fashion_trends_dsw (1).ipynb

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          "text": [
            "Archive:  /content/sample_data/DSW ML Test-APSIT.zip\n",
            "  inflating: /content/DSW - ML Assignment v2.pdf  \n",
            "  inflating: /content/historic.csv   \n",
            "  inflating: /content/prediction_input.csv  \n"
          ]
        }
      ],
      "source": [
        "# prompt: generate code to unzip files /content/sample_data/DSW ML Test-APSIT.zip\n",
        "\n",
        "!unzip -j /content/sample_data/DSW\\ ML\\ Test-APSIT.zip -d /content\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "OeF1A8ybnqwU"
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      "execution_count": 4,
      "outputs": []
    },
    {
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      "source": [
        "df = pd.read_csv(\"historic.csv\")\n",
        "df.head()"
      ],
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          "height": 206
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      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   item_no    category      main_promotion  color  stars success_indicator\n",
              "0   739157       Tunic             Catalog  Green    3.1              flop\n",
              "1   591846      Hoodie  Category_Highlight    Red    1.5              flop\n",
              "2   337574  Sweatshirt             Catalog    Red    4.4               top\n",
              "3   401933  Polo-Shirt  Category_Highlight   Blue    3.1              flop\n",
              "4   812151      Hoodie  Category_Highlight  Green    4.1               top"
            ],
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              "      <td>739157</td>\n",
              "      <td>Tunic</td>\n",
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              "      <td>3.1</td>\n",
              "      <td>flop</td>\n",
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        "df.shape\n"
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        "outputId": "8a92a084-5c83-459f-8191-887bc703d54d"
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          "data": {
            "text/plain": [
              "(8000, 6)"
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          "metadata": {},
          "execution_count": 8
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        "df.duplicated().sum()"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8000 entries, 0 to 7999\n",
            "Data columns (total 6 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   item_no            8000 non-null   int64  \n",
            " 1   category           8000 non-null   object \n",
            " 2   main_promotion     8000 non-null   object \n",
            " 3   color              8000 non-null   object \n",
            " 4   stars              8000 non-null   float64\n",
            " 5   success_indicator  8000 non-null   object \n",
            "dtypes: float64(1), int64(1), object(4)\n",
            "memory usage: 375.1+ KB\n"
          ]
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      "execution_count": 11,
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              "             item_no        stars\n",
              "count    8000.000000  8000.000000\n",
              "mean   546474.338375     3.473075\n",
              "std    256513.463874     0.978810\n",
              "min    100171.000000     0.300000\n",
              "25%    325001.500000     2.800000\n",
              "50%    547081.000000     3.500000\n",
              "75%    764312.250000     4.200000\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",
              "\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-7f552253-ac88-4d6e-a128-5a26cea23d06 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-7f552253-ac88-4d6e-a128-5a26cea23d06');\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-da1a5503-5f10-44e8-bbde-b24339d20955\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-da1a5503-5f10-44e8-bbde-b24339d20955')\"\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-da1a5503-5f10-44e8-bbde-b24339d20955 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RI-WZYYHqXB0",
        "outputId": "1391e977-9949-4b3d-a088-ce998b06ba64"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "item_no              8000\n",
              "category                6\n",
              "main_promotion          4\n",
              "color                  10\n",
              "stars                  57\n",
