Long_Short_Term_Memory_.ipynb

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      "name": "Long Short Term Memory .ipynb",
      "provenance": [],
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_Xqah7ZTeWvd"
      },
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      "source": [
        "#LSTM predict the closing stock price of a corportaion (AAPL) using the past 60 days data. \n",
        "#Remeber important information and foreget unuseful data \n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Import the libraries \n",
        "import math \n",
        "import pandas_datareader as web \n",
        "import numpy as np \n",
        "import pandas as pd \n",
        "from sklearn.preprocessing import MinMaxScaler \n",
        "from keras.models import Sequential \n",
        "from keras.layers import Dense,LSTM \n",
        "import matplotlib.pyplot as plt \n",
        "plt.style.use('fivethirtyeight')\n",
        "import os"
      ],
      "metadata": {
        "id": "Nmhnoq0zeyvP"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/AAPL.csv\")\n",
        "df.head()"
      ],
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      "execution_count": 7,
      "outputs": [
        {
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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>2012-01-03</td>\n",
              "      <td>14.621429</td>\n",
              "      <td>14.732143</td>\n",
              "      <td>14.607143</td>\n",
              "      <td>14.686786</td>\n",
              "      <td>12.575915</td>\n",
              "      <td>302220800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012-01-04</td>\n",
              "      <td>14.642857</td>\n",
              "      <td>14.810000</td>\n",
              "      <td>14.617143</td>\n",
              "      <td>14.765714</td>\n",
              "      <td>12.643497</td>\n",
              "      <td>260022000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2012-01-05</td>\n",
              "      <td>14.819643</td>\n",
              "      <td>14.948214</td>\n",
              "      <td>14.738214</td>\n",
              "      <td>14.929643</td>\n",
              "      <td>12.783869</td>\n",
              "      <td>271269600</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012-01-06</td>\n",
              "      <td>14.991786</td>\n",
              "      <td>15.098214</td>\n",
              "      <td>14.972143</td>\n",
              "      <td>15.085714</td>\n",
              "      <td>12.917507</td>\n",
              "      <td>318292800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2012-01-09</td>\n",
              "      <td>15.196429</td>\n",
              "      <td>15.276786</td>\n",
              "      <td>15.048214</td>\n",
              "      <td>15.061786</td>\n",
              "      <td>12.897017</td>\n",
              "      <td>394024400</td>\n",
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            "text/plain": [
              "         Date       Open       High        Low      Close  Adj Close     Volume\n",
              "0  2012-01-03  14.621429  14.732143  14.607143  14.686786  12.575915  302220800\n",
              "1  2012-01-04  14.642857  14.810000  14.617143  14.765714  12.643497  260022000\n",
              "2  2012-01-05  14.819643  14.948214  14.738214  14.929643  12.783869  271269600\n",
              "3  2012-01-06  14.991786  15.098214  14.972143  15.085714  12.917507  318292800\n",
              "4  2012-01-09  15.196429  15.276786  15.048214  15.061786  12.897017  394024400"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "#Get number of rows and columns in the data set \n",
        "df.shape "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OLmh8jnvnBC3",
        "outputId": "c9a5d219-1a38-4e2d-ec9d-5baf5d5db295"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2003, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualize the clsoing price history \n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title ('Close Price History')\n",
        "plt.plot(df['Close'])\n",
        "plt.xlabel ('Date', fontsize=16)\n",
        "plt.ylabel ('Close Price USD ($)',fontsize =16)\n",
        "plt.show "
      ],
      "metadata": {
        "colab": {
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          "height": 572
        },
        "id": "Mz4mNcOUnIPy",
        "outputId": "c3169955-cd95-4dbc-b5ff-bffcfd8b61e1"
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      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
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          "data": {
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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a new dataframe with only the \"Close column\"\n",
        "data = df.filter (['Close'])\n",
        "# Convert the dataframe to a numpy array \n",
        "dataset = data.values \n",
        "#Get the number of rows to train the model on\n",
        "training_data_len =math.ceil (len(dataset) * .8 )\n",
