House Sales_in_King_Count_USA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <a href=\"https://www.bigdatauniversity.com\"><img src = \"https://ibm.box.com/shared/static/ugcqz6ohbvff804xp84y4kqnvvk3bq1g.png\" width = 300, align = \"center\"></a>\n",
    "\n",
    "<h1 align=center><font size = 5>Data Analysis with Python</font></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# House Sales in King County, USA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>id</b> : A notation for a house\n",
    "\n",
    "<b> date</b>: Date house was sold\n",
    "\n",
    "\n",
    "<b>price</b>: Price is prediction target\n",
    "\n",
    "\n",
    "<b>bedrooms</b>: Number of bedrooms\n",
    "\n",
    "\n",
    "<b>bathrooms</b>: Number of bathrooms\n",
    "\n",
    "<b>sqft_living</b>: Square footage of the home\n",
    "\n",
    "<b>sqft_lot</b>: Square footage of the lot\n",
    "\n",
    "\n",
    "<b>floors</b> :Total floors (levels) in house\n",
    "\n",
    "\n",
    "<b>waterfront</b> :House which has a view to a waterfront\n",
    "\n",
    "\n",
    "<b>view</b>: Has been viewed\n",
    "\n",
    "\n",
    "<b>condition</b> :How good the condition is overall\n",
    "\n",
    "<b>grade</b>: overall grade given to the housing unit, based on King County grading system\n",
    "\n",
    "\n",
    "<b>sqft_above</b> : Square footage of house apart from basement\n",
    "\n",
    "\n",
    "<b>sqft_basement</b>: Square footage of the basement\n",
    "\n",
    "<b>yr_built</b> : Built Year\n",
    "\n",
    "\n",
    "<b>yr_renovated</b> : Year when house was renovated\n",
    "\n",
    "<b>zipcode</b>: Zip code\n",
    "\n",
    "\n",
    "<b>lat</b>: Latitude coordinate\n",
    "\n",
    "<b>long</b>: Longitude coordinate\n",
    "\n",
    "<b>sqft_living15</b> : Living room area in 2015(implies-- some renovations) This might or might not have affected the lotsize area\n",
    "\n",
    "\n",
    "<b>sqft_lot15</b> : LotSize area in 2015(implies-- some renovations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will require the following libraries: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler,PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 1: Importing Data Sets "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Load the csv:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "file_name='https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/coursera/project/kc_house_data_NaN.csv'\n",
    "df=pd.read_csv(file_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We use the method <code>head</code> to display the first 5 columns of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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>...</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",
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       "      <th>0</th>\n",
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       "      <td>5650</td>\n",
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       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>6414100192</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>180000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2487200875</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1954400510</td>\n",
       "      <td>20150218T000000</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 22 columns</p>\n",
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      "text/plain": [
       "   Unnamed: 0          id             date     price  bedrooms  bathrooms  \\\n",
       "0           0  7129300520  20141013T000000  221900.0       3.0       1.00   \n",
       "1           1  6414100192  20141209T000000  538000.0       3.0       2.25   \n",
       "2           2  5631500400  20150225T000000  180000.0       2.0       1.00   \n",
       "3           3  2487200875  20141209T000000  604000.0       4.0       3.00   \n",
       "4           4  1954400510  20150218T000000  510000.0       3.0       2.00   \n",
       "\n",
       "   sqft_living  sqft_lot  floors  waterfront  ...  grade  sqft_above  \\\n",
       "0         1180      5650     1.0           0  ...      7        1180   \n",
       "1         2570      7242     2.0           0  ...      7        2170   \n",
       "2          770     10000     1.0           0  ...      6         770   \n",
       "3         1960      5000     1.0           0  ...      7        1050   \n",
       "4         1680      8080     1.0           0  ...      8        1680   \n",
       "\n",
       "   sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
       "0              0      1955             0    98178  47.5112 -122.257   \n",
       "1            400      1951          1991    98125  47.7210 -122.319   \n",
       "2              0      1933             0    98028  47.7379 -122.233   \n",
       "3            910      1965             0    98136  47.5208 -122.393   \n",
       "4              0      1987             0    98074  47.6168 -122.045   \n",
       "\n",
       "   sqft_living15  sqft_lot15  \n",
       "0           1340        5650  \n",
       "1           1690        7639  \n",
       "2           2720        8062  \n",
       "3           1360        5000  \n",
       "4           1800        7503  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1 \n",
    "Display the data types of each column using the attribute dtype, then take a screenshot and submit it, include your code in the image. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0         int64\n",
       "id                 int64\n",
       "date              object\n",
       "price            float64\n",
       "bedrooms         float64\n",
       "bathrooms        float64\n",
       "sqft_living        int64\n",
       "sqft_lot           int64\n",
       "floors           float64\n",
       "waterfront         int64\n",
       "view               int64\n",
       "condition          int64\n",
       "grade              int64\n",
       "sqft_above         int64\n",
       "sqft_basement      int64\n",
       "yr_built           int64\n",
       "yr_renovated       int64\n",
       "zipcode            int64\n",
       "lat              float64\n",
       "long             float64\n",
       "sqft_living15      int64\n",
       "sqft_lot15         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of        Unnamed: 0          id             date     price  bedrooms  bathrooms  \\\n",
       "0               0  7129300520  20141013T000000  221900.0       3.0       1.00   \n",
       "1               1  6414100192  20141209T000000  538000.0       3.0       2.25   \n",
       "2               2  5631500400  20150225T000000  180000.0       2.0       1.00   \n",
       "3               3  2487200875  20141209T000000  604000.0       4.0       3.00   \n",
       "4               4  1954400510  20150218T000000  510000.0       3.0       2.00   \n",
       "...           ...         ...              ...       ...       ...        ...   \n",
       "21608       21608   263000018  20140521T000000  360000.0       3.0       2.50   \n",
       "21609       21609  6600060120  20150223T000000  400000.0       4.0       2.50   \n",
       "21610       21610  1523300141  20140623T000000  402101.0       2.0       0.75   \n",
       "21611       21611   291310100  20150116T000000  400000.0       3.0       2.50   \n",
       "21612       21612  1523300157  20141015T000000  325000.0       2.0       0.75   \n",
       "\n",
       "       sqft_living  sqft_lot  floors  waterfront  ...  grade  sqft_above  \\\n",
       "0             1180      5650     1.0           0  ...      7        1180   \n",
       "1             2570      7242     2.0           0  ...      7        2170   \n",
       "2              770     10000     1.0           0  ...      6         770   \n",
       "3             1960      5000     1.0           0  ...      7        1050   \n",
       "4             1680      8080     1.0           0  ...      8        1680   \n",
       "...            ...       ...     ...         ...  ...    ...         ...   \n",
       "21608         1530      1131     3.0           0  ...      8        1530   \n",
       "21609         2310      5813     2.0           0  ...      8        2310   \n",
       "21610         1020      1350     2.0           0  ...      7        1020   \n",
       "21611         1600      2388     2.0           0  ...      8        1600   \n",
