WalmartSales.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
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      "source": [
        "df = pd.read_csv('Walmart_Sales.csv')"
      ],
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        "df.head()"
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              "   Store        Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
              "0      1  05-02-2010    1643690.90             0        42.31       2.572   \n",
              "1      1  12-02-2010    1641957.44             1        38.51       2.548   \n",
              "2      1  19-02-2010    1611968.17             0        39.93       2.514   \n",
              "3      1  26-02-2010    1409727.59             0        46.63       2.561   \n",
              "4      1  05-03-2010    1554806.68             0        46.50       2.625   \n",
              "\n",
              "          CPI  Unemployment  \n",
              "0  211.096358         8.106  \n",
              "1  211.242170         8.106  \n",
              "2  211.289143         8.106  \n",
              "3  211.319643         8.106  \n",
              "4  211.350143         8.106  "
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 6435,\n  \"fields\": [\n    {\n      \"column\": \"Store\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 1,\n        \"max\": 45,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          40,\n          26,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 143,\n        \"samples\": [\n          \"04-05-2012\",\n          \"18-06-2010\",\n          \"02-09-2011\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weekly_Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 564366.6220536975,\n        \"min\": 209986.25,\n        \"max\": 3818686.45,\n        \"num_unique_values\": 6435,\n        \"samples\": [\n          1138800.32,\n          1304850.67,\n          1769296.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Holiday_Flag\",\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\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.44493287581158,\n        \"min\": -2.06,\n        \"max\": 100.14,\n        \"num_unique_values\": 3528,\n        \"samples\": [\n          51.13,\n          98.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuel_Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4590197071928525,\n        \"min\": 2.472,\n        \"max\": 4.468,\n        \"num_unique_values\": 892,\n        \"samples\": [\n          2.84,\n          3.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39.35671229566418,\n        \"min\": 126.064,\n        \"max\": 227.2328068,\n        \"num_unique_values\": 2145,\n        \"samples\": [\n          184.613419,\n          214.1083654\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unemployment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8758847818627977,\n        \"min\": 3.879,\n        \"max\": 14.313,\n        \"num_unique_values\": 349,\n        \"samples\": [\n          8.185,\n          7.804\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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              "       '09-12-2011', '16-12-2011', '23-12-2011', '30-12-2011',\n",
              "       '06-01-2012', '13-01-2012', '20-01-2012', '27-01-2012',\n",
              "       '03-02-2012', '10-02-2012', '17-02-2012', '24-02-2012',\n",
              "       '02-03-2012', '09-03-2012', '16-03-2012', '23-03-2012',\n",
              "       '30-03-2012', '06-04-2012', '13-04-2012', '20-04-2012',\n",
              "       '27-04-2012', '04-05-2012', '11-05-2012', '18-05-2012',\n",
              "       '25-05-2012', '01-06-2012', '08-06-2012', '15-06-2012',\n",
              "       '22-06-2012', '29-06-2012', '06-07-2012', '13-07-2012',\n",
              "       '20-07-2012', '27-07-2012', '03-08-2012', '10-08-2012',\n",
              "       '17-08-2012', '24-08-2012', '31-08-2012', '07-09-2012',\n",
              "       '14-09-2012', '21-09-2012', '28-09-2012', '05-10-2012',\n",
              "       '12-10-2012', '19-10-2012', '26-10-2012'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['Date'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "CDZ4bfRwUUIj"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
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        "df.head()"
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        "id": "DLOZwaLkVXYy",
        "outputId": "d796b0fd-3d9a-4141-826a-999493bfc063"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Store  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price         CPI  \\\n",
              "0      1    1643690.90             0        42.31       2.572  211.096358   \n",
              "1      1    1641957.44             1        38.51       2.548  211.242170   \n",
              "2      1    1611968.17             0        39.93       2.514  211.289143   \n",
              "3      1    1409727.59             0        46.63       2.561  211.319643   \n",
              "4      1    1554806.68             0        46.50       2.625  211.350143   \n",
              "\n",
              "   Unemployment  \n",
              "0         8.106  \n",
              "1         8.106  \n",
              "2         8.106  \n",
              "3         8.106  \n",
              "4         8.106  "
