Sculpture_cost_prediction.ipynb

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      "name": "Sculpture cost prediction.ipynb",
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      "metadata": {
        "id": "-N0RQuFtVhHy"
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      "source": [
        "# Importing necessary libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "artist = pd.read_csv(\"Artist Sculpture Cost.csv\")\n"
      ],
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        "id": "L_RHWXvDZ6tV"
      },
      "execution_count": 2,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "artist.head(5)"
      ],
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        "outputId": "30178582-6154-4f99-c9b8-3f3daab025a1"
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      "execution_count": 3,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "            Customer Id     Artist Name  Artist Reputation  Height  Width  \\\n",
              "0  fffe3900350033003300   Billy Jenkins               0.26    17.0    6.0   \n",
              "1  fffe3800330031003900     Jean Bryant               0.28     3.0    3.0   \n",
              "2  fffe3600370035003100    Laura Miller               0.07     8.0    5.0   \n",
              "3      fffe350031003300  Robert Chaires               0.12     9.0    NaN   \n",
              "4  fffe3900320038003400    Rosalyn Krol               0.15    17.0    6.0   \n",
              "\n",
              "   Weight   Material  Price Of Sculpture  Base Shipping Price International  \\\n",
              "0  4128.0      Brass               13.91                16.27           Yes   \n",
              "1    61.0      Brass                6.83                15.00            No   \n",
              "2   237.0       Clay                4.96                21.18            No   \n",
              "3     NaN  Aluminium                5.81                16.31            No   \n",
              "4   324.0  Aluminium                3.18                11.94           Yes   \n",
              "\n",
              "  Express Shipment Installation Included Transport Fragile  \\\n",
              "0              Yes                    No   Airways      No   \n",
              "1               No                    No  Roadways      No   \n",
              "2               No                    No  Roadways     Yes   \n",
              "3               No                    No       NaN      No   \n",
              "4              Yes                   Yes   Airways      No   \n",
              "\n",
              "  Customer Information Remote Location Scheduled Date Delivery Date  \\\n",
              "0        Working Class              No     06-07-2015    06-03-2015   \n",
              "1        Working Class              No     03-06-2017    03-05-2017   \n",
              "2        Working Class             Yes     03-09-2015    03-08-2015   \n",
              "3              Wealthy             Yes       05/24/15      05/20/15   \n",
              "4        Working Class              No       12/18/16      12/14/16   \n",
              "\n",
              "           Customer Location    Cost  \n",
              "0     New Michelle, OH 50777 -283.29  \n",
              "1  New Michaelport, WY 12072 -159.96  \n",
              "2      Bowmanshire, WA 19241 -154.29  \n",
              "3       East Robyn, KY 86375 -161.16  \n",
              "4        Aprilside, PA 52793 -159.23  "
            ],
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              "      <th>Customer Id</th>\n",
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              "      <th>Express Shipment</th>\n",
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              "      <th>Cost</th>\n",
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              "      <th>0</th>\n",
              "      <td>fffe3900350033003300</td>\n",
              "      <td>Billy Jenkins</td>\n",
              "      <td>0.26</td>\n",
              "      <td>17.0</td>\n",
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              "      <td>Brass</td>\n",
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              "      <td>06-07-2015</td>\n",
              "      <td>06-03-2015</td>\n",
              "      <td>New Michelle, OH 50777</td>\n",
              "      <td>-283.29</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>fffe3800330031003900</td>\n",
              "      <td>Jean Bryant</td>\n",
              "      <td>0.28</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>Brass</td>\n",
              "      <td>6.83</td>\n",
              "      <td>15.00</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Roadways</td>\n",
              "      <td>No</td>\n",
              "      <td>Working Class</td>\n",
              "      <td>No</td>\n",
              "      <td>03-06-2017</td>\n",
              "      <td>03-05-2017</td>\n",
              "      <td>New Michaelport, WY 12072</td>\n",
              "      <td>-159.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>fffe3600370035003100</td>\n",
              "      <td>Laura Miller</td>\n",
              "      <td>0.07</td>\n",
              "      <td>8.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>237.0</td>\n",
