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  "cells": [
    {
      "cell_type": "markdown",
      "source": "# House Sales in King County, USA",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015.",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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)",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "The following libraries are required ",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler,PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression\n%matplotlib inline",
      "metadata": {},
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "# Section 1: Importing Data Sets ",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": " Loading the csv:  ",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "file_name='https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/coursera/project/kc_house_data_NaN.csv'\ndf=pd.read_csv(file_name)",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "collapsed": false
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "\nWe use the method <code>head</code> to display the first 5 columns of the dataframe.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.head()",
      "metadata": {},
      "execution_count": 3,
      "outputs": [
        {
          "execution_count": 3,
          "output_type": "execute_result",
          "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>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  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>7129300520</td>\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>...</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  </tbody>\n</table>\n<p>5 rows × 22 columns</p>\n</div>",
            "text/plain": "   Unnamed: 0          id             date     price  bedrooms  bathrooms  \\\n0           0  7129300520  20141013T000000  221900.0       3.0       1.00   \n1           1  6414100192  20141209T000000  538000.0       3.0       2.25   \n2           2  5631500400  20150225T000000  180000.0       2.0       1.00   \n3           3  2487200875  20141209T000000  604000.0       4.0       3.00   \n4           4  1954400510  20150218T000000  510000.0       3.0       2.00   \n\n   sqft_living  sqft_lot  floors  waterfront  ...  grade  sqft_above  \\\n0         1180      5650     1.0           0  ...      7        1180   \n1         2570      7242     2.0           0  ...      7        2170   \n2          770     10000     1.0           0  ...      6         770   \n3         1960      5000     1.0           0  ...      7        1050   \n4         1680      8080     1.0           0  ...      8        1680   \n\n   sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n0              0      1955             0    98178  47.5112 -122.257   \n1            400      1951          1991    98125  47.7210 -122.319   \n2              0      1933             0    98028  47.7379 -122.233   \n3            910      1965             0    98136  47.5208 -122.393   \n4              0      1987             0    98074  47.6168 -122.045   \n\n   sqft_living15  sqft_lot15  \n0           1340        5650  \n1           1690        7639  \n2           2720        8062  \n3           1360        5000  \n4           1800        7503  \n\n[5 rows x 22 columns]"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Task 1 \nDisplaing the data types of each column using the attribute dtype.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.dtypes",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "collapsed": false
      },
      "execution_count": 4,
      "outputs": [
        {
          "execution_count": 4,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Unnamed: 0         int64\nid                 int64\ndate              object\nprice            float64\nbedrooms         float64\nbathrooms        float64\nsqft_living        int64\nsqft_lot           int64\nfloors           float64\nwaterfront         int64\nview               int64\ncondition          int64\ngrade              int64\nsqft_above         int64\nsqft_basement      int64\nyr_built           int64\nyr_renovated       int64\nzipcode            int64\nlat              float64\nlong             float64\nsqft_living15      int64\nsqft_lot15         int64\ndtype: object"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We use the method describe to obtain a statistical summary of the dataframe.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.describe()",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "collapsed": false
      },
      "execution_count": 5,
      "outputs": [
        {
          "execution_count": 5,
          "output_type": "execute_result",
