HouseSales_DataAnalysis_Python.ipynb

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      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
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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": {},
      "execution_count": null,
      "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": 8,
      "outputs": [
        {
          "execution_count": 8,
          "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": 13,
      "outputs": [
        {
          "execution_count": 13,
          "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": 14,
      "outputs": [
        {
          "execution_count": 14,
          "output_type": "execute_result",
          "data": {
            "text/plain": "        Unnamed: 0            id         price      bedrooms     bathrooms  \\\ncount  21613.00000  2.161300e+04  2.161300e+04  21600.000000  21603.000000   \nmean   10806.00000  4.580302e+09  5.400881e+05      3.372870      2.115736   \nstd     6239.28002  2.876566e+09  3.671272e+05      0.926657      0.768996   \nmin        0.00000  1.000102e+06  7.500000e+04      1.000000      0.500000   \n25%     5403.00000  2.123049e+09  3.219500e+05      3.000000      1.750000   \n50%    10806.00000  3.904930e+09  4.500000e+05      3.000000      2.250000   \n75%    16209.00000  7.308900e+09  6.450000e+05      4.000000      2.500000   \nmax    21612.00000  9.900000e+09  7.700000e+06     33.000000      8.000000   \n\n        sqft_living      sqft_lot        floors    waterfront          view  \\\ncount  21613.000000  2.161300e+04  21613.000000  21613.000000  21613.000000   \nmean    2079.899736  1.510697e+04      1.494309      0.007542      0.234303   \nstd      918.440897  4.142051e+04      0.539989      0.086517      0.766318   \nmin      290.000000  5.200000e+02      1.000000      0.000000      0.000000   \n25%     1427.000000  5.040000e+03      1.000000      0.000000      0.000000   \n50%     1910.000000  7.618000e+03      1.500000      0.000000      0.000000   \n75%     2550.000000  1.068800e+04      2.000000      0.000000      0.000000   \nmax    13540.000000  1.651359e+06      3.500000      1.000000      4.000000   \n\n       ...         grade    sqft_above  sqft_basement      yr_built  \\\ncount  ...  21613.000000  21613.000000   21613.000000  21613.000000   \nmean   ...      7.656873   1788.390691     291.509045   1971.005136   \nstd    ...      1.175459    828.090978     442.575043     29.373411   \nmin    ...      1.000000    290.000000       0.000000   1900.000000   \n25%    ...      7.000000   1190.000000       0.000000   1951.000000   \n50%    ...      7.000000   1560.000000       0.000000   1975.000000   \n75%    ...      8.000000   2210.000000     560.000000   1997.000000   \nmax    ...     13.000000   9410.000000    4820.000000   2015.000000   \n\n       yr_renovated       zipcode           lat          long  sqft_living15  \\\ncount  21613.000000  21613.000000  21613.000000  21613.000000   21613.000000   \nmean      84.402258  98077.939805     47.560053   -122.213896    1986.552492   \nstd      401.679240     53.505026      0.138564      0.140828     685.391304   \nmin        0.000000  98001.000000     47.155900   -122.519000     399.000000   \n25%        0.000000  98033.000000     47.471000   -122.328000    1490.000000   \n50%        0.000000  98065.000000     47.571800   -122.230000    1840.000000   \n75%        0.000000  98118.000000     47.678000   -122.125000    2360.000000   \nmax     2015.000000  98199.000000     47.777600   -121.315000    6210.000000   \n\n          sqft_lot15  \ncount   21613.000000  \nmean    12768.455652  \nstd     27304.179631  \nmin       651.000000  \n25%      5100.000000  \n50%      7620.000000  \n75%     10083.000000  \nmax    871200.000000  \n\n[8 rows x 21 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>id</th>\n      <th>price</th>\n      <th>bedrooms</th>\n      <th>bathrooms</th>\n      <th>sqft_living</th>\n      <th>sqft_lot</th>\n      <th>floors</th>\n      <th>waterfront</th>\n      <th>view</th>\n      <th>...</th>\n      <th>grade</th>\n      <th>sqft_above</th>\n      <th>sqft_basement</th>\n      <th>yr_built</th>\n      <th>yr_renovated</th>\n      <th>zipcode</th>\n      <th>lat</th>\n      <th>long</th>\n      <th>sqft_living15</th>\n      <th>sqft_lot15</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>21613.00000</td>\n      <td>2.161300e+04</td>\n      <td>2.161300e+04</td>\n      <td>21600.000000</td>\n      <td>21603.000000</td>\n      <td>21613.000000</td>\n      <td>2.161300e+04</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>...</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>10806.00000</td>\n      <td>4.580302e+09</td>\n      <td>5.400881e+05</td>\n      <td>3.372870</td>\n      <td>2.115736</td>\n      <td>2079.899736</td>\n      <td>1.510697e+04</td>\n      <td>1.494309</td>\n      <td>0.007542</td>\n      <td>0.234303</td>\n      <td>...