       "21612         1020      1076     2.0           0  ...      7        1020   \n",
       "\n",
       "       sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
       "0                  0      1955             0    98178  47.5112 -122.257   \n",
       "1                400      1951          1991    98125  47.7210 -122.319   \n",
       "2                  0      1933             0    98028  47.7379 -122.233   \n",
       "3                910      1965             0    98136  47.5208 -122.393   \n",
       "4                  0      1987             0    98074  47.6168 -122.045   \n",
       "...              ...       ...           ...      ...      ...      ...   \n",
       "21608              0      2009             0    98103  47.6993 -122.346   \n",
       "21609              0      2014             0    98146  47.5107 -122.362   \n",
       "21610              0      2009             0    98144  47.5944 -122.299   \n",
       "21611              0      2004             0    98027  47.5345 -122.069   \n",
       "21612              0      2008             0    98144  47.5941 -122.299   \n",
       "\n",
       "       sqft_living15  sqft_lot15  \n",
       "0               1340        5650  \n",
       "1               1690        7639  \n",
       "2               2720        8062  \n",
       "3               1360        5000  \n",
       "4               1800        7503  \n",
       "...              ...         ...  \n",
       "21608           1530        1509  \n",
       "21609           1830        7200  \n",
       "21610           1020        2007  \n",
       "21611           1410        1287  \n",
       "21612           1020        1357  \n",
       "\n",
       "[21613 rows x 22 columns]>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21613 entries, 0 to 21612\n",
      "Data columns (total 22 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Unnamed: 0     21613 non-null  int64  \n",
      " 1   id             21613 non-null  int64  \n",
      " 2   date           21613 non-null  object \n",
      " 3   price          21613 non-null  float64\n",
      " 4   bedrooms       21600 non-null  float64\n",
      " 5   bathrooms      21603 non-null  float64\n",
      " 6   sqft_living    21613 non-null  int64  \n",
      " 7   sqft_lot       21613 non-null  int64  \n",
      " 8   floors         21613 non-null  float64\n",
      " 9   waterfront     21613 non-null  int64  \n",
      " 10  view           21613 non-null  int64  \n",
      " 11  condition      21613 non-null  int64  \n",
      " 12  grade          21613 non-null  int64  \n",
      " 13  sqft_above     21613 non-null  int64  \n",
      " 14  sqft_basement  21613 non-null  int64  \n",
      " 15  yr_built       21613 non-null  int64  \n",
      " 16  yr_renovated   21613 non-null  int64  \n",
      " 17  zipcode        21613 non-null  int64  \n",
      " 18  lat            21613 non-null  float64\n",
      " 19  long           21613 non-null  float64\n",
      " 20  sqft_living15  21613 non-null  int64  \n",
      " 21  sqft_lot15     21613 non-null  int64  \n",
      "dtypes: float64(6), int64(15), object(1)\n",
      "memory usage: 3.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <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>...</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",
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       "      <td>20141013T000000</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>6414100192</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5631500400</td>\n",
       "      <td>20150225T000000</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2487200875</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1954400510</td>\n",
       "      <td>20150218T000000</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21608</th>\n",
       "      <td>21608</td>\n",
       "      <td>263000018</td>\n",
       "      <td>20140521T000000</td>\n",
       "      <td>360000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1530</td>\n",
       "      <td>1131</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1530</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98103</td>\n",
       "      <td>47.6993</td>\n",
       "      <td>-122.346</td>\n",
       "      <td>1530</td>\n",
       "      <td>1509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21609</th>\n",
       "      <td>21609</td>\n",
       "      <td>6600060120</td>\n",
       "      <td>20150223T000000</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2310</td>\n",
       "      <td>5813</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2310</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>98146</td>\n",
       "      <td>47.5107</td>\n",
       "      <td>-122.362</td>\n",
       "      <td>1830</td>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21610</th>\n",
       "      <td>21610</td>\n",
       "      <td>1523300141</td>\n",
       "      <td>20140623T000000</td>\n",
       "      <td>402101.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1020</td>\n",
       "      <td>1350</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1020</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.5944</td>\n",
       "      <td>-122.299</td>\n",
       "      <td>1020</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21611</th>\n",
       "      <td>21611</td>\n",
       "      <td>291310100</td>\n",
       "      <td>20150116T000000</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1600</td>\n",
       "      <td>2388</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1600</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>98027</td>\n",
       "      <td>47.5345</td>\n",
       "      <td>-122.069</td>\n",
       "      <td>1410</td>\n",
       "      <td>1287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21612</th>\n",
       "      <td>21612</td>\n",
       "      <td>1523300157</td>\n",
       "      <td>20141015T000000</td>\n",
       "      <td>325000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1020</td>\n",
       "      <td>1076</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1020</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.5941</td>\n",
       "      <td>-122.299</td>\n",
       "      <td>1020</td>\n",
       "      <td>1357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21613 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0          id             date     price  bedrooms  bathrooms  \\\n",
       "0               0  7129300520  20141013T000000  221900.0       3.0       1.00   \n",
       "1               1  6414100192  20141209T000000  538000.0       3.0       2.25   \n",
       "2               2  5631500400  20150225T000000  180000.0       2.0       1.00   \n",
       "3               3  2487200875  20141209T000000  604000.0       4.0       3.00   \n",
       "4               4  1954400510  20150218T000000  510000.0       3.0       2.00   \n",
       "...           ...         ...              ...       ...       ...        ...   \n",
       "21608       21608   263000018  20140521T000000  360000.0       3.0       2.50   \n",
       "21609       21609  6600060120  20150223T000000  400000.0       4.0       2.50   \n",
       "21610       21610  1523300141  20140623T000000  402101.0       2.0       0.75   \n",
       "21611       21611   291310100  20150116T000000  400000.0       3.0       2.50   \n",
       "21612       21612  1523300157  20141015T000000  325000.0       2.0       0.75   \n",
       "\n",
       "       sqft_living  sqft_lot  floors  waterfront  ...  grade  sqft_above  \\\n",
       "0             1180      5650     1.0           0  ...      7        1180   \n",
       "1             2570      7242     2.0           0  ...      7        2170   \n",
       "2              770     10000     1.0           0  ...      6         770   \n",
       "3             1960      5000     1.0           0  ...      7        1050   \n",
       "4             1680      8080     1.0           0  ...      8        1680   \n",
       "...            ...       ...     ...         ...  ...    ...         ...   \n",
       "21608         1530      1131     3.0           0  ...      8        1530   \n",
       "21609         2310      5813     2.0           0  ...      8        2310   \n",
       "21610         1020      1350     2.0           0  ...      7        1020   \n",
