StockPricePredictorVer4.ipynb

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            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.70-py2.py3-none-any.whl (26 kB)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)\n",
            "\u001b[K     |████████████████████████████████| 63 kB 996 kB/s \n",
            "\u001b[?25hCollecting lxml>=4.5.1\n",
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            "\u001b[K     |████████████████████████████████| 6.4 MB 2.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.10)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->yfinance) (1.15.0)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.0.12)\n",
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            "Installing collected packages: requests, lxml, yfinance\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Attempting uninstall: lxml\n",
            "    Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "Successfully installed lxml-4.8.0 requests-2.27.1 yfinance-0.1.70\n"
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      "source": [
        "#yahoo finance as data source\n",
        "!pip install yfinance\n",
        "import yfinance as yf"
      ]
    },
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      "source": [
        "#See the yahoo finance ticker for your stock symbol\n",
        "stock_symbol = 'AAPL'"
      ]
    },
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      "id": "d365868a",
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
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      ],
      "source": [
        "#last 5 years data with interval of 1 day\n",
        "data = yf.download(tickers=stock_symbol,period='5y',interval='1d')"
      ]
    },
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              "                 Open       High        Low      Close  Adj Close     Volume\n",
              "Date                                                                        \n",
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              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2022-04-04  174.570007  178.490005  174.440002  178.440002  178.440002   \n",
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              "    [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-154a963f-cdb3-434b-b09e-8c7563dbe449 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-154a963f-cdb3-434b-b09e-8c7563dbe449');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "data.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "6292e20a",
      "metadata": {
        "id": "6292e20a"
      },
      "outputs": [],
      "source": [
        "opn = data[['Open']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "21e132d8",
      "metadata": {
        "id": "21e132d8",
        "outputId": "c91eae1a-2030-4e4a-bef6-a295523c0fb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f41e0e9b650>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "opn.plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "c5194440",
      "metadata": {
        "id": "c5194440"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "8038cb6a",
      "metadata": {
        "id": "8038cb6a"
      },
      "outputs": [],
      "source": [
        "ds = opn.values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "69b08672",
      "metadata": {
        "id": "69b08672",
        "outputId": "a3941305-528c-452e-afa1-8efd127baf13",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 35.90000153],\n",
              "       [ 35.73500061],\n",
              "       [ 35.40000153],\n",
              "       ...,\n",
              "       [172.36000061],\n",
              "       [171.16000366],\n",
              "       [171.77999878]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "ds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "af022e3c",
      "metadata": {
        "id": "af022e3c",
        "outputId": "847c099e-041f-4482-9e98-fc2e581a679c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f41e08bd250>]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(ds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "0e73b789",
      "metadata": {
        "id": "0e73b789"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "0e6c1c22",
      "metadata": {
        "id": "0e6c1c22"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "cf6a3e1b",
      "metadata": {
        "id": "cf6a3e1b"
      },
      "outputs": [],
      "source": [
