Road_Friction_Forecasting.ipynb

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      "metadata": {
        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/Abrar2652/Road-Friction-Forecasting/blob/master/Road_Friction_Forecasting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FWbrw-7EbpLw"
      },
      "source": [
        "# Authors: \n",
        "**Md. Abrar Jahin**\n",
        "\n",
        "*   LinkedIn: https://www.linkedin.com/in/md-abrar-jahin-9a026018b\n",
        "*   Facebook: https://www.facebook.com/\n",
        "*   Github: https://github.com/Abrar2652\n",
        "*   email: abrar.jahin.2652@gmail.com\n",
        "\n",
        "\n",
        "**Andrii Krutsylo**\n",
        "\n",
        "*  Website: https://krutsylo.neocities.org\n",
        "*  email: krutsylo@airmail.cc"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jmzvHUnOkrbD"
      },
      "source": [
        "# Import Libraries and Packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DA6SWGg6czU2"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wso4uVlwlCCD"
      },
      "source": [
        "After importing libraries and packages, we start off by defining a function `transform_to_supervised` that creates desired **lag** *(24 hours in this case)* and **forecasting** features *(1 hour)* of our independent variables concatening with the dataframe and returns the final dataframe.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZSvtdZC0gzd"
      },
      "source": [
        "import os\n",
        "import optuna\n",
        "import pickle\n",
        "import pandas as pd\n",
        "from optuna import Trial\n",
        "from optuna.samplers import TPESampler\n",
        "from sklearn.impute import KNNImputer\n",
        "from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
        "from xgboost import XGBClassifier, XGBRegressor\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.metrics import mean_absolute_error, accuracy_score, balanced_accuracy_score\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "def transform_to_supervised(df,\n",
        "                            previous_steps=1, \n",
        "                            forecast_steps=1,\n",
        "                            dropnan=False):\n",
        "\n",
        "    \"\"\"\n",
        "    https://gist.github.com/monocongo/6e0df19c9dd845f3f465a9a6ccfcef37\n",
        "    \n",
        "    \n",
        "    Transforms a DataFrame containing time series data into a DataFrame\n",
        "    containing data suitable for use as a supervised learning problem.\n",
        "    \n",
        "    Derived from code originally found at \n",
        "    https://machinelearningmastery.com/convert-time-series-supervised-learning-problem-python/\n",
        "    \n",
        "    :param df: pandas DataFrame object containing columns of time series values\n",
        "    :param previous_steps: the number of previous steps that will be included in the\n",
        "                           output DataFrame corresponding to each input column\n",
        "    :param forecast_steps: the number of forecast steps that will be included in the\n",
        "                           output DataFrame corresponding to each input column\n",
        "    :return Pandas DataFrame containing original columns, renamed <orig_name>(t), as well as\n",
        "            columns for previous steps, <orig_name>(t-1) ... <orig_name>(t-n) and columns \n",
        "            for forecast steps, <orig_name>(t+1) ... <orig_name>(t+n)\n",
        "    \"\"\"\n",
        "    \n",
        "    # original column names\n",
        "    col_names = df.columns\n",
        "    \n",
        "    # list of columns and corresponding names we'll build from \n",
        "    # the originals found in the input DataFrame\n",
        "    cols, names = list(), list()\n",
        "\n",
        "    # input sequence (t-n, ... t-1)\n",
        "    # Lag features\n",
        "    for i in range(previous_steps, 0, -1):\n",
        "        cols.append(df.shift(i))\n",
        "        names += [('%s(t-%d)' % (col_name, i)) for col_name in col_names]\n",
        "\n",
        "    # forecast sequence (t, t+1, ... t+n)\n",
        "    for i in range(0, forecast_steps):\n",
        "        cols.append(df.shift(-i))\n",
        "        if i == 0:\n",
        "            names += [('%s(t)' % col_name) for col_name in col_names]\n",
        "        else:\n",
        "            names += [('%s(t+%d)' % (col_name, i)) for col_name in col_names]\n",
        "\n",
        "    # put all the columns together into a single aggregated DataFrame\n",
        "    agg = pd.concat(cols, axis=1)\n",
        "    agg.columns = names\n",
        "\n",
        "    # drop rows with NaN values\n",
        "    if dropnan:\n",
        "        agg.dropna(inplace=True)\n",
