INFOSYS_Stock_Market_Prediction_LSTM.ipynb

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      "name": "INFOSYS_Stock_Market_Prediction_LSTM.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kaScslrtfw9E",
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      "source": [
        "## INFOSYS STOCK MARKET PRICE PREDICTION USING LSTM(Long short-term memory) \n",
        "##              FROM [01-07-2015 t0 01-07-2020(5 Years)]\n",
        "### DATASET FROM NSE INDIA(National Stock Exchange of India Ltd)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Q0k4Bds6XQX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM, Dropout\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zQJTHnPP7Q2I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "INFOSYS = pd.read_csv(\"/content/INFY.NS.csv\")"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 343
        },
        "outputId": "7a7096d5-7a86-4461-cc91-7c56940e2608"
      },
      "source": [
        "INFOSYS.head(10)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-07-01</td>\n",
              "      <td>494.500000</td>\n",
              "      <td>502.500000</td>\n",
              "      <td>493.000000</td>\n",
              "      <td>498.700012</td>\n",
              "      <td>415.561249</td>\n",
              "      <td>6880852.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-07-02</td>\n",
              "      <td>499.500000</td>\n",
              "      <td>500.700012</td>\n",
              "      <td>492.524994</td>\n",
              "      <td>494.000000</td>\n",
              "      <td>411.644745</td>\n",
              "      <td>4007568.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-07-03</td>\n",
              "      <td>494.000000</td>\n",
              "      <td>496.500000</td>\n",
              "      <td>491.000000</td>\n",
              "      <td>495.149994</td>\n",
              "      <td>412.603058</td>\n",
              "      <td>2695306.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-07-06</td>\n",
              "      <td>492.500000</td>\n",
              "      <td>494.000000</td>\n",
              "      <td>487.500000</td>\n",
              "      <td>491.649994</td>\n",
              "      <td>409.686493</td>\n",
              "      <td>4305602.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-07-07</td>\n",
              "      <td>492.500000</td>\n",
              "      <td>495.000000</td>\n",
              "      <td>489.500000</td>\n",
              "      <td>490.250000</td>\n",
              "      <td>408.519897</td>\n",
              "      <td>3497418.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2015-07-08</td>\n",
              "      <td>489.500000</td>\n",
              "      <td>489.500000</td>\n",
              "      <td>477.399994</td>\n",
              "      <td>478.750000</td>\n",
              "      <td>398.937103</td>\n",
              "      <td>7024178.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2015-07-09</td>\n",
              "      <td>478.750000</td>\n",
              "      <td>483.000000</td>\n",
              "      <td>467.024994</td>\n",
              "      <td>469.000000</td>\n",
              "      <td>390.812531</td>\n",
              "      <td>8587772.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2015-07-10</td>\n",
              "      <td>474.049988</td>\n",
              "      <td>476.049988</td>\n",
              "      <td>466.325012</td>\n",
              "      <td>468.750000</td>\n",
              "      <td>390.604187</td>\n",
              "      <td>7411522.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2015-07-13</td>\n",
              "      <td>470.500000</td>\n",
              "      <td>477.500000</td>\n",
              "      <td>467.024994</td>\n",
              "      <td>475.075012</td>\n",
              "      <td>395.874756</td>\n",
              "      <td>6531768.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2015-07-14</td>\n",
              "      <td>477.500000</td>\n",
              "      <td>486.750000</td>\n",
              "      <td>472.750000</td>\n",
              "      <td>485.625000</td>\n",
              "      <td>404.665955</td>\n",
              "      <td>5367074.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date        Open        High  ...       Close   Adj Close     Volume\n",
              "0  2015-07-01  494.500000  502.500000  ...  498.700012  415.561249  6880852.0\n",
              "1  2015-07-02  499.500000  500.700012  ...  494.000000  411.644745  4007568.0\n",
              "2  2015-07-03  494.000000  496.500000  ...  495.149994  412.603058  2695306.0\n",
              "3  2015-07-06  492.500000  494.000000  ...  491.649994  409.686493  4305602.0\n",
              "4  2015-07-07  492.500000  495.000000  ...  490.250000  408.519897  3497418.0\n",
