.idle": "2024-05-31T23:14:01.796535Z",
     "shell.execute_reply": "2024-05-31T23:14:01.795877Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "Metrics: Linear Regression (All Features)\n",
      "--------------------------------------------------------------\n",
      "RMSE (Train): 203680.13271422562\n",
      "MAE (Train): 133387.29115116654\n",
      "R2 (Train): 0.6831735158523966\n",
      "--------------------------------------------------------------\n",
      "RMSE (Test): 215930.28643225055\n",
      "MAE (Test): 137547.23127374452\n",
      "R2 (Test): 0.6893404625153258\n",
      "--------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(reg_lin, X_train, y_train, X_test, y_test, label = 'Linear Regression (All Features)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performance of the model has improved. Since we have a large sample size but relatively few regressors it is unlikely to overfit. Note, however, that if we add more regressors, e.g., squared and cubed features, etc. we might run into trouble at a certain point. That's why it's important to use the train-test split to check that our model generalizes.\n",
    "\n",
    "\n",
    "### LASSO Regression\n",
    "\n",
    "One way to deal with overfitting in a linear regression is to use LASSO regression. LASSO regression is a type of linear regression that uses a penalty (or regularization) term to shrink the coefficients of the regressors towards zero. Essentially, LASSO selects a subset of features, which can help to prevent overfitting. We will use the `Lasso` class from the `sklearn.linear_model` module to train a LASSO regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.799749Z",
     "iopub.status.busy": "2024-05-31T23:14:01.799507Z",
     "iopub.status.idle": "2024-05-31T23:14:01.815189Z",
     "shell.execute_reply": "2024-05-31T23:14:01.814466Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_lasso = Lasso(alpha=0.1).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can evaluate the model using the function we defined earlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.818538Z",
     "iopub.status.busy": "2024-05-31T23:14:01.818306Z",
     "iopub.status.idle": "2024-05-31T23:14:01.830424Z",
     "shell.execute_reply": "2024-05-31T23:14:01.829762Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "Metrics: LASSO Regression\n",
      "--------------------------------------------------------------\n",
      "RMSE (Train): 254661.31873140397\n",
      "MAE (Train): 163764.59579244166\n",
      "R2 (Train): 0.5047207803287292\n",
      "--------------------------------------------------------------\n",
      "RMSE (Test): 273446.0239203981\n",
      "MAE (Test): 168730.33198849438\n",
      "R2 (Test): 0.5018033587261654\n",
      "--------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(reg_lasso, X_train, y_train, X_test, y_test, label = 'LASSO Regression');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model is doing a bit worse than a standard linear regression. However, we just chose the value of the penalty term $\\alpha$ arbitrarily. We can use cross-validation to find the best value of $\\alpha$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:01.833497Z",
     "iopub.status.busy": "2024-05-31T23:14:01.833267Z",
     "iopub.status.idle": "2024-05-31T23:14:02.046053Z",
     "shell.execute_reply": "2024-05-31T23:14:02.045320Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_lasso_cv = LassoCV(cv=5, random_state=42).fit(X_train, y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This command repeatedly runs 5-fold cross-validation for a LASSO regression using different values of $\\alpha$. The $\\alpha$ that minimizes the mean squared error is then stored in the `alpha_` attribute of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:02.049501Z",
     "iopub.status.busy": "2024-05-31T23:14:02.049266Z",
     "iopub.status.idle": "2024-05-31T23:14:02.053431Z",
     "shell.execute_reply": "2024-05-31T23:14:02.052656Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0007018253833076978"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_lasso_cv.alpha_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This $\\alpha$ is much smaller than our initial value. Let's see how well it does in terms of the RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:02.056518Z",
     "iopub.status.busy": "2024-05-31T23:14:02.056297Z",
     "iopub.status.idle": "2024-05-31T23:14:02.068465Z",