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 6435,\n  \"fields\": [\n    {\n      \"column\": \"Store\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 1,\n        \"max\": 45,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          40,\n          26,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weekly_Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 564366.6220536975,\n        \"min\": 209986.25,\n        \"max\": 3818686.45,\n        \"num_unique_values\": 6435,\n        \"samples\": [\n          1138800.32,\n          1304850.67,\n          1769296.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Holiday_Flag\",\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\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.44493287581158,\n        \"min\": -2.06,\n        \"max\": 100.14,\n        \"num_unique_values\": 3528,\n        \"samples\": [\n          51.13,\n          98.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuel_Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4590197071928525,\n        \"min\": 2.472,\n        \"max\": 4.468,\n        \"num_unique_values\": 892,\n        \"samples\": [\n          2.84,\n          3.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39.35671229566418,\n        \"min\": 126.064,\n        \"max\": 227.2328068,\n        \"num_unique_values\": 2145,\n        \"samples\": [\n          184.613419,\n          214.1083654\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unemployment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8758847818627977,\n        \"min\": 3.879,\n        \"max\": 14.313,\n        \"num_unique_values\": 349,\n        \"samples\": [\n          8.185,\n          7.804\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
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          "height": 300
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        "id": "4l2MtGQ-VthK",
        "outputId": "f7cad27d-f165-4e23-d3e1-2da2c1e8794b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Store  Weekly_Sales  Holiday_Flag  Temperature   Fuel_Price  \\\n",
              "count  6435.000000  6.435000e+03   6435.000000  6435.000000  6435.000000   \n",
              "mean     23.000000  1.046965e+06      0.069930    60.663782     3.358607   \n",
              "std      12.988182  5.643666e+05      0.255049    18.444933     0.459020   \n",
              "min       1.000000  2.099862e+05      0.000000    -2.060000     2.472000   \n",
              "25%      12.000000  5.533501e+05      0.000000    47.460000     2.933000   \n",
              "50%      23.000000  9.607460e+05      0.000000    62.670000     3.445000   \n",
              "75%      34.000000  1.420159e+06      0.000000    74.940000     3.735000   \n",
              "max      45.000000  3.818686e+06      1.000000   100.140000     4.468000   \n",
              "\n",
              "               CPI  Unemployment  \n",
              "count  6435.000000   6435.000000  \n",
              "mean    171.578394      7.999151  \n",
              "std      39.356712      1.875885  \n",
              "min     126.064000      3.879000  \n",
              "25%     131.735000      6.891000  \n",
              "50%     182.616521      7.874000  \n",
              "75%     212.743293      8.622000  \n",
              "max     227.232807     14.313000  "
            ],
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              "      <th>Weekly_Sales</th>\n",
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              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>6435.000000</td>\n",
              "      <td>6.435000e+03</td>\n",
              "      <td>6435.000000</td>\n",
              "      <td>6435.000000</td>\n",
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              "      <td>6435.000000</td>\n",
              "      <td>6435.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>1.046965e+06</td>\n",
              "      <td>0.069930</td>\n",
              "      <td>60.663782</td>\n",
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              "      <td>7.999151</td>\n",
              "    </tr>\n",
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              "      <th>std</th>\n",
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              "      <td>5.643666e+05</td>\n",
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              "      <td>18.444933</td>\n",
              "      <td>0.459020</td>\n",
              "      <td>39.356712</td>\n",
              "      <td>1.875885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.099862e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-2.060000</td>\n",
              "      <td>2.472000</td>\n",
              "      <td>126.064000</td>\n",
              "      <td>3.879000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>5.533501e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>47.460000</td>\n",
              "      <td>2.933000</td>\n",
              "      <td>131.735000</td>\n",
              "      <td>6.891000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>9.607460e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>62.670000</td>\n",
              "      <td>3.445000</td>\n",
              "      <td>182.616521</td>\n",
              "      <td>7.874000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>34.000000</td>\n",
              "      <td>1.420159e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>74.940000</td>\n",
              "      <td>3.735000</td>\n",
              "      <td>212.743293</td>\n",
              "      <td>8.622000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>3.818686e+06</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>100.140000</td>\n",