        "#Using MinMaxScaler for normalizing data between 0 & 1\n",
        "normalizer = MinMaxScaler(feature_range=(0,1))\n",
        "ds_scaled = normalizer.fit_transform(np.array(ds).reshape(-1,1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "7dbea1d5",
      "metadata": {
        "id": "7dbea1d5",
        "outputId": "14c9bfd0-8a40-400e-8173-0465c20aaf67",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1260, 1260)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "len(ds_scaled), len(ds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "a20c5654",
      "metadata": {
        "id": "a20c5654"
      },
      "outputs": [],
      "source": [
        "#Defining test and train data sizes\n",
        "train_size = int(len(ds_scaled)*0.70)\n",
        "test_size = len(ds_scaled) - train_size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "b964aa4e",
      "metadata": {
        "id": "b964aa4e",
        "outputId": "603a872b-ec13-4872-a57a-b210221c09a3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(882, 378)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "train_size,test_size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "25ca615e",
      "metadata": {
        "id": "25ca615e"
      },
      "outputs": [],
      "source": [
        "#Splitting data between train and test\n",
        "ds_train, ds_test = ds_scaled[0:train_size,:], ds_scaled[train_size:len(ds_scaled),:1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "95913f28",
      "metadata": {
        "id": "95913f28",
        "outputId": "3e1eae22-4a83-4292-8b54-e1434c1e013a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(882, 378)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "len(ds_train),len(ds_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "5dd0b45e",
      "metadata": {
        "id": "5dd0b45e"
      },
      "outputs": [],
      "source": [
        "#creating dataset in time series for LSTM model \n",
        "#X[100,120,140,160,180] : Y[200]\n",
        "def create_ds(dataset,step):\n",
        "    Xtrain, Ytrain = [], []\n",
        "    for i in range(len(dataset)-step-1):\n",
        "        a = dataset[i:(i+step), 0]\n",
        "        Xtrain.append(a)\n",
        "        Ytrain.append(dataset[i + step, 0])\n",
        "    return np.array(Xtrain), np.array(Ytrain)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "d28b7d1d",
      "metadata": {
        "id": "d28b7d1d"
      },
      "outputs": [],
      "source": [
        "#Taking 100 days price as one record for training\n",
        "time_stamp = 100\n",
        "X_train, y_train = create_ds(ds_train,time_stamp)\n",
        "X_test, y_test = create_ds(ds_test,time_stamp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "03ce9096",
      "metadata": {
        "id": "03ce9096",
        "outputId": "71ccb2e7-0854-440d-af02-37b63b0670f5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((781, 100), (781,))"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "X_train.shape,y_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "84acdd72",
      "metadata": {
        "id": "84acdd72",
        "outputId": "9afa82d8-85ea-463d-fabe-bf4d45303462",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((277, 100), (277,))"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "X_test.shape, y_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "c657d193",
      "metadata": {
        "id": "c657d193"
      },
      "outputs": [],
      "source": [
        "#Reshaping data to fit into LSTM model\n",
        "X_train = X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)\n",
        "X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "82844671",
      "metadata": {
        "id": "82844671"
      },
      "outputs": [],
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "b5250913",
      "metadata": {
        "id": "b5250913",
        "outputId": "0a39d46c-10f6-45a9-868e-8d2334b54196",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 100, 50)           10400     \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 100, 50)           20200     \n",
            "                                                                 \n",
            " lstm_2 (LSTM)               (None, 50)                20200     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 51        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 50,851\n",
            "Trainable params: 50,851\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#Creating LSTM model using keras\n",