     "shell.execute_reply": "2024-05-31T23:14:02.067629Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "Metrics: LASSO Regression (CV)\n",
      "--------------------------------------------------------------\n",
      "RMSE (Train): 204360.76389300276\n",
      "MAE (Train): 134141.73731503487\n",
      "R2 (Train): 0.6810525207180653\n",
      "--------------------------------------------------------------\n",
      "RMSE (Test): 217146.28161042708\n",
      "MAE (Test): 138085.31092628682\n",
      "R2 (Test): 0.6858316989155063\n",
      "--------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(reg_lasso_cv, X_train, y_train, X_test, y_test, label = 'LASSO Regression (CV)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's always a good idea to use cross-validation to find the best hyperparameters for your model. For more complicated models with several hyperparameter choices, one can use `GridSearchCV` or `RandomizedSearchCV` from `sklearn` to find the hyperparameters.\n",
    "\n",
    "We can check which coefficients the LASSO regression has shrunk to zero because of the regularization term"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:02.071644Z",
     "iopub.status.busy": "2024-05-31T23:14:02.071406Z",
     "iopub.status.idle": "2024-05-31T23:14:02.075894Z",
     "shell.execute_reply": "2024-05-31T23:14:02.075170Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sqft_above', 'view_1', 'view_3', 'condition_1', 'condition_3',\n",
       "       'grade_1', 'grade_3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns[np.abs(reg_lasso_cv.coef_) < 1e-12]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare this to the linear regression where none of the coefficients were zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:02.079038Z",
     "iopub.status.busy": "2024-05-31T23:14:02.078817Z",
     "iopub.status.idle": "2024-05-31T23:14:02.083378Z",
     "shell.execute_reply": "2024-05-31T23:14:02.082452Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns[(np.abs(reg_lin.coef_) < 1e-12).reshape(-1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree\n",
    "\n",
    "We will now train a decision tree regressor on the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:02.086665Z",
     "iopub.status.busy": "2024-05-31T23:14:02.086433Z",
     "iopub.status.idle": "2024-05-31T23:14:02.286059Z",
     "shell.execute_reply": "2024-05-31T23:14:02.285379Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_tree = DecisionTreeRegressor(random_state=42).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can evaluate the model using the function we defined earlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:02.290576Z",
     "iopub.status.busy": "2024-05-31T23:14:02.289986Z",
     "iopub.status.idle": "2024-05-31T23:14:02.312923Z",
     "shell.execute_reply": "2024-05-31T23:14:02.311963Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "Metrics: Decision Tree\n",
      "--------------------------------------------------------------\n",
      "RMSE (Train): 0.0\n",
      "MAE (Train): 0.0\n",
      "R2 (Train): 1.0\n",
      "--------------------------------------------------------------\n",
      "RMSE (Test): 276927.1448808092\n",
      "MAE (Test): 163650.69442516772\n",
      "R2 (Test): 0.48903797314415076\n",
      "--------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(reg_tree, X_train, y_train, X_test, y_test, label = 'Decision Tree');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decision tree perfectly fits the training data but does not generalize well to the test data. Why did this happen? We did not change any of the default hyperparameters of the decision tree which resulted in the decision tree overfitting, i.e., it learned the noise in the training data. We can try to reduce the depth of the tree to prevent overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:02.316621Z",
     "iopub.status.busy": "2024-05-31T23:14:02.315979Z",
     "iopub.status.idle": "2024-05-31T23:14:02.418370Z",
     "shell.execute_reply": "2024-05-31T23:14:02.417859Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "Metrics: Decision Tree\n",
      "--------------------------------------------------------------\n",
      "RMSE (Train): 151229.68262754745\n",
      "MAE (Train): 104245.46532488744\n",
      "R2 (Train): 0.8253380836313531\n",
      "--------------------------------------------------------------\n",
      "RMSE (Test): 242624.93510350658\n",
      "MAE (Test): 139455.8139319333\n",
      "R2 (Test): 0.6077811751936795\n",