              "success_indicator       2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['category'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-WxKdacqlFK",
        "outputId": "046261c4-c8f7-42ef-ea13-46772e3795ca"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Tunic' 'Hoodie' 'Sweatshirt' 'Polo-Shirt' 'T-Shirt' 'Blouse']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['main_promotion'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VuajM_gkqzeH",
        "outputId": "ef9205d1-a168-4048-c6d6-1e46629a18a6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Catalog' 'Category_Highlight' 'Display_Ad_Campaign' 'Frontpage_Header']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_features = [feature for feature in df.columns if df[feature].dtype != 'O']\n",
        "categorical_features = [feature for feature in df.columns if df[feature].dtype == 'O']"
      ],
      "metadata": {
        "id": "tsqKW2axq9oD"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# print columns\n",
        "print('We have {} numerical features : {}'.format(len(numeric_features), numeric_features))\n",
        "print('\\nWe have {} categorical features : {}'.format(len(categorical_features), categorical_features))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lKfCNGo_rHWe",
        "outputId": "59445120-a80a-487d-aaa1-7de68be2be56"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We have 2 numerical features : ['item_no', 'stars']\n",
            "\n",
            "We have 4 categorical features : ['category', 'main_promotion', 'color', 'success_indicator']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('category')['stars'].mean().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "QEb9nisDrMTn",
        "outputId": "9ef666c9-5af7-4687-c7d8-c7ec98dc5fa6"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='category'>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "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": [
        "# prompt: plot the average rating for each colour\n",
        "\n",
        "df.groupby('color')['stars'].mean().plot(kind='bar')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "zE_554A8rgSM",
        "outputId": "c9de8948-d0db-488f-cbfb-d971061a6443"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='color'>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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KFSpo165duv/++6+6z9SpU/Xggw9qxIgRkqSxY8dq9erVmj59umbOnJnPsgEAQGFxQ3NG0tPTJUnlypXLtc/WrVvVoUMHl7bw8HBt3bo1130cDocyMjJcNgAAUDjlO4zk5OTo+eefV5s2bdSoUaNc+6WmpiogIMClLSAgQKmpqbnuExUVJT8/P+cWFBSU3zIBAEABl+8wMmjQIO3fv18LFy68mfVIkiIjI5Wenu7ckpOTb/oxAABAweDWnJE/DB48WF9++aU2bNigKlWqXLNvYGCg0tLSXNrS0tIUGBiY6z52u112uz0/pQEAAA/j1siIZVkaPHiwli1bpnXr1qlGjRrX3Sc0NFRr1651aVu9erVCQ0PdqxQAABRKbo2MDBo0SDExMVqxYoXKli3rnPfh5+cnHx8fSVJERIQqV66sqKgoSdLQoUPVtm1bTZo0SV26dNHChQu1c+dOzZ49+yafCgAA8ERujYy8//77Sk9PV1hYmCpWrOjcPvvsM2efpKQkpaSkOF+3bt1aMTExmj17tpo2barFixdr+fLl15z0CgAAig63RkYsy7pun9jY2CvaHn/8cT3++OPuHAoAABQRrE0DAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKLfDyIYNG/Twww+rUqVKstlsWr58+TX7x8bGymazXbGlpqbmt2YAAFCIuB1GsrKy1LRpU7333ntu7Xf48GGlpKQ4twoVKrh7aAAAUAgVd3eHzp07q3Pnzm4fqEKFCrrjjjvc3g8AABRut23OSLNmzVSxYkV17NhRmzdvvmZfh8OhjIwMlw0AABROtzyMVKxYUTNnztSSJUu0ZMkSBQUFKSwsTLt37851n6ioKPn5+Tm3oKCgW10mAAAwxO3LNO6qW7eu6tat63zdunVrJSYmavLkyfrkk0+uuk9kZKSGDx/ufJ2RkUEgAQCgkLrlYeRq7rnnHm3atCnX9+12u+x2+22sCAAAmGLkOSPx8fGqWLGiiUMDAIACxu2RkczMTB09etT5+tixY4qPj1e5cuVUtWpVRUZG6uTJk5o3b54kacqUKapRo4YaNmyoCxcu6MMPP9S6dev07bff3ryzAAAAHsvtMLJz5061a9fO+fqPuR29evVSdHS0UlJSlJSU5Hz/4sWLeuGFF3Ty5EmVKlVKTZo00Zo1a1y+BgAAKLrcDiNhYWGyLCvX96Ojo11ejxw5UiNHjnS7MAAAUDSwNg0AADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACj3A4jGzZs0MMPP6xKlSrJZrNp+fLl190nNjZWzZs3l91uV3BwsKKjo/NRKgAAKIzcDiNZWVlq2rSp3nvvvTz1P3bsmLp06aJ27dopPj5ezz//vPr3769Vq1a5XSwAACh8iru7Q+fOndW5c+c89585c6Zq1KihSZMmSZLq16+vTZs2afLkyQoPD3f38AAAoJC55XNGtm7dqg4dOri0hYeHa+vWrbnu43A4lJGR4bIBAIDC6ZaHkdTUVAUEBLi0BQQEKCMjQ+fPn7/qPlFRUfLz83NuQUFBt7pMAABgSIG8myYyMlLp6enOLTk52XRJAADgFnF7zoi7AgMDlZaW5tKWlpYmX19f+fj4XHUfu90uu91+q0sDAAAFwC0fGQkNDdXatWtd2lavXq3Q0NBbfWgAAOAB3A4jmZmZio+PV3x8vKTLt+7Gx8crKSlJ0uVLLBEREc7+AwcO1A8//KCRI0fq0KFDmjFjhj7//HMNGzbs5pwBAADwaG6HkZ07dyokJEQhISGSpOHDhyskJESjR4+WJKWkpDiDiSTVqFFDX331lVavXq2mTZtq0qRJ+vDDD7mtFwAASMrHnJGwsDBZlpXr+1d7umpYWJj27Nnj7qEAAEARUCDvpgEAAEUHYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABG5SuMvPfee6pevbq8vb117733avv27bn2jY6Ols1mc9m8vb3zXTAAAChc3A4jn332mYYPH67XXntNu3fvVtOmTRUeHq5Tp07luo+vr69SUlKc24kTJ26oaAAAUHi4HUbeeecdDRgwQH369FGDBg00c+ZMlSpVSnPmzMl1H5vNpsDAQOcWEBBwQ0UDAIDCw60wcvHiRe3atUsdOnT4/y/g5aUOHTpo69atue6XmZmpatWqKSgoSN26ddOBAweueRyHw6GMjAyXDQAAFE5uhZGff/5Z2dnZV4xsBAQEKDU19ar71K1bV3