              "5  2015-07-08  489.500000  489.500000  ...  478.750000  398.937103  7024178.0\n",
              "6  2015-07-09  478.750000  483.000000  ...  469.000000  390.812531  8587772.0\n",
              "7  2015-07-10  474.049988  476.049988  ...  468.750000  390.604187  7411522.0\n",
              "8  2015-07-13  470.500000  477.500000  ...  475.075012  395.874756  6531768.0\n",
              "9  2015-07-14  477.500000  486.750000  ...  485.625000  404.665955  5367074.0\n",
              "\n",
              "[10 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 5
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    {
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        "id": "Nn4iEga77RGR",
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        "outputId": "424bab60-0269-4b40-a8b5-3a2e912c3377"
      },
      "source": [
        "INFOSYS.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1228 entries, 0 to 1227\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Date       1228 non-null   object \n",
            " 1   Open       1227 non-null   float64\n",
            " 2   High       1227 non-null   float64\n",
            " 3   Low        1227 non-null   float64\n",
            " 4   Close      1227 non-null   float64\n",
            " 5   Adj Close  1227 non-null   float64\n",
            " 6   Volume     1227 non-null   float64\n",
            "dtypes: float64(6), object(1)\n",
            "memory usage: 67.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lcz8GT-h7RJg",
        "colab_type": "code",
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          "height": 402
        },
        "outputId": "4de8f95e-7516-475e-f3cf-fc0b3fa7fd25"
      },
      "source": [
        " INFOSYS_NEW=INFOSYS.dropna(axis=0)\n",
        " INFOSYS_NEW"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-07-01</td>\n",
              "      <td>494.500000</td>\n",
              "      <td>502.500000</td>\n",
              "      <td>493.000000</td>\n",
              "      <td>498.700012</td>\n",
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              "      <td>6880852.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-07-02</td>\n",
              "      <td>499.500000</td>\n",
              "      <td>500.700012</td>\n",
              "      <td>492.524994</td>\n",
              "      <td>494.000000</td>\n",
              "      <td>411.644745</td>\n",
              "      <td>4007568.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-07-03</td>\n",
              "      <td>494.000000</td>\n",
              "      <td>496.500000</td>\n",
              "      <td>491.000000</td>\n",
              "      <td>495.149994</td>\n",
              "      <td>412.603058</td>\n",
              "      <td>2695306.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-07-06</td>\n",
              "      <td>492.500000</td>\n",
              "      <td>494.000000</td>\n",
              "      <td>487.500000</td>\n",
              "      <td>491.649994</td>\n",
              "      <td>409.686493</td>\n",
              "      <td>4305602.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-07-07</td>\n",
              "      <td>492.500000</td>\n",
              "      <td>495.000000</td>\n",
              "      <td>489.500000</td>\n",
              "      <td>490.250000</td>\n",
              "      <td>408.519897</td>\n",
              "      <td>3497418.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1223</th>\n",
              "      <td>2020-06-23</td>\n",
              "      <td>695.700012</td>\n",
              "      <td>724.500000</td>\n",
              "      <td>692.099976</td>\n",
              "      <td>720.650024</td>\n",
              "      <td>720.650024</td>\n",
              "      <td>12157390.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1224</th>\n",
              "      <td>2020-06-24</td>\n",
              "      <td>716.900024</td>\n",
              "      <td>726.799988</td>\n",
              "      <td>709.400024</td>\n",
              "      <td>714.150024</td>\n",
              "      <td>714.150024</td>\n",
              "      <td>10220838.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1225</th>\n",
              "      <td>2020-06-25</td>\n",
              "      <td>704.849976</td>\n",
              "      <td>705.599976</td>\n",
              "      <td>693.500000</td>\n",
              "      <td>700.500000</td>\n",
              "      <td>700.500000</td>\n",
              "      <td>14945284.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1226</th>\n",
              "      <td>2020-06-26</td>\n",
              "      <td>710.200012</td>\n",
              "      <td>751.599976</td>\n",
              "      <td>710.000000</td>\n",
              "      <td>748.200012</td>\n",
              "      <td>748.200012</td>\n",
              "      <td>26030458.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1227</th>\n",
