House_Sales_in_King_Count_USA.jupyterlite.ipynb

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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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    "kernelspec": {
      "name": "python",
      "display_name": "Pyolite",
      "language": "python"
    },
    "toc-showcode": false
  },
  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "source": "<p style=\"text-align:center\">\n    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork971-2022-01-01\" target=\"_blank\">\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n    </a>\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# House Sales in King County, USA\n",
      "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.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "| Variable      | Description                                                                                                 |\n| ------------- | ----------------------------------------------------------------------------------------------------------- |\n| id            | A notation for a house                                                                                      |\n| date          | Date house was sold                                                                                         |\n| price         | Price is prediction target                                                                                  |\n| bedrooms      | Number of bedrooms                                                                                          |\n| bathrooms     | Number of bathrooms                                                                                         |\n| sqft_living   | Square footage of the home                                                                                  |\n| sqft_lot      | Square footage of the lot                                                                                   |\n| floors        | Total floors (levels) in house                                                                              |\n| waterfront    | House which has a view to a waterfront                                                                      |\n| view          | Has been viewed                                                                                             |\n| condition     | How good the condition is overall                                                                           |\n| grade         | overall grade given to the housing unit, based on King County grading system                                |\n| sqft_above    | Square footage of house apart from basement                                                                 |\n| sqft_basement | Square footage of the basement                                                                              |\n| yr_built      | Built Year                                                                                                  |\n| yr_renovated  | Year when house was renovated                                                                               |\n| zipcode       | Zip code                                                                                                    |\n| lat           | Latitude coordinate                                                                                         |\n| long          | Longitude coordinate                                                                                        |\n| sqft_living15 | Living room area in 2015(implies-- some renovations) This might or might not have affected the lotsize area |\n| sqft_lot15    | LotSize area in 2015(implies-- some renovations)                                                            |\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "If you run the lab locally using Anaconda, you can load the correct library and versions by uncommenting the following:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# All Libraries required for this lab are listed below. The libraries pre-installed on Skills Network Labs are commented.