       "21611         1600      2388     2.0           0  ...      8        1600   \n",
       "21612         1020      1076     2.0           0  ...      7        1020   \n",
       "\n",
       "       sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
       "0                  0      1955             0    98178  47.5112 -122.257   \n",
       "1                400      1951          1991    98125  47.7210 -122.319   \n",
       "2                  0      1933             0    98028  47.7379 -122.233   \n",
       "3                910      1965             0    98136  47.5208 -122.393   \n",
       "4                  0      1987             0    98074  47.6168 -122.045   \n",
       "...              ...       ...           ...      ...      ...      ...   \n",
       "21608              0      2009             0    98103  47.6993 -122.346   \n",
       "21609              0      2014             0    98146  47.5107 -122.362   \n",
       "21610              0      2009             0    98144  47.5944 -122.299   \n",
       "21611              0      2004             0    98027  47.5345 -122.069   \n",
       "21612              0      2008             0    98144  47.5941 -122.299   \n",
       "\n",
       "       sqft_living15  sqft_lot15  \n",
       "0               1340        5650  \n",
       "1               1690        7639  \n",
       "2               2720        8062  \n",
       "3               1360        5000  \n",
       "4               1800        7503  \n",
       "...              ...         ...  \n",
       "21608           1530        1509  \n",
       "21609           1830        7200  \n",
       "21610           1020        2007  \n",
       "21611           1410        1287  \n",
       "21612           1020        1357  \n",
       "\n",
       "[21613 rows x 22 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the method describe to obtain a statistical summary of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21613.00000</td>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>21600.000000</td>\n",
       "      <td>21603.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10806.00000</td>\n",
       "      <td>4.580302e+09</td>\n",
       "      <td>5.400881e+05</td>\n",
       "      <td>3.372870</td>\n",
       "      <td>2.115736</td>\n",
       "      <td>2079.899736</td>\n",
       "      <td>1.510697e+04</td>\n",
       "      <td>1.494309</td>\n",
       "      <td>0.007542</td>\n",
       "      <td>0.234303</td>\n",
       "      <td>...</td>\n",
       "      <td>7.656873</td>\n",
       "      <td>1788.390691</td>\n",
       "      <td>291.509045</td>\n",
       "      <td>1971.005136</td>\n",
       "      <td>84.402258</td>\n",
       "      <td>98077.939805</td>\n",
       "      <td>47.560053</td>\n",
       "      <td>-122.213896</td>\n",
       "      <td>1986.552492</td>\n",
       "      <td>12768.455652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6239.28002</td>\n",
       "      <td>2.876566e+09</td>\n",
       "      <td>3.671272e+05</td>\n",
       "      <td>0.926657</td>\n",
       "      <td>0.768996</td>\n",
       "      <td>918.440897</td>\n",
       "      <td>4.142051e+04</td>\n",
       "      <td>0.539989</td>\n",
       "      <td>0.086517</td>\n",
       "      <td>0.766318</td>\n",
       "      <td>...</td>\n",
       "      <td>1.175459</td>\n",
       "      <td>828.090978</td>\n",
       "      <td>442.575043</td>\n",
       "      <td>29.373411</td>\n",
       "      <td>401.679240</td>\n",
       "      <td>53.505026</td>\n",
       "      <td>0.138564</td>\n",
       "      <td>0.140828</td>\n",
       "      <td>685.391304</td>\n",
       "      <td>27304.179631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000102e+06</td>\n",
       "      <td>7.500000e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>5.200000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98001.000000</td>\n",
       "      <td>47.155900</td>\n",
       "      <td>-122.519000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>651.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5403.00000</td>\n",
       "      <td>2.123049e+09</td>\n",
       "      <td>3.219500e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1427.000000</td>\n",
       "      <td>5.040000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1190.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1951.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98033.000000</td>\n",
       "      <td>47.471000</td>\n",
       "      <td>-122.328000</td>\n",
       "      <td>1490.000000</td>\n",
       "      <td>5100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10806.00000</td>\n",
       "      <td>3.904930e+09</td>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1910.000000</td>\n",
       "      <td>7.618000e+03</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1560.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98065.000000</td>\n",
       "      <td>47.571800</td>\n",
       "      <td>-122.230000</td>\n",
       "      <td>1840.000000</td>\n",
       "      <td>7620.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16209.00000</td>\n",
       "      <td>7.308900e+09</td>\n",
       "      <td>6.450000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2550.000000</td>\n",
       "      <td>1.068800e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2210.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98118.000000</td>\n",
       "      <td>47.678000</td>\n",
       "      <td>-122.125000</td>\n",
       "      <td>2360.000000</td>\n",
       "      <td>10083.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21612.00000</td>\n",
       "      <td>9.900000e+09</td>\n",
       "      <td>7.700000e+06</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13540.000000</td>\n",
       "      <td>1.651359e+06</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9410.000000</td>\n",
       "      <td>4820.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>98199.000000</td>\n",
       "      <td>47.777600</td>\n",
       "      <td>-121.315000</td>\n",
       "      <td>6210.000000</td>\n",
       "      <td>871200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0            id         price      bedrooms     bathrooms  \\\n",
       "count  21613.00000  2.161300e+04  2.161300e+04  21600.000000  21603.000000   \n",
       "mean   10806.00000  4.580302e+09  5.400881e+05      3.372870      2.115736   \n",
       "std     6239.28002  2.876566e+09  3.671272e+05      0.926657      0.768996   \n",
       "min        0.00000  1.000102e+06  7.500000e+04      1.000000      0.500000   \n",
       "25%     5403.00000  2.123049e+09  3.219500e+05      3.000000      1.750000   \n",
       "50%    10806.00000  3.904930e+09  4.500000e+05      3.000000      2.250000   \n",
       "75%    16209.00000  7.308900e+09  6.450000e+05      4.000000      2.500000   \n",
       "max    21612.00000  9.900000e+09  7.700000e+06     33.000000      8.000000   \n",
       "\n",
       "        sqft_living      sqft_lot        floors    waterfront          view  \\\n",
       "count  21613.000000  2.161300e+04  21613.000000  21613.000000  21613.000000   \n",
       "mean    2079.899736  1.510697e+04      1.494309      0.007542      0.234303   \n",
       "std      918.440897  4.142051e+04      0.539989      0.086517      0.766318   \n",
       "min      290.000000  5.200000e+02      1.000000      0.000000      0.000000   \n",
       "25%     1427.000000  5.040000e+03      1.000000      0.000000      0.000000   \n",
       "50%     1910.000000  7.618000e+03      1.500000      0.000000      0.000000   \n",
       "75%     2550.000000  1.068800e+04      2.000000      0.000000      0.000000   \n",
       "max    13540.000000  1.651359e+06      3.500000      1.000000      4.000000   \n",
       "\n",
       "       ...         grade    sqft_above  sqft_basement      yr_built  \\\n",
       "count  ...  21613.000000  21613.000000   21613.000000  21613.000000   \n",
       "mean   ...      7.656873   1788.390691     291.509045   1971.005136   \n",
       "std    ...      1.175459    828.090978     442.575043     29.373411   \n",
       "min    ...      1.000000    290.000000       0.000000   1900.000000   \n",
       "25%    ...      7.000000   1190.000000       0.000000   1951.000000   \n",
       "50%    ...      7.000000   1560.000000       0.000000   1975.000000   \n",
