Assign2.ipynb

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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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    "gpuClass": "standard"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yawningwinner/InvestX/blob/main/Assign2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "id": "F3NmyDkdaw3f"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import seaborn as sns\n",
        "#from datetime import datetime"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('Train.csv')\n",
        "print(df.head())\n",
        "df=df.head(150)\n",
        "# df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9aH7bA5fdNV8",
        "outputId": "2c358afa-b32d-4584-afb2-eb9e9792edc3"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         Date    Stock  liabilities      equity  total_assets  current_assets  \\\n",
            "0  2006-07-31  Stock 1   625.898686  925.086518   1550.985204      675.789905   \n",
            "1  2006-08-31  Stock 1   407.387467  903.249093   1310.636561     1023.110983   \n",
            "2  2006-09-29  Stock 1   464.069891  602.668832   1066.738722      709.711673   \n",
            "3  2006-10-31  Stock 1   855.854937  391.375369   1247.230306      649.979564   \n",
            "4  2006-11-30  Stock 1   453.954969  956.490566   1410.445534     1115.594551   \n",
            "\n",
            "   current_liabilities  total_revenue  net_income   dividend  \\\n",
            "0           555.931329     548.488451  251.282553  68.184030   \n",
            "1          1295.186240     513.446432  207.576940  42.232672   \n",
            "2           598.719338     503.519871  133.719700  28.835722   \n",
            "3           733.594339     596.006397  -76.881818   0.000000   \n",
            "4          1152.274711     965.939247 -309.204301   0.000000   \n",
            "\n",
            "   shares_outstanding       price  \n",
            "0             1040831  100.000000  \n",
            "1             1046649  101.924169  \n",
            "2             1094158  103.166191  \n",
            "3             1018574  104.478956  \n",
            "4             1036293  104.160907  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols = list(df)[2:12]\n",
        "print(cols)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YLtCdVuQdaqD",
        "outputId": "1d651589-a795-4724-c724-57d8f0acfb90"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['liabilities', 'equity', 'total_assets', 'current_assets', 'current_liabilities', 'total_revenue', 'net_income', 'dividend', 'shares_outstanding', 'price']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_for_training = df[cols].astype(float)"
      ],
      "metadata": {
        "id": "kW6fJrsveCsy"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "scaler = scaler.fit(df_for_training)\n",
        "df_for_training_scaled = scaler.transform(df_for_training)"
      ],
      "metadata": {
        "id": "sMJHDh0Eea0u"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainX = []\n",
        "trainY = []"
      ],
      "metadata": {
        "id": "UXLCuxpYefYU"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_future = 1   # Number of days we want to look into the future based on the past days.\n",
        "n_past = 14 # Number of past days we want to use to predict the future."
      ],
      "metadata": {
        "id": "SlZmQQHaepNo"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(n_past, len(df_for_training_scaled) - n_future +1):\n",
        "    trainX.append(df_for_training_scaled[i - n_past:i, 0:df_for_training.shape[1]])\n",
        "    trainY.append(df_for_training_scaled[i + n_future - 1:i + n_future, 9])\n",
        "\n",
        "trainX, trainY = np.array(trainX), np.array(trainY)\n",
        "\n",
        "print('trainX shape == {}.'.format(trainX.shape))\n",
        "print('trainY shape == {}.'.format(trainY.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9UGlhknUfFXB",
        "outputId": "fb7ef938-7309-44fb-f28d-5c724bd0966f"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainX shape == (136, 14, 10).\n",
            "trainY shape == (136, 1).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_for_training_scaled"
      ],
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        "outputId": "02d41285-7edb-43e8-ee28-aa6b139bae9b"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "array([[ 0.25220378,  1.41884066,  1.28105593, ...,  1.83696687,\n",
              "        -0.33248576, -2.39597646],\n",
              "       [-0.59414981,  1.34029146,  0.61107448, ...,  0.87868441,\n",
              "        -0.12707445, -2.14167174],\n",
              "       [-0.37460332,  0.25910443, -0.0688005 , ...,  0.38398725,\n",
              "         1.55028652, -1.97752193],\n",
              "       ...,\n",
              "       [-1.25021177,  0.92718225, -0.18122825, ..., -0.68080352,\n",
              "         1.33922622,  0.45098837],\n",
              "       [ 0.95077629,  0.38434975,  0.98211698, ..., -0.68080352,\n",
              "        -0.14949387,  0.29755242],\n",