\n# !mamba install -qy pandas==1.3.4 numpy==1.21.4 seaborn==0.9.0 matplotlib==3.5.0 scikit-learn==0.20.1\n# Note: If your environment doesn't support \"!mamba install\", use \"!pip install\"",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": " # Surpress warnings:\ndef warn(*args, **kwargs):\n    pass\nimport warnings\nwarnings.warn = warn",
      "metadata": {
        "trusted": true
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "You will require the following libraries:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install(['pandas','matplotlib','scikit-learn','seaborn', 'numpy'])\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "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": {
        "trusted": true
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "# Module 1: Importing Data Sets\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "The functions below will download the dataset into your browser:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from pyodide.http import pyfetch\n\nasync def download(url, filename):\n    response = await pyfetch(url)\n    if response.status == 200:\n        with open(filename, \"wb\") as f:\n            f.write(await response.bytes())",
      "metadata": {
        "trusted": true
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "file_name='https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/kc_house_data_NaN.csv'",
      "metadata": {
        "trusted": true
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "You will need to download the dataset; if you are running locally, please comment out the following code: \n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "await download(file_name, \"kc_house_data_NaN.csv\")\nfile_name=\"kc_house_data_NaN.csv\"",
      "metadata": {
        "trusted": true
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Use the Pandas method <b>read_csv()</b> to load the data from the web address.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df = pd.read_csv(file_name)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "We use the method <code>head</code> to display the first 5 columns of the dataframe.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 9,
      "outputs": [
        {
          "execution_count": 9,
          "output_type": "execute_result",
          "data": {
            "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]",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Question 1\n\nDisplay the data types of each column using the function dtypes, then take a screenshot and submit it, include your code in the image.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.dtypes",
      "metadata": {
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "execution_count": 10,
          "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.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.describe()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 11,
      "outputs": [
        {
          "execution_count": 11,
          "output_type": "execute_result",
          "data": {