</td>\n      <td>7.656873</td>\n      <td>1788.390691</td>\n      <td>291.509045</td>\n      <td>1971.005136</td>\n      <td>84.402258</td>\n      <td>98077.939805</td>\n      <td>47.560053</td>\n      <td>-122.213896</td>\n      <td>1986.552492</td>\n      <td>12768.455652</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>6239.28002</td>\n      <td>2.876566e+09</td>\n      <td>3.671272e+05</td>\n      <td>0.926657</td>\n      <td>0.768996</td>\n      <td>918.440897</td>\n      <td>4.142051e+04</td>\n      <td>0.539989</td>\n      <td>0.086517</td>\n      <td>0.766318</td>\n      <td>...</td>\n      <td>1.175459</td>\n      <td>828.090978</td>\n      <td>442.575043</td>\n      <td>29.373411</td>\n      <td>401.679240</td>\n      <td>53.505026</td>\n      <td>0.138564</td>\n      <td>0.140828</td>\n      <td>685.391304</td>\n      <td>27304.179631</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.00000</td>\n      <td>1.000102e+06</td>\n      <td>7.500000e+04</td>\n      <td>1.000000</td>\n      <td>0.500000</td>\n      <td>290.000000</td>\n      <td>5.200000e+02</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>290.000000</td>\n      <td>0.000000</td>\n      <td>1900.000000</td>\n      <td>0.000000</td>\n      <td>98001.000000</td>\n      <td>47.155900</td>\n      <td>-122.519000</td>\n      <td>399.000000</td>\n      <td>651.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>5403.00000</td>\n      <td>2.123049e+09</td>\n      <td>3.219500e+05</td>\n      <td>3.000000</td>\n      <td>1.750000</td>\n      <td>1427.000000</td>\n      <td>5.040000e+03</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>7.000000</td>\n      <td>1190.000000</td>\n      <td>0.000000</td>\n      <td>1951.000000</td>\n      <td>0.000000</td>\n      <td>98033.000000</td>\n      <td>47.471000</td>\n      <td>-122.328000</td>\n      <td>1490.000000</td>\n      <td>5100.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>10806.00000</td>\n      <td>3.904930e+09</td>\n      <td>4.500000e+05</td>\n      <td>3.000000</td>\n      <td>2.250000</td>\n      <td>1910.000000</td>\n      <td>7.618000e+03</td>\n      <td>1.500000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>7.000000</td>\n      <td>1560.000000</td>\n      <td>0.000000</td>\n      <td>1975.000000</td>\n      <td>0.000000</td>\n      <td>98065.000000</td>\n      <td>47.571800</td>\n      <td>-122.230000</td>\n      <td>1840.000000</td>\n      <td>7620.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>16209.00000</td>\n      <td>7.308900e+09</td>\n      <td>6.450000e+05</td>\n      <td>4.000000</td>\n      <td>2.500000</td>\n      <td>2550.000000</td>\n      <td>1.068800e+04</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>8.000000</td>\n      <td>2210.000000</td>\n      <td>560.000000</td>\n      <td>1997.000000</td>\n      <td>0.000000</td>\n      <td>98118.000000</td>\n      <td>47.678000</td>\n      <td>-122.125000</td>\n      <td>2360.000000</td>\n      <td>10083.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>21612.00000</td>\n      <td>9.900000e+09</td>\n      <td>7.700000e+06</td>\n      <td>33.000000</td>\n      <td>8.000000</td>\n      <td>13540.000000</td>\n      <td>1.651359e+06</td>\n      <td>3.500000</td>\n      <td>1.000000</td>\n      <td>4.000000</td>\n      <td>...</td>\n      <td>13.000000</td>\n      <td>9410.000000</td>\n      <td>4820.000000</td>\n      <td>2015.000000</td>\n      <td>2015.000000</td>\n      <td>98199.000000</td>\n      <td>47.777600</td>\n      <td>-121.315000</td>\n      <td>6210.000000</td>\n      <td>871200.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 21 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "# Module 2: Data Wrangling\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Question 2\n\nDrop the columns <code>\"id\"</code>  and <code>\"Unnamed: 0\"</code> from axis 1 using the method <code>drop()</code>, then use the method <code>describe()</code> to obtain a statistical summary of the data. Take a screenshot and submit it, make sure the <code>inplace</code> parameter is set to <code>True</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.drop(['id','Unnamed: 0'], axis=1, inplace=True)\ndf.describe()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 15,
      "outputs": [
        {
          "execution_count": 15,
          "output_type": "execute_result",
          "data": {
