07. Lab House_Sales_in_King_Count_USA.jupyterlite.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(['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())\n",
      "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": "number_of_houses=df[\"floors\"].value_counts(normalize=False, sort=True, ascending=False, dropna=True).to_frame()\nnumber_of_houses",
      "metadata": {
        "trusted": true
      },
      "execution_count": 17,
      "outputs": [
        {
          "execution_count": 17,
          "output_type": "execute_result",
          "data": {
            "text/plain": "     floors\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>floors</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1.0</th>\n      <td>10680</td>\n    </tr>\n    <tr>\n      <th>2.0</th>\n      <td>8241</td>\n    </tr>\n    <tr>\n      <th>1.5</th>\n      <td>1910</td>\n    </tr>\n    <tr>\n      <th>3.0</th>\n      <td>613</td>\n    </tr>\n    <tr>\n      <th>2.5</th>\n      <td>161</td>\n    </tr>\n    <tr>\n      <th>3.5</th>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "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\", legend=\"auto\", palette=\"bright\")",
      "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": "sns.regplot(data=df, x=\"sqft_above\", y=\"price\")",
      "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": "X1 = df[['sqft_living']]\nY1 = df[['price']]\nlm1 = LinearRegression().fit(X1, Y1)\nlm1.score(X1, Y1)",
      "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": "lm2 = LinearRegression().fit(df[features], df[['price']])\nlm2.score(df[features], df[['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)\npipe.fit(df[features], df[['price']])\npipe.score(df[features], df[['price']])",
      "metadata": {
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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": null,
      "outputs": []
    },
    {
      "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": null,
      "outputs": []
    },
    {
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "RR = Ridge(alpha=0.1).fit(x_train, y_train)\nRR.score(x_test, y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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\npoly = PolynomialFeatures(degree=2)\nx_train_poly = poly.fit_transform(x_train)\nx_test_poly = poly.transform(x_test)\nRR1 = Ridge(alpha=0.1).fit(x_train_poly, y_train)\nRR1.score(x_test_poly, y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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": {}
    }
  ]
}