BOOSTING_ON_REGRESSION_BY_MS.ipynb

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            "Requirement already satisfied: xgboost in /usr/local/lib/python3.10/dist-packages (2.0.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.25.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.11.4)\n"
          ]
        }
      ],
      "source": [
        "pip install xgboost"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "IPwm1LUeOLSE"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/supermarket_sales -.csv\")"
      ],
      "metadata": {
        "id": "nHsaw563OefG"
      },
      "execution_count": 24,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "df.isna().sum()"
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        "id": "u_uAF4meOnh8",
        "outputId": "c94456e1-19eb-4166-e80b-46fdd5291441"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Invoice ID                 0\n",
              "Branch                     0\n",
              "City                       0\n",
              "Customer type              0\n",
              "Gender                     0\n",
              "Product line               0\n",
              "Unit price                 0\n",
              "Quantity                   0\n",
              "Tax 5%                     0\n",
              "Total                      0\n",
              "Date                       0\n",
              "Time                       0\n",
              "Payment                    0\n",
              "cogs                       0\n",
              "gross margin percentage    0\n",
              "gross income               0\n",
              "Rating                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
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      "source": [
        "df"
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      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Invoice ID Branch       City Customer type  Gender  \\\n",
              "0    750-67-8428      A     Yangon        Member  Female   \n",
              "1    226-31-3081      C  Naypyitaw        Normal  Female   \n",
              "2    631-41-3108      A     Yangon        Normal    Male   \n",
              "3    123-19-1176      A     Yangon        Member    Male   \n",
              "4    373-73-7910      A     Yangon        Normal    Male   \n",
              "..           ...    ...        ...           ...     ...   \n",
              "995  233-67-5758      C  Naypyitaw        Normal    Male   \n",
              "996  303-96-2227      B   Mandalay        Normal  Female   \n",
              "997  727-02-1313      A     Yangon        Member    Male   \n",
              "998  347-56-2442      A     Yangon        Normal    Male   \n",
              "999  849-09-3807      A     Yangon        Member  Female   \n",
              "\n",
              "               Product line  Unit price  Quantity   Tax 5%      Total  \\\n",
              "0         Health and beauty       74.69         7  26.1415   548.9715   \n",
              "1    Electronic accessories       15.28         5   3.8200    80.2200   \n",
              "2        Home and lifestyle       46.33         7  16.2155   340.5255   \n",
              "3         Health and beauty       58.22         8  23.2880   489.0480   \n",
              "4         Sports and travel       86.31         7  30.2085   634.3785   \n",
              "..                      ...         ...       ...      ...        ...   \n",
              "995       Health and beauty       40.35         1   2.0175    42.3675   \n",
              "996      Home and lifestyle       97.38        10  48.6900  1022.4900   \n",
              "997      Food and beverages       31.84         1   1.5920    33.4320   \n",
              "998      Home and lifestyle       65.82         1   3.2910    69.1110   \n",
              "999     Fashion accessories       88.34         7  30.9190   649.2990   \n",
              "\n",
              "          Date   Time      Payment    cogs  gross margin percentage  \\\n",
              "0     1/5/2019  13:08      Ewallet  522.83                 4.761905   \n",
              "1     3/8/2019  10:29         Cash   76.40                 4.761905   \n",
              "2     3/3/2019  13:23  Credit card  324.31                 4.761905   \n",
              "3    1/27/2019  20:33      Ewallet  465.76                 4.761905   \n",
              "4     2/8/2019  10:37      Ewallet  604.17                 4.761905   \n",
              "..         ...    ...          ...     ...                      ...   \n",
              "995  1/29/2019  13:46      Ewallet   40.35                 4.761905   \n",
              "996   3/2/2019  17:16      Ewallet  973.80                 4.761905   \n",
              "997   2/9/2019  13:22         Cash   31.84                 4.761905   \n",
              "998  2/22/2019  15:33         Cash   65.82                 4.761905   \n",
              "999  2/18/2019  13:28         Cash  618.38                 4.761905   \n",
              "\n",
              "     gross income  Rating  \n",
              "0         26.1415     9.1  \n",
              "1          3.8200     9.6  \n",
              "2         16.2155     7.4  \n",
              "3         23.2880     8.4  \n",
              "4         30.2085     5.3  \n",
              "..            ...     ...  \n",
              "995        2.0175     6.2  \n",
              "996       48.6900     4.4  \n",
              "997        1.5920     7.7  \n",
              "998        3.2910     4.1  \n",
              "999       30.9190     6.6  \n",
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              "[1000 rows x 17 columns]"
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              "      <th>Invoice ID</th>\n",
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              "      <th>0</th>\n",
              "      <td>750-67-8428</td>\n",
              "      <td>A</td>\n",
              "      <td>Yangon</td>\n",
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              "      <td>Female</td>\n",
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              "      <td>13:08</td>\n",
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              "      <td>522.83</td>\n",
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              "      <td>9.1</td>\n",
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              "      <th>1</th>\n",
              "      <td>226-31-3081</td>\n",
              "      <td>C</td>\n",
              "      <td>Naypyitaw</td>\n",
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              "      <td>3/8/2019</td>\n",
              "      <td>10:29</td>\n",
              "      <td>Cash</td>\n",
              "      <td>76.40</td>\n",
              "      <td>4.761905</td>\n",
              "      <td>3.8200</td>\n",
              "      <td>9.6</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>631-41-3108</td>\n",
              "      <td>A</td>\n",
              "      <td>Yangon</td>\n",
              "      <td>Normal</td>\n",
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              "      <td>46.33</td>\n",
              "      <td>7</td>\n",
              "      <td>16.2155</td>\n",
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              "      <td>3/3/2019</td>\n",
              "      <td>13:23</td>\n",
              "      <td>Credit card</td>\n",
              "      <td>324.31</td>\n",
              "      <td>4.761905</td>\n",
              "      <td>16.2155</td>\n",
              "      <td>7.4</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>123-19-1176</td>\n",
              "      <td>A</td>\n",
              "      <td>Yangon</td>\n",
              "      <td>Member</td>\n",
              "      <td>Male</td>\n",
              "      <td>Health and beauty</td>\n",
              "      <td>58.22</td>\n",
              "      <td>8</td>\n",
              "      <td>23.2880</td>\n",
              "      <td>489.0480</td>\n",
              "      <td>1/27/2019</td>\n",
              "      <td>20:33</td>\n",
              "      <td>Ewallet</td>\n",
              "      <td>465.76</td>\n",
              "      <td>4.761905</td>\n",
              "      <td>23.2880</td>\n",
              "      <td>8.4</td>\n",
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              "      <th>4</th>\n",
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              "      <td>A</td>\n",
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              "      <td>Normal</td>\n",
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              "      <td>86.31</td>\n",
              "      <td>7</td>\n",
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              "      <td>634.3785</td>\n",
              "      <td>2/8/2019</td>\n",
              "      <td>10:37</td>\n",
              "      <td>Ewallet</td>\n",
              "      <td>604.17</td>\n",
              "      <td>4.761905</td>\n",
              "      <td>30.2085</td>\n",
              "      <td>5.3</td>\n",
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              "      <td>Health and beauty</td>\n",
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              "      <td>1</td>\n",
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              "      <td>13:46</td>\n",
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              "      <td>40.35</td>\n",
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              "      <td>303-96-2227</td>\n",
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              "      <td>Female</td>\n",
              "      <td>Home and lifestyle</td>\n",
              "      <td>97.38</td>\n",
              "      <td>10</td>\n",
              "      <td>48.6900</td>\n",
              "      <td>1022.4900</td>\n",