              "      <td>Clay</td>\n",
              "      <td>4.96</td>\n",
              "      <td>21.18</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Roadways</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Working Class</td>\n",
              "      <td>Yes</td>\n",
              "      <td>03-09-2015</td>\n",
              "      <td>03-08-2015</td>\n",
              "      <td>Bowmanshire, WA 19241</td>\n",
              "      <td>-154.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>fffe350031003300</td>\n",
              "      <td>Robert Chaires</td>\n",
              "      <td>0.12</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Aluminium</td>\n",
              "      <td>5.81</td>\n",
              "      <td>16.31</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Wealthy</td>\n",
              "      <td>Yes</td>\n",
              "      <td>05/24/15</td>\n",
              "      <td>05/20/15</td>\n",
              "      <td>East Robyn, KY 86375</td>\n",
              "      <td>-161.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>fffe3900320038003400</td>\n",
              "      <td>Rosalyn Krol</td>\n",
              "      <td>0.15</td>\n",
              "      <td>17.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>324.0</td>\n",
              "      <td>Aluminium</td>\n",
              "      <td>3.18</td>\n",
              "      <td>11.94</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Airways</td>\n",
              "      <td>No</td>\n",
              "      <td>Working Class</td>\n",
              "      <td>No</td>\n",
              "      <td>12/18/16</td>\n",
              "      <td>12/14/16</td>\n",
              "      <td>Aprilside, PA 52793</td>\n",
              "      <td>-159.23</td>\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "# So we are predicting the cost(price) a customer might pay to get the painting."
      ],
      "metadata": {
        "id": "qExP3cN4ePZt"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "artist.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xMS-mpoGeSUU",
        "outputId": "76ca5bff-7004-4cdb-8408-88b6ca110a0a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Customer Id                 0\n",
              "Artist Name                 0\n",
              "Artist Reputation         750\n",
              "Height                    375\n",
              "Width                     584\n",
              "Weight                    587\n",
              "Material                  764\n",
              "Price Of Sculpture          0\n",
              "Base Shipping Price         0\n",
              "International               0\n",
              "Express Shipment            0\n",
              "Installation Included       0\n",
              "Transport                1392\n",
              "Fragile                     0\n",
              "Customer Information        0\n",
              "Remote Location           771\n",
              "Scheduled Date              0\n",
              "Delivery Date               0\n",
              "Customer Location           0\n",
              "Cost                        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "artist = artist.dropna(how = 'any')\n",
        "artist.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FpsODGVbeVGE",
        "outputId": "98160806-7d67-417f-c364-fcb15df25b95"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3362, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "artist_cost_positive = artist[artist['Cost'] > 0]\n",
        "artist_cost_positive.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sNjC8WIMeXO1",
        "outputId": "1c3b66ce-d070-47f7-e0dd-9aae0640ddb7"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3030, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# As you could see above, there were some costs that were negative, so I have excluded those costs from the dataset.\n",
        "\n",
        "artist_cost_positive.describe()"
      ],
      "metadata": {
        "colab": {
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      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Artist Reputation       Height        Width        Weight  \\\n",
              "count        3030.000000  3030.000000  3030.000000  3.030000e+03   \n",
              "mean            0.462419    21.938944     9.660396  4.221486e+05   \n",
              "std             0.266421    11.894853     5.460551  3.099541e+06   \n",
              "min             0.000000     3.000000     2.000000  4.000000e+00   \n",
              "25%             0.240000    13.000000     6.000000  5.080000e+02   \n",
              "50%             0.450000    21.000000     8.000000  3.007500e+03   \n",
              "75%             0.680000    30.000000    12.000000  3.579700e+04   \n",
              "max             1.000000    73.000000    45.000000  1.179279e+08   \n",
              "\n",
              "       Price Of Sculpture  Base Shipping Price          Cost  \n",
              "count         3030.000000          3030.000000  3.030000e+03  \n",
              "mean          1300.495635            36.939416  1.642879e+04  \n",
              "std          10080.916618            26.453719  1.539658e+05  \n",
              "min              3.000000            10.000000  8.607000e+01  \n",