      "--------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "reg_tree = DecisionTreeRegressor(max_depth=10, random_state=42).fit(X_train, y_train)\n",
    "evaluate_model(reg_tree, X_train, y_train, X_test, y_test, label = 'Decision Tree');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This seems to have improved the performance of the model. However, we need a more rigorous way to find the best hyperparameters. One such way is to use grid search, which tries many different hyperparameter values. We, then, combine this with cross-validation to find the best hyperparameters for the decision tree. `GridSearchCV` from the `sklearn` package does exactly that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:02.421145Z",
     "iopub.status.busy": "2024-05-31T23:14:02.420926Z",
     "iopub.status.idle": "2024-05-31T23:14:27.567819Z",
     "shell.execute_reply": "2024-05-31T23:14:27.567267Z"
    }
   },
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'max_depth': [5, 10, 15, 20],\n",
    "    'min_samples_split': [2, 5, 10, 15],\n",
    "    'min_samples_leaf': [1, 2, 5, 10]\n",
    "}\n",
    "\n",
    "reg_tree_cv = GridSearchCV(DecisionTreeRegressor(random_state=42), param_grid, cv=5).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that `param_grid` is a dictionary where the keys are the hyperparameters of the decision tree and the values are lists of the values we want to try. The best hyperparameters are stored in the `best_params_` attribute of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:27.570712Z",
     "iopub.status.busy": "2024-05-31T23:14:27.570502Z",
     "iopub.status.idle": "2024-05-31T23:14:27.574236Z",
     "shell.execute_reply": "2024-05-31T23:14:27.573728Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 10, 'min_samples_leaf': 5, 'min_samples_split': 15}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_tree_cv.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then evaluate the model using the best hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:27.576831Z",
     "iopub.status.busy": "2024-05-31T23:14:27.576655Z",
     "iopub.status.idle": "2024-05-31T23:14:27.588503Z",
     "shell.execute_reply": "2024-05-31T23:14:27.588029Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "Metrics: Decision Tree (CV)\n",
      "--------------------------------------------------------------\n",
      "RMSE (Train): 165756.35013566245\n",
      "MAE (Train): 111454.50933401058\n",
      "R2 (Train): 0.7901714932986897\n",
      "--------------------------------------------------------------\n",
      "RMSE (Test): 233527.64612876996\n",
      "MAE (Test): 137387.4802938534\n",
      "R2 (Test): 0.6366424628160059\n",
      "--------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(reg_tree_cv, X_train, y_train, X_test, y_test, label = 'Decision Tree (CV)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that using `reg_tree_cv` as the model to be evaluated uses automatically the best estimator. Alternatively, we could also use `best_estimator_` attribute in `evaluate_model`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:27.591359Z",
     "iopub.status.busy": "2024-05-31T23:14:27.591114Z",
     "iopub.status.idle": "2024-05-31T23:14:27.596596Z",
     "shell.execute_reply": "2024-05-31T23:14:27.596062Z"
    }
   },
   "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=10, min_samples_leaf=5, min_samples_split=15,\n",
       "                      random_state=42)</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=10, min_samples_leaf=5, min_samples_split=15,\n",
       "                      random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=10, min_samples_leaf=5, min_samples_split=15,\n",
       "                      random_state=42)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_tree_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest\n",
    "\n",
    "We will now train a random forest regressor on the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:27.599355Z",
     "iopub.status.busy": "2024-05-31T23:14:27.599108Z",
     "iopub.status.idle": "2024-05-31T23:14:40.075987Z",
     "shell.execute_reply": "2024-05-31T23:14:40.074755Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/datascience_course_cemfi_dev/lib/python3.8/site-packages/sklearn/base.py:1151: 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",
      "  return fit_method(estimator, *args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "reg_rf = RandomForestRegressor(random_state=42).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can evaluate the model using the function we defined earlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:40.078852Z",