        "model = Sequential()\n",
        "model.add(LSTM(units=50,return_sequences=True,input_shape=(X_train.shape[1],1)))\n",
        "model.add(LSTM(units=50,return_sequences=True))\n",
        "model.add(LSTM(units=50))\n",
        "model.add(Dense(units=1,activation='linear'))\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "7e8ff062",
      "metadata": {
        "id": "7e8ff062",
        "outputId": "cd241571-f366-481f-f1e9-c64e943be385",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "13/13 [==============================] - 16s 645ms/step - loss: 0.0128 - val_loss: 0.0368\n",
            "Epoch 2/100\n",
            "13/13 [==============================] - 5s 370ms/step - loss: 0.0016 - val_loss: 0.0028\n",
            "Epoch 3/100\n",
            "13/13 [==============================] - 5s 342ms/step - loss: 9.3652e-04 - val_loss: 0.0070\n",
            "Epoch 4/100\n",
            "13/13 [==============================] - 4s 308ms/step - loss: 9.7036e-04 - val_loss: 0.0046\n",
            "Epoch 5/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 8.9698e-04 - val_loss: 0.0073\n",
            "Epoch 6/100\n",
            "13/13 [==============================] - 2s 178ms/step - loss: 9.3049e-04 - val_loss: 0.0090\n",
            "Epoch 7/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 7.9522e-04 - val_loss: 0.0050\n",
            "Epoch 8/100\n",
            "13/13 [==============================] - 2s 178ms/step - loss: 6.7380e-04 - val_loss: 0.0045\n",
            "Epoch 9/100\n",
            "13/13 [==============================] - 2s 179ms/step - loss: 6.2256e-04 - val_loss: 0.0054\n",
            "Epoch 10/100\n",
            "13/13 [==============================] - 2s 179ms/step - loss: 5.8954e-04 - val_loss: 0.0050\n",
            "Epoch 11/100\n",
            "13/13 [==============================] - 2s 179ms/step - loss: 5.5710e-04 - val_loss: 0.0041\n",
            "Epoch 12/100\n",
            "13/13 [==============================] - 2s 177ms/step - loss: 5.6411e-04 - val_loss: 0.0038\n",
            "Epoch 13/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 5.3223e-04 - val_loss: 0.0069\n",
            "Epoch 14/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 5.5058e-04 - val_loss: 0.0083\n",
            "Epoch 15/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 4.6997e-04 - val_loss: 0.0053\n",
            "Epoch 16/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 4.6834e-04 - val_loss: 0.0016\n",
            "Epoch 17/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 4.7192e-04 - val_loss: 0.0040\n",
            "Epoch 18/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 4.9204e-04 - val_loss: 0.0121\n",
            "Epoch 19/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 5.4022e-04 - val_loss: 0.0034\n",
            "Epoch 20/100\n",
            "13/13 [==============================] - 2s 182ms/step - loss: 4.7837e-04 - val_loss: 0.0014\n",
            "Epoch 21/100\n",
            "13/13 [==============================] - 2s 178ms/step - loss: 5.0654e-04 - val_loss: 0.0058\n",
            "Epoch 22/100\n",
            "13/13 [==============================] - 2s 177ms/step - loss: 5.3508e-04 - val_loss: 0.0059\n",
            "Epoch 23/100\n",
            "13/13 [==============================] - 2s 178ms/step - loss: 4.2547e-04 - val_loss: 0.0034\n",
            "Epoch 24/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 4.2115e-04 - val_loss: 0.0015\n",
            "Epoch 25/100\n",
            "13/13 [==============================] - 2s 178ms/step - loss: 5.9901e-04 - val_loss: 0.0032\n",
            "Epoch 26/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 6.3193e-04 - val_loss: 0.0066\n",
            "Epoch 27/100\n",
            "13/13 [==============================] - 2s 179ms/step - loss: 4.0845e-04 - val_loss: 0.0078\n",
            "Epoch 28/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 3.7544e-04 - val_loss: 0.0015\n",
            "Epoch 29/100\n",
            "13/13 [==============================] - 2s 179ms/step - loss: 3.9600e-04 - val_loss: 0.0061\n",
            "Epoch 30/100\n",
            "13/13 [==============================] - 2s 177ms/step - loss: 3.5777e-04 - val_loss: 0.0026\n",
            "Epoch 31/100\n",
            "13/13 [==============================] - 2s 183ms/step - loss: 3.4219e-04 - val_loss: 0.0017\n",
            "Epoch 32/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 3.3301e-04 - val_loss: 0.0014\n",