              "      <td>4.468000</td>\n",
              "      <td>227.232807</td>\n",
              "      <td>14.313000</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-a878816e-80de-4549-b2e4-81cc7bf07930')\"\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-a878816e-80de-4549-b2e4-81cc7bf07930 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-a878816e-80de-4549-b2e4-81cc7bf07930');\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-4d105798-9140-4189-bb7b-7d842a22d4c6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4d105798-9140-4189-bb7b-7d842a22d4c6')\"\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",
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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-4d105798-9140-4189-bb7b-7d842a22d4c6 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\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Store\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2267.530836160574,\n        \"min\": 1.0,\n        \"max\": 6435.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6435.0,\n          23.0,\n          34.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weekly_Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1200219.5149406516,\n        \"min\": 6435.0,\n        \"max\": 3818686.45,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1046964.8775617715,\n          960746.04,\n          6435.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Holiday_Flag\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2275.0491726398463,\n        \"min\": 0.0,\n        \"max\": 6435.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.06993006993006994,\n          1.0,\n          0.25504894436982795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2257.0439834306026,\n        \"min\": -2.06,\n        \"max\": 6435.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          60.66378243978244,\n          62.67,\n          6435.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuel_Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2274.0622491841236,\n        \"min\": 0.4590197071928525,\n        \"max\": 6435.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.358606837606838,\n          3.445,\n          6435.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2220.778606683361,\n        \"min\": 39.35671229566418,\n        \"max\": 6435.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          171.57839384878014,\n          182.6165205,\n          6435.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unemployment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2272.5201710166866,\n        \"min\": 1.8758847818627977,\n        \"max\": 6435.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.99915104895105,\n          7.874,\n          6435.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h1PNPPHZVzN6",
        "outputId": "1eb8c7c3-47c1-4120-9668-8d078f281727"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6435 entries, 0 to 6434\n",
            "Data columns (total 7 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   Store         6435 non-null   int64  \n",
            " 1   Weekly_Sales  6435 non-null   float64\n",
            " 2   Holiday_Flag  6435 non-null   int64  \n",
            " 3   Temperature   6435 non-null   float64\n",
            " 4   Fuel_Price    6435 non-null   float64\n",
            " 5   CPI           6435 non-null   float64\n",
            " 6   Unemployment  6435 non-null   float64\n",
            "dtypes: float64(5), int64(2)\n",
            "memory usage: 352.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['Weekly_Sales'].values"
      ],
      "metadata": {
        "id": "N7BaS6ewVYRS"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vKDtjESVVpCZ",
        "outputId": "a7b7ee94-3498-4486-fca8-1735d6b3fc38"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ABKAvvMeWTir",
        "outputId": "4b628e20-b7ab-45c7-a47e-0b5d028a9f2b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6435,)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6nAM6hKkaQo2",
        "outputId": "badc62fc-17ee-4895-eed4-43ba9227a182"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1643690.900,  1641957.440,  1611968.170,  1409727.590,\n",
              "        1554806.680])"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_scaled = y/100000"
      ],
      "metadata": {
        "id": "BSEDe-sSaXqT"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FppBJwsWaeNA",
        "outputId": "db5ada3e-0e58-4359-bdd0-6350b19c647d"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 16.437,  16.420,  16.120, ...,  7.345,  7.181,  7.603])"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:, [0,2,3,4,5,6]].values"
      ],
      "metadata": {
        "id": "pqD795RTVqCF"
      },
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(X[:, 4])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "9ez4pzZ9iZYR",
        "outputId": "0f355d18-f5f2-4bed-a12d-423d151b6056"
      },
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 2264.000,  882.000,  0.000,  0.000,  0.000,  168.000,  538.000,\n",
              "         331.000,  1361.000,  891.000]),\n",
              " array([ 126.064,  136.181,  146.298,  156.415,  166.532,  176.648,\n",
              "         186.765,  196.882,  206.999,  217.116,  227.233]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 190
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = X.copy()"
      ],
      "metadata": {
