Final_Project_Stock_Market_Prediction_Team_19.ipynb

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        "###Name:K Manoj\n",
        "### Registration ID: SIRSS2309\n",
        "### College: East Point College of Engineering and Technology,Bangalore\n",
        "## ***Team Project- Stock Market Prediction***\n",
        "## ***Team 19***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "85iHaC07nml3"
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      "source": [
        "import os  \n",
        "import pandas as pd\n",
        "import pandas_datareader as pdr\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import zipfile\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras import datasets,layers,models,Model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FJNMq73BvzMR"
      },
      "source": [
        "path=\"MSFT.csv\"\n",
        "df=pd.read_csv(path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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      "metadata": {
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          "height": 204
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        "id": "PM3UkJ6Dwr2Q",
        "outputId": "eb164830-3d7a-42b6-9fe6-98573bf468f6"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1986-03-13</td>\n",
              "      <td>0.088542</td>\n",
              "      <td>0.101563</td>\n",
              "      <td>0.088542</td>\n",
              "      <td>0.097222</td>\n",
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              "      <td>1031788800</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1986-03-14</td>\n",
              "      <td>0.097222</td>\n",
              "      <td>0.102431</td>\n",
              "      <td>0.097222</td>\n",
              "      <td>0.100694</td>\n",
              "      <td>0.063956</td>\n",
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              "      <th>2</th>\n",
              "      <td>1986-03-17</td>\n",
              "      <td>0.100694</td>\n",
              "      <td>0.103299</td>\n",
              "      <td>0.100694</td>\n",
              "      <td>0.102431</td>\n",
              "      <td>0.065059</td>\n",
              "      <td>133171200</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1986-03-18</td>\n",
              "      <td>0.102431</td>\n",
              "      <td>0.103299</td>\n",
              "      <td>0.098958</td>\n",
              "      <td>0.099826</td>\n",
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              "      <th>4</th>\n",
              "      <td>1986-03-19</td>\n",
              "      <td>0.099826</td>\n",
              "      <td>0.100694</td>\n",
              "      <td>0.097222</td>\n",
              "      <td>0.098090</td>\n",
              "      <td>0.062302</td>\n",
              "      <td>47894400</td>\n",
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              "         Date      Open      High       Low     Close  Adj Close      Volume\n",
              "0  1986-03-13  0.088542  0.101563  0.088542  0.097222   0.061751  1031788800\n",
              "1  1986-03-14  0.097222  0.102431  0.097222  0.100694   0.063956   308160000\n",
              "2  1986-03-17  0.100694  0.103299  0.100694  0.102431   0.065059   133171200\n",
              "3  1986-03-18  0.102431  0.103299  0.098958  0.099826   0.063405    67766400\n",
              "4  1986-03-19  0.099826  0.100694  0.097222  0.098090   0.062302    47894400"
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      "source": [
        "\n",
        "df.tail()"
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      "outputs": [
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              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
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              "      <th>8852</th>\n",
              "      <td>2021-04-27</td>\n",
              "      <td>261.579987</td>\n",
              "      <td>263.190002</td>\n",
              "      <td>260.119995</td>\n",
              "      <td>261.970001</td>\n",
              "      <td>261.970001</td>\n",
              "      <td>31014200</td>\n",
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              "      <th>8853</th>\n",
              "      <td>2021-04-28</td>\n",
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              "      <td>256.540009</td>\n",
              "      <td>252.949997</td>\n",
              "      <td>254.559998</td>\n",
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              "      <td>2021-04-29</td>\n",
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              "      <td>256.100006</td>\n",
              "      <td>249.000000</td>\n",
              "      <td>252.509995</td>\n",
              "      <td>252.509995</td>\n",
              "      <td>40589000</td>\n",
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              "      <td>2021-04-30</td>\n",