              "      <td>3/2/2019</td>\n",
              "      <td>17:16</td>\n",
              "      <td>Ewallet</td>\n",
              "      <td>973.80</td>\n",
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              "      <td>4.4</td>\n",
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              "      <td>31.84</td>\n",
              "      <td>1</td>\n",
              "      <td>1.5920</td>\n",
              "      <td>33.4320</td>\n",
              "      <td>2/9/2019</td>\n",
              "      <td>13:22</td>\n",
              "      <td>Cash</td>\n",
              "      <td>31.84</td>\n",
              "      <td>4.761905</td>\n",
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              "      <td>7.7</td>\n",
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              "      <td>Home and lifestyle</td>\n",
              "      <td>65.82</td>\n",
              "      <td>1</td>\n",
              "      <td>3.2910</td>\n",
              "      <td>69.1110</td>\n",
              "      <td>2/22/2019</td>\n",
              "      <td>15:33</td>\n",
              "      <td>Cash</td>\n",
              "      <td>65.82</td>\n",
              "      <td>4.761905</td>\n",
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              "      <td>4.1</td>\n",
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              "      <td>7</td>\n",
              "      <td>30.9190</td>\n",
              "      <td>649.2990</td>\n",
              "      <td>2/18/2019</td>\n",
              "      <td>13:28</td>\n",
              "      <td>Cash</td>\n",
              "      <td>618.38</td>\n",
              "      <td>4.761905</td>\n",
              "      <td>30.9190</td>\n",
              "      <td>6.6</td>\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      .colab-df-generate:hover {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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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              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_40363264-cada-4256-b340-018258bb0a88 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Invoice ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          \"451-28-5717\",\n          \"137-63-5492\",\n          \"733-29-1227\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"A\",\n          \"C\",\n          \"B\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Yangon\",\n          \"Naypyitaw\",\n          \"Mandalay\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Normal\",\n          \"Member\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product line\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Health and beauty\",\n          \"Electronic accessories\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unit price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.49462834791978,\n        \"min\": 10.08,\n        \"max\": 99.96,\n        \"num_unique_values\": 943,\n        \"samples\": [\n          12.45,\n          70.74\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tax 5%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.708825480998659,\n        \"min\": 0.5085,\n        \"max\": 49.65,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.914,\n          2.348\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 245.88533510097187,\n        \"min\": 10.6785,\n        \"max\": 1042.65,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          19.194,\n          49.308\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 89,\n        \"samples\": [\n          \"2/14/2019\",\n          \"1/26/2019\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 506,\n        \"samples\": [\n          \"10:13\",\n          \"11:21\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Ewallet\",\n          \"Cash\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cogs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 234.1765096199732,\n        \"min\": 10.17,\n        \"max\": 993.0,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          18.28,\n          46.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gross margin percentage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 4.761904762,\n        \"max\": 4.761904762,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4.761904762\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gross income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.708825480998659,\n        \"min\": 0.5085,\n        \"max\": 49.65,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.914\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7185802943791215,\n        \"min\": 4.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          9.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bV3x-nHQOtHN",
        "outputId": "50672b11-fda5-4c39-e6da-b38d59fbb391"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Invoice ID                 1000\n",
              "Branch                        3\n",
              "City                          3\n",
              "Customer type                 2\n",
              "Gender                        2\n",
              "Product line                  6\n",
              "Unit price                  943\n",
              "Quantity                     10\n",
              "Tax 5%                      990\n",
              "Total                       990\n",
              "Date                         89\n",
              "Time                        506\n",
              "Payment                       3\n",
              "cogs                        990\n",
              "gross margin percentage       1\n",
              "gross income                990\n",
              "Rating                       61\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "LFnXjt9HOygP",
        "outputId": "c5011b75-e3b3-45f9-f2b0-4d506413896e"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Invoice ID Branch       City Customer type  Gender  \\\n",
              "0  750-67-8428      A     Yangon        Member  Female   \n",
              "1  226-31-3081      C  Naypyitaw        Normal  Female   \n",
              "2  631-41-3108      A     Yangon        Normal    Male   \n",
              "3  123-19-1176      A     Yangon        Member    Male   \n",
              "4  373-73-7910      A     Yangon        Normal    Male   \n",
              "\n",
              "             Product line  Unit price  Quantity   Tax 5%     Total       Date  \\\n",
              "0       Health and beauty       74.69         7  26.1415  548.9715   1/5/2019   \n",
              "1  Electronic accessories       15.28         5   3.8200   80.2200   3/8/2019   \n",
              "2      Home and lifestyle       46.33         7  16.2155  340.5255   3/3/2019   \n",
              "3       Health and beauty       58.22         8  23.2880  489.0480  1/27/2019   \n",
              "4       Sports and travel       86.31         7  30.2085  634.3785   2/8/2019   \n",
              "\n",
              "    Time      Payment    cogs  gross margin percentage  gross income  Rating  \n",
              "0  13:08      Ewallet  522.83                 4.761905       26.1415     9.1  \n",
              "1  10:29         Cash   76.40                 4.761905        3.8200     9.6  \n",
              "2  13:23  Credit card  324.31                 4.761905       16.2155     7.4  \n",
              "3  20:33      Ewallet  465.76                 4.761905       23.2880     8.4  \n",
              "4  10:37      Ewallet  604.17                 4.761905       30.2085     5.3  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Invoice ID</th>\n",
              "      <th>Branch</th>\n",
              "      <th>City</th>\n",
              "      <th>Customer type</th>\n",
              "      <th>Gender</th>\n",
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              "      <th>Payment</th>\n",
              "      <th>cogs</th>\n",
              "      <th>gross margin percentage</th>\n",
              "      <th>gross income</th>\n",
              "      <th>Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>750-67-8428</td>\n",
              "      <td>A</td>\n",
              "      <td>Yangon</td>\n",
              "      <td>Member</td>\n",
              "      <td>Female</td>\n",
              "      <td>Health and beauty</td>\n",
              "      <td>74.69</td>\n",
              "      <td>7</td>\n",
              "      <td>26.1415</td>\n",
              "      <td>548.9715</td>\n",
              "      <td>1/5/2019</td>\n",
              "      <td>13:08</td>\n",
              "      <td>Ewallet</td>\n",
              "      <td>522.83</td>\n",
              "      <td>4.761905</td>\n",
              "      <td>26.1415</td>\n",
              "      <td>9.1</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>226-31-3081</td>\n",
              "      <td>C</td>\n",
              "      <td>Naypyitaw</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Female</td>\n",
              "      <td>Electronic accessories</td>\n",
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              "      <td>5</td>\n",
              "      <td>3.8200</td>\n",
              "      <td>80.2200</td>\n",
              "      <td>3/8/2019</td>\n",
              "      <td>10:29</td>\n",
              "      <td>Cash</td>\n",
              "      <td>76.40</td>\n",
              "      <td>4.761905</td>\n",
              "      <td>3.8200</td>\n",
              "      <td>9.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>631-41-3108</td>\n",
              "      <td>A</td>\n",
              "      <td>Yangon</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Male</td>\n",
              "      <td>Home and lifestyle</td>\n",
              "      <td>46.33</td>\n",
              "      <td>7</td>\n",
              "      <td>16.2155</td>\n",