            "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]",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "# Module 2: Data Wrangling\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Question 2\n\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>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.drop(['id', 'Unnamed: 0'], axis=1, inplace=True)\ndf.describe()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 15,
      "outputs": [
        {
          "execution_count": 15,
          "output_type": "execute_result",
          "data": {
            "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  ",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can see we have missing values for the columns <code> bedrooms</code>  and <code> bathrooms </code>\n",
      "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": {
        "trusted": true
      },
      "execution_count": 16,
      "outputs": [
        {
          "name": "stdout",
          "text": "number of NaN values for the column bedrooms : 13\nnumber of NaN values for the column bathrooms : 10\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can replace the missing values of the column <code>'bedrooms'</code> with the mean of the column  <code>'bedrooms' </code> using the method <code>replace()</code>. Don't forget to set the <code>inplace</code> parameter to <code>True</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "mean=df['bedrooms'].mean()\ndf['bedrooms'].replace(np.nan,mean, inplace=True)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "We also replace the missing values of the column <code>'bathrooms'</code> with the mean of the column  <code>'bathrooms' </code> using the method <code>replace()</code>. Don't forget to set the <code> inplace </code>  parameter top <code> True </code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "mean=df['bathrooms'].mean()\ndf['bathrooms'].replace(np.nan,mean, inplace=True)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 18,
      "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": {
        "trusted": true
      },
      "execution_count": 20,
      "outputs": [
        {
          "name": "stdout",
          "text": "number of NaN values for the column bedrooms : 0\nnumber of NaN values for the column bathrooms : 0\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "# Module 3: Exploratory Data Analysis\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Question 3\n\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",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df[['floors']].value_counts().to_frame()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 29,
      "outputs": [
        {
          "execution_count": 29,
          "output_type": "execute_result",
          "data": {
            "text/plain": "            0\nfloors       \n1.0     10680\n2.0      8241\n1.5      1910\n3.0       613\n2.5       161\n3.5         8",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n    </tr>\n    <tr>\n      <th>floors</th>\n      <th></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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Question 4\n\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.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "sns.boxplot(data=df, x=\"waterfront\", y=\"price\")\n#houses without a waterfront have more price outliers",
      "metadata": {
        "trusted": true
      },
      "execution_count": 32,
      "outputs": [
        {