              "25%              5.260000            16.765000  2.303725e+02  \n",
              "50%              7.765000            23.465000  4.583750e+02  \n",
              "75%             91.322500            56.630000  1.359648e+03  \n",
              "max         382385.670000            99.950000  4.020732e+06  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Artist Reputation</th>\n",
              "      <th>Height</th>\n",
              "      <th>Width</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Price Of Sculpture</th>\n",
              "      <th>Base Shipping Price</th>\n",
              "      <th>Cost</th>\n",
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              "      <th>count</th>\n",
              "      <td>3030.000000</td>\n",
              "      <td>3030.000000</td>\n",
              "      <td>3030.000000</td>\n",
              "      <td>3.030000e+03</td>\n",
              "      <td>3030.000000</td>\n",
              "      <td>3030.000000</td>\n",
              "      <td>3.030000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.462419</td>\n",
              "      <td>21.938944</td>\n",
              "      <td>9.660396</td>\n",
              "      <td>4.221486e+05</td>\n",
              "      <td>1300.495635</td>\n",
              "      <td>36.939416</td>\n",
              "      <td>1.642879e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.266421</td>\n",
              "      <td>11.894853</td>\n",
              "      <td>5.460551</td>\n",
              "      <td>3.099541e+06</td>\n",
              "      <td>10080.916618</td>\n",
              "      <td>26.453719</td>\n",
              "      <td>1.539658e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>8.607000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.240000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.080000e+02</td>\n",
              "      <td>5.260000</td>\n",
              "      <td>16.765000</td>\n",
              "      <td>2.303725e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.450000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3.007500e+03</td>\n",
              "      <td>7.765000</td>\n",
              "      <td>23.465000</td>\n",
              "      <td>4.583750e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.680000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>3.579700e+04</td>\n",
              "      <td>91.322500</td>\n",
              "      <td>56.630000</td>\n",
              "      <td>1.359648e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>1.179279e+08</td>\n",
              "      <td>382385.670000</td>\n",
              "      <td>99.950000</td>\n",
              "      <td>4.020732e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2eaef6a6-264c-4361-924d-c485ab453434');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# First converting dates into datetime format in the dataframe.\n",
        "\n",
        "artist_cost_positive['Scheduled Date'] = pd.to_datetime(artist_cost_positive['Scheduled Date'])\n",
        "artist_cost_positive['Delivery Date'] = pd.to_datetime(artist_cost_positive['Delivery Date'])\n",
        "\n",
        "artist_cost_positive.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "amboOgCnefX6",
        "outputId": "377f0b67-2a45-46cf-a273-2c7c73ec139b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Customer Id     Artist Name  Artist Reputation  Height  Width  \\\n",
              "650  fffe3800340030003400  George Hartman               0.94     5.0    5.0   \n",
              "651  fffe3600360039003000   Vincent Daves               0.64     5.0    5.0   \n",
              "\n",
              "     Weight Material  Price Of Sculpture  Base Shipping Price International  \\\n",
              "650    62.0     Wood                6.17                23.53            No   \n",
              "651   315.0    Brass                5.74                15.91            No   \n",
              "\n",
              "    Express Shipment Installation Included Transport Fragile  \\\n",
              "650              Yes                    No  Roadways      No   \n",
              "651               No                    No  Roadways      No   \n",
              "\n",
              "    Customer Information Remote Location Scheduled Date Delivery Date  \\\n",
              "650        Working Class              No     2018-02-13    2018-02-12   \n",
              "651        Working Class              No     2018-11-28    2018-11-26   \n",
              "\n",
              "            Customer Location    Cost  \n",
              "650  Lake Debraport, MI 42385  561.98  \n",
              "651       Monicaton, AK 90457  243.11  "
            ],
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              "\n",
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              "      <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Customer Id</th>\n",
              "      <th>Artist Name</th>\n",
              "      <th>Artist Reputation</th>\n",
              "      <th>Height</th>\n",
              "      <th>Width</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Material</th>\n",
              "      <th>Price Of Sculpture</th>\n",
              "      <th>Base Shipping Price</th>\n",
              "      <th>International</th>\n",
