Netflix_Stock_Prediction.ipynb

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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ofnGsD34thig"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "2tX5Ske8uFqv",
        "outputId": "5f6a57b4-f984-4451-dd1f-95bc9ade1bf4"
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/netflix.csv'\n",
        "df = pd.read_csv(url,error_bad_lines=False)\n",
        "df.head()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Date</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2002-05-23</td>\n",
              "      <td>1.242857</td>\n",
              "      <td>1.145714</td>\n",
              "      <td>1.156429</td>\n",
              "      <td>1.196429</td>\n",
              "      <td>104790000.0</td>\n",
              "      <td>1.196429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2002-05-24</td>\n",
              "      <td>1.225000</td>\n",
              "      <td>1.197143</td>\n",
              "      <td>1.214286</td>\n",
              "      <td>1.210000</td>\n",
              "      <td>11104800.0</td>\n",
              "      <td>1.210000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2002-05-28</td>\n",
              "      <td>1.232143</td>\n",
              "      <td>1.157143</td>\n",
              "      <td>1.213571</td>\n",
              "      <td>1.157143</td>\n",
              "      <td>6609400.0</td>\n",
              "      <td>1.157143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2002-05-29</td>\n",
              "      <td>1.164286</td>\n",
              "      <td>1.085714</td>\n",
              "      <td>1.164286</td>\n",
              "      <td>1.103571</td>\n",
              "      <td>6757800.0</td>\n",
              "      <td>1.103571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2002-05-30</td>\n",
              "      <td>1.107857</td>\n",
              "      <td>1.071429</td>\n",
              "      <td>1.107857</td>\n",
              "      <td>1.071429</td>\n",
              "      <td>10154200.0</td>\n",
              "      <td>1.071429</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date      High       Low      Open     Close       Volume  Adj Close\n",
              "0  2002-05-23  1.242857  1.145714  1.156429  1.196429  104790000.0   1.196429\n",
              "1  2002-05-24  1.225000  1.197143  1.214286  1.210000   11104800.0   1.210000\n",
              "2  2002-05-28  1.232143  1.157143  1.213571  1.157143    6609400.0   1.157143\n",
              "3  2002-05-29  1.164286  1.085714  1.164286  1.103571    6757800.0   1.103571\n",
              "4  2002-05-30  1.107857  1.071429  1.107857  1.071429   10154200.0   1.071429"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QtNdgAaX3gHn"
      },
      "source": [
        "## 1. Close Value of Netflix Stocks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "gIMwD33cuOfn",
        "outputId": "f9076bd6-1ce8-4e5a-dd2e-857db2625183"
      },
      "source": [
        "df1 = df[['Date','Close']]\n",
        "df1.head()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Date</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2002-05-23</td>\n",
              "      <td>1.196429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2002-05-24</td>\n",
              "      <td>1.210000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2002-05-28</td>\n",
              "      <td>1.157143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2002-05-29</td>\n",
              "      <td>1.103571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2002-05-30</td>\n",
              "      <td>1.071429</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date     Close\n",
              "0  2002-05-23  1.196429\n",
              "1  2002-05-24  1.210000\n",
              "2  2002-05-28  1.157143\n",
              "3  2002-05-29  1.103571\n",
              "4  2002-05-30  1.071429"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ohFracGHuWyl",
        "outputId": "95e4c201-a503-4197-879f-f09d33b22d6d"
      },
      "source": [
        "df1.info()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4856 entries, 0 to 4855\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Date    4856 non-null   object \n",
            " 1   Close   4856 non-null   float64\n",
            "dtypes: float64(1), object(1)\n",
            "memory usage: 76.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "inWXJCo9uaGP"
      },
      "source": [
        "from pycaret.internal.pycaret_experiment import TimeSeriesExperiment\n",
        "from sktime.utils.plotting import plot_series   #pycaret includes time series and related plotting tools"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "id": "CmPsCX-zuyja",
        "outputId": "b9189669-bd0f-4044-ece3-ca3303166bdd"
      },
      "source": [
        "df1.index = pd.DatetimeIndex(df1[\"Date\"])\n",
        "df1.drop([\"Date\"],axis=1,inplace=True)   # replace date with the index\n",
