app2.ipynb

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      "metadata": {
        "id": "y4Oj5Kg15s1Y",
        "colab_type": "code",
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      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2OvbcHm_5s1c",
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        },
        "outputId": "3e9035ae-e3b3-4a7f-a057-070d3d6bbc8a"
      },
      "source": [
        "import pandas_datareader as pdr\n",
        "key = \"4412be54f9ecbfe7303a63f224bb63e7d0474e30\""
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas_datareader/compat/__init__.py:7: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  from pandas.util.testing import assert_frame_equal\n"
          ],
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        }
      ]
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    {
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      "metadata": {
        "id": "nCT8fkTf5s1f",
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        "colab": {}
      },
      "source": [
        "df = pdr.get_data_tiingo('AAPL', api_key=key)\n",
        "df.to_csv('AAPL.csv')\n",
        "df=pd.read_csv('AAPL.csv')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YIb-BHDE5s1i",
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        "outputId": "27f7c45a-12c5-4b56-f0e3-e6f22c9d954c"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
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              "      <td>AAPL</td>\n",
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              "      <td>AAPL</td>\n",
              "      <td>2015-07-02 00:00:00+00:00</td>\n",
              "      <td>126.44</td>\n",
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              "      <td>AAPL</td>\n",
              "      <td>2015-07-06 00:00:00+00:00</td>\n",
              "      <td>126.00</td>\n",
              "      <td>126.23</td>\n",
              "      <td>124.85</td>\n",
              "      <td>124.94</td>\n",
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              "      <td>AAPL</td>\n",
              "      <td>2015-07-07 00:00:00+00:00</td>\n",
              "      <td>125.69</td>\n",
              "      <td>126.15</td>\n",
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              "      <th>4</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2015-07-08 00:00:00+00:00</td>\n",
              "      <td>122.57</td>\n",
              "      <td>124.64</td>\n",
              "      <td>122.54</td>\n",
              "      <td>124.48</td>\n",
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              "  symbol                       date   close  ...  adjVolume  divCash  splitFactor\n",
              "0   AAPL  2015-07-01 00:00:00+00:00  126.60  ...   30238811      0.0          1.0\n",
              "1   AAPL  2015-07-02 00:00:00+00:00  126.44  ...   27210952      0.0          1.0\n",
              "2   AAPL  2015-07-06 00:00:00+00:00  126.00  ...   28060431      0.0          1.0\n",
              "3   AAPL  2015-07-07 00:00:00+00:00  125.69  ...   46946811      0.0          1.0\n",
              "4   AAPL  2015-07-08 00:00:00+00:00  122.57  ...   60761614      0.0          1.0\n",
              "\n",
              "[5 rows x 14 columns]"
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        "outputId": "934c8bc5-8639-4b62-8faf-cf355425858c"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>AAPL</td>\n",
              "      <td>2020-06-22 00:00:00+00:00</td>\n",
              "      <td>358.87</td>\n",
              "      <td>359.46</td>\n",
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              "      <td>351.34</td>\n",
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              "      <td>AAPL</td>\n",
              "      <td>2020-06-23 00:00:00+00:00</td>\n",
              "      <td>366.53</td>\n",
              "      <td>372.38</td>\n",
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              "    <tr>\n",
              "      <th>1254</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2020-06-24 00:00:00+00:00</td>\n",
              "      <td>360.06</td>\n",
              "      <td>368.79</td>\n",
              "      <td>358.5200</td>\n",
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              "      <td>48155849</td>\n",
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              "    <tr>\n",
              "      <th>1255</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2020-06-25 00:00:00+00:00</td>\n",
              "      <td>364.84</td>\n",
              "      <td>365.00</td>\n",
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              "      <td>360.70</td>\n",
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              "    <tr>\n",
              "      <th>1256</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2020-06-26 00:00:00+00:00</td>\n",
              "      <td>353.63</td>\n",
              "      <td>365.32</td>\n",
              "      <td>353.0200</td>\n",