            "Epoch 33/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 3.6059e-04 - val_loss: 0.0050\n",
            "Epoch 34/100\n",
            "13/13 [==============================] - 2s 184ms/step - loss: 3.5234e-04 - val_loss: 0.0040\n",
            "Epoch 35/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 3.5027e-04 - val_loss: 0.0015\n",
            "Epoch 36/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 3.5260e-04 - val_loss: 0.0032\n",
            "Epoch 37/100\n",
            "13/13 [==============================] - 2s 182ms/step - loss: 3.3889e-04 - val_loss: 0.0030\n",
            "Epoch 38/100\n",
            "13/13 [==============================] - 2s 182ms/step - loss: 3.1877e-04 - val_loss: 0.0013\n",
            "Epoch 39/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 3.3277e-04 - val_loss: 0.0030\n",
            "Epoch 40/100\n",
            "13/13 [==============================] - 2s 182ms/step - loss: 3.4639e-04 - val_loss: 0.0047\n",
            "Epoch 41/100\n",
            "13/13 [==============================] - 2s 185ms/step - loss: 3.9383e-04 - val_loss: 0.0012\n",
            "Epoch 42/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 3.6800e-04 - val_loss: 0.0011\n",
            "Epoch 43/100\n",
            "13/13 [==============================] - 2s 183ms/step - loss: 3.6350e-04 - val_loss: 0.0051\n",
            "Epoch 44/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 3.3116e-04 - val_loss: 0.0012\n",
            "Epoch 45/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 3.1929e-04 - val_loss: 0.0016\n",
            "Epoch 46/100\n",
            "13/13 [==============================] - 4s 328ms/step - loss: 3.1006e-04 - val_loss: 0.0022\n",
            "Epoch 47/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 3.4811e-04 - val_loss: 0.0085\n",
            "Epoch 48/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 4.1075e-04 - val_loss: 0.0017\n",
            "Epoch 49/100\n",
            "13/13 [==============================] - 2s 182ms/step - loss: 3.6837e-04 - val_loss: 0.0021\n",
            "Epoch 50/100\n",
            "13/13 [==============================] - 2s 179ms/step - loss: 2.9588e-04 - val_loss: 0.0014\n",
            "Epoch 51/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 2.8444e-04 - val_loss: 0.0014\n",
            "Epoch 52/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 3.0715e-04 - val_loss: 0.0047\n",
            "Epoch 53/100\n",
            "13/13 [==============================] - 2s 184ms/step - loss: 3.1420e-04 - val_loss: 0.0013\n",
            "Epoch 54/100\n",
            "13/13 [==============================] - 2s 185ms/step - loss: 3.2635e-04 - val_loss: 0.0014\n",
            "Epoch 55/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 3.3091e-04 - val_loss: 0.0050\n",
            "Epoch 56/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 3.7274e-04 - val_loss: 0.0045\n",
            "Epoch 57/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 3.8666e-04 - val_loss: 0.0012\n",
            "Epoch 58/100\n",
            "13/13 [==============================] - 2s 184ms/step - loss: 3.6708e-04 - val_loss: 0.0038\n",
            "Epoch 59/100\n",
            "13/13 [==============================] - 2s 182ms/step - loss: 3.3285e-04 - val_loss: 0.0034\n",
            "Epoch 60/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 2.6548e-04 - val_loss: 0.0031\n",
            "Epoch 61/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 2.5325e-04 - val_loss: 0.0039\n",
            "Epoch 62/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 2.5082e-04 - val_loss: 0.0104\n",
            "Epoch 63/100\n",
            "13/13 [==============================] - 2s 182ms/step - loss: 5.3733e-04 - val_loss: 0.0012\n",
            "Epoch 64/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 3.2349e-04 - val_loss: 0.0015\n",
            "Epoch 65/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 3.2505e-04 - val_loss: 0.0017\n",
            "Epoch 66/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 2.7851e-04 - val_loss: 8.8803e-04\n",
            "Epoch 67/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 2.7223e-04 - val_loss: 0.0030\n",
            "Epoch 68/100\n",
            "13/13 [==============================] - 2s 183ms/step - loss: 2.3936e-04 - val_loss: 0.0024\n",
            "Epoch 69/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 2.3842e-04 - val_loss: 0.0017\n",
