Decoding_the_Gold_Market_Insights_from_Data_Analysis.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPJHoIiqAN6NwZTP0/CwfDt",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/montassar-ML/project/blob/main/Decoding_the_Gold_Market_Insights_from_Data_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "nOJyMHOlizhN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "# Load the dataset\n",
        "gold_data = pd.read_csv('gld_price_data.csv')\n",
        "\n",
        "# Examine the dataset\n",
        "print(gold_data.head())\n",
        "print(gold_data.info())\n",
        "print(gold_data.describe())\n",
        "\n",
        "# Check for missing values\n",
        "print(gold_data.isnull().sum())\n",
        "\n",
        "# Convert 'Date' column to datetime format\n",
        "gold_data['Date'] = pd.to_datetime(gold_data['Date'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oKEvL69vlWNC",
        "outputId": "b5ee4332-0d74-4f3a-e882-e36f7dcdc177"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       Date          SPX        GLD        USO     SLV   EUR/USD\n",
            "0  1/2/2008  1447.160034  84.860001  78.470001  15.180  1.471692\n",
            "1  1/3/2008  1447.160034  85.570000  78.370003  15.285  1.474491\n",
            "2  1/4/2008  1411.630005  85.129997  77.309998  15.167  1.475492\n",
            "3  1/7/2008  1416.180054  84.769997  75.500000  15.053  1.468299\n",
            "4  1/8/2008  1390.189941  86.779999  76.059998  15.590  1.557099\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2290 entries, 0 to 2289\n",
            "Data columns (total 6 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   Date     2290 non-null   object \n",
            " 1   SPX      2290 non-null   float64\n",
            " 2   GLD      2290 non-null   float64\n",
            " 3   USO      2290 non-null   float64\n",
            " 4   SLV      2290 non-null   float64\n",
            " 5   EUR/USD  2290 non-null   float64\n",
            "dtypes: float64(5), object(1)\n",
            "memory usage: 107.5+ KB\n",
            "None\n",
            "               SPX          GLD          USO          SLV      EUR/USD\n",
            "count  2290.000000  2290.000000  2290.000000  2290.000000  2290.000000\n",
            "mean   1654.315776   122.732875    31.842221    20.084997     1.283653\n",
            "std     519.111540    23.283346    19.523517     7.092566     0.131547\n",
            "min     676.530029    70.000000     7.960000     8.850000     1.039047\n",
            "25%    1239.874969   109.725000    14.380000    15.570000     1.171313\n",
            "50%    1551.434998   120.580002    33.869999    17.268500     1.303297\n",
            "75%    2073.010070   132.840004    37.827501    22.882500     1.369971\n",
            "max    2872.870117   184.589996   117.480003    47.259998     1.598798\n",
            "Date       0\n",
            "SPX        0\n",
            "GLD        0\n",
            "USO        0\n",
            "SLV        0\n",
            "EUR/USD    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "0uiPRSKrlZE3"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform exploratory data analysis\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.lineplot(x='Date', y='GLD', data=gold_data)\n",
        "plt.title('Gold Price Over Time')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Gold Price')\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "OIR7zf6Klhup",
        "outputId": "c7f3e8de-ac33-4f82-92db-7bbe8d9d2fd3"
      },
      "execution_count": 14,
      "outputs": [
        {
          "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": [
        "# Analyze correlation between features\n",
        "correlation_matrix = gold_data.corr()\n",
        "print(correlation_matrix)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UHn8dX-5ljp2",
        "outputId": "654c8aa0-a919-4b3b-b817-36fbf8c2cf95"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             Date       SPX       GLD       USO       SLV   EUR/USD\n",
            "Date     1.000000  0.944033  0.209118 -0.782230 -0.174108 -0.794540\n",
            "SPX      0.944033  1.000000  0.049345 -0.591573 -0.274055 -0.672017\n",
            "GLD      0.209118  0.049345  1.000000 -0.186360  0.866632 -0.024375\n",
            "USO     -0.782230 -0.591573 -0.186360  1.000000  0.167547  0.829317\n",
            "SLV     -0.174108 -0.274055  0.866632  0.167547  1.000000  0.321631\n",
            "EUR/USD -0.794540 -0.672017 -0.024375  0.829317  0.321631  1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create new features (if necessary)\n",