              "      <td>364.41</td>\n",
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              "     symbol                       date   close  ...  adjVolume  divCash  splitFactor\n",
              "1252   AAPL  2020-06-22 00:00:00+00:00  358.87  ...   33861316      0.0          1.0\n",
              "1253   AAPL  2020-06-23 00:00:00+00:00  366.53  ...   53038869      0.0          1.0\n",
              "1254   AAPL  2020-06-24 00:00:00+00:00  360.06  ...   48155849      0.0          1.0\n",
              "1255   AAPL  2020-06-25 00:00:00+00:00  364.84  ...   34380628      0.0          1.0\n",
              "1256   AAPL  2020-06-26 00:00:00+00:00  353.63  ...   51314211      0.0          1.0\n",
              "\n",
              "[5 rows x 14 columns]"
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        "outputId": "b111d350-1983-409e-b8c3-8126cfff6b65"
      },
      "source": [
        "df.head(1)"
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      "execution_count": 7,
      "outputs": [
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              "  symbol                       date  close  ...  adjVolume  divCash  splitFactor\n",
              "0   AAPL  2015-07-01 00:00:00+00:00  126.6  ...   30238811      0.0          1.0\n",
              "\n",
              "[1 rows x 14 columns]"
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      },
      "source": [
        "df.tail(100)"
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      "execution_count": 8,
      "outputs": [
        {
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          "data": {
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              "      <th>1157</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2020-02-05 00:00:00+00:00</td>\n",
              "      <td>321.45</td>\n",
              "      <td>324.76</td>\n",
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              "      <td>AAPL</td>\n",
              "      <td>2020-02-06 00:00:00+00:00</td>\n",
              "      <td>325.21</td>\n",
              "      <td>325.22</td>\n",
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              "      <td>AAPL</td>\n",
              "      <td>2020-02-07 00:00:00+00:00</td>\n",
              "      <td>320.03</td>\n",
              "      <td>323.40</td>\n",
              "      <td>318.0000</td>\n",
              "      <td>322.37</td>\n",
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              "      <td>0.77</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1160</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2020-02-10 00:00:00+00:00</td>\n",
              "      <td>321.55</td>\n",
              "      <td>321.55</td>\n",
              "      <td>313.8500</td>\n",
              "      <td>314.18</td>\n",
              "      <td>27337215</td>\n",
              "      <td>320.702047</td>\n",
              "      <td>320.702047</td>\n",
              "      <td>313.022352</td>\n",
              "      <td>313.351482</td>\n",
              "      <td>27337215</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1161</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2020-02-11 00:00:00+00:00</td>\n",
              "      <td>319.61</td>\n",
              "      <td>323.90</td>\n",
              "      <td>318.7100</td>\n",
              "      <td>323.60</td>\n",
              "      <td>23580780</td>\n",
              "      <td>318.767163</td>\n",
              "      <td>323.045850</td>\n",
              "      <td>317.869536</td>\n",
              "      <td>322.746641</td>\n",
              "      <td>23580780</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</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",
              "      <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>1252</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2020-06-22 00:00:00+00:00</td>\n",
              "      <td>358.87</td>\n",
              "      <td>359.46</td>\n",
              "      <td>351.1500</td>\n",
              "      <td>351.34</td>\n",
              "      <td>33861316</td>\n",
              "      <td>358.870000</td>\n",
              "      <td>359.460000</td>\n",
              "      <td>351.150000</td>\n",
              "      <td>351.340000</td>\n",
              "      <td>33861316</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1253</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2020-06-23 00:00:00+00:00</td>\n",
              "      <td>366.53</td>\n",
              "      <td>372.38</td>\n",
              "      <td>362.2701</td>\n",
              "      <td>364.00</td>\n",
              "      <td>53038869</td>\n",
              "      <td>366.530000</td>\n",
              "      <td>372.380000</td>\n",
              "      <td>362.270100</td>\n",
              "      <td>364.000000</td>\n",
              "      <td>53038869</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1254</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2020-06-24 00:00:00+00:00</td>\n",
