LSTM_NFLX.ipynb

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      "source": [
        "#Author: Ivan L. Ihwani\n",
        "#Last Update: 9/30/2024\n",
        "\n",
        "\n",
        "#import the libraries\n",
        "import math\n",
        "import pandas_datareader as web\n",
        "import numpy as np\n",
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('fivethirtyeight')\n",
        "\n",
        "#Get thr stock quote\n",
        "df = yf.download('NFLX', start='2016-05-01', end=None)\n",
        "#show the data\n",
        "df"
      ],
      "execution_count": 26,
      "outputs": [
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              "                  Open        High         Low       Close   Adj Close  \\\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2117,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2016-05-02 00:00:00\",\n        \"max\": \"2024-09-27 00:00:00\",\n        \"num_unique_values\": 2117,\n        \"samples\": [\n          \"2017-12-12 00:00:00\",\n          \"2020-09-28 00:00:00\",\n          \"2017-05-11 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 159.76178847141216,\n        \"min\": 85.43000030517578,\n        \"max\": 724.72998046875,\n        \"num_unique_values\": 2025,\n        \"samples\": [\n          687.0,\n          417.3900146484375,\n          419.9800109863281\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 161.5452916913429,\n        \"min\": 86.5,\n        \"max\": 725.260009765625,\n        \"num_unique_values\": 2056,\n        \"samples\": [\n          141.0399932861328,\n          367.1499938964844,\n          161.77999877929688\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157.82448243627545,\n        \"min\": 84.5,\n        \"max\": 716.2100219726562,\n        \"num_unique_values\": 2063,\n        \"samples\": [\n          539.0999755859375,\n          622.989990234375,\n          359.3299865722656\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 159.65244113434377,\n        \"min\": 85.33000183105469,\n        \"max\": 722.260009765625,\n        \"num_unique_values\": 2066,\n        \"samples\": [\n          177.19000244140625,\n          565.1500244140625,\n          631.8499755859375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 159.65244113434377,\n        \"min\": 85.33000183105469,\n        \"max\": 722.260009765625,\n        \"num_unique_values\": 2066,\n        \"samples\": [\n          177.19000244140625,\n          565.1500244140625,\n          631.8499755859375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6162265,\n        \"min\": 1144000,\n        \"max\": 133387500,\n        \"num_unique_values\": 2091,\n        \"samples\": [\n          3526600,\n          13484300,\n          6119900\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        },
        "id": "LxBmKjlkaRHK",
        "outputId": "16699f57-fdca-4c0d-c9da-ec7789cc0887"
      },
      "source": [
        "# Get the number of rows and columns in the data set\n",
        "df.shape\n",
        "#Visualize the closing price history\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Close price History')\n",
        "plt.plot(df['Close'])\n",
        "plt.xlabel('Data', fontsize=18)\n",
        "plt.ylabel('Close Price in USD ($)', fontsize=18)\n",
        "plt.show()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kmofGd5-a5_Y",
        "outputId": "5bba21c4-3946-40fb-d6d7-c329c5479b17"
      },
      "source": [
        "# Create a new dataframe with only the 'Close' column\n",
        "data=df.filter(['Close'])\n",
        "#Convert the dataframe to a numpy array\n",
        "dataset = data.values\n",
        "# Get the number of rows to train the model on\n",
        "training_data_len = math.ceil(len(dataset)* .8)\n",
        "training_data_len"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1694"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "etddcWcPbFVX",
        "outputId": "6f41203b-1587-4fb7-e8d7-f2e8580baa4e"
      },
      "source": [
        "#Scale the data\n",
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "scaled_data = scaler.fit_transform(dataset)\n",
        "scaled_data"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.01221484],\n",
              "       [0.00974989],\n",
              "       [0.00857237],\n",
              "       ...,\n",
              "       [0.99890096],\n",
              "       [0.98299654],\n",
              "       [0.97659078]])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqBbDGK_cE-8",
        "outputId": "2f3ff443-6632-4698-b83c-f4bc6b889ef1"
      },
      "source": [
        "#Create the training dataset\n",
        "#Create the scaled training dataset\n",
        "train_data = scaled_data[0:training_data_len, :]\n",
        "#Split the data into x_train and y_train data sets\n",
        "x_train = []\n",
        "y_train = []\n",
        "for i in range(60, len(train_data)):\n",
        "    x_train.append(train_data[i-60: i, 0])\n",
        "    y_train.append(train_data[i,0])\n",
        "    if i<=61:\n",
        "        print(x_train)\n",
        "        print(y_train)\n",
        "        print()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([0.01221484, 0.00974989, 0.00857237, 0.00634293, 0.00865086,\n",
            "       0.00817986, 0.01186943, 0.00736344, 0.00378377, 0.00400357,\n",
            "       0.00595042, 0.0051811 , 0.00811706, 0.00662553, 0.01124142,\n",
            "       0.01500949, 0.01971959, 0.02334636, 0.02744414, 0.02821346,\n",