        "\n",
        "    return agg\n"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RN0QaeNAnyjS"
      },
      "source": [
        "# Data Collection and Import data\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PEM4qwzTn-4u"
      },
      "source": [
        "The dataset has been collected from the **Smart Road - Winter Road Maintenance Challenge 2021** organized by *UiT The Arctic University of Norway* on Devpost.\n",
        "\n",
        "Dataset download link: https://uitno.app.box.com/s/bch09z27weq0wpcv8dbbc18sxz6cycjt\n",
        "\n",
        "After downloading the `smart_road_measurements.csv` file from the competition page, wehad added extra columns collecting data from the external resources authorized the organizers. The links of the external datasets are:\n",
        "\n",
        "[1] Weather data https://pypi.org/project/wwo-hist/ \n",
        "\n",
        "[2] UV Index data https://pyowm.readthedocs.io/en/latest/v3/uv-api-usage-examples.html "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jyfszC3Gslij"
      },
      "source": [
        "After merging these 3 files together based on the same dates, we finalized our main dataset `smart_road_measurements_new_d_weather.csv` on top of which we will build our model after preprocessing."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bv3p1JcP0v-g"
      },
      "source": [
        "df = pd.read_csv(\"/content/smart_road_measurements_new_d_weather.csv\", header=0)\n",
        "df2 = df.copy()"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "BlDP2wIV0_02",
        "outputId": "3dff76b4-93d1-4dd8-ab53-41ee2dddadc4"
      },
      "source": [
        "df.head(15)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Time(+01:00)</th>\n",
              "      <th>Friction</th>\n",
              "      <th>State</th>\n",
              "      <th>Ta</th>\n",
              "      <th>Tsurf</th>\n",
              "      <th>Water</th>\n",
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              "      <th>maxtempC</th>\n",
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              "      <th>moon_illumination</th>\n",
              "      <th>DewPointC</th>\n",
              "      <th>FeelsLikeC</th>\n",
              "      <th>HeatIndexC</th>\n",
              "      <th>WindChillC</th>\n",
              "      <th>WindGustKmph</th>\n",
              "      <th>cloudcover</th>\n",
              "      <th>humidity</th>\n",
              "      <th>precipMM</th>\n",
              "      <th>pressure</th>\n",
              "      <th>tempC</th>\n",
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              "      <th>winddirDegree</th>\n",
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              "      <td>2021.02.16</td>\n",
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              "      <th>2</th>\n",
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              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>-15</td>\n",
              "      <td>-16</td>\n",
              "      <td>-9</td>\n",
              "      <td>-16</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>07:11:23</td>\n",
              "      <td>0.33</td>\n",
              "      <td>6</td>\n",
              "      <td>-2.4</td>\n",
              "      <td>-6.17</td>\n",
              "      <td>0.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>14</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>-15</td>\n",
              "      <td>-16</td>\n",
              "      <td>-9</td>\n",
              "      <td>-16</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>07:11:24</td>\n",
              "      <td>0.34</td>\n",
              "      <td>6</td>\n",
              "      <td>-2.4</td>\n",
              "      <td>-6.67</td>\n",
              "      <td>0.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>18</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>-15</td>\n",
              "      <td>-16</td>\n",
              "      <td>-9</td>\n",
              "      <td>-16</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>07:11:25</td>\n",
              "      <td>0.34</td>\n",
              "      <td>6</td>\n",
              "      <td>-2.4</td>\n",
              "      <td>-6.77</td>\n",
              "      <td>0.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>22</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>-15</td>\n",
              "      <td>-16</td>\n",
              "      <td>-9</td>\n",
              "      <td>-16</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>07:11:26</td>\n",
              "      <td>0.35</td>\n",
              "      <td>6</td>\n",
              "      <td>-2.4</td>\n",
              "      <td>-6.83</td>\n",
