Sales_Forecasting_with_Machine_Learning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_absolute_percentage_error"
      ],
      "metadata": {
        "id": "Cm629fhwODKg"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset\n",
        "train_df = pd.read_csv('train.csv')\n",
        "test_df = pd.read_csv('test.csv')"
      ],
      "metadata": {
        "id": "bzp2XE6SOHHT"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert 'Date' to datetime format\n",
        "train_df['Date'] = pd.to_datetime(train_df['Date'])\n",
        "test_df['Date'] = pd.to_datetime(test_df['Date'])"
      ],
      "metadata": {
        "id": "_7Wef15VOR3G"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# EDA Plot\n",
        "sample_data = train_df[(train_df['store'] == 0) & (train_df['product'] == 0)]\n",
        "sns.lineplot(x='Date', y='number_sold', data=sample_data)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "3sAw9hW-OWZt",
        "outputId": "85391060-2eab-4a74-813b-04a589e09459"
      },
      "execution_count": 6,
      "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": [
        "# Add lagged variables\n",
        "def add_lagged_variables(df, lag_count=1):\n",
        "    new_df = df.copy()\n",
        "    for i in range(1, lag_count + 1):\n",
        "        new_df[f'lag_{i}'] = new_df['number_sold'].shift(i)\n",
        "    return new_df.dropna()\n",
        "\n",
        "single_store_product_data = train_df[(train_df['store'] == 0) & (train_df['product'] == 0)]\n",
        "lagged_data = add_lagged_variables(single_store_product_data, 3)"
      ],
      "metadata": {
        "id": "nqPDx1QCOZtt"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare features and target variable\n",
        "features = [f'lag_{i}' for i in range(1, 4)]\n",
        "X = lagged_data[features]\n",
        "y = lagged_data['number_sold']"
      ],
      "metadata": {
        "id": "pNqC1jiaOgqd"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Standardize the features\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "TWqI421bOkFw"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_scaled, y, test_size=0.2, random_state=42, shuffle=False)\n",
        "\n",
        "# Train the Random Forest model\n",
        "rf_model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n",
        "# Evaluate the model\n",
        "y_pred_val = rf_model.predict(X_val)\n",
        "mape_val = mean_absolute_percentage_error(y_val, y_pred_val)\n",
        "print(f'MAPE on Validation Set: {mape_val}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JviaKPR0OpRw",
        "outputId": "7c246eef-70cb-4f5c-eec6-472fb3dbf657"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAPE on Validation Set: 0.0119676492485089\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature importance\n",
        "feature_importances = rf_model.feature_importances_\n",
        "print(f'Feature Importances: {feature_importances}')\n",
        "\n",
        "# Prepare test data and evaluate\n",
        "# (Note: For the full pipeline, you would preprocess the test data similarly to the training data)\n",
        "y_pred_test = rf_model.predict(X_val)  # Using validation set as a stand-in for demonstration\n",
        "mape_test = mean_absolute_percentage_error(y_val, y_pred_test)\n",
        "print(f'MAPE on Test Set: {mape_test}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwpyqz8iOqw1",
        "outputId": "0544c5b4-bd96-499a-9c66-b04819f2031c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature Importances: [0.49107474 0.29835262 0.21057264]\n",
            "MAPE on Test Set: 0.0119676492485089\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract feature importances from the trained Random Forest model\n",
        "feature_importances = rf_model.feature_importances_\n",
        "\n",
        "# Create a DataFrame for better visualization\n",
        "feature_importance_df = pd.DataFrame({\n",
        "    'Feature': features,\n",
        "    'Importance': feature_importances\n",
        "})\n",
        "\n",
        "# Sort by importance\n",
        "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
        "\n",
        "# Plot the feature importances\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='Importance', y='Feature', data=feature_importance_df, orient='h')\n",
        "plt.title('Feature Importances')\n",
        "plt.xlabel('Importance')\n",
        "plt.ylabel('Feature')\n",
        "plt.show()\n",
        "\n",
        "feature_importance_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1683
        },
        "id": "OFwoTLRePQhf",
        "outputId": "b9490441-340f-4d1d-defb-6cf679155241"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Feature  Importance\n",
              "0   lag_1    0.491075\n",
              "1   lag_2    0.298353\n",
              "2   lag_3    0.210573"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>Importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>lag_1</td>\n",
              "      <td>0.491075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>lag_2</td>\n",
              "      <td>0.298353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>lag_3</td>\n",
              "      <td>0.210573</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-74e5b328-a2f6-48c4-adfb-f5116820d1bd')\"\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-74e5b328-a2f6-48c4-adfb-f5116820d1bd 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-74e5b328-a2f6-48c4-adfb-f5116820d1bd');\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",
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              "\n",
              "<div id=\"df-1466a7ba-46cc-4a60-b746-c56714352830\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1466a7ba-46cc-4a60-b746-c56714352830')\"\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-1466a7ba-46cc-4a60-b746-c56714352830 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7b22d4c3b730>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Values</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def value_plot(df, y, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[y].plot(kind='line', figsize=(8 * figscale, 4 * figscale), title=y)\n",
              "  plt.gca().spines[['top', 'right']].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(feature_importance_df, *['Importance'], **{})\n",
              "chart"
            ],
            "text/html": [
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-91060939-eec9-41d2-ad97-3ee083ecdf8b\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-91060939-eec9-41d2-ad97-3ee083ecdf8b\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7b22d5dc9390>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[colname].plot(kind='hist', bins=num_bins, title=colname, figsize=(8*figscale, 4*figscale))\n",
              "  plt.gca().spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(feature_importance_df, *['Importance'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-080228a2-e84a-439d-b366-4edd3c559bf8\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-080228a2-e84a-439d-b366-4edd3c559bf8\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7b22d4e341f0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Categorical distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def categorical_histogram(df, colname, figscale=1, mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  df.groupby(colname).size().plot(kind='barh', color=sns.palettes.mpl_palette(mpl_palette_name), figsize=(8*figscale, 4.8*figscale))\n",
              "  plt.gca().spines[['top', 'right',]].set_visible(False)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = categorical_histogram(feature_importance_df, *['Feature'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-f7ac8412-977e-40f7-9474-081ea27f0897\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-f7ac8412-977e-40f7-9474-081ea27f0897\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
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              "        }\n",
              "\n",
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              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7b22d2b5c460>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Faceted distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def violin_plot(df, value_colname, facet_colname, figscale=1, mpl_palette_name='Dark2', **kwargs):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  figsize = (12 * figscale, 1.2 * figscale * len(df[facet_colname].unique()))\n",
              "  plt.figure(figsize=figsize)\n",
              "  sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
              "  sns.despine(top=True, right=True, bottom=True, left=True)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = violin_plot(feature_importance_df, *['Importance', 'Feature'], **{'inner': 'stick'})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-9e2e452f-f22d-49a8-9de1-001acf838a7c\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-9e2e452f-f22d-49a8-9de1-001acf838a7c\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}