              "      <td>2020-06-29</td>\n",
              "      <td>735.400024</td>\n",
              "      <td>744.700012</td>\n",
              "      <td>730.099976</td>\n",
              "      <td>731.750000</td>\n",
              "      <td>731.750000</td>\n",
              "      <td>8954428.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1227 rows × 7 columns</p>\n",
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            ],
            "text/plain": [
              "            Date        Open        High  ...       Close   Adj Close      Volume\n",
              "0     2015-07-01  494.500000  502.500000  ...  498.700012  415.561249   6880852.0\n",
              "1     2015-07-02  499.500000  500.700012  ...  494.000000  411.644745   4007568.0\n",
              "2     2015-07-03  494.000000  496.500000  ...  495.149994  412.603058   2695306.0\n",
              "3     2015-07-06  492.500000  494.000000  ...  491.649994  409.686493   4305602.0\n",
              "4     2015-07-07  492.500000  495.000000  ...  490.250000  408.519897   3497418.0\n",
              "...          ...         ...         ...  ...         ...         ...         ...\n",
              "1223  2020-06-23  695.700012  724.500000  ...  720.650024  720.650024  12157390.0\n",
              "1224  2020-06-24  716.900024  726.799988  ...  714.150024  714.150024  10220838.0\n",
              "1225  2020-06-25  704.849976  705.599976  ...  700.500000  700.500000  14945284.0\n",
              "1226  2020-06-26  710.200012  751.599976  ...  748.200012  748.200012  26030458.0\n",
              "1227  2020-06-29  735.400024  744.700012  ...  731.750000  731.750000   8954428.0\n",
              "\n",
              "[1227 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FsssqV3l7RMZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "ec075b6a-d3cd-4622-9e02-55923e9343a0"
      },
      "source": [
        "INFOSYS_NEW.info()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1227 entries, 0 to 1227\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Date       1227 non-null   object \n",
            " 1   Open       1227 non-null   float64\n",
            " 2   High       1227 non-null   float64\n",
            " 3   Low        1227 non-null   float64\n",
            " 4   Close      1227 non-null   float64\n",
            " 5   Adj Close  1227 non-null   float64\n",
            " 6   Volume     1227 non-null   float64\n",
            "dtypes: float64(6), object(1)\n",
            "memory usage: 76.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-rCDrGYN8mfb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "INFOSYS_NEW['Close']=pd.to_numeric(INFOSYS_NEW.Close,errors='coerce')\n",
        "INFOSYS_NEW = INFOSYS_NEW.dropna()\n",
        "INFOSYS_TRAIN = INFOSYS_NEW.iloc[:,4:5].values"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZjynYeX8mj-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "effeb358-df0a-4e0d-85b6-3a2cac1cc88b"
      },
      "source": [
        "INFOSYS_NEW.info()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1227 entries, 0 to 1227\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Date       1227 non-null   object \n",
            " 1   Open       1227 non-null   float64\n",
            " 2   High       1227 non-null   float64\n",
            " 3   Low        1227 non-null   float64\n",
            " 4   Close      1227 non-null   float64\n",
            " 5   Adj Close  1227 non-null   float64\n",
            " 6   Volume     1227 non-null   float64\n",
            "dtypes: float64(6), object(1)\n",
            "memory usage: 76.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BKCAbDZ8mpZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ab9cb7ea-3f2a-47a4-df2d-66bac17e91a2"
      },
      "source": [
        "sc = MinMaxScaler(feature_range=(0,1))\n",
        "INFOSYS_TRAIN = sc.fit_transform(INFOSYS_TRAIN)\n",
        "INFOSYS_TRAIN.shape"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1227, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fmGB6cne8mtE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = []\n",
        "y_train = []\n",
        "\n",
        "for i in range (60,1227):\n",
        "  x_train.append(INFOSYS_TRAIN[i-60:i,0])\n",
        "  y_train.append(INFOSYS_TRAIN[i,0])\n",
        "\n",
        "x_train, y_train = np.array(x_train), np.array(y_train)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bt_Rugc08mnw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c8eb92ae-b97f-404e-805c-afc442828ca5"
      },
      "source": [
        "x_train = np.reshape(x_train,(x_train.shape[0],x_train.shape[1],1))\n",
        "x_train.shape"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1167, 60, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "THpXUqtY8mip",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(LSTM(units=100, return_sequences= True, input_shape=(x_train.shape[1],1)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(LSTM(units=100, return_sequences= True))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "\n",