     "iopub.status.busy": "2024-05-31T23:14:40.078637Z",
     "iopub.status.idle": "2024-05-31T23:14:40.482555Z",
     "shell.execute_reply": "2024-05-31T23:14:40.481937Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "Metrics: Random Forest\n",
      "--------------------------------------------------------------\n",
      "RMSE (Train): 66638.2969151954\n",
      "MAE (Train): 42418.54030134768\n",
      "R2 (Train): 0.9660865542789411\n",
      "--------------------------------------------------------------\n",
      "RMSE (Test): 207350.8910584885\n",
      "MAE (Test): 120473.06515845477\n",
      "R2 (Test): 0.713536442057418\n",
      "--------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(reg_rf, X_train, y_train, X_test, y_test, label = 'Random Forest');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use grid search with cross-validation to find the best hyperparameters for the random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:40.485531Z",
     "iopub.status.busy": "2024-05-31T23:14:40.485287Z",
     "iopub.status.idle": "2024-05-31T23:14:40.488575Z",
     "shell.execute_reply": "2024-05-31T23:14:40.487925Z"
    }
   },
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'max_depth': [5, 10, 15, 20],\n",
    "    'n_estimators': [50, 100, 150, 200, 300],\n",
    "}\n",
    "\n",
    "#reg_rf_cv = GridSearchCV(RandomForestRegressor(random_state=42), param_grid, cv=5).fit(X_train, y_train)\n",
    "#dump(reg_rf_cv, 'reg_rf_cv.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:40.491214Z",
     "iopub.status.busy": "2024-05-31T23:14:40.490995Z",
     "iopub.status.idle": "2024-05-31T23:14:41.028301Z",
     "shell.execute_reply": "2024-05-31T23:14:41.027513Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_rf_cv = load('reg_rf_cv.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are trying 20 different hyperparameter combinations and for each parameter combination we will have to estimate the model 5 times (5-fold cross-validation). This might take a while. The best hyperparameters are stored in the `best_params_` attribute of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:41.031404Z",
     "iopub.status.busy": "2024-05-31T23:14:41.031145Z",
     "iopub.status.idle": "2024-05-31T23:14:41.034874Z",
     "shell.execute_reply": "2024-05-31T23:14:41.034329Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 20, 'n_estimators': 300}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_rf_cv.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then evaluate the model using the best hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:41.037792Z",
     "iopub.status.busy": "2024-05-31T23:14:41.037569Z",
     "iopub.status.idle": "2024-05-31T23:14:42.060821Z",
     "shell.execute_reply": "2024-05-31T23:14:42.060213Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "Metrics: Random Forest (CV)\n",
      "--------------------------------------------------------------\n",
      "RMSE (Train): 72654.48965997726\n",
      "MAE (Train): 49716.92916875867\n",
      "R2 (Train): 0.959686634834322\n",
      "--------------------------------------------------------------\n",
      "RMSE (Test): 206073.35722748775\n",
      "MAE (Test): 120254.77646893183\n",
      "R2 (Test): 0.7170554960056299\n",
      "--------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(reg_rf_cv, X_train, y_train, X_test, y_test, label = 'Random Forest (CV)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tuned random forest model performs a bit better than the one with the default values. However, the improvement is not that big. This is likely because the default values of the random forest are already quite good. We could try to test more hyperparameters in the grid search. Note that we chose the highest value for both parameters. Thus, we could try even higher values. However, this would increase the computational time.\n",
    "\n",
    "\n",
    "### XGBoost\n",
    "\n",
    "We will now train an XGBoost regressor on the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:42.063821Z",
     "iopub.status.busy": "2024-05-31T23:14:42.063573Z",
     "iopub.status.idle": "2024-05-31T23:14:42.219292Z",
     "shell.execute_reply": "2024-05-31T23:14:42.218355Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_xgb = XGBRegressor(random_state=42).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can evaluate the model using the function we defined earlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:42.223180Z",
