ML Model Building.ipynb

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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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  "cells": [
    {
      "cell_type": "markdown",
      "source": "<p style=\"text-align:center\">\n    <a href=\"https://skills.network\" 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": 8,
      "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(df[['id', 'Unnamed: 0']], axis =1, inplace = True)\ndf.describe()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 12,
      "outputs": [
        {
          "execution_count": 12,
          "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())",
      "metadata": {
        "trusted": true
      },
      "execution_count": 13,
      "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": 14,
      "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": 15,
      "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": 16,
      "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": "no_of_houses = df['floors'].value_counts().to_frame()\nno_of_houses.rename(columns={'floors':'no_of_houses'}, inplace = True)\nno_of_houses.index.name = 'floors_pattern'\nno_of_houses",
      "metadata": {
        "trusted": true
      },
      "execution_count": 17,
      "outputs": [
        {
          "execution_count": 17,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                no_of_houses\nfloors_pattern              \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>no_of_houses</th>\n    </tr>\n    <tr>\n      <th>floors_pattern</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": "df.head()\n\n#let's create boxplot to visualize the difference between waterfront view or without a waterfront view\n\nsns.boxplot(x = df['waterfront'], y = df['price'], data = df)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 18,
      "outputs": [
        {
          "execution_count": 18,
          "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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"
          },
          "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": "#here we create scatter plot with regression fitted line\n\nsns.regplot(x = df['sqft_above'], y = df['price'], data=df)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 19,
      "outputs": [
        {
          "execution_count": 19,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='sqft_above', ylabel='price'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "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": 20,
      "outputs": [
        {
          "execution_count": 20,
          "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": 21,
      "outputs": [
        {
          "execution_count": 21,
          "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": "x_sqft_living = df[['sqft_living']]\ny_price  = df['price']\n\n#fitting a linear regression model\nlm.fit(x_sqft_living, y_price)\nlm.score(x_sqft_living, y_price)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 22,
      "outputs": [
        {
          "execution_count": 22,
          "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": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Then calculate the R^2. Take a screenshot of your code.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "x_features = df[features]\ny_price = df['price']\n\nlm.fit(x_features, y_price)\nlm.score(x_features, y_price)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 24,
      "outputs": [
        {
          "execution_count": 24,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.6576950629068081"
          },
          "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": 25,
      "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) \n\ndf[features].astype(float)\n\npipe.fit(df[features], df['price'])\npipe.score(df[features], df['price'])",
      "metadata": {
        "trusted": true
      },
      "execution_count": 26,
      "outputs": [
        {
          "execution_count": 26,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.7512786321941719"
          },
          "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": 27,
      "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": 28,
      "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": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "#first we need to create ridge object with parameter of value of 0.1\nridgeModel = Ridge(alpha = 0.1)\n\n#let's fit the model with training data\nridgeModel.fit(x_train, y_train)\n\n# Calculating the R-squared based on the testing set\nridgeModel.score(x_test, y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 30,
      "outputs": [
        {
          "execution_count": 30,
          "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. 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.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import Ridge\n#first need to create polynomial object with polynomial regression order which is 2 \npoly = PolynomialFeatures(degree=2) \n\n# now let's transform the data into polynomial regression using degree = 2 for both training dataset and testing dataset\n\nx_train_poly = poly.fit_transform(x_train)\nx_test_poly = poly.fit_transform(x_test)\n\n#create ridge regression object using regularization parameter of 0.1\nridge_model = Ridge(alpha = 0.1) \n\n#perform the ridge regression method on training set\nridge_model.fit(x_train_poly, y_train)\n\n#Find R-squared based on testing set\nridge_model.score(x_test_poly, y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 31,
      "outputs": [
        {
          "execution_count": 31,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.7002744263350642"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Once you complete your notebook, you can download the notebook. To download the notebook, navigate to <b>File</b> and click <b>Download</b>.\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": {}
    }
  ]
}