              "      <td>0.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>26</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>-15</td>\n",
              "      <td>-16</td>\n",
              "      <td>-9</td>\n",
              "      <td>-16</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>07:11:27</td>\n",
              "      <td>0.35</td>\n",
              "      <td>6</td>\n",
              "      <td>-2.4</td>\n",
              "      <td>-6.97</td>\n",
              "      <td>0.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>30</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>-15</td>\n",
              "      <td>-16</td>\n",
              "      <td>-9</td>\n",
              "      <td>-16</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>07:11:28</td>\n",
              "      <td>0.35</td>\n",
              "      <td>6</td>\n",
              "      <td>-2.4</td>\n",
              "      <td>-6.99</td>\n",
              "      <td>0.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>34</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>-15</td>\n",
              "      <td>-16</td>\n",
              "      <td>-9</td>\n",
              "      <td>-16</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>07:11:29</td>\n",
              "      <td>0.36</td>\n",
              "      <td>6</td>\n",
              "      <td>-2.4</td>\n",
              "      <td>-7.03</td>\n",
              "      <td>0.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>40</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>-15</td>\n",
              "      <td>-16</td>\n",
              "      <td>-9</td>\n",
              "      <td>-16</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>07:11:30</td>\n",
              "      <td>0.37</td>\n",
              "      <td>6</td>\n",
              "      <td>-2.4</td>\n",
              "      <td>-7.08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>40</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>-15</td>\n",
              "      <td>-16</td>\n",
              "      <td>-9</td>\n",
              "      <td>-16</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>07:11:31</td>\n",
              "      <td>0.39</td>\n",
              "      <td>6</td>\n",
              "      <td>-2.4</td>\n",
              "      <td>-7.15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>40</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>-15</td>\n",
              "      <td>-16</td>\n",
              "      <td>-9</td>\n",
              "      <td>-16</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>07:11:32</td>\n",
              "      <td>0.40</td>\n",
              "      <td>6</td>\n",
              "      <td>-2.4</td>\n",
              "      <td>-7.02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>40</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>-15</td>\n",
              "      <td>-16</td>\n",
              "      <td>-9</td>\n",
              "      <td>-16</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>07:11:33</td>\n",
              "      <td>0.41</td>\n",
              "      <td>6</td>\n",
              "      <td>-2.4</td>\n",
              "      <td>-7.01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>40</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>-15</td>\n",
              "      <td>-16</td>\n",
              "      <td>-9</td>\n",
              "      <td>-16</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date Time(+01:00)  ...  windspeedKmph  UV radiation\n",
              "0   2021.02.16     07:11:19  ...             14           0.2\n",
              "1   2021.02.16     07:11:20  ...             14           0.2\n",
              "2   2021.02.16     07:11:21  ...             14           0.2\n",
              "3   2021.02.16     07:11:22  ...             14           0.2\n",
              "4   2021.02.16     07:11:23  ...             14           0.2\n",
              "5   2021.02.16     07:11:24  ...             14           0.2\n",
              "6   2021.02.16     07:11:25  ...             14           0.2\n",
              "7   2021.02.16     07:11:26  ...             14           0.2\n",
              "8   2021.02.16     07:11:27  ...             14           0.2\n",
              "9   2021.02.16     07:11:28  ...             14           0.2\n",
              "10  2021.02.16     07:11:29  ...             14           0.2\n",
              "11  2021.02.16     07:11:30  ...             14           0.2\n",
              "12  2021.02.16     07:11:31  ...             14           0.2\n",
              "13  2021.02.16     07:11:32  ...             14           0.2\n",
              "14  2021.02.16     07:11:33  ...             14           0.2\n",
              "\n",
              "[15 rows x 29 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P0G82MJs_Rd0"
