Project_6.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": 45,
            "metadata": {},
            "outputs": [],
            "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"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Module 1: Importing Data Sets "
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": " Load the csv:  "
        },
        {
            "cell_type": "code",
            "execution_count": 46,
            "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'\ndf=pd.read_csv(file_name)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "\nWe use the method <code>head</code> to display the first 5 columns of the dataframe."
        },
        {
            "cell_type": "code",
            "execution_count": 47,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>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 \u00d7 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]"
                    },
                    "execution_count": 47,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Question 1 \nDisplay 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": 48,
            "metadata": {
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "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\n"
                }
            ],
            "source": "df_data_types = df.dtypes\nprint(df_data_types)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We use the method describe to obtain a statistical summary of the dataframe."
        },
        {
            "cell_type": "code",
            "execution_count": 49,
            "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 \u00d7 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]"
                    },
                    "execution_count": 49,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df.describe()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Module 2: Data Wrangling"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Question 2 \nDrop 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": 50,
            "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>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  "
                    },
                    "execution_count": 50,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df.drop(['id', 'Unnamed: 0'], axis=1, inplace = True)\ndf.describe()\n\n"
        },
        {
            "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": 51,
            "metadata": {
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "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"
                }
            ],
            "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"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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>"
        },
        {
            "cell_type": "code",
            "execution_count": 52,
            "metadata": {},
            "outputs": [],
            "source": "mean=df['bedrooms'].mean()\ndf['bedrooms'].replace(np.nan,mean, inplace=True)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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>"
        },
        {
            "cell_type": "code",
            "execution_count": 53,
            "metadata": {},
            "outputs": [],
            "source": "mean=df['bathrooms'].mean()\ndf['bathrooms'].replace(np.nan,mean, inplace=True)"
        },
        {
            "cell_type": "code",
            "execution_count": 54,
            "metadata": {
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "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"
                }
            ],
            "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())"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Module 3: Exploratory Data Analysis"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Question 3\nUse 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": 55,
            "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\n1.0   10680\n2.0    8241\n1.5    1910\n3.0     613\n2.5     161\n3.5       8"
                    },
                    "execution_count": 55,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "unique_floor_counts = df['floors'].value_counts().to_frame()\nunique_floor_counts"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Question 4\nUse 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": 56,
            "metadata": {
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fe7d458abe0>"
                    },
                    "execution_count": 56,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": "iVBORw0KGgoAAAANSUhEUgAAAa4AAAESCAYAAACl/TGUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAG5VJREFUeJzt3X+U3XV95/HnnUn4oaGZOCSLoYGgkrc/jj9qZQEzBldN2906bLGtu7ES0W0V6oo97XZlu/VH1W6xumurxCY9XXoQiqe6KyzRVqu2/EgM1bWwHmt9ExRISIAMQyYSbSCZmf3jfidMEiD3kpn7/XyZ5+OcOXe+38/93vuem8m87ufz/dzvpzU5OYkkSU3RV3cBkiR1w+CSJDWKwSVJahSDS5LUKAaXJKlRDC5JUqMYXJKkRjG4JEmNYnBJkhrF4JIkNcq8Xj1RRLwe+BDQoh2YH8jMz0fECuAqYBAYBdZm5tbqmGLaJEll