PmzNGKFSs0f/585eTkqHXr1vrxxx9zPU5UVJT8/PycW1BQkDtlAgAAD3LL76YJDQ1VRESEmjVrprZt22rp0qUqX768Zs2ales+kZGRSk9Pd27Jycm3ukwAAGBIcXc633XXXSpWrJjS0tJc2tPS0hQYGJinr1GiRAmFhITo6NGjufax2+2y2+3ulAYAADyUWyMjJUuWVIsWLbR27VpnW05OjtauXavQ0NA8fY3s7GwlJCSoYsWK7lUKAAAKJbdGRiRp+PDh6tWrl1q2bKl77rlHU6ZMUVZWlvr06SNJioiIUOXKlRUVFSVJGjNmjFq1aqXg4GCdPXtWb7/9tk6cOKH+/fvf3DMBAAAeye0w8re//U2nT5/W6NGjlZqaqmbNmmnlypXOSa1JSUny8vr/AZdff/1VAwYMUGpqqvz9/dWiRQtt2bJFDRo0uHlnAQAAPJbbYUSSBg8erMGDB1/1vdjYWJfXkydP1uTJk/NzGAAAUASwNg0AADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACj8hVG3nvvPVWvXl3e3t669957tX379mv2X7RokerVqydvb281btxYX3/9db6KBQAAhY/bYeSzzz7T8OHD9dprr2n37t1q2rSpwsPDderUqav237Jli3r06KF+/fppz5496t69u7p37679+/ffcPEAAMDzFXd3h3feeUcDBgxQnz59JEkzZ87UV199pTlz5ujll1++ov/UqVP14IMPasSIEZKksWPHavXq1Zo+fbpmzpx51WM4HA45HA7n6/T0dElSRkZGnuvMcfyW57755U49+VUYzqMwnIPEeeRVYTgHifPIq8JwDhLnkVfunsMf/S3LunZHyw0Oh8MqVqyYtWzZMpf2iIgIq2vXrlfdJygoyJo8ebJL2+jRo60mTZrkepzXXnvNksTGxsbGxsZWCLbk5ORr5gu3RkZ+/vlnZWdnKyAgwKU9ICBAhw4duuo+qampV+2fmpqa63EiIyM1fPhw5+ucnBydOXNGd955p2w2mzsl51lGRoaCgoKUnJwsX1/fW3KMW60wnINUOM6jMJyDxHkUJIXhHKTCcR6F4Ryk23MelmXp3LlzqlSp0jX7uX2Z5naw2+2y2+0ubXfcccdtObavr69Hf7ikwnEOUuE4j8JwDhLnUZAUhnOQCsd5FIZzkG79efj5+V23j1sTWO+66y4VK1ZMaWlpLu1paWkKDAy86j6BgYFu9QcAAEWLW2GkZMmSatGihdauXetsy8nJ0dq1axUaGnrVfUJDQ136S9Lq1atz7Q8AAIoWty/TDB8+XL169VLLli11zz33aMqUKcrKynLeXRMREaHKlSsrKipKkjR06FC1bdtWkyZNUpcuXbRw4ULt3LlTs2fPvrlncoPsdrtee+21Ky4PeZLCcA5S4TiPwnAOEudRkBSGc5AKx3kUhnOQCtZ52CzrevfbXGn69Ol6++23lZqaqmbNmmnatGm69957JUlhYWGqXr26oqOjnf0XLVqkV199VcePH1ft2rU1ceJEPfTQQzftJAAAgOfKVxgBAAC4WVibBgAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgB4JSdna0NGzbo7Nmzpku5YYmJiXr11VfVo0cPnTp1SpL0zTff6MCBA4Yrg6fy9M9U1apVFRERoY8++kiJiYmmy3FRpMPI/v37c31v+fLlt6+Qm+TChQumSyjSsrKyNGrUKLVu3VrBwcGqWbOmy+YJihUrpk6dOunXX381XcoNiYuLU+PGjfXf//5XS5cuVWZmpiRp7969eu211wxXl3ee/DMqIyMjz5snKAyfqfHjx8vb21tvvfWWateuraCgID311FP64IMPdOTIEaO1FennjFSuXFmbNm1SjRo1XNqXLFmiiIgIZWVlGaos73JycjRu3DjNnDlTaWlp+v7771WzZk2NGjVK1atXV79+/UyXmGdr167V2rVrderUKeXk5Li8N2fOHENV5V2PHj0UFxenp59+WhUrVrxihemhQ4caqsw9LVu21FtvvaUHHnjAdCn5Fhoaqscff1zDhw9X2bJltXfvXtWsWVPbt2/Xo48+qh9//NF0iXniyT+jvLy88rzKenZ29i2u5sYVls/UH1JSUhQXF6cvv/xSn332mXJycoz+PxTIVXtvl/79+6tDhw7avHmzc+G+zz77TH379nV5gmxB9uabb2ru3LmaOHGiBgwY4Gxv1KiRpkyZ4jFh5I033tCYMWPUsmXLq/4i9wTffPONvvrqK7Vp08Z0KTfkzTff1IsvvqixY8eqRYsWKl26tMv7nrBKaUJCgmJiYq5or1Chgn7++WcDFeWPJ/+MWr9+vfPfx48f18svv6zevXs71yXbunWr5s6d61w6pKArLJ+p3377TZs2bVJsbKzWr1+vPXv2qFGjRgoLCzNbmFXEDR482GrYsKH1yy+/WAsWLLB8fHysxYsXmy4rz2rVqmWtWbPGsizLKlOmjJWYmGhZlmV999131h133GGyNLcEBgZa8+bNM13GDalevbp18OBB02XcMJvN5ty8vLyc2x+vPUHlypWtzZs3W5bl+n2xdOlSq2bNmiZLc5un/4yyLMtq3769FRMTc0X7ggULrLZt297+gvKhMHymQkNDLW9vbyskJMQaNmyYtXz5cuvMmTOmy7Isy7KK9MiIJL377rvq2bOnWrVqpZMnT+rTTz9Vt27dTJeVZydPnlRwcPAV7Tk5Obp06ZKBivLn4sWLat26tekybsjYsWM1evRozZ07V6VKlTJdTr79+S9aT/Xkk0/qpZde0qJFi2Sz2ZSTk6PNmzfrxRdfVEREhOny3OLpP6Oky6MgM2fOvKK9ZcuW6t+/v4GK3FcYPlOHDh1S6dKlVa9ePdWrV0/169eXv7+/6bIkFcE5I1988cUVbZcuXdKwYcPUqVMnde3a1dn+538XVC1atNCwYcP01FNPuVzHHDNmjFavXq2NGzeaLjFPXnrpJZUpU0ajRo0yXUq+hYSEKDExUZZlqXr16ipRooTL+7t37zZUWdFz8eJFDRo0SNHR0crOzlbx4sWVnZ2tv//974qOjlaxYsVMl5irwvYzSpLq1q2rbt26aeLEiS7tI0eO1IoVK3T48GFDleWdJ3+m/mBZlhISEhQbG6u4uDht2LBBJUuWVNu2bdWuXTuXS/23W5ELI15eebuByGazecSkqhUrVqhXr16KjIzUmDFj9MYbb+jw4cOaN2+evvzyS3Xs2NF0iXkydOhQzZs3T02aNFGTJk2u+EX+zjvvGKos7954441rvu8pM+4l6ezZs/roo4/03XffSZIaNmyovn37ys/Pz3Bl7klKStL+/fuVmZmpkJAQ1a5d23RJ11XYfkZJ0tdff63HHntMwcHBzhXet2/friNHjmjJkiUetYp7cnKyEhISPOozdTWWZWnXrl2aPn26FixYYHwCa5ELI4XRxo0bNWbMGO3du1eZmZlq3ry5Ro8erU6dOpkuLc/atWuX63s2m03r1q27jdUUbTt37lR4eLh8fHx0zz33SJJ27Nih8+fP69tvv1Xz5s0NVwhPlJycrPfff1+HDh2SJNWvX18DBw5UUFCQ4cryZsyYMXrxxRevuAR7/vx5vf322xo9erShyvJu9+7dio2NVWxsrDZt2qRz586pcePGCgsLU9u2bY1e/iOMADfR2bNntXjxYiUmJmrEiBEqV66cdu/erYCAAFWuXNl0eXly3333KTg4WB988IGKF788rez3339X//799cMPP2jDhg2GK7y+4cOHX7XdZrPJ29tbwcHB6tatm8qVK3ebK4OnKlasmFJSUlShQgWX9l9++UUVKlTwiFGq4sWLKyQkRG3btlXbtm11//33F5jRziIdRoYMGaLg4GANGTLEpX369Ok6evSopkyZYqawImjdunVq06aN7Ha76VLybd++ferQoYP8/Px0/PhxHT58WDVr1tSrr76qpKQkzZs3z3SJeeLj46M9e/aoXr16Lu0HDx5Uy5Yt9dtvvxmqLO/atWun3bt3Kzs7W3Xr1pUkff/99ypWrJjq1aunw4cPy2azadOmTWrQoIHhaq/N05+/84eNGzdq1qxZ+uGHH7Ro0SJVrlxZn3zyiWrUqKG//OUvpsu7Li8vL6Wlpal8+fIu7evWrdPf/vY3nT592lBleZeRkVFgb80v0k9gXbJkyVWfCdG6dWstXrzYQEXu8/LyUrFixXLdPEXXrl3l5+en++67T6NGjdKaNWt0/vx502W5Zfjw4erdu7eOHDkib29vZ/tDDz3kEaMJf/D19VVSUtIV7cnJySpbtqyBitzXrVs3dejQQT/99JN27dqlXbt26ccff1THjh3Vo0cPnTx5Uvfff7+GDRtmutRreuONN9SpUyetXbtWP//8s3799VeXzVMsWbLEeelv9+7dcjgckqT09HSNHz/ecHXX5u/vr3Llyslms6lOnToqV66cc/Pz81PHjh31xBNPmC4zT/4IIrt27dL8+fM1f/78gjOx3sgNxQWE3W63jhw5ckX7kSNHLLvdbqAi9y1fvtxlW7RokfWvf/3Lqly5svXhhx+aLi/PLl68aG3atMkaN26c1alTJ6tMmTJWyZIlrdatW1uvvPKK6fLyxNfX1zp69KhlWa7PITh+/LjHfJ4sy7Kee+45q0qVKtbChQutpKQkKykpyfr000+tKlWqWEOHDjVdXp5UqlTJOnDgwBXt+/fvtypVqmRZlmXt2rXLuvPOO293aW4pDM/fsSzLatasmTV37lzLsly/N3bv3m0FBASYLO26oqOjrY8//tiy2WzW1KlTrejoaOcWExNjbdmyxXSJeZaWlmaFhYVZNpvN8vf3t/z9/S2bzWa1b9/eOnXqlNHainQYadiwofXuu+9e0T5t2jSrfv36Biq6eRYsWGB17drVdBn5tn//fqtXr15W8eLFPeZBW+XLl7d2795tWZbrD9xvv/3WqlKlisnS3OJwOKwhQ4ZYJUuWdD7wzG63W88//7x14cIF0+XlSenSpa3169df0b5+/XqrTJkylmVZVmJiolW2bNnbXJl7ypUr5wy4nszHx8c6duyYZVmu3xuJiYkeE9RjY2Otixcvmi7jhjzxxBNWy5YtXR7OeODAAatly5bWk08+abCyIv7Qs+HDh2vw4ME6ffq02rdvL+ny9dlJkyZ5/HyRVq1a6R//+IfpMvLs+++/d87yjouLk8Ph0H333ad///vf5h9TnEddu3bVmDFj9Pnnn0u6PFkyKSlJL730kh577DHD1eVdyZIlNXXqVEVFRTlX9qxVq5ZHPcitW7du6tu3ryZNmqS7775b0uU7gl588UV1795d0uVbS+vUqWOwyuvr37+/YmJiPPr5O5IUGBioo0ePqnr16i7tmzZtKtCLSP55jkVISIjOnz+f6+XjgjoX489WrlypNWvWqH79+s62Bg0a6L333jN/96XRKFQAzJgxw6pcubLz8dc1atRwDid6qt9++80aOnSoVadOHdOl5JnNZrMqVKhgjRs3ztq7d6+Vk5NjuiS3nT171urQoYN1xx13WMWKFbOCgoKsEiVKWPfff7+VmZlpurwi5dy5c1b//v1dRndKlixpDRgwwPl/sWfPHmvPnj1mC72OIUOGWHfccYd1//33W4MHD7aGDRvmsnmK8ePHWw0aNLC2bdtmlS1b1tq4caM1f/5866677rKmTZtmurxceXl5WWlpaZZlWVcsj+CJyySUKVPmqp/53bt3Gx8lLNJ30/zZ6dOn5ePjozJlypguxS3+/v4ui8pZlqVz586pVKlSmj9/vsc8ofH555/Xhg0bdPDgQTVv3lxhYWEKCwvTX/7yF4/6i1y6/Nfevn37nM986dChg+mSruvRRx/Nc9+lS5fewkpurszMTP3www+SpJo1a3rc93dhef6OZVkaP368oqKinHdj2e12jRgxQpGRkfLx8TFc4dXFxcWpTZs2Kl68uOLi4q7Zt23btrepqvzr1q2bzp49q08//VSVKlWSdHlJkZ49e8rf31/Lli0zVhthxMNFR0e7hBEvLy+VL19e9957b4FZc8AdZ8+e1caNGxUXF6e4uDgdOHBAISEh2rx5s+nS3HLhwgXZ7XaPWX24T58+ee778ccf38JKUJhdvHhRR48eVWZmpho0aKBZs2bp7bffVmpqqunSioTk5GR17dpVBw4ccD5sLjk5WY0aNdIXX3yhKlWqGKutSM8ZkaTFixfr888/V1JSki5evOjyXoG55ekaevfubbqEmyo7O1uXLl2Sw+HQhQsX5HA4PGLdCuny4oTjxo3TzJkzlZaWpu+//141a9bUqFGjVL16dfXr1890ibkqbAEjKytLEyZMyPX5HH+MluDWcjgcev3117V69WrnSEj37t318ccf65FHHlGxYsUK/O3Vf3b27Flt3779qp8pT1gsLygoSLt379aaNWtcnoRbEEZvi3QYmTZtml555RX17t1bK1asUJ8+fZSYmKgdO3Zo0KBBpsvL1b59+/Lct0mTJrewkptnyJAhio2N1cGDB+Xv76/7779fAwYMUFhYmBo3bmy6vDx58803NXfuXE2cONFlwalGjRppypQpBTqMXM3p06edQbBu3bpXPOypIOvfv7/i4uL09NNPq2LFih4zQiVdvmQWHR0tX19fPfLII9esvaBfMhs9erRmzZqlDh06aMuWLXr88cfVp08fbdu2TZMmTdLjjz/uMc9D+s9//qOePXsqMzNTvr6+Lv8vNpvNI8KIdLnWjh07Frh1y4p0GJkxY4Zmz56tHj16KDo6WiNHjlTNmjU1evRonTlzxnR5uWrWrJlsNpuud4XNkxbSSklJ0T/+8Q+FhYWpUaNGpsvJl3nz5mn27Nl64IEHNHDgQGd706ZNnX+FeIKsrCw999xzmjdvnvOvv2LFiikiIkLvvvuuR8zh+eabb/TVV19d9aGGBZ2fn5/zF90dd9yRp+/1gmrRokWaN2+eunbtqv3796tJkyb6/ffftXfvXo8KiJL0wgsvqG/fvho/frxHfA/8Ydq0aXnu+79PI7+divSckVKlSum7775TtWrVVKFCBa1evVpNmzbVkSNH1KpVK/3yyy+mS7yqEydO5LlvtWrVbmEl+DMfHx8dOnRI1apVU9myZbV3717VrFlTBw8e1D333KPMzEzTJebJM888ozVr1mj69OnOX+abNm3SkCFD1LFjR73//vuGK7y+GjVq6Ouvv3a5hdGTZGdn69///re++OILXbx4Ue3bt9frr79eYCd65qZkyZI6duyYc10mHx8fbd++3WNGO/+sdOnSSkhIKNC3Il9NjRo18tTPZrMZvXxZpEdGAgMDdebMGVWrVk1Vq1bVtm3b1LRpUx07dqxA/yXy54Dxyy+/6M4775R0eSLSBx98oPPnz6tr16667777TJWYL4mJiZoyZYpz2foGDRpo6NChqlWrluHK8qZBgwbauHHjFQFw8eLFCgkJMVSV+5YsWaLFixe7PN/loYceko+Pj5544gmPCCNjx47V6NGjNXfuXI/6K/YP48eP1+uvv64OHTrIx8dH06ZN0+nTpz1qLRrpcqgqWbKk83Xx4sU97o6mP4SHh2vnzp0eF0aOHTtmuoQ8KdJhpH379vriiy8UEhKiPn36aNiwYVq8eLF27tzp1q2OJiQkJOjhhx9WcnKyateurYULF+rBBx9UVlaWvLy8NHnyZC1evNj5gKeCbtWqVeratauaNWvm/Gt88+bNatiwof7zn/8UuOubVzN69Gj16tVLJ0+eVE5OjpYuXarDhw9r3rx5+vLLL02Xl2e//fabAgICrmivUKGCRyySJ0mTJk1SYmKiAgICVL16dZUoUcLl/YI+OX3evHmaMWOGnnnmGUnSmjVr1KVLF3344Yfy8vKcJcUsy1Lv3r2dC2BeuHBBAwcOVOnSpV36FdS5L1988YXz3126dNGIESN08OBBNW7c+IrPlKc8RqGgKtKXaXJycpSTk+NcJn3hwoXasmWLateurWeeecYl0Rc0nTt3VvHixfXyyy/rk08+0Zdffqnw8HB98MEHkqTnnntOu3bt0rZt2wxXmjchISEKDw/XhAkTXNpffvllffvttwX+l8cfNm7cqDFjxmjv3r3O54yMHj3a/NMN3fDAAw/ozjvv1Lx585wL/p0/f169evXSmTNntGbNGsMVXt8bb7xxzfdfe+2121RJ/tjtdh09etR5+6UkeXt76+jRo0Zvv3RXXm8ZL6h3c+U1+BXk+XnDhw/Pc9933nnnFlZybUU6jHiyu+66S+vWrVOTJk2cs7t37NihFi1aSJIOHTqkVq1a6ezZs2YLzSNvb28lJCSodu3aLu3ff/+9mjRpogsXLhiqLG9+//13jR8/Xn379vWoXxZXs3//foWHh8vhcKhp06aSpL1798rb21urVq1Sw4YNDVdY+BUrVkypqakudzCVLVtW+/bty/McANwcP/zwg8ddmvmzaz04789MP0SvyF2mKSy3xZ45c0aBgYGSpDJlyqh06dIuDznz9/fXuXPnTJXntvLlyys+Pv6KMBIfH68KFSoYqirvihcvrokTJ3rM7X3X0qhRIx05ckQLFixw3gXUo0cP9ezZ0+MmUHqq/728IV39EkdBvbxRmAQHB6tatWpq37692rVrp3bt2jkn5HqC9evXmy4hT4pcGClMt8X+761xnnar3J8NGDBA//jHP/TDDz+odevWki7PGXnrrbfcGmY06YEHHlBcXNwVi4F5olKlSrk8K8XTZGdna/Lkybk+0LAg37ovSb169bqi7amnnjJQCdatW+dcxDMmJkYXL15UzZo1XcLJ1eZYFWRHjx5VYmKi7r//fvn4+MiyLOO/P4rcZZrCclusl5eXOnfu7PzL6T//+Y/at2/v/KvJ4XBo5cqVBT5Q/cGyLE2ZMkWTJk3STz/9JEmqVKmSRowYoSFDhhj/RsmLmTNn6o033lDPnj3VokWLKybpFfQJbrt27dKLL76oFStWXLECaXp6urp3764pU6Y4L90UZKNHj9aHH36oF154Qa+++qpeeeUVHT9+XMuXL9fo0aONPk8BnuvChQvasmWLM5xs375dly5dUr169XTgwAHT5V3XL7/8oieeeELr16+XzWbTkSNHVLNmTfXt21f+/v6aNGmSsdqKXBj5M0++LdbTJ4b92e+//66YmBiFh4crICDAeXmpbNmyhitzz7Umu3nCSNvf//531a9fP9fl6sePH6+DBw9q/vz5t7ky99WqVUvTpk1Tly5dVLZsWcXHxzvbtm3bppiYGNMlwoNdvHhRmzdv1jfffKNZs2YpMzOzwH9/S5cfWX/q1Cl9+OGHql+/vvNZSKtWrdLw4cPNBqrbt0BwwbFv3z6rWrVqlpeXl1W3bl1rz549VkBAgFWmTBnL19fXKlasmLVs2TLTZRYpPj4+1vHjx02XUaTVrFnT2rt3b67v79u3z6pRo8ZtrCj/SpUqZZ04ccKyLMsKDAy0du3aZVmWZSUmJlq+vr4mS4MHcjgcVlxcnPX6669bYWFhlo+Pj1WnTh2rf//+1rx585yftYIuICDAio+PtyzLssqUKWMlJiZalnX5+6J06dImS7OK3JwRSRo5cqQaN26sBQsW6JNPPtFf//pXdenSxeW22AkTJnjMMzoKg3vuuUd79uwp0JfGcnP+/HmtXbtWf/3rXyVJkZGRcjgczveLFy+uMWPGOG+TLahOnjx5zdGoMmXKKCUl5TZWlH9VqlRRSkqKqlatqlq1aunbb79V8+bNtWPHDpdJocD1tG/fXv/9739Vo0YNtW3bVs8884xiYmJUsWJF06W5LSsr66oPATxz5ozx74siGUZ27NjhvC22adOmmj17tp599lnnMPtzzz2nVq1aGa6yaHn22Wf1wgsv6Mcff7zqfIuCfGfT3Llz9dVXXznDyPTp09WwYUPnnSeHDh1SYGBggZ+IW758eR0+fDjXW0cPHTqku+666zZXlT+PPPKI1q5dq3vvvVfPPfecnnrqKX300UdKSkryqFViYd7GjRtVsWJFtW/fXmFhYWrbtq3z8r6n+Omnn1SpUiXdd999mjdvnsaOHSvp8uXjnJwcTZw4Mc+3AN8qRXLOiJeXl1JTU523jP55HRFJSktLU6VKlTziGmBh4cnzLe677z6NHDlSDz/8sKQrP0/z58/Xe++9p61bt5os87r69Omjo0ePauPGjVe8Z1mW7rvvPtWuXdsj5iH9r23btjkfaPjH/xOQF1lZWdq4caNiY2O1fv16xcfHq06dOmrbtq0znBT0Fa39/f313nvvqWnTpmrfvr2aN2+udevWqWvXrjpw4IDOnDmjzZs3G116o8iGkbS0NOcH6H8fJkQYuf2ud5dTQb58U7FiRW3dutV5S2/58uW1Y8cO5+vvv/9ed999t9LT080VmQeJiYlq0aKF6tatqxdeeEF169aVdHlEZNKkSfr++++1c+dOBQcHG6702i5duqRnnnlGo0aN4gFhuOnOnTunTZs2af369YqNjdXevXtVu3Zt7d+/33RpuZoxY4ZeeuklPfjgg5o5c6Zmzpzp8pToQYMGGb/sVCQv00i65noJf77ej9ujTJkyHntn09mzZ10+M6dPn3Z5PycnxyM+U7Vq1dKaNWvUu3dvPfnkk87bqS3LUoMGDbR69eoCH0QkqUSJElqyZEmudwUBN6J06dIqV66cypUrJ39/fxUvXty5uGdB9eyzz6pz587q16+fGjZsqNmzZ+uVV14xXZaLIjkyUphui/V011vwLysrq8Av+Fe7dm1NmDBBjz322FXf//zzz/Wvf/1LR48evc2V5V98fLyOHDkiy7JUp04dNWvWzHRJbunVq5eaNWvG/BDcsJycHO3cudN5mWbz5s3KyspS5cqVnQ89a9euXYEevf2z6dOna9iwYapfv75zXbY/mFwDrEiGERQchWHBv6FDh2rNmjXatWvXFXfMnD9/Xi1btlSHDh00depUQxXm3+bNm9WyZUvjM+3d9eabb2rSpElq3769WrZsecWEaB56hrzy9fVVVlaWAgMDncEjLCzM6PyK/Dpx4oT69Omj/fv365lnnrkijJhcQJIwAqMKw4J/aWlpatasmUqWLKnBgwerTp06kqTDhw9r+vTp+v3337Vnzx6Pe2S0dPkHcXx8vMctFHatuSI2m00//PDDbawGnmzWrFlq166d8/vaU33wwQd64YUX1KFDB82aNavATboljMCownJn07Fjx/TPf/5Tq1evdq57ZLPZ1LFjR82YMcPjfpn/4X//PzzNzz//LEkec0sycCs8+OCD2r59u6ZMmVJgF/MsshNYUXAUhgX/atSooZUrV+rMmTPOuSHBwcEqV66c4cqKnrNnz+qVV17RZ599pl9//VXS5Vsbn3zySY0bN05+fn6GKwRur+zsbO3bt09VqlQxXUquGBmBUYVtwb/CJiYmRt26dbtizkVBdebMGYWGhurkyZPq2bOn6tevL0k6ePCgYmJiFBQUpC1btsjf399wpQD+jDACo7izCTfT888/r7Vr12rNmjVXzNFJTU1Vp06d9MADD2jy5MmGKgRwNYQRAHr00UcVHR0tX19fPfroo9fsu3Tp0ttUlfuqV6+uWbNmKTw8/Krvr1y5UgMHDtTx48dvb2EArok5IwDk5+fnnKvj6+vrkfN2JCklJUUNGzbM9f1GjRopNTX1NlYEIC8IIwBcLoNFR0ebK+QG3XXXXTp+/HiuE/WOHTvGpGKgAMp9dTIARVL79u2v+lyXjIwMtW/f/vYX5Ibw8HC98sorunjx4hXvORwOjRo1Sg8++KCBygBcC3NGALj432e//OHUqVOqXLmyLl26ZKiy6/vxxx+dT4wdNGiQ6tWrJ8uy9N1332nGjBlyOBzauXOngoKCTJcK4E+4TANAkrRv3z7nvw8ePOgytyI7O1srV65U5cqVTZSWZ1WqVNHWrVv17LPPKjIy8ooH0E2fPp0gAhRAjIwAkHR5ROTPK/X+Lx8fH7377rvq27fv7S4tX3799VcdOXJEEg+gAwo6wggASZcX0bIsSzVr1tT27dtd1q4oWbKkKlSooGLFihmsEEBhRRgBAABGMWcEgL744os89+3atestrARAUcTICAB5eeXtLn+bzcY6QQBuOsIIAAAwioeeAQAAo5gzAsDFmDFjrvn+6NGjb1MlAIoKLtMAcBESEuLy+tKlSzp27JiKFy+uWrVqaffu3YYqA1BYMTICwMWePXuuaMvIyFDv3r31yCOPGKgIQGHHyAiAPElISNDDDz+s48ePmy4FQCHDBFYAeZKenq709HTTZQAohLhMA8DFtGnTXF5blqWUlBR98skn6ty5s6GqABRmXKYB4KJGjRour728vFS+fHm1b99ekZGRKlu2rKHKABRWhBEAAGAUl2kASJL69u2bp35z5sy5xZUAKGoYGQEg6fLlmGrVqikkJETX+rGwbNmy21gVgKKAkREAkqR//vOf+vTTT3Xs2DH16dNHTz31lMqVK2e6LABFACMjAJwcDoeWLl2qOXPmaMuWLerSpYv69eunTp06yWazmS4PQCFFGAFwVSdOnFB0dLTmzZun33//XQcOHFCZMmVMlwWgEOKhZwCuysvLSzabTZZlKTs723Q5AAoxwggAJ4fDoU8//VQdO3ZUnTp1lJCQoOnTpyspKYlREQC3DBNYAUiSnn32WS1cuFBBQUHq27evPv30U911112mywJQBDBnBICky5dlqlatqpCQkGtOVl26dOltrApAUcDICABJUkREBHfMADCCkREAAGAUE1gBAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAY1bt3b3Xv3t10GQAMIowAAACjCCMAPJplWfr9999NlwHgBhBGANywnJwcTZw4UcHBwbLb7apatarGjRsnSUpISFD79u3l4+OjO++8U//4xz+UmZmZ69dyOBwaMmSIKlSoIG9vb/3lL3/Rjh07nO/HxsbKZrPpm2++UYsWLWS327Vp06Zbfo4Abh3CCIAbFhkZqQkTJmjUqFE6ePCgYmJiFBAQoKysLIWHh8vf3187duzQokWLtGbNGg0ePDjXrzVy5EgtWbJEc+fO1e7duxUcHKzw8HCdOXPGpd/LL7+sCRMm6LvvvlOTJk1u9SkCuJUsALgBGRkZlt1utz744IMr3ps9e7bl7+9vZWZmOtu++uory8vLy0pNTbUsy7J69epldevWzbIsy8rMzLRKlChhLViwwNn/4sWLVqVKlayJEydalmVZ69evtyRZy5cvv4VnBeB2YmQEwA357rvv5HA49MADD1z1vaZNm6p06dLOtjZt2ignJ0eHDx++on9iYqIuXbqkNm3aONtKlCihe+65R999951L35YtW97EswBgEmEEwA3x8fExctw/BxwAno0wAuCG1K5dWz4+Plq7du0V79WvX1979+5VVlaWs23z5s3y8vJS3bp1r+hfq1YtlSxZUps3b3a2Xbp0STt27FCDBg1uzQkAMK646QIAeDZvb2+99NJLGjlypEqWLKk2bdro9OnTOnDggHr27KnXXntNvXr10uuvv67Tp0/rueee09NPP62AgIArvlbp0qX1z3/+UyNGjFC5cuVUtWpVTZw4Ub/99pv69etn4OwA3A6EEQA3bNSoUSpevLhGjx6tn376SRUrVtTAgQNVqlQprVq1SkOHDtXdd9+tUqVK6bHHHtM777yT69eaMGGCcnJy9PTTT+vcuXNq2bKlVq1aJX9//9t4RgBuJ5tlWZbpIgAAQNHFnBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABG/R+o5I5Bg7wxhwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['main_promotion'].value_counts().plot(kind='bar')\n",