       "75%    ...      8.000000   2210.000000     560.000000   1997.000000   \n",
       "max    ...     13.000000   9410.000000    4820.000000   2015.000000   \n",
       "\n",
       "       yr_renovated       zipcode           lat          long  sqft_living15  \\\n",
       "count  21613.000000  21613.000000  21613.000000  21613.000000   21613.000000   \n",
       "mean      84.402258  98077.939805     47.560053   -122.213896    1986.552492   \n",
       "std      401.679240     53.505026      0.138564      0.140828     685.391304   \n",
       "min        0.000000  98001.000000     47.155900   -122.519000     399.000000   \n",
       "25%        0.000000  98033.000000     47.471000   -122.328000    1490.000000   \n",
       "50%        0.000000  98065.000000     47.571800   -122.230000    1840.000000   \n",
       "75%        0.000000  98118.000000     47.678000   -122.125000    2360.000000   \n",
       "max     2015.000000  98199.000000     47.777600   -121.315000    6210.000000   \n",
       "\n",
       "          sqft_lot15  \n",
       "count   21613.000000  \n",
       "mean    12768.455652  \n",
       "std     27304.179631  \n",
       "min       651.000000  \n",
       "25%      5100.000000  \n",
       "50%      7620.000000  \n",
       "75%     10083.000000  \n",
       "max    871200.000000  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 2: Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2 \n",
    "Drop the columns <code>\"id\"</code>  and <code>\"Unnamed: 0\"</code> from axis 1 using the method <code>drop()</code>, then use the method <code>describe()</code> to obtain a statistical summary of the data. Take a screenshot and submit it, make sure the <code>inplace</code> parameter is set to <code>True</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>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>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20141013T000000</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20150225T000000</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20150218T000000</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21608</th>\n",
       "      <td>20140521T000000</td>\n",
       "      <td>360000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1530</td>\n",
       "      <td>1131</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1530</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98103</td>\n",
       "      <td>47.6993</td>\n",
       "      <td>-122.346</td>\n",
       "      <td>1530</td>\n",
       "      <td>1509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21609</th>\n",
       "      <td>20150223T000000</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2310</td>\n",
       "      <td>5813</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2310</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>98146</td>\n",
       "      <td>47.5107</td>\n",
       "      <td>-122.362</td>\n",
       "      <td>1830</td>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21610</th>\n",
       "      <td>20140623T000000</td>\n",
       "      <td>402101.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1020</td>\n",
       "      <td>1350</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1020</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.5944</td>\n",
       "      <td>-122.299</td>\n",
       "      <td>1020</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21611</th>\n",
       "      <td>20150116T000000</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1600</td>\n",
       "      <td>2388</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1600</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>98027</td>\n",
       "      <td>47.5345</td>\n",
       "      <td>-122.069</td>\n",
       "      <td>1410</td>\n",
       "      <td>1287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21612</th>\n",
       "      <td>20141015T000000</td>\n",
       "      <td>325000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1020</td>\n",
       "      <td>1076</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1020</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.5941</td>\n",
       "      <td>-122.299</td>\n",
       "      <td>1020</td>\n",
       "      <td>1357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21613 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
       "0      20141013T000000  221900.0       3.0       1.00         1180      5650   \n",
       "1      20141209T000000  538000.0       3.0       2.25         2570      7242   \n",
       "2      20150225T000000  180000.0       2.0       1.00          770     10000   \n",
       "3      20141209T000000  604000.0       4.0       3.00         1960      5000   \n",
       "4      20150218T000000  510000.0       3.0       2.00         1680      8080   \n",
       "...                ...       ...       ...        ...          ...       ...   \n",
       "21608  20140521T000000  360000.0       3.0       2.50         1530      1131   \n",
       "21609  20150223T000000  400000.0       4.0       2.50         2310      5813   \n",
       "21610  20140623T000000  402101.0       2.0       0.75         1020      1350   \n",
       "21611  20150116T000000  400000.0       3.0       2.50         1600      2388   \n",
       "21612  20141015T000000  325000.0       2.0       0.75         1020      1076   \n",
       "\n",
       "       floors  waterfront  view  condition  grade  sqft_above  sqft_basement  \\\n",
       "0         1.0           0     0          3      7        1180              0   \n",
       "1         2.0           0     0          3      7        2170            400   \n",
       "2         1.0           0     0          3      6         770              0   \n",
       "3         1.0           0     0          5      7        1050            910   \n",
       "4         1.0           0     0          3      8        1680              0   \n",
       "...       ...         ...   ...        ...    ...         ...            ...   \n",
       "21608     3.0           0     0          3      8        1530              0   \n",
       "21609     2.0           0     0          3      8        2310              0   \n",
       "21610     2.0           0     0          3      7        1020              0   \n",
       "21611     2.0           0     0          3      8        1600              0   \n",
       "21612     2.0           0     0          3      7        1020              0   \n",
       "\n",
       "       yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "0          1955             0    98178  47.5112 -122.257           1340   \n",
       "1          1951          1991    98125  47.7210 -122.319           1690   \n",
       "2          1933             0    98028  47.7379 -122.233           2720   \n",
       "3          1965             0    98136  47.5208 -122.393           1360   \n",
       "4          1987             0    98074  47.6168 -122.045           1800   \n",
       "...         ...           ...      ...      ...      ...            ...   \n",
       "21608      2009             0    98103  47.6993 -122.346           1530   \n",
       "21609      2014             0    98146  47.5107 -122.362           1830   \n",
       "21610      2009             0    98144  47.5944 -122.299           1020   \n",
       "21611      2004             0    98027  47.5345 -122.069           1410   \n",
       "21612      2008             0    98144  47.5941 -122.299           1020   \n",
       "\n",
       "       sqft_lot15  \n",
       "0            5650  \n",
       "1            7639  \n",
       "2            8062  \n",
       "3            5000  \n",
       "4            7503  \n",
       "...           ...  \n",
       "21608        1509  \n",
       "21609        7200  \n",
       "21610        2007  \n",
       "21611        1287  \n",
       "21612        1357  \n",
       "\n",
       "[21613 rows x 20 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['id','Unnamed: 0'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21613.00000</td>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>21600.000000</td>\n",
       "      <td>21603.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10806.00000</td>\n",
       "      <td>4.580302e+09</td>\n",
       "      <td>5.400881e+05</td>\n",
       "      <td>3.372870</td>\n",
       "      <td>2.115736</td>\n",