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              "      <td>252.179993</td>\n",
              "      <td>252.179993</td>\n",
              "      <td>30929200</td>\n",
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              "      <th>8856</th>\n",
              "      <td>2021-05-03</td>\n",
              "      <td>253.399994</td>\n",
              "      <td>254.350006</td>\n",
              "      <td>251.119995</td>\n",
              "      <td>251.860001</td>\n",
              "      <td>251.860001</td>\n",
              "      <td>19598900</td>\n",
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              "            Date        Open        High         Low       Close   Adj Close  \\\n",
              "8852  2021-04-27  261.579987  263.190002  260.119995  261.970001  261.970001   \n",
              "8853  2021-04-28  256.079987  256.540009  252.949997  254.559998  254.559998   \n",
              "8854  2021-04-29  255.460007  256.100006  249.000000  252.509995  252.509995   \n",
              "8855  2021-04-30  249.740005  253.080002  249.600006  252.179993  252.179993   \n",
              "8856  2021-05-03  253.399994  254.350006  251.119995  251.860001  251.860001   \n",
              "\n",
              "        Volume  \n",
              "8852  31014200  \n",
              "8853  46903100  \n",
              "8854  40589000  \n",
              "8855  30929200  \n",
              "8856  19598900  "
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      "source": [
        "df1=df.reset_index()['Close']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "u7t4e0WtnPpF",
        "outputId": "df760705-bf71-4ab8-ef84-e8ba93c15114"
      },
      "source": [
        "df1\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0         0.097222\n",
              "1         0.100694\n",
              "2         0.102431\n",
              "3         0.099826\n",
              "4         0.098090\n",
              "           ...    \n",
              "8852    261.970001\n",
              "8853    254.559998\n",
              "8854    252.509995\n",
              "8855    252.179993\n",
              "8856    251.860001\n",
              "Name: Close, Length: 8857, dtype: float64"
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          "execution_count": 6,
          "metadata": {},
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    {
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        "id": "eFct_35mnnlo",
        "outputId": "2bbdc14f-da5c-4565-d7c2-eb7dda8d6199"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(df1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x20e68f297c0>]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VmYCIKxRnvl4"
      },
      "source": [
        "### LSTM are sensitive to the scale of the data. so we apply MinMax scaler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oB_cyDTXnyqV",
        "outputId": "0dec01d5-3059-4976-f50d-2812058484d9"
      },
      "source": [
        "df1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0         0.097222\n",
              "1         0.100694\n",
              "2         0.102431\n",
              "3         0.099826\n",
              "4         0.098090\n",
              "           ...    \n",
              "8852    261.970001\n",
              "8853    254.559998\n",
              "8854    252.509995\n",
              "8855    252.179993\n",
              "8856    251.860001\n",
              "Name: Close, Length: 8857, dtype: float64"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Wle39trn5bD"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler=MinMaxScaler(feature_range=(0,1))\n",
        "df1=scaler.fit_transform(np.array(df1).reshape(-1,1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nc6UlKHtn8iR",
        "outputId": "32d3659a-f49a-4193-dc18-18cf65993e29"
      },
      "source": [
        "print(df1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[2.65159923e-05]\n",
            " [3.97739884e-05]\n",
            " [4.64067989e-05]\n",
            " ...\n",
            " [9.63876521e-01]\n",
            " [9.62616393e-01]\n",
            " [9.61394489e-01]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lT1Gg-wrn_UV"
      },
      "source": [
        "##splitting dataset into train and test split\n",
        "training_size=int(len(df1)*0.65)\n",
        "test_size=len(df1)-training_size\n",
        "train_data,test_data=df1[0:training_size,:],df1[training_size:len(df1),:1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mM9wNErWoCWm",
        "outputId": "c8a2f375-c1a1-4206-b56a-18dcd26aa907"
      },
      "source": [
        "training_size,test_size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(5757, 3100)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YUDQqP91oFk5",
        "outputId": "d07e87db-1d76-4b9c-81ae-58d009489153"
      },
      "source": [
        "train_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[2.65159923e-05],\n",