        "gold_data['SMA_50'] = gold_data['GLD'].rolling(window=50).mean()\n",
        "gold_data['SMA_200'] = gold_data['GLD'].rolling(window=200).mean()"
      ],
      "metadata": {
        "id": "WxM8ejpYlnol"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "0y3yXb_Gl7pr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# Select relevant features and target variable\n",
        "features = ['SPX', 'USO', 'SLV', 'EUR/USD', 'SMA_50', 'SMA_200']\n",
        "target = 'GLD'\n",
        "\n",
        "X = gold_data[features]\n",
        "y = gold_data[target]\n",
        "\n",
        "# Handle missing values using SimpleImputer\n",
        "imputer = SimpleImputer(strategy='mean')\n",
        "X_imputed = imputer.fit_transform(X)\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_imputed, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Normalize the features\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "K0PDGsC2lpIr"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "sSqnrRJtl6EK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "# Create a neural network model\n",
        "model = Sequential()\n",
        "model.add(Dense(64, activation='relu', input_shape=(X_train_scaled.shape[1],)))\n",
        "model.add(Dense(32, activation='relu'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MuTc5hJOltgP",
        "outputId": "12f55306-0f12-4d24-bcd5-cb235be66236"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "58/58 [==============================] - 1s 3ms/step - loss: 15375.8330\n",
            "Epoch 2/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 13673.9297\n",
            "Epoch 3/100\n",
            "58/58 [==============================] - 0s 2ms/step - loss: 8413.4521\n",
            "Epoch 4/100\n",
            "58/58 [==============================] - 0s 2ms/step - loss: 2115.6252\n",
            "Epoch 5/100\n",
            "58/58 [==============================] - 0s 2ms/step - loss: 343.0334\n",
            "Epoch 6/100\n",
            "58/58 [==============================] - 0s 2ms/step - loss: 281.2090\n",
            "Epoch 7/100\n",
            "58/58 [==============================] - 0s 2ms/step - loss: 257.2371\n",
            "Epoch 8/100\n",
            "58/58 [==============================] - 0s 2ms/step - loss: 235.3377\n",
            "Epoch 9/100\n",
            "58/58 [==============================] - 0s 2ms/step - loss: 215.6826\n",
            "Epoch 10/100\n",
            "58/58 [==============================] - 0s 2ms/step - loss: 197.1637\n",
            "Epoch 11/100\n",
            "58/58 [==============================] - 0s 2ms/step - loss: 180.8970\n",
            "Epoch 12/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 165.8578\n",
            "Epoch 13/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 152.9108\n",
            "Epoch 14/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 140.6216\n",
            "Epoch 15/100\n",
            "58/58 [==============================] - 0s 8ms/step - loss: 129.5257\n",
            "Epoch 16/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 119.5462\n",
            "Epoch 17/100\n",
            "58/58 [==============================] - 0s 5ms/step - loss: 110.2352\n",
            "Epoch 18/100\n",
            "58/58 [==============================] - 0s 6ms/step - loss: 101.2882\n",
            "Epoch 19/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 92.8628\n",
            "Epoch 20/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 84.8690\n",
            "Epoch 21/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 77.4868\n",
            "Epoch 22/100\n",
            "58/58 [==============================] - 0s 5ms/step - loss: 71.0558\n",
            "Epoch 23/100\n",
            "58/58 [==============================] - 0s 7ms/step - loss: 64.4261\n",
            "Epoch 24/100\n",
            "58/58 [==============================] - 1s 10ms/step - loss: 59.0086\n",
            "Epoch 25/100\n",
            "58/58 [==============================] - 1s 11ms/step - loss: 53.7345\n",
            "Epoch 26/100\n",
            "58/58 [==============================] - 1s 10ms/step - loss: 49.3844\n",
            "Epoch 27/100\n",
            "58/58 [==============================] - 0s 6ms/step - loss: 45.6927\n",
            "Epoch 28/100\n",
            "58/58 [==============================] - 0s 8ms/step - loss: 42.2180\n",
            "Epoch 29/100\n",
            "58/58 [==============================] - 0s 6ms/step - loss: 39.5095\n",
            "Epoch 30/100\n",