              "      <td>360.06</td>\n",
              "      <td>368.79</td>\n",
              "      <td>358.5200</td>\n",
              "      <td>365.00</td>\n",
              "      <td>48155849</td>\n",
              "      <td>360.060000</td>\n",
              "      <td>368.790000</td>\n",
              "      <td>358.520000</td>\n",
              "      <td>365.000000</td>\n",
              "      <td>48155849</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1255</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2020-06-25 00:00:00+00:00</td>\n",
              "      <td>364.84</td>\n",
              "      <td>365.00</td>\n",
              "      <td>357.5700</td>\n",
              "      <td>360.70</td>\n",
              "      <td>34380628</td>\n",
              "      <td>364.840000</td>\n",
              "      <td>365.000000</td>\n",
              "      <td>357.570000</td>\n",
              "      <td>360.700000</td>\n",
              "      <td>34380628</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1256</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2020-06-26 00:00:00+00:00</td>\n",
              "      <td>353.63</td>\n",
              "      <td>365.32</td>\n",
              "      <td>353.0200</td>\n",
              "      <td>364.41</td>\n",
              "      <td>51314211</td>\n",
              "      <td>353.630000</td>\n",
              "      <td>365.320000</td>\n",
              "      <td>353.020000</td>\n",
              "      <td>364.410000</td>\n",
              "      <td>51314211</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     symbol                       date   close  ...  adjVolume  divCash  splitFactor\n",
              "1157   AAPL  2020-02-05 00:00:00+00:00  321.45  ...   29706718     0.00          1.0\n",
              "1158   AAPL  2020-02-06 00:00:00+00:00  325.21  ...   26356385     0.00          1.0\n",
              "1159   AAPL  2020-02-07 00:00:00+00:00  320.03  ...   29421012     0.77          1.0\n",
              "1160   AAPL  2020-02-10 00:00:00+00:00  321.55  ...   27337215     0.00          1.0\n",
              "1161   AAPL  2020-02-11 00:00:00+00:00  319.61  ...   23580780     0.00          1.0\n",
              "...     ...                        ...     ...  ...        ...      ...          ...\n",
              "1252   AAPL  2020-06-22 00:00:00+00:00  358.87  ...   33861316     0.00          1.0\n",
              "1253   AAPL  2020-06-23 00:00:00+00:00  366.53  ...   53038869     0.00          1.0\n",
              "1254   AAPL  2020-06-24 00:00:00+00:00  360.06  ...   48155849     0.00          1.0\n",
              "1255   AAPL  2020-06-25 00:00:00+00:00  364.84  ...   34380628     0.00          1.0\n",
              "1256   AAPL  2020-06-26 00:00:00+00:00  353.63  ...   51314211     0.00          1.0\n",
              "\n",
              "[100 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ao5mu4EZ5s1v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df1=df.reset_index()['close']"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aKiUTDNu5s1x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "013171e5-2450-4f01-fb20-804d5bc9bb64"
      },
      "source": [
        "df1"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       126.60\n",
              "1       126.44\n",
              "2       126.00\n",
              "3       125.69\n",
              "4       122.57\n",
              "         ...  \n",
              "1252    358.87\n",
              "1253    366.53\n",
              "1254    360.06\n",
              "1255    364.84\n",
              "1256    353.63\n",
              "Name: close, Length: 1257, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eskz4qVf5s10",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "87423978-91d5-4994-a0f4-45180b64245b"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(df1)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fbc494dc5f8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "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": "bjOQKvA65s13",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        },
        "outputId": "ec6f0ace-b8d6-4044-a151-08f3cd54104d"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler=MinMaxScaler(feature_range=(0,1))\n",
        "df1=scaler.fit_transform(np.array(df1).reshape(-1,1))\n",
        "print(df1)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.13128643]\n",
            " [0.13070712]\n",
            " [0.12911402]\n",
            " ...\n",
            " [0.9765741 ]\n",
            " [0.99388102]\n",
            " [0.95329302]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s7Tprygr5s15",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "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": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t5Cl6iLW5s18",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8e1c7f5d-1435-4989-9b8b-6b73a46c6b35"
      },
      "source": [