            "text/plain": "              price      bedrooms     bathrooms   sqft_living      sqft_lot  \\\ncount  2.161300e+04  21600.000000  21603.000000  21613.000000  2.161300e+04   \nmean   5.400881e+05      3.372870      2.115736   2079.899736  1.510697e+04   \nstd    3.671272e+05      0.926657      0.768996    918.440897  4.142051e+04   \nmin    7.500000e+04      1.000000      0.500000    290.000000  5.200000e+02   \n25%    3.219500e+05      3.000000      1.750000   1427.000000  5.040000e+03   \n50%    4.500000e+05      3.000000      2.250000   1910.000000  7.618000e+03   \n75%    6.450000e+05      4.000000      2.500000   2550.000000  1.068800e+04   \nmax    7.700000e+06     33.000000      8.000000  13540.000000  1.651359e+06   \n\n             floors    waterfront          view     condition         grade  \\\ncount  21613.000000  21613.000000  21613.000000  21613.000000  21613.000000   \nmean       1.494309      0.007542      0.234303      3.409430      7.656873   \nstd        0.539989      0.086517      0.766318      0.650743      1.175459   \nmin        1.000000      0.000000      0.000000      1.000000      1.000000   \n25%        1.000000      0.000000      0.000000      3.000000      7.000000   \n50%        1.500000      0.000000      0.000000      3.000000      7.000000   \n75%        2.000000      0.000000      0.000000      4.000000      8.000000   \nmax        3.500000      1.000000      4.000000      5.000000     13.000000   \n\n         sqft_above  sqft_basement      yr_built  yr_renovated       zipcode  \\\ncount  21613.000000   21613.000000  21613.000000  21613.000000  21613.000000   \nmean    1788.390691     291.509045   1971.005136     84.402258  98077.939805   \nstd      828.090978     442.575043     29.373411    401.679240     53.505026   \nmin      290.000000       0.000000   1900.000000      0.000000  98001.000000   \n25%     1190.000000       0.000000   1951.000000      0.000000  98033.000000   \n50%     1560.000000       0.000000   1975.000000      0.000000  98065.000000   \n75%     2210.000000     560.000000   1997.000000      0.000000  98118.000000   \nmax     9410.000000    4820.000000   2015.000000   2015.000000  98199.000000   \n\n                lat          long  sqft_living15     sqft_lot15  \ncount  21613.000000  21613.000000   21613.000000   21613.000000  \nmean      47.560053   -122.213896    1986.552492   12768.455652  \nstd        0.138564      0.140828     685.391304   27304.179631  \nmin       47.155900   -122.519000     399.000000     651.000000  \n25%       47.471000   -122.328000    1490.000000    5100.000000  \n50%       47.571800   -122.230000    1840.000000    7620.000000  \n75%       47.678000   -122.125000    2360.000000   10083.000000  \nmax       47.777600   -121.315000    6210.000000  871200.000000  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>price</th>\n      <th>bedrooms</th>\n      <th>bathrooms</th>\n      <th>sqft_living</th>\n      <th>sqft_lot</th>\n      <th>floors</th>\n      <th>waterfront</th>\n      <th>view</th>\n      <th>condition</th>\n      <th>grade</th>\n      <th>sqft_above</th>\n      <th>sqft_basement</th>\n      <th>yr_built</th>\n      <th>yr_renovated</th>\n      <th>zipcode</th>\n      <th>lat</th>\n      <th>long</th>\n      <th>sqft_living15</th>\n      <th>sqft_lot15</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>2.161300e+04</td>\n      <td>21600.000000</td>\n      <td>21603.000000</td>\n      <td>21613.000000</td>\n      <td>2.161300e+04</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n      <td>21613.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>5.400881e+05</td>\n      <td>3.372870</td>\n      <td>2.115736</td>\n      <td>2079.899736</td>\n      <td>1.510697e+04</td>\n      <td>1.494309</td>\n      <td>0.007542</td>\n      <td>0.234303</td>\n      <td>3.409430</td>\n      <td>7.656873</td>\n      <td>1788.390691</td>\n      <td>291.509045</td>\n      <td>1971.005136</td>\n      <td>84.402258</td>\n      <td>98077.939805</td>\n      <td>47.560053</td>\n      <td>-122.213896</td>\n      <td>1986.552492</td>\n      <td>12768.455652</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>3.671272e+05</td>\n      <td>0.926657</td>\n      <td>0.768996</td>\n      <td>918.440897</td>\n      <td>4.142051e+04</td>\n      <td>0.539989</td>\n      <td>0.086517</td>\n      <td>0.766318</td>\n      <td>0.650743</td>\n      <td>1.175459</td>\n      <td>828.090978</td>\n      <td>442.575043</td>\n      <td>29.373411</td>\n      <td>401.679240</td>\n      <td>53.505026</td>\n      <td>0.138564</td>\n      <td>0.140828</td>\n      <td>685.391304</td>\n      <td>27304.179631</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>7.500000e+04</td>\n      <td>1.000000</td>\n      <td>0.500000</td>\n      <td>290.000000</td>\n      <td>5.200000e+02</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>290.000000</td>\n      <td>0.000000</td>\n      <td>1900.000000</td>\n      <td>0.000000</td>\n      <td>98001.000000</td>\n      <td>47.155900</td>\n      <td>-122.519000</td>\n      <td>399.000000</td>\n      <td>651.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>3.219500e+05</td>\n      <td>3.000000</td>\n      <td>1.750000</td>\n      <td>1427.000000</td>\n      <td>5.040000e+03</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>7.000000</td>\n      <td>1190.000000</td>\n      <td>0.000000</td>\n      <td>1951.000000</td>\n      <td>0.000000</td>\n      <td>98033.000000</td>\n      <td>47.471000</td>\n      <td>-122.328000</td>\n      <td>1490.000000</td>\n      <td>5100.