     "iopub.status.busy": "2024-05-31T23:14:42.222912Z",
     "iopub.status.idle": "2024-05-31T23:14:42.251128Z",
     "shell.execute_reply": "2024-05-31T23:14:42.250378Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "Metrics: XGBoost\n",
      "--------------------------------------------------------------\n",
      "RMSE (Train): 101571.54001161143\n",
      "MAE (Train): 76626.15270638122\n",
      "R2 (Train): 0.9212105237017153\n",
      "--------------------------------------------------------------\n",
      "RMSE (Test): 204360.86407090884\n",
      "MAE (Test): 120757.43200613001\n",
      "R2 (Test): 0.7217385587721041\n",
      "--------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(reg_xgb, X_train, y_train, X_test, y_test, label = 'XGBoost');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use grid search with cross-validation to find the best hyperparameters for the XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:42.254740Z",
     "iopub.status.busy": "2024-05-31T23:14:42.254449Z",
     "iopub.status.idle": "2024-05-31T23:14:42.258261Z",
     "shell.execute_reply": "2024-05-31T23:14:42.257406Z"
    }
   },
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'max_depth': [5, 10, 15, 20],\n",
    "    'n_estimators': [50, 100, 150, 200, 300],\n",
    "}\n",
    "\n",
    "#reg_xgb_cv = GridSearchCV(XGBRegressor(random_state=42), param_grid, cv=5).fit(X_train, y_train)\n",
    "#dump(reg_xgb_cv, 'reg_xgb_cv.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:42.261589Z",
     "iopub.status.busy": "2024-05-31T23:14:42.261290Z",
     "iopub.status.idle": "2024-05-31T23:14:42.268606Z",
     "shell.execute_reply": "2024-05-31T23:14:42.268011Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_xgb_cv = load('reg_xgb_cv.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best hyperparameters are stored in the `best_params_` attribute of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:42.272067Z",
     "iopub.status.busy": "2024-05-31T23:14:42.271802Z",
     "iopub.status.idle": "2024-05-31T23:14:42.276427Z",
     "shell.execute_reply": "2024-05-31T23:14:42.275711Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 5, 'n_estimators': 50}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_xgb_cv.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then evaluate the model using the best hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:42.279858Z",
     "iopub.status.busy": "2024-05-31T23:14:42.279601Z",
     "iopub.status.idle": "2024-05-31T23:14:42.305111Z",
     "shell.execute_reply": "2024-05-31T23:14:42.304397Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "Metrics: XGBoost (CV)\n",
      "--------------------------------------------------------------\n",
      "RMSE (Train): 134323.31888964228\n",
      "MAE (Train): 99419.23493894239\n",
      "R2 (Train): 0.862207059779255\n",
      "--------------------------------------------------------------\n",
      "RMSE (Test): 201839.3762899356\n",
      "MAE (Test): 123656.21579704488\n",
      "R2 (Test): 0.7285628038252234\n",
      "--------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(reg_xgb_cv, X_train, y_train, X_test, y_test, label = 'XGBoost (CV)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the tuned XGBoost model performs a bit better than the one with the default values. However, the improvement is not that big.\n",
    "\n",
    "\n",
    "### Neural Network\n",
    "\n",
    "Finally, let's try to train a neural network on the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:42.308639Z",
     "iopub.status.busy": "2024-05-31T23:14:42.308363Z",
     "iopub.status.idle": "2024-05-31T23:14:42.311626Z",
     "shell.execute_reply": "2024-05-31T23:14:42.310735Z"
    }
   },
   "outputs": [],
   "source": [
    "#reg_nn = MLPRegressor(random_state=42, verbose=True).fit(X_train, y_train)\n",
    "#dump(reg_nn, 'reg_nn.joblib') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:42.315025Z",
     "iopub.status.busy": "2024-05-31T23:14:42.314744Z",
     "iopub.status.idle": "2024-05-31T23:14:42.323913Z",
     "shell.execute_reply": "2024-05-31T23:14:42.323202Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_nn = load('reg_nn.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can evaluate the model using the function we defined earlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:42.327437Z",