        "model.add(LSTM(units=100, return_sequences= True))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "\n",
        "model.add(LSTM(units=100, return_sequences= False))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "\n",
        "model.add(Dense(units=1))\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RlfBDOer8mdw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        },
        "outputId": "513c2575-d500-4863-b4ef-a21da1fb6381"
      },
      "source": [
        "hist = model.fit(x_train, y_train, epochs=20, batch_size=32, verbose=2)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            " - 9s - loss: 0.0063\n",
            "Epoch 2/20\n",
            " - 9s - loss: 0.0054\n",
            "Epoch 3/20\n",
            " - 9s - loss: 0.0049\n",
            "Epoch 4/20\n",
            " - 9s - loss: 0.0054\n",
            "Epoch 5/20\n",
            " - 9s - loss: 0.0049\n",
            "Epoch 6/20\n",
            " - 9s - loss: 0.0047\n",
            "Epoch 7/20\n",
            " - 8s - loss: 0.0054\n",
            "Epoch 8/20\n",
            " - 9s - loss: 0.0051\n",
            "Epoch 9/20\n",
            " - 9s - loss: 0.0042\n",
            "Epoch 10/20\n",
            " - 8s - loss: 0.0042\n",
            "Epoch 11/20\n",
            " - 9s - loss: 0.0045\n",
            "Epoch 12/20\n",
            " - 9s - loss: 0.0038\n",
            "Epoch 13/20\n",
            " - 9s - loss: 0.0036\n",
            "Epoch 14/20\n",
            " - 9s - loss: 0.0036\n",
            "Epoch 15/20\n",
            " - 9s - loss: 0.0037\n",
            "Epoch 16/20\n",
            " - 9s - loss: 0.0035\n",
            "Epoch 17/20\n",
            " - 9s - loss: 0.0034\n",
            "Epoch 18/20\n",
            " - 9s - loss: 0.0035\n",
            "Epoch 19/20\n",
            " - 9s - loss: 0.0030\n",
            "Epoch 20/20\n",
            " - 9s - loss: 0.0036\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XeYfbRoML55j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "44a4bd02-e0b6-4d8b-ff56-0aefbf58196b"
      },
      "source": [
        "plt.plot(hist.history['loss'])\n",
        "plt.title('Training model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train'],loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ahGa-Rd3Z_Vf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5f224f47-9c7d-48b5-ba0c-201a5bd89e98"
      },
      "source": [
        "INFOSYS_NEW.columns"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UOTEDQi_NHfa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b17eb3b8-51ae-456b-f4ab-586f89f14551"
      },
      "source": [
        "INFOSYS_TEST = pd.read_csv(\"/content/INFY.NS.csv\")\n",
        "INFOSYS_TEST[\"Close\"]=pd.to_numeric(INFOSYS_TEST.Close, errors='coerce')\n",
        "INFOSYS_TEST = INFOSYS_TEST.dropna()\n",
        "INFOSYS_TEST = INFOSYS_TEST.iloc[:,4:5]\n",
        "y_test = INFOSYS_TEST.iloc[60:,0:].values\n",
        "\n",
        "inputClosing = INFOSYS_TEST.iloc[:,0:].values\n",
        "inputClosing_scaled = sc.transform(inputClosing)\n",
        "inputClosing_scaled.shape\n",
        "x_test = []\n",
        "length = len(INFOSYS_TEST)\n",
        "timestep = 60\n",
        "for i in range (timestep, length):\n",
        "  x_test.append(inputClosing_scaled[i-timestep:i,0])\n",
        "x_test = np.array(x_test)\n",
        "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n",
        "x_test.shape\n",
        "\n",
        "\n"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1167, 60, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YNFmNoncNHzM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "61829c26-f793-4439-edc5-562a13257a7f"
      },
      "source": [
        "y_predict = model.predict(x_test)\n",
        "y_predict"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.29796383],\n",
              "       [0.30271685],\n",
              "       [0.30667093],\n",
              "       ...,\n",
              "       [0.6671327 ],\n",
              "       [0.6693521 ],\n",
              "       [0.67803097]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-o0gjVRoNH8M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predicted_price = sc.inverse_transform(y_predict)"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HNzEChcUNIAO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "619386c9-c74d-4437-8f8e-19bf4a3e6c0f"
      },
      "source": [
        "plt.plot(y_test, color='red', label='ACTUAL STOCK PRICE')\n",
        "plt.plot(predicted_price, color='cyan', label='PREDICTED STOCK PRICE')\n",
        "plt.title('INFOSYS STOCK PRICE PREDICTION')\n",
        "plt.xlabel('TIME')\n",
        "plt.ylabel('STOCK PRICE')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}