              "      <th>Express Shipment</th>\n",
              "      <th>Installation Included</th>\n",
              "      <th>Transport</th>\n",
              "      <th>Fragile</th>\n",
              "      <th>Customer Information</th>\n",
              "      <th>Remote Location</th>\n",
              "      <th>Scheduled Date</th>\n",
              "      <th>Delivery Date</th>\n",
              "      <th>Customer Location</th>\n",
              "      <th>Cost</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>650</th>\n",
              "      <td>fffe3800340030003400</td>\n",
              "      <td>George Hartman</td>\n",
              "      <td>0.94</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>Wood</td>\n",
              "      <td>6.17</td>\n",
              "      <td>23.53</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Roadways</td>\n",
              "      <td>No</td>\n",
              "      <td>Working Class</td>\n",
              "      <td>No</td>\n",
              "      <td>2018-02-13</td>\n",
              "      <td>2018-02-12</td>\n",
              "      <td>Lake Debraport, MI 42385</td>\n",
              "      <td>561.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>651</th>\n",
              "      <td>fffe3600360039003000</td>\n",
              "      <td>Vincent Daves</td>\n",
              "      <td>0.64</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>315.0</td>\n",
              "      <td>Brass</td>\n",
              "      <td>5.74</td>\n",
              "      <td>15.91</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Roadways</td>\n",
              "      <td>No</td>\n",
              "      <td>Working Class</td>\n",
              "      <td>No</td>\n",
              "      <td>2018-11-28</td>\n",
              "      <td>2018-11-26</td>\n",
              "      <td>Monicaton, AK 90457</td>\n",
              "      <td>243.11</td>\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e3335561-f7e5-4f99-8a32-06ceb3438a40');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "artist_cost_positive['Delivery Date'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "515wsMEVeiwF",
        "outputId": "67c8f61f-ae2a-446a-b1da-e69688b09010"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "artist_cost_positive['Scheduled Date'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UE31PahKelWc",
        "outputId": "bdd242c0-c806-4ef1-e35b-fdaeec458506"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding categorical data in the dataset:\n",
        "\n",
        "artist_cost_positive['Material'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gic1aRRaeo5t",
        "outputId": "92295dc9-8609-4577-cdf0-dfd12376fe5b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Aluminium    470\n",
              "Marble       451\n",
              "Brass        433\n",
              "Clay         432\n",
              "Wood         425\n",
              "Bronze       415\n",
              "Stone        404\n",
              "Name: Material, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "artist_cost_positive['International'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LHtk96qwetUW",
        "outputId": "d5bce8c7-9c15-4fde-e9d1-00291cef151e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "No     2007\n",
              "Yes    1023\n",
              "Name: International, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "artist_cost_positive['Express Shipment'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1GE19By-ewYl",
        "outputId": "b3d4ddcf-3b86-40ef-d442-47643437466a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "No     2041\n",
              "Yes     989\n",
              "Name: Express Shipment, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "artist_cost_positive['Installation Included'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lONuN2M3e0TD",
        "outputId": "743a83cd-7776-4b63-b049-5a74379dbf92"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "No     1799\n",
              "Yes    1231\n",
              "Name: Installation Included, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "artist_cost_positive['Transport'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JF5nm3mfe3S3",
        "outputId": "d3ae8b12-ecb6-4558-bbd2-a930b984f3c0"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Roadways     1239\n",
              "Airways      1071\n",
              "Waterways     720\n",
              "Name: Transport, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "artist_cost_positive['Fragile'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yu-F08BYe550",
        "outputId": "bc458be7-e215-489b-ab2b-330cffea1133"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "No     2547\n",
              "Yes     483\n",
              "Name: Fragile, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "artist_cost_positive['Customer Information'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6UNEwMYJe8P0",
        "outputId": "15033e35-2741-4f15-f1b1-06aa75fbe3c1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Working Class    2211\n",
              "Wealthy           819\n",
              "Name: Customer Information, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "artist_cost_positive['Remote Location'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVp6Oa6Fe_SW",