              "       [3.97739884e-05],\n",
              "       [4.64067989e-05],\n",
              "       ...,\n",
              "       [7.80118529e-02],\n",
              "       [7.89283033e-02],\n",
              "       [7.41551197e-02]])"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8NEy9y4oIHi"
      },
      "source": [
        "import numpy\n",
        "# convert an array of values into a dataset matrix\n",
        "def create_dataset(dataset, time_step=1):\n",
        "\tdataX, dataY = [], []\n",
        "\tfor i in range(len(dataset)-time_step-1):\n",
        "\t\ta = dataset[i:(i+time_step), 0]   ###i=0, 0,1,2,3-----99   100 \n",
        "\t\tdataX.append(a)\n",
        "\t\tdataY.append(dataset[i + time_step, 0])\n",
        "\treturn numpy.array(dataX), numpy.array(dataY)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "65x2GZxioQDR"
      },
      "source": [
        "# reshape into X=t,t+1,t+2,t+3 and Y=t+4\n",
        "time_step = 100\n",
        "X_train, y_train = create_dataset(train_data, time_step)\n",
        "X_test, ytest = create_dataset(test_data, time_step)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hkvG5yIDoS1B",
        "outputId": "3f8436fb-2270-4ea2-ab77-6bb5605b3f93"
      },
      "source": [
        "\n",
        "print(X_train.shape), print(y_train.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(5656, 100)\n",
            "(5656,)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-33z82jEoVa5",
        "outputId": "f50eeaaf-01fc-4ea6-ecb6-9974b0d7f228"
      },
      "source": [
        "print(X_test.shape), print(ytest.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(2999, 100)\n",
            "(2999,)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zYC1cG_uoZH-"
      },
      "source": [
        "# reshape input to be [samples, time steps, features] which is required for LSTM\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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ryn5zrhdocRh"
      },
      "source": [
        "### Create the Stacked LSTM model\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import LSTM"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BbotkiXiogTH"
      },
      "source": [
        "model=Sequential()\n",
        "model.add(LSTM(50,return_sequences=True,input_shape=(100,1)))\n",
        "model.add(LSTM(50,return_sequences=True))\n",
        "model.add(LSTM(50))\n",
        "model.add(Dense(1))\n",
        "model.compile(loss='mean_squared_error',optimizer='adam')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zeemBUheoizp",
        "outputId": "829306a8-1026-4aa9-cf8b-d520cf2777bb"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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",
            "Total params: 50,851\n",
            "Trainable params: 50,851\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sH72FRFzolzy",
        "outputId": "6eb2da67-9017-40c8-b8fb-b89492a0984c"
      },
      "source": [
        "from keras.utils.vis_utils import plot_model\n",
        "plot_model(model, to_file='model.png', show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "('You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) ', 'for plot_model/model_to_dot to work.')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kWOZ9ooAosWF",
        "scrolled": false,
        "outputId": "b911c305-c627-4c0a-d411-92ec552e1b86"
      },
      "source": [
        "model.fit(X_train,y_train,validation_data=(X_test,ytest),epochs=100,batch_size=64,verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "89/89 [==============================] - 7s 27ms/step - loss: 3.9287e-04 - val_loss: 8.5699e-04\n",
            "Epoch 2/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 3.1468e-05 - val_loss: 0.0015\n",
            "Epoch 3/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 2.9512e-05 - val_loss: 0.0023\n",
            "Epoch 4/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 2.7099e-05 - val_loss: 0.0017\n",
            "Epoch 5/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 2.5282e-05 - val_loss: 0.0020\n",
            "Epoch 6/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 2.4127e-05 - val_loss: 0.0019\n",
            "Epoch 7/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 2.1439e-05 - val_loss: 0.0027\n",
            "Epoch 8/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 2.2876e-05 - val_loss: 0.0026\n",
            "Epoch 9/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 2.1849e-05 - val_loss: 0.0016\n",
            "Epoch 10/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.7527e-05 - val_loss: 0.0016\n",
            "Epoch 11/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.5113e-05 - val_loss: 0.0014\n",
            "Epoch 12/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.6394e-05 - val_loss: 0.0012\n",
            "Epoch 13/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.8381e-05 - val_loss: 0.0013\n",