              "      <td>340.5255</td>\n",
              "      <td>3/3/2019</td>\n",
              "      <td>13:23</td>\n",
              "      <td>Credit card</td>\n",
              "      <td>324.31</td>\n",
              "      <td>4.761905</td>\n",
              "      <td>16.2155</td>\n",
              "      <td>7.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>123-19-1176</td>\n",
              "      <td>A</td>\n",
              "      <td>Yangon</td>\n",
              "      <td>Member</td>\n",
              "      <td>Male</td>\n",
              "      <td>Health and beauty</td>\n",
              "      <td>58.22</td>\n",
              "      <td>8</td>\n",
              "      <td>23.2880</td>\n",
              "      <td>489.0480</td>\n",
              "      <td>1/27/2019</td>\n",
              "      <td>20:33</td>\n",
              "      <td>Ewallet</td>\n",
              "      <td>465.76</td>\n",
              "      <td>4.761905</td>\n",
              "      <td>23.2880</td>\n",
              "      <td>8.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>373-73-7910</td>\n",
              "      <td>A</td>\n",
              "      <td>Yangon</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Male</td>\n",
              "      <td>Sports and travel</td>\n",
              "      <td>86.31</td>\n",
              "      <td>7</td>\n",
              "      <td>30.2085</td>\n",
              "      <td>634.3785</td>\n",
              "      <td>2/8/2019</td>\n",
              "      <td>10:37</td>\n",
              "      <td>Ewallet</td>\n",
              "      <td>604.17</td>\n",
              "      <td>4.761905</td>\n",
              "      <td>30.2085</td>\n",
              "      <td>5.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Invoice ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          \"451-28-5717\",\n          \"137-63-5492\",\n          \"733-29-1227\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"A\",\n          \"C\",\n          \"B\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Yangon\",\n          \"Naypyitaw\",\n          \"Mandalay\"\n        ],\n        \"semantic_type\": \"\",\n 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\"Unit price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.49462834791978,\n        \"min\": 10.08,\n        \"max\": 99.96,\n        \"num_unique_values\": 943,\n        \"samples\": [\n          12.45,\n          70.74\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tax 5%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.708825480998659,\n        \"min\": 0.5085,\n        \"max\": 49.65,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.914,\n          2.348\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 245.88533510097187,\n        \"min\": 10.6785,\n        \"max\": 1042.65,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          19.194,\n          49.308\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 89,\n        \"samples\": [\n          \"2/14/2019\",\n          \"1/26/2019\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 506,\n        \"samples\": [\n          \"10:13\",\n          \"11:21\"\n        ],\n        \"semantic_type\": \"\",\n        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            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop('Invoice ID',axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "UJsaVxJwO4R3"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop('gross margin percentage',axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "UX0qwDkLPDeJ"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['Date','Time'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "hx28KmcQPXQn"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "V55ZwYq-PQLE",
        "outputId": "ddcd29ec-17af-431d-b294-4af230a020b0"
      },
      "execution_count": 32,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Branch       City Customer type  Gender            Product line  Unit price  \\\n",
              "0      A     Yangon        Member  Female       Health and beauty       74.69   \n",
              "1      C  Naypyitaw        Normal  Female  Electronic accessories       15.28   \n",
              "2      A     Yangon        Normal    Male      Home and lifestyle       46.33   \n",
              "3      A     Yangon        Member    Male       Health and beauty       58.22   \n",
              "4      A     Yangon        Normal    Male       Sports and travel       86.31   \n",
              "\n",
              "   Quantity   Tax 5%     Total      Payment    cogs  gross income  Rating  \n",
              "0         7  26.1415  548.9715      Ewallet  522.83       26.1415     9.1  \n",
              "1         5   3.8200   80.2200         Cash   76.40        3.8200     9.6  \n",
              "2         7  16.2155  340.5255  Credit card  324.31       16.2155     7.4  \n",
              "3         8  23.2880  489.0480      Ewallet  465.76       23.2880     8.4  \n",
              "4         7  30.2085  634.3785      Ewallet  604.17       30.2085     5.3  "
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              "      <th>Branch</th>\n",
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              "      <th>0</th>\n",
              "      <td>A</td>\n",
              "      <td>Yangon</td>\n",
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              "      <td>9.1</td>\n",
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              "      <td>C</td>\n",
              "      <td>Naypyitaw</td>\n",
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              "      <td>Electronic accessories</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>Yangon</td>\n",
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              "      <td>Home and lifestyle</td>\n",
              "      <td>46.33</td>\n",
              "      <td>7</td>\n",
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              "      <td>Credit card</td>\n",
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              "      <th>3</th>\n",
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              "      <td>Yangon</td>\n",
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              "      <td>58.22</td>\n",
              "      <td>8</td>\n",
              "      <td>23.2880</td>\n",
              "      <td>489.0480</td>\n",
              "      <td>Ewallet</td>\n",
              "      <td>465.76</td>\n",
              "      <td>23.2880</td>\n",
              "      <td>8.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A</td>\n",
              "      <td>Yangon</td>\n",
              "      <td>Normal</td>\n",
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              "      <td>Sports and travel</td>\n",
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              "      <td>7</td>\n",
              "      <td>30.2085</td>\n",
              "      <td>634.3785</td>\n",
              "      <td>Ewallet</td>\n",
              "      <td>604.17</td>\n",
              "      <td>30.2085</td>\n",
              "      <td>5.3</td>\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-87e8a649-f2b1-4a44-ad79-603e204c1565 button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-87e8a649-f2b1-4a44-ad79-603e204c1565');\n",
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              "                                                    [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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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"A\",\n          \"C\",\n          \"B\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Yangon\",\n          \"Naypyitaw\",\n          \"Mandalay\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Normal\",\n          \"Member\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product line\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Health and beauty\",\n          \"Electronic accessories\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unit price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.49462834791978,\n        \"min\": 10.08,\n        \"max\": 99.96,\n        \"num_unique_values\": 943,\n        \"samples\": [\n          12.45,\n          70.74\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tax 5%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.708825480998659,\n        \"min\": 0.5085,\n        \"max\": 49.65,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.914,\n          2.348\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 245.88533510097187,\n        \"min\": 10.6785,\n        \"max\": 1042.65,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          19.194,\n          49.308\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Ewallet\",\n          \"Cash\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cogs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 234.1765096199732,\n        \"min\": 10.17,\n        \"max\": 993.0,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          18.28,\n          46.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gross income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.708825480998659,\n        \"min\": 0.5085,\n        \"max\": 49.65,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.914,\n          2.348\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7185802943791215,\n        \"min\": 4.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          9.1,\n          4.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()"
      ],
      "metadata": {
        "id": "UFXb5EazQcsj"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in ['Branch','City','Customer type','Gender','Product line','Payment']:\n",
        "  df[i] = le.fit_transform(df[i])"
      ],
      "metadata": {