       "      <td>2079.899736</td>\n",
       "      <td>1.510697e+04</td>\n",
       "      <td>1.494309</td>\n",
       "      <td>0.007542</td>\n",
       "      <td>0.234303</td>\n",
       "      <td>...</td>\n",
       "      <td>7.656873</td>\n",
       "      <td>1788.390691</td>\n",
       "      <td>291.509045</td>\n",
       "      <td>1971.005136</td>\n",
       "      <td>84.402258</td>\n",
       "      <td>98077.939805</td>\n",
       "      <td>47.560053</td>\n",
       "      <td>-122.213896</td>\n",
       "      <td>1986.552492</td>\n",
       "      <td>12768.455652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6239.28002</td>\n",
       "      <td>2.876566e+09</td>\n",
       "      <td>3.671272e+05</td>\n",
       "      <td>0.926657</td>\n",
       "      <td>0.768996</td>\n",
       "      <td>918.440897</td>\n",
       "      <td>4.142051e+04</td>\n",
       "      <td>0.539989</td>\n",
       "      <td>0.086517</td>\n",
       "      <td>0.766318</td>\n",
       "      <td>...</td>\n",
       "      <td>1.175459</td>\n",
       "      <td>828.090978</td>\n",
       "      <td>442.575043</td>\n",
       "      <td>29.373411</td>\n",
       "      <td>401.679240</td>\n",
       "      <td>53.505026</td>\n",
       "      <td>0.138564</td>\n",
       "      <td>0.140828</td>\n",
       "      <td>685.391304</td>\n",
       "      <td>27304.179631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000102e+06</td>\n",
       "      <td>7.500000e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>5.200000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98001.000000</td>\n",
       "      <td>47.155900</td>\n",
       "      <td>-122.519000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>651.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5403.00000</td>\n",
       "      <td>2.123049e+09</td>\n",
       "      <td>3.219500e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1427.000000</td>\n",
       "      <td>5.040000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1190.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1951.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98033.000000</td>\n",
       "      <td>47.471000</td>\n",
       "      <td>-122.328000</td>\n",
       "      <td>1490.000000</td>\n",
       "      <td>5100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10806.00000</td>\n",
       "      <td>3.904930e+09</td>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1910.000000</td>\n",
       "      <td>7.618000e+03</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1560.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98065.000000</td>\n",
       "      <td>47.571800</td>\n",
       "      <td>-122.230000</td>\n",
       "      <td>1840.000000</td>\n",
       "      <td>7620.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16209.00000</td>\n",
       "      <td>7.308900e+09</td>\n",
       "      <td>6.450000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2550.000000</td>\n",
       "      <td>1.068800e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2210.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98118.000000</td>\n",
       "      <td>47.678000</td>\n",
       "      <td>-122.125000</td>\n",
       "      <td>2360.000000</td>\n",
       "      <td>10083.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21612.00000</td>\n",
       "      <td>9.900000e+09</td>\n",
       "      <td>7.700000e+06</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13540.000000</td>\n",
       "      <td>1.651359e+06</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9410.000000</td>\n",
       "      <td>4820.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>98199.000000</td>\n",
       "      <td>47.777600</td>\n",
       "      <td>-121.315000</td>\n",
       "      <td>6210.000000</td>\n",
       "      <td>871200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0            id         price      bedrooms     bathrooms  \\\n",
       "count  21613.00000  2.161300e+04  2.161300e+04  21600.000000  21603.000000   \n",
       "mean   10806.00000  4.580302e+09  5.400881e+05      3.372870      2.115736   \n",
       "std     6239.28002  2.876566e+09  3.671272e+05      0.926657      0.768996   \n",
       "min        0.00000  1.000102e+06  7.500000e+04      1.000000      0.500000   \n",
       "25%     5403.00000  2.123049e+09  3.219500e+05      3.000000      1.750000   \n",
       "50%    10806.00000  3.904930e+09  4.500000e+05      3.000000      2.250000   \n",
       "75%    16209.00000  7.308900e+09  6.450000e+05      4.000000      2.500000   \n",
       "max    21612.00000  9.900000e+09  7.700000e+06     33.000000      8.000000   \n",
       "\n",
       "        sqft_living      sqft_lot        floors    waterfront          view  \\\n",
       "count  21613.000000  2.161300e+04  21613.000000  21613.000000  21613.000000   \n",
       "mean    2079.899736  1.510697e+04      1.494309      0.007542      0.234303   \n",
       "std      918.440897  4.142051e+04      0.539989      0.086517      0.766318   \n",
       "min      290.000000  5.200000e+02      1.000000      0.000000      0.000000   \n",
       "25%     1427.000000  5.040000e+03      1.000000      0.000000      0.000000   \n",
       "50%     1910.000000  7.618000e+03      1.500000      0.000000      0.000000   \n",
       "75%     2550.000000  1.068800e+04      2.000000      0.000000      0.000000   \n",
       "max    13540.000000  1.651359e+06      3.500000      1.000000      4.000000   \n",
       "\n",
       "       ...         grade    sqft_above  sqft_basement      yr_built  \\\n",
       "count  ...  21613.000000  21613.000000   21613.000000  21613.000000   \n",
       "mean   ...      7.656873   1788.390691     291.509045   1971.005136   \n",
       "std    ...      1.175459    828.090978     442.575043     29.373411   \n",
       "min    ...      1.000000    290.000000       0.000000   1900.000000   \n",
       "25%    ...      7.000000   1190.000000       0.000000   1951.000000   \n",
       "50%    ...      7.000000   1560.000000       0.000000   1975.000000   \n",
       "75%    ...      8.000000   2210.000000     560.000000   1997.000000   \n",
       "max    ...     13.000000   9410.000000    4820.000000   2015.000000   \n",
       "\n",
       "       yr_renovated       zipcode           lat          long  sqft_living15  \\\n",
       "count  21613.000000  21613.000000  21613.000000  21613.000000   21613.000000   \n",
       "mean      84.402258  98077.939805     47.560053   -122.213896    1986.552492   \n",
       "std      401.679240     53.505026      0.138564      0.140828     685.391304   \n",
       "min        0.000000  98001.000000     47.155900   -122.519000     399.000000   \n",
       "25%        0.000000  98033.000000     47.471000   -122.328000    1490.000000   \n",
       "50%        0.000000  98065.000000     47.571800   -122.230000    1840.000000   \n",
       "75%        0.000000  98118.000000     47.678000   -122.125000    2360.000000   \n",
       "max     2015.000000  98199.000000     47.777600   -121.315000    6210.000000   \n",
       "\n",
       "          sqft_lot15  \n",
       "count   21613.000000  \n",
       "mean    12768.455652  \n",
       "std     27304.179631  \n",
       "min       651.000000  \n",
       "25%      5100.000000  \n",
       "50%      7620.000000  \n",
       "75%     10083.000000  \n",
       "max    871200.000000  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see we have missing values for the columns <code> bedrooms</code>  and <code> bathrooms </code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of NaN values for the column bedrooms : 13\n",
      "number of NaN values for the column bathrooms : 10\n"
     ]
    }
   ],
   "source": [
    "print(\"number of NaN values for the column bedrooms :\", df['bedrooms'].isnull().sum())\n",
    "print(\"number of NaN values for the column bathrooms :\", df['bathrooms'].isnull().sum())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We can replace the missing values of the column <code>'bedrooms'</code> with the mean of the column  <code>'bedrooms' </code> using the method <code>replace()</code>. Don't forget to set the <code>inplace</code> parameter to <code>True</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean=df['bedrooms'].mean()\n",