          "execution_count": 32,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='waterfront', ylabel='price'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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UVHXq1CmyZGZmmo4EAIgB5lFBQxgvKjt37lRWVpbOPfdc5efns6sPAJII86jgVIwWleHDh2vp0qV6/fXXtWTJEn355ZcaPXq0Dh48WOf2wWBQfr8/agEAxDdukYH6GC0qEyZM0LXXXqv+/ftr/Pjx+u///m8dOHBAL730Up3bFxUVKSMjI7Lk5OTEODEAoLlxiwzUx3Etu/5r6NChuvTSS1VUVHTCWDAYVDAYjDz2+/3KyclRTU2N0tPTYxkTAAA0kt/vV0ZGRoM+v42fo3K82tpa7dq1S507d65z3Ov1Kj09PWoBAACJy2hRuf3227V27Vrt3r1bpaWlmjRpkjwejyZPnmwyFgAAsITRSUsqKys1efJkVVdXq3379rr44ou1YcMGtW/f3mQsAABgCaNF5YUXXjD57QEAgOWsOkcFAADgeBQVAABgLYoKAACwFkUFAABYi6ICADCutLRU119/vUpLS01HgWUoKgAAowKBgIqLi7V3714VFxcrEAiYjgSLUFQAAEYtX75c1dXVkqTq6mqVlJQYTgSbUFQAAMZUVlaqpKREx24757quSkpKVFlZaTgZbEFRAQAY4bquFi1adNL1lt0zF4ZQVAAARpSXl6usrEyhUChqfSgUUllZmcrLyw0lg00oKgAAI3JzczV06FB5PJ6o9R6PR8OGDVNubq6hZLAJRQVxg8sXgcTiOI5mz5590vWO4xhIBdtQVBAXuHwRSEzZ2dm67rrrotZdd9116tKli6FEsA1FBXGByxcBIDlRVGA9Ll8EEldlZaVeeumlqHUvvfQS729EUFRgNS5fBBIX7280BEUFVuPyRSBx8f5GQ1BUYDUuXwQSF+9vNARFBVbj8kUgcfH+RkNQVGC97Oxs5eXlRf5oOY6jvLw8Ll8EEgDvb5wKRQVxIT8/X+3atZMkZWZmKi8vz3AiAM2F9zfqQ1FBXPD5fCooKFDHjh01d+5c+Xw+05EANBPe36iP48bx9V9+v18ZGRmqqalRenq66TgAAKABTufzmz0qAADAWhQVxA1uSggAyYeigrjATQkBIDlRVBAXuCkhACQnigqsx00JASB5UVRgNW5aBgDJrdFF5bnnntOoUaOUlZWlr776SpK0cOFC/ed//mezhQO4aRkAJLdGFZUlS5aooKBAV1xxhQ4cOBD5EGnTpo0WLlzYnPmQ5LhpGQAkt0YVld///vd68skndccdd0R9gAwZMkQff/xxs4UDuGkZACS3RhWVL7/8UgMHDjxhvdfr1aFDh5ocCjgeNy0DgOTVqKLSvXt3bd269YT1r7/+unr37t3UTMAJ8vPz1bp1a0lS69atuWkZACSJRhWVgoICzZgxQy+++KJc19UHH3yg+fPnq7CwUP/+7//eqCAPPvigHMfRnDlzGvV8JL7jL08GACSH1MY86ZZbblHLli1155136vvvv1deXp6ysrK0aNEi3XDDDaf9emVlZXr88cfVv3//xsRBEli+fLlqa2slSbW1tSopKdH06dMNpwIAnGmNvjw5Pz9fO3fuVG1traqqqlRZWal//ud/Pu3Xqa2tVX5+vp588kmdc845jY2DBMaEbwCQvBp9Mu3OnTslSWeffbY6dOggSdq5c6d27959Wq81Y8YMXXnllbr00ktPuW0wGJTf749akNiY8A0Aklujisq0adPqvIPtxo0bNW3atAa/zgsvvKAtW7aoqKioQdsXFRUpIyMjsuTk5DT4eyE+MeEbACS3RhWVDz/8UKNGjTph/UUXXVTn1UB1qaio0OzZs7V8+XL5fL4GPaewsFA1NTWRpaKi4nRiIw4x4RsAJLdGFRXHcXTw4MET1tfU1JzwL9+T2bx5s/bt26dBgwYpNTVVqampWrt2rRYvXqzU1NQ6X8fr9So9PT1qQWJjwjcASG6NKiqXXHKJioqKospEKBRSUVGRLr744ga9xj/+4z/q448/1tatWyPLkCFDlJ+fr61bt57wL2gkLyZ8A4Dk1ajLkx966CFdcskl6tWrl0aPHi1J+utf/yq/36933nmnQa/RunVr9e3bN2pdq1at1K5duxPWA/n5+frTn/6kH374QT6fjwnfACBJNKqo9OnTR9u2bdOjjz6qjz76SC1bttRNN92kmTNnqm3bts2dEVAgEFAgEIj6uqHnNgGJzHXdyHsjXrmuq2AwKOnHQ/yJcEjX5/MlxM9hA8eN4+s7/X6/MjIyVFNTw/kqCW7WrFlRN7zs37+/Fi9ebDARYIcffvhBEyZMMB0DP7Fq1Sq1bNnSdAxrnc7nd4P3qGzbtk19+/ZVSkqKtm3bVu+2zDCL5rRp06YT7sq9bds2bdq0SUOGDDGUCgAQCw3eo5KSkqKqqip16NBBKSkpchynzsm2HMdp8JU/TcUelcQXDoc1ceLEOif3S09P18qVK5WS0ugJloG4lwiHfgKBgCZNmiRJWrFiRUIc1uXQT/3OyB6VL7/8Uu3bt498DcTCxo0bTzoDsd/v18aNGzVixIgYpwLs4ThOQh1i8Pl8CfXzoOkaXFS6du0qSTpy5Ijuu+8+3XXXXerevfsZCwZI0vDhw5Wenl5nWcnIyNDw4cMNpAIAxMpp7zM/66yz9Morr5yJLMAJUlJS9Otf/7rOsV//+tcc9gGABNeov/ITJ07UypUrmzkKcCLXdbV69eo6x95++21uSggACa5R86j06NFD999/v9avX6/BgwerVatWUeO33XZbs4QDjt2UsC7Hbkp47LAkACDxNKqoPPXUU2rTpo02b96szZs3R405jkNRQbPJyclRWlqaamtrTxhLS0vjDtoAkOAaVVSOv+rn2K53LsPCmVBeXl5nSZGk2tpalZeXq1u3brENBQCImUafifjUU0+pb9++8vl88vl86tu3r/7whz80ZzYAAJDkGrVH5e6771ZxcbFmzZoVmcPi/fff19y5c1VeXq7777+/WUMieXXt2lX9+vU7YWZa6ccZkDk/BQASW6Pu9dO+fXstXrxYkydPjlr//PPPa9asWdq/f3+zBawPM9Mmh8rKSt14441RV/ikpKToueeeU5cuXQwmA9Acjr9fEffISQ6n8/ndqEM/R44cqfMeK4MHD9bRo0cb85LASWVnZ+uGG26IWnfDDTdQUgAgCTSqqNx4441asmTJCeufeOIJ5efnNzkU8FNTp06NtO709HTddNNNhhMBAGKhUeeoSD+eTPvmm2/qoosukvTjPVnKy8t10003qaCgILJdcXFx01Mi6fl8Pg0aNEhr1qzRoEGDEuKmZQCAU2tUUfnkk080aNAgSdKuXbskSZmZmcrMzNQnn3wS2Y5LltFcDhw4oHXr1kmS1q1bpwMHDqhNmzZmQwEAzrhGFZV33323uXMA9brrrrsUDoclSeFwWHfffbcWL15sOBUA4Ezjjm6w3qZNm064PHnbtm3atGmToUQAgFihqMBq4XD4pPPy3H///ZG9LACAxERRgdU2btwov99f55jf79fGjRtjnAgAEEsUFVht+PDhSktLq3MsLS1Nw4cPj3EiAEAsUVRgNcdxlJWVVedYly5duLIMABIcRQVWKy8v144dO+oc2759u8rLy2OcCAAQSxQVWC07O1sej6fOMY/Ho+zs7BgnAgDEEkUFVvvggw8UCoXqHAuFQvrggw9inAgAEEsUFVht+PDhJ72zZkZGBifTAkCCo6jAaikpKRo9enSdYxdffLFSUvhfGAASGX/lYbWjR4/qL3/5S51jf/nLX3T06NEYJwIAxBJFBVZ79tlnmzQOAIhvFBVY7cYbb2zSOAAgvlFUYLWvv/66SeMAgPhGUYHVcnNz1bNnzzrHevXqpdzc3BgnAgDEEkUFVnNdV5WVlXWOVVRUyHXdGCcCAMSS0aKyZMkS9e/fX+np6UpPT9eIESO0atUqk5FgmQ0bNuj777+vc+z777/Xhg0bYpwIABBLRotKdna2HnzwQW3evFmbNm3SP/zDP+jqq6/W3/72N5OxYJHOnTs3aRwAEN+MFpWrrrpKV1xxhXr06KGePXtq/vz5SktL41/JiOjWrZsyMzPrHMvMzFS3bt1iGwgAEFOppgMcEwqF9PLLL+vQoUMaMWJEndsEg0EFg8HIY7/fH6t4MCQUCmn//v11ju3fv1+hUEipqdb8bwwAaGbGT6b9+OOPlZaWJq/Xq3/913/VihUr1KdPnzq3LSoqUkZGRmTJycmJcVrEGhO+AUByM15UevXqpa1bt2rjxo269dZbNXXqVP3P//xPndsWFhaqpqYmslRUVMQ4LWJt1KhRTRoHAMQ34/vMW7RoofPPP1+SNHjwYJWVlWnRokV6/PHHT9jW6/XK6/XGOiIMWr9+/SnHe/XqFaM0AIBYM75H5afC4XDUeShIbnl5eU0aBwDEN6NFpbCwUOvWrdPu3bv18ccfq7CwUGvWrFF+fr7JWLDIc88916RxAEB8M3roZ9++fbrpppu0Z88eZWRkqH///nrjjTd02WWXmYwFAAAsYbSoPPXUUya/PeLAtGnTtHz58nrHAQCJy7pzVIDjcfdkAEhuFBVYLScnR+np6XWOpaenM5cOACQ4igqsVlFRcdIZiP1+P3PpAECCo6jAatnZ2UpJqft/05SUFGVnZ8c4EQAgligqsNqGDRsUDofrHAuHw9zAEgASHEUFVjtZSWnoOAAgvlFUYDXHcZo0DgCIbxQVWC0rK6tJ4wCA+EZRAQAA1qKowGpVVVVNGgcAxDeKCqx20UUXyev11jnm9Xp10UUXxTgRACCWKCqwmuu6CgaDdY4Fg0G5rhvjRACAWKKowGorV65s0jgAIL5RVGC1/fv3N2kcABDfKCqwWps2bZo0DgCIbxQVWO2rr75q0jgAIL5RVGC13NzcJo0DAOIbRQVW27RpU5PGAQDxjaICq911111NGgcAxDeKCqx2xx13NGkcABDfKCqw2ujRo5s0DgCIbxQVWG3gwIFNGgcAxDeKCqy2du3aJo0DAOIbRQVWS09Pb9I4ACC+UVRgtY0bNzZpHAAQ3ygqsNr48eObNA4AiG+ppgMA9XnmmWdOOU5ZQWO4rqtAIGA6BqSo3wO/E3v4fD45jmM6BkUFdhs5cqT+9Kc/1TsONEYgENCECRNMx8BPTJo0yXQE/J9Vq1apZcuWpmNw6Ad2+/zzz5s0DgCIb+xRgdU6dOjQpHGgIR69+Ft5Pa7pGEnLdaXD4R+/bpEiWXC0IWkFQ45mvtfWdIwoFBVYrW3b+t8wpxoHGsLrceX1mE6R3HymA+D/2FfYOfQDq53qRC4bTvQCAJw5FBVY7YILLmjSOAAgvhktKkVFRRo6dKhat26tDh06aOLEidq+fbvJSLBMSkr9/4ueahwAEN+M/pVfu3atZsyYoQ0bNuitt97SkSNHdPnll+vQoUMmY8Eirlv/8dJTjQMA4pvRk2lff/31qMdLly5Vhw4dtHnzZl1yySWGUsEm+/fvb9I4ACC+WXXVT01NjaSTX8kRDAYVDAYjj/1+f0xywZzevXs3aRwAEN+sOcAfDoc1Z84cjRo1Sn379q1zm6KiImVkZESWnJycGKdErC1atKhJ4wCA+GZNUZkxY4Y++eQTvfDCCyfdprCwUDU1NZGloqIihglhgtfrbdI4ACC+WXHoZ+bMmXrttde0bt06ZWdnn3Q7r9fLB1OS2bNnT5PGAQDxzegeFdd1NXPmTK1YsULvvPOOunfvbjIOLJSRkdGkcQBAfDNaVGbMmKFly5appKRErVu3VlVVlaqqqvTDDz+YjAWLdOrUqUnjAID4ZrSoLFmyRDU1NRo7dqw6d+4cWV588UWTsWCRDRs2NGkcABDfjJ6jwmRdOJXMzMx6z0PJzMyMYRoAQKxZc9UPUJdTXYLOJeoAkNgoKrDaF1980aRxAEB8o6jAaqc6sZoTrwEgsVFUYLVWrVo1aRwAEN8oKrBaIBBo0jgAIL5RVGA1Dv0AQHKjqMBqR44cadI4ACC+UVQAAIC1KCqwWs+ePZs0DgCIbxQVWG3Xrl1NGgcAxDeKCqwWCoWaNA4AiG8UFQAAYC2KCgAAsBZFBQAAWIuiAgAArEVRAQAA1qKoAAAAa1FUAACAtVJNBwAAE1zXjXwdZDoeQFL0e+H494hJFBVYLTU1VUePHq13HGiMYDAY+Xrme+0MJgHsFAwGdfbZZ5uOwaEf2K2+ktKQcQBAfOOfowCSktfrjXz96MXV8noMhgEsEQz9/x7G498jJlFUACQlx3EiX3s9oqgAP3H8e8QkDv0AAABrUVQAAIC1KCoAAMBaFBUAAGAtigoAALAWRQUAAFiLogIAAKzFPCpJwHVdBQIB0zHOmB9++MF0hEbx+XzWzFMAALaiqCSBQCCgCRMmmI5xxsTrz7Zq1Sq1bNnSdAwAsBqHfgAAgLWM7lFZt26dFixYoM2bN2vPnj1asWKFJk6caDJSQvL5fFq1apXpGI1SXV2tKVOmnHR82bJlatcuPu986/P5TEcAAOsZLSqHDh3SgAEDNH36dF1zzTUmoyQ0x3Hi9hBDdna2fD5fnefY+Hw+ZWdnG0gFAIgVo0VlwoQJcXt+AWLn9ddf19ixY+tcDwBIbHF1jkowGJTf749akBx+8YtfRD3+5S9/aSgJACCW4qqoFBUVKSMjI7Lk5OSYjoQYufXWW6Mez5o1y1ASAEAsxVVRKSwsVE1NTWSpqKgwHQkGxOuJwQCA0xdX86h4vV55vV7TMQAAQIzE1R4VAACQXIzuUamtrdXnn38eefzll19q69atatu2rXJzcw0mAwAANjBaVDZt2qRx48ZFHhcUFEiSpk6dqqVLlxpKBQAAbGG0qIwdO1au65qMAAAALMY5KgAAwFoUFQAAYK24ujwZAM6EYMiRxGFoU1xXOhz+8esWKZLjmM2TzH58L9iFogIg6c18r63pCABOgkM/AADAWuxRAZCUfD4ft2OwRCAQ0KRJkyRJK1askM/nM5wIkqz5PVBUACQlx3HUsmVL0zHwEz6fj98LolBU6uG6rgKBgOkYkKJ+D/xO7OHz+eRw5iOAM4iiUo9AIKAJEyaYjoGfOLaLGOatWrWKf/0COKM4mRYAAFiLPSoNVPuzyXJT+M9ljOtK4aM/fp2SykQLBjnho0rb+rzpGACSBJ+8DeSmpEqes0zHSHItTAeAmBYNQGxx6AcAAFiLogIAAKxFUQEAANbiHJV6uO5xR+NDR8wFAWxy3Hsh6j0CAGcARaUewWAw8nXrj14wmASwUzAY1Nlnn206BoAExqEfAABgLfao1MPr9Ua+PjjgBi5PBiQpdCSyh/H49wgAnAkUlXpE3cPEcxZFBfgJ7vMD4Ezj0A8AALAWe1QayAkfZUZOk5hC3xrOsd8DAMQARaWBuLcJAACxR1EBgDjmuq4CgYDpGE1yfP54/1mO8fl8nMPVTBw3jmds8vv9ysjIUE1NjdLT05v99RPhD0CiCAQCmjRpkiRpxYoV8vl8hhNB4o+xDX744QdNmDDBdAz8xKpVq9SyZUvTMax1Op/f7FGph+M4/I9mIZ/Px+8FAJIERQUA4pjP59OqVatMx2gS13UjM4F7vd6E2EvHXt/mQ1EBgDiWKHt+uRUDToZ5VAAAgLUoKgAAwFoUFQAAYC2KCgAAsBYn0yaBRJgPhgmhACA5GS8qjz32mBYsWKCqqioNGDBAv//97zVs2DDTsRJKIBBIqAmhjk38Fu+YEAoATs3ooZ8XX3xRBQUFuueee7RlyxYNGDBA48eP1759+0zGAgAAljA6hf7w4cM1dOhQPfroo5KkcDisnJwczZo1S/PmzTvl88/0FPqJIhEO/STqhFCJ8HMAwOmKiyn0Dx8+rM2bN6uwsDCyLiUlRZdeeqnef//9Op8TDAYjH1bSjz8oTo0JoQAA8crYoZ/9+/crFAqpY8eOUes7duyoqqqqOp9TVFSkjIyMyJKTkxOLqAAAwJC4ujy5sLBQNTU1kaWiosJ0JAAAcAYZO/STmZkpj8ejvXv3Rq3fu3evOnXqVOdzvF6vvF5vLOIBAAALGNuj0qJFCw0ePFirV6+OrAuHw1q9erVGjBhhKhYAALCI0XlUCgoKNHXqVA0ZMkTDhg3TwoULdejQId18880mYwEAAEsYLSrXX3+9vvnmG919992qqqrSz372M73++usnnGALAACSk9F5VJqKeVQAAIg/p/P5HVdX/QAAgORCUQEAANaiqAAAAGtRVAAAgLUoKgAAwFoUFQAAYC2j86g01bErq7mLMgAA8ePY53ZDZkiJ66Jy8OBBSeIuygAAxKGDBw8qIyOj3m3iesK3cDisr7/+Wq1bt5bjOKbj4Azz+/3KyclRRUUFE/wBCYb3d3JxXVcHDx5UVlaWUlLqPwslrveopKSkKDs723QMxFh6ejp/yIAExfs7eZxqT8oxnEwLAACsRVEBAADWoqggbni9Xt1zzz3yer2mowBoZry/cTJxfTItAABIbOxRAQAA1qKoAAAAa1FUAACAtSgqiBuPPfaYunXrJp/Pp+HDh+uDDz4wHQlAE61bt05XXXWVsrKy5DiOVq5caToSLENRQVx48cUXVVBQoHvuuUdbtmzRgAEDNH78eO3bt890NABNcOjQIQ0YMECPPfaY6SiwFFf9IC4MHz5cQ4cO1aOPPirpx9sn5OTkaNasWZo3b57hdACag+M4WrFihSZOnGg6CizCHhVY7/Dhw9q8ebMuvfTSyLqUlBRdeumlev/99w0mAwCcaRQVWG///v0KhULq2LFj1PqOHTuqqqrKUCoAQCxQVAAAgLUoKrBeZmamPB6P9u7dG7V+79696tSpk6FUAIBYoKjAei1atNDgwYO1evXqyLpwOKzVq1drxIgRBpMBAM60VNMBgIYoKCjQ1KlTNWTIEA0bNkwLFy7UoUOHdPPNN5uOBqAJamtr9fnnn0cef/nll9q6davatm2r3Nxcg8lgCy5PRtx49NFHtWDBAlVVVelnP/uZFi9erOHDh5uOBaAJ1qxZo3Hjxp2wfurUqVq6dGnsA8E6FBUAAGAtzlEBAADWoqgAAABrUVQAAIC1KCoAAMBaFBUAAGAtigoAALAWRQUAAFiLogIAAKxFUQEQN6qqqnTZZZepVatWatOmjek4AGKAogLgjBk7dqzmzJnTbK/3u9/9Tnv27NHWrVu1Y8eOZnvduqxZs0aO4+jAgQNn9PsAqB83JQRgvcOHD6tFixbatWuXBg8erB49epx02yNHjuiss86KYToAZxJ7VABEvPbaa2rTpo1CoZAkaevWrXIcR/PmzYtsc8stt2jKlCmqrq7W5MmT1aVLF5199tnq16+fnn/++ch206ZN09q1a7Vo0SI5jiPHcbR7925J0ieffKIJEyYoLS1NHTt21I033qj9+/dHnjt27FjNnDlTc+bMUWZmpsaPH69u3brplVde0bPPPivHcTRt2jRJkuM4WrJkiX7xi1+oVatWmj9/viRpyZIlOu+889SiRQv16tVLzz33XNTP6jiO/vCHP2jSpEk6++yz1aNHD/35z3+WJO3evTtyo7xzzjkn6vsBiDEXAP7PgQMH3JSUFLesrMx1XddduHChm5mZ6Q4fPjyyzfnnn+8++eSTbmVlpbtgwQL3ww8/dHft2uUuXrzY9Xg87saNGyOvNWLECPdXv/qVu2fPHnfPnj3u0aNH3e+++85t3769W1hY6H766afuli1b3Msuu8wdN25c5HuMGTPGTUtLc3/zm9+4n332mfvZZ5+5+/btc3/+85+71113nbtnzx73wIEDruu6riS3Q4cO7h//+Ed3165d7ldffeW++uqr7llnneU+9thj7vbt291HHnnE9Xg87jvvvBP5HpLc7Oxst6SkxN25c6d72223uWlpaW51dbV79OhR95VXXnEludu3b4/6fgBii6ICIMqgQYPcBQsWuK7ruhMnTnTnz5/vtmjRwj148KBbWVnpSnJ37NhR53OvvPJK99/+7d8ij8eMGePOnj07apsHHnjAvfzyy6PWVVRURErBsecNHDjwhNe/+uqr3alTp0atk+TOmTMnat3IkSPdX/3qV1Hrrr32WveKK66Iet6dd94ZeVxbW+tKcletWuW6ruu+++67riT3u+++q/NnBRAbHPoBEGXMmDFas2aNXNfVX//6V11zzTXq3bu33nvvPa1du1ZZWVnq0aOHQqGQHnjgAfXr109t27ZVWlqa3njjDZWXl9f7+h999JHeffddpaWlRZYLLrhAkrRr167IdoMHD25w5iFDhkQ9/vTTTzVq1KiodaNGjdKnn34ata5///6Rr1u1aqX09HTt27evwd8XwJnHybQAoowdO1Z//OMf9dFHH+mss87SBRdcoLFjx2rNmjX67rvvNGbMGEnSggULtGjRIi1cuFD9+vVTq1atNGfOHB0+fLje16+trdVVV12lhx566ISxzp07R75u1apVgzOfzrbH++lJt47jKBwON+q1AJwZ7FEBEGX06NE6ePCgfve730VKybGismbNGo0dO1aStH79el199dWaMmWKBgwYoHPPPfeES4ZbtGgROTH3mEGDBulvf/ubunXrpvPPPz9qaWzh+KnevXtr/fr1UevWr1+vPn36NPg1WrRoIUkn5AcQWxQVAFHOOecc9e/fX8uXL4+UkksuuURbtmzRjh07IuWlR48eeuutt1RaWqpPP/1U//Iv/6K9e/dGvVa3bt20ceNG7d69W/v371c4HNaMGTP07bffavLkySorK9OuXbv0xhtv6Oabb262UvCb3/xGS5cu1ZIlS7Rz504VFxfr1Vdf1e23397g1+jatascx9Frr72mb775RrW1tc2SDcDpoagAOMGYMWMUCoUiRaVt27bq06ePOnXqpF69ekmS7rzzTg0aNEjjx4/X2LFj1alTJ02cODHqdW6//XZ5PB716dNH7du3V3l5ubKysrR+/XqFQiFdfvnl6tevn+bMmaM2bdooJaV5/iRNnDhRixYt0sMPP6wLL7xQjz/+uJ5++unIz9MQXbp00X333ad58+apY8eOmjlzZrNkA3B6HNd1XdMhAAAA6sIeFQAAYC2KCgAAsBZFBQAAWIuiAgAArEVRAQAA1qKoAAAAa1FUAACAtSgqAADAWhQVAABgLYoKAACwFkUFAABYi6ICAACs9b/dInZl94D9GgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Question 5\n\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.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "sns.regplot(x = \"price\", \n            y = \"sqft_above\", \n            data = df)\n# price is positively correlated with price",