        "training_size"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "817"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X7GsAmVh5s1-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3c92556d-3fd5-4178-d326-6268bafd512e"
      },
      "source": [
        "test_size"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "440"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0fzsOa4g5s2B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "abc19e41-d305-4e08-b97d-749198291c7e"
      },
      "source": [
        "train_data"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.13128643],\n",
              "       [0.13070712],\n",
              "       [0.12911402],\n",
              "       [0.1279916 ],\n",
              "       [0.11669503],\n",
              "       [0.10764329],\n",
              "       [0.11926572],\n",
              "       [0.12788298],\n",
              "       [0.12770194],\n",
              "       [0.13208299],\n",
              "       [0.13820196],\n",
              "       [0.14222093],\n",
              "       [0.15109164],\n",
              "       [0.14631232],\n",
              "       [0.12628987],\n",
              "       [0.12607263],\n",
              "       [0.12368297],\n",
              "       [0.11741917],\n",
              "       [0.11962779],\n",
              "       [0.11821572],\n",
              "       [0.11597089],\n",
              "       [0.11209674],\n",
              "       [0.10174155],\n",
              "       [0.08798291],\n",
              "       [0.09073464],\n",
              "       [0.08975705],\n",
              "       [0.09116912],\n",
              "       [0.10637605],\n",
              "       [0.08381911],\n",
              "       [0.09015533],\n",
              "       [0.08982947],\n",
              "       [0.09276223],\n",
              "       [0.09710706],\n",
              "       [0.0947174 ],\n",
              "       [0.08932257],\n",
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              "       [0.4620008 ],\n",
              "       [0.46837322],\n",
              "       [0.4802491 ],\n",
              "       [0.48767153],\n",
              "       [0.49708534],\n",
              "       [0.49972845],\n",
              "       [0.49433361],\n",
              "       [0.48068359],\n",
              "       [0.47416633],\n",
              "       [0.46341287],\n",
              "       [0.48339911],\n",
              "       [0.47333357],\n",
              "       [0.49266809],\n",
              "       [0.4833629 ],\n",
              "       [0.46178355],\n",
              "       [0.46308701],\n",
              "       [0.4635577 ],\n",
              "       [0.46956805],\n",
              "       [0.460987  ],\n",
              "       [0.47231978],\n",
              "       [0.47738875],\n",
              "       [0.47098012]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RVpCGe_S5s2D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy\n",
        "# convert 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] \n",
        "\t\tdataX.append(a)\n",
        "\t\tdataY.append(dataset[i + time_step, 0])\n",
        "\treturn numpy.array(dataX), numpy.array(dataY)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5JAZfU05s2F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "time_step = 100\n",
        "X_train,y_train = create_dataset(train_data,time_step)\n",
        "X_test,y_test = create_dataset(test_data,time_step)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vs0K9dK95s2K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "outputId": "ffb57c56-2ac9-4107-826d-bfa422da55bc"
      },
      "source": [
        "X_train"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.13128643, 0.13070712, 0.12911402, ..., 0.08454325, 0.09757775,\n",
              "        0.10297259],\n",
              "       [0.13070712, 0.12911402, 0.1279916 , ..., 0.09757775, 0.10297259,\n",
              "        0.10485535],\n",
              "       [0.12911402, 0.1279916 , 0.11669503, ..., 0.10297259, 0.10485535,\n",
              "        0.09924327],\n",
              "       ...,\n",
              "       [0.28516601, 0.31221261, 0.31337123, ..., 0.46308701, 0.4635577 ,\n",
              "        0.46956805],\n",
              "       [0.31221261, 0.31337123, 0.33849886, ..., 0.4635577 , 0.46956805,\n",
              "        0.460987  ],\n",
              "       [0.31337123, 0.33849886, 0.34331439, ..., 0.46956805, 0.460987  ,\n",
              "        0.47231978]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXvkXdHw5s2N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1943932f-8ac6-4a09-920f-ccb5df753887"
      },
      "source": [
        "print(X_train.shape)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(716, 100)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xkWliGLy5s2P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        },
        "outputId": "0d4ee538-0f7a-4763-8bad-61bf0b4a2da5"
      },
      "source": [