      },
      "source": [
        "# Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X84aMHcprVQW"
      },
      "source": [
        "Our dataset contains 349613 rows and 29 columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZbAtEeTa1EdD",
        "outputId": "d9ec1b1c-13ea-46f2-deaa-9ac497afdc39"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(349613, 29)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uBfLuVRZ1LcW",
        "outputId": "c4f198ae-db87-4934-c40a-97d45ad42c51"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 349613 entries, 0 to 349612\n",
            "Data columns (total 29 columns):\n",
            " #   Column             Non-Null Count   Dtype  \n",
            "---  ------             --------------   -----  \n",
            " 0   Date               349613 non-null  object \n",
            " 1   Time(+01:00)       349613 non-null  object \n",
            " 2   Friction           349613 non-null  float64\n",
            " 3   State              349613 non-null  int64  \n",
            " 4   Ta                 349613 non-null  float64\n",
            " 5   Tsurf              349613 non-null  float64\n",
            " 6   Water              349613 non-null  float64\n",
            " 7   Height             347863 non-null  float64\n",
            " 8   Distance           349613 non-null  int64  \n",
            " 9   maxtempC           349613 non-null  int64  \n",
            " 10  mintempC           349613 non-null  int64  \n",
            " 11  totalSnow_cm       349613 non-null  float64\n",
            " 12  sunHour            349613 non-null  float64\n",
            " 13  uvIndex            349613 non-null  int64  \n",
            " 14  moon_illumination  349613 non-null  int64  \n",
            " 15  DewPointC          349613 non-null  int64  \n",
            " 16  FeelsLikeC         349613 non-null  int64  \n",
            " 17  HeatIndexC         349613 non-null  int64  \n",
            " 18  WindChillC         349613 non-null  int64  \n",
            " 19  WindGustKmph       349613 non-null  int64  \n",
            " 20  cloudcover         349613 non-null  int64  \n",
            " 21  humidity           349613 non-null  int64  \n",
            " 22  precipMM           349613 non-null  float64\n",
            " 23  pressure           349613 non-null  int64  \n",
            " 24  tempC              349613 non-null  int64  \n",
            " 25  visibility         349613 non-null  int64  \n",
            " 26  winddirDegree      349613 non-null  int64  \n",
            " 27  windspeedKmph      349613 non-null  int64  \n",
            " 28  UV radiation       349613 non-null  float64\n",
            "dtypes: float64(9), int64(18), object(2)\n",
            "memory usage: 77.4+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "1nNMtP5pBEVw",
        "outputId": "5191f67f-347f-4eef-d7b8-dae2f9a44f56"
      },
      "source": [
        "import numpy as np \n",
        "np.random.seed(0)\n",
        "import seaborn as sns\n",
        "sns.set_theme()\n",
        "_ = sns.heatmap(df2.iloc[:,2:11].corr())"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "Xjs2RJiYCgV5",
        "outputId": "0e2b638a-7409-42c2-b3ef-2b7028309c57"
      },
      "source": [
        "_ = sns.heatmap(df2.corr())"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPxRF4yPuaVJ"
      },
      "source": [
        "We want to predict Friction of the road by weather conditions. So,\n",
        "this is a classification task. Every day the car drives on a new route.\n",
        "This means that all 11 days we receive data on new road sections. So, the\n",
        "only link between the road sections is the average weather conditions.\n",
        "\n",
        "\n",
        "This can be achieved by filtering the rows on **Microsoft Excel** for each date and get the total distance covered (the last row on each date because the column is cumulative in nature)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ge53BO25yegF"
      },
      "source": [
        "**Max Distance traveled, Date**\n",
        "\n",
        "42441, 16/02/2021\n",
        "\n",
        "92311, 17/02/2021\n",
        "\n",
        "150216, 18/02/2021\n",
        "\n",
        "39007, 19/02/2021\n",
        "\n",
        "71358, 22/02/2021\n",
        "\n",
        "81999, 23/02/2021\n",
        "\n",
        "55958, 24/02/2021\n",
        "\n",
        "77315, 25/02/2021\n",
        "\n",
        "55647, 26/02/2021\n",
        "\n",
        "61534, 1/03/2021\n",
        "\n",
        "12409, 2/03/2021\n",
        "\n",
        "\n",
        "**Therefore, we can see from the above data that for all 11 days the car was driving at different routes**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1-ZAnELFyOvI"
      },
      "source": [