        "id": "OGAgjEe3Qo9U"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "uZhGJmPqWnRt",
        "outputId": "b2046ee7-115c-4592-8872-ba313522e430"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Branch  City  Customer type  Gender  Product line  Unit price  Quantity  \\\n",
              "0       0     2              0       0             3       74.69         7   \n",
              "1       2     1              1       0             0       15.28         5   \n",
              "2       0     2              1       1             4       46.33         7   \n",
              "3       0     2              0       1             3       58.22         8   \n",
              "4       0     2              1       1             5       86.31         7   \n",
              "\n",
              "    Tax 5%     Total  Payment    cogs  gross income  Rating  \n",
              "0  26.1415  548.9715        2  522.83       26.1415     9.1  \n",
              "1   3.8200   80.2200        0   76.40        3.8200     9.6  \n",
              "2  16.2155  340.5255        1  324.31       16.2155     7.4  \n",
              "3  23.2880  489.0480        2  465.76       23.2880     8.4  \n",
              "4  30.2085  634.3785        2  604.17       30.2085     5.3  "
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Branch</th>\n",
              "      <th>City</th>\n",
              "      <th>Customer type</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Product line</th>\n",
              "      <th>Unit price</th>\n",
              "      <th>Quantity</th>\n",
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              "      <th>Total</th>\n",
              "      <th>Payment</th>\n",
              "      <th>cogs</th>\n",
              "      <th>gross income</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
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              "      <td>74.69</td>\n",
              "      <td>7</td>\n",
              "      <td>26.1415</td>\n",
              "      <td>548.9715</td>\n",
              "      <td>2</td>\n",
              "      <td>522.83</td>\n",
              "      <td>26.1415</td>\n",
              "      <td>9.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15.28</td>\n",
              "      <td>5</td>\n",
              "      <td>3.8200</td>\n",
              "      <td>80.2200</td>\n",
              "      <td>0</td>\n",
              "      <td>76.40</td>\n",
              "      <td>3.8200</td>\n",
              "      <td>9.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>46.33</td>\n",
              "      <td>7</td>\n",
              "      <td>16.2155</td>\n",
              "      <td>340.5255</td>\n",
              "      <td>1</td>\n",
              "      <td>324.31</td>\n",
              "      <td>16.2155</td>\n",
              "      <td>7.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>58.22</td>\n",
              "      <td>8</td>\n",
              "      <td>23.2880</td>\n",
              "      <td>489.0480</td>\n",
              "      <td>2</td>\n",
              "      <td>465.76</td>\n",
              "      <td>23.2880</td>\n",
              "      <td>8.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>86.31</td>\n",
              "      <td>7</td>\n",
              "      <td>30.2085</td>\n",
              "      <td>634.3785</td>\n",
              "      <td>2</td>\n",
              "      <td>604.17</td>\n",
              "      <td>30.2085</td>\n",
              "      <td>5.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2b24daa-2cb6-493e-8e35-59145d3edd4d')\"\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",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
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              "    [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",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f2b24daa-2cb6-493e-8e35-59145d3edd4d 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-f2b24daa-2cb6-493e-8e35-59145d3edd4d');\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",
              "\n",
              "\n",
              "<div id=\"df-59b7911c-3277-4e2f-b799-8caba783693d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-59b7911c-3277-4e2f-b799-8caba783693d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-59b7911c-3277-4e2f-b799-8caba783693d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer type\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product line\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unit price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.49462834791978,\n        \"min\": 10.08,\n        \"max\": 99.96,\n        \"num_unique_values\": 943,\n        \"samples\": [\n          12.45,\n          70.74\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tax 5%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.708825480998659,\n        \"min\": 0.5085,\n        \"max\": 49.65,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.914,\n          2.348\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 245.88533510097187,\n        \"min\": 10.6785,\n        \"max\": 1042.65,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          19.194,\n          49.308\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cogs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 234.1765096199732,\n        \"min\": 10.17,\n        \"max\": 993.0,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          18.28,\n          46.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gross income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.708825480998659,\n        \"min\": 0.5085,\n        \"max\": 49.65,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.914,\n          2.348\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7185802943791215,\n        \"min\": 4.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          9.1,\n          4.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.drop(['Branch','City','Customer type','Gender','Product line','Payment','Quantity'],axis=1)"
      ],
      "metadata": {
        "id": "xe-s60YLXBm-"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y  = df[['Branch','City','Customer type','Gender','Product line','Payment','Quantity']]"
      ],
      "metadata": {
        "id": "dhVrQ6p_XkKs"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "eFwkFxQaXv28",
        "outputId": "bb61290b-5958-4aa5-8c2b-0980cb2754e3"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unit price   Tax 5%     Total    cogs  gross income  Rating\n",
              "0       74.69  26.1415  548.9715  522.83       26.1415     9.1\n",
              "1       15.28   3.8200   80.2200   76.40        3.8200     9.6\n",
              "2       46.33  16.2155  340.5255  324.31       16.2155     7.4\n",
              "3       58.22  23.2880  489.0480  465.76       23.2880     8.4\n",
              "4       86.31  30.2085  634.3785  604.17       30.2085     5.3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fc55b9ae-c1fd-404b-859e-88a820103c45\" 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>Unit price</th>\n",
              "      <th>Tax 5%</th>\n",
              "      <th>Total</th>\n",
              "      <th>cogs</th>\n",
              "      <th>gross income</th>\n",
              "      <th>Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>74.69</td>\n",
              "      <td>26.1415</td>\n",
              "      <td>548.9715</td>\n",
              "      <td>522.83</td>\n",
              "      <td>26.1415</td>\n",
              "      <td>9.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15.28</td>\n",
              "      <td>3.8200</td>\n",
              "      <td>80.2200</td>\n",
              "      <td>76.40</td>\n",
              "      <td>3.8200</td>\n",
              "      <td>9.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>46.33</td>\n",
              "      <td>16.2155</td>\n",
              "      <td>340.5255</td>\n",
              "      <td>324.31</td>\n",
              "      <td>16.2155</td>\n",
              "      <td>7.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>58.22</td>\n",
              "      <td>23.2880</td>\n",
              "      <td>489.0480</td>\n",
              "      <td>465.76</td>\n",
              "      <td>23.2880</td>\n",
              "      <td>8.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>86.31</td>\n",
              "      <td>30.2085</td>\n",
              "      <td>634.3785</td>\n",
              "      <td>604.17</td>\n",
              "      <td>30.2085</td>\n",
              "      <td>5.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fc55b9ae-c1fd-404b-859e-88a820103c45')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-fc55b9ae-c1fd-404b-859e-88a820103c45 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-fc55b9ae-c1fd-404b-859e-88a820103c45');\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",
              "\n",
              "\n",
              "<div id=\"df-bd6becf9-2044-4aef-8575-2380ea19bcc3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bd6becf9-2044-4aef-8575-2380ea19bcc3')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bd6becf9-2044-4aef-8575-2380ea19bcc3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Unit price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.49462834791978,\n        \"min\": 10.08,\n        \"max\": 99.96,\n        \"num_unique_values\": 943,\n        \"samples\": [\n          12.45,\n          70.74,\n          74.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tax 5%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.708825480998659,\n        \"min\": 0.5085,\n        \"max\": 49.65,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.914,\n          2.348,\n          4.4225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 245.88533510097187,\n        \"min\": 10.6785,\n        \"max\": 1042.65,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          19.194,\n          49.308,\n          92.8725\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cogs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 234.1765096199732,\n        \"min\": 10.17,\n        \"max\": 993.0,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          18.28,\n          46.96,\n          88.45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gross income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.708825480998659,\n        \"min\": 0.5085,\n        \"max\": 49.65,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.914,\n          2.348,\n          4.4225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7185802943791215,\n        \"min\": 4.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          9.1,\n          4.1,\n          5.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "minmax = MinMaxScaler()"