    "df['bedrooms'].replace(np.nan,mean, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We also replace the missing values of the column <code>'bathrooms'</code> with the mean of the column  <code>'bathrooms' </code> using the method <code>replace()</code>. Don't forget to set the <code> inplace </code>  parameter top <code> True </code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean=df['bathrooms'].mean()\n",
    "df['bathrooms'].replace(np.nan,mean, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of NaN values for the column bedrooms : 0\n",
      "number of NaN values for the column bathrooms : 0\n"
     ]
    }
   ],
   "source": [
    "print(\"number of NaN values for the column bedrooms :\", df['bedrooms'].isnull().sum())\n",
    "print(\"number of NaN values for the column bathrooms :\", df['bathrooms'].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 3: Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "Use the method <code>value_counts</code> to count the number of houses with unique floor values, use the method <code>.to_frame()</code> to convert it to a dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>floors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>10680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>8241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>1910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5</th>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_floor_counts=df['floors'].value_counts().to_frame()\n",
    "house_floor_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "Use the function <code>boxplot</code> in the seaborn library  to  determine whether houses with a waterfront view or without a waterfront view have more price outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='waterfront', ylabel='price'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='waterfront',y='price',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5\n",
    "Use the function <code>regplot</code>  in the seaborn library  to  determine if the feature <code>sqft_above</code> is negatively or positively correlated with price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sqft_above', ylabel='price'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='sqft_above',y='price',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We can use the Pandas method <code>corr()</code>  to find the feature other than price that is most correlated with price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "zipcode         -0.053203\n",
       "id              -0.016762\n",
       "long             0.021626\n",
       "Unnamed: 0       0.027372\n",
       "condition        0.036362\n",
       "yr_built         0.054012\n",
       "sqft_lot15       0.082447\n",
       "sqft_lot         0.089661\n",
       "yr_renovated     0.126434\n",
       "floors           0.256794\n",
       "waterfront       0.266369\n",
       "lat              0.307003\n",
       "bedrooms         0.308797\n",
       "sqft_basement    0.323816\n",
       "view             0.397293\n",
       "bathrooms        0.525738\n",
       "sqft_living15    0.585379\n",
       "sqft_above       0.605567\n",
       "grade            0.667434\n",
       "sqft_living      0.702035\n",
       "price            1.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['price'].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 4: Model Development"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We can Fit a linear regression model using the  longitude feature <code>'long'</code> and  caculate the R^2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00046769430149007363"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[['long']]\n",
    "Y = df['price']\n",
    "lm = LinearRegression()\n",
    "lm.fit(X,Y)\n",
    "lm.score(X, Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question  6\n",
    "Fit a linear regression model to predict the <code>'price'</code> using the feature <code>'sqft_living'</code> then calculate the R^2. Take a screenshot of your code and the value of the R^2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4928532179037931"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[['sqft_living']]\n",
    "Y = df['price']\n",
    "lm = LinearRegression()\n",
    "lm.fit(X,Y)\n",
    "lm.score(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sqft_living', ylabel='price'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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znmpd/9B6Y0HqI+oKBd+nOMDpiboWWE1H8H0llQ8Cejrn4fkLo/TlculLjdIdkSCgh0E96ta3hKneAP8GguB+PUGg/y7wZ3W+16wDt92wmw/+xRFcCf6BlLeh8zXIOaoGn792qHq65UPX7+LTDzyPI0rUFfzw9b/64zaOMO0r7/mksh6pfIFM3i+lXlY7So9HXXqiQVBPLDNSr6XeAP8Fgs0+7gq/fn/42M9d0FFNW1vpptHF1wuQ9xRXg7z7xv44Z+eyKMEo3nWEjfEo/+JNW3n/gYfP+/5v3raRrf0xXp7Jogo9MZdfvWE3H3nXFWt27cYsR1XJ5H1SuQKpitr0RaP0k1PMZ+sfpUddh0TUpSccpVfeCV+IegP861T1qrKvvy0iT6766KbtLLWatFqQL3/9pRsTvHwuA8BQX4yI6+Crsrk3xnwuKCm8bvcmDj7+cun7Hz87x213HyUeccgWfAZ7oly5daBUpfLmbRvX9g/AmCo8X0tpl3R+IfWy0oqX8lG6I0IiupB2iUXq3iK7bvUG+CdE5FpVfRhARN4O/GPDz8a03Epb4la+HoRXZzO8MpPl6h2D/N5PvGHR+95/4OHS62fSeSbmg43CZjMFIq4wMZ8jHnEZSEatFa9pqVxhYZSeKatNX00uPR4NgnlPzCUecRBZ/Sh9KfUG+LcDvyQiJ8OvdwDPiMhTgKrqm5tydmbNPfvqDJm8T87zibkOQ31x+hORmis2K1d4DiSj9CciTKfzfOnWa5d8/dm5LA6COGFqxxHUDx4fSEZXtVJ0pWmmRr//Qr9vs45rllerLUC9ufS37dzE23cvHqVHHIdEzKEnFiEZdXEbkHZZiXoD/E1NPQvTFg6NjDGXDZZGuxLUtJ+eTrO5EGPXUF/V96x0hWf564u9ZlTBkWBiVgRy4Q/Wha4UXWmaqdHvv9Dv26zjmto8X5kvpl7K2gKcncvy2PFJHjkRjtKr5NKvfs0gb68YpYsEu30lw9RLM9IuK1Fvs7EXm30ipvX2Hx5lU2+Uibmggl0cwA8WJ/1hWIdeOcIs5tRTuQIFz+fV2Sx5T4m5DodGxs4LTOUrQmOuQ87zEYThvjhTqTw+wXtXs1n1/sOj5D2PiblC6U5kIBmpO91TLU01PpvhI/c8EdyhxCOoKnM5b0Wj7OXSX7Zj1NrIFryw6mWhLcCKRum7NnHlJQuj9FjECdMuERLR5qddVqLeEbxZB16aSrG5N0484jI+myVb8PF8RUTZf3iU7586t2iCdGw2w8HHX+aWqy/lG0+d4cREmqjjsG1jgpznVx19lq8InU7lKPjKpt4om3vjOA5MzudJRh229CcuOD3x3Ngs06k8jiO4jlDwlbOzOfLebN1/DuVpp+Jcga/KYE+U58aCvW8u3ZhY0Sh7uYZl1tCsOWq1BSiN0leYS3cdKU2MJqPuotLGdmMB3pQU0yf9iSDInD6XQVyIuQ5jsxn+67efL+VRYq7DcH+cqCs8NDrJYG+cnb4uStXUGn2Wrwgt3hGcmkqxc3Mff/DTq8855wo+SFClAEHaxxddtJKwnj+H4rWcnQtasiYiLmfnckEeVeHsXI7dw311j7KXS2dZQ7PGKXZkTGUX2gJc6ChdREhEnVLaJR65sJr0VrAAv84cGhnjzvtGSu0Cdm3u4ZPveT379mxZlD4Zm8mgYauBob44nq/kPUUAEWXe80hNpBjui5Er+CgsOfqsNXnYjOX/UVdI54NVhSILi65ibn23zpWNxbKFYPGKJ0qm4OMIuCLkws5o9Y6yl2tYZg3NVqdaW4B6cunVRulR1wnq0cORejulXVbCAvw6cmhkjI8ffJKpVJ7iZP7z4/N87OCT/NK1r+Gh0Unms3nyXhDIklGXob44A8koo+NBWkLD/4kEH8fmcrz+4n429sRqjj7XevLwiosGOH52jtnMQg6+PxGtOVFcqbKxWMx1yBY8lIXJ4Lwq8XACrd5R9nINy6yh2cpUawtwoaN01wkmRxOxYPVoO6ddVqJpAV5EEsBhIB4e56CqfqpZxzPL2394lNlMAdeRhfSFr0yncnzm0AtsG0yydUOSdN4jW/DpT0QYSEaZSedJleUntfS/wGw6xydu2sPt9x7j7FyG6VSerOcTcRzee9Ul7D88Sq6weNKzP7Ew6dno0sDiSPjiDZG6R8LVzqFY5nnTf/4Oz4/PgwYj93zxlkB1xZPBy92xWEOzpVVrC3Aho3QRIR4JRumJ6IW3Amh3zRzBZ4EbVXVORKLAgyLyd8XFUmbtvTSVouD7i0YnIlDwwMFfVL2xqTfK5HywK+PEfK48np/nTLht2C1XX8pnDr2A5yuJiEt/IsLBx19maj4bpDbCLpMFT5mYz1HwZpoyul/pSHi5c5jLeVy6McHZuRw5T0lEHHzfp6CcNxlsdeyNVa0twIWO0ss7MDaqFUC7a1qA16Djzlz4ZTT8b6k4YZps+2APr06nyRb8UprFIfhLiVfckm7ujTOXKTA+l1vUEa+SEHyP/YdHAdjUG2UmHYzUZzMFBpKQygd56+IPlEhwe53ztGmlgcX3FoNt8fzqWY1beQ7Fyc/dwwspnlSuwJb+xKLFXFbH3hieHy44ygZB3a9zlH71awZ5286gJcBwf/yCOzB2k6bm4EXEBY4CrwU+o6qPVHnNrcCtADt27Gjm6axL5SNKfJ8wtgNBLtknyCtv6Fk8QXp2LksunFSNVPRoLxc0EoPnXp0Bkarlib6veEDe85Cy96EeT708harUtXL2rvuf5bMPHmc+59Ebc/nQ9btqNiJbSbBdrjyx3snPdqhj79Q7iMq2AJ6vHDs9zSMrHKWXd2Bci1YA7a6pAV5VPeAtIrIR+JqIvFFVn654zQHgAMDevXtthN9AlUHu+fG50ki6EFaARF3hooEEIrIogE2l8qXReDH/Hs6rLiIELX1TeZ+II+eVJxbwF72n+LkrwfNz2WB0H3OdJVfO3nX/s2Er4eAXTjrv8ekHngeoGuRXEmyXK0+sN+XT6jr2TrqDqNYW4EJG6RHHWTRKX+tWAO1uTapoVPWciBwiaHnw9DIvNw1SGeQ8P+j3EnUd9lwcBFBVZTqdP29D6+l0ns29cWKuy4uTyweofMEnmYycV57o1Rj5exr843Md8MO7imorZ4s+++DxMLgXO/FBwff57IPHqwb4lQTbekbo9Ux+trqOvR3uIJZSrE1PlwX1Y6enw1z6FM+Pz533nspReiwSTIi2SyuAdtfMKpphIB8G9yTwLmCJ7WFNo1UGuZjrkPf8Uq8XWAhAlQHs/QceZmw2w0AySjzilGrdKynBaNxxpGp5YtYjTPMI+Ypcvh8+Xgg/5jyfqCP0xCPnBaT5nEflz7Ij1NxhfiXBtlHlia2uY2/1HUQ1lW0BJsJOjLVG6T3FipeyUXosstCsq91aAbS7Zo7gtwJfCPPwDvBlVf16E49nKlQGuaG+OC+fSxNxhJl0blHfmLvuf5aHRier9pgZSLiMz9VeBeopbO2NVi1PfGF8Phh5uw6eevhKKQ9f7EWTiDilCczi5GWl4nZ+5XfgvgaPV1M8l/KyTSHI9V5/5wPn5acbUZ7Y6jr2Vt9BwPltAbIFb8W59EQ0QiK20N/F0i4XrplVNN8H3tqs72+WVz6iLDYC83zFEXhpKk3MDfrGnEvn+PQDzzPcFyMecXji5BSPnphga3+cnDpMhOWS1YiAKPQnY1UDXK7gc2Y6E3aoDLfvA+JuUIc8n/PwPI/R8Tn6ExFiEbfqiLe4nV/BD3L2xe38PnT9rqrntW/PFm45da5UthmRYNL3zHRmxT1kVmKlvygaOSnaqjuIguczHwb0dN7j7GyGR09M8cjxibpG6VsGEiSiDj1hYO+kVgDtTor7B7aDvXv36pEjR1p9Gl3l0MgYf/h3z/Dc+BxRx+GigTivzmYpeMq2wST9iWCVarF1r6/F2vggJVPMqkTCUXimrJwm6krY7lcpKLxt56aq/c0/fvBJZjMFCn4wiiZsqZot+PTEHLJ5v7Qw