        "print(X_test)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.48738187 0.49024222 0.49574568 ... 0.29179188 0.29577465 0.29226257]\n",
            " [0.49024222 0.49574568 0.50305949 ... 0.29577465 0.29226257 0.2991781 ]\n",
            " [0.49574568 0.50305949 0.51316123 ... 0.29226257 0.2991781  0.30374018]\n",
            " ...\n",
            " [0.84554111 0.79354792 0.79047033 ... 0.9464137  0.9391361  0.97226547]\n",
            " [0.79354792 0.79047033 0.82736522 ... 0.9391361  0.97226547 1.        ]\n",
            " [0.79047033 0.82736522 0.83677903 ... 0.97226547 1.         0.9765741 ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lbTcROFf5s2R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "33721b95-75d1-44de-9a20-2727be8067ff"
      },
      "source": [
        "print(X_test.shape)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(339, 100)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_r2ZLsG5s2U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f37799d4-b4ce-4fd2-ab73-1c3c217dddcd"
      },
      "source": [
        "print(y_test.shape)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(339,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8wURFBsF5s2W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3b29a9e1-69af-4b4f-8fed-86fcff83268f"
      },
      "source": [
        "print(y_train.shape)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(716,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tlKt2Gj85s2Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#time_step = 101\n",
        "#X_train,y_train = create_dataset(train_data,time_step)\n",
        "#X_test,y_test = create_dataset(test_data,time_step)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ociix2ro5s2b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e224e8bc-b3d5-4752-b849-1c32bac5c273"
      },
      "source": [
        "print(X_test.shape)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(339, 100)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zj0bihS5s2e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "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": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j3pRUpsF5xwO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# creating a stack LSTM Model #\n",
        "from tensorflow.keras.layers import LSTM\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADnLwJfj5_1x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RLyJ37j66AX-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "569608cd-c3aa-474a-aa01-032601b0b19f"
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'2.2.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZF-EF3KM7bjI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow import keras"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Vvu4P5P7ixh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = keras.Sequential()"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EnkGiMHf7nIF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.add(LSTM(50,return_sequences=True,input_shape=(100,1)))"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9SnR8B6m7ybj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "outputId": "892d4ab2-262c-4bd4-8800-56ca5b6152ab"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm (LSTM)                  (None, 100, 50)           10400     \n",
            "=================================================================\n",
            "Total params: 10,400\n",
            "Trainable params: 10,400\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KtzR52Wd70jC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.add(LSTM(50,return_sequences=True))"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8yMQGhe-9e66",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.add(LSTM(50))"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oRSn7LyY9gwT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.add(Dense(1))"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fNzUqyE9iYE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "6d949f79-f4be-4e3b-983d-7b8176266205"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5qnpC7xI_qK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "62504b5f-2ed5-4139-c270-b9e79971bbde"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HLDk_LpZK-hz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "outputId": "4c24fa07-5190-4cc4-ed5e-027c3ce3f4a1"
      },
      "source": [