            "Epoch 14/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.5038e-05 - val_loss: 0.0013\n",
            "Epoch 15/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.4877e-05 - val_loss: 0.0012\n",
            "Epoch 16/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.4352e-05 - val_loss: 0.0011\n",
            "Epoch 17/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.6158e-05 - val_loss: 0.0012\n",
            "Epoch 18/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.3867e-05 - val_loss: 6.1184e-04\n",
            "Epoch 19/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.4381e-05 - val_loss: 0.0011\n",
            "Epoch 20/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.2815e-05 - val_loss: 0.0014\n",
            "Epoch 21/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.3568e-05 - val_loss: 8.6797e-04\n",
            "Epoch 22/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.1230e-05 - val_loss: 5.4410e-04\n",
            "Epoch 23/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.1494e-05 - val_loss: 0.0011\n",
            "Epoch 24/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.2140e-05 - val_loss: 9.5673e-04\n",
            "Epoch 25/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.1148e-05 - val_loss: 0.0011\n",
            "Epoch 26/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 1.2350e-05 - val_loss: 0.0011\n",
            "Epoch 27/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.1130e-05 - val_loss: 4.8310e-04\n",
            "Epoch 28/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 9.9487e-06 - val_loss: 5.1568e-04\n",
            "Epoch 29/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.0773e-05 - val_loss: 6.2506e-04\n",
            "Epoch 30/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 1.0238e-05 - val_loss: 4.6722e-04\n",
            "Epoch 31/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 9.2540e-06 - val_loss: 3.6130e-04\n",
            "Epoch 32/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 9.8483e-06 - val_loss: 5.0199e-04\n",
            "Epoch 33/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 8.8827e-06 - val_loss: 4.8681e-04\n",
            "Epoch 34/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 8.9901e-06 - val_loss: 4.6087e-04\n",
            "Epoch 35/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 9.2896e-06 - val_loss: 5.4843e-04\n",
            "Epoch 36/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 8.0987e-06 - val_loss: 5.2315e-04\n",
            "Epoch 37/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 7.9907e-06 - val_loss: 2.4620e-04\n",
            "Epoch 38/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 7.9234e-06 - val_loss: 3.9326e-04\n",
            "Epoch 39/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 6.7834e-06 - val_loss: 2.8650e-04\n",
            "Epoch 40/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 8.3488e-06 - val_loss: 1.5509e-04\n",
            "Epoch 41/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 7.3569e-06 - val_loss: 2.9169e-04\n",
            "Epoch 42/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 6.7634e-06 - val_loss: 2.1037e-04\n",
            "Epoch 43/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 7.5724e-06 - val_loss: 1.5066e-04\n",
            "Epoch 44/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 6.7377e-06 - val_loss: 3.8233e-04\n",
            "Epoch 45/100\n",
            "89/89 [==============================] - 2s 21ms/step - loss: 6.4723e-06 - val_loss: 3.0057e-04\n",
            "Epoch 46/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 6.7196e-06 - val_loss: 2.2706e-04\n",
            "Epoch 47/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 7.4468e-06 - val_loss: 1.9609e-04\n",
            "Epoch 48/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 5.7783e-06 - val_loss: 2.8256e-04\n",
            "Epoch 49/100\n",
            "89/89 [==============================] - 2s 23ms/step - loss: 6.2561e-06 - val_loss: 2.6987e-04\n",
            "Epoch 50/100\n",
            "89/89 [==============================] - 2s 27ms/step - loss: 6.7371e-06 - val_loss: 2.0662e-04\n",
            "Epoch 51/100\n",
            "89/89 [==============================] - 2s 23ms/step - loss: 5.5163e-06 - val_loss: 4.9813e-04\n",
            "Epoch 52/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 6.5575e-06 - val_loss: 2.3247e-04\n",
            "Epoch 53/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 4.8988e-06 - val_loss: 2.6795e-04\n",
            "Epoch 54/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 5.3783e-06 - val_loss: 2.0933e-04\n",
            "Epoch 55/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 5.5201e-06 - val_loss: 1.8858e-04\n",
            "Epoch 56/100\n",
            "89/89 [==============================] - 2s 21ms/step - loss: 5.8846e-06 - val_loss: 4.4075e-04\n",
