8) Final-Project - House_Sales_in_King_Count_USA.ipynb

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      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    },
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      "name": "python",
      "display_name": "Pyolite",
      "language": "python"
    }
  },
  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "source": "<p style=\"text-align:center\">\n    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork971-2022-01-01\" target=\"_blank\">\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n    </a>\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# House Sales in King County, USA\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "| Variable      | Description                                                                                                 |\n| ------------- | ----------------------------------------------------------------------------------------------------------- |\n| id            | A notation for a house                                                                                      |\n| date          | Date house was sold                                                                                         |\n| price         | Price is prediction target                                                                                  |\n| bedrooms      | Number of bedrooms                                                                                          |\n| bathrooms     | Number of bathrooms                                                                                         |\n| sqft_living   | Square footage of the home                                                                                  |\n| sqft_lot      | Square footage of the lot                                                                                   |\n| floors        | Total floors (levels) in house                                                                              |\n| waterfront    | House which has a view to a waterfront                                                                      |\n| view          | Has been viewed                                                                                             |\n| condition     | How good the condition is overall                                                                           |\n| grade         | overall grade given to the housing unit, based on King County grading system                                |\n| sqft_above    | Square footage of house apart from basement                                                                 |\n| sqft_basement | Square footage of the basement                                                                              |\n| yr_built      | Built Year                                                                                                  |\n| yr_renovated  | Year when house was renovated                                                                               |\n| zipcode       | Zip code                                                                                                    |\n| lat           | Latitude coordinate                                                                                         |\n| long          | Longitude coordinate                                                                                        |\n| sqft_living15 | Living room area in 2015(implies-- some renovations) This might or might not have affected the lotsize area |\n| sqft_lot15    | LotSize area in 2015(implies-- some renovations)                                                            |\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "If you run the lab locally using Anaconda, you can load the correct library and versions by uncommenting the following:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# All Libraries required for this lab are listed below. The libraries pre-installed on Skills Network Labs are commented.\n# !mamba install -qy pandas==1.3.4 numpy==1.21.4 seaborn==0.9.0 matplotlib==3.5.0 scikit-learn==0.20.1\n# Note: If your environment doesn't support \"!mamba install\", use \"!pip install\"",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": " # Surpress warnings:\ndef warn(*args, **kwargs):\n    pass\nimport warnings\nwarnings.warn = warn",
      "metadata": {
        "trusted": true
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "You will require the following libraries:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install(['pandas','matplotlib','scikit-learn','seaborn', 'numpy'])\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler,PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression\n%matplotlib inline",
      "metadata": {
        "trusted": true
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "# Module 1: Importing Data Sets\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "The functions below will download the dataset into your browser:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from pyodide.http import pyfetch\n\nasync def download(url, filename):\n    response = await pyfetch(url)\n    if response.status == 200:\n        with open(filename, \"wb\") as f:\n            f.write(await response.bytes())",
      "metadata": {
        "trusted": true
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "file_name='https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/kc_house_data_NaN.csv'",
      "metadata": {
        "trusted": true
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "You will need to download the dataset; if you are running locally, please comment out the following code: \n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "await download(file_name, \"kc_house_data_NaN.csv\")\nfile_name=\"kc_house_data_NaN.csv\"",
      "metadata": {
        "trusted": true
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Use the Pandas method <b>read_csv()</b> to load the data from the web address.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df = pd.read_csv(file_name)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 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": "print(df.dtypes)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "name": "stdout",