        "id": "DkcSVHGlgp4b"
      },
      "execution_count": 191,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cpi_norm = np.linalg.norm(X[:, 4]) # root of sum of squares"
      ],
      "metadata": {
        "id": "73cAcsi3hG6u"
      },
      "execution_count": 192,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x[:, 4] = x[:, 4] / cpi_norm"
      ],
      "metadata": {
        "id": "mG6hmKYjgsXV"
      },
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ayjCfk2fguwy",
        "outputId": "440a60ea-cc21-475e-e332-9efff88c6fb0"
      },
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.000,  0.000,  42.310,  2.572,  0.015,  8.106],\n",
              "       [ 1.000,  1.000,  38.510,  2.548,  0.015,  8.106],\n",
              "       [ 1.000,  0.000,  39.930,  2.514,  0.015,  8.106],\n",
              "       [ 1.000,  0.000,  46.630,  2.561,  0.015,  8.106],\n",
              "       [ 1.000,  0.000,  46.500,  2.625,  0.015,  8.106]])"
            ]
          },
          "metadata": {},
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rows = X.shape[0]"
      ],
      "metadata": {
        "id": "vthHAz1PXORY"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#X_new = np.hstack(((np.ones((rows,1))), x))# Adding one more column for bias\n",
        "X_new = x.copy()"
      ],
      "metadata": {
        "id": "eypmoLlwXC0D"
      },
      "execution_count": 195,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_new.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s5gi79q6XZiF",
        "outputId": "35f2d4e2-0813-4eac-f77b-872e56df4c03"
      },
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6435, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(42) # Let's set the zero for time being\n",
        "theta = np.random.randn(1,X_new.shape[1]) # Setting values of theta randomly"
      ],
      "metadata": {
        "id": "LGq3pE_VXMQt"
      },
      "execution_count": 197,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "theta.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PARFPY_QXxNz",
        "outputId": "9ab69e73-7d41-4fc8-8f24-cc5c8065e448"
      },
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "theta"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "27nOR83VYiTb",
        "outputId": "3b215517-fdf5-4ab0-cd4a-062320e153ca"
      },
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.497, -0.138,  0.648,  1.523, -0.234, -0.234]])"
            ]
          },
          "metadata": {},
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_hat = np.dot(X_new, theta.T)"
      ],
      "metadata": {
        "id": "fJQsGWfOX2Gc"
      },
      "execution_count": 200,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_hat.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fSxht5j2X43f",
        "outputId": "74ed67a2-2a08-4fed-9541-6f1970e80550"
      },
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6435, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_hat[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vTtu5jUxX-bD",
        "outputId": "6b8d8f00-3f3b-45d8-8180-236930bd30f3"
      },
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 29.916],\n",
              "       [ 27.280],\n",
              "       [ 28.286],\n",
              "       [ 32.697],\n",
              "       [ 32.711]])"
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "error = y_hat.ravel() - y_scaled"
      ],
      "metadata": {
        "id": "LbtqdVBFYETr"
      },
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "error.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lippyqZcYGgv",
        "outputId": "b83520c2-3fb0-4aa5-b426-e0121d7c20d2"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6435,)"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "error.reshape(1,rows).shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EdFcejsDm29l",
        "outputId": "6e513dd9-218f-407f-8a92-1f74fdf6bf34"
      },
      "execution_count": 217,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 6435)"
            ]
          },
          "metadata": {},
          "execution_count": 217
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "error[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nMDCcIxaYdtn",
        "outputId": "0e8ecce0-7160-4d6e-b696-4768b684ede8"
      },
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 60.556,  55.439,  57.262,  69.478,  67.814])"
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "squared_error = error ** 2"
      ],
      "metadata": {
        "id": "50_WyUe7b24A"
      },
      "execution_count": 177,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "squared_error_sum = np.sum(squared_error)"
      ],
      "metadata": {
        "id": "8gqN0UXncFQZ"
      },
      "execution_count": 178,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "squared_error_sum"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hqwRNwr3cHfF",
        "outputId": "db408544-c42f-466f-c91d-0ede14bb61fc"
      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "59000407.63639484"
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "1/(2 * rows) * squared_error_sum"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6nz8W7HscQAi",
        "outputId": "b06826a0-b02a-4858-c1bb-330a5714826e"
      },
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4584.336257684137"
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "update = ((learning_rate/rows) * np.dot(error.reshape(1,rows), X))"
      ],
      "metadata": {
        "id": "Shv9b7y6cbGf"
      },