        "* We drop the `Distance` because the condition of the road does not depend on how much the car has traveled before. We use this column to get the speed and slope of the road.\n",
        "\n",
        "* This means that we are using normalized data + lag (time-series\n",
        "classification with engineered features instead of time-series\n",
        "classification with deep learning, because we have shallow data). \n",
        "We won't focus on any complicated models, just XGBClassifier to win.\n",
        "\n",
        "* Now we need to define at what Friction the road is dangerous (label 0),\n",
        "requires caution (label-1) and safe (label-2)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YICMpCyB6_kM"
      },
      "source": [
        "Ta, Tsurf, friction are **highly correlated** which has been shown in our pandas profiling \n",
        "https://krutsylo.neocities.org/SmartRoads/pandas3.html of the smart road dataset.\n",
        "\n",
        "Yet we'll drop State, Height, Distance, Ta, Tsurf, Water, moon-illumination, uvIndex columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "YGaiuxed1bF7",
        "outputId": "a0dc193f-e86c-470c-b338-8ceb3473a141"
      },
      "source": [
        "df = df.drop(\"Height\", axis=1) # contain N/A\n",
        "\n",
        "df = df.drop(\"Distance\", axis=1)\n",
        "df = df.drop(\"State\", axis=1)\n",
        "df = df.drop(\"Ta\", axis=1)\n",
        "df = df.drop(\"Tsurf\", axis=1)\n",
        "df = df.drop(\"Water\", axis=1)\n",
        "\n",
        "df = df.drop(\"moon_illumination\", axis=1)\n",
        "df = df.drop(\"uvIndex\", axis=1)\n",
        "df.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>Time(+01:00)</th>\n",
              "      <th>Friction</th>\n",
              "      <th>maxtempC</th>\n",
              "      <th>mintempC</th>\n",
              "      <th>totalSnow_cm</th>\n",
              "      <th>sunHour</th>\n",
              "      <th>DewPointC</th>\n",
              "      <th>FeelsLikeC</th>\n",
              "      <th>HeatIndexC</th>\n",
              "      <th>WindChillC</th>\n",
              "      <th>WindGustKmph</th>\n",
              "      <th>cloudcover</th>\n",
              "      <th>humidity</th>\n",
              "      <th>precipMM</th>\n",
              "      <th>pressure</th>\n",
              "      <th>tempC</th>\n",
              "      <th>visibility</th>\n",
              "      <th>winddirDegree</th>\n",
              "      <th>windspeedKmph</th>\n",
              "      <th>UV radiation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>07:11:19</td>\n",
              "      <td>0.33</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-15</td>\n",
              "      <td>-16</td>\n",
              "      <td>-9</td>\n",
              "      <td>-16</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>07:11:20</td>\n",
              "      <td>0.33</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-15</td>\n",
              "      <td>-16</td>\n",
              "      <td>-9</td>\n",
              "      <td>-16</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>07:11:21</td>\n",
              "      <td>0.33</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-15</td>\n",
              "      <td>-16</td>\n",
              "      <td>-9</td>\n",
              "      <td>-16</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>07:11:22</td>\n",
              "      <td>0.34</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-15</td>\n",
              "      <td>-16</td>\n",
              "      <td>-9</td>\n",
              "      <td>-16</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>07:11:23</td>\n",
              "      <td>0.33</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-15</td>\n",
              "      <td>-16</td>\n",
              "      <td>-9</td>\n",
              "      <td>-16</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date Time(+01:00)  ...  windspeedKmph  UV radiation\n",
              "0  2021.02.16     07:11:19  ...             14           0.2\n",
              "1  2021.02.16     07:11:20  ...             14           0.2\n",
              "2  2021.02.16     07:11:21  ...             14           0.2\n",
              "3  2021.02.16     07:11:22  ...             14           0.2\n",
              "4  2021.02.16     07:11:23  ...             14           0.2\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gVOmwNHjuu2S"
      },
      "source": [
        "  We have grouped the data by calculating the mean of the rows in each hour based on the individual dates. For instance, if there are 8 rows for each hour, we calculated the mean of 8 rows and thus converted into a single row belonging to the distinct dates.\n",
        "\n",
        "  We also avoided duplicates to reduce the noise in the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "djnVHmFrdmzY",