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>4.500000e+05</td>\n      <td>3.000000</td>\n      <td>2.250000</td>\n      <td>1910.000000</td>\n      <td>7.618000e+03</td>\n      <td>1.500000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>7.000000</td>\n      <td>1560.000000</td>\n      <td>0.000000</td>\n      <td>1975.000000</td>\n      <td>0.000000</td>\n      <td>98065.000000</td>\n      <td>47.571800</td>\n      <td>-122.230000</td>\n      <td>1840.000000</td>\n      <td>7620.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>6.450000e+05</td>\n      <td>4.000000</td>\n      <td>2.500000</td>\n      <td>2550.000000</td>\n      <td>1.068800e+04</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>4.000000</td>\n      <td>8.000000</td>\n      <td>2210.000000</td>\n      <td>560.000000</td>\n      <td>1997.000000</td>\n      <td>0.000000</td>\n      <td>98118.000000</td>\n      <td>47.678000</td>\n      <td>-122.125000</td>\n      <td>2360.000000</td>\n      <td>10083.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>7.700000e+06</td>\n      <td>33.000000</td>\n      <td>8.000000</td>\n      <td>13540.000000</td>\n      <td>1.651359e+06</td>\n      <td>3.500000</td>\n      <td>1.000000</td>\n      <td>4.000000</td>\n      <td>5.000000</td>\n      <td>13.000000</td>\n      <td>9410.000000</td>\n      <td>4820.000000</td>\n      <td>2015.000000</td>\n      <td>2015.000000</td>\n      <td>98199.000000</td>\n      <td>47.777600</td>\n      <td>-121.315000</td>\n      <td>6210.000000</td>\n      <td>871200.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can see we have missing values for the columns <code> bedrooms</code>  and <code> bathrooms </code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print(\"number of NaN values for the column bedrooms :\", df['bedrooms'].isnull().sum())\nprint(\"number of NaN values for the column bathrooms :\", df['bathrooms'].isnull().sum())\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 16,
      "outputs": [
        {
          "name": "stdout",
          "text": "number of NaN values for the column bedrooms : 13\nnumber of NaN values for the column bathrooms : 10\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can replace the missing values of the column <code>'bedrooms'</code> with the mean of the column  <code>'bedrooms' </code> using the method <code>replace()</code>. Don't forget to set the <code>inplace</code> parameter to <code>True</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "mean=df['bedrooms'].mean()\ndf['bedrooms'].replace(np.nan,mean, inplace=True)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "We also replace the missing values of the column <code>'bathrooms'</code> with the mean of the column  <code>'bathrooms' </code> using the method <code>replace()</code>. Don't forget to set the <code> inplace </code>  parameter top <code> True </code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "mean=df['bathrooms'].mean()\ndf['bathrooms'].replace(np.nan,mean, inplace=True)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "print(\"number of NaN values for the column bedrooms :\", df['bedrooms'].isnull().sum())\nprint(\"number of NaN values for the column bathrooms :\", df['bathrooms'].isnull().sum())",
      "metadata": {
        "trusted": true
      },
      "execution_count": 19,
      "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": "unique_counts = df['floors'].value_counts().to_frame().reset_index()\nunique_counts.columns = ['Floors', 'Hose Count']\n\nprint(unique_counts)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 20,
      "outputs": [
        {
          "name": "stdout",
          "text": "   Floors  Hose Count\n0     1.0       10680\n1     2.0        8241\n2     1.5        1910\n3     3.0         613\n4     2.5         161\n5     3.5           8\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "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)\n\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 21,
      "outputs": [
        {
          "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(x='sqft_above', y='price', data=df)\n\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 22,
      "outputs": [
        {
          "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": 23,
      "outputs": [
        {
          "execution_count": 23,
          "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": 24,
      "outputs": [
        {
          "execution_count": 24,
          "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 = LinearRegression()\nlm.fit(X,Y)\nlm.score(X, Y)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 25,
      "outputs": [
        {
          "execution_count": 25,