            "       0.02706733, 0.02540311, 0.02499489, 0.02238864, 0.02419417,\n",
            "       0.02285965, 0.01967249, 0.01846356, 0.01321966, 0.01337666,\n",
            "       0.01380058, 0.01406748, 0.01587302, 0.01431868, 0.01329817,\n",
            "       0.00888637, 0.00734775, 0.0099383 , 0.0048828 , 0.        ,\n",
            "       0.00414488, 0.00899627, 0.00965569, 0.01780415, 0.019751  ,\n",
            "       0.01455418, 0.0153392 , 0.01841646, 0.01466409, 0.01670513,\n",
            "       0.01742734, 0.01992369, 0.0205046 , 0.02116401, 0.00080071,\n",
            "       0.00405068, 0.00103621, 0.00087921, 0.00365818, 0.00954579])]\n",
            "[0.010534908076056532]\n",
            "\n",
            "[array([0.01221484, 0.00974989, 0.00857237, 0.00634293, 0.00865086,\n",
            "       0.00817986, 0.01186943, 0.00736344, 0.00378377, 0.00400357,\n",
            "       0.00595042, 0.0051811 , 0.00811706, 0.00662553, 0.01124142,\n",
            "       0.01500949, 0.01971959, 0.02334636, 0.02744414, 0.02821346,\n",
            "       0.02706733, 0.02540311, 0.02499489, 0.02238864, 0.02419417,\n",
            "       0.02285965, 0.01967249, 0.01846356, 0.01321966, 0.01337666,\n",
            "       0.01380058, 0.01406748, 0.01587302, 0.01431868, 0.01329817,\n",
            "       0.00888637, 0.00734775, 0.0099383 , 0.0048828 , 0.        ,\n",
            "       0.00414488, 0.00899627, 0.00965569, 0.01780415, 0.019751  ,\n",
            "       0.01455418, 0.0153392 , 0.01841646, 0.01466409, 0.01670513,\n",
            "       0.01742734, 0.01992369, 0.0205046 , 0.02116401, 0.00080071,\n",
            "       0.00405068, 0.00103621, 0.00087921, 0.00365818, 0.00954579]), array([0.00974989, 0.00857237, 0.00634293, 0.00865086, 0.00817986,\n",
            "       0.01186943, 0.00736344, 0.00378377, 0.00400357, 0.00595042,\n",
            "       0.0051811 , 0.00811706, 0.00662553, 0.01124142, 0.01500949,\n",
            "       0.01971959, 0.02334636, 0.02744414, 0.02821346, 0.02706733,\n",
            "       0.02540311, 0.02499489, 0.02238864, 0.02419417, 0.02285965,\n",
            "       0.01967249, 0.01846356, 0.01321966, 0.01337666, 0.01380058,\n",
            "       0.01406748, 0.01587302, 0.01431868, 0.01329817, 0.00888637,\n",
            "       0.00734775, 0.0099383 , 0.0048828 , 0.        , 0.00414488,\n",
            "       0.00899627, 0.00965569, 0.01780415, 0.019751  , 0.01455418,\n",
            "       0.0153392 , 0.01841646, 0.01466409, 0.01670513, 0.01742734,\n",
            "       0.01992369, 0.0205046 , 0.02116401, 0.00080071, 0.00405068,\n",
            "       0.00103621, 0.00087921, 0.00365818, 0.00954579, 0.01053491])]\n",
            "[0.010534908076056532, 0.009922596856942939]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "90Jcl2ljcnW1",
        "outputId": "48e040fd-848d-4ad9-db51-4b2dfe6bc1ed"
      },
      "source": [
        "# Convert the x_train and y_train to numpy arrays\n",
        "x_train, y_train =np.array(x_train),np.array(y_train)\n",
        "#Reshape the data\n",
        "x_train =np.reshape(x_train,(x_train.shape[0], x_train.shape[1],1))\n",
        "x_train.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1634, 60, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWZVfBUDczeW"
      },
      "source": [
        "#Build the LSTM model\n",
        "model = Sequential()\n",
        "model.add(LSTM(50, return_sequences=True, input_shape=(x_train.shape[1], 1)))\n",
        "model.add(LSTM(50, return_sequences=False))\n",
        "model.add(Dense(25))\n",
        "model.add(Dense(1))"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hNggUa19eqvn",
        "outputId": "18280d7b-19f7-4647-a971-8422fa806e37"
      },
      "source": [
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "\n",
        "# Train the model\n",
        "model.fit(x_train, y_train, batch_size=1, epochs=1)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1634/1634\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 31ms/step - loss: 0.0090\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7fdf88171f90>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gvnTeYx-euJc"
      },
      "source": [
        "# Create the testing dataset\n",
        "# Create a new array containing scaled values from index (training_data_len - 60) to 2003\n",
        "test_data = scaled_data[training_data_len - 60:2003]\n",
        "\n",
        "# Create the data sets x_test and y_test\n",
        "x_test = []\n",
        "y_test = dataset[training_data_len:, :]  # y_test should come from original dataset\n",
        "\n",
        "# Create x_test sequences\n",
        "for i in range(60, len(test_data)):\n",
        "    x_test.append(test_data[i-60:i, 0])  # Corrected the array indexing\n",
        "\n",
        "# Convert x_test to numpy array\n",
        "x_test = np.array(x_test)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dKX8L-40e1x_",
        "outputId": "d652a26b-75c4-4e2c-a2d8-7648d7983e7d"
      },
      "source": [
        "# Ensure that y_test and predictions have the same length\n",
        "min_length = min(len(predictions), len(y_test))\n",
        "\n",
        "# Slice y_test to the same length as predictions\n",
        "y_test = y_test[:min_length]\n",
        "\n",
        "# Calculate RMSE\n",
        "rmse = np.sqrt(np.mean((predictions[:min_length] - y_test) ** 2))\n",
        "\n",
        "rmse"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "40.58410783269582"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "id": "Icu4_oQrfERW",