          "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  \\\ncount  21613.00000  2.161300e+04  2.161300e+04  21600.000000  21603.000000   \nmean   10806.00000  4.580302e+09  5.400881e+05      3.372870      2.115736   \nstd     6239.28002  2.876566e+09  3.671272e+05      0.926657      0.768996   \nmin        0.00000  1.000102e+06  7.500000e+04      1.000000      0.500000   \n25%     5403.00000  2.123049e+09  3.219500e+05      3.000000      1.750000   \n50%    10806.00000  3.904930e+09  4.500000e+05      3.000000      2.250000   \n75%    16209.00000  7.308900e+09  6.450000e+05      4.000000      2.500000   \nmax    21612.00000  9.900000e+09  7.700000e+06     33.000000      8.000000   \n\n        sqft_living      sqft_lot        floors    waterfront          view  \\\ncount  21613.000000  2.161300e+04  21613.000000  21613.000000  21613.000000   \nmean    2079.899736  1.510697e+04      1.494309      0.007542      0.234303   \nstd      918.440897  4.142051e+04      0.539989      0.086517      0.766318   \nmin      290.000000  5.200000e+02      1.000000      0.000000      0.000000   \n25%     1427.000000  5.040000e+03      1.000000      0.000000      0.000000   \n50%     1910.000000  7.618000e+03      1.500000      0.000000      0.000000   \n75%     2550.000000  1.068800e+04      2.000000      0.000000      0.000000   \nmax    13540.000000  1.651359e+06      3.500000      1.000000      4.000000   \n\n       ...         grade    sqft_above  sqft_basement      yr_built  \\\ncount  ...  21613.000000  21613.000000   21613.000000  21613.000000   \nmean   ...      7.656873   1788.390691     291.509045   1971.005136   \nstd    ...      1.175459    828.090978     442.575043     29.373411   \nmin    ...      1.000000    290.000000       0.000000   1900.000000   \n25%    ...      7.000000   1190.000000       0.000000   1951.000000   \n50%    ...      7.000000   1560.000000       0.000000   1975.000000   \n75%    ...      8.000000   2210.000000     560.000000   1997.000000   \nmax    ...     13.000000   9410.000000    4820.000000   2015.000000   \n\n       yr_renovated       zipcode           lat          long  sqft_living15  \\\ncount  21613.000000  21613.000000  21613.000000  21613.000000   21613.000000   \nmean      84.402258  98077.939805     47.560053   -122.213896    1986.552492   \nstd      401.679240     53.505026      0.138564      0.140828     685.391304   \nmin        0.000000  98001.000000     47.155900   -122.519000     399.000000   \n25%        0.000000  98033.000000     47.471000   -122.328000    1490.000000   \n50%        0.000000  98065.000000     47.571800   -122.230000    1840.000000   \n75%        0.000000  98118.000000     47.678000   -122.125000    2360.000000   \nmax     2015.000000  98199.000000     47.777600   -121.315000    6210.000000   \n\n          sqft_lot15  \ncount   21613.000000  \nmean    12768.455652  \nstd     27304.179631  \nmin       651.000000  \n25%      5100.000000  \n50%      7620.000000  \n75%     10083.000000  \nmax    871200.000000  \n\n[8 rows x 21 columns]"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "# Section 2: Data Wrangling",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Task 2 \nDroping the columns <code>\"id\"</code>  and <code>\"Unnamed: 0\"</code> from axis 1 using the method <code>drop()</code>, then I use the method <code>describe()</code> to obtain a statistical summary of the data.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.drop(\"id\", axis = 1, inplace = True)\ndf.drop(\"Unnamed: 0\", axis = 1, inplace = True)\n\ndf.describe()",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "collapsed": false
      },
      "execution_count": 6,
      "outputs": [
        {
          "execution_count": 6,
          "output_type": "execute_result",
          "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>price</th>\n      <th>bedrooms</th>\n      <th>bathrooms</th>\n      <th>sqft_living</th>\n      <th>sqft_lot</th>\n      <th>floors</th>\n      <th>waterfront</th>\n      <th>view</th>\n      <th>condition</th>\n      <th>grade</th>\n      <th>sqft_above</th>\n      <th>sqft_basement</th>\n      <th>yr_built</th>\n      <th>yr_renovated</th>\n      <th>zipcode</th>\n      <th>lat</th>\n      <th>long</th>\n      <th>sqft_living15</th>\n      <th>sqft_lot15</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>2.161300e+04</td>\n      <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>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>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>3.409430</td>\n      <td>7.656873</td>\n      <td>1788.390691</td>\n      <td>291.509045</td>\n      <td>1971.005136</td>\n      <td>84.402258</td>\n      <td>98077.939805</td>\n      <td>47.560053</td>\n      <td>-122.213896</td>\n      <td>1986.552492</td>\n      <td>12768.455652</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>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>0.650743</td>\n      <td>1.175459</td>\n      <td>828.090978</td>\n      <td>442.575043</td>\n      <td>29.373411</td>\n      <td>401.679240</td>\n      <td>53.505026</td>\n      <td>0.138564</td>\n      <td>0.140828</td>\n      <td>685.391304</td>\n      <td>27304.179631</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>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>1.000000</td>\n      <td>1.000000</td>\n      <td>290.000000</td>\n      <td>0.000000</td>\n      <td>1900.000000</td>\n      <td>0.000000</td>\n      <td>98001.000000</td>\n      <td>47.155900</td>\n      <td>-122.519000</td>\n      <td>399.000000</td>\n      <td>651.