              "       [ 0.68338604,  0.6752647 ,  1.01512818, ..., -0.68080352,\n",
              "         0.83872591,  0.20597296]])"
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          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainY"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A4HvqbAkv1ZW",
        "outputId": "b69f3be0-af64-485a-849e-d424961027e7"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
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          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(64, activation='relu', input_shape=(trainX.shape[1], trainX.shape[2]), return_sequences=True)) \n",
        "model.add(LSTM(32, activation='relu', return_sequences=False))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(trainY.shape[1]))\n",
        "\n",
        "model.compile(optimizer='adam', loss='mse')\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xLJ_fEZ8foI3",
        "outputId": "a4138f82-0f79-458f-8c18-c4126b05d90a"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_6 (LSTM)               (None, 14, 64)            19200     \n",
            "                                                                 \n",
            " lstm_7 (LSTM)               (None, 32)                12416     \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 32)                0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 33        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 31,649\n",
            "Trainable params: 31,649\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(trainX, trainY, epochs=5, batch_size=3, validation_split=0.1, verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gR-gELFqhCt3",
        "outputId": "134a260b-7c78-4e3d-8c6d-6ffbb692ca70"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "41/41 [==============================] - 4s 24ms/step - loss: 0.4086 - val_loss: 0.0353\n",
            "Epoch 2/5\n",
            "41/41 [==============================] - 1s 14ms/step - loss: 0.1006 - val_loss: 0.0353\n",
            "Epoch 3/5\n",
            "41/41 [==============================] - 1s 15ms/step - loss: 0.0535 - val_loss: 0.0389\n",
            "Epoch 4/5\n",
            "41/41 [==============================] - 1s 18ms/step - loss: 0.0550 - val_loss: 0.0463\n",
            "Epoch 5/5\n",
            "41/41 [==============================] - 1s 23ms/step - loss: 0.0555 - val_loss: 0.0403\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'], label='Training loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation loss')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "ReHWBQ1VhKzd",
        "outputId": "d9542245-0cee-49fc-a24f-e9bb490b4492"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f35cea5d870>"
            ]
          },
          "metadata": {},
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.tseries.holiday import USFederalHolidayCalendar\n",
        "from pandas.tseries.offsets import CustomBusinessDay\n",
        "us_bd = CustomBusinessDay(calendar=USFederalHolidayCalendar())"
      ],
      "metadata": {
        "id": "ncph6lCWjfQ_"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2= df.head(150) #Since each stock has 150 entries. So we can not take more than 150.. less than 150 would give less efficiency\n",
        "train_dates = pd.to_datetime(df2['Date'])\n",
        "print(train_dates.tail(15)) \n",
        "# df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DSZrHFBYj7iK",
        "outputId": "c2339fd0-70f1-4c4e-bdb0-92391959b48a"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "135   2017-10-31\n",
            "136   2017-11-30\n",
            "137   2017-12-29\n",
            "138   2018-01-31\n",
            "139   2018-02-28\n",
            "140   2018-03-30\n",
            "141   2018-04-30\n",
            "142   2018-05-31\n",
            "143   2018-06-29\n",
            "144   2018-07-31\n",
            "145   2018-08-31\n",
            "146   2018-09-28\n",
            "147   2018-10-31\n",
            "148   2018-11-30\n",
            "149   2018-12-31\n",
            "Name: Date, dtype: datetime64[ns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_past = 150\n",
        "n_days_for_prediction= 136 \n",
        "\n",
        "predict_period_dates = pd.date_range(list(train_dates)[-n_past], periods=n_days_for_prediction, freq='M').tolist()\n",
        "print(predict_period_dates)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yAZn0dxejZ-b",
        "outputId": "6178166b-fea3-4eb4-f1d1-960c62398093"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Timestamp('2006-07-31 00:00:00', freq='M'), Timestamp('2006-08-31 00:00:00', freq='M'), Timestamp('2006-09-30 00:00:00', freq='M'), Timestamp('2006-10-31 00:00:00', freq='M'), Timestamp('2006-11-30 00:00:00', freq='M'), Timestamp('2006-12-31 00:00:00', freq='M'), Timestamp('2007-01-31 00:00:00', freq='M'), Timestamp('2007-02-28 00:00:00', freq='M'), Timestamp('2007-03-31 00:00:00', freq='M'), Timestamp('2007-04-30 00:00:00', freq='M'), Timestamp('2007-05-31 00:00:00', freq='M'), Timestamp('2007-06-30 00:00:00', freq='M'), Timestamp('2007-07-31 00:00:00', freq='M'), Timestamp('2007-08-31 00:00:00', freq='M'), Timestamp('2007-09-30 00:00:00', freq='M'), Timestamp('2007-10-31 00:00:00', freq='M'), Timestamp('2007-11-30 00:00:00', freq='M'), Timestamp('2007-12-31 00:00:00', freq='M'), Timestamp('2008-01-31 00:00:00', freq='M'), Timestamp('2008-02-29 00:00:00', freq='M'), Timestamp('2008-03-31 00:00:00', freq='M'), Timestamp('2008-04-30 00:00:00', freq='M'), 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        "prediction = model.predict(trainX[-n_days_for_prediction:]) "