        "outputId": "39be9155-f0fc-4eb7-c387-80647a2cc498"
      },
      "source": [
        "# Truncate the 'valid' DataFrame to match the length of 'predictions'\n",
        "valid = valid[:len(predictions)]\n",
        "\n",
        "# Now you can safely assign the predictions to the 'valid' DataFrame\n",
        "valid['Predictions'] = predictions\n",
        "\n",
        "# Visualize the data\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Model')\n",
        "plt.xlabel('Date', fontsize=18)\n",
        "plt.ylabel('IDR', fontsize=18)\n",
        "plt.plot(train['Close'])\n",
        "plt.plot(valid[['Close', 'Predictions']])\n",
        "plt.legend(['Train', 'Val', 'Predictions'], loc='lower right')\n",
        "plt.show()\n"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-3d2adfde7c49>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  valid['Predictions'] = predictions\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valid\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "IxOKo72am8bQ",
        "outputId": "c2255e5b-76bf-4e47-b1e6-7ca0dd643465"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Close  Predictions\n",
              "Date                               \n",
              "2023-01-24  363.829987   303.698792\n",
              "2023-01-25  367.959991   309.481781\n",
              "2023-01-26  364.869995   316.052765\n",
              "2023-01-27  360.769989   321.897552\n",
              "2023-01-30  353.109985   326.137329\n",
              "...                ...          ...\n",
              "2024-04-10  618.580017   581.071472\n",
              "2024-04-11  628.780029   580.773743\n",
              "2024-04-12  622.830017   581.408142\n",
              "2024-04-15  607.150024   581.583618\n",
              "2024-04-16  617.520020   579.447815\n",
              "\n",
              "[309 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-acf632f9-8108-4d54-aedd-a0e291386922\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\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>Close</th>\n",
              "      <th>Predictions</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2023-01-24</th>\n",
              "      <td>363.829987</td>\n",
              "      <td>303.698792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-25</th>\n",
              "      <td>367.959991</td>\n",
              "      <td>309.481781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-26</th>\n",
              "      <td>364.869995</td>\n",
              "      <td>316.052765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-27</th>\n",
              "      <td>360.769989</td>\n",
              "      <td>321.897552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-30</th>\n",
              "      <td>353.109985</td>\n",
              "      <td>326.137329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-04-10</th>\n",
              "      <td>618.580017</td>\n",
              "      <td>581.071472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-04-11</th>\n",
              "      <td>628.780029</td>\n",
              "      <td>580.773743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-04-12</th>\n",
              "      <td>622.830017</td>\n",
              "      <td>581.408142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-04-15</th>\n",
              "      <td>607.150024</td>\n",
              "      <td>581.583618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-04-16</th>\n",
              "      <td>617.520020</td>\n",
              "      <td>579.447815</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>309 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-acf632f9-8108-4d54-aedd-a0e291386922')\"\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-acf632f9-8108-4d54-aedd-a0e291386922 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-acf632f9-8108-4d54-aedd-a0e291386922');\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-9d206e28-d797-480b-87cd-d8597020b0cd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9d206e28-d797-480b-87cd-d8597020b0cd')\"\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-9d206e28-d797-480b-87cd-d8597020b0cd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_afa640fb-6032-4b8b-8ae6-ca7fdaa3c169\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('valid')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_afa640fb-6032-4b8b-8ae6-ca7fdaa3c169 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('valid');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "valid",
              "summary": "{\n  \"name\": \"valid\",\n  \"rows\": 309,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-01-24 00:00:00\",\n        \"max\": \"2024-04-16 00:00:00\",\n        \"num_unique_values\": 309,\n        \"samples\": [\n          \"2024-03-18 00:00:00\",\n          \"2023-02-06 00:00:00\",\n          \"2023-04-17 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 93.00217400563241,\n        \"min\": 292.760009765625,\n        \"max\": 636.1799926757812,\n        \"num_unique_values\": 306,\n        \"samples\": [\n          360.82000732421875,\n          445.760009765625,\n          445.8999938964844\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predictions\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 309,\n        \"samples\": [\n          566.1673583984375,\n          331.1062927246094,\n          309.8642578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PGYlwYhWnVFg"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}