        "df1.head()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2002-05-23</th>\n",
              "      <td>1.196429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-05-24</th>\n",
              "      <td>1.210000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-05-28</th>\n",
              "      <td>1.157143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-05-29</th>\n",
              "      <td>1.103571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-05-30</th>\n",
              "      <td>1.071429</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Close\n",
              "Date                \n",
              "2002-05-23  1.196429\n",
              "2002-05-24  1.210000\n",
              "2002-05-28  1.157143\n",
              "2002-05-29  1.103571\n",
              "2002-05-30  1.071429"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hXWdL_lPu_EL",
        "outputId": "56966e53-5620-412a-9008-7fc5b04eaa14"
      },
      "source": [
        "df1 = df1.asfreq('d')   # day-wise frequency of the data\n",
        "df1.index"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2002-05-23', '2002-05-24', '2002-05-25', '2002-05-26',\n",
              "               '2002-05-27', '2002-05-28', '2002-05-29', '2002-05-30',\n",
              "               '2002-05-31', '2002-06-01',\n",
              "               ...\n",
              "               '2021-08-25', '2021-08-26', '2021-08-27', '2021-08-28',\n",
              "               '2021-08-29', '2021-08-30', '2021-08-31', '2021-09-01',\n",
              "               '2021-09-02', '2021-09-03'],\n",
              "              dtype='datetime64[ns]', name='Date', length=7044, freq='D')"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "id": "0wtZw181vEqv",
        "outputId": "996f25b9-5b6a-4f55-e181-f99ad9d7a42c"
      },
      "source": [
        "df1=df1.fillna(method='ffill')  # we apply forward fill for time series data\n",
        "df1.head()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2002-05-23</th>\n",
              "      <td>1.196429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-05-24</th>\n",
              "      <td>1.210000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-05-25</th>\n",
              "      <td>1.210000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-05-26</th>\n",
              "      <td>1.210000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-05-27</th>\n",
              "      <td>1.210000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Close\n",
              "Date                \n",
              "2002-05-23  1.196429\n",
              "2002-05-24  1.210000\n",
              "2002-05-25  1.210000\n",
              "2002-05-26  1.210000\n",
              "2002-05-27  1.210000"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XOs_mByJvL3Z",
        "outputId": "cc61485c-5da1-4eea-e96b-b6a5cd4b1345"
      },
      "source": [
        "data=df1.Close   \n",
        "data"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2002-05-23      1.196429\n",
              "2002-05-24      1.210000\n",
              "2002-05-25      1.210000\n",
              "2002-05-26      1.210000\n",
              "2002-05-27      1.210000\n",
              "                 ...    \n",
              "2021-08-30    566.179993\n",
              "2021-08-31    569.190002\n",
              "2021-09-01    582.070007\n",
              "2021-09-02    588.549988\n",
              "2021-09-03    589.520081\n",
              "Freq: D, Name: Close, Length: 7044, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "uvG8hmF21Rha",
        "outputId": "5775bcbc-665a-4f07-d0b2-c6ef2d2cd50b"
      },
      "source": [
        "pl=plot_series(data)\n",
        "plt.xticks(rotation=90)\n",
        "plt.grid()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "1_XYQMj-1XFW",
        "outputId": "65bf32fe-d31d-4666-fc15-8540d336854e"
      },
      "source": [
        "exp = TimeSeriesExperiment()\n",
        "exp.setup(data=data,session_id=42,fh=365)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>Description</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>session_id</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Original Data</td>\n",
              "      <td>(7044, 1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Missing Values</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Transformed Train Set</td>\n",
              "      <td>(6679,)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Transformed Test Set</td>\n",
              "      <td>(365,)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Fold Generator</td>\n",
              "      <td>ExpandingWindowSplitter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Fold Number</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>CPU Jobs</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Use GPU</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Log Experiment</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Experiment Name</td>\n",