        "outputId": "6609b2a7-fec8-44f6-dae9-c8b24dc2c5e5"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "No     2442\n",
              "Yes     588\n",
              "Name: Remote Location, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# So there are more than 3000 artists in the dataset that we have narrowed down.\n",
        "\n",
        "artist_cost_positive['Artist Name'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Mkq0cyyfBbd",
        "outputId": "6fd63637-d1f7-41e5-fa8d-01f2d30841b8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "John Brown          3\n",
              "James Moore         3\n",
              "Charles Jones       2\n",
              "Mary Williams       2\n",
              "Catherine Martin    2\n",
              "                   ..\n",
              "Yvonne Hough        1\n",
              "Peggy Gregory       1\n",
              "Ron Pigott          1\n",
              "Keith Hileman       1\n",
              "Vernon Carroll      1\n",
              "Name: Artist Name, Length: 3019, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Converting the categorical datatypes into numerical ones\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "def label_encoded(feat):\n",
        "    le = LabelEncoder()\n",
        "    le.fit(feat)\n",
        "    print(feat.name,le.classes_)\n",
        "    return le.transform(feat)"
      ],
      "metadata": {
        "id": "SrinxkdKfD6I"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "name_list = ['Material', 'International', 'Express Shipment', 'Installation Included', 'Transport', 'Fragile',\n",
        "            'Customer Information', 'Remote Location']\n",
        "\n",
        "for name in name_list:\n",
        "  artist_cost_positive[name] = label_encoded(artist_cost_positive[name])\n",
        "\n",
        "artist_cost_positive.head(3) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "RMF4EgSJfG9V",
        "outputId": "5188b7cc-0915-440d-f616-82af1a90b437"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Material ['Aluminium' 'Brass' 'Bronze' 'Clay' 'Marble' 'Stone' 'Wood']\n",
            "International ['No' 'Yes']\n",
            "Express Shipment ['No' 'Yes']\n",
            "Installation Included ['No' 'Yes']\n",
            "Transport ['Airways' 'Roadways' 'Waterways']\n",
            "Fragile ['No' 'Yes']\n",
            "Customer Information ['Wealthy' 'Working Class']\n",
            "Remote Location ['No' 'Yes']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Customer Id       Artist Name  Artist Reputation  Height  Width  \\\n",
              "650  fffe3800340030003400    George Hartman               0.94     5.0    5.0   \n",
              "651  fffe3600360039003000     Vincent Daves               0.64     5.0    5.0   \n",
              "653      fffe330037003500  Marcia Chevrette               0.74    25.0   18.0   \n",
              "\n",
              "       Weight  Material  Price Of Sculpture  Base Shipping Price  \\\n",
              "650      62.0         6                6.17                23.53   \n",
              "651     315.0         1                5.74                15.91   \n",
              "653  917520.0         4              848.75                52.62   \n",
              "\n",
              "     International  Express Shipment  Installation Included  Transport  \\\n",
              "650              0                 1                      0          1   \n",
              "651              0                 0                      0          1   \n",
              "653              0                 1                      1          1   \n",
              "\n",
              "     Fragile  Customer Information  Remote Location Scheduled Date  \\\n",
              "650        0                     1                0     2018-02-13   \n",
              "651        0                     1                0     2018-11-28   \n",
              "653        0                     0                0     2017-12-18   \n",
              "\n",
              "    Delivery Date         Customer Location      Cost  \n",
              "650    2018-02-12  Lake Debraport, MI 42385    561.98  \n",
              "651    2018-11-26       Monicaton, AK 90457    243.11  \n",
              "653    2017-12-18   South Richard, HI 75014  87760.07  "
            ],
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "artist_cost_positive.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZAmS9dw-fXDL",
        "outputId": "7eadfa4e-2e67-41c1-86cb-fb839e54528f"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Customer Id', 'Artist Name', 'Artist Reputation', 'Height', 'Width',\n",
              "       'Weight', 'Material', 'Price Of Sculpture', 'Base Shipping Price',\n",
              "       'International', 'Express Shipment', 'Installation Included',\n",
              "       'Transport', 'Fragile', 'Customer Information', 'Remote Location',\n",
              "       'Scheduled Date', 'Delivery Date', 'Customer Location', 'Cost'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_theme(style=\"whitegrid\")\n",
        "sns.boxplot(artist_cost_positive['Height']) # change the column name to see the outliers for all the columns."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "YX4uP2Rqfg0u",