          "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": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Then calculate the R^2. Take a screenshot of your code.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "X = df[features]\nY = df['price']\nlm = LinearRegression()\nlm.fit(X,Y)\nlm.score(X, Y)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 29,
      "outputs": [
        {
          "execution_count": 29,
          "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": 10,
      "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": "pipeline = Pipeline(Input)\n\nX = df[features]\nY = df['price']\n\npipeline.fit(X,Y)\n\npipeline.score(X,Y)\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 31,
      "outputs": [
        {
          "execution_count": 31,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.7512786321941719"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "# Module 5: Model Evaluation and Refinement\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Import the necessary modules:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import train_test_split\nprint(\"done\")",
      "metadata": {
        "trusted": true
      },
      "execution_count": 11,
      "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": 22,
      "outputs": [
        {
          "name": "stdout",
          "text": "number of test samples: 3242\nnumber of training samples: 18371\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "nan_count = df[features].isna().sum()\nprint(nan_count)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 15,
      "outputs": [
        {
          "name": "stdout",
          "text": "floors            0\nwaterfront        0\nlat               0\nbedrooms         13\nsqft_basement     0\nview              0\nbathrooms        10\nsqft_living15     0\nsqft_above        0\ngrade             0\nsqft_living       0\ndtype: int64\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "df['bedrooms'].fillna(df['bedrooms'].mean(), inplace=True)\ndf['bathrooms'].fillna(df['bathrooms'].mean(), inplace=True)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "nan_count = df[features].isna().sum()\nprint(nan_count)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 20,
      "outputs": [
        {
          "name": "stdout",
          "text": "floors           0\nwaterfront       0\nlat              0\nbedrooms         0\nsqft_basement    0\nview             0\nbathrooms        0\nsqft_living15    0\nsqft_above       0\ngrade            0\nsqft_living      0\ndtype: int64\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "nan_count2 = df['price'].isna().sum()\nprint(nan_count2)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 16,
      "outputs": [
        {
          "name": "stdout",
          "text": "0\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": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "ridge = Ridge(alpha=0.1)\nridge.fit(x_train, y_train)\nridge.score(x_train, y_train)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 23,
      "outputs": [
        {
          "execution_count": 23,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.6594378534950235"
          },
          "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)\nx_test_pr=pr.fit_transform(x_test)\n\nridge2 = Ridge(alpha=0.1)\nridge2.fit(x_train_pr, y_train)\ntest_score = ridge2.score(x_test_pr, y_test)\ntest_score    \n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 25,
      "outputs": [
        {
          "execution_count": 25,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.7002744263350642"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Once you complete your notebook, you can download the notebook. To download the notebook, navigate to <b>File</b> and click <b>Download</b>.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2>About the Authors:</h2> \n\n<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2022-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Other contributors: <a href=\"https://www.linkedin.com/in/michelleccarey/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2022-01-01\">Michelle Carey</a>, <a href=\"https://www.linkedin.com/in/jiahui-mavis-zhou-a4537814a?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2022-01-01\">Mavis Zhou</a>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Change Log\n\n| Date (YYYY-MM-DD) | Version | Changed By      | Change Description                           |\n| ----------------- | ------- | --------------- | -------------------------------------------- |\n| 2020-12-01        | 2.2     | Aije Egwaikhide | Coverted Data describtion from text to table |\n| 2020-10-06        | 2.1     | Lakshmi Holla   | Changed markdown instruction of Question1    |\n| 2020-08-27        | 2.0     | Malika Singla   | Added lab to GitLab                          |\n\n<hr>\n\n## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n\n<p>\n",
      "metadata": {}
    }
  ]
}