              "      <td>ts-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>USI</td>\n",
              "      <td>5c83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Imputation Type</td>\n",
              "      <td>simple</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Description                    Value\n",
              "0              session_id                       42\n",
              "1           Original Data                (7044, 1)\n",
              "2          Missing Values                    False\n",
              "3   Transformed Train Set                  (6679,)\n",
              "4    Transformed Test Set                   (365,)\n",
              "5          Fold Generator  ExpandingWindowSplitter\n",
              "6             Fold Number                        3\n",
              "7                CPU Jobs                       -1\n",
              "8                 Use GPU                    False\n",
              "9          Log Experiment                    False\n",
              "10        Experiment Name          ts-default-name\n",
              "11                    USI                     5c83\n",
              "12        Imputation Type                   simple"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pycaret.internal.pycaret_experiment.time_series_experiment.TimeSeriesExperiment at 0x7f2b0a129350>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269,
          "referenced_widgets": [
            "ac58d1eaf5fb421c8ee1cf10b3c9ea1b",
            "b59aa2b3038a46fdba46a5634dbf209c",
            "443afce0748e4739b5f77d23ee6eb22c"
          ]
        },
        "id": "ahunrZZV1f7K",
        "outputId": "1c0e9d7c-b670-4d44-9254-51b0bf9cb58a"
      },
      "source": [
        "model = exp.create_model(\"arima\")\n",
        "model"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>cutoff</th>\n",
              "      <th>MAE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>SMAPE</th>\n",
              "      <th>R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017-09-04</td>\n",
              "      <td>92.8573</td>\n",
              "      <td>13750.4568</td>\n",
              "      <td>0.2831</td>\n",
              "      <td>0.3571</td>\n",
              "      <td>-1.4474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-09-04</td>\n",
              "      <td>40.4565</td>\n",
              "      <td>2817.6045</td>\n",
              "      <td>0.1348</td>\n",
              "      <td>0.1197</td>\n",
              "      <td>-1.1677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-09-04</td>\n",
              "      <td>81.8853</td>\n",
              "      <td>11251.4573</td>\n",
              "      <td>0.1906</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>-0.8430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>71.7331</td>\n",
              "      <td>9273.1729</td>\n",
              "      <td>0.2028</td>\n",
              "      <td>0.2328</td>\n",
              "      <td>-1.1527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>NaN</td>\n",
              "      <td>22.5649</td>\n",
              "      <td>4677.3940</td>\n",
              "      <td>0.0611</td>\n",
              "      <td>0.0972</td>\n",
              "      <td>0.2469</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          cutoff      MAE        RMSE    MAPE   SMAPE      R2\n",
              "0     2017-09-04  92.8573  13750.4568  0.2831  0.3571 -1.4474\n",
              "1     2018-09-04  40.4565   2817.6045  0.1348  0.1197 -1.1677\n",
              "2     2019-09-04  81.8853  11251.4573  0.1906  0.2216 -0.8430\n",
              "Mean         NaN  71.7331   9273.1729  0.2028  0.2328 -1.1527\n",
              "SD           NaN  22.5649   4677.3940  0.0611  0.0972  0.2469"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ARIMA(maxiter=50, method='lbfgs', order=(1, 0, 0), out_of_sample_size=0,\n",
              "      scoring='mse', scoring_args=None, seasonal_order=(0, 1, 0, 7),\n",
              "      start_params=None, suppress_warnings=False, trend=None,\n",
              "      with_intercept=True)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k4c-8OtQ1k5q",
        "outputId": "46e24a6b-8f9d-409d-bcb7-77ffa5e5f83f"
      },
      "source": [
        "y_train = exp.get_config(\"y_train\")\n",
        "y_train[:5]"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2002-05-23    1.196429\n",
              "2002-05-24    1.210000\n",
              "2002-05-25    1.210000\n",
              "2002-05-26    1.210000\n",
              "2002-05-27    1.210000\n",
              "Freq: D, Name: Close, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "7SDQ1FHb1rPy",
        "outputId": "016f1805-3df8-4666-e6f4-a70c8e0b7b50"
      },
      "source": [
        "y_predict = exp.predict_model(model)\n",