      "metadata": {
        "trusted": true
      },
      "execution_count": 33,
      "outputs": [
        {
          "execution_count": 33,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='price', ylabel='sqft_above'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can use the Pandas method <code>corr()</code>  to find the feature other than price that is most correlated with price.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.corr()['price'].sort_values()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 34,
      "outputs": [
        {
          "execution_count": 34,
          "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": "# Module 4: Model Development\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can Fit a linear regression model using the  longitude feature <code>'long'</code> and  caculate the R^2.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "X = df[['long']]\nY = df['price']\nlm = LinearRegression()\nlm.fit(X,Y)\nlm.score(X, Y)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 35,
      "outputs": [
        {
          "execution_count": 35,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.00046769430149007363"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Question  6\n\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.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "X1 = df[['sqft_living']]\nY1 = df[['price']]\nlm1 = LinearRegression()\nlm1.fit(X1,Y1)\nlm1.score(X1, Y1)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 71,
      "outputs": [
        {
          "execution_count": 71,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.4928532179037931"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Question 7\n\nFit a linear regression model to predict the <code>'price'</code> using the list of features:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "features =[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"]     ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Then calculate the R^2. Take a screenshot of your code.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Z2 = df[features]\nY2 = df[['price']]\nlm2 = LinearRegression()\nlm2.fit(Z2,Y2)\nlm2.score(Z2, Y2)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 49,
      "outputs": [
        {
          "execution_count": 49,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.6576890354915759"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Input=[('scale',StandardScaler()),('polynomial', PolynomialFeatures(include_bias=False)),('model',LinearRegression())]",
      "metadata": {
        "trusted": true
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "### Question 8\n\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.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pipe = Pipeline(Input)\npipe\n\nZ3 = df[features]\nY3 = df[['price']]\n\npipe.fit(Z3, Y3)\npipe.score(Z3, Y3)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 72,
      "outputs": [
        {