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "update.ravel()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n_rZjDqlcntj",
        "outputId": "de2bb0dd-dd12-4f68-e987-1f7cbed82fe6"
      },
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 6.292,  0.016,  18.175,  0.923,  47.483,  2.221])"
            ]
          },
          "metadata": {},
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt # data plotting"
      ],
      "metadata": {
        "id": "WML-oHHvYXeN"
      },
      "execution_count": 183,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "theta = np.random.randn(1,X_new.shape[1]) # Setting values of theta randomly\n",
        "\n",
        "iteration = 100000\n",
        "learning_rate = 0.0003\n",
        "J = np.zeros(iteration)\n",
        "\n",
        "# Let's train our model to compute values of theta\n",
        "for i in range(iteration):\n",
        "    # the predicted value is what we call y_hat\n",
        "    y_hat = np.dot(X_new, theta.T)\n",
        "    # the residue, or the error term is difference between what the\n",
        "    # prediction was and what the true value is\n",
        "    error = y_hat.ravel() - y_scaled\n",
        "    # we square the error (MSE) - why?\n",
        "    squared_error = error ** 2\n",
        "    # now we sum up the error across all samples\n",
        "    squared_error_mean = np.mean(np.sum(squared_error))\n",
        "    # we take the mean of this accumulated error\n",
        "    J[i] = squared_error_mean\n",
        "    # this is the update rule -\n",
        "    # we correct our estimate (theta) by a small faction of the gradient\n",
        "    # gradient caclulation requires calculus - the\n",
        "    update = ((learning_rate/rows) * np.dot(error.reshape(1,rows), X))\n",
        "    theta -= update.ravel()\n",
        "\n",
        "prediction = np.round(np.dot(X_new, theta.T))\n",
        "\n",
        "ax = plt.subplot(111)\n",
        "ax.plot(np.arange(iteration), np.log(J))\n",
        "ax.set_ylim([0,int(np.max(np.log(J)))])\n",
        "plt.ylabel(\"Cost Values\", color=\"Green\")\n",
        "plt.xlabel(\"No. of Iterations\", color=\"Green\")\n",
        "plt.title(\"Mean Squared Error vs Iterations\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "CLb3U_ugXjk3",
        "outputId": "30d3f115-5a21-4181-c1b9-db0f9a90fd3f"
      },
      "execution_count": 257,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "theta"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RMxbvt-vXoi9",
        "outputId": "ffe453b8-ceb9-4013-eeae-677772e877bf"
      },
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.137,  0.955, -0.010,  2.994,  117.232,  0.319]])"
            ]
          },
          "metadata": {},
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "J[:-10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GyQg2ViMYV1Q",
        "outputId": "e56c9368-6f9a-40a4-a64c-1d6197aaed62"
      },
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 77596866.112,  11590633.511,  2066024.754, ...,  195124.579,\n",
              "        195124.577,  195124.576])"
            ]
          },
          "metadata": {},
          "execution_count": 259
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mse = np.sum(np.sqrt((prediction - y_hat)**2)/float(len(y_scaled)) * 100)\n",
        "print(\"The model predicted values of Walmart dataset with an overall MSE of %s\" % (mse))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7HyCk0WomAwG",
        "outputId": "8d3309ff-dd2d-4f31-d9bf-3c8339f1aab8"
      },
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The model predicted values of Walmart dataset with an overall MSE of 24.824062456103714\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W-LzEMjkox1-",
        "outputId": "9e1a5ba3-d619-45ec-e1be-42570dc3f9d6"
      },
      "execution_count": 248,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 11.000],\n",
              "       [ 12.000],\n",
              "       [ 11.000],\n",
              "       ...,\n",
              "       [ 10.000],\n",
              "       [ 9.000],\n",
              "       [ 9.000]])"
            ]
          },
          "metadata": {},
          "execution_count": 248
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "togSZQZHo2bB",
        "outputId": "7b787607-c7ef-413e-ec1f-98f1be2ba499"
      },
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 16.437,  16.420,  16.120, ...,  7.345,  7.181,  7.603])"
            ]
          },
          "metadata": {},
          "execution_count": 249
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "differences = np.abs(prediction - y_scaled)"
      ],
      "metadata": {
        "id": "tgkx6qoIo5xl"
      },
      "execution_count": 262,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a figure and axis\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# Plot the points\n",
        "ax.scatter(range(len(prediction)), prediction, color='blue', label='prediction')\n",
        "ax.scatter(range(len(y_scaled)), y_scaled, color='red', label='sales (scaled)')\n",
        "\n",
        "# Set labels and title\n",
        "ax.set_xlabel('Index')\n",
        "ax.set_ylabel('Value')\n",
        "ax.set_title('Plot of Two Arrays with Distances')\n",
        "ax.legend()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "SIgab6jjrO3_",
        "outputId": "1bb9e5d9-3795-4119-f8c3-d012f334879e"
      },
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "L6lpk5KdrdvC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}