          "text": "Unnamed: 0         int64\nid                 int64\ndate              object\nprice            float64\nbedrooms         float64\nbathrooms        float64\nsqft_living        int64\nsqft_lot           int64\nfloors           float64\nwaterfront         int64\nview               int64\ncondition          int64\ngrade              int64\nsqft_above         int64\nsqft_basement      int64\nyr_built           int64\nyr_renovated       int64\nzipcode            int64\nlat              float64\nlong             float64\nsqft_living15      int64\nsqft_lot15         int64\ndtype: object\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "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=pd.read_csv(file_name)\n\ndf.drop([\"id\", \"Unnamed: 0\"], axis=1, inplace = True)\n\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": "df['floors'].value_counts()\n\ndf['floors'].value_counts().to_frame()",
      "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(x=\"waterfront\", y=\"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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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Question 5\n\nUse the function <code>regplot</code>  in the seaborn library  to  determine if the feature <code>sqft_above</code> is negatively or positively correlated with price.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "sns.regplot(x=\"sqft_above\", y=\"price\", data=df)\nplt.ylim(0,)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 19,
      "outputs": [
        {
          "execution_count": 19,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(0.0, 8081250.0)"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can use the Pandas method <code>corr()</code>  to find the feature other than price that is most correlated with price.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.corr()['price'].sort_values()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 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 = df[['sqft_living']]\nY = df['price']\nlm.fit(X,Y)\nlm.score(X, Y)",
      "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": "lm.fit(df[features], Y)\nlm.score(df[features], Y)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 24,
      "outputs": [
        {
          "execution_count": 24,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.6576890354915759"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### This will help with Question 8\n\nCreate a list of tuples, the first element in the tuple contains the name of the estimator:\n\n<code>'scale'</code>\n\n<code>'polynomial'</code>\n\n<code>'model'</code>\n\nThe second element in the tuple  contains the model constructor\n\n<code>StandardScaler()</code>\n\n<code>PolynomialFeatures(include_bias=False)</code>\n\n<code>LinearRegression()</code>\n",
      "metadata": {
        "tags": []
      }
    },
    {
      "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],Y)\nPipe.score(df[features],Y)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 26,
      "outputs": [
        {
          "execution_count": 26,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.7512398529081656"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "# Module 5: Model Evaluation and Refinement\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Import the necessary modules:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import train_test_split\nprint(\"done\")",
      "metadata": {
        "trusted": true
      },
      "execution_count": 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": "RM = Ridge(alpha = 0.1)\nRM.fit(x_train,y_train)\nRM.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": "pr = PolynomialFeatures(degree = 2)\nx_train_pr = pr.fit_transform(x_train[features])\nx_test_pr = pr.fit_transform(x_test[features])\n\nRM = Ridge(alpha = 0.1)\nRM.fit(x_train_pr, y_train)\nRM.score(x_test_pr, y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 31,
      "outputs": [
        {
          "execution_count": 31,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.7002744270151646"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>Once you complete your notebook you will have to share it. Select the icon on the top right a marked in red in the image below, a dialogue box should open, and select the option all&nbsp;content excluding sensitive code cells.</p>\n        <p><img width=\"600\" src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/FinalModule_Coursera/images/share_notebook.png\" alt=\"share notebook\" style=\"display: block; margin-left: auto; margin-right: auto;\"></p>\n        <p></p>\n        <p>You can then share the notebook&nbsp; via a&nbsp; URL by scrolling down as shown in the following image:</p>\n        <p style=\"text-align: center;\"><img width=\"600\" src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/FinalModule_Coursera/images/url_notebook.png\" alt=\"HTML\" style=\"display: block; margin-left: auto; margin-right: auto;\"></p>\n        <p>&nbsp;</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2>About the Authors:</h2> \n\n<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2022-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Other contributors: <a href=\"https://www.linkedin.com/in/michelleccarey/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2022-01-01\">Michelle Carey</a>, <a href=\"https://www.linkedin.com/in/jiahui-mavis-zhou-a4537814a?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2022-01-01\">Mavis Zhou</a>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Change Log\n\n| Date (YYYY-MM-DD) | Version | Changed By      | Change Description                           |\n| ----------------- | ------- | --------------- | -------------------------------------------- |\n| 2020-12-01        | 2.2     | Aije Egwaikhide | Coverted Data describtion from text to table |\n| 2020-10-06        | 2.1     | Lakshmi Holla   | Changed markdown instruction of Question1    |\n| 2020-08-27        | 2.0     | Malika Singla   | Added lab to GitLab                          |\n\n<hr>\n\n## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n\n<p>\n",
      "metadata": {}
    }
  ]
}