        "outputId": "df650cea-d866-47d1-f845-67775176d6c5"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a1fec1a90>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "artist_cost_positive = artist_cost_positive[artist_cost_positive['Height'] < 53]\n",
        "artist_cost_positive = artist_cost_positive[artist_cost_positive['Width'] < 22]\n",
        "artist_cost_positive = artist_cost_positive[artist_cost_positive['Price Of Sculpture'] < 4000]\n",
        "artist_cost_positive = artist_cost_positive[artist_cost_positive['Cost'] < 25000]"
      ],
      "metadata": {
        "id": "HjK_04iYfkSU"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(artist_cost_positive['Cost'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "VpXDIqeCfnLF",
        "outputId": "6525f177-e7bc-4039-c67b-3e5109eb5fd2"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a1fdf1ad0>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "artist_cost_positive.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mstiZccHfqnm",
        "outputId": "23fe200d-aeef-4939-93d3-a3fe1b17aa3e"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2765, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing columns that are not required:\n",
        "\n",
        "X = artist_cost_positive.drop(['Customer Id', 'Artist Name', 'Customer Location'], axis = 1)"
      ],
      "metadata": {
        "id": "dHPcYgyPftS_"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(13,13))\n",
        "sns.heatmap(X.corr(),annot=True,cmap='viridis',linewidths=.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 818
        },
        "id": "dC27NHOtfvSp",
        "outputId": "ec67b36c-7e23-4e0d-9dc6-5aa45db57972"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a1f91ad10>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 936x936 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Some interesting findings that you can see from the above graph is, first the price of the painting or sculpture is not affected much\n",
        "# by whom it has been created as you can see the correlation of price(cost) to that of artists reputation is very low.\n",
        "# Next you can see the base price of the painting or sculpture is very much correlated to the weight, which is bizarre.\n",
        "# You can try to find other meaning from the correlational graph as well."
      ],
      "metadata": {
        "id": "ESNiLyO9fxis"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X['Waiting time'] = X['Scheduled Date'] - X[\"Delivery Date\"]\n",
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "31pBnRFCfzlO",
        "outputId": "2b3171d4-3525-499b-b19e-e11ebef1be51"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Artist Reputation  Height  Width    Weight  Material  Price Of Sculpture  \\\n",
              "650               0.94     5.0    5.0      62.0         6                6.17   \n",
              "651               0.64     5.0    5.0     315.0         1                5.74   \n",
              "662               0.65    22.0    6.0    8737.0         1               22.05   \n",
              "664               0.59    26.0    6.0  133987.0         4              409.78   \n",
              "667               0.24    19.0    6.0  170477.0         5              529.95   \n",
              "\n",
              "     Base Shipping Price  International  Express Shipment  \\\n",
              "650                23.53              0                 1   \n",
              "651                15.91              0                 0   \n",
              "662                68.33              0                 1   \n",
              "664                55.05              0                 1   \n",
              "667                48.53              0                 0   \n",
              "\n",
              "     Installation Included  Transport  Fragile  Customer Information  \\\n",
              "650                      0          1        0                     1   \n",
              "651                      0          1        0                     1   \n",
              "662                      1          0        0                     1   \n",
              "664                      0          0        0                     0   \n",
              "667                      0          0        0                     0   \n",
              "\n",
              "     Remote Location Scheduled Date Delivery Date     Cost Waiting time  \n",
              "650                0     2018-02-13    2018-02-12   561.98       1 days  \n",
              "651                0     2018-11-28    2018-11-26   243.11       2 days  \n",
              "662                0     2015-12-15    2015-12-13  1271.89       2 days  \n",
              "664                0     2016-07-24    2016-07-28  3619.57      -4 days  \n",
              "667                1     2016-12-16    2016-12-18  2122.79      -2 days  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-511b2866-10da-49c2-a14f-1473f71ca670\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Artist Reputation</th>\n",
              "      <th>Height</th>\n",
              "      <th>Width</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Material</th>\n",
              "      <th>Price Of Sculpture</th>\n",
              "      <th>Base Shipping Price</th>\n",