        "training_data_len"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3pJ5AF2Eo3HI",
        "outputId": "d35e1891-d044-4a6f-86a0-73dbf029c8a0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1603"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Sclae the data \n",
        "scaler = MinMaxScaler (feature_range=(0,1))\n",
        "scaled_data = scaler.fit_transform(dataset) \n",
        "\n",
        "scaled_data "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVhqwl3zsKkB",
        "outputId": "9bd5c438-39d3-4438-bff0-8b315bda21b7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.0131651 ],\n",
              "       [0.01457063],\n",
              "       [0.01748986],\n",
              "       ...,\n",
              "       [0.97658262],\n",
              "       [0.99755133],\n",
              "       [1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the training data set \n",
        "# Create the scaled training data set \n",
        "train_data = scaled_data [0:training_data_len,:]\n",
        "#Split the data into x_train and y_train data sets \n",
        "x_train = []\n",
        "y_train = []\n",
        "\n",
        "for i in range(60,len(train_data)):\n",
        " x_train.append(train_data[i-60:i,0])\n",
        " y_train.append(train_data[i,0])\n",
        " if i <= 60: \n",
        "   print(x_train)\n",
        "   print(y_train)\n",
        "   print ()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0OFbV_VsniZ",
        "outputId": "3821e535-f011-4f76-924d-11509be33001"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([0.0131651 , 0.01457063, 0.01748986, 0.02026915, 0.01984304,\n",
            "       0.02080338, 0.02036454, 0.0196268 , 0.01862192, 0.02173194,\n",
            "       0.02453667, 0.02367173, 0.01893356, 0.02345549, 0.01900353,\n",
            "       0.03569839, 0.03440732, 0.03609271, 0.03973694, 0.04194383,\n",
            "       0.04175942, 0.04107891, 0.04397904, 0.04670743, 0.0497984 ,\n",
            "       0.05479095, 0.0652785 , 0.0654375 , 0.07127595, 0.07563886,\n",
            "       0.0681405 , 0.07102789, 0.07097067, 0.07906688, 0.07791571,\n",
            "       0.08004628, 0.08387497, 0.08600557, 0.09214292, 0.09661394,\n",
            "       0.09790501, 0.0983566 , 0.09071196, 0.08886754, 0.08914104,\n",
            "       0.09632779, 0.09835024, 0.10269409, 0.11293359, 0.12659476,\n",
            "       0.12403805, 0.12404441, 0.13392141, 0.13701237, 0.1348118 ,\n",
            "       0.13280208, 0.13070964, 0.13766104, 0.14243104, 0.14442806])]\n",
            "[0.139492723007876]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Convert the x_train and y_train to numpy arrays \n",
        "x_train,y_train = np.array(x_train), np.array(y_train)"
      ],
      "metadata": {
        "id": "uNL5fzDl-kX1"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Reshape the data \n",
        "x_train = np.reshape (x_train, (x_train.shape[0],x_train.shape[1],1))\n",
        "x_train.shape \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M3PMYcL1ApKG",
        "outputId": "175a3b32-5e63-4b68-bd62-f55614e0866a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1543, 60, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Build the LSTM Model \n",
        "model = Sequential()\n",
        "model.add(LSTM(50,return_sequences = True, input_shape =(x_train.shape[1],1)))\n",
        "model.add(LSTM(50,return_sequences = False))\n",
        "model.add(Dense(25))\n",
        "model.add(Dense(1))"
      ],
      "metadata": {
        "id": "rWBytwRQA9o_"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Compile the model \n",
        "model.compile(optimizer ='adam',loss='mean_squared_error')"
      ],
      "metadata": {
        "id": "te-NVoiyB7Rj"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Train the model \n",
        "model.fit(x_train,y_train,batch_size=1,epochs=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qAmK08TbDZhk",
        "outputId": "805cbb23-b91c-463c-bd01-728c00f03f28"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1543/1543 [==============================] - 46s 27ms/step - loss: 5.8784e-04\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc5d1761810>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Created the testing data set \n",
        "#Created a new arrray containing sclaed values from index 1543 to 2003\n",
        "test_data = scaled_data [training_data_len - 60: , :]\n",
        "#Created the data sets x_test and y_test \n",
        "x_test = []\n",
        "y_test = dataset [training_data_len: , :]\n",
        "for i in range(60,len(test_data)):\n",
        "  x_test.append(test_data[i-60:i,0])"
      ],
      "metadata": {
        "id": "h8dbrYhaDvRf"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Convert the data to a numpy array \n",
        "x_test = np.array(x_test)"
      ],
      "metadata": {
        "id": "_jNnIATTEfUY"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Reshape the data set \n",