6ElwRUQLuBp4VWZ+JyJeAmyOiOuB9cC6zLwmIt4MbABeUx1aUtvRHA+cBdwHjHfx8kjSXNYPPBv4JvBIJwf0rMcFTAALq+8HaP+BPxl4ObC62v8Z4IqIWEy7Z1ZEW2aOdPDznQXc0sH9JElHehWwqZM79iS4MnMyIt4I/J+I+BFwEvDzwDJgR2aOV/cbj4id1f5WQW2dBNd9ALt3/4iJCa+4L0md6OtrsWjRM6H6G9qJXg0VzgP+C/BvM3NzRKwE/hK4sBfP3yPjwNQ/gCSpOx2fYmn1Yj2uiHgF8OnMfOG0ff8EXAR8GRisejj9tCdFnEm7B3RHCW0dDhUuB+4aHd1rj0uSOtTX12JwcAHAGcDdHR0zmwVNcy/wkxERABHxAuAUYCtwO7Cmut8a4LbMHMnMXaW0zegrIUk6Jj3pcQFExK8Al9GepAHw/sy8PiKeT3sK+iJgN+0p6FkdU0xbB5Zjj0uSuvJUelw9C645YDkG14wYG9vN+vWf5JJLLmXhwoG6y5E0i0oeKpQ6tnHjdWzdmtxww+frLkVSgQwuFWVsbDebNt3E5OQkmzbdzJ49Y3WXJKkwBpeKsnHjdQeHWicmJux1STqCwaWibNmymfHxAwCMjx9gy5bNNVckqTQGl4py7rkr6e9vfy6+v38e5567suaKJJXG4FJRhocvoK+vBUBfXx/nn/+GmiuSVBqDS0UZGFjE0NB5tFothoZWOR1e0hF6eXV4qSPDwxewY8e99rYkPS4/gDxzluMHkCWpK34AWZL0tGdwSZIaxeCSJDWKwSVJahSDS5LUKAaXJKlRDC5J6sLY2G4uv/yDrlxQI4NLkrrgenH168mVMyJiOXD9tF0DwE9k5rMiYgVwFTAIjAJrM3NrdVwxbZJ0+Hpx55//Bi9LVoOe9Lgy8+7MfNnUF+0Qu7ZqXg+sy8wVwDpgw7RDS2qTNMe5XlwZen7Jp4g4DtgB/CxwL3AHMJiZ4xHRT7uncybQKqUtM0c6+NGW4yWfpKe1X//1/8C+ff98cPuEE07kU5/6nzVW1HxNueTT+cCOzPwHYFn1/ThAdbuz2l9Sm3rIk98qlevFlaGOq8O/Dbiyhuftieqdg47B5z53NVu3Jl/5yhe45JJL6i5HOuiiiy5k8+abGR+H/v4+3vrWtSxadFLdZc05PQ2uiFgKnAdcWO3aDpwaEf3ThueWVvtbBbV1zKHCYzM2tpuvfvWrTE5O8pWvfJXVq1/vyW8VZD4rV67ixhu/xsqVqzhwYB4jIw/XXVSjTRsq7PyYWarliVwEfDEzRwEycxdwO7Cmal8D3JaZIyW1zexLoCfjyW+Vbnj4As48M1wvrkZ1BNfhw4QXA++KiDuAd1XbJbapB7Zs2cz4+AEAxscPsGXL5porkg41MLCIyy57nyMBNXIhyZmzHGcVHrOrr76Sm2++kfHxA/T3z2PVqldz4YVvq7ssSbOkKbMKpSc0PHwBfX0tAPr6+hyOkXQEg0tFGRhYxNDQebRaLYaGVjkcI+kIdUyHl57U8PAF7Nhxr70tSY/Lc1wzZzme45KkrniOS5L0tGdwSZIaxeCSJDWKwSVJahSDS5LUKAaXJKlRDC5JUqMYXJKkRjG4JEmNYnBJkhrF4JIkNYrBJUlqFINLktQoPVvWJCJOAD4OvA7YB2zJzLdHxArgKmAQGAXWZubW6phi2iRJZehlj+sPaQfWisx8MfDeav96YF1mrgDWARumHVNSmySpAD1ZjysiFgD3Aj+ZmXun7V8C3AEMZuZ4RPTT7umcCbRKacvMkQ5+zOW4HpckdaXk9bieSzsE3h8R/zciboyIIWAZsCMzxwGq253V/pLaJEmF6NU5rnnAc4DbMvO3I+JsYCPwyz16/p6p3jlIkmZJr4YKTwbuA47LzMlq33eBi4C/oYDhwCdrc6hQkmZHsUOFmfkg8HfAajg4e2/q/NbtwJrqrmto98pGMnNXKW0z+VpIko5NT3pcABHxHOBK2lPN9wP/NTP/OiKeT3sK+iJgN+0p6FkdU0xbB5Zjj0uSuvJUelw9C645YDkGlyR1pdihQkmSZorBJUlqFINLxRkb283ll3+QPXvG6i5FUoEMLhVn48br2Lo1ueGGz9ddiqQCGVwqytjYbjZtuonJyUk2bbrZXpekIxhcKsrGjdcdnJU5MTFhr0vSEQwuFWXLls2Mjx8AYHz8AFu2bK65IkmlMbhUlHPPXUl/f/sSmv398zj33JU1VySpNAaXijI8fAGtVgtofzDx/PPfUHNFkkpjcKkoAwOLWLJkCQCLFy9h4cKBmiuSDuXHNepncKkoY2O72bXrAQB27drlHwcVx49r1M/gUlE2bryOqctnTk5O+sdBRfHjGmUwuFQUZxWqZH5cowwGl4rirEKVzDdWZTC4VJTh4Qvo65uaVdjnrEIVpf3Gqh+A/v5+31jVxOBSUQYGFjE0dB6tVouhoVXOKlRRhocvYGoNw8nJSd9Y1WRe3QVIhxsevoAdO+71j4KK9Fhw1VzIHNaz4IqIu4F91RfAezLzyxFxDrABOJH26pdvzsxd1THFtKl3BgYWcdll76u7DOkIGzdeR6vVx+TkOK1Wixtu+DwXXvi2usuac3o9VPhLmfmy6uvLEdECrgHemZkrgJuBywFKapMkaE/OmJgYB2BiYtzJGTWp+xzXK4B9mbmp2l4PvLHANkly1msheh1cfxER346IT0XEAHAacM9UY2Y+CPRFxLMKa5MkZ70WopeTM16Vmdsj4njgj4ArgOt6+Pw9MTi4oO4SJM2SxYtP4nWvex1f+tKXWL36dTzvecvqLmlO6llwZeb26vaRiPgUcAPwx8DpU/eJiJOBycx8KCK2ldLWzc85Orr34CfrJT39rF79er7//btYvfr1jIw8XHc5jdfX1+r6DX9Phgoj4pkRsbD6vgX8e+B24FvAiRExVN31YuCz1fcltUkS8NisVz9jWJ/WZA8+jBARzwH+N9BffX0XuDQz74uIV9Kegn4Cj01Bf6A6rpi2DiwH7rLHJUmdm9bjOoP2392j6klwzRHLMbikp72xsd2sX/9JLrnkUntdM+CpBFfd0+ElqVFcj6t+Bpckdcj1uMpgcElSh1yPqwwGlyR1yPW4ymBwqThjY7u5/PIPOgyj4rgeVxkMLhXHk98qletxlcHgUlE8+a3SuR5X/QwuFcWT3yrZ1HpcwMH1uNR7BpeK4slvlcz1uMpgcKkornekkvn7WQaDS0VxvSOVzN/PMhhcKsrAwCLOOuscAM4662yvBaeiDAwsYmjoPFqtFkNDq/z9rEnX63FFxDLg1My8dRbqkQ5qtVp1lyAdYXj4AnbsuNfeVo067nFFxGkRsRn4HvDVat8vRcSfzVZxmnvGxnbzzW+23xN94xu3Oh1exXE9rvp1M1S4AfgicBKwv9r3FWD1TBelucvp8JKOppvg+pfA5Zk5AUwCZOYeYOFsFKa5yenwko6mm+B6AHje9B0R8UJg24xWpDnN6caSjqabyRkfA74QEX8AzIuINcDvAJd384QR8X7gA8CLM/M7EXEO7WHIE2mvfvnmzNxV3beYNvXG8PAF3HLLTUB7ZVRPgEs6XMc9rsy8EvjPwC8D24G1wHsz8y86fYyIeDlwDlUvLSJawDXAOzNzBXAzVRCW1KbeGRhYxJIlSwBYvHiJJ8AlHaGr6fCZeT1w/VN5oog4HlgHvAn4u2r3K4B9mbmp2l5Pu6fztsLa1CNjY7vZtesBAHbt2sWePWOGl6RDdDMd/hMR8crD9r0yIv6ow4f4IHBNZt41bd9pwD1TG5n5INAXEc8qrE09snHjdQevuj05OemsQklH6KbHtQb4T4ft+xbtHthvPNmBEXEucBZwWVfVNdDg4IK6S2i0W289dFbhrbdu5jd/8901VyWpJN0E1yRH9tD6H2ff4zkPeD5wV0QA/CTwZeATwOlTd4qIk4HJzHwoIraV0tbBz3fQ6Ojeg59DUvfOOWclN974t0xOTtBq9XHOOSsZGXm47rIkzZK+vlbXb/i7mQ5/C/DhiOgDqG4/UO1/Upl5eWYuzczlmbkcuBf4WeCjwIkRMVTd9WLgs9X33yqoTT3SXmF2AoDJyQlnFUo6Qjc9rncDXwDui4h7aJ8Tug8YfqpPnpkTEXEhsCEiTqCagl5am3rnhz/cc8S2kzMEsHnzzWzadFPdZRy8DFndv5dDQ+excuWqWmuoS2uyi/Wnq17W2bSH+rYD36iupCFYDtzlUOGx+d3f/W127txxcHvp0lP58Ic/WmNFKkUpwbVtW3sO12mnnX6Ue86up0twTRsqPIN2h+Goup0OPwFs6boyqUPTQ+vxtjV3rVy5qog/1B/5yIcAeM973ltzJXPXkwZXRPxTZr6g+n471TUKD5eZp81CbZqDnvGMZ/LjH//okG1Jmu5oPa5fm/a953s06x599NEn3ZakJw2uqatIREQ/7StIvD0zH+lFYZqbjjvuOA4c2H/ItiRN19F0+MwcB34GcCKGZtX0YcLH25akbj7H9XHg9yJi/mwVIy1deuqTbktSN8H1LuC3gYcjYntEbJu6naXaNAe9/e3vPGT7He/4jzVVIqlU3UyHd3KGZt1ppy2nv38e4+MH6O+fx7Jl9X5WRlJ5uulxbQFeC/wZ8FfV7euAv5+FujRHbdt29yEX2d2+/Z6jHCFprukmuP4EeA1wKe0rvV9K++K5n5qFujRH/emfrjtke8OGK2qqRFKpuhkq/AXguZk5Vm1/NyL+HrgTF1vUDPHKGZKOppse1/3AMw7bdyLtC+1KM+L440940m1J6qbHdTXwpYj4JO1lSZYB7wQ+HRGvmbpTZv7tzJaoueTRRx950m1J6ia43lHd/s5h+y+uvqB9LcPnHGtRmrsOX62gm9ULJM0NHQdXZp4xm4VIktSJbs5xSZJUO4NLktQoXS0keSwi4nraK1xOAHuBd2Xm7RGxArgKGARGgbWZubU6ppg29cb8+fPZv3//tG2vDi/pUL3scb0lM1+amT8FfAy4stq/HliXmSuAdcCGaceU1KYemB5a7W3X45J0qJ71uDJzz7TNhcBERCwBXg6srvZ/BrgiIhYDrVLaMnNkBl4CSdIM6Ok5roj4s+pq8r8PvIX2Z8F2VOt9Ta37tbPaX1KbJKkQPetxAWTmrwJExIXAR4H39vL5e2FwcEHdJTztLF58Ut0lSAfNn98P+HtZp54G15TMvDoi/pT2FThOjYj+zByPiH5gKbCd9tBdKW0dGx3dy8SEH5qdSSMjD9ddgnTQ/v3jgL+XM6Wvr9X1G/6eDBVGxIKIWDZtexh4CNgF3A6sqZrWALdl5khmFtM2c6+EJOlY9arH9UzgcxHxTGCcdmgNZ+ZkRFwMXBUR7wN2A2unHVdSmySpAD0Jrsx8ADjnCdq+B5xdepskqQxeOUOS1CgGlySpUQwuSVKjGFySpEYxuCRJjWJwSZIaxeCSJDWKwSVJahSDS5LUKAaXJKlRDC5JUqMYXJKkRjG4JEmNYnBJkhqllhWQJTXLtdd+mu3b76m7jCJs29Z+HT7ykQ/VXEkZli07nTe9qbdLFxpcko5q+/Z7uPvO73HKAv9kPIMJAPbdf2fNldTv/r0HanlefwsldeSUBfN460ueVXcZKsiff/uhWp63J8EVEYPA1cBzgUeAO4F3ZOZIRJwDbABOBO4G3pyZu6rjimmTJJWhV5MzJoE/zMzIzJcA3wcuj4gWcA3wzsxcAdwMXA5QUpskqRw9Ca7MfCgzb5y261bgdOAVwL7M3FTtXw+8sfq+pDZJUiF6Ph0+IvqAS4AbgNOAg1OVMvNBoC8inlVYmySpEHVMzvgksBe4ArighuefVYODC+ou4Wln8eKT6i5hzps/v599dRehIs2f39/z/6M9Da6I+BhwJjCcmRMRsY32kOFU+8nAZGY+VFJbNz/j6OheJiYmuzlERzEy8nDdJcx5+/eP112CCrV///gx/R/t62t1/Ya/Z0OFEfH7wE8Dv5CZj1S7vwWcGBFD1fbFwGcLbJMkFaInwRURLwJ+B1gKfD0ibo+I6zJzArgQ+JOI2AqcB1wGUFKbJKkcPRkqzMx/BFpP0PZ14MWlt0mSyuBFdiVJjWJwSZIaxeCSJDWKwSVJahSDS5LUKAaXJKlRDC5JUqMYXJKkRjG4JEmNUsfV4SU1zJ49Y+zee6C2pdpVpvv3HmDRnrGeP689LklSo9jjknRUCxcOcPw/P8hbX+K6qnrMn3/7IU5YONDz57XHJUlqFINLktQoBpckqVEMLklSozg5Q4fYvPlmNm26qe4yDvGRj3yotuceGjqPlStX1fb8ko7Uk+CKiI8BvwgsB16cmd+p9q8ArgIGgVFgbWZuLa1NklSOXvW4rgf+GLjlsP3rgXWZeU1EvBnYALymwLY5Y+XKVbX2MN72tjcdse8973lvDZVIKlVPgiszNwFExMF9EbEEeDmwutr1GeCKiFgMtEppy8yRmXgNJEkzo85zXMuAHZk5DpCZ4xGxs9rfKqitq+AaHFzw1F8RsXHjRoaHhw/ZVv3mz+9nX91FqEjz5/ezePFJPX1OJ2fMsNHRvUxMTNZdxtPGyMjDdZcgYP/+8bpLUKH27x8/pv+nfX2trt/w1zkdfjtwakT0A1S3S6v9JbWpxyJeQMQLuPLKa+suRVKBaguuzNwF3A6sqXatAW7LzJGS2mb+J5ckHYteTYf/BPAG4BTgqxExmpkvAi4GroqI9wG7gbXTDiupTZJUiF7NKrwUuPRx9n8POPsJjimmTZJUDidnSOrI/S4kCcDeRycAWHCcV8y7f+8BltfwvAaXpKNatuz0uksoxq5t9wBw8im+Jsup53fD4JJ0VG96k6d8p0xdO9MrutTH4CrEtdd+mu3b76m7jCJsq97R1nlx3ZIsW3a6wSFNY3AVYvv2e8itd9J/Qu+XwS7NxHg/AHduf7DmSuo3vm+s7hKk4hhcBek/YYBnnP7austQQX58z9fqLkEqjtNiJEmNYnBJkhrFocJC7Nkzxvi+MYeGdIjxfWPs2eN/U2k6e1ySpEbxrVwhFi4cYOSHB5ycoUP8+J6vsXChM02l6QyugjhU2DZxoL1kYd+8E2qupH7t6fAn112GVBSDqxBeUucxUx9APm2Zf7DhZH83Kps338ymTTfVXUYxH5AfGjqPlStX1VpDXQyuQnhlhMd4SR2VbOHChXWXMOcZXJIaYeXKVXO2h6FDOatQktQoBpckqVFak5OTdddQlIhYAVwFDAKjwNrM3NrBocuBu0ZH9zIx0dzXtIQT4AcnZ5xW/6SEuXwCXOqFvr4Wg4MLAM4A7u7omNksqKHWA+sycwWwDthQcz1zzsKFCz0BLukJ2eOaJiKWAHcAg5k5HhH9tHtdZ2bmyFEOX87ToMclSb30VHpczio81DJgR2aOA1ThtbPaf7TgApj6B5AkzRKDa4bZ45Kkzk3rcXV+zCzV0lTbgVOrIUKq26XVfklSAQyuaTJzF3A7sKbatQa4rYPzW5KkHnGo8EgXA1dFxPuA3YDXYpKkghhch8nM7wFn112HJOnxOVQoSWoUe1wzpx/aM2QkSZ2Z9jezv9NjDK6Z82yARYueWXcdktREzwa+38kdvXLGzDkeOAu4DxivuRZJaop+2qH1TeCRTg4wuCRJjeLkDElSoxhckqRGMbgkSY1icEmSGsXgkiQ1isElSWoUg0uS1CheOUPFiYgVwFXAIDAKrM3MrfVWJUFEfAz4RWA58OLM/E69Fc1N9rhUovXAusxcAawDNtRcjzTlemAVcE/dhcxlBpeKEhFLgJcDn6l2fQZ4eUQsrq8qqS0zN2WmK6LXzOBSaZYBOzJzHKC63VntlySDS5LULAaXSrMdODUi+gGq26XVfkkyuFSWzNwF3A6sqXatAW7LzJH6qpJUEpc1UXEi4vm0p8MvAnbTng6f9VYlQUR8AngDcArwIDCamS+qt6q5x+CSJDWKQ4WSpEYxuCRJjWJwSZIaxeCSJDWKwSVJahSvDi/NQRHxYeBi4EBmnlJ3PVI37HFJDRIRH4iIa47xMZYBvwW8cDZCKyJeHRH3zvTjSlMMLmkOiYh5wOm0Pzi760nuIxXLDyBLPRIRbwXekJnD1fadwD9k5hur7e3AMPBW2ldnWAhsBX4jM2+JiJ8DbgBawCPA9zPzpRGxEPgfwL8BJoA/B96fmeMRcRHwa8A3gLcA3wV+Gjge+DHwv4APAHcBvwq8H7g7M1dFxPnAHwCn0r4M1yWZ+U9VrXcDVwBraQfhl6rH76d9RYmpxwdYkZk7Z+p1lOxxSb1zE/CqiOiLiGcD84GVABHxHGAB8G3gm8DLgGcB1wKfi4gTMvNLwH8D/jIzF2TmS6vHvQo4ADwP+CngZ2iH0JSzgR8AS4DVwL8GdlaPcdG0+50HvAD42WoV6s8AvwEsBv4K2BgRx027/xuBnwPOAF4CXJSZPzrs8RcYWpppDglIPZKZP4iIh2mH0grgy8DLqmszngvckpkTwPRzWP89In4XCOD/Hf6YEfEvaAfFQGb+M/CjiPg48HYeWzl6Z2Z+svr+QEQ8UYkfqIKHiPh3wBcz8yvV9seAdwOvBG6s7v+JqVCKiI3VzyXNOoNL6q2bgFfT7h3dBIzR7umcW20TEb9Fu8e0FJgEfgI4+Qke73TaPbf7pgVSH4cuA9PpkjDT77eUacvTZ+ZENZR56rT73D/t+x9Xx0izzuCSeusm2uexzqA97DcG/Art4LoiIl4FvAd4LfCPVWDspn1eC9pBNt122ue7Ts7MA0/wnJ2eyJ5+v53Ai6c2IqJFtTp1l48jzTjPcUm9dRPwr4ATM/Ne4Bba54kGgduAk2ifrxoB5kXE+2j3uKY8ACyPiD6AzLwP+BvaQ4o/UZ0/e25EnHeMdX4W+PmIeG1EzKc9ff4R4OsdHPsAMFhNGpFmnMEl9VBm3gHspR1YZOYPaU+c2JyZ47TPe/01cAftobp9HDqE97nqdjQi/qH6fi1wHO0Zg7tpzxR89jHWmcCbgU/SniU4DAxn5qMdHPs92hM7fhARYxHhEKJmlNPhJUmNYo9LktQoBpckqVEMLklSoxhckqRGMbgkSY1icEmSGsXgkiQ1isElSWoUg0uS1Cj/HwDFxsw91mLvAAAAAElFTkSuQmCC\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\nUse 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": 57,
            "metadata": {
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fe7d456fcc0>"
                    },
                    "execution_count": 57,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 1008x576 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "sns.set(rc={'figure.figsize':(14,8)},color_codes=True)\nsns.regplot(x= 'sqft_above', y='price', data=df, fit_reg=True)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "\nWe 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": 58,
            "metadata": {
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "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"
                    },
                    "execution_count": 58,
                    "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": "\nWe can Fit a linear regression model using the  longitude feature <code>'long'</code> and  caculate the R^2."