            "Epoch 70/100\n",
            "13/13 [==============================] - 2s 179ms/step - loss: 2.3513e-04 - val_loss: 0.0012\n",
            "Epoch 71/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 2.3894e-04 - val_loss: 0.0030\n",
            "Epoch 72/100\n",
            "13/13 [==============================] - 2s 182ms/step - loss: 2.3524e-04 - val_loss: 0.0056\n",
            "Epoch 73/100\n",
            "13/13 [==============================] - 2s 178ms/step - loss: 4.2560e-04 - val_loss: 0.0025\n",
            "Epoch 74/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 3.1088e-04 - val_loss: 0.0016\n",
            "Epoch 75/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 3.1982e-04 - val_loss: 0.0025\n",
            "Epoch 76/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 2.9819e-04 - val_loss: 0.0038\n",
            "Epoch 77/100\n",
            "13/13 [==============================] - 2s 179ms/step - loss: 2.5668e-04 - val_loss: 0.0019\n",
            "Epoch 78/100\n",
            "13/13 [==============================] - 2s 179ms/step - loss: 2.7730e-04 - val_loss: 9.9515e-04\n",
            "Epoch 79/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 2.6816e-04 - val_loss: 0.0042\n",
            "Epoch 80/100\n",
            "13/13 [==============================] - 2s 182ms/step - loss: 2.7108e-04 - val_loss: 0.0019\n",
            "Epoch 81/100\n",
            "13/13 [==============================] - 2s 183ms/step - loss: 2.2263e-04 - val_loss: 0.0029\n",
            "Epoch 82/100\n",
            "13/13 [==============================] - 2s 179ms/step - loss: 2.2222e-04 - val_loss: 0.0019\n",
            "Epoch 83/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 2.1286e-04 - val_loss: 0.0017\n",
            "Epoch 84/100\n",
            "13/13 [==============================] - 2s 188ms/step - loss: 2.1744e-04 - val_loss: 0.0024\n",
            "Epoch 85/100\n",
            "13/13 [==============================] - 2s 185ms/step - loss: 2.0425e-04 - val_loss: 0.0056\n",
            "Epoch 86/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 2.2714e-04 - val_loss: 0.0018\n",
            "Epoch 87/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 2.1144e-04 - val_loss: 0.0023\n",
            "Epoch 88/100\n",
            "13/13 [==============================] - 2s 185ms/step - loss: 1.9830e-04 - val_loss: 0.0031\n",
            "Epoch 89/100\n",
            "13/13 [==============================] - 2s 180ms/step - loss: 1.9480e-04 - val_loss: 0.0019\n",
            "Epoch 90/100\n",
            "13/13 [==============================] - 2s 183ms/step - loss: 1.9542e-04 - val_loss: 0.0051\n",
            "Epoch 91/100\n",
            "13/13 [==============================] - 2s 185ms/step - loss: 2.0301e-04 - val_loss: 0.0036\n",
            "Epoch 92/100\n",
            "13/13 [==============================] - 2s 182ms/step - loss: 2.1712e-04 - val_loss: 0.0011\n",
            "Epoch 93/100\n",
            "13/13 [==============================] - 2s 183ms/step - loss: 2.2540e-04 - val_loss: 0.0038\n",
            "Epoch 94/100\n",
            "13/13 [==============================] - 2s 186ms/step - loss: 2.0450e-04 - val_loss: 0.0031\n",
            "Epoch 95/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 2.5841e-04 - val_loss: 0.0012\n",
            "Epoch 96/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 2.3946e-04 - val_loss: 0.0030\n",
            "Epoch 97/100\n",
            "13/13 [==============================] - 2s 182ms/step - loss: 2.1002e-04 - val_loss: 0.0019\n",
            "Epoch 98/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 2.3687e-04 - val_loss: 0.0021\n",
            "Epoch 99/100\n",
            "13/13 [==============================] - 2s 181ms/step - loss: 2.4782e-04 - val_loss: 0.0049\n",
            "Epoch 100/100\n",
            "13/13 [==============================] - 2s 182ms/step - loss: 2.0123e-04 - val_loss: 0.0027\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f41648eb6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "#Training model with adam optimizer and mean squared error loss function\n",
        "model.compile(loss='mean_squared_error',optimizer='adam')\n",
        "model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=100,batch_size=64)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "18b1ba6a",
      "metadata": {
        "id": "18b1ba6a",