        "x_test = np.reshape(x_test,(x_test.shape[0],x_test.shape[1],1))"
      ],
      "metadata": {
        "id": "vpuVyixuEreN"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the models predicted price values \n",
        "predictions = model.predict (x_test)\n",
        "predictions = scaler.inverse_transform(predictions)"
      ],
      "metadata": {
        "id": "mGCsp5dnHJVE"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the root mean squared error (RMSE)\n",
        "rmse = np.sqrt (np.mean(predictions - y_test)**2)\n",
        "rmse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F3LTmxxAH1cB",
        "outputId": "eacac48d-ec60-4b7c-a7b7-da5f63ba207c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.27637874520874"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the data \n",
        "train = data[:training_data_len]\n",
        "valid = data [training_data_len:]\n",
        "valid['Predictions']= predictions\n",
        "# Visualize the data \n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Model')\n",
        "plt.xlabel('Data',fontsize=18)\n",
        "plt.ylabel('Close Price USD ($)', fontsize=18)\n",
        "plt.plot(train['Close'])\n",
        "plt.plot(valid[['Close','Predictions']])\n",
        "plt.legend(['Train','Val','Predictions'],loc='lower right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "Ei27HL9oILBT",
        "outputId": "341934a9-2f8f-4db9-9208-84adadc04f65"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Valid \n",
        "valid "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "WHYDQYlbj0hX",
        "outputId": "7148d0f2-8e1a-4ad9-e028-82c140367c6a"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-caed6203-5dce-4ca5-aa1e-f296e43ba374\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "      <th>Predictions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1603</th>\n",
              "      <td>46.747501</td>\n",
              "      <td>44.217026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1604</th>\n",
              "      <td>46.577499</td>\n",
              "      <td>44.174747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1605</th>\n",
              "      <td>46.907501</td>\n",
              "      <td>44.098713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1606</th>\n",
              "      <td>46.790001</td>\n",
              "      <td>44.075363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1607</th>\n",
              "      <td>47.090000</td>\n",
              "      <td>44.064922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>67.692497</td>\n",
              "      <td>60.934372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>67.864998</td>\n",
              "      <td>61.169605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000</th>\n",
              "      <td>68.787498</td>\n",
              "      <td>61.402657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001</th>\n",
              "      <td>69.964996</td>\n",
              "      <td>61.730396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002</th>\n",
              "      <td>70.102501</td>\n",
              "      <td>62.210285</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-caed6203-5dce-4ca5-aa1e-f296e43ba374')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-caed6203-5dce-4ca5-aa1e-f296e43ba374 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-caed6203-5dce-4ca5-aa1e-f296e43ba374');\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",
              "  "
            ],
            "text/plain": [
              "          Close  Predictions\n",
              "1603  46.747501    44.217026\n",
              "1604  46.577499    44.174747\n",
              "1605  46.907501    44.098713\n",
              "1606  46.790001    44.075363\n",
              "1607  47.090000    44.064922\n",
              "...         ...          ...\n",
              "1998  67.692497    60.934372\n",
              "1999  67.864998    61.169605\n",
              "2000  68.787498    61.402657\n",
              "2001  69.964996    61.730396\n",
              "2002  70.102501    62.210285\n",
              "\n",
              "[400 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the quote \n",
        "apple_quote = web.DataReader ('AAPL',data_source='yahoo',start='2012-01-01',end='2019-12-17')\n",
        "#Created a new dataframe \n",
        "new_df = apple_quote.fliter (['Close'])\n",
        "#Get the last 60 day closing price values and convert the dataframe to set an array \n",
        "last_60_days = new_df[-60:].values\n",
        "#Scale the data to be values between 0 and 1 \n",
        "last_60_days_scaled= scaler.transfrom(last_60_days)\n",
        "#Created an empty list \n",
        "x_test = []\n",
        "#Append the past 60 days \n",
        "X_test.append(last_60_days_scaled)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "BB8hyIUij3ny",