            "Epoch 57/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 5.1790e-06 - val_loss: 1.6930e-04\n",
            "Epoch 58/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 5.4571e-06 - val_loss: 4.5950e-04\n",
            "Epoch 59/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 5.3703e-06 - val_loss: 3.7825e-04\n",
            "Epoch 60/100\n",
            "89/89 [==============================] - 2s 21ms/step - loss: 4.9040e-06 - val_loss: 3.4758e-04\n",
            "Epoch 61/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 5.3794e-06 - val_loss: 2.3942e-04\n",
            "Epoch 62/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 4.7146e-06 - val_loss: 2.8690e-04\n",
            "Epoch 63/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 5.3507e-06 - val_loss: 2.7501e-04\n",
            "Epoch 64/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 5.4796e-06 - val_loss: 1.9537e-04\n",
            "Epoch 65/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 5.6501e-06 - val_loss: 1.7561e-04\n",
            "Epoch 66/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 5.0933e-06 - val_loss: 1.8026e-04\n",
            "Epoch 67/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 4.5779e-06 - val_loss: 4.5619e-04\n",
            "Epoch 68/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 5.1135e-06 - val_loss: 1.8718e-04\n",
            "Epoch 69/100\n",
            "89/89 [==============================] - 2s 22ms/step - loss: 4.7258e-06 - val_loss: 3.0497e-04\n",
            "Epoch 70/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 4.9419e-06 - val_loss: 2.6353e-04\n",
            "Epoch 71/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 5.1846e-06 - val_loss: 2.3200e-04\n",
            "Epoch 72/100\n",
            "89/89 [==============================] - 2s 24ms/step - loss: 4.3425e-06 - val_loss: 2.3026e-04\n",
            "Epoch 73/100\n",
            "89/89 [==============================] - 2s 23ms/step - loss: 4.2642e-06 - val_loss: 2.6247e-04\n",
            "Epoch 74/100\n",
            "89/89 [==============================] - 2s 23ms/step - loss: 5.8412e-06 - val_loss: 2.9834e-04\n",
            "Epoch 75/100\n",
            "89/89 [==============================] - 2s 23ms/step - loss: 6.1075e-06 - val_loss: 3.1870e-04\n",
            "Epoch 76/100\n",
            "89/89 [==============================] - 2s 22ms/step - loss: 5.0102e-06 - val_loss: 3.4309e-04\n",
            "Epoch 77/100\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "89/89 [==============================] - 2s 20ms/step - loss: 4.7274e-06 - val_loss: 2.5707e-04\n",
            "Epoch 78/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 4.7966e-06 - val_loss: 6.2758e-04\n",
            "Epoch 79/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 5.4899e-06 - val_loss: 2.8402e-04\n",
            "Epoch 80/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 4.3386e-06 - val_loss: 4.4869e-04\n",
            "Epoch 81/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 4.4743e-06 - val_loss: 3.7242e-04\n",
            "Epoch 82/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 5.3157e-06 - val_loss: 1.2485e-04\n",
            "Epoch 83/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 4.8020e-06 - val_loss: 1.7539e-04\n",
            "Epoch 84/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 5.1443e-06 - val_loss: 4.0213e-04\n",
            "Epoch 85/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 5.6048e-06 - val_loss: 3.5925e-04\n",
            "Epoch 86/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 4.5943e-06 - val_loss: 2.2685e-04\n",
            "Epoch 87/100\n",
            "89/89 [==============================] - 2s 23ms/step - loss: 5.1205e-06 - val_loss: 6.1894e-04\n",
            "Epoch 88/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 5.6145e-06 - val_loss: 2.3077e-04\n",
            "Epoch 89/100\n",
            "89/89 [==============================] - 2s 22ms/step - loss: 5.8168e-06 - val_loss: 3.0074e-04\n",
            "Epoch 90/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 4.7178e-06 - val_loss: 2.3085e-04\n",
            "Epoch 91/100\n",
            "89/89 [==============================] - 2s 22ms/step - loss: 4.5105e-06 - val_loss: 3.9814e-04\n",
            "Epoch 92/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 4.3765e-06 - val_loss: 4.6077e-04\n",
            "Epoch 93/100\n",
            "89/89 [==============================] - 2s 21ms/step - loss: 5.2229e-06 - val_loss: 5.7553e-04\n",
            "Epoch 94/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 4.3579e-06 - val_loss: 3.6326e-04\n",
            "Epoch 95/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 4.8903e-06 - val_loss: 3.1557e-04\n",
            "Epoch 96/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 4.8923e-06 - val_loss: 4.0094e-04\n",
            "Epoch 97/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 5.3884e-06 - val_loss: 2.7810e-04\n",