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>3.219500e+05</td>\n      <td>3.000000</td>\n      <td>1.750000</td>\n      <td>1427.000000</td>\n      <td>5.040000e+03</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>7.000000</td>\n      <td>1190.000000</td>\n      <td>0.000000</td>\n      <td>1951.000000</td>\n      <td>0.000000</td>\n      <td>98033.000000</td>\n      <td>47.471000</td>\n      <td>-122.328000</td>\n      <td>1490.000000</td>\n      <td>5100.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>4.500000e+05</td>\n      <td>3.000000</td>\n      <td>2.250000</td>\n      <td>1910.000000</td>\n      <td>7.618000e+03</td>\n      <td>1.500000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>7.000000</td>\n      <td>1560.000000</td>\n      <td>0.000000</td>\n      <td>1975.000000</td>\n      <td>0.000000</td>\n      <td>98065.000000</td>\n      <td>47.571800</td>\n      <td>-122.230000</td>\n      <td>1840.000000</td>\n      <td>7620.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>6.450000e+05</td>\n      <td>4.000000</td>\n      <td>2.500000</td>\n      <td>2550.000000</td>\n      <td>1.068800e+04</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>4.000000</td>\n      <td>8.000000</td>\n      <td>2210.000000</td>\n      <td>560.000000</td>\n      <td>1997.000000</td>\n      <td>0.000000</td>\n      <td>98118.000000</td>\n      <td>47.678000</td>\n      <td>-122.125000</td>\n      <td>2360.000000</td>\n      <td>10083.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>7.700000e+06</td>\n      <td>33.000000</td>\n      <td>8.000000</td>\n      <td>13540.000000</td>\n      <td>1.651359e+06</td>\n      <td>3.500000</td>\n      <td>1.000000</td>\n      <td>4.000000</td>\n      <td>5.000000</td>\n      <td>13.000000</td>\n      <td>9410.000000</td>\n      <td>4820.000000</td>\n      <td>2015.000000</td>\n      <td>2015.000000</td>\n      <td>98199.000000</td>\n      <td>47.777600</td>\n      <td>-121.315000</td>\n      <td>6210.000000</td>\n      <td>871200.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "              price      bedrooms     bathrooms   sqft_living      sqft_lot  \\\ncount  2.161300e+04  21600.000000  21603.000000  21613.000000  2.161300e+04   \nmean   5.400881e+05      3.372870      2.115736   2079.899736  1.510697e+04   \nstd    3.671272e+05      0.926657      0.768996    918.440897  4.142051e+04   \nmin    7.500000e+04      1.000000      0.500000    290.000000  5.200000e+02   \n25%    3.219500e+05      3.000000      1.750000   1427.000000  5.040000e+03   \n50%    4.500000e+05      3.000000      2.250000   1910.000000  7.618000e+03   \n75%    6.450000e+05      4.000000      2.500000   2550.000000  1.068800e+04   \nmax    7.700000e+06     33.000000      8.000000  13540.000000  1.651359e+06   \n\n             floors    waterfront          view     condition         grade  \\\ncount  21613.000000  21613.000000  21613.000000  21613.000000  21613.000000   \nmean       1.494309      0.007542      0.234303      3.409430      7.656873   \nstd        0.539989      0.086517      0.766318      0.650743      1.175459   \nmin        1.000000      0.000000      0.000000      1.000000      1.000000   \n25%        1.000000      0.000000      0.000000      3.000000      7.000000   \n50%        1.500000      0.000000      0.000000      3.000000      7.000000   \n75%        2.000000      0.000000      0.000000      4.000000      8.000000   \nmax        3.500000      1.000000      4.000000      5.000000     13.000000   \n\n         sqft_above  sqft_basement      yr_built  yr_renovated       zipcode  \\\ncount  21613.000000   21613.000000  21613.000000  21613.000000  21613.000000   \nmean    1788.390691     291.509045   1971.005136     84.402258  98077.939805   \nstd      828.090978     442.575043     29.373411    401.679240     53.505026   \nmin      290.000000       0.000000   1900.000000      0.000000  98001.000000   \n25%     1190.000000       0.000000   1951.000000      0.000000  98033.000000   \n50%     1560.000000       0.000000   1975.000000      0.000000  98065.000000   \n75%     2210.000000     560.000000   1997.000000      0.000000  98118.000000   \nmax     9410.000000    4820.000000   2015.000000   2015.000000  98199.000000   \n\n                lat          long  sqft_living15     sqft_lot15  \ncount  21613.000000  21613.000000   21613.000000   21613.000000  \nmean      47.560053   -122.213896    1986.552492   12768.455652  \nstd        0.138564      0.140828     685.391304   27304.179631  \nmin       47.155900   -122.519000     399.000000     651.000000  \n25%       47.471000   -122.328000    1490.000000    5100.000000  \n50%       47.571800   -122.230000    1840.000000    7620.000000  \n75%       47.678000   -122.125000    2360.000000   10083.000000  \nmax       47.777600   -121.315000    6210.000000  871200.000000  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can see we have missing values for the columns <code> bedrooms</code>  and <code> bathrooms </code>",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print(\"number of NaN values for the column bedrooms :\", df['bedrooms'].isnull().sum())\nprint(\"number of NaN values for the column bathrooms :\", df['bathrooms'].isnull().sum())\n",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "collapsed": false