      ],
      "metadata": {
        "id": "YP-UxRi4Xw3U"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newx = minmax.fit_transform(x)"
      ],
      "metadata": {
        "id": "1BrpWnfDX6Ck"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = pd.DataFrame(newx,columns=('Unit price','Tax 5%','Total','cogs','gross income','Rating'))"
      ],
      "metadata": {
        "id": "wGvcpmbhYBON"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([x,y],axis=1)"
      ],
      "metadata": {
        "id": "Y1MLkatcYPFF"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "fm1G0j0MY4-1",
        "outputId": "aac12a2e-91a0-4955-9ec7-a0b2994711af"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Unit price    Tax 5%     Total      cogs  gross income    Rating  Branch  \\\n",
              "0      0.718847  0.521616  0.521616  0.521616      0.521616  0.850000       0   \n",
              "1      0.057855  0.067387  0.067387  0.067387      0.067387  0.933333       2   \n",
              "2      0.403316  0.319628  0.319628  0.319628      0.319628  0.566667       0   \n",
              "3      0.535603  0.463549  0.463549  0.463549      0.463549  0.733333       0   \n",
              "4      0.848131  0.604377  0.604377  0.604377      0.604377  0.216667       0   \n",
              "..          ...       ...       ...       ...           ...       ...     ...   \n",
              "995    0.336782  0.030707  0.030707  0.030707      0.030707  0.366667       2   \n",
              "996    0.971295  0.980465  0.980465  0.980465      0.980465  0.066667       1   \n",
              "997    0.242101  0.022049  0.022049  0.022049      0.022049  0.616667       0   \n",
              "998    0.620160  0.056622  0.056622  0.056622      0.056622  0.016667       0   \n",
              "999    0.870717  0.618835  0.618835  0.618835      0.618835  0.433333       0   \n",
              "\n",
              "     City  Customer type  Gender  Product line  Payment  Quantity  \n",
              "0       2              0       0             3        2         7  \n",
              "1       1              1       0             0        0         5  \n",
              "2       2              1       1             4        1         7  \n",
              "3       2              0       1             3        2         8  \n",
              "4       2              1       1             5        2         7  \n",
              "..    ...            ...     ...           ...      ...       ...  \n",
              "995     1              1       1             3        2         1  \n",
              "996     0              1       0             4        2        10  \n",
              "997     2              0       1             2        0         1  \n",
              "998     2              1       1             4        0         1  \n",
              "999     2              0       0             1        0         7  \n",
              "\n",
              "[1000 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-51446ad4-6c51-472a-b5b0-92a3e38b9b9d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\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>Unit price</th>\n",
              "      <th>Tax 5%</th>\n",
              "      <th>Total</th>\n",
              "      <th>cogs</th>\n",
              "      <th>gross income</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Branch</th>\n",
              "      <th>City</th>\n",
              "      <th>Customer type</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Product line</th>\n",
              "      <th>Payment</th>\n",
              "      <th>Quantity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0.718847</td>\n",
              "      <td>0.521616</td>\n",
              "      <td>0.521616</td>\n",
              "      <td>0.521616</td>\n",
              "      <td>0.521616</td>\n",
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              "      <td>7</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.057855</td>\n",
              "      <td>0.067387</td>\n",
              "      <td>0.067387</td>\n",
              "      <td>0.067387</td>\n",
              "      <td>0.067387</td>\n",
              "      <td>0.933333</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.403316</td>\n",
              "      <td>0.319628</td>\n",
              "      <td>0.319628</td>\n",
              "      <td>0.319628</td>\n",
              "      <td>0.319628</td>\n",
              "      <td>0.566667</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.535603</td>\n",
              "      <td>0.463549</td>\n",
              "      <td>0.463549</td>\n",
              "      <td>0.463549</td>\n",
              "      <td>0.463549</td>\n",
              "      <td>0.733333</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.848131</td>\n",
              "      <td>0.604377</td>\n",
              "      <td>0.604377</td>\n",
              "      <td>0.604377</td>\n",
              "      <td>0.604377</td>\n",
              "      <td>0.216667</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>0.336782</td>\n",
              "      <td>0.030707</td>\n",
              "      <td>0.030707</td>\n",
              "      <td>0.030707</td>\n",
              "      <td>0.030707</td>\n",
              "      <td>0.366667</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>0.971295</td>\n",
              "      <td>0.980465</td>\n",
              "      <td>0.980465</td>\n",
              "      <td>0.980465</td>\n",
              "      <td>0.980465</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>0.242101</td>\n",
              "      <td>0.022049</td>\n",
              "      <td>0.022049</td>\n",
              "      <td>0.022049</td>\n",
              "      <td>0.022049</td>\n",
              "      <td>0.616667</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>0.620160</td>\n",
              "      <td>0.056622</td>\n",
              "      <td>0.056622</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>0.870717</td>\n",
              "      <td>0.618835</td>\n",
              "      <td>0.618835</td>\n",
              "      <td>0.618835</td>\n",
              "      <td>0.618835</td>\n",
              "      <td>0.433333</td>\n",
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              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 13 columns</p>\n",
              "</div>\n",
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              "\n",
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              "  <style>\n",
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              "      display:flex;\n",
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              "\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",
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              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Unit price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.29477779648330865,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 943,\n        \"samples\": [\n          0.026368491321762333,\n          0.6748998664886515,\n          0.712283044058745\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tax 5%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2382675636885048,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.008251681369107578,\n          0.03743271979894794,\n          0.0796475484061333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23826756368850477,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.008251681369107575,\n          0.03743271979894794,\n          0.0796475484061333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cogs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2382675636885048,\n        \"min\": 0.0,\n        \"max\": 1.0000000000000002,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.00825168136910758,\n          0.03743271979894794,\n          0.07964754840613332\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gross income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2382675636885048,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.008251681369107578,\n          0.03743271979894794,\n          0.0796475484061333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2864300490631869,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          0.85,\n          0.016666666666666607,\n          0.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer type\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product line\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "pif1F7sGY7qE",
        "outputId": "65ff1ce0-c431-4797-a09f-0dbab400ab97"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Unit price    Tax 5%     Total      cogs  gross income  \\\n",
              "Unit price       1.000000  0.633962  0.633962  0.633962      0.633962   \n",
              "Tax 5%           0.633962  1.000000  1.000000  1.000000      1.000000   \n",