            "58/58 [==============================] - 1s 11ms/step - loss: 37.2546\n",
            "Epoch 31/100\n",
            "58/58 [==============================] - 0s 6ms/step - loss: 35.3514\n",
            "Epoch 32/100\n",
            "58/58 [==============================] - 1s 10ms/step - loss: 34.0285\n",
            "Epoch 33/100\n",
            "58/58 [==============================] - 0s 5ms/step - loss: 32.1672\n",
            "Epoch 34/100\n",
            "58/58 [==============================] - 0s 6ms/step - loss: 31.4324\n",
            "Epoch 35/100\n",
            "58/58 [==============================] - 0s 6ms/step - loss: 30.1855\n",
            "Epoch 36/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 29.4812\n",
            "Epoch 37/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 28.4075\n",
            "Epoch 38/100\n",
            "58/58 [==============================] - 0s 5ms/step - loss: 27.8636\n",
            "Epoch 39/100\n",
            "58/58 [==============================] - 0s 6ms/step - loss: 26.9660\n",
            "Epoch 40/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 26.4814\n",
            "Epoch 41/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 25.7844\n",
            "Epoch 42/100\n",
            "58/58 [==============================] - 0s 6ms/step - loss: 25.1177\n",
            "Epoch 43/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 24.8113\n",
            "Epoch 44/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 24.5991\n",
            "Epoch 45/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 24.2834\n",
            "Epoch 46/100\n",
            "58/58 [==============================] - 0s 7ms/step - loss: 24.0851\n",
            "Epoch 47/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 23.7078\n",
            "Epoch 48/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 23.6136\n",
            "Epoch 49/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 23.5171\n",
            "Epoch 50/100\n",
            "58/58 [==============================] - 0s 6ms/step - loss: 23.3237\n",
            "Epoch 51/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 23.0544\n",
            "Epoch 52/100\n",
            "58/58 [==============================] - 0s 5ms/step - loss: 22.8929\n",
            "Epoch 53/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 23.0439\n",
            "Epoch 54/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 22.8908\n",
            "Epoch 55/100\n",
            "58/58 [==============================] - 0s 7ms/step - loss: 22.9426\n",
            "Epoch 56/100\n",
            "58/58 [==============================] - 1s 9ms/step - loss: 22.7370\n",
            "Epoch 57/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 22.9002\n",
            "Epoch 58/100\n",
            "58/58 [==============================] - 0s 5ms/step - loss: 22.3655\n",
            "Epoch 59/100\n",
            "58/58 [==============================] - 0s 8ms/step - loss: 22.3410\n",
            "Epoch 60/100\n",
            "58/58 [==============================] - 0s 5ms/step - loss: 22.1673\n",
            "Epoch 61/100\n",
            "58/58 [==============================] - 0s 5ms/step - loss: 22.1342\n",
            "Epoch 62/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 22.0728\n",
            "Epoch 63/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 21.8892\n",
            "Epoch 64/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 21.8134\n",
            "Epoch 65/100\n",
            "58/58 [==============================] - 0s 7ms/step - loss: 21.7456\n",
            "Epoch 66/100\n",
            "58/58 [==============================] - 0s 6ms/step - loss: 21.6298\n",
            "Epoch 67/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 21.6704\n",
            "Epoch 68/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 21.7145\n",
            "Epoch 69/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 21.8266\n",
            "Epoch 70/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 21.4562\n",
            "Epoch 71/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 21.3190\n",
            "Epoch 72/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 21.4741\n",
            "Epoch 73/100\n",
            "58/58 [==============================] - 0s 2ms/step - loss: 21.5160\n",
            "Epoch 74/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 21.5410\n",
            "Epoch 75/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 21.2862\n",