            "Epoch 98/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 5.1921e-06 - val_loss: 5.3464e-04\n",
            "Epoch 99/100\n",
            "89/89 [==============================] - 2s 19ms/step - loss: 4.6443e-06 - val_loss: 4.0703e-04\n",
            "Epoch 100/100\n",
            "89/89 [==============================] - 2s 20ms/step - loss: 4.6829e-06 - val_loss: 3.3471e-04\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x20e0e560550>"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8u4h3ybNozVl"
      },
      "source": [
        "### Lets Do the prediction and check performance metrics\n",
        "train_predict=model.predict(X_train)\n",
        "test_predict=model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ytY-_Fb3v1-8"
      },
      "source": [
        "##Transformback to original form\n",
        "train_predict=scaler.inverse_transform(train_predict)\n",
        "test_predict=scaler.inverse_transform(test_predict)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPwf_s7Ov72I",
        "outputId": "ea6b5f4a-215f-4ded-ee41-2881d4c582cb"
      },
      "source": [
        "### Calculate RMSE performance metrics\n",
        "import math\n",
        "from sklearn.metrics import mean_squared_error\n",
        "math.sqrt(mean_squared_error(y_train,train_predict))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "21.921029517793514"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OXnYo-JJwAuc",
        "outputId": "eace4246-e454-4046-a091-6a7df1419f2d"
      },
      "source": [
        "### Test Data RMSE\n",
        "math.sqrt(mean_squared_error(ytest,test_predict))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "87.89226551455482"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "E33hPT7TwFHv",
        "outputId": "45a9fc71-875c-459f-e48b-f7f1c3644ec1"
      },
      "source": [
        "### Plotting \n",
        "# shift train predictions for plotting\n",
        "look_back=100\n",
        "trainPredictPlot = numpy.empty_like(df1)\n",
        "trainPredictPlot[:, :] = np.nan\n",
        "trainPredictPlot[look_back:len(train_predict)+look_back, :] = train_predict\n",
        "# shift test predictions for plotting\n",
        "testPredictPlot = numpy.empty_like(df1)\n",
        "testPredictPlot[:, :] = numpy.nan\n",
        "testPredictPlot[len(train_predict)+(look_back*2)+1:len(df1)-1, :] = test_predict\n",
        "# plot baseline and predictions\n",
        "plt.plot(scaler.inverse_transform(df1))\n",
        "plt.plot(trainPredictPlot)\n",
        "plt.plot(testPredictPlot)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "txIphGn5wHot",
        "outputId": "f778012f-535e-4393-d113-c1a68371742e"
      },
      "source": [
        "len(test_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3100"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F87b10YVwMSB",
        "outputId": "2609289f-0061-403a-95e2-8ee9582d39c2"
      },
      "source": [
        "x_input=test_data[341:].reshape(1,-1)\n",
        "x_input.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1, 2759)"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KFOFmLKLwOei"
      },
      "source": [
        "temp_input=list(x_input)\n",
        "temp_input=temp_input[0].tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBpIP6rmwRoI",
        "scrolled": true,
        "outputId": "233d7178-aa2e-46f1-d3ff-0a61fca1a0ac"
      },
      "source": [
        "temp_input"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[0.10886571144633435,\n",
              " 0.10749103223893473,\n",
              " 0.10317607746936396,\n",
              " 0.10214506806381425,\n",
              " 0.09996849750763163,\n",
              " 0.09920478521834293,\n",
              " 0.09515712756503317,\n",
              " 0.09893748810208193,\n",
              " 0.09817377581279321,\n",
              " 0.09851744561464314,\n",
              " 0.10069401617082573,\n",
              " 0.10222144074940315,\n",
              " 0.09813559675329893,\n",
              " 0.09622632331337731,\n",
              " 0.09553898370967749,\n",
              " 0.09431704987345571,\n",
              " 0.0951189412222387,\n",
              " 0.09763918158027121,\n",
              " 0.09702821466216031,\n",
              " 0.10115224500105897,\n",
              " 0.10015942193830374,\n",
              " 0.100350353652276,\n",
              " 0.100617650768537,\n",
              " 0.09874656367140984,\n",
              " 0.09805922406771003,\n",
              " 0.09630268871566605,\n",
              " 0.0951189412222387,\n",
              " 0.09332422681070045,\n",
              " 0.09248414183582283,\n",
              " 0.0886655949559796,\n",
              " 0.08752003380534672,\n",
              " 0.08809281438066316,\n",
              " 0.08851285686810197,\n",
              " 0.09061305473869567,\n",
              " 0.09244595549302838,\n",
              " 0.09286599798046719,\n",
              " 0.09233140374794518,\n",