      },
      "execution_count": 8,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "number of NaN values for the column bedrooms : 13\nnumber of NaN values for the column bathrooms : 10\n"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "\nWe 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>",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "mean=df['bedrooms'].mean()\ndf['bedrooms'].replace(np.nan,mean, inplace=True)",
      "metadata": {},
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "\nWe 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>",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "mean=df['bathrooms'].mean()\ndf['bathrooms'].replace(np.nan,mean, inplace=True)",
      "metadata": {},
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "print(\"number of NaN values for the column bedrooms :\", df['bedrooms'].isnull().sum())\nprint(\"number of NaN values for the column bathrooms :\", df['bathrooms'].isnull().sum())",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "collapsed": false
      },
      "execution_count": 11,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "number of NaN values for the column bedrooms : 0\nnumber of NaN values for the column bathrooms : 0\n"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "# Section 3: Exploratory Data Analysis",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Task 3\nUsing the method <code>value_counts</code> to count the number of houses with unique floor values.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df['floors'].value_counts().to_frame()",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "collapsed": false
      },
      "execution_count": 12,
      "outputs": [
        {
          "execution_count": 12,
          "output_type": "execute_result",
          "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\n1.0   10680\n2.0    8241\n1.5    1910\n3.0     613\n2.5     161\n3.5       8"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Task 4\nUsing 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.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "sns.boxplot(x=\"waterfront\", y=\"price\", data=df)",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "collapsed": false
      },
      "execution_count": 13,
      "outputs": [
        {
          "execution_count": 13,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f5bced0ff50>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Task 5\nUsing 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.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "sns.regplot(x=\"sqft_above\", y=\"price\", data=df, ci = None)",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "collapsed": false
      },
      "execution_count": 14,
      "outputs": [
        {
          "execution_count": 14,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f5bce199090>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "\nWe can use the Pandas method <code>corr()</code>  to find the feature other than price that is most correlated with price.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.corr()['price'].sort_values()",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "collapsed": false
      },
      "execution_count": 15,
      "outputs": [
        {
          "execution_count": 15,
          "output_type": "execute_result",
          "data": {
            "text/plain": "zipcode         -0.053203\nlong             0.021626\ncondition        0.036362\nyr_built         0.054012\nsqft_lot15       0.082447\nsqft_lot         0.089661\nyr_renovated     0.126434\nfloors           0.256794\nwaterfront       0.266369\nlat              0.307003\nbedrooms         0.308797\nsqft_basement    0.323816\nview             0.397293\nbathrooms        0.525738\nsqft_living15    0.585379\nsqft_above       0.605567\ngrade            0.667434\nsqft_living      0.702035\nprice            1.000000\nName: price, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "# Section 4: Model Development",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "\nWe can Fit a linear regression model using the  longitude feature <code>'long'</code> and  caculate the R^2.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "X = df[['long']]\nY = df['price']\nlm = LinearRegression()\nlm.fit(X,Y)\nlm.score(X, Y)",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "collapsed": false
      },
      "execution_count": 16,
      "outputs": [
        {