        "outputId": "c80da389-a993-4e8d-bcd1-aaea60fb78f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4164696ed0>]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#PLotting loss, it shows that loss has decreased significantly and model trained well\n",
        "loss = model.history.history['loss']\n",
        "plt.plot(loss)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "3e7f2fc8",
      "metadata": {
        "id": "3e7f2fc8"
      },
      "outputs": [],
      "source": [
        "#Predicitng on train and test data\n",
        "train_predict = model.predict(X_train)\n",
        "test_predict = model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "c2d6c2d5",
      "metadata": {
        "id": "c2d6c2d5"
      },
      "outputs": [],
      "source": [
        "#Inverse transform to get actual value\n",
        "train_predict = normalizer.inverse_transform(train_predict)\n",
        "test_predict = normalizer.inverse_transform(test_predict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "9d0543ad",
      "metadata": {
        "id": "9d0543ad",
        "outputId": "746f7f86-7410-4015-defb-cae88f7e6022",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f41644b7a10>]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Comparing using visuals\n",
        "plt.plot(normalizer.inverse_transform(ds_scaled))\n",
        "plt.plot(train_predict)\n",
        "plt.plot(test_predict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "55300bae",
      "metadata": {
        "id": "55300bae",
        "outputId": "9d1bbf04-7f59-4301-9870-0927dd1a480a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "type(train_predict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "bf6d5613",
      "metadata": {
        "id": "bf6d5613"
      },
      "outputs": [],
      "source": [
        "test = np.vstack((train_predict,test_predict))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "41b272ff",
      "metadata": {
        "id": "41b272ff",
        "outputId": "efec4e17-93a7-4c06-f181-9870d9af8ef0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f416447bf90>]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Combining the predited data to create uniform data visualization\n",
        "plt.plot(normalizer.inverse_transform(ds_scaled))\n",
        "plt.plot(test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "16af9283",
      "metadata": {
        "id": "16af9283",
        "outputId": "10cada3a-338b-44be-eb62-4f2aece47afa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "378"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "len(ds_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "c13df7b8",
      "metadata": {
        "id": "c13df7b8"
      },
      "outputs": [],
      "source": [
        "#Getting the last 100 days records\n",
        "last_100 = len(ds_test)-100\n",
        "fut_inp = ds_test[last_100:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "52b30579",
      "metadata": {
        "id": "52b30579"
      },
      "outputs": [],
      "source": [
        "fut_inp = fut_inp.reshape(1,-1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "ac2686a6",
      "metadata": {
        "id": "ac2686a6"
      },
      "outputs": [],
      "source": [
        "tmp_inp = list(fut_inp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "dad5024f",
      "metadata": {
        "id": "dad5024f",
        "outputId": "75583534-ca73-42e2-f116-60592baa1299",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 100)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "fut_inp.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "98042a30",
      "metadata": {
        "id": "98042a30"
      },
      "outputs": [],
      "source": [
        "#Creating list of the last 100 data\n",
        "tmp_inp = tmp_inp[0].tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "44799d02",
      "metadata": {
        "id": "44799d02",
        "outputId": "c38e8f12-1d23-44b9-8146-94bb370a445f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.8319033980369568], [0.7991958856582642], [0.7562515735626221], [0.7143570780754089], [0.6783904433250427], [0.6500413417816162], [0.6295497417449951], [0.6164284944534302], [0.6098190546035767], [0.6087045669555664], [0.6120427846908569], [0.6188366413116455], [0.6281672120094299], [0.6392039060592651], [0.6512055993080139], [0.6635186076164246], [0.6755761504173279], [0.6868992447853088], [0.6970992684364319], [0.7058807015419006], [0.7130426168441772], [0.7184768319129944], [0.7221621870994568], [0.7241548299789429], [0.7245755791664124], [0.7235935926437378], [0.7214099764823914], [0.7182415723800659], [0.7143058776855469], [0.7098103165626526]]\n"