            "Epoch 76/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 21.0880\n",
            "Epoch 77/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 20.9127\n",
            "Epoch 78/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 20.9589\n",
            "Epoch 79/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 21.0134\n",
            "Epoch 80/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 20.8223\n",
            "Epoch 81/100\n",
            "58/58 [==============================] - 0s 6ms/step - loss: 21.2049\n",
            "Epoch 82/100\n",
            "58/58 [==============================] - 0s 5ms/step - loss: 20.7202\n",
            "Epoch 83/100\n",
            "58/58 [==============================] - 0s 6ms/step - loss: 20.6164\n",
            "Epoch 84/100\n",
            "58/58 [==============================] - 0s 5ms/step - loss: 20.7550\n",
            "Epoch 85/100\n",
            "58/58 [==============================] - 0s 5ms/step - loss: 20.6221\n",
            "Epoch 86/100\n",
            "58/58 [==============================] - 0s 6ms/step - loss: 20.5475\n",
            "Epoch 87/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 20.6116\n",
            "Epoch 88/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 20.5355\n",
            "Epoch 89/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 20.4767\n",
            "Epoch 90/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 20.4249\n",
            "Epoch 91/100\n",
            "58/58 [==============================] - 0s 2ms/step - loss: 20.3913\n",
            "Epoch 92/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 20.2337\n",
            "Epoch 93/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 20.3391\n",
            "Epoch 94/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 20.5568\n",
            "Epoch 95/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 20.4563\n",
            "Epoch 96/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 20.2816\n",
            "Epoch 97/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 19.9531\n",
            "Epoch 98/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 20.2164\n",
            "Epoch 99/100\n",
            "58/58 [==============================] - 0s 3ms/step - loss: 20.1070\n",
            "Epoch 100/100\n",
            "58/58 [==============================] - 0s 4ms/step - loss: 20.1836\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7f985f55ae90>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "g29HIDzll3X0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
        "\n",
        "# Make predictions on the testing set\n",
        "y_pred = model.predict(X_test_scaled)\n",
        "\n",
        "# Calculate evaluation metrics\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(\"Mean Squared Error (MSE):\", mse)\n",
        "print(\"Mean Absolute Error (MAE):\", mae)\n",
        "print(\"R-squared (R2):\", r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YQ1GxyUnlw2r",
        "outputId": "09d8cda4-180b-4696-c942-012ac4e7cfb7"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15/15 [==============================] - 0s 4ms/step\n",
            "Mean Squared Error (MSE): 18.57092984432851\n",
            "Mean Absolute Error (MAE): 3.3998190166793893\n",
            "R-squared (R2): 0.9661299774245254\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create a DataFrame to store the actual and predicted values\n",
        "comparison_data = pd.DataFrame({'Actual': y_test, 'Predicted': test_predictions.flatten()})\n",
        "\n",
        "# Sort the DataFrame by the actual values\n",
        "comparison_data = comparison_data.sort_values(by='Actual')\n",
        "\n",
        "# Reset the index\n",
        "comparison_data = comparison_data.reset_index(drop=True)\n",
        "\n",
        "# Plot the actual and predicted values\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(comparison_data.index, comparison_data['Actual'], marker='o', label='Actual')\n",
        "plt.plot(comparison_data.index, comparison_data['Predicted'], marker='o', label='Predicted')\n",
        "plt.xlabel('Index')\n",
        "plt.ylabel('Gold Price')\n",
        "plt.title('Actual vs. Predicted Gold Prices')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "0_j8wMzvp7hw",
        "outputId": "6020df96-af38-4be9-8877-c05a3bcd1a25"
      },
      "execution_count": 30,
      "outputs": [
        {
          "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": [],
      "metadata": {
        "id": "qFJH37FNqu2y"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}