              "Total            0.633962  1.000000  1.000000  1.000000      1.000000   \n",
              "cogs             0.633962  1.000000  1.000000  1.000000      1.000000   \n",
              "gross income     0.633962  1.000000  1.000000  1.000000      1.000000   \n",
              "Rating          -0.008778 -0.036442 -0.036442 -0.036442     -0.036442   \n",
              "Branch           0.028202  0.041047  0.041047  0.041047      0.041047   \n",
              "City            -0.013763 -0.012812 -0.012812 -0.012812     -0.012812   \n",
              "Customer type   -0.020238 -0.019670 -0.019670 -0.019670     -0.019670   \n",
              "Gender           0.015445 -0.049451 -0.049451 -0.049451     -0.049451   \n",
              "Product line     0.019321  0.031621  0.031621  0.031621      0.031621   \n",
              "Payment         -0.015941 -0.012434 -0.012434 -0.012434     -0.012434   \n",
              "Quantity         0.010778  0.705510  0.705510  0.705510      0.705510   \n",
              "\n",
              "                 Rating    Branch      City  Customer type    Gender  \\\n",
              "Unit price    -0.008778  0.028202 -0.013763      -0.020238  0.015445   \n",
              "Tax 5%        -0.036442  0.041047 -0.012812      -0.019670 -0.049451   \n",
              "Total         -0.036442  0.041047 -0.012812      -0.019670 -0.049451   \n",
              "cogs          -0.036442  0.041047 -0.012812      -0.019670 -0.049451   \n",
              "gross income  -0.036442  0.041047 -0.012812      -0.019670 -0.049451   \n",
              "Rating         1.000000  0.010238  0.049585       0.018889  0.004800   \n",
              "Branch         0.010238  1.000000 -0.507401      -0.019608 -0.056318   \n",
              "City           0.049585 -0.507401  1.000000       0.004899  0.012219   \n",
              "Customer type  0.018889 -0.019608  0.004899       1.000000  0.039996   \n",
              "Gender         0.004800 -0.056318  0.012219       0.039996  1.000000   \n",
              "Product line  -0.020529 -0.053938  0.008811      -0.036800  0.005193   \n",
              "Payment       -0.005381 -0.050104  0.019094       0.018073  0.044578   \n",
              "Quantity      -0.015815  0.015964 -0.002121      -0.016763 -0.074258   \n",
              "\n",
              "               Product line   Payment  Quantity  \n",
              "Unit price         0.019321 -0.015941  0.010778  \n",
              "Tax 5%             0.031621 -0.012434  0.705510  \n",
              "Total              0.031621 -0.012434  0.705510  \n",
              "cogs               0.031621 -0.012434  0.705510  \n",
              "gross income       0.031621 -0.012434  0.705510  \n",
              "Rating            -0.020529 -0.005381 -0.015815  \n",
              "Branch            -0.053938 -0.050104  0.015964  \n",
              "City               0.008811  0.019094 -0.002121  \n",
              "Customer type     -0.036800  0.018073 -0.016763  \n",
              "Gender             0.005193  0.044578 -0.074258  \n",
              "Product line       1.000000  0.029896  0.020256  \n",
              "Payment            0.029896  1.000000 -0.003921  \n",
              "Quantity           0.020256 -0.003921  1.000000  "
            ],
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              "\n",
              "  <div id=\"df-a142e7d7-1d37-4eb2-831e-9ddddd00d015\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unit price</th>\n",
              "      <th>Tax 5%</th>\n",
              "      <th>Total</th>\n",
              "      <th>cogs</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Unit price</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.633962</td>\n",
              "      <td>0.633962</td>\n",
              "      <td>0.633962</td>\n",
              "      <td>0.633962</td>\n",
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              "      <td>0.010778</td>\n",
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              "    <tr>\n",
              "      <th>Tax 5%</th>\n",
              "      <td>0.633962</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.036442</td>\n",
              "      <td>0.041047</td>\n",
              "      <td>-0.012812</td>\n",
              "      <td>-0.019670</td>\n",
              "      <td>-0.049451</td>\n",
              "      <td>0.031621</td>\n",
              "      <td>-0.012434</td>\n",
              "      <td>0.705510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total</th>\n",
              "      <td>0.633962</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.036442</td>\n",
              "      <td>0.041047</td>\n",
              "      <td>-0.012812</td>\n",
              "      <td>-0.019670</td>\n",
              "      <td>-0.049451</td>\n",
              "      <td>0.031621</td>\n",
              "      <td>-0.012434</td>\n",
              "      <td>0.705510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cogs</th>\n",
              "      <td>0.633962</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.036442</td>\n",
              "      <td>0.041047</td>\n",
              "      <td>-0.012812</td>\n",
              "      <td>-0.019670</td>\n",
              "      <td>-0.049451</td>\n",
              "      <td>0.031621</td>\n",
              "      <td>-0.012434</td>\n",
              "      <td>0.705510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gross income</th>\n",
              "      <td>0.633962</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.036442</td>\n",
              "      <td>0.041047</td>\n",
              "      <td>-0.012812</td>\n",
              "      <td>-0.019670</td>\n",
              "      <td>-0.049451</td>\n",
              "      <td>0.031621</td>\n",
              "      <td>-0.012434</td>\n",
              "      <td>0.705510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rating</th>\n",
              "      <td>-0.008778</td>\n",
              "      <td>-0.036442</td>\n",
              "      <td>-0.036442</td>\n",
              "      <td>-0.036442</td>\n",
              "      <td>-0.036442</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.010238</td>\n",
              "      <td>0.049585</td>\n",
              "      <td>0.018889</td>\n",
              "      <td>0.004800</td>\n",
              "      <td>-0.020529</td>\n",
              "      <td>-0.005381</td>\n",
              "      <td>-0.015815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Branch</th>\n",
              "      <td>0.028202</td>\n",
              "      <td>0.041047</td>\n",
              "      <td>0.041047</td>\n",
              "      <td>0.041047</td>\n",
              "      <td>0.041047</td>\n",
              "      <td>0.010238</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.507401</td>\n",
              "      <td>-0.019608</td>\n",
              "      <td>-0.056318</td>\n",
              "      <td>-0.053938</td>\n",
              "      <td>-0.050104</td>\n",
              "      <td>0.015964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>City</th>\n",
              "      <td>-0.013763</td>\n",
              "      <td>-0.012812</td>\n",
              "      <td>-0.012812</td>\n",
              "      <td>-0.012812</td>\n",
              "      <td>-0.012812</td>\n",
              "      <td>0.049585</td>\n",
              "      <td>-0.507401</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.004899</td>\n",
              "      <td>0.012219</td>\n",
              "      <td>0.008811</td>\n",
              "      <td>0.019094</td>\n",
              "      <td>-0.002121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Customer type</th>\n",
              "      <td>-0.020238</td>\n",
              "      <td>-0.019670</td>\n",
              "      <td>-0.019670</td>\n",
              "      <td>-0.019670</td>\n",
              "      <td>-0.019670</td>\n",
              "      <td>0.018889</td>\n",
              "      <td>-0.019608</td>\n",
              "      <td>0.004899</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.039996</td>\n",
              "      <td>-0.036800</td>\n",
              "      <td>0.018073</td>\n",
              "      <td>-0.016763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>0.015445</td>\n",
              "      <td>-0.049451</td>\n",
              "      <td>-0.049451</td>\n",
              "      <td>-0.049451</td>\n",
              "      <td>-0.049451</td>\n",
              "      <td>0.004800</td>\n",
              "      <td>-0.056318</td>\n",
              "      <td>0.012219</td>\n",
              "      <td>0.039996</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.005193</td>\n",
              "      <td>0.044578</td>\n",
              "      <td>-0.074258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Product line</th>\n",
              "      <td>0.019321</td>\n",
              "      <td>0.031621</td>\n",
              "      <td>0.031621</td>\n",
              "      <td>0.031621</td>\n",
              "      <td>0.031621</td>\n",
              "      <td>-0.020529</td>\n",
              "      <td>-0.053938</td>\n",
              "      <td>0.008811</td>\n",
              "      <td>-0.036800</td>\n",
              "      <td>0.005193</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.029896</td>\n",
              "      <td>0.020256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Payment</th>\n",
              "      <td>-0.015941</td>\n",
              "      <td>-0.012434</td>\n",
              "      <td>-0.012434</td>\n",
              "      <td>-0.012434</td>\n",
              "      <td>-0.012434</td>\n",
              "      <td>-0.005381</td>\n",
              "      <td>-0.050104</td>\n",
              "      <td>0.019094</td>\n",
              "      <td>0.018073</td>\n",
              "      <td>0.044578</td>\n",