              "      <th>International</th>\n",
              "      <th>Express Shipment</th>\n",
              "      <th>Installation Included</th>\n",
              "      <th>Transport</th>\n",
              "      <th>Fragile</th>\n",
              "      <th>Customer Information</th>\n",
              "      <th>Remote Location</th>\n",
              "      <th>Scheduled Date</th>\n",
              "      <th>Delivery Date</th>\n",
              "      <th>Cost</th>\n",
              "      <th>Waiting time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>650</th>\n",
              "      <td>0.94</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>6</td>\n",
              "      <td>6.17</td>\n",
              "      <td>23.53</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2018-02-13</td>\n",
              "      <td>2018-02-12</td>\n",
              "      <td>561.98</td>\n",
              "      <td>1 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>651</th>\n",
              "      <td>0.64</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>315.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.74</td>\n",
              "      <td>15.91</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2018-11-28</td>\n",
              "      <td>2018-11-26</td>\n",
              "      <td>243.11</td>\n",
              "      <td>2 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>662</th>\n",
              "      <td>0.65</td>\n",
              "      <td>22.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8737.0</td>\n",
              "      <td>1</td>\n",
              "      <td>22.05</td>\n",
              "      <td>68.33</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2015-12-15</td>\n",
              "      <td>2015-12-13</td>\n",
              "      <td>1271.89</td>\n",
              "      <td>2 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>664</th>\n",
              "      <td>0.59</td>\n",
              "      <td>26.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>133987.0</td>\n",
              "      <td>4</td>\n",
              "      <td>409.78</td>\n",
              "      <td>55.05</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-07-24</td>\n",
              "      <td>2016-07-28</td>\n",
              "      <td>3619.57</td>\n",
              "      <td>-4 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>667</th>\n",
              "      <td>0.24</td>\n",
              "      <td>19.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>170477.0</td>\n",
              "      <td>5</td>\n",
              "      <td>529.95</td>\n",
              "      <td>48.53</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-12-16</td>\n",
              "      <td>2016-12-18</td>\n",
              "      <td>2122.79</td>\n",
              "      <td>-2 days</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-511b2866-10da-49c2-a14f-1473f71ca670')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-511b2866-10da-49c2-a14f-1473f71ca670 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-511b2866-10da-49c2-a14f-1473f71ca670');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing the 'days' from the Waiting time data column.\n",
        "\n",
        "time_list = X['Waiting time'].to_list()\n",
        "print(str(time_list[0]).split(\" \")[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MgZA1LWKf2Sw",
        "outputId": "d6eaecf9-812a-4dca-b730-d7404adaaa28"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def extractingSize(item):\n",
        "  count = 0\n",
        "  try:\n",
        "    s = str(item).split(\" \")\n",
        "    return float(s[0])\n",
        "  except:\n",
        "    count = count + 1\n",
        "    pass\n",
        "\n",
        "X['Waiting time'] = X['Waiting time'].map(extractingSize)"
      ],
      "metadata": {
        "id": "-cviyhFsf6eN"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "Z8mETGR9f-OS",
        "outputId": "f9d6e0a5-9fa4-4133-bfa7-53dacfbfda88"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Artist Reputation  Height  Width    Weight  Material  Price Of Sculpture  \\\n",
              "650               0.94     5.0    5.0      62.0         6                6.17   \n",
              "651               0.64     5.0    5.0     315.0         1                5.74   \n",
              "662               0.65    22.0    6.0    8737.0         1               22.05   \n",
              "664               0.59    26.0    6.0  133987.0         4              409.78   \n",
              "667               0.24    19.0    6.0  170477.0         5              529.95   \n",
              "\n",
              "     Base Shipping Price  International  Express Shipment  \\\n",
              "650                23.53              0                 1   \n",
              "651                15.91              0                 0   \n",
              "662                68.33              0                 1   \n",
              "664                55.05              0                 1   \n",
              "667                48.53              0                 0   \n",
              "\n",
              "     Installation Included  Transport  Fragile  Customer Information  \\\n",
              "650                      0          1        0                     1   \n",