          ]
        }
      ],
      "source": [
        "#Predicting next 30 days price suing the current data\n",
        "#It will predict in sliding window manner (algorithm) with stride 1\n",
        "lst_output=[]\n",
        "n_steps=100\n",
        "i=0\n",
        "while(i<30):\n",
        "    \n",
        "    if(len(tmp_inp)>100):\n",
        "        fut_inp = np.array(tmp_inp[1:])\n",
        "        fut_inp=fut_inp.reshape(1,-1)\n",
        "        fut_inp = fut_inp.reshape((1, n_steps, 1))\n",
        "        yhat = model.predict(fut_inp, verbose=0)\n",
        "        tmp_inp.extend(yhat[0].tolist())\n",
        "        tmp_inp = tmp_inp[1:]\n",
        "        lst_output.extend(yhat.tolist())\n",
        "        i=i+1\n",
        "    else:\n",
        "        fut_inp = fut_inp.reshape((1, n_steps,1))\n",
        "        yhat = model.predict(fut_inp, verbose=0)\n",
        "        tmp_inp.extend(yhat[0].tolist())\n",
        "        lst_output.extend(yhat.tolist())\n",
        "        i=i+1\n",
        "    \n",
        "\n",
        "print(lst_output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "d49a83e5",
      "metadata": {
        "id": "d49a83e5",
        "outputId": "8fe98428-77ce-46da-e843-bca75345fc06",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1260"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "len(ds_scaled)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "49450ea7",
      "metadata": {
        "id": "49450ea7"
      },
      "outputs": [],
      "source": [
        "#Creating a dummy plane to plot graph one after another\n",
        "plot_new=np.arange(1,101)\n",
        "plot_pred=np.arange(101,131)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "29f84e31",
      "metadata": {
        "id": "29f84e31",
        "outputId": "4ce2c5ec-5cc0-4b2a-edf8-7285d9c0f244",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f416438f610>]"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "ds_scaled_minus_100 = len(ds_scaled) - 100\n",
        "plt.plot(plot_new, normalizer.inverse_transform(ds_scaled[ds_scaled_minus_100:]))\n",
        "plt.plot(plot_pred, normalizer.inverse_transform(lst_output))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "e41c9362",
      "metadata": {
        "id": "e41c9362"
      },
      "outputs": [],
      "source": [
        "ds_new = ds_scaled.tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "d6c8d3c1",
      "metadata": {
        "id": "d6c8d3c1",
        "outputId": "b52788f3-5d9c-4297-d095-57b9b365af84",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1260"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "len(ds_new)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "24e99607",
      "metadata": {
        "id": "24e99607",
        "outputId": "585ba77d-77b7-436a-c2f7-dbc3abda9600",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f41642a0750>]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Entends helps us to fill the missing value with approx value\n",
        "ds_new.extend(lst_output)\n",
        "plt.plot(ds_new[1200:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "7f04f058",
      "metadata": {
        "id": "7f04f058"
      },
      "outputs": [],
      "source": [
        "#Creating final data for plotting\n",
        "final_graph = normalizer.inverse_transform(ds_new).tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "2dfdef30",
      "metadata": {
        "id": "2dfdef30",
        "outputId": "cd74b533-01c7-440a-b234-82bcaef0a613",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f41643c8a90>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Plotting final results with predicted value after 30 Days\n",
        "plt.plot(final_graph,)\n",
        "plt.ylabel(\"Price\")\n",
        "plt.xlabel(\"Time\")\n",
        "plt.title(\"{0} prediction of next month open\".format(stock_symbol))\n",
        "plt.axhline(y=final_graph[len(final_graph)-1], color = 'red', linestyle = ':', label = 'NEXT 30D: {0}'.format(round(float(*final_graph[len(final_graph)-1]),2)))\n",
        "plt.legend()\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.10"
    },
    "colab": {
      "name": "StockPricePredictorVer4.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
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