      ],
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      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 0s 8ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_copies = np.repeat(prediction, df_for_training.shape[1], axis=-1)\n",
        "y_pred_future = scaler.inverse_transform(prediction_copies)[:,9]"
      ],
      "metadata": {
        "id": "A68cGGUlkLp7"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
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      "source": [
        "forecast_dates = []\n",
        "for time_i in predict_period_dates:\n",
        "    forecast_dates.append(time_i.date())\n",
        "    \n",
        "df_forecast = pd.DataFrame({'Date':np.array(forecast_dates), 'price':y_pred_future})\n",
        "df_forecast['Date']=pd.to_datetime(df_forecast['Date'])"
      ],
      "metadata": {
        "id": "RRg0e30rkPIK"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "original = df[['Date', 'price']]\n",
        "original['Date']=pd.to_datetime(original['Date'])\n",
        "original\n"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      "execution_count": 107,
      "outputs": [
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          "text": [
            "<ipython-input-107-20f60c629b61>:2: 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",
            "  original['Date']=pd.to_datetime(original['Date'])\n"
          ]
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              "3   2006-10-31  104.478956\n",
              "4   2006-11-30  104.160907\n",
              "..         ...         ...\n",
              "145 2018-08-31  120.895608\n",
              "146 2018-09-28  120.817216\n",
              "147 2018-10-31  121.541253\n",
              "148 2018-11-30  120.380297\n",
              "149 2018-12-31  119.687371\n",
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              "4   2006-11-30  108.933769\n",
              "..         ...         ...\n",
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              "133 2017-08-31  122.590515\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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      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-96a29864-c225-416a-86c0-c115b88fad24 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-96a29864-c225-416a-86c0-c115b88fad24');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "original.plot(x='Date',y='price')\n",
        "original= pd.concat([original,df_forecast],axis=0)\n",
        "original.plot(x='Date',y='price')\n",
        "#f_forecast.plot(x='Date',y='price')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 871
        },
        "id": "RLBZ0Dddla_5",
        "outputId": "5ab51298-3938-4a18-bee7-a713bab9dc8e"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 109
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def rmse(predictions, targets):\n",
        "    return np.sqrt(((((predictions - targets)) ** 2)).mean())\n",
        "def mae(predictions, targets):\n",
        "  return (abs(predictions - targets)).mean()\n",
        "def mape(predictions, targets):\n",
        "  return ((abs(predictions - targets)/targets)*100).mean()"
      ],
      "metadata": {
        "id": "VRRck0fWDvc4"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_forecast['price']-original['price'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mE2Ytc-yzfYD",
        "outputId": "ebeb67cd-735a-495e-d43d-3f985eef3944"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0      10.658012\n",
            "0       0.000000\n",
            "1       8.421366\n",
            "1       0.000000\n",
            "2       6.910713\n",
            "         ...    \n",
            "145          NaN\n",
            "146          NaN\n",
            "147          NaN\n",
            "148          NaN\n",
            "149          NaN\n",
            "Name: price, Length: 286, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rmse_error =rmse(df_forecast['price'],original['price'])\n",
        "mae_error = mae(df_forecast['price'],original['price'])\n",
        "mape_error = mape(df_forecast['price'],original['price'])\n",
        "print(\"rmse error is:\" , rmse_error)\n",
        "print(\"mae error is:\" , mae_error)\n",
        "print(\"mape error is:\" , mape_error)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Mp1c2pqDyUj",
        "outputId": "43fa658a-9669-48d2-f4c1-5809e7491d43"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rmse error is: 2.808242077693432\n",
            "mae error is: 1.583980014178664\n",
            "mape error is: 1.3758097152887485\n"
          ]
        }
      ]
    }
  ]
}