        "outputId": "6c0803e5-02aa-4046-edd5-593d19381c24"
      },
      "source": [
        "df['Time(+01:00)'] = pd.to_datetime(df['Time(+01:00)'], format='%H:%M:%S').dt.hour\n",
        "df = df.groupby(['Date','Time(+01:00)']).mean()\n",
        "df = df.drop_duplicates()\n",
        "df.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Friction</th>\n",
              "      <th>maxtempC</th>\n",
              "      <th>mintempC</th>\n",
              "      <th>totalSnow_cm</th>\n",
              "      <th>sunHour</th>\n",
              "      <th>DewPointC</th>\n",
              "      <th>FeelsLikeC</th>\n",
              "      <th>HeatIndexC</th>\n",
              "      <th>WindChillC</th>\n",
              "      <th>WindGustKmph</th>\n",
              "      <th>cloudcover</th>\n",
              "      <th>humidity</th>\n",
              "      <th>precipMM</th>\n",
              "      <th>pressure</th>\n",
              "      <th>tempC</th>\n",
              "      <th>visibility</th>\n",
              "      <th>winddirDegree</th>\n",
              "      <th>windspeedKmph</th>\n",
              "      <th>UV radiation</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th>Time(+01:00)</th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">2021.02.16</th>\n",
              "      <th>7</th>\n",
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              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.379456</td>\n",
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              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.374158</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-13</td>\n",
              "      <td>-14</td>\n",
              "      <td>-8</td>\n",
              "      <td>-14</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <th>10</th>\n",
              "      <td>0.377506</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-12</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>112</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
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              "      <th>11</th>\n",
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              "      <td>-11</td>\n",
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              "      <td>-11</td>\n",
              "      <td>-12</td>\n",
              "      <td>-6</td>\n",
              "      <td>-12</td>\n",
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              "      <td>14</td>\n",
              "      <td>69</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-6</td>\n",
              "      <td>10</td>\n",
              "      <td>112</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Friction  maxtempC  ...  windspeedKmph  UV radiation\n",
              "Date       Time(+01:00)                      ...                             \n",
              "2021.02.16 7             0.359750        -6  ...             14           0.2\n",
              "           8             0.379456        -6  ...             14           0.2\n",
              "           9             0.374158        -6  ...             14           0.2\n",
              "           10            0.377506        -6  ...             14           0.2\n",
              "           11            0.450421        -6  ...             14           0.2\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OOjgUpaYwIDL"
      },
      "source": [
        "Now we will work on the target feature that is `Friction` column to accomplish our objective since we want to perform a supervised machine learning model. Here we applied our knowledge of physics and research capabilities.\n",
        "\n",
        "Icy: These roads typically have the lowest coefficient of friction. For drivers, this is the most dangerous surface to be on. The small coefficient of friction gives the driver the least amount of traction when accelerating, braking, or turning (which has angular acceleration). Icy roads have a frictional coefficient of around 0.1.\n",
        "\n",
        "Wet: Roads wet with water have a coefficient of friction of around .4.  This is around 4 times higher than an icy road. Although these roads are much safer to drive on, there is still the possibility of hydroplaning. Hydroplaning occurs when there is standing or flowing water on the road (typically from rainfall) that causes a tire to lose contact with the road's surface. The treads are designed to allow water to fill the crevices so that contact may be maintained between the road and the tire. However, if there is too much water, this may not be achieved, and hydroplaning will occur. This is precisely the reason that racing slicks have such a high coefficient of friction on dry roads (about .9) and a much lower coefficient on wet roads (as low as .1).  \n",