        "plot_series(data, y_train, y_predict, labels=['All', 'Train', 'Test Predictions'])\n",
        "plt.grid()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>MAE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>SMAPE</th>\n",
              "      <th>R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ARIMA</td>\n",
              "      <td>34.4321</td>\n",
              "      <td>40.807</td>\n",
              "      <td>0.0682</td>\n",
              "      <td>0.0651</td>\n",
              "      <td>-1.7227</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Model      MAE    RMSE    MAPE   SMAPE      R2\n",
              "0  ARIMA  34.4321  40.807  0.0682  0.0651 -1.7227"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269,
          "referenced_widgets": [
            "5681e3e4b1f2463f95674498757ffcab",
            "d426c8926abd4e4db8ace5b0c9d721d1",
            "9989362a8db44d1ab0cbcabf87457a7f"
          ]
        },
        "id": "34BJ6REa12dq",
        "outputId": "0c1c250e-ddd1-47bf-f739-eba9d535894c"
      },
      "source": [
        "tuned_model = exp.tune_model(model)\n",
        "tuned_model"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>cutoff</th>\n",
              "      <th>MAE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>SMAPE</th>\n",
              "      <th>R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017-09-04</td>\n",
              "      <td>98.3421</td>\n",
              "      <td>14839.8016</td>\n",
              "      <td>0.3039</td>\n",
              "      <td>0.3853</td>\n",
              "      <td>-1.6413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-09-04</td>\n",
              "      <td>40.7548</td>\n",
              "      <td>2849.0392</td>\n",
              "      <td>0.1357</td>\n",
              "      <td>0.1205</td>\n",
              "      <td>-1.1919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-09-04</td>\n",
              "      <td>82.6572</td>\n",
              "      <td>11443.8804</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.2241</td>\n",
              "      <td>-0.8745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>73.9180</td>\n",
              "      <td>9710.9071</td>\n",
              "      <td>0.2107</td>\n",
              "      <td>0.2433</td>\n",
              "      <td>-1.2359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>NaN</td>\n",
              "      <td>24.3085</td>\n",
              "      <td>5046.2523</td>\n",
              "      <td>0.0699</td>\n",
              "      <td>0.1090</td>\n",
              "      <td>0.3145</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          cutoff      MAE        RMSE    MAPE   SMAPE      R2\n",
              "0     2017-09-04  98.3421  14839.8016  0.3039  0.3853 -1.6413\n",
              "1     2018-09-04  40.7548   2849.0392  0.1357  0.1205 -1.1919\n",
              "2     2019-09-04  82.6572  11443.8804  0.1924  0.2241 -0.8745\n",
              "Mean         NaN  73.9180   9710.9071  0.2107  0.2433 -1.2359\n",
              "SD           NaN  24.3085   5046.2523  0.0699  0.1090  0.3145"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ARIMA(maxiter=50, method='lbfgs', order=(0, 1, 1), out_of_sample_size=0,\n",
              "      scoring='mse', scoring_args=None, seasonal_order=(1, 0, 0, 14),\n",
              "      start_params=None, suppress_warnings=False, trend=None,\n",
              "      with_intercept=True)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "8SGZVDgF16xi",
        "outputId": "af162d13-0554-458e-b4ba-0c0c01a4dffa"
      },
      "source": [
        "exp.predict_model(tuned_model)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>MAE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>SMAPE</th>\n",
              "      <th>R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ARIMA</td>\n",
              "      <td>28.4813</td>\n",
              "      <td>33.3711</td>\n",
              "      <td>0.0565</td>\n",
              "      <td>0.0545</td>\n",
              "      <td>-0.8209</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Model      MAE     RMSE    MAPE   SMAPE      R2\n",
              "0  ARIMA  28.4813  33.3711  0.0565  0.0545 -0.8209"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2020-09-04    527.8955\n",
              "2020-09-05    527.9777\n",
              "2020-09-06    528.0599\n",
              "2020-09-07    528.2857\n",
              "2020-09-08    528.2952\n",
              "                ...   \n",
              "2021-08-30    554.9593\n",
              "2021-08-31    555.0382\n",
              "2021-09-01    555.1172\n",
              "2021-09-02    555.1961\n",
              "2021-09-03    555.2751\n",
              "Freq: D, Length: 365, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "YJ785kyR2j3Z",
        "outputId": "376b8c4b-9276-49af-8bf4-ca057387b190"
      },
      "source": [
        "y_pred = exp.predict_model(tuned_model)\n",
        "\n",
        "# Finalize Model\n",
        "final_model = exp.finalize_model(tuned_model)\n",
        "\n",
        "# Future Predictions\n",
        "y_pred_future = exp.predict_model(final_model, verbose=False)\n",
        "\n",
        "# Plot Results\n",
        "y_train = exp.get_config(\"y_train\")\n",
        "_ = plot_series(data, y_train, y_pred, y_pred_future, labels=['All', 'Train', 'Test Predictions', 'Future Pred (Unknown)'])\n",
        "plt.grid()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>MAE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>SMAPE</th>\n",
              "      <th>R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ARIMA</td>\n",
              "      <td>28.4813</td>\n",
              "      <td>33.3711</td>\n",
              "      <td>0.0565</td>\n",
              "      <td>0.0545</td>\n",
              "      <td>-0.8209</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Model      MAE     RMSE    MAPE   SMAPE      R2\n",
              "0  ARIMA  28.4813  33.3711  0.0565  0.0545 -0.8209"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "npb__lNy5P9i"
      },
      "source": [
        "## Open Values of Netflix Stocks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "LZA1eE9s2pXL",
        "outputId": "83f0f2f6-e9e1-410f-e29f-e6280f6384ce"
      },
      "source": [
        "df2 = df[['Date','Open']]\n",
        "df2.head()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Date</th>\n",
              "      <th>Open</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2002-05-23</td>\n",
              "      <td>1.156429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2002-05-24</td>\n",
              "      <td>1.214286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2002-05-28</td>\n",
              "      <td>1.213571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2002-05-29</td>\n",
              "      <td>1.164286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2002-05-30</td>\n",
              "      <td>1.107857</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date      Open\n",
              "0  2002-05-23  1.156429\n",
              "1  2002-05-24  1.214286\n",
              "2  2002-05-28  1.213571\n",
              "3  2002-05-29  1.164286\n",
              "4  2002-05-30  1.107857"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FbqGUHIc3pls",
        "outputId": "b9774087-7489-4acc-dfd4-5fbfa45c8c6d"
      },
      "source": [
        "df2.info()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4856 entries, 0 to 4855\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Date    4856 non-null   object \n",
            " 1   Open    4856 non-null   float64\n",
            "dtypes: float64(1), object(1)\n",
            "memory usage: 76.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "id": "3N7aTxxL32D2",
        "outputId": "14a8dcea-79f4-41e4-9634-a95a827a9a7e"
      },
      "source": [
        "df2.index = pd.DatetimeIndex(df2[\"Date\"])\n",
        "df2.drop([\"Date\"],axis=1,inplace=True)   # replace date with the index\n",
        "df2.head()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Open</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2002-05-23</th>\n",
              "      <td>1.156429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-05-24</th>\n",
              "      <td>1.214286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-05-28</th>\n",
              "      <td>1.213571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-05-29</th>\n",
              "      <td>1.164286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-05-30</th>\n",
              "      <td>1.107857</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Open\n",
              "Date                \n",
              "2002-05-23  1.156429\n",
              "2002-05-24  1.214286\n",
              "2002-05-28  1.213571\n",
              "2002-05-29  1.164286\n",
              "2002-05-30  1.107857"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gkwpuj0-3_Ls",
        "outputId": "30cb8cdb-60fb-4959-a607-93cd8201d675"
      },
      "source": [
        "df2 = df2.asfreq('d')   # day-wise frequency of the data\n",
        "df2.index"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2002-05-23', '2002-05-24', '2002-05-25', '2002-05-26',\n",
              "               '2002-05-27', '2002-05-28', '2002-05-29', '2002-05-30',\n",
              "               '2002-05-31', '2002-06-01',\n",
              "               ...\n",
              "               '2021-08-25', '2021-08-26', '2021-08-27', '2021-08-28',\n",
              "               '2021-08-29', '2021-08-30', '2021-08-31', '2021-09-01',\n",
              "               '2021-09-02', '2021-09-03'],\n",
              "              dtype='datetime64[ns]', name='Date', length=7044, freq='D')"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "id": "0IpavWmO5l50",