              " 0.09446978796133333,\n",
              " 0.09561534911196624,\n",
              " 0.09679910388869377,\n",
              " 0.09706640100495478,\n",
              " 0.0946988987347999,\n",
              " 0.09599720525661061,\n",
              " 0.0969518419765714,\n",
              " 0.09557717005247196,\n",
              " 0.09832652118397102,\n",
              " 0.09821196215558767,\n",
              " 0.09931934424672628,\n",
              " 0.09954845502019283,\n",
              " 0.09874656367140984,\n",
              " 0.09905204713046528,\n",
              " 0.09821196215558767,\n",
              " 0.10019760828109817,\n",
              " 0.09954845502019283,\n",
              " 0.09790647869653221,\n",
              " 0.09653180677243277,\n",
              " 0.0972191390928324,\n",
              " 0.09744825714959911,\n",
              " 0.0953862383384997,\n",
              " 0.0945843469897167,\n",
              " 0.09317148143952264,\n",
              " 0.09282781163767274,\n",
              " 0.0932096677823171,\n",
              " 0.09401155913110008,\n",
              " 0.09443160161853889,\n",
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              " ...]"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kBnIQBu2xnXz"
      },
      "source": [
        "day_new=np.arange(1,101)\n",
        "day_pred=np.arange(101,131)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "letcfYyeybu3",
        "outputId": "53535b93-5036-4fce-cddc-e6111f108a6d"
      },
      "source": [
        "len(df1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "8857"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v0Wxe7I1zBnS",
        "outputId": "0c9eb72e-7d6b-45bb-997c-f004eedfd691"
      },
      "source": [
        "# demonstrate prediction for next 10 days\n",
        "from numpy import array\n",
        "\n",
        "lst_output=[]\n",
        "n_steps=2758\n",
        "i=0\n",
        "while(i<30):\n",
        "    \n",
        "    if(len(temp_input)>100):\n",
        "        #print(temp_input)\n",
        "        x_input=np.array(temp_input[1:])\n",
        "        print(\"{} day input {}\".format(i,x_input))\n",
        "        x_input=x_input.reshape(1,-1)\n",
        "        x_input = x_input.reshape((1, n_steps, 1))\n",
        "        #print(x_input)\n",
        "        yhat = model.predict(x_input, verbose=0)\n",
        "        print(\"{} day output {}\".format(i,yhat))\n",
        "        temp_input.extend(yhat[0].tolist())\n",
        "        temp_input=temp_input[1:]\n",
        "        #print(temp_input)\n",
        "        lst_output.extend(yhat.tolist())\n",
        "        i=i+1\n",
        "    else:\n",
        "        x_input = x_input.reshape((1, n_steps,1))\n",
        "        yhat = model.predict(x_input, verbose=0)\n",
        "        print(yhat[0])\n",
        "        temp_input.extend(yhat[0].tolist())\n",
        "        print(len(temp_input))\n",
        "        lst_output.extend(yhat.tolist())\n",
        "        i=i+1\n",
        "    \n",
        "\n",
        "print(lst_output)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0 day input [0.10749103 0.10317608 0.10214507 ... 0.96387652 0.96261639 0.96139449]\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 100, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 100, 1), dtype=tf.float32, name='lstm_input'), name='lstm_input', description=\"created by layer 'lstm_input'\"), but it was called on an input with incompatible shape (None, 2758, 1).\n",
            "0 day output [[0.87783545]]\n",
            "1 day input [0.10317608 0.10214507 0.0999685  ... 0.96261639 0.96139449 0.87783545]\n",
            "1 day output [[0.7794197]]\n",
            "2 day input [0.10214507 0.0999685  0.09920479 ... 0.96139449 0.87783545 0.77941972]\n",
            "2 day output [[0.68774146]]\n",
            "3 day input [0.0999685  0.09920479 0.09515713 ... 0.87783545 0.77941972 0.68774146]\n",
            "3 day output [[0.6121928]]\n",
            "4 day input [0.09920479 0.09515713 0.09893749 ... 0.77941972 0.68774146 0.61219281]\n",
            "4 day output [[0.5488404]]\n",
            "5 day input [0.09515713 0.09893749 0.09817378 ... 0.68774146 0.61219281 0.5488404 ]\n",
            "5 day output [[0.493029]]\n",
            "6 day input [0.09893749 0.09817378 0.09851745 ... 0.61219281 0.5488404  0.493029  ]\n",
            "6 day output [[0.44197673]]\n",
            "7 day input [0.09817378 0.09851745 0.10069402 ... 0.5488404  0.493029   0.44197673]\n",
            "7 day output [[0.39477122]]\n",
            "8 day input [0.09851745 0.10069402 0.10222144 ... 0.493029   0.44197673 0.39477122]\n",
            "8 day output [[0.35157216]]\n",
            "9 day input [0.10069402 0.10222144 0.0981356  ... 0.44197673 0.39477122 0.35157216]\n",
            "9 day output [[0.31287813]]\n",
            "10 day input [0.10222144 0.0981356  0.09622632 ... 0.39477122 0.35157216 0.31287813]\n",