        "outputId": "a7495733-2837-4df8-d96e-687ea196c3dd"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RemoteDataError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRemoteDataError\u001b[0m                           Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-40-452b8f30ea1c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Get the quote\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mapple_quote\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataReader\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'AAPL'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata_source\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'yahoo'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'2012-01-01'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'2019-12-17'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m#Created a new dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mnew_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mapple_quote\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfliter\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Close'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#Get the last 60 day closing price values and convert the dataframe to set an array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    205\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas_datareader/data.py\u001b[0m in \u001b[0;36mDataReader\u001b[0;34m(name, data_source, start, end, retry_count, pause, session, api_key)\u001b[0m\n\u001b[1;32m    382\u001b[0m             \u001b[0mretry_count\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mretry_count\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m             \u001b[0mpause\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpause\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m             \u001b[0msession\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    385\u001b[0m         ).read()\n\u001b[1;32m    386\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas_datareader/base.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    251\u001b[0m         \u001b[0;31m# If a single symbol, (e.g., 'GOOG')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m             \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_one_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    254\u001b[0m         \u001b[0;31m# Or multiple symbols, (e.g., ['GOOG', 'AAPL', 'MSFT'])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas_datareader/yahoo/daily.py\u001b[0m in \u001b[0;36m_read_one_data\u001b[0;34m(self, url, params)\u001b[0m\n\u001b[1;32m    151\u001b[0m         \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msymbol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m         \u001b[0mptrn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mr\"root\\.App\\.main = (.*?);\\n}\\(this\\)\\);\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas_datareader/base.py\u001b[0m in \u001b[0;36m_get_response\u001b[0;34m(self, url, params, headers)\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"\\nResponse Text:\\n{0}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlast_response_text\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mRemoteDataError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_crumb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRemoteDataError\u001b[0m: Unable to read URL: https://finance.yahoo.com/quote/AAPL/history?period1=1325390400&period2=1576641599&interval=1d&frequency=1d&filter=history\nResponse Text:\nb'<!DOCTYPE html>\\n  <html lang=\"en-us\"><head>\\n  <meta http-equiv=\"content-type\" content=\"text/html; charset=UTF-8\">\\n      <meta charset=\"utf-8\">\\n      <title>Yahoo</title>\\n      <meta name=\"viewport\" content=\"width=device-width,initial-scale=1,minimal-ui\">\\n      <meta http-equiv=\"X-UA-Compatible\" content=\"IE=edge,chrome=1\">\\n      <style>\\n  html {\\n      height: 100%;\\n  }\\n  body {\\n      background: #fafafc url(https://s.yimg.com/nn/img/sad-panda-201402200631.png) 50% 50%;\\n      background-size: cover;\\n      height: 100%;\\n      text-align: center;\\n      font: 300 18px \"helvetica neue\", helvetica, verdana, tahoma, arial, sans-serif;\\n  }\\n  table {\\n      height: 100%;\\n      width: 100%;\\n      table-layout: fixed;\\n      border-collapse: collapse;\\n      border-spacing: 0;\\n      border: none;\\n  }\\n  h1 {\\n      font-size: 42px;\\n      font-weight: 400;\\n      color: #400090;\\n  }\\n  p {\\n      color: #1A1A1A;\\n  }\\n  #message-1 {\\n      font-weight: bold;\\n      margin: 0;\\n  }\\n  #message-2 {\\n      display: inline-block;\\n      *display: inline;\\n      zoom: 1;\\n      max-width: 17em;\\n      _width: 17em;\\n  }\\n      </style>\\n  <script>\\n    document.write(\\'<img src=\"//geo.yahoo.com/b?s=1197757129&t=\\'+new Date().getTime()+\\'&src=aws&err_url=\\'+encodeURIComponent(document.URL)+\\'&err=%<pssc>&test=\\'+encodeURIComponent(\\'%<{Bucket}cqh[:200]>\\')+\\'\" width=\"0px\" height=\"0px\"/>\\');var beacon = new Image();beacon.src=\"//bcn.fp.yahoo.com/p?s=1197757129&t=\"+ne..."
          ]
        }
      ]
    }
  ]
}