6sP7LqtZGbOSKhpYSDP1xCKMjs8FE8sSpIOKPWQqSx3XUvmkaHlAvuPmKy84yJf39mnmHUR5W4B0zls+lz4U7GpUHKUnY5GuaAXQDkTkqKrurfqcBfjuVx7oAEZemUEI+m3sHu5j5JUZHIFcQYlFHHxfz8uXA0QdwVMtpVmG+2KMz+VKS/i39MeJRdzzAlS1oLP/8Oh56YRiwC0+v9pR7fV3PsDGZBQRYeSVGVwJqnw8X9lz8UBpgvm7n7hxxd+7ESr/XqB6fX07qGwLMDYTjNIfPT7JkRcnlx2lX7whUZoYbfcOjJ1mqQBvvWg6TPktfV8sGPnMZgtLBsLlJluLuXAIRu9e+Eu/mAopKg/6CqXgDkHAf3UmiwAfuecJ7nrfW5fMb3/s4JOksgXyvpY6U/bFIzw3NrtoxH92NsvHDz7JH91y1YqDfHlOuliGWbxeaH2nxnacFC1X3hZgPlvg6ZfrG6W/bdcm3njpBvrika5vBdDuLMB3kPJbeleCRmFQuy95MaUxkylw+lyaod4YF21ILppsVVUGkhHGZnMIkM37i4J2pfJa+OLI3RHB88PekwLzucJ5OxVVbhJSDOCqlDpTOmUlk1FXiLgOqkHZ5J33jayqqmWoLxZsCK5w8UB8VRuKNEo7TIqWq2wLcGY6vWRdeuUo/ZKNSZKxYL/RRGR9tAJodxbgO0ixztnzlZem0qVUyelzaWKRIKddHD1//9S50sKgqAN5P+j8qCiuEzQo8BSeG5tj91Av1+7axNefeoW8tzBK96pk75Qgh+2rohqkefJlZZeqkIi6RF0ptQeoXHzzmUMvEHUgW1j8fctr5vOelkbcCoy8Mlt1h6ilVE76vna4FxFhLluoe0ORZq4MbXVZJQTpqmLaZSaTX9Eo/U2Xbgj2zl3HrQDaneXgO8j1dz6AK3BmOruolr1ICEbUlw72MD6boeBraWGQ52u4PV6wxVhxF6ViUOmNueS8YAenkxMpltoaY0tfjPmwFA7OX916UX+c4f440+k8vTGXE5MpvDAVU7x78PygDUJ9W3AE17Z9U09DJiCffXUmKA+NOFy+pb/mxtfPjc0ymykw2BNlqC/ekEnQWue0lmWV5W0BXp5KrajiZdumHmsF0GYsB98ltg/28MTJqaBJWEV+vMjXoOdLOu8Tjyz88LmOEBchWwiqZypXO46enefyLX3MZQvLdoQ7ly4QcxdSNJVjhKlUHseBjckYz47N4oos2sS72KdmJUOLmCulu4ILCYDF9Fau4DGTCW4d0jmPExNzNTe+TmUL+Bp0voxHXAaS0aatDG3mMKuYepnPFZjN5Pn+S9M8emKSR0Ynl614uWrbRvrLRulWk95ZLMC3oVppget2b+Kh0Yma7yumT8bD9r3ZggIeDkDZL4RCxei/4PnkCj5Pn56p6/w832cuHOhV/pJxBDz1eWU6y/hsFt8Hxw12m5dwyO77C8ev1t+mmos3JEsTkCtNmxwaGeMj9zxR6kzoSNCuwVdlJl3g4g0LvenLl/vnw9YO6gfN1waS0YZOgjazd0wx9ZLKeZyaTPFwuNDo6ItTzJXnxghG6VfvCPqlX7t7Ezs29ZYCurUC6GwW4NtMrR/6W8INr2PuwlZx1RSqDOt9KEVR1yGYbEQYSEaZzeQ5NZVe0QiyePhibl8I7hA8PyyhDOcGJNz2Ke8peS9YhVqsg++JOaRywS5JywV5RyiNnntj7oqCYvHPcz5XCNoiFAiDvOI4kPP8mhtfx1yHQthXp5gSa+QkaKN7x2QLwWKj6UyeJ16cqjuX/tYdG9mQjFkrgC5kAb7N1Pqh/+yDxxnuj7MxGV1UnrgSF/XHScZcTk2lOTOd5uxctmYvl3oUgzsEQTPmOuR9H9eRsCxxcf+a4u+ezckIV1w8wA9fmeFcOo+vtVNOxfcV5xRSOY9cwScecUrbCS4VFIt/nomIS8EPAruvQaOyqDjEXKfmxtfD/XFOn8vgE1xboytvVlsmWezIOJ/1eGkyxUOjE0vWpV+9Y5C379rEdZdtZsfmHmsFsA5YgG8zlT/0s5k8YzMZUnmfTK6Aj4RpkOW/VzE/XnzploGgx8umngJjczny3oUH9+Koe3FveS3l44f64rwyk6F8bF58z0y2wMUDMR49EWwfGC22HvAW2hmUU2A6nacvHiFb8IKReJjPB2r2j4eFP89isC5vc+D7ykBvtObG133xCJv7gp2tklGn7sqbel1ImWSxI+NMOs/jL07x8OhEXatHr9k5yIZEzFoBdJFiqjI6vPNNtV5jAb7NlP/Qz2bynD6XQQn6x+R9ULRqfXo1lYFydHyOnpjLxHwOWN3EnkjQviBfls4veMHEad7TUqVMNXlP+dr3zuAAbsTBC0f/qEfeh0RZdUaxh42nxTSNF4zEHYEwNx5xpWZQLP559ieiXLIRxmezeDkPxxE29ETZublvyY2vd27u4w9+evkc/4WUUtZbJpkJW+y+ODnPPz4/UVcu/R2v3czOzb3WCqBLladyUb9Q63VWJtlmyv/izpxLl1aPFlMznaQ8tx4JK2eq2dIXLMCaSed4cTIdLORyJLj7UEqj6GKVz8nJ1KJfXomIsGuor+qK3mb0eym32u9frUzyhiuGSec9pjM5jpyY4uEXlh+lX7s72FC6Pxmlx1oBdL3yNhffvOMXUrnxF3urvc5G8G2mfAQ5Oj4fdGsUIRX2Q/f85pbUNUpxw4+iWsEdgpYHyViESNjdcnwuR97zibsOG3qjRF2X3UMx0uFItvJbZQrKmek028LRevmka7Nb+K52orR4jnnPJ5XzODk5z5995wUeWS6XvnsT1792iF1DvdYKYB2qNn9TjQX4NlQMDI+fnApTFEK24NfcF7UdRcJVV8mIMJNd+sQVODOdRkToi7vs2txTWnFaDMgQrIgdC0tAK81kCohI1QDbiF7vtaxmojSTD1aPPnZ8kn96YYJHjk/y/FjtUfqPXraZH9m1iYFEUK5prQDWr2rzN9VYgG9T+w+Psqk3ysRcnoKvS46A11Jx4laWqHoBKCj0RBxydZ53zlOijtIXj5P3lbzn8/vvfeOiwHwH8IHPP1Y6D1cWFkyVH2YtG3atZKK02JHx5MQ833l2nIdHl86lX7t7EzdcMcxrNvfSE7NWAGZB+fzNUizAt6mXplJs7o0Tj7icrNgTdamSwqbToNVBvkqrhEUvU0pNzNw6qn6CAb/D6ekMl2xIVl21um/PFgYSEdJ5b6EFg3poWGZZtJYNu5abKM0VfGYzeR49PsmDz5+tOUrfNdTL23YOcv3lw7x916bSVok2OWqqKU89Ik7NON7MPVm3A38BXEyw1uaAqn66WcfrBEtVW1RuZhGLODw/m8VTXZTLLm48nfeq75F6IVwHok7QMni5Xxw+QVnixDITvlFX2Lm5j43JHMfPzuPX2NO1KOKwqDJm11Bv1VWr79wzzL3ff6W0s1O4loqBRKRUF75crXojG4hV5vgv3ZjkV96xk60bE+z/zgv80wsTS47Sr9u9mRteN8yuoV5rBWBWpJh6lNtOPFXrNU2rohGRrcBWVX1cRPqBo8BPqeoPar2nW6toDo2Mced9Izw7NkfUFS7qjxNxnVK1RXnnR0eCCUnPDzaydRwW5d5dAbfOAF8M3K1I8bz+4n4+cdMeAD5+8Ekm53NLjuIjDkRdF1XFU2XHph6ijpDK++dVqFyzYwPfGhkv/TJ8555hXpnJLZpEBaoG8WZU1Xi+MpPO8diJKQ4/O84DI2Ocns6c97pdQ728fdcmfuzyId6+ezMDiai1AjCr1hY7OonI3wJ/qqr/UOs13RjgiwFlbCbYl1QQfJRLNiSJuMKW/gTHTk8vSjtkC+dXilSz3BL/4q5LY3P1pUkaKRF16ItH+ONbrgLgzvtGeH5srlT2mQgDW9AmWINukxEH1aDGfUt/otThcqU7Hi0VxJfaSWoluyhlCx4nJ1J89rujfPPYq0yn8+f9XTgC8YhDLOLwf7zrCn72R3aQiDp854fjTWtBbNaflgd4EdkJHAbeqKozFc/dCtwKsGPHjmtefPHFpp/PWirWq56cTCEEi3yKf+JxV+hPRoN0R1m0XunfSND3pYV5+SribrCA6bXDvdz30R8vpUUeOT4R5MwdIRrWxhdX5kYdIe/7XD7cxyff83p+92+fLm25VzSTzvHKTJbh/njN4LjUVnjFqpe5bIHx2aDtctQReuIRjvzuu2tej6qGufQpvvPsOA+NTlTNpQMkYw5DvcEOVa4jpPNe6RdIs+vyzfrT0nbBItIH/DXwm5XBHUBVDwAHIBjBN/t81loxoLgiizasBsh6Sq7YV2YVV66c37K31YrNuV44O18KannPK7VO8HzFDytghGAh1Ft3DC5Kpcyk85yZTpOIuAz3x4GgUVpkmUZjS5Uubh/s4cTEHBNzeUSCJml5X5nNFM7bUKTg+ZycTPHtH47x4HNneezE+bl0AXrjLn3xSGkDc1HY2BM779jQ+AZjxiylqQFeRKIEwf2LqvrVZh6rXRXL6Gpps7jcOGE5Zd5T7rxvhKgrTMwVcMvmFMq3/vNUuW73pkV58p5YMMLNeT4vT6UpDuQv6k+cV/MOCzn3mXSegucz3J8onc6pqRSzWY+Xzy3shBWPOKgf3vmoz213H+WqbRt4x2VDTKRy/NML1Ufpu4Z6uW73Ju479goX9cVx3YVql0TUJVNYvDipvKqn3fdhNd2lmVU0Avw58Iyq/kmzjtOOKjfGnknnq+7A1M2KdxQCPD82x+su7ifn+URch4J//p+Fr/DpB57ny4+d5OWZbLD1X8RhMBllPueFC72Uod4or85kSqWjUSfoL1O+UbcTLpKCoOnZqakU59LBL5eoG2x6okC24Je2QESgUPB57MUpHj0xtejcktFgV6Mfu3yIG/dsYddQLxHX4fjZVJAKKttYpT8RoZDSmmWT7bYPq+luzRzBvwP418BTIvK98LHfVtVvNPGYLVfZzz2d98jkvbZLoTRarQlfBfK+8syZmWXvVjxfeXk6W3pdpuBTSOXZNpikLx5h5JUZJufziyaLcz7kMgUcIBpxSht1C8pMpkDUdZjNergOxNzicv6FrQaLe9B65bcTBJPE/+rqbey7YphrL9tMf+L8ZeHVauBjEZcP79vBQ6OTVVsjtMM+rGb9aFqAV9UHoe7Gh12jMsc6lykwm73wtrydYiARYTpTe1VdMYD6y5TyFLcBLO0+5SsnJ1NcNBDHdRyyNfo1BO8L/rkVc+uer3z3Ezdy2W9/AwfIez5+xW/a8q8SUYeNySj98WAx1X/66ZpdWIHza+DLg/lHKl5beVcnIkyn82u2D6tZn2wla4OV51jHZjK8WqN3SrdZKrivhHD+hLGv8GqYtqml8ik/3Frq/7z3GKpKvsZMdtDyALZuSLChJ5jITeUKbN9UtTnfeerpc1Ptrq5aKwZjGs1WWTTY9sEezs5lGR2fWzfBvZHKNxGpfGK5VfvpvFf6r6DBZO7n/+nEovLRsAcaANs2JtjUG8NxHHKeoqoN37UJFt/VFSeHi60YjGkmG8E3QPntdyZX4Ox8vtWn1HXK95VdiYsHErz7DRcxm85z/zOvkC4ocdch4kA86rKxJ0bUzTI5H1TeXH7RQMNTJlY5Y1rFAvwqld9+uwITYXBfbpWpWRuDPVF+/6feuOixyoVQQ30JemKRFa9mrZdVzphWsQBfp1oNqspvv0fH5yyor1KjfzFWq2MvjqhnM/lFq1mn04vvvBrVlMwqZ0yrWICvQ+Uk2dhsho8ffJLNvTGeG58n7gp98Qip3EK1jAX6C9PoP7dClZnZelazVvs7r7Zqth7N3lXKmFoswNfhzvtGGJvJ4IW7K3mq5D1lYj6HI5DKK6l8Z+2Xul64VSZmb7thN7fdfTTYzBxBw8rLwZ5oqWVAo1sKNHNXKWNqsQC/jLvuf5ZnXpkFgvRBec9AX9urwZdZzAFeO9x33uP79myhL+6SyfvkPJ+Y6zDUl6A/ESlNfNrEqOkGFuCXcGhkjM8ceqGUF7ZY3jmiDmzoifHJ97y+9Fh5Tj3vKQPJCEN9C/1qUrlCaeLTJkZNN7AAX6ZyUm1qPovnq1XEtIjI8l0yq5XGC+A4DsN98dJjlTn1gufz6kyWibkcvioRx6E/EeH3fuINgE2Mmu5gAT5UbVLthfH5Vp/WulIZ0CuDe3GhU/nj8YjDxRsS9CeCqphTU2kEJeIIz43NcdvdR/nwvst4aHRyUU49EXVBg1YIriNB98uyY9nEqOkGa7ajUz1ataPToZExPnLPE8xnC6UfcgcotM8fzbpUHJ0v9ddQ3MLwko0JxmezZPIengYbjogEbYgdEfriLls3JEttfUfH50rbHl6yIcnZuSzZgk9PzOWu973VArnpGEtt+LHuWxUcGhnj4wefZCZTwNOFiVML7u0h6i79TzT4O/NL9ezFXu+OI/gadKjMFnzOpQucnVtoHVFs3+wAp6fTFDzFdWA+V+D2e49xaGSsiVdlzNpY9ymaO+8bCbbMM21HWb7/DAQ9Zwq5xR07854f9HknvBNQZTz8ex7qi+OKlNIzji78QkhEnFKfmGI9fD2LnRq1KMqYRlr3I/jRs/Osr604Oks+HGnXE+jLFfyFvW9dR0hEXbb0x0jlPKbTeXYN9bKxJxpu9qH4qqjCcH+8VA5Z2jB9NrNosVPl6L74uhMTc0zOZXn0xAS33X2Uu+5/tgF/AsZcuHU1gq82ysrX6C9u2oPrCOppXfn4Wu8XCUbt/YkIESfPdz9xI1A295IrkIg4DPfH6U9ES+WS9S522n94lLznlVbGRl0Hz1c+c+gF3rxto43kTcs0bQQvIp8TkTERebpZx1iJytHYM2em+cDnH7PRe5uLuQ7DfTEijlP3KN6RhZbA8YjDJRuSDIR92Mvr2Pft2cJd73srl27s4eINibDdxEK74JemUiSj7qLvXW2x00tTKaZTQXB3RBAkWPHsq7UENi3VzBTN54Gbmvj9V6R8NPbqTIZz6cZsUGGaQ4DhvmAl6VQ6jyNQ3PrUWSLQiwRBeEt/vKyEMlKzz/u+PVu44+Yr2dKfYDqdZ0t/gjtuvpJ9e7awfbCHdL72BtpF2wd7yHr+ol9AqsEvF1v5alqpmVv2HRaRnc36/vW66/5n+eyDx5lp0I5DZm0oMD630N2x2CLCATb3xnAdYXwuS+Ve5qIwkIwQdZfeG7VcrT4x9S52uu2G3Tx+cgrPV9ywlt9H6U9EbeWraamuzcEfGhnjYwef5KxVyHSkWquHfWB8Lsf2wSSDySipnF/aa9XX4I3zWY8PXb+Dj7zrivP2Rl2JWoudIOgpXz6X8+F9l/GZQy9Q8JV4xKE/ESUWcW3lq2mppi50CkfwX1fVNy7xmluBWwF27NhxzYsvvrjq4951/7P8128/T36ZDZ5N+1quPUQ84rD/F685r7Vv+Ui7mGpppKWOBbby1ay9pRY6tXwEr6oHgAMQrGRd7fe76/5n+ZP7n1v1eZnWWu4fQsH3m9badylLHetLt15rAd20lZYH+EY6NDLGp79lwX098H14/OQUh0bG1rS1b73HsoVPph00s0zyS8BDwOtE5JSIfLBZx4LgB+q2u49iWZn1QQFV5fZ7j9EXc+uqdmmEeipr6l0gZUyzNS3Aq+r7VXWrqkZVdZuq/nmzjvXRex7nVz7/GFlbtNT2wsaNK1Lr9b5C3vMQEfKeksoVUNWaJZGNcNsNu5c9VnkaRyT4WGx/YMxa6vhWBR+953G+9r0z1q+9A4jA5375R4hFnKAHzDKRPh5x6Ik6bBtM8vqL+0uPOxIsgHIdYTqVZy5bqFnL3mhL1c0X1btAyphm6+gc/KGRMb72vTOtPg1Tp6gj/O7fPk084rAp7jLUl+DY6ema2x6qwobeoJb8pakUvTGXgqc44W8GRcl6PtsGe9Z0z9PljmW7QZl20dEj+H/3xaOtPgWzApt6Y2xMRumJuYzN5jg7l1lULlM5oO+Lu0TdoJZ8+2AP/YkIPorvK6qK5wc7MbVbrXk9aRxj1kJHjeDLKxNm0nnSecu5dwoHiEdcRITh/mAf1PmsRzTi4Ps+hHn0cjOZPB/40Z2l0fLt9x5jc2+M2UyBbMHHdYQP77us7apTbDco0y46Zkenu+5/ls8ceiHYhUdtj9ROE3OFqOuwe7gPCCpgptN5fv+9b+T2e49x5lyK8t/XkTAN89rhXu776I8DC7/gLWgas6CtFzrV49DImK1M7XCer2hZ785iTro42v2VLzwGBBOoESeYQPV8n+MTCxOTa5lnN6YbdESA/92/ecqCewcq30TbU/A8ZXR8jv5EZFGfln17thBzHVQV1+noaSFj2kpb/zTddf+zXPE73+DUuUyrT8VciIrfyXFXyBQ8plJ5brn60kWj8d1DveF+uIqipeZhu4d61/ikjekebRvgiz1lcjZyb3vF6peoW7uwPRFxuOLiAd6wdQPbBpM8NDq56PlP3LSHwZ4oAhQ8HwEGe6J84qY9TTtvY7pd26VoihNpD41OtPpUzDKccHs6EbioP8FAMsor02nOzuVQoCfmks55uI5w8YZE6X3VFv3s27OFP7rlKptENaaB2irAj81k+OAXHrN+Mh3AEfjNd17Om7dt5PZ7jxFxBVVlIBlFgeG+OHPZAtNOnt64S39ioUFXrUU/NolqTGO1VYB/dTbLVgvuba8n5vKrN+zmI++6AuC8mu/f+4k3lAJ1sfHWcrsiGWMar60CvGkNV8BxBN/XqndP8YjDYE+wQ1G1Hi9Ljbxt0Y8xrWMBfp0RwHVgYzLK5RcNcN3uTTX3LW3UwiJLvRjTGhbgu1hf3CWT94JNoBUirnDZUC+ffM/rFwXcWvuWWmA2prNZgO9CAvQnIlx5yQZLhxizjlmA7yARB7ZuSJLOe+QKPqo+Ig6xiMPlW/otmBtjFmlqgBeRm4BPAy7wWVX9w2Yer1sUR+Afun5XqVLFGGNWqmkBXkRc4DPAu4FTwGMicq+q/qBZx2xH2weT/Ow122pOZBpjTLM0cwT/NuB5VR0FEJF7gPcCXRPgReCnrtrKf37f1cu+ttZEpjHGNEszA/ylwEtlX58C3l75IhG5FbgVILZ5GzFXiDhCqgWbeQjwuot6OTmZIZX3EIFLNyT4v3/qTTbiNsZ0nGYG+Gqdp85bRqOqB4ADEGz48cf/ei/7D4/y3KszTM7naVSYj0cc+hMRm4w0xqwbzQzwp4DtZV9vA04v96Zatde2m48xxqxMMwP8Y8DlIrILeBl4H/C/X+g3s0U3xhizMk0L8KpaEJF/D3yToEzyc6p6rFnHM8YYs1hT6+BV9RvAN5p5DGOMMdW17Y5OxhhjVscCvDHGdCkL8MYY06UswBtjTJcS1fbZI09ExoEXW30edRoCzrb6JBqgW64DuudauuU6oHuupZ2v4zWqOlztibYK8J1ERI6o6t5Wn8dqdct1QPdcS7dcB3TPtXTqdViKxhhjupQFeGOM6VIW4C/cgVafQIN0y3VA91xLt1wHdM+1dOR1WA7eGGO6lI3gjTGmS1mAN8aYLmUBPiQi20Xk2yLyjIgcE5HfCB/fJCL/ICLPhR8Hy97zWyLyvIj8UET+t7LHrxGRp8Ln7hKRapufNPt6XBF5QkS+3uHXsVFEDorISPh3c10nXouIfDT8d/W0iHxJRBKdch0i8jkRGRORp8sea9i5i0hcRP4qfPwREdm5xtfyR+G/r++LyNdEZGMnXEtdVNX+C+YhtgJXh5/3A88CbwD+H+CT4eOfBO4MP38D8CQQB3YBLwBu+NyjwHUEu1r9HfCeFlzPfwD+Evh6+HWnXscXgA+Fn8eAjZ12LQTbVx4HkuHXXwY+0CnXAdwAXA08XfZYw84d+DXgz8LP3wf81Rpfyz8HIuHnd3bKtdR1va08eDv/B/wt8G7gh8DW8LGtwA/Dz38L+K2y138z/AvfCoyUPf5+YP8an/s24FvAjSwE+E68joEwMErF4x11LSzsT7yJoEX318Og0jHXAeysCIoNO/fia8LPIwQrRmWtrqXiuZ8Gvtgp17Lcf5aiqSK8rXor8AhwkaqeAQg/FreVqrap+KXhf6eqPL6W/gvwH2HRlradeB27gXHgf4Tpps+KSC8ddi2q+jLwx8BJ4Awwrap/T4ddR4VGnnvpPapaAKaBzU0786X9G4IR+aLzCnXatViAryQifcBfA7+pqjNLvbTKY7rE42tCRH4SGFPVo/W+pcpjLb+OUITgdvq/q+pbgXmCdEAtbXktYX76vQS3+ZcAvSLyi0u9pcpjLb+OOl3IubfFdYnI7wAF4IvFh6q8rCOupcgCfBkRiRIE9y+q6lfDh18Vka3h81uBsfDxWpuKnwo/r3x8rbwDuFlETgD3ADeKyN103nUQnsMpVX0k/PogQcDvtGt5F3BcVcdVNQ98FfhROu86yjXy3EvvEZEIsAGYbNqZVyEivwz8JPALGuZX6NBrKWcBPhTOgv858Iyq/knZU/cCvxx+/ssEufni4+8LZ813AZcDj4a3q7Micm34PX+p7D1Np6q/parbVHUnwSTPA6r6i512HeG1vAK8JCKvCx96J/ADOu9aTgLXikhPePx3As904HWUa+S5l3+vWwj+za7lHdZNwCeAm1U1VfZUx13LeVqV/G+3/4DrCW6lvg98L/zvXxDkz74FPBd+3FT2nt8hmFn/IWXVDMBe4OnwuT+lRZMswD4WJlk78jqAtwBHwr+XvwEGO/FagP8LGAnP4X8SVGZ0xHUAXyKYO8gTjFA/2MhzBxLAV4DnCapTdq/xtTxPkDcv/tz/WSdcSz3/WasCY4zpUpaiMcaYLmUB3hhjupQFeGOM6VIW4I0xpktZgDfGmC5lAd4YY7qUBXizroSLVu4Xke+JyM+LyG/X8Z658OMlInJwmdfeLCJLtVMwZs1YHbxZV0TkWoJ2sD8efj2nqn3LvGfZ1xjTjmwEbzqeiPSKyP8SkScl2FDj50XkpnAThwfDDRm+LiJbgLuBt4Qj+K8AyfDzLy5zGERkZ3GjiHAzhyvLnjsUbgLxARH50/Cxz4fH/icRGRWRW8LHHRH5bxJsAPJ1EflG8TljGinS6hMwpgFuAk6r6k8AiMgGgmXkNxIsGf8rAFUdE5EPAR9T1Z8MXzunqm+5gGPeA/wc8Kmw2dYlqnpURN5U8bqtBG0w9hD0KTkI/AxBT/I3EbTZfQb43AWcgzFLshG86QZPAe8SkTtF5McI2vIeV9XnNMhB3t2EY34Z+Nnw858j6D9Szd+oqq+qPwAuCh+7HvhK+PgrwLebcH7GWIA3nU9VnwWuIQj0fwDcTJN7cGuwiceEiLwZ+HmCEX012bLPpeKjMU1lAd50PBG5BEip6t0EOyf9KLBLRC4LX/L+Jd6eD/cBuBD3EOyctUFVn1rB+x4E/lWYi7+IoOunMQ1nOXjTDd4E/JGI+ARtYP8dMAT8LxE5SxBQ31jjvQeA74vI46r6Cys87kHg08Dvr/B9f03QE/5pgs3dHyHY2s2YhrIySdP1RGQfZROr7UBE+lR1TkQ2E/QNf0eYjzemYWwEb0xrfF1ENgIx4PctuJtmsBG8MUA4kv5WlafeqaoTa30+xjSCBXhjjOlSVkVjjDFdygK8McZ0KQvwxhjTpSzAG2NMl/r/AS72gNszf7WkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=X,y='price',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7\n",
    "Fit a linear regression model to predict the <code>'price'</code> using the list of features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.0, 3000000.0)]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features =[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"]\n",
    "lm = LinearRegression()\n",
    "X = df[features]\n",
    "Y = df['price']\n",
    "model = lm.fit(X,Y)\n",
    "Y_hat = lm.predict(X)\n",
    "\n",
    "g=sns.kdeplot(x=Y)\n",
    "sns.kdeplot(x=Y_hat)\n",
    "g.set(xlim=(0,3e6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then calculate the R^2. Take a screenshot of your code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6576951666037502"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.score(X,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This will help with Question 8\n",
    "\n",
    "Create a list of tuples, the first element in the tuple contains the name of the estimator:\n",
    "\n",
    "<code>'scale'</code>\n",
    "\n",
    "<code>'polynomial'</code>\n",
    "\n",
    "<code>'model'</code>\n",
    "\n",
    "The second element in the tuple  contains the model constructor \n",
    "\n",
    "<code>StandardScaler()</code>\n",
    "\n",
    "<code>PolynomialFeatures(include_bias=False)</code>\n",
    "\n",
    "<code>LinearRegression()</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "Input=[('scale',StandardScaler()),('polynomial', PolynomialFeatures(include_bias=False)),('model',LinearRegression())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 8\n",
    "Use the list to create a pipeline object to predict the 'price', fit the object using the features in the list <code>features</code>, and calculate the R^2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7513406905914715"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe=Pipeline(Input)\n",
    "X = df[features]\n",
    "y = df['price']\n",
    "pipe.fit(X,y)\n",
    "pipe.score(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 5: Model Evaluation and Refinement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the necessary modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will split the data into training and testing sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of test samples: 3242\n",
      "number of training samples: 18371\n"
     ]
    }
   ],
   "source": [
    "features =[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"]    \n",
    "X = df[features]\n",
    "Y = df['price']\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=1)\n",
    "\n",
    "\n",
    "print(\"number of test samples:\", x_test.shape[0])\n",
    "print(\"number of training samples:\",x_train.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 9\n",
    "Create and fit a Ridge regression object using the training data, set the regularization parameter to 0.1, and calculate the R^2 using the test data. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6594378534950238"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rm=Ridge(alpha=0.1)\n",
    "rm.fit(x_train,y_train)\n",
    "rm.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 10\n",
    "Perform a second order polynomial transform on both the training data and testing data. Create and fit a Ridge regression object using the training data, set the regularisation parameter to 0.1, and calculate the R^2 utilising the test data provided. Take a screenshot of your code and the R^2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7002744260564684"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr=PolynomialFeatures(degree=2, include_bias=False)\n",
    "x_polly_train=pr.fit_transform(x_train)\n",
    "x_polly_test=pr.fit_transform(x_test)\n",
    "rm=Ridge(alpha=0.1)\n",
    "rm.fit(x_polly_train,y_train)\n",
    "rm.score(x_polly_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Once you complete your notebook you will have to share it. Select the icon on the top right a marked in red in the image below, a dialogue box should open, and select the option all&nbsp;content excluding sensitive code cells.</p>\n",
    "        <p><img width=\"600\" src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/coursera/project/save_notebook.png\" alt=\"share notebook\"  style=\"display: block; margin-left: auto; margin-right: auto;\"/></p>\n",
    "        <p></p>\n",
    "        <p>You can then share the notebook&nbsp; via a&nbsp; URL by scrolling down as shown in the following image:</p>\n",
    "        <p style=\"text-align: center;\"><img width=\"600\"  src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/coursera/project/url_notebook.png\" alt=\"HTML\" style=\"display: block; margin-left: auto; margin-right: auto;\" /></p>\n",
    "        <p>&nbsp;</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>About the Authors:</h2> \n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other contributors: <a href=\"https://www.linkedin.com/in/michelleccarey/\">Michelle Carey</a>, <a href=\"www.linkedin.com/in/jiahui-mavis-zhou-a4537814a\">Mavis Zhou</a> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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