              "      <td>0.029896</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.003921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Quantity</th>\n",
              "      <td>0.010778</td>\n",
              "      <td>0.705510</td>\n",
              "      <td>0.705510</td>\n",
              "      <td>0.705510</td>\n",
              "      <td>0.705510</td>\n",
              "      <td>-0.015815</td>\n",
              "      <td>0.015964</td>\n",
              "      <td>-0.002121</td>\n",
              "      <td>-0.016763</td>\n",
              "      <td>-0.074258</td>\n",
              "      <td>0.020256</td>\n",
              "      <td>-0.003921</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a142e7d7-1d37-4eb2-831e-9ddddd00d015')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a142e7d7-1d37-4eb2-831e-9ddddd00d015 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-a142e7d7-1d37-4eb2-831e-9ddddd00d015');\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",
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              "\n",
              "\n",
              "<div id=\"df-ee6d336c-fd86-416e-9521-9b092fb6b002\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ee6d336c-fd86-416e-9521-9b092fb6b002')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ee6d336c-fd86-416e-9521-9b092fb6b002 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": \"Unit price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3697053696182865,\n        \"min\": -0.02023787498958826,\n        \"max\": 1.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          -0.013763476750283153,\n          1.0,\n          -0.015941047861448978\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tax 5%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.47969109754467404,\n        \"min\": -0.04945098885448819,\n        \"max\": 1.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -0.012433636619037083,\n          1.0,\n          -0.019670282859210703\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.47969109754467404,\n        \"min\": -0.049450988854488176,\n        \"max\": 1.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -0.012433636619037097,\n          1.0,\n          -0.0196702828592107\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cogs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4796910975446741,\n        \"min\": -0.049450988854488225,\n        \"max\": 1.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -0.012433636619037115,\n          1.0,\n          -0.019670282859210728\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gross income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.47969109754467404,\n        \"min\": -0.04945098885448819,\n        \"max\": 1.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -0.012433636619037083,\n          1.0,\n          -0.019670282859210703\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2811403883965318,\n        \"min\": -0.036441704997018466,\n        \"max\": 1.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.049585347972680084,\n          -0.008777507200779005,\n          -0.005381289418876156\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.32308640546099265,\n        \"min\": -0.5074005160020203,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.05010428831755317,\n          -0.053937557447478,\n          0.028202439875982782\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.32152940743502,\n        \"min\": -0.5074005160020203,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.019094256893597197,\n          0.008811417467895068,\n          -0.013763476750283153\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer type\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28027020064492325,\n        \"min\": -0.0368003107258994,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.01807343634843152,\n          -0.0368003107258994,\n          -0.02023787498958826\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28489989601017174,\n        \"min\": -0.07425830664537177,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.044577609103097215,\n          0.005193197139333984,\n          0.015444629894692443\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product line\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2765276359545256,\n        \"min\": -0.053937557447478,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.029896382687947025,\n          1.0,\n          0.019321027730208128\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2787068740842358,\n        \"min\": -0.05010428831755317,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          1.0,\n          0.029896382687947025,\n          -0.015941047861448978\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3992218859951013,\n        \"min\": -0.07425830664537177,\n        \"max\": 1.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          -0.002120919697472665,\n          0.010777564342497362,\n          -0.003920989589602425\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr(),linewidth=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "CtseJwZmY9zp",
        "outputId": "aad01ded-8518-439f-cb0b-6276ae122a88"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# BOOSTING MODEL WITH REGRESSION\n",
        "# ADA BOOST\n",
        "# GRADIENT\n",
        "# XGBOOST"
      ],
      "metadata": {
        "id": "PGF_FI2tZBhR"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "8-7J1fABZesR",
        "outputId": "13a011ac-f4e1-49c8-8306-6a4ba273ac96"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unit price    Tax 5%     Total      cogs  gross income    Rating  Branch  \\\n",
              "0    0.718847  0.521616  0.521616  0.521616      0.521616  0.850000       0   \n",
              "1    0.057855  0.067387  0.067387  0.067387      0.067387  0.933333       2   \n",
              "2    0.403316  0.319628  0.319628  0.319628      0.319628  0.566667       0   \n",
              "3    0.535603  0.463549  0.463549  0.463549      0.463549  0.733333       0   \n",
              "4    0.848131  0.604377  0.604377  0.604377      0.604377  0.216667       0   \n",
              "\n",
              "   City  Customer type  Gender  Product line  Payment  Quantity  \n",
              "0     2              0       0             3        2         7  \n",
              "1     1              1       0             0        0         5  \n",
              "2     2              1       1             4        1         7  \n",
              "3     2              0       1             3        2         8  \n",
              "4     2              1       1             5        2         7  "
            ],
            "text/html": [
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              "  <div id=\"df-66270e1e-1cdc-49d8-b811-22969b7548fa\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unit price</th>\n",
              "      <th>Tax 5%</th>\n",
              "      <th>Total</th>\n",
              "      <th>cogs</th>\n",
              "      <th>gross income</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Branch</th>\n",
              "      <th>City</th>\n",
              "      <th>Customer type</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Product line</th>\n",
              "      <th>Payment</th>\n",
              "      <th>Quantity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.718847</td>\n",
              "      <td>0.521616</td>\n",
              "      <td>0.521616</td>\n",
              "      <td>0.521616</td>\n",
              "      <td>0.521616</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.057855</td>\n",
              "      <td>0.067387</td>\n",
              "      <td>0.067387</td>\n",
              "      <td>0.067387</td>\n",
              "      <td>0.067387</td>\n",
              "      <td>0.933333</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.403316</td>\n",
              "      <td>0.319628</td>\n",
              "      <td>0.319628</td>\n",
              "      <td>0.319628</td>\n",
              "      <td>0.319628</td>\n",
              "      <td>0.566667</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.535603</td>\n",
              "      <td>0.463549</td>\n",
              "      <td>0.463549</td>\n",
              "      <td>0.463549</td>\n",
              "      <td>0.463549</td>\n",
              "      <td>0.733333</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.848131</td>\n",
              "      <td>0.604377</td>\n",
              "      <td>0.604377</td>\n",
              "      <td>0.604377</td>\n",
              "      <td>0.604377</td>\n",
              "      <td>0.216667</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-66270e1e-1cdc-49d8-b811-22969b7548fa')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-66270e1e-1cdc-49d8-b811-22969b7548fa 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-66270e1e-1cdc-49d8-b811-22969b7548fa');\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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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e4f292bd-1695-41f4-baca-631247d86550 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Unit price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.29477779648330865,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 943,\n        \"samples\": [\n          0.026368491321762333,\n          0.6748998664886515,\n          0.712283044058745\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tax 5%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2382675636885048,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.008251681369107578,\n          0.03743271979894794,\n          0.0796475484061333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23826756368850477,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.008251681369107575,\n          0.03743271979894794,\n          0.0796475484061333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cogs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2382675636885048,\n        \"min\": 0.0,\n        \"max\": 1.0000000000000002,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.00825168136910758,\n          0.03743271979894794,\n          0.07964754840613332\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gross income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2382675636885048,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.008251681369107578,\n          0.03743271979894794,\n          0.0796475484061333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2864300490631869,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          0.85,\n          0.016666666666666607,\n          0.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer type\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product line\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jMUasrGsZlao",