          "execution_count": 16,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.00046769430149007363"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Task  6\nFiting a linear regression model to predict the <code>'price'</code> using the feature <code>'sqft_living'</code> then calculating the R^2.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "X1 = df[['sqft_living']]\nY1 = df['price']\nlm = LinearRegression()\nlm\nlm.fit(X1,Y1)\nlm.score(X1, Y1)",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "collapsed": false
      },
      "execution_count": 17,
      "outputs": [
        {
          "execution_count": 17,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.4928532179037931"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Task 7\nFitting a linear regression model to predict the <code>'price'</code> using the list of features:",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "features =[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"]     ",
      "metadata": {},
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Then calculating the R^2.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "X2 = df[features]\nY2 = df['price']\nlm.fit(X2,Y2)\nlm.score(X2,Y2)",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "collapsed": false
      },
      "execution_count": 19,
      "outputs": [
        {
          "execution_count": 19,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.657679183672129"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### This will help with Task 8\n\nCreating 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\nThe 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",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Input=[('scale',StandardScaler()),('polynomial', PolynomialFeatures(include_bias=False)),('model',LinearRegression())]",
      "metadata": {},
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "### Task 8\nUsing the list to create a pipeline object to predict the 'price', fitting the object using the features in the list <code>features</code>, and then calculating the R^2.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pipe=Pipeline(Input)\npipe\nX = df[features]\nY = df['price']\npipe.fit(X,Y)\npipe.score(X,Y)",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "collapsed": false
      },
      "execution_count": 25,
      "outputs": [
        {
          "execution_count": 25,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.7513408553309376"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "# Section 5: Model Evaluation and Refinement",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Import the necessary modules:",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import train_test_split\nprint(\"done\")",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "collapsed": false
      },
      "execution_count": 26,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "done\n"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We will split the data into training and testing sets:",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "features =[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"]    \nX = df[features]\nY = df['price']\n\nx_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=1)\n\n\nprint(\"number of test samples:\", x_test.shape[0])\nprint(\"number of training samples:\",x_train.shape[0])",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "collapsed": false
      },
      "execution_count": 27,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "number of test samples: 3242\nnumber of training samples: 18371\n"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Task 9\nCreating and fitting a Ridge regression object using the training data, set the regularization parameter to 0.1, and calculating the R^2 using the test data. \n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.linear_model import Ridge",
      "metadata": {},
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "RidgeModel = Ridge(alpha=0.1) \nRidgeModel.fit(x_train, y_train)\nRidgeModel.score(x_test, y_test)",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "collapsed": false
      },
      "execution_count": 30,
      "outputs": [
        {
          "execution_count": 30,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.6478759163939122"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Task 10\nPerforming a second order polynomial transform on both the training data and testing data. Creating and fitting a Ridge regression object using the training data, setting the regularisation parameter to 0.1, and calculating the R^2 utilising the test data provided.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pr = PolynomialFeatures(degree = 2)\nx_train_pr = pr.fit_transform(x_train[features])\nx_test_pr = pr.fit_transform(x_test[features])\n\nRidgeModel1 = Ridge(alpha = 0.1) \nRidgeModel1.fit(x_train_pr, y_train)\nRidgeModel1.score(x_test_pr, y_test)",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "collapsed": false
      },
      "execution_count": 31,
      "outputs": [
        {
          "execution_count": 31,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.7002744279896707"
          },
          "metadata": {}
        }
      ]
    }
  ]
}