     "iopub.status.busy": "2024-05-31T23:14:42.327193Z",
     "iopub.status.idle": "2024-05-31T23:14:42.365803Z",
     "shell.execute_reply": "2024-05-31T23:14:42.364957Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "Metrics: Neural Network\n",
      "--------------------------------------------------------------\n",
      "RMSE (Train): 142063.16391660276\n",
      "MAE (Train): 101370.39765456515\n",
      "R2 (Train): 0.8458700261274621\n",
      "--------------------------------------------------------------\n",
      "RMSE (Test): 201217.79803902542\n",
      "MAE (Test): 125534.39997216879\n",
      "R2 (Test): 0.7302320486389542\n",
      "--------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(reg_nn, X_train, y_train, X_test, y_test, label = 'Neural Network');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can try to improve the performance of the neural network by tuning the hyperparameters. We will use grid search with cross-validation to find the best hyperparameters for the neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:42.369955Z",
     "iopub.status.busy": "2024-05-31T23:14:42.369627Z",
     "iopub.status.idle": "2024-05-31T23:14:42.373691Z",
     "shell.execute_reply": "2024-05-31T23:14:42.372929Z"
    }
   },
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'hidden_layer_sizes': [(100,), (100, 100), (200,), (200, 100)],\n",
    "    'alpha': [0.0001, 0.001, 0.01, 0.1],\n",
    "}\n",
    "\n",
    "#reg_nn_cv = GridSearchCV(MLPRegressor(random_state=42, verbose=True), param_grid, cv=5).fit(X_train, y_train)\n",
    "#dump(reg_nn_cv, 'reg_nn_cv.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:42.377423Z",
     "iopub.status.busy": "2024-05-31T23:14:42.377143Z",
     "iopub.status.idle": "2024-05-31T23:14:42.387038Z",
     "shell.execute_reply": "2024-05-31T23:14:42.386281Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_nn_cv = load('reg_nn_cv.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best hyperparameters are stored in the `best_params_` attribute of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:42.390949Z",
     "iopub.status.busy": "2024-05-31T23:14:42.390665Z",
     "iopub.status.idle": "2024-05-31T23:14:42.395712Z",
     "shell.execute_reply": "2024-05-31T23:14:42.394840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.1, 'hidden_layer_sizes': (100,)}"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_nn_cv.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then evaluate the model using the best hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:42.399526Z",
     "iopub.status.busy": "2024-05-31T23:14:42.399256Z",
     "iopub.status.idle": "2024-05-31T23:14:42.421485Z",
     "shell.execute_reply": "2024-05-31T23:14:42.420688Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "Metrics: Neural Network\n",
      "--------------------------------------------------------------\n",
      "RMSE (Train): 155891.59268622578\n",
      "MAE (Train): 108538.19421278372\n",
      "R2 (Train): 0.814403608721182\n",
      "--------------------------------------------------------------\n",
      "RMSE (Test): 192879.83144507415\n",
      "MAE (Test): 122558.79964553488\n",
      "R2 (Test): 0.7521258686276469\n",
      "--------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(reg_nn_cv, X_train, y_train, X_test, y_test, label = 'Neural Network');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tuned neural network model performs a bit better than the one with the default values.\n",
    "\n",
    "\n",
    "## Model Evaluation\n",
    "\n",
    "Let's summarize the results of our models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-31T23:14:42.425653Z",
     "iopub.status.busy": "2024-05-31T23:14:42.425365Z",
     "iopub.status.idle": "2024-05-31T23:14:43.528163Z",
     "shell.execute_reply": "2024-05-31T23:14:43.527311Z"
    }
   },
   "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>Model</th>\n",
       "      <th>RMSE Train</th>\n",
       "      <th>RMSE Test</th>\n",
       "      <th>MAE Train</th>\n",
       "      <th>MAE Test</th>\n",
       "      <th>R2 Train</th>\n",
       "      <th>R2 Test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Neural Network</td>\n",
       "      <td>155891.592686</td>\n",
       "      <td>192879.831445</td>\n",
       "      <td>108538.194213</td>\n",
       "      <td>122558.799646</td>\n",
       "      <td>0.814404</td>\n",
       "      <td>0.752126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>134323.318890</td>\n",
