Salespredicition.ipynb

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        "<a href=\"https://colab.research.google.com/github/kartheek453/Sales-prediction/blob/main/Salespredicition.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ]
    },
    {
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      "source": [
        "import pandas as pd\n",
        "\n",
        "# Sample data\n",
        "data = {\n",
        "    'Store': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5],\n",
        "    'DayOfWeek': [5, 5, 5, 5, 5, 4, 4, 4, 4, 4],\n",
        "    'Customers': [555, 625, 821, 148, 720, 555, 625, 821, 148, 720],\n",
        "    'Open': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
        "    'Promo': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
        "    'StateHoliday': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "    'SchoolHoliday': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
        "    'Sales': [5263, 6064, 8314, 13995, 4822, 5020, 6080, 7890, 14437, 4711]\n",
        "}\n",
        "\n",
        "# Create DataFrame\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Save to CSV\n",
        "df.to_csv('sales_data.csv', index=False)\n"
      ],
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      "source": [
        "# Load the dataset\n",
        "data = pd.read_csv('sales_data.csv')\n",
        "\n",
        "# Display the first few rows\n",
        "data.head()\n"
      ],
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      "execution_count": 3,
      "outputs": [
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          "data": {
            "text/plain": [
              "   Store  DayOfWeek  Customers  Open  Promo  StateHoliday  SchoolHoliday  \\\n",
              "0      1          5        555     1      1             0              1   \n",
              "1      2          5        625     1      1             0              1   \n",
              "2      3          5        821     1      1             0              1   \n",
              "3      4          5        148     1      1             0              1   \n",
              "4      5          5        720     1      1             0              1   \n",
              "\n",
              "   Sales  \n",
              "0   5263  \n",
              "1   6064  \n",
              "2   8314  \n",
              "3  13995  \n",
              "4   4822  "
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              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Store\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DayOfWeek\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 4,\n        \"max\": 5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 243,\n        \"min\": 148,\n        \"max\": 821,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          625,\n          720\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Promo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StateHoliday\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SchoolHoliday\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3667,\n        \"min\": 4711,\n        \"max\": 14437,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          14437\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      "source": [
        "# Check for missing values\n",
        "print(data.isnull().sum())\n",
        "\n",
        "# Summary statistics\n",
        "print(data.describe())\n",
        "\n",
        "# Correlation matrix\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(data.corr(), annot=True, cmap='coolwarm')\n",
        "plt.show()\n"
      ],
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        "colab": {
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      "execution_count": 4,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Store            0\n",
            "DayOfWeek        0\n",
            "Customers        0\n",
            "Open             0\n",
            "Promo            0\n",
            "StateHoliday     0\n",
            "SchoolHoliday    0\n",
            "Sales            0\n",
            "dtype: int64\n",
            "           Store  DayOfWeek   Customers  Open  Promo  StateHoliday  \\\n",
            "count  10.000000  10.000000   10.000000  10.0   10.0          10.0   \n",
            "mean    3.000000   4.500000  573.800000   1.0    1.0           0.0   \n",
            "std     1.490712   0.527046  243.476122   0.0    0.0           0.0   \n",
            "min     1.000000   4.000000  148.000000   1.0    1.0           0.0   \n",
            "25%     2.000000   4.000000  555.000000   1.0    1.0           0.0   \n",
            "50%     3.000000   4.500000  625.000000   1.0    1.0           0.0   \n",
            "75%     4.000000   5.000000  720.000000   1.0    1.0           0.0   \n",
            "max     5.000000   5.000000  821.000000   1.0    1.0           0.0   \n",
            "\n",
            "       SchoolHoliday         Sales  \n",
            "count           10.0     10.000000  \n",
            "mean             1.0   7659.600000  \n",
            "std              0.0   3667.427776  \n",
            "min              1.0   4711.000000  \n",
            "25%              1.0   5080.750000  \n",
            "50%              1.0   6072.000000  \n",
            "75%              1.0   8208.000000  \n",
            "max              1.0  14437.000000  \n"
          ]
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fill missing values (example: fill with mean)\n",
        "data.fillna(data.mean(), inplace=True)\n",
        "\n",
        "# Encode categorical variables (if any)\n",
        "data = pd.get_dummies(data, drop_first=True)\n",
        "\n",
        "# Split the data into features and target\n",
        "X = data.drop('Sales', axis=1)  # Assuming 'Sales' is the target column\n",
        "Y = data['Sales']\n"
      ],
      "metadata": {
        "id": "ZYfamTnOPhon"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "J3GFI09FPlwP"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the model\n",
        "model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, Y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "fOv41TV9Ppsi",
        "outputId": "f97aef81-29da-4a43-a1ec-225b2a6c7576"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the model\n",
        "model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, Y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "v7STPpCzPupN",
        "outputId": "77c14af6-b576-4531-bc90-85b33d689f45"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions on the test set\n",
        "Y_pred = model.predict(X_test)\n"
      ],
      "metadata": {
        "id": "m5-NyKUxPzQh"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate Mean Squared Error and R2 Score\n",
        "mse = mean_squared_error(Y_test, Y_pred)\n",
        "r2 = r2_score(Y_test, Y_pred)\n",
        "\n",
        "print(f'Mean Squared Error: {mse}')\n",
        "print(f'R2 Score: {r2}')\n",
        "\n",
        "# Plot actual vs predicted values\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(Y_test, Y_pred, alpha=0.5)\n",
        "plt.xlabel(\"Actual Sales\")\n",
        "plt.ylabel(\"Predicted Sales\")\n",
        "plt.title(\"Actual vs Predicted Sales\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "KS_pcW1PP4W1",
        "outputId": "d0734d63-904a-4df4-b89f-7480cf357fe5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 8772618.885850001\n",
            "R2 Score: 0.49947350513526223\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get feature importances\n",
        "importances = model.feature_importances_\n",
        "features = X.columns\n",
        "\n",
        "# Create a DataFrame for visualization\n",
        "feature_importances = pd.DataFrame({'Feature': features, 'Importance': importances})\n",
        "feature_importances = feature_importances.sort_values(by='Importance', ascending=False)\n",
        "\n",
        "# Plot feature importances\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.barplot(x='Importance', y='Feature', data=feature_importances)\n",
        "plt.title('Feature Importances')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "6FJ91xauP8Zq",
        "outputId": "f3d1be1f-ce1c-4efe-8237-35367cf7b051"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}