        "outputId": "58116e16-a44b-40d4-878e-ca0be4d594e8"
      },
      "source": [
        "df2=df2.fillna(method='ffill')  # we apply forward fill for time series data\n",
        "df2.head()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Open</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2002-05-23</th>\n",
              "      <td>1.156429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-05-24</th>\n",
              "      <td>1.214286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-05-25</th>\n",
              "      <td>1.214286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-05-26</th>\n",
              "      <td>1.214286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-05-27</th>\n",
              "      <td>1.214286</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Open\n",
              "Date                \n",
              "2002-05-23  1.156429\n",
              "2002-05-24  1.214286\n",
              "2002-05-25  1.214286\n",
              "2002-05-26  1.214286\n",
              "2002-05-27  1.214286"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6cS8Nlvw5rO4",
        "outputId": "a16aad16-fdcf-4ab7-ded4-d114dc062e75"
      },
      "source": [
        "data1=df2.Open \n",
        "data1"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2002-05-23      1.156429\n",
              "2002-05-24      1.214286\n",
              "2002-05-25      1.214286\n",
              "2002-05-26      1.214286\n",
              "2002-05-27      1.214286\n",
              "                 ...    \n",
              "2021-08-30    557.250000\n",
              "2021-08-31    566.119995\n",
              "2021-09-01    569.000000\n",
              "2021-09-02    583.679993\n",
              "2021-09-03    585.799988\n",
              "Freq: D, Name: Open, Length: 7044, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "w5Oc9eoN5Idk",
        "outputId": "21f92824-e862-4644-a944-ee0898d034f8"
      },
      "source": [
        "open=plot_series(data1)\n",
        "plt.xticks(rotation=90)\n",
        "plt.grid()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "3U-SO28S5VlC",
        "outputId": "de8983ea-7c5b-410d-85b3-0f12e2eb3f5d"
      },
      "source": [
        "exp = TimeSeriesExperiment()\n",
        "exp.setup(data=data1,session_id=42,fh=365)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>Description</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>session_id</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Original Data</td>\n",
              "      <td>(7044, 1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Missing Values</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Transformed Train Set</td>\n",
              "      <td>(6679,)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Transformed Test Set</td>\n",
              "      <td>(365,)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Fold Generator</td>\n",
              "      <td>ExpandingWindowSplitter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Fold Number</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>CPU Jobs</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Use GPU</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Log Experiment</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Experiment Name</td>\n",
              "      <td>ts-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>USI</td>\n",
              "      <td>d6d8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Imputation Type</td>\n",
              "      <td>simple</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Description                    Value\n",
              "0              session_id                       42\n",
              "1           Original Data                (7044, 1)\n",
              "2          Missing Values                    False\n",
              "3   Transformed Train Set                  (6679,)\n",
              "4    Transformed Test Set                   (365,)\n",
              "5          Fold Generator  ExpandingWindowSplitter\n",
              "6             Fold Number                        3\n",
              "7                CPU Jobs                       -1\n",
              "8                 Use GPU                    False\n",
              "9          Log Experiment                    False\n",
              "10        Experiment Name          ts-default-name\n",
              "11                    USI                     d6d8\n",
              "12        Imputation Type                   simple"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pycaret.internal.pycaret_experiment.time_series_experiment.TimeSeriesExperiment at 0x7f2b2df7d550>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269,
          "referenced_widgets": [
            "95b940c42d094af0921355a5e34e1a9a",
            "0024b938194344e682ae41451720012b",
            "2f8aa8a8ef2d4278b1587ecb18cf29db"
          ]
        },