        "\n",
        "Dry: Roads without precipitation are considered optimal for driving conditions. They have the highest coefficient of friction, around 0.9, which creates the most traction. This allows corners, acceleration, and braking to reach higher values without loss of control. Oftentimes, if roads are not dry, races will be canceled due to the extreme dangers that a less than optimal frictional surface can pose. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nL2j79FX2NOi"
      },
      "source": [
        "So, we'll take (0 <= friction < 0.5) as *dangerous*, and (0.5 < friction <= 1) as *safe*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "MzhIWOf_dnCV",
        "outputId": "1c86afef-6661-4d46-b03e-22a9013d9a2f"
      },
      "source": [
        "bins = [0, 0.5, 1]\n",
        "labels = [0, 1]\n",
        "df[\"Friction\"] = pd.cut(df[\"Friction\"], bins, labels=labels)\n",
        "\n",
        "#df = df.drop(\"Date\", axis=1)\n",
        "#df = df.drop(\"Time(+01:00)\", axis=1)\n",
        "df.head()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Friction</th>\n",
              "      <th>maxtempC</th>\n",
              "      <th>mintempC</th>\n",
              "      <th>totalSnow_cm</th>\n",
              "      <th>sunHour</th>\n",
              "      <th>DewPointC</th>\n",
              "      <th>FeelsLikeC</th>\n",
              "      <th>HeatIndexC</th>\n",
              "      <th>WindChillC</th>\n",
              "      <th>WindGustKmph</th>\n",
              "      <th>cloudcover</th>\n",
              "      <th>humidity</th>\n",
              "      <th>precipMM</th>\n",
              "      <th>pressure</th>\n",
              "      <th>tempC</th>\n",
              "      <th>visibility</th>\n",
              "      <th>winddirDegree</th>\n",
              "      <th>windspeedKmph</th>\n",
              "      <th>UV radiation</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th>Time(+01:00)</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">2021.02.16</th>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
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              "      <td>-16</td>\n",
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              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
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              "      <td>-14</td>\n",
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              "      <td>-9</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>-9</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-13</td>\n",
              "      <td>-14</td>\n",
              "      <td>-8</td>\n",
              "      <td>-14</td>\n",
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              "      <td>0</td>\n",
              "      <td>67</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-8</td>\n",
              "      <td>10</td>\n",
              "      <td>113</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-12</td>\n",
              "      <td>-13</td>\n",
              "      <td>-7</td>\n",
              "      <td>-13</td>\n",
              "      <td>24</td>\n",
              "      <td>7</td>\n",
              "      <td>68</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-7</td>\n",
              "      <td>10</td>\n",
              "      <td>112</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-11</td>\n",
              "      <td>-12</td>\n",
              "      <td>-6</td>\n",
              "      <td>-12</td>\n",
              "      <td>22</td>\n",
              "      <td>14</td>\n",
              "      <td>69</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020</td>\n",
              "      <td>-6</td>\n",
              "      <td>10</td>\n",
              "      <td>112</td>\n",
              "      <td>14</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Friction  maxtempC  ...  windspeedKmph  UV radiation\n",
              "Date       Time(+01:00)                     ...                             \n",
              "2021.02.16 7                   0        -6  ...             14           0.2\n",
              "           8                   0        -6  ...             14           0.2\n",
              "           9                   0        -6  ...             14           0.2\n",
              "           10                  0        -6  ...             14           0.2\n",
              "           11                  0        -6  ...             14           0.2\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1w2rqcY8dlV"
      },
      "source": [
        "Now we'll perform lagging and forecasting feature columns by shifting simply using our pre-defined `transform_to_supervise` function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PllI_p8kdnE3",