        "plt.xlabel('Main Promotion')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Count of Items by Main Promotion')\n",
        "plt.show()\n",
        "\n",
        "df['color'].value_counts().plot(kind='bar')\n",
        "plt.xlabel('Color')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Count of Items by Color')\n",
        "plt.show()\n",
        "\n",
        "df['success_indicator'].value_counts().plot(kind='bar')\n",
        "plt.xlabel('Success Indicator')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Count of Items by Success Indicator')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "B7wUW3WksFaF",
        "outputId": "71953a90-1ee2-4901-fe19-ea9c9cd8a32a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "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": [
        "df.groupby(['category', 'success_indicator'])['success_indicator'].count().unstack().plot(kind='bar')\n",
        "plt.xlabel('Category')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Success Indicator by Category')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "B4wd-VT1sbTb",
        "outputId": "f5773ea2-1c59-455b-cfb8-29d26837c7cc"
      },
      "execution_count": 20,
      "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": [
        "# Plot bar charts for categorical features\n",
        "df['category'].value_counts().plot(kind='bar')\n",
        "plt.xlabel('Category')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Count of Items by Category')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "a0QzzfxOtAmJ",
        "outputId": "1119957f-3259-4764-a2d4-08d869bca7cd"
      },
      "execution_count": 21,
      "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": [
        "df.groupby(['color', 'success_indicator'])['success_indicator'].count().unstack().plot(kind='bar')\n",
        "plt.xlabel('Color')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Success Indicator by Category')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "6-GESuiItNvJ",
        "outputId": "a035c5b3-b48b-4a8f-9d37-d30f92ea3089"
      },
      "execution_count": 22,
      "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": [
        "# prompt: plot volin plot form seabon for each promotion against sucess\n",
        "\n",
        "sns.violinplot(x = \"main_promotion\", y = \"stars\", data = df)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "sVjvuoOdtdzI",
        "outputId": "5d63f6ab-7200-4a90-8cad-d6b1f65b20f2"
      },
      "execution_count": 23,
      "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": [
        "# prompt: plot frequency of stars between 0-5 change size of fig 20,4 give in sorted on x axis\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "df['stars'].value_counts().sort_index().plot(kind='bar', figsize=(20, 4))\n",
        "plt.xlabel('Stars')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Frequency of Stars')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "UDHUdDqat7ln",
        "outputId": "727d6e93-d7bd-4ead-d9e4-552c8588e8a6"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x400 with 1 Axes>"
            ],
            "image/png": 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FF+U0p6aOP4w9csP444eryWOPlGru+COfY4+UjD9+KOOP6jf+mDhxYqpdu3Y6+OCD04QJE9KDDz6YHnzwwTRhwoR0yCGHpDp16qQ//vGPOcv7Nrm+/5FSSoos38M222yTrrnmmpRSSo899lhq0KBBuvLKKzPtl19+edp5551zklWvXr3Mzbutttoq3XnnnRXa77///rTZZpvlJCulLwcar732WrrqqqvS1ltvnQoLC9OPfvSjdN1116WPPvooZzkppdS+fftMseHNN99MhYWF6d577820P/LII6ljx445ydpmm23S+PHjK5y7a9euKaWUVqxYkfbcc8909NFH5yQrpZr7QfHwww+nunXrpi233DJ16NAhtWjRIj3++OOZ9lz+oHLVVVelRo0apQMPPDC1adMm/epXv0otWrRIv/rVr9LIkSNTUVFR+t3vfpeTrJRS2nbbbdf4KigoSN26dcts50JhYWGm0PLMM8+kOnXqpN122y2dc845aa+99kq1a9dO06ZNy0lW586d03PPPZdSSunss89OnTp1SnfffXeaM2dOuvfee9MWW2yRzjnnnJxkFRQUpBYtWqRatWqlAQMGpL/85S/piy++yMm5v+nYY49NW221VbruuuvSbrvtlvbbb7/Uo0eP9PTTT6dnn302bb/99unII4/MWd4f//jH1KRJk3TWWWelX/7yl6mkpCSdf/75afz48Wm33XZLG220UfrXv/71g3N69OiRLr744rW2jxgxIm299dY/OCellK655ppvfZ177rk5HWT069cvHXPMMWnVqlWptLQ0nXzyyalFixbppZdeSinl9vMjn9+nCwoK0m677ZaOPvroCq/CwsK0//77Z7ZzlfXVZ8d+++2XBg8enPk/tmrVqnTwwQenfffdNydZ+Rx/5HPskVLNHX/U1JukKblRmgtukuZGTb1RWlPHH8YeuWH88cPV1LFHSjV3/JHPsUdKxh+5YPxR/cYfm2++ebr++uvX2j5u3LicfeafeeaZ3/o6/PDDFVkqQ6NGjdJbb72V2a5Tp06FD4U5c+akFi1a5CSrTZs2afr06SmllFq3bp0ZEH7lX//6V2rQoEFOslKqOIBKKaUZM2akE088MRUXF6cGDRqkQw45JFNV/6EaNGhQ4be/69SpU+Gb1ttvv50aNmyYk6z69eunt99+O7NdXl6e6tSpk/lN96eeeiq1bNkyJ1kp1dwPij59+qQLL7wwpfTl3+Gll16aGjdunB566KGUUm4HGl27dk2TJk1KKaX00ksvpdq1a6c//OEPmfY//OEPqWfPnjnJSiml2rVrp7333jtdcsklmdeIESNSYWFhOuWUUzLv5cLX/5/ttdde6dhjj63Qfvrpp6c99tgjJ1lf/0Fliy22yPxbfWXatGmpQ4cOOckqKChI77//frrnnnvS4MGDU+3atVPLli3TWWedlfPfiGnTpk165plnUkpfft0VFBSkRx55JNP+9NNPp4033jhneV27dk133HFHZvuFF15I7dq1y/wmzEEHHZSTgWj9+vXTG2+8sdb2N954I9WvX/8H56T05b9X27ZtU6dOndb4+uq3fnKlWbNmae7cuRXeGzNmTGrWrFl6/vnnc/r5kc/v03/6059Su3btVvvtstq1a+f8N/e+/tnRvn37zA8RX3nppZdSmzZtcpKVz/FHPsceKdXc8UdNvUmakhulucpyk/SHq6k3Smvq+MPYIzeMP364mjr2SKnmjj/yOfZIyfgjF4w/OuYkK5/jj3r16uVt/FFYWJi222671Ldv3zW+evXqpchSGZo2bVrhi6Bx48bpzTffzGy/9dZbOfsGecopp6R99903rVy5Mp144onp+OOPrzC97bTTTkt9+vTJSVZKqw80vvLpp5+mCRMmpB//+Mc5+6Lr0qVL5qbl888/n+rWrVthoHjHHXekzTffPCdZm266aYXfnpg3b16qVatWZsrnW2+9ldNiVU39oCgqKkrz58+v8N6kSZNSo0aN0n333ZfTgcY3B6L16tWrMBCdN29eatq0aU6yUvrypvymm26ahg8fnlatWpV5f33fKP36DxJfmT17dtpoo41yktWxY8fMbKONN944vfDCCxXaX3/99dSoUaOcZH3z82PhwoXpN7/5Tdp8880zj/C6+eabc5LVsGHDtGDBgsx2nTp10quvvprZfuutt3J2XSl9+fX49R9WUvrya+P9999PKX35Q1kuvh67du2arrjiirW2X3HFFalLly4/OCellDp16pT+/Oc/r7X95ZdfznmRZU2/pfTb3/42NW3aNN199905y8vn9+mUvvzBeOedd04HHnhgZlC9Pj47CgsLM78J1rFjx9X+Pt96662cfW/J5/gjn2OPlGru+KOm3iRNyY3SXHCTNDdq6o3Smjr+MPbIDeOPH66mjj1Sqrnjj3yOPVIy/sgF44/qN/7YbrvtvvXJKueee27abrvtcpK1xRZbpNtuu22t7bm+/5GSIsv30qtXrwoVw+XLl1f4xv/oo4+mLbbYIidZy5YtS7169UqbbbZZOuKII1L9+vVTx44d01577ZU6d+6ciouLM48CyoW1DTS+7pvfaLJ11VVXpfr166d+/fqlZs2apWuvvTaVlJSkc889N51//vmpuLg4jRo1KidZI0eOTO3atUvjx49Pt9xyS9pqq60q/Nb53Xffnbp3756TrJRq7gdFy5Yt1zgN8k9/+lNq2LBhGj9+fM6yWrRoUWH2Q7t27SrcVJ83b15q3LhxTrK+smzZsnTwwQen3r17Z4pJ62ugMX/+/LR8+fLUuXPn1b75z58/P2ffIC+88MLUp0+f9N///jedf/75afDgwenjjz9OKX35A8TPfvaz1L9//5xkff0xaN/0xBNPpMMPPzxnhY8f/ehHmdliDz74YGrSpEmFmwPjx49PW221VU6yUkqpW7duFaZnz5w5M9WtWzetXLkypfTl12Muru3OO+9MtWvXToMHD07XXHNNuuOOO9Idd9yRrrnmmvSTn/wk1a1bN/3lL3/5wTkppTRkyJB07rnnrrV91qxZqaCgICdZKaW0yy67VPiNmK+79NJLM2vd5EI+v09/ZdWqVWn48OGpffv2acqUKalOnTrr5bOjadOmqVmzZqlOnTqrffY/8sgjqVOnTjnJyuf4I59jj5Rq7vijpt4kTcmN0lxwkzQ3auqN0po6/jD2yA3jjx+upo49Uqq54498jj1SMv7IBeOP6jf+eOKJJ1KjRo3S1ltvnc4888w0duzYNHbs2HTmmWemHj16pMaNG+fsUfqHHnpoOuOMM9banuv7Hykpsnwvd99997f+I48ZMyZddNFFOctbsWJFGj9+fNpnn31S165d0xZbbJF22223dOGFF6Z33303ZzkppdS3b9/03//+N6fn/DaTJk1KQ4cOTZMnT04pffkfbJdddkk9e/ZMl1xySYUZBT/EF198kc4999zUtm3b1KJFi3TooYdWWLR9xowZOfuPm1LN/aDYa6+90m9/+9s1tk2ePDnVqVMnZ99Idt555wqPZ/qm++67L6c30b/ulltuSSUlJel3v/vdertR+tXC6QUFBen3v/99hfa//e1vOZvGWlZWln7yk5+kZs2apb322iuzaNnmm2+eGjVqlDp06JCzHx6+zw8quVok7fbbb0+1atVKm222WapXr1666667Utu2bdPPfvazdPDBB6e6det+6yP71tX111+fiouL07nnnpuGDx+e2rZtm4477rgK/cnVmj3PPPNMOuigg1KHDh1S3bp1U926dVOHDh3SQQcdlJ599tmcZKT05TNxvzmz6etWrFhRobD5Q910003p8MMPX2v72LFjc/YDer6/T3/dP/7xj9S5c+dUWFiY88+OiRMnVnh9cxbcqFGj0plnnpmzvHyNP/I99kipZo4/aupN0pTcKM0FN0lzoybfKK2J4w9jj9ww/siNmjj2SKnmjj/yOfZIyfgjF4w/quf44+23307nnntu2nXXXdMWW2yRtthii7Trrrum8847b7WnifwQixYtyun9je+jIKWUAvjBFixYEOPHj4/nnnsuFi9eHBERJSUl0adPnzjppJOiU6dOOclZvHhxlJWVRceOHXNyvm9zzz33xFNPPRVXXXXVGtsnT54cN910UzzxxBM/OOuZZ56JRo0axTbbbLPG9htuuCHKy8tj6NChPzhrTebNmxeHHXZYvPjiizF79uzo3r17zs49bdq0Cttt2rSJLbbYIrN9zTXXxIoVK+Kcc87JWeaUKVPivvvui7feeivKy8ujTZs2sfPOO8ehhx4ajRo1yknGMcccE9dee200adIkJ+f7Ls8880w899xz0adPn9hpp53i9ddfj7Fjx8b//ve/GDx4cBx11FE5zRs/fnzcfvvtUVZWFgMGDIiLL7446tevHxFffr2sWrUqunbtmtNMqqdPPvkk3nzzzejWrVvUrVu3srvDBuTZZ5+Na6+9NqZPn77a2OP000+PPn365CTn9ddfj//973/Rq1evNbZ/8cUXsXDhwpyNTf7whz/EtGnT4rbbbltj+6WXXho33nhjvP322z8465577okWLVrErrvuusb2sWPHxqeffhqjR4/+wVnf9PTTT8eRRx4Z//73v+PVV1/N6djj1ltvrbDdpUuX2HHHHTPbo0ePjv/+979x5ZVX5iTviy++iJtvvnmNY4+TTz452rVrl5Oc3XffPe65555o2rRpTs73fUyePDmmT58eO+20UxxyyCHx5JNPxvDhwzPjj4svvjgKCwt/cM7KlSvjl7/8ZYWxxzXXXBMbbbRRREQ8//zz8fnnn6/1a5UNi7EHlakmjj/yOfaIMP4w/vhuxh/VjyILwP9TXl4eH3/8cRQVFUVBQUFldwcAqOHcKAUA8s34A3Lvh5e8iAsvvDCOPfbYGpeV77yamkX1UVhYGMXFxQosVBk1+XOxpl6brOqVle884w++qXHjxvGjH/3IDQ6qlJr6uSir+uXJql5ZVB/GH1RFRx11VOy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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px"
      ],
      "metadata": {
        "id": "EAEpu4Mp6miW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "1o2qewH851Wf",
        "outputId": "dcc9f598-3211-4a29-ace2-d170553f0a71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: do numeric encoding to colour\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "df['color'] = le.fit_transform(df['color'])\n",
        "df['success_indicator']= le.fit_transform(df['success_indicator'])\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "EO8i_U9NuVoL",
        "outputId": "b40db32e-f20c-45ac-e45c-a1e2cf1a4632"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   item_no    category      main_promotion  color  stars  success_indicator\n",
              "0   739157       Tunic             Catalog      3    3.1                  0\n",
              "1   591846      Hoodie  Category_Highlight      7    1.5                  0\n",