        "id": "UHxJkd7L53KA",
        "outputId": "e18d51d6-bed4-4884-c281-e58d188ee463"
      },
      "source": [
        "model = exp.create_model(\"arima\")\n",
        "model"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>cutoff</th>\n",
              "      <th>MAE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>SMAPE</th>\n",
              "      <th>R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017-09-04</td>\n",
              "      <td>92.8828</td>\n",
              "      <td>13802.4129</td>\n",
              "      <td>0.2831</td>\n",
              "      <td>0.3573</td>\n",
              "      <td>-1.4385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-09-04</td>\n",
              "      <td>44.5433</td>\n",
              "      <td>3177.0334</td>\n",
              "      <td>0.1465</td>\n",
              "      <td>0.1300</td>\n",
              "      <td>-1.4911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-09-04</td>\n",
              "      <td>78.9054</td>\n",
              "      <td>10497.3618</td>\n",
              "      <td>0.1842</td>\n",
              "      <td>0.2126</td>\n",
              "      <td>-0.7283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>72.1105</td>\n",
              "      <td>9158.9360</td>\n",
              "      <td>0.2046</td>\n",
              "      <td>0.2333</td>\n",
              "      <td>-1.2193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>NaN</td>\n",
              "      <td>20.3110</td>\n",
              "      <td>4439.8356</td>\n",
              "      <td>0.0576</td>\n",
              "      <td>0.0940</td>\n",
              "      <td>0.3479</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          cutoff      MAE        RMSE    MAPE   SMAPE      R2\n",
              "0     2017-09-04  92.8828  13802.4129  0.2831  0.3573 -1.4385\n",
              "1     2018-09-04  44.5433   3177.0334  0.1465  0.1300 -1.4911\n",
              "2     2019-09-04  78.9054  10497.3618  0.1842  0.2126 -0.7283\n",
              "Mean         NaN  72.1105   9158.9360  0.2046  0.2333 -1.2193\n",
              "SD           NaN  20.3110   4439.8356  0.0576  0.0940  0.3479"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ARIMA(maxiter=50, method='lbfgs', order=(1, 0, 0), out_of_sample_size=0,\n",
              "      scoring='mse', scoring_args=None, seasonal_order=(0, 1, 0, 7),\n",
              "      start_params=None, suppress_warnings=False, trend=None,\n",
              "      with_intercept=True)"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-WNOhsY58YX",
        "outputId": "961c6b15-3d39-4082-967b-1137f2f93f64"
      },
      "source": [
        "y_train = exp.get_config(\"y_train\")\n",
        "y_train[:5]"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2002-05-23    1.156429\n",
              "2002-05-24    1.214286\n",
              "2002-05-25    1.214286\n",
              "2002-05-26    1.214286\n",
              "2002-05-27    1.214286\n",
              "Freq: D, Name: Open, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "YX_BRiF76C85",
        "outputId": "b92b59b9-1657-4364-83f3-25ebd0b0d347"
      },
      "source": [
        "y_predict = exp.predict_model(model)\n",
        "plot_series(data1, y_train, y_predict, labels=['All', 'Train', 'Test Predictions'])\n",
        "plt.grid()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>MAE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>SMAPE</th>\n",
              "      <th>R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ARIMA</td>\n",
              "      <td>39.6341</td>\n",
              "      <td>45.4377</td>\n",
              "      <td>0.0785</td>\n",
              "      <td>0.0746</td>\n",
              "      <td>-2.3974</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Model      MAE     RMSE    MAPE   SMAPE      R2\n",
              "0  ARIMA  39.6341  45.4377  0.0785  0.0746 -2.3974"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269,
          "referenced_widgets": [
            "9ea81800504f44d79b4f6adf7ef7bba3",
            "6607db82b1a54c34a09b875f286d4ee5",
            "51da4c81b8dc4be3bd5695093822ba02"
          ]
        },
        "id": "8mwsLtJB6Klg",
        "outputId": "e3ebd65a-31b8-4e26-cabc-331c600ec140"
      },
      "source": [
        "tuned_model = exp.tune_model(model)\n",
        "tuned_model"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>cutoff</th>\n",
              "      <th>MAE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>SMAPE</th>\n",
              "      <th>R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017-09-04</td>\n",
              "      <td>134.4969</td>\n",
              "      <td>23203.9657</td>\n",
              "      <td>0.4634</td>\n",
              "      <td>0.3652</td>\n",
              "      <td>-3.0995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-09-04</td>\n",
              "      <td>32.9113</td>\n",