        "model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=100,batch_size=64,verbose=1)\n"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-41-55faf346c940>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\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[0mkwargs\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     65\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\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[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     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;31m# Running inside `run_distribute_coordinator` already.\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/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m    786\u001b[0m     \u001b[0;31m# Legacy graph support is contained in `training_v1.Model`.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    787\u001b[0m     \u001b[0mversion_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisallow_legacy_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Model'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fit'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 788\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_assert_compile_was_called\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    789\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_call_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'fit'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    790\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_assert_compile_was_called\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1633\u001b[0m     \u001b[0;31m# (i.e. whether the model is built and its inputs/outputs are set).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1634\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_compiled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1635\u001b[0;31m       raise RuntimeError('You must compile your model before '\n\u001b[0m\u001b[1;32m   1636\u001b[0m                          \u001b[0;34m'training/testing. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1637\u001b[0m                          'Use `model.compile(optimizer, loss)`.')\n",
            "\u001b[0;31mRuntimeError\u001b[0m: You must compile your model before training/testing. Use `model.compile(optimizer, loss)`."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xU27KTwzLBsE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_predict=model.predict(X_train)\n",
        "test_predict=model.predict(X_test)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IzjYBao8MfFj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "268902a9-aa02-4f08-cce1-e62764d5a3b1"
      },
      "source": [
        "print(train_predict)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.00611554]\n",
            " [0.00609665]\n",
            " [0.00607671]\n",
            " [0.00605892]\n",
            " [0.00604458]\n",
            " [0.00603401]\n",
            " [0.00602758]\n",
            " [0.00602434]\n",
            " [0.00602217]\n",
            " [0.00601829]\n",
            " [0.00601469]\n",
            " [0.00601249]\n",
            " [0.00601226]\n",
            " [0.00601155]\n",
            " [0.00600895]\n",
            " [0.00600059]\n",
            " [0.00598317]\n",
            " [0.005953  ]\n",
            " [0.0059093 ]\n",
            " [0.00585034]\n",
            " [0.00577287]\n",
            " [0.00567748]\n",
            " [0.0055664 ]\n",
            " [0.0054444 ]\n",
            " [0.00531564]\n",
            " [0.00518268]\n",
            " [0.00505023]\n",
            " [0.00492072]\n",
            " [0.00479377]\n",
            " [0.00466948]\n",
            " [0.00454537]\n",
            " [0.00441783]\n",
            " [0.00428018]\n",
            " [0.00412964]\n",
            " [0.00396798]\n",
            " [0.00379998]\n",
            " [0.00362732]\n",
            " [0.00345477]\n",
            " [0.00328374]\n",
            " [0.00311528]\n",
            " [0.00295091]\n",
            " [0.00279172]\n",
            " [0.00264507]\n",
            " [0.00251379]\n",
            " [0.00239988]\n",
            " [0.00229613]\n",
            " [0.0021979 ]\n",
            " [0.00210627]\n",
            " [0.00202114]\n",
            " [0.00194005]\n",
            " [0.00186375]\n",
            " [0.00179325]\n",
            " [0.00172609]\n",
            " [0.00166186]\n",
            " [0.00160048]\n",
            " [0.00154121]\n",
            " [0.00148299]\n",
            " [0.00142573]\n",
            " [0.00137295]\n",
            " [0.00132895]\n",
            " [0.00129405]\n",
            " [0.00126758]\n",
            " [0.00124967]\n",
            " [0.00123724]\n",
            " [0.00122952]\n",
            " [0.00122663]\n",
            " [0.00122855]\n",
            " [0.00123469]\n",
            " [0.00124898]\n",
            " [0.0012739 ]\n",
            " [0.00131111]\n",
            " [0.00136249]\n",
            " [0.00142673]\n",
            " [0.00150058]\n",
            " [0.00158085]\n",
            " [0.00166454]\n",
            " [0.00175028]\n",
            " [0.00183697]\n",
            " [0.00192575]\n",
            " [0.00201859]\n",
            " [0.00211591]\n",
            " [0.00221729]\n",
            " [0.00232156]\n",
            " [0.00242812]\n",
            " [0.00253519]\n",
            " [0.0026404 ]\n",
            " [0.00274113]\n",
            " [0.00283816]\n",
            " [0.00293411]\n",
            " [0.00302986]\n",
            " [0.00312654]\n",
            " [0.00322563]\n",
            " [0.00332607]\n",
            " [0.00342761]\n",
            " [0.00352689]\n",
            " [0.00362114]\n",