              "2   337574  Sweatshirt             Catalog      7    4.4                  1\n",
              "3   401933  Polo-Shirt  Category_Highlight      1    3.1                  0\n",
              "4   812151      Hoodie  Category_Highlight      3    4.1                  1"
            ],
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              "\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-4418b4e7-f7a0-4b4a-b170-2c5408661823 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns=['success_indicator'],axis=1)\n",
        "y= df.success_indicator"
      ],
      "metadata": {
        "id": "rhIIgHk8wQOS"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Sk1811yQv_AM",
        "outputId": "12b68ce0-6419-4541-820b-e0e64952230d"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   item_no    category      main_promotion  color  stars\n",
              "0   739157       Tunic             Catalog      3    3.1\n",
              "1   591846      Hoodie  Category_Highlight      7    1.5\n",
              "2   337574  Sweatshirt             Catalog      7    4.4\n",
              "3   401933  Polo-Shirt  Category_Highlight      1    3.1\n",
              "4   812151      Hoodie  Category_Highlight      3    4.1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e00d7b57-262c-49a6-89e5-67b4eb0918b0\" 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>item_no</th>\n",
              "      <th>category</th>\n",
              "      <th>main_promotion</th>\n",
              "      <th>color</th>\n",
              "      <th>stars</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>739157</td>\n",
              "      <td>Tunic</td>\n",
              "      <td>Catalog</td>\n",
              "      <td>3</td>\n",
              "      <td>3.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>591846</td>\n",
              "      <td>Hoodie</td>\n",
              "      <td>Category_Highlight</td>\n",
              "      <td>7</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>337574</td>\n",
              "      <td>Sweatshirt</td>\n",
              "      <td>Catalog</td>\n",
              "      <td>7</td>\n",
              "      <td>4.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>401933</td>\n",
              "      <td>Polo-Shirt</td>\n",
              "      <td>Category_Highlight</td>\n",
              "      <td>1</td>\n",
              "      <td>3.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>812151</td>\n",
              "      <td>Hoodie</td>\n",
              "      <td>Category_Highlight</td>\n",
              "      <td>3</td>\n",
              "      <td>4.1</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-e00d7b57-262c-49a6-89e5-67b4eb0918b0')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-e00d7b57-262c-49a6-89e5-67b4eb0918b0 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-e00d7b57-262c-49a6-89e5-67b4eb0918b0');\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-a3545662-315e-41a1-a35c-e56d101ffa54\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a3545662-315e-41a1-a35c-e56d101ffa54')\"\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-a3545662-315e-41a1-a35c-e56d101ffa54 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Column Transformer with 3 types of transformers\n",
        "num_features = X.select_dtypes(exclude=\"object\").columns\n",
        "cat_features = X.select_dtypes(include=\"object\").columns\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "numeric_transformer = StandardScaler()\n",
        "oh_transformer = OneHotEncoder()\n",
        "\n",
        "preprocessor = ColumnTransformer(\n",
        "    [\n",
        "        (\"OneHotEncoder\", oh_transformer, cat_features),\n",
        "         (\"StandardScaler\", numeric_transformer, num_features),\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "id": "cGNjXRqNw4nH"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = preprocessor.fit_transform(X)"
      ],
      "metadata": {
        "id": "-od3hzz_y_nr"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MS2rLFiJzDb6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# separate dataset into train and test\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=42)\n",
        "X_train.shape, X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ubEfJLUWzFbu",
        "outputId": "0c2c9613-bcb4-4cd4-9b9c-c716e32ea075"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((6400, 13), (1600, 13))"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "KsYuLrhd0LKN"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "In9WhIEB3r__"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}