              "651                      0          1        0                     1   \n",
              "662                      1          0        0                     1   \n",
              "664                      0          0        0                     0   \n",
              "667                      0          0        0                     0   \n",
              "\n",
              "     Remote Location Scheduled Date Delivery Date     Cost  Waiting time  \n",
              "650                0     2018-02-13    2018-02-12   561.98           1.0  \n",
              "651                0     2018-11-28    2018-11-26   243.11           2.0  \n",
              "662                0     2015-12-15    2015-12-13  1271.89           2.0  \n",
              "664                0     2016-07-24    2016-07-28  3619.57          -4.0  \n",
              "667                1     2016-12-16    2016-12-18  2122.79          -2.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ea8d20d6-4766-4940-b004-8ab3d0b98f73\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    .dataframe thead th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Artist Reputation</th>\n",
              "      <th>Height</th>\n",
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              "      <th>Weight</th>\n",
              "      <th>Material</th>\n",
              "      <th>Price Of Sculpture</th>\n",
              "      <th>Base Shipping Price</th>\n",
              "      <th>International</th>\n",
              "      <th>Express Shipment</th>\n",
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              "      <th>Fragile</th>\n",
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              "      <th>Remote Location</th>\n",
              "      <th>Scheduled Date</th>\n",
              "      <th>Delivery Date</th>\n",
              "      <th>Cost</th>\n",
              "      <th>Waiting time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>650</th>\n",
              "      <td>0.94</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
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              "      <td>2018-11-28</td>\n",
              "      <td>2018-11-26</td>\n",
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              "    <tr>\n",
              "      <th>662</th>\n",
              "      <td>0.65</td>\n",
              "      <td>22.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8737.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>2015-12-15</td>\n",
              "      <td>2015-12-13</td>\n",
              "      <td>1271.89</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>664</th>\n",
              "      <td>0.59</td>\n",
              "      <td>26.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>133987.0</td>\n",
              "      <td>4</td>\n",
              "      <td>409.78</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>2016-07-24</td>\n",
              "      <td>2016-07-28</td>\n",
              "      <td>3619.57</td>\n",
              "      <td>-4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>667</th>\n",
              "      <td>0.24</td>\n",
              "      <td>19.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>170477.0</td>\n",
              "      <td>5</td>\n",
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              "      <td>2016-12-16</td>\n",
              "      <td>2016-12-18</td>\n",
              "      <td>2122.79</td>\n",
              "      <td>-2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea8d20d6-4766-4940-b004-8ab3d0b98f73')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ea8d20d6-4766-4940-b004-8ab3d0b98f73 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ea8d20d6-4766-4940-b004-8ab3d0b98f73');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = X['Cost']\n",
        "X = X.drop(['Scheduled Date', 'Delivery Date', 'Cost'], axis = 1)"
      ],
      "metadata": {
        "id": "U5yxfVopgAkC"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.decomposition import PCA"
      ],
      "metadata": {
        "id": "QWe_Fh9ZgDj-"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "HPyOF6WSgF8U"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_model = RandomForestRegressor(n_estimators=200, n_jobs = -1)"
      ],
      "metadata": {
        "id": "eMyVep_ygIBi"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Fit\n",
        "random_model.fit(Xtrain, ytrain)\n",
        "\n",
        "y_pred = random_model.predict(Xtest)\n",
        "\n",
        "#Checking the accuracy\n",
        "random_model_accuracy = round(random_model.score(Xtrain, ytrain)*100,2)\n",
        "print(round(random_model_accuracy, 2), '%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "px-M1fYngLTK",
        "outputId": "b88a8195-8b80-4784-86d6-2f9e30ca0392"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "97.62 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_model_accuracy1 = round(random_model.score(Xtest, ytest)*100,2)\n",
        "print(round(random_model_accuracy1, 2), '%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j8HV9dJGgNaE",
        "outputId": "9e29e47a-9a93-4a9e-d101-2a2a19961e33"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "84.65 %\n"
          ]
        }
      ]
    }
  ]
}