          "execution_count": 72,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.7512398529081656"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "# Module 5: Model Evaluation and Refinement\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Import the necessary modules:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import train_test_split\nprint(\"done\")",
      "metadata": {
        "trusted": true
      },
      "execution_count": 57,
      "outputs": [
        {
          "name": "stdout",
          "text": "done\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We will split the data into training and testing sets:\n",
      "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": {
        "trusted": true
      },
      "execution_count": 58,
      "outputs": [
        {
          "name": "stdout",
          "text": "number of test samples: 3242\nnumber of training samples: 18371\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Question 9\n\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",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.linear_model import Ridge",
      "metadata": {
        "trusted": true
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "RigeModel=Ridge(alpha=0.1)\nRigeModel.fit(x_train, y_train)\nRigeModel.score(x_test, y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 69,
      "outputs": [
        {
          "execution_count": 69,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.647875916393906"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Question 10\n\nPerform a second order polynomial transform on both the training data and testing data. \n\nCreate 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. \n\nTake a screenshot of your code and the R^2.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pr=PolynomialFeatures(degree=2)\n\nx_train_pr=pr.fit_transform(x_train)\nx_test_pr=pr.fit_transform(x_test)\n\nRigeModel=Ridge(alpha=0.1)\nRigeModel.fit(x_train_pr, y_train)\nRigeModel.score(x_test_pr, y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 70,
      "outputs": [
        {
          "execution_count": 70,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.7002744270151646"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/FinalModule_Coursera/images/share_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://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/FinalModule_Coursera/images/url_notebook.png\" alt=\"HTML\" style=\"display: block; margin-left: auto; margin-right: auto;\"></p>\n        <p>&nbsp;</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2>About the Authors:</h2> \n\n<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2022-01-01\">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.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Other contributors: <a href=\"https://www.linkedin.com/in/michelleccarey/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2022-01-01\">Michelle Carey</a>, <a href=\"https://www.linkedin.com/in/jiahui-mavis-zhou-a4537814a?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2022-01-01\">Mavis Zhou</a>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Change Log\n\n| Date (YYYY-MM-DD) | Version | Changed By      | Change Description                           |\n| ----------------- | ------- | --------------- | -------------------------------------------- |\n| 2020-12-01        | 2.2     | Aije Egwaikhide | Coverted Data describtion from text to table |\n| 2020-10-06        | 2.1     | Lakshmi Holla   | Changed markdown instruction of Question1    |\n| 2020-08-27        | 2.0     | Malika Singla   | Added lab to GitLab                          |\n\n<hr>\n\n## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n\n<p>\n",
      "metadata": {}
    }
  ]
}