        },
        {
            "cell_type": "code",
            "execution_count": 59,
            "metadata": {
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "0.00046769430149007363"
                    },
                    "execution_count": 59,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "X = df[['long']]\nY = df['price']\nlm = LinearRegression()\nlm.fit(X,Y)\nlm.score(X, Y)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Question  6\nFit 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": 60,
            "metadata": {
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Price prediction\n [[287555.1]\n [677621.8]\n [172499.4]\n ...\n [242655.3]\n [405417. ]\n [242655.3]]\nThe R^2 is:\n 0.49285321790379316\n"
                }
            ],
            "source": "X=df[['sqft_living']]\nY=df[['price']]\nlm=LinearRegression()\nlm.fit(X,Y)\nYhat=lm.predict(X).round(decimals=1)\nprint('Price prediction\\n',Yhat)\nprint('The R^2 is:\\n',lm.score(X, Y))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Question 7\nFit a linear regression model to predict the <code>'price'</code> using the list of features:"
        },
        {
            "cell_type": "code",
            "execution_count": 61,
            "metadata": {},
            "outputs": [],
            "source": "features =[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"]  \n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Then calculate the R^2. Take a screenshot of your code."
        },
        {
            "cell_type": "code",
            "execution_count": 62,
            "metadata": {
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "The R-Square is :\n 0.657679183672129\n"
                }
            ],
            "source": "lm.fit(df[features], df['price'])\nprint('The R-Square is :\\n',lm.score(df[features], df['price']) )"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### This will help with Question 8\n\nCreate 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"
        },
        {
            "cell_type": "code",
            "execution_count": 63,
            "metadata": {},
            "outputs": [],
            "source": "Input=[('scale',StandardScaler()),('polynomial', PolynomialFeatures(include_bias=False)),('model',LinearRegression())]"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Question 8\nUse 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": 82,
            "metadata": {
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "R-Square is : 0.657679183672129\n"
                },
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": "/opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n  return self.partial_fit(X, y)\n/opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/base.py:467: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n  return self.fit(X, y, **fit_params).transform(X)\n/opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/pipeline.py:331: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n  Xt = transform.transform(Xt)\n"
                }
            ],
            "source": "pipe=Pipeline(Input)\n\npipe.fit(df[features],Y)\nYhat=pipe.predict(df[features])\nlm.fit(df[features],Y)\n\nprint(\"R-Square is :\", lm.score(df[features],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": 86,
            "metadata": {
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "done\n"
                }
            ],
            "source": "from sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import train_test_split\nprint(\"done\")"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We will split the data into training and testing sets:"
        },
        {
            "cell_type": "code",
            "execution_count": 87,
            "metadata": {
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "number of test samples: 3242\nnumber of training samples: 18371\n"
                }
            ],
            "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])"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Question 9\nCreate 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": 88,
            "metadata": {},
            "outputs": [],
            "source": "from sklearn.linear_model import Ridge"
        },
        {
            "cell_type": "code",
            "execution_count": 91,
            "metadata": {
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "R-Square is : 0.6478759163939121\n"
                }
            ],
            "source": "RidgeModel = Ridge(alpha=0.1)\nRidgeModel.fit(x_train,y_train)\nprint(\"R-Square is :\", RidgeModel.score(x_test,y_test))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Question 10\nPerform 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": 95,
            "metadata": {
                "jupyter": {
                    "outputs_hidden": false
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "R-Square is : 0.7002744279699229\n"
                }
            ],
            "source": "pr = PolynomialFeatures(degree=2)\nx_train_pr = pr.fit_transform(x_train[features])\nx_test_pr = pr.fit_transform(x_test[features])\npoly = LinearRegression()\n\nRidgeModel = Ridge(alpha=0.1)\nRidgeModel.fit(x_train_pr,y_train)\nprint(\"R-Square is :\", RidgeModel.score(x_test_pr,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": ""
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.6",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.6.9"
        },
        "widgets": {
            "state": {},
            "version": "1.1.2"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 4
}