            " [0.00370869]\n",
            " [0.00379007]\n",
            " [0.00386751]\n",
            " [0.00394264]\n",
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            " [0.00416753]\n",
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            " [0.00419311]\n",
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            " [0.00412719]\n",
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            " [0.00118905]\n",
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            " [0.00133411]\n",
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            " [0.00147365]\n",
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            " [0.00174539]\n",
            " [0.00174141]\n",
            " [0.00172729]\n",
            " [0.00170527]\n",
            " [0.00167423]\n",
            " [0.00163235]\n",
            " [0.00158126]\n",
            " [0.00152406]\n",
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            " [0.00140822]\n",
            " [0.00135486]\n",
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            " [0.00126525]\n",
            " [0.00123212]\n",
            " [0.00120839]\n",
            " [0.00119478]\n",
            " [0.00119044]\n",
            " [0.00119647]\n",
            " [0.00121291]\n",
            " [0.00124026]\n",
            " [0.001278  ]\n",
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            " [0.00137885]\n",
            " [0.00143635]\n",
            " [0.00149307]\n",
            " [0.00154493]\n",
            " [0.00159684]\n",
            " [0.00165386]\n",
            " [0.00171872]\n",
            " [0.0017944 ]\n",
            " [0.00188002]\n",
            " [0.00197518]\n",
            " [0.00207857]\n",
            " [0.00219001]\n",
            " [0.00230934]\n",
            " [0.00243584]\n",
            " [0.00256694]\n",
            " [0.00269971]\n",
            " [0.00283162]\n",
            " [0.00296179]\n",
            " [0.00308911]\n",
            " [0.00321217]\n",
            " [0.00332948]\n",
            " [0.00344012]\n",
            " [0.00354245]\n",
            " [0.00363566]\n",
            " [0.0037185 ]\n",
            " [0.0037898 ]\n",
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            " [0.00389402]\n",
            " [0.00392605]\n",
            " [0.00394495]\n",
            " [0.00395257]\n",
            " [0.00395205]\n",
            " [0.00394615]\n",
            " [0.00393783]\n",
            " [0.00392613]\n",
            " [0.00390765]\n",
            " [0.00388303]\n",
            " [0.00385629]\n",
            " [0.00383495]\n",
            " [0.00382832]\n",
            " [0.00384128]\n",
            " [0.00387409]\n",
            " [0.00392505]\n",
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            " [0.00407095]\n",
            " [0.00415893]\n",
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            " [0.00434388]\n",
            " [0.00443554]\n",
            " [0.00452263]\n",
            " [0.00460347]\n",
            " [0.00467651]\n",
            " [0.00474134]\n",
            " [0.00479808]\n",
            " [0.00484799]\n",
            " [0.00489243]\n",
            " [0.00493459]\n",
            " [0.00497706]\n",
            " [0.00502242]\n",
            " [0.0050717 ]\n",
            " [0.00512564]\n",
            " [0.00518406]\n",
            " [0.0052461 ]\n",
            " [0.00531019]\n",
            " [0.00537465]\n",
            " [0.00543748]\n",
            " [0.00549811]\n",
            " [0.00555675]\n",
            " [0.00561076]\n",
            " [0.00565686]\n",
            " [0.00569218]\n",
            " [0.00571505]\n",
            " [0.00572295]\n",
            " [0.00571502]\n",
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            " [0.00564701]\n",
            " [0.00558865]\n",
            " [0.00551888]\n",
            " [0.00544149]\n",
            " [0.00535654]\n",
            " [0.00526538]\n",
            " [0.00516684]\n",
            " [0.00506242]\n",
            " [0.0049574 ]\n",
            " [0.00485622]\n",
            " [0.00476225]\n",
            " [0.00467955]\n",
            " [0.00461069]\n",
            " [0.00455618]\n",
            " [0.00451613]\n",
            " [0.00448959]\n",
            " [0.004475  ]\n",
            " [0.00446948]\n",
            " [0.00446927]\n",
            " [0.00447183]\n",
            " [0.00447445]\n",
            " [0.00447615]\n",
            " [0.00447769]\n",
            " [0.00448091]\n",
            " [0.00448908]\n",
            " [0.00450353]\n",
            " [0.00452659]\n",
            " [0.00455931]\n",
            " [0.00460221]\n",
            " [0.00465489]\n",
            " [0.00471685]\n",
            " [0.00478713]\n",
            " [0.00486432]\n",
            " [0.00494574]\n",
            " [0.00502903]\n",
            " [0.0051128 ]\n",
            " [0.00519525]\n",
            " [0.00527474]\n",
            " [0.00534903]\n",
            " [0.0054168 ]\n",