       "      <td>201839.376290</td>\n",
       "      <td>99419.234939</td>\n",
       "      <td>123656.215797</td>\n",
       "      <td>0.862207</td>\n",
       "      <td>0.728563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>72654.489660</td>\n",
       "      <td>206073.357227</td>\n",
       "      <td>49716.929169</td>\n",
       "      <td>120254.776469</td>\n",
       "      <td>0.959687</td>\n",
       "      <td>0.717055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>203680.132714</td>\n",
       "      <td>215930.286432</td>\n",
       "      <td>133387.291151</td>\n",
       "      <td>137547.231274</td>\n",
       "      <td>0.683174</td>\n",
       "      <td>0.689340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LASSO Regression</td>\n",
       "      <td>204360.763893</td>\n",
       "      <td>217146.281610</td>\n",
       "      <td>134141.737315</td>\n",
       "      <td>138085.310926</td>\n",
       "      <td>0.681053</td>\n",
       "      <td>0.685832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>165756.350136</td>\n",
       "      <td>233527.646129</td>\n",
       "      <td>111454.509334</td>\n",
       "      <td>137387.480294</td>\n",
       "      <td>0.790171</td>\n",
       "      <td>0.636642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Model     RMSE Train      RMSE Test      MAE Train  \\\n",
       "0     Neural Network  155891.592686  192879.831445  108538.194213   \n",
       "1            XGBoost  134323.318890  201839.376290   99419.234939   \n",
       "2      Random Forest   72654.489660  206073.357227   49716.929169   \n",
       "3  Linear Regression  203680.132714  215930.286432  133387.291151   \n",
       "4   LASSO Regression  204360.763893  217146.281610  134141.737315   \n",
       "5      Decision Tree  165756.350136  233527.646129  111454.509334   \n",
       "\n",
       "        MAE Test  R2 Train   R2 Test  \n",
       "0  122558.799646  0.814404  0.752126  \n",
       "1  123656.215797  0.862207  0.728563  \n",
       "2  120254.776469  0.959687  0.717055  \n",
       "3  137547.231274  0.683174  0.689340  \n",
       "4  138085.310926  0.681053  0.685832  \n",
       "5  137387.480294  0.790171  0.636642  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = {\n",
    "    \"Linear Regression\" : reg_lin,\n",
    "    \"LASSO Regression\" : reg_lasso_cv,\n",
    "    \"Decision Tree\" : reg_tree_cv,\n",
    "    \"Random Forest\" : reg_rf_cv,\n",
    "    \"XGBoost\" : reg_xgb_cv,\n",
    "    \"Neural Network\" : reg_nn_cv\n",
    "}\n",
    "\n",
    "results = pd.DataFrame(columns=['Model', 'RMSE Train', 'RMSE Test', 'MAE Train', 'MAE Test', 'R2 Train', 'R2 Test'])\n",
    "\n",
    "for modelName in models:\n",
    "\n",
    "    # Evaluate the current model\n",
    "    rmse_train, rmse_test, mae_train, mae_test, r2_train, r2_test = evaluate_model(models[modelName], X_train, y_train, X_test, y_test, print_results=False)\n",
    "\n",
    "    # Store the results\n",
    "    res = {\n",
    "        'Model': modelName,\n",
    "        'RMSE Train': rmse_train, \n",
    "        'RMSE Test': rmse_test,\n",
    "        'MAE Train': mae_train,\n",
    "        'MAE Test': mae_test,\n",
    "        'R2 Train': r2_train,\n",
    "        'R2 Test': r2_test\n",
    "    }\n",
    "\n",
    "    df_tmp = pd.DataFrame(res, index=[0])\n",
    "\n",
    "    results = pd.concat([results, df_tmp], axis=0, ignore_index=True)\n",
    "\n",
    "# Sort the results by the RMSE of the test set\n",
    "results = results.sort_values(by='RMSE Test').reset_index(drop=True)\n",
    "\n",
    "results"
   ]
  },
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   "source": [
    "## Conclusion\n",
    "\n",
    "In this application, we have seen how to implement machine learning models for regression problems. We have used a dataset of house prices in King County, USA, to predict the price of a house based on a set of features. We have trained several models, including linear regression, LASSO regression, decision trees, random forests, XGBoost, and neural networks. We have used grid search with cross-validation to find the best hyperparameters for the models. We have evaluated the models based on the root mean squared error, mean absolute error, and R-squared. With a bit more careful hyperparameter tuning, we could likely improve the performance of the models even further and the ranking of the models might change. "
   ]
  }
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