        "outputId": "1be9556f-a693-4794-c765-d2b04704545d"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unit price       943\n",
              "Tax 5%           990\n",
              "Total            990\n",
              "cogs             990\n",
              "gross income     990\n",
              "Rating            61\n",
              "Branch             3\n",
              "City               3\n",
              "Customer type      2\n",
              "Gender             2\n",
              "Product line       6\n",
              "Payment            3\n",
              "Quantity          10\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.drop('gross income',axis=1)\n",
        "x1 = pd.DataFrame(x)\n",
        "y = df['gross income']\n",
        "y1 = pd.DataFrame(y)"
      ],
      "metadata": {
        "id": "A40_FdehZiQd"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x1_train,x1_test,y1_train,y1_test = train_test_split(x1,y1,test_size=0.25,random_state=42)"
      ],
      "metadata": {
        "id": "UPSs3D4oZTce"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error,r2_score"
      ],
      "metadata": {
        "id": "zlConT7famU3"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ada boost\n",
        "\n",
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "model1 = AdaBoostRegressor()\n",
        "model1.fit(x1_train,y1_train)\n",
        "y1_predADB = model1.predict(x1_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a_j_X4jUaISf",
        "outputId": "06aecda4-4da0-43f0-f5c7-33ea6ef45f20"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"the mse is:\", mean_squared_error(y1_test,y1_predADB))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PNRlB_18ai2y",
        "outputId": "9803982c-7169-4c63-b615-4d1ae2792002"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the mse is: 0.00016126463782886142\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"the r2_score is:\", r2_score(y1_test,y1_predADB))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Racb5EHraxCU",
        "outputId": "6089b4f3-54dd-4592-c63b-a2d59c860c23"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the r2_score is: 0.9974654662008128\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# gradient\n",
        "\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "model2 = GradientBoostingRegressor()\n",
        "model2.fit(x1_train,y1_train)\n",
        "y1_predGBR = model2.predict(x1_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1mpHZZ2PbxNh",
        "outputId": "305ff148-ee08-415f-c7aa-250336b247c6"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_gb.py:437: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"the mse is:\", mean_squared_error(y1_test,y1_predGBR))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejfq8hwNcMFa",
        "outputId": "ecc616b8-c170-4dfb-9b23-dfa10c77d56a"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the mse is: 4.008467657204207e-06\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"the r2_score is:\", r2_score(y1_test,y1_predGBR))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W3DOIFUqcRkM",
        "outputId": "c946d035-3c96-4ec1-c064-3b82bd9dc0cc"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the r2_score is: 0.9999370004676976\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install xgboost"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "92WKTkaWc_hW",
        "outputId": "07102e3d-9730-44b0-80c2-c30a55d82a47"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.10/dist-packages (2.0.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.25.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.11.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# XGBOOST\n",
        "\n",
        "from xgboost import XGBRegressor\n",
        "model3 = XGBRegressor()\n",
        "model3.fit(x1_train,y1_train)\n",
        "y1_predXGR = model3.predict(x1_test)"
      ],
      "metadata": {
        "id": "1EKo8jKzcWYY"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"the mse is:\", mean_squared_error(y1_test,y1_predXGR))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iBYKkPsDcfqb",
        "outputId": "1f0aeed9-dd88-48d4-d97c-12a4604d7d8a"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the mse is: 1.4207216843807189e-05\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"the r2_score is:\", r2_score(y1_test,y1_predXGR))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pW9qOL8XdTfN",
        "outputId": "e9ef0aca-7398-491e-c066-d5374b0500a5"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the r2_score is: 0.9997767106802347\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "jR8SbeoCde00",
        "outputId": "7cec0325-5a30-49d4-8a34-0668fa80b091"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unit price    Tax 5%     Total      cogs  gross income    Rating  Branch  \\\n",
              "0    0.718847  0.521616  0.521616  0.521616      0.521616  0.850000       0   \n",
              "1    0.057855  0.067387  0.067387  0.067387      0.067387  0.933333       2   \n",
              "2    0.403316  0.319628  0.319628  0.319628      0.319628  0.566667       0   \n",
              "3    0.535603  0.463549  0.463549  0.463549      0.463549  0.733333       0   \n",
              "4    0.848131  0.604377  0.604377  0.604377      0.604377  0.216667       0   \n",
              "\n",
              "   City  Customer type  Gender  Product line  Payment  Quantity  \n",
              "0     2              0       0             3        2         7  \n",
              "1     1              1       0             0        0         5  \n",
              "2     2              1       1             4        1         7  \n",
              "3     2              0       1             3        2         8  \n",
              "4     2              1       1             5        2         7  "
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Unit price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.29477779648330865,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 943,\n        \"samples\": [\n          0.026368491321762333,\n          0.6748998664886515,\n          0.712283044058745\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tax 5%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2382675636885048,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.008251681369107578,\n          0.03743271979894794,\n          0.0796475484061333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23826756368850477,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.008251681369107575,\n          0.03743271979894794,\n          0.0796475484061333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cogs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2382675636885048,\n        \"min\": 0.0,\n        \"max\": 1.0000000000000002,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.00825168136910758,\n          0.03743271979894794,\n          0.07964754840613332\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gross income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2382675636885048,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 990,\n        \"samples\": [\n          0.008251681369107578,\n          0.03743271979894794,\n          0.0796475484061333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2864300490631869,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          0.85,\n          0.016666666666666607,\n          0.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer type\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product line\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Payment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model1.predict([[0.718847,\t0.521616,\t0.521616,\t0.521616,\t0.850000,\t0,\t2,\t0,\t0,\t3,\t2,\t7]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y97hCeCmdY8Y",
        "outputId": "e8eacf50-8c2e-4895-8a3c-f6cdac6c8b8b"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but AdaBoostRegressor was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.53350967])"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2.predict([[0.718847,\t0.521616,\t0.521616,\t0.521616,\t0.850000,\t0,\t2,\t0,\t0,\t3,\t2,\t7]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-p0pd3gdttj",
        "outputId": "55c0c54b-8ec6-4457-c6f4-c0f9f04e7008"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but GradientBoostingRegressor was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.52148503])"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model3.predict([[0.718847,\t0.521616,\t0.521616,\t0.521616,\t0.850000,\t0,\t2,\t0,\t0,\t3,\t2,\t7]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3x7e3QM8d-8w",
        "outputId": "0198ef1c-1145-4f2b-f453-8186655d80c7"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.52226293], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wbm_89iKeGFE"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}