            " [0.00547718]\n",
            " [0.0055304 ]\n",
            " [0.00557826]\n",
            " [0.0056234 ]\n",
            " [0.00566781]\n",
            " [0.00571331]\n",
            " [0.00576038]\n",
            " [0.00580863]\n",
            " [0.00585837]\n",
            " [0.00590955]\n",
            " [0.00596153]\n",
            " [0.0060137 ]\n",
            " [0.00606547]\n",
            " [0.00611607]\n",
            " [0.00616669]\n",
            " [0.00621819]\n",
            " [0.00627085]\n",
            " [0.00632418]\n",
            " [0.00637719]\n",
            " [0.00643604]\n",
            " [0.00650514]\n",
            " [0.00658691]\n",
            " [0.00668285]\n",
            " [0.00679376]\n",
            " [0.00691921]\n",
            " [0.00705765]\n",
            " [0.00720615]\n",
            " [0.00736225]\n",
            " [0.00752464]\n",
            " [0.00769194]\n",
            " [0.007862  ]\n",
            " [0.00803257]\n",
            " [0.00820214]\n",
            " [0.00836941]\n",
            " [0.00853243]\n",
            " [0.00868932]\n",
            " [0.00883866]\n",
            " [0.00897934]\n",
            " [0.0091128 ]\n",
            " [0.00923976]\n",
            " [0.00936103]\n",
            " [0.00947672]\n",
            " [0.00958668]\n",
            " [0.00969025]\n",
            " [0.00978644]\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uAGJsRbvMisz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "401629c0-aca4-49ed-897c-eefb6cffc2f2"
      },
      "source": [
        "\n",
        "print(test_predict)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.01550296]\n",
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            " [0.03390085]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bwafSwwmMnCD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "train_predict = scaler.inverse_transform(train_predict)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DLLkz_3MzLO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "38ed3353-647a-4dc5-9919-d2c2c094c4c8"
      },
      "source": [
        "\n",
        "\n",
        "print(train_predict)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ZkWr6DVM2jJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_predict = scaler.inverse_transform(test_predict)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VnfNyaVAM7_T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "ae409e15-59c1-42f6-8858-7d54edc666b2"
      },
      "source": [
        "\n",
        "print(tesst_predict)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-48-4c1ea042de6c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtesst_predict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'tesst_predict' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Te_Hhl7CM-lK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a057d6fc-6291-4939-9a49-d77ed8b1c569"
      },
      "source": [
        "\n",
        "print(test_predict)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            " [98.83634 ]\n",
            " [98.879135]\n",
            " [98.924675]\n",
            " [98.97033 ]\n",
            " [99.016136]\n",
            " [99.06032 ]\n",
            " [99.10365 ]\n",
            " [99.14571 ]\n",
            " [99.18711 ]\n",
            " [99.22733 ]\n",
            " [99.26616 ]\n",
            " [99.30349 ]\n",
            " [99.33815 ]\n",
            " [99.36996 ]\n",
            " [99.39896 ]\n",
            " [99.426506]\n",
            " [99.45055 ]\n",
            " [99.47156 ]\n",
            " [99.488   ]\n",
            " [99.49947 ]\n",
            " [99.51398 ]\n",
            " [99.53184 ]\n",
            " [99.55192 ]\n",
            " [99.57154 ]\n",
            " [99.591644]\n",
            " [99.61304 ]\n",
            " [99.636894]\n",
            " [99.66032 ]\n",
            " [99.68121 ]\n",
            " [99.70308 ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UXV3ZyV7NEhv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b73693f1-ef94-4336-cd1c-73f387342fe7"
      },
      "source": [
        "\n",
        "import math\n",
        "from sklearn.metrics import mean_squared_error\n",
        "math.sqrt(mean_squared_error(y_train,train_predict))"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "93.00921781479842"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XzOfxxe0xetW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "301530f9-4f27-4be9-c2e2-7a3c8d09be6a"
      },
      "source": [
        "\n",
        "math.sqrt(mean_squared_error(y_test,test_predict))\n"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96.82946760469602"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-KtYqvkx0ze",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "b5fc6dc7-bb1e-4f0e-c06b-8b5b2049b90e"
      },
      "source": [
        "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": 53,
      "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": "Lu3OhFN5yPdT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}