              "      <td>2135.9365</td>\n",
              "      <td>0.1107</td>\n",
              "      <td>0.0991</td>\n",
              "      <td>-0.6748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-09-04</td>\n",
              "      <td>55.6068</td>\n",
              "      <td>5353.9851</td>\n",
              "      <td>0.1311</td>\n",
              "      <td>0.1435</td>\n",
              "      <td>0.1185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>74.3383</td>\n",
              "      <td>10231.2958</td>\n",
              "      <td>0.2351</td>\n",
              "      <td>0.2026</td>\n",
              "      <td>-1.2186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>NaN</td>\n",
              "      <td>43.5359</td>\n",
              "      <td>9266.6637</td>\n",
              "      <td>0.1617</td>\n",
              "      <td>0.1164</td>\n",
              "      <td>1.3689</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          cutoff       MAE        RMSE    MAPE   SMAPE      R2\n",
              "0     2017-09-04  134.4969  23203.9657  0.4634  0.3652 -3.0995\n",
              "1     2018-09-04   32.9113   2135.9365  0.1107  0.0991 -0.6748\n",
              "2     2019-09-04   55.6068   5353.9851  0.1311  0.1435  0.1185\n",
              "Mean         NaN   74.3383  10231.2958  0.2351  0.2026 -1.2186\n",
              "SD           NaN   43.5359   9266.6637  0.1617  0.1164  1.3689"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ARIMA(maxiter=50, method='lbfgs', order=(1, 1, 1), out_of_sample_size=0,\n",
              "      scoring='mse', scoring_args=None, seasonal_order=(0, 1, 0, 7),\n",
              "      start_params=None, suppress_warnings=False, trend=None,\n",
              "      with_intercept=False)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "A38fjSWa6gTM",
        "outputId": "2a9847d4-9f07-4363-a6da-50b3253d68bb"
      },
      "source": [
        "# Finalize Model\n",
        "final_model = exp.finalize_model(tuned_model)\n",
        "# Future Predictions\n",
        "y_pred_future = exp.predict_model(final_model, verbose=False)\n",
        "_=plot_series(data1, y_pred_future, labels=['All', 'Future Pred (Unknown)'])\n",
        "plt.grid()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "1TJDDDw06pHG",
        "outputId": "6eb7b3b0-9d29-4917-8e28-fb92e411a50b"
      },
      "source": [
        "exp.predict_model(tuned_model)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>MAE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>SMAPE</th>\n",
              "      <th>R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ARIMA</td>\n",
              "      <td>351.782</td>\n",
              "      <td>396.865</td>\n",
              "      <td>0.6788</td>\n",
              "      <td>0.4802</td>\n",
              "      <td>-258.1816</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Model      MAE     RMSE    MAPE   SMAPE        R2\n",
              "0  ARIMA  351.782  396.865  0.6788  0.4802 -258.1816"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2020-09-04     544.5239\n",
              "2020-09-05     544.5520\n",
              "2020-09-06     544.5521\n",
              "2020-09-07     533.7121\n",
              "2020-09-08     545.1521\n",
              "                ...    \n",
              "2021-08-30    1173.8695\n",
              "2021-08-31    1185.3095\n",
              "2021-09-01    1206.4896\n",
              "2021-09-02    1197.7095\n",
              "2021-09-03    1197.2335\n",
              "Freq: D, Length: 365, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "JqLad_Sz7TNV",
        "outputId": "61176b8f-0051-49d8-bc3b-269462046893"
      },
      "source": [
        "y_pred = exp.predict_model(tuned_model)\n",
        "\n",
        "# Finalize Model\n",
        "final_model = exp.finalize_model(tuned_model)\n",
        "\n",
        "# Future Predictions\n",
        "y_pred_future = exp.predict_model(final_model, verbose=False)\n",
        "\n",
        "# Plot Results\n",
        "y_train = exp.get_config(\"y_train\")\n",
        "_ = plot_series(data, y_train, y_pred, y_pred_future, labels=['All', 'Train', 'Test Predictions', 'Future Pred (Unknown)'])\n",
        "plt.grid()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>MAE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>SMAPE</th>\n",
              "      <th>R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ARIMA</td>\n",
              "      <td>351.782</td>\n",
              "      <td>396.865</td>\n",
              "      <td>0.6788</td>\n",
              "      <td>0.4802</td>\n",
              "      <td>-258.1816</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Model      MAE     RMSE    MAPE   SMAPE        R2\n",
              "0  ARIMA  351.782  396.865  0.6788  0.4802 -258.1816"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z5TfOtmn7gZ2"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}