            "10 day output [[0.27908248]]\n",
            "11 day input [0.0981356  0.09622632 0.09553898 ... 0.35157216 0.31287813 0.27908248]\n",
            "11 day output [[0.25029767]]\n",
            "12 day input [0.09622632 0.09553898 0.09431705 ... 0.31287813 0.27908248 0.25029767]\n",
            "12 day output [[0.22635883]]\n",
            "13 day input [0.09553898 0.09431705 0.09511894 ... 0.27908248 0.25029767 0.22635883]\n",
            "13 day output [[0.2069103]]\n",
            "14 day input [0.09431705 0.09511894 0.09763918 ... 0.25029767 0.22635883 0.2069103 ]\n",
            "14 day output [[0.19150445]]\n",
            "15 day input [0.09511894 0.09763918 0.09702821 ... 0.22635883 0.2069103  0.19150445]\n",
            "15 day output [[0.17967807]]\n",
            "16 day input [0.09763918 0.09702821 0.10115225 ... 0.2069103  0.19150445 0.17967807]\n",
            "16 day output [[0.17099716]]\n",
            "17 day input [0.09702821 0.10115225 0.10015942 ... 0.19150445 0.17967807 0.17099716]\n",
            "17 day output [[0.16507594]]\n",
            "18 day input [0.10115225 0.10015942 0.10035035 ... 0.17967807 0.17099716 0.16507594]\n",
            "18 day output [[0.16157979]]\n",
            "19 day input [0.10015942 0.10035035 0.10061765 ... 0.17099716 0.16507594 0.16157979]\n",
            "19 day output [[0.16022015]]\n",
            "20 day input [0.10035035 0.10061765 0.09874656 ... 0.16507594 0.16157979 0.16022015]\n",
            "20 day output [[0.16074692]]\n",
            "21 day input [0.10061765 0.09874656 0.09805922 ... 0.16157979 0.16022015 0.16074692]\n",
            "21 day output [[0.16294068]]\n",
            "22 day input [0.09874656 0.09805922 0.09630269 ... 0.16022015 0.16074692 0.16294068]\n",
            "22 day output [[0.16660608]]\n",
            "23 day input [0.09805922 0.09630269 0.09511894 ... 0.16074692 0.16294068 0.16660608]\n",
            "23 day output [[0.17156668]]\n",
            "24 day input [0.09630269 0.09511894 0.09332423 ... 0.16294068 0.16660608 0.17156668]\n",
            "24 day output [[0.17766061]]\n",
            "25 day input [0.09511894 0.09332423 0.09248414 ... 0.16660608 0.17156668 0.17766061]\n",
            "25 day output [[0.18473761]]\n",
            "26 day input [0.09332423 0.09248414 0.08866559 ... 0.17156668 0.17766061 0.18473761]\n",
            "26 day output [[0.19265638]]\n",
            "27 day input [0.09248414 0.08866559 0.08752003 ... 0.17766061 0.18473761 0.19265638]\n",
            "27 day output [[0.20128267]]\n",
            "28 day input [0.08866559 0.08752003 0.08809281 ... 0.18473761 0.19265638 0.20128267]\n",
            "28 day output [[0.21048771]]\n",
            "29 day input [0.08752003 0.08809281 0.08851286 ... 0.19265638 0.20128267 0.21048771]\n",
            "29 day output [[0.22014695]]\n",
            "[[0.8778354525566101], [0.7794197201728821], [0.6877414584159851], [0.6121928095817566], [0.5488404035568237], [0.49302899837493896], [0.44197672605514526], [0.3947712182998657], [0.3515721559524536], [0.3128781318664551], [0.2790824770927429], [0.2502976655960083], [0.2263588309288025], [0.20691029727458954], [0.19150444865226746], [0.1796780675649643], [0.17099715769290924], [0.16507594287395477], [0.16157978773117065], [0.16022014617919922], [0.16074691712856293], [0.16294068098068237], [0.1666060835123062], [0.1715666800737381], [0.17766061425209045], [0.18473760783672333], [0.19265638291835785], [0.20128266513347626], [0.2104877084493637], [0.22014695405960083]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "WCZAaWOzyeau",
        "outputId": "62cc9d84-5a13-45b5-d313-c09c91f329d9"
      },
      "source": [
        "plt.plot(day_new,scaler.inverse_transform(df1[8757:]))\n",
        "plt.plot(day_pred,scaler.inverse_transform(lst_output))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x20f0510caf0>]"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "pYfpecAoyhod",
        "outputId": "465dee48-03d9-4330-c137-97379656a33a"
      },
      "source": [
        "df3=df1.tolist()\n",
        "df3.extend(lst_output)\n",
        "plt.plot(df3[1200:])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x20f01e171f0>]"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CMvB4l-5ynEB"
      },
      "source": [
        "df3=scaler.inverse_transform(df3).tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "mdGWZ5yPyqOP",
        "outputId": "65fcf1d4-cec2-4f65-f85d-9c792f8596ac"
      },
      "source": [
        "plt.plot(df3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x20eda0c5b50>]"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GRrOKokbMWEV",
        "outputId": "b2a70349-71a9-430b-8039-506b2d8a8b01"
      },
      "source": [
        "test_predict1=model.predict(X_test)\n",
        "from sklearn.metrics import r2_score\n",
        "print(\"R2 score : %.2f\" % r2_score(ytest,test_predict1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R2 score : 0.99\n"
          ]
        }
      ]
    }
  ]
}