        "outputId": "779e8511-f58f-444b-98e3-e63b7c918aea"
      },
      "source": [
        "df = transform_to_supervised(df, previous_steps=24, forecast_steps=1, dropnan=True)\n",
        "\n",
        "Y = df.loc[:, \"Friction(t)\"].to_numpy()\n",
        "\n",
        "cols = [c for c in df.columns if '(t)' not in c]\n",
        "data=df[cols]\n",
        "\n",
        "data['Friction'] = Y\n",
        "data.to_csv('/content/test.csv')\n",
        "data = data.values.tolist()\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning:\n",
            "\n",
            "\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",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "CJcDoWVlfw7c",
        "outputId": "a477182e-6672-4b57-d325-b31ca1ab546a"
      },
      "source": [
        "df[cols].head()"
      ],
      "execution_count": 29,
      "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></th>\n",
              "      <th>Friction(t-24)</th>\n",
              "      <th>maxtempC(t-24)</th>\n",
              "      <th>mintempC(t-24)</th>\n",
              "      <th>totalSnow_cm(t-24)</th>\n",
              "      <th>sunHour(t-24)</th>\n",
              "      <th>DewPointC(t-24)</th>\n",
              "      <th>FeelsLikeC(t-24)</th>\n",
              "      <th>HeatIndexC(t-24)</th>\n",
              "      <th>WindChillC(t-24)</th>\n",
              "      <th>WindGustKmph(t-24)</th>\n",
              "      <th>cloudcover(t-24)</th>\n",
              "      <th>humidity(t-24)</th>\n",
              "      <th>precipMM(t-24)</th>\n",
              "      <th>pressure(t-24)</th>\n",
              "      <th>tempC(t-24)</th>\n",
              "      <th>visibility(t-24)</th>\n",
              "      <th>winddirDegree(t-24)</th>\n",
              "      <th>windspeedKmph(t-24)</th>\n",
              "      <th>UV radiation(t-24)</th>\n",
              "      <th>Friction(t-23)</th>\n",
              "      <th>maxtempC(t-23)</th>\n",
              "      <th>mintempC(t-23)</th>\n",
              "      <th>totalSnow_cm(t-23)</th>\n",
              "      <th>sunHour(t-23)</th>\n",
              "      <th>DewPointC(t-23)</th>\n",
              "      <th>FeelsLikeC(t-23)</th>\n",
              "      <th>HeatIndexC(t-23)</th>\n",
              "      <th>WindChillC(t-23)</th>\n",
              "      <th>WindGustKmph(t-23)</th>\n",
              "      <th>cloudcover(t-23)</th>\n",
              "      <th>humidity(t-23)</th>\n",
              "      <th>precipMM(t-23)</th>\n",
              "      <th>pressure(t-23)</th>\n",
              "      <th>tempC(t-23)</th>\n",
              "      <th>visibility(t-23)</th>\n",
              "      <th>winddirDegree(t-23)</th>\n",
              "      <th>windspeedKmph(t-23)</th>\n",
              "      <th>UV radiation(t-23)</th>\n",
              "      <th>Friction(t-22)</th>\n",
              "      <th>maxtempC(t-22)</th>\n",
              "      <th>...</th>\n",
              "      <th>windspeedKmph(t-3)</th>\n",
              "      <th>UV radiation(t-3)</th>\n",
              "      <th>Friction(t-2)</th>\n",
              "      <th>maxtempC(t-2)</th>\n",
              "      <th>mintempC(t-2)</th>\n",
              "      <th>totalSnow_cm(t-2)</th>\n",
              "      <th>sunHour(t-2)</th>\n",
              "      <th>DewPointC(t-2)</th>\n",
              "      <th>FeelsLikeC(t-2)</th>\n",
              "      <th>HeatIndexC(t-2)</th>\n",
              "      <th>WindChillC(t-2)</th>\n",
              "      <th>WindGustKmph(t-2)</th>\n",
              "      <th>cloudcover(t-2)</th>\n",
              "      <th>humidity(t-2)</th>\n",
              "      <th>precipMM(t-2)</th>\n",
              "      <th>pressure(t-2)</th>\n",
              "      <th>tempC(t-2)</th>\n",
              "      <th>visibility(t-2)</th>\n",
              "      <th>winddirDegree(t-2)</th>\n",
              "      <th>windspeedKmph(t-2)</th>\n",
              "      <th>UV radiation(t-2)</th>\n",
              "      <th>Friction(t-1)</th>\n",
              "      <th>maxtempC(t-1)</th>\n",
              "      <th>mintempC(t-1)</th>\n",
              "      <th>totalSnow_cm(t-1)</th>\n",
              "      <th>sunHour(t-1)</th>\n",
              "      <th>DewPointC(t-1)</th>\n",
              "      <th>FeelsLikeC(t-1)</th>\n",
              "      <th>HeatIndexC(t-1)</th>\n",
              "      <th>WindChillC(t-1)</th>\n",
              "      <th>WindGustKmph(t-1)</th>\n",
              "      <th>cloudcover(t-1)</th>\n",
              "      <th>humidity(t-1)</th>\n",
              "      <th>precipMM(t-1)</th>\n",
              "      <th>pressure(t-1)</th>\n",
              "      <th>tempC(t-1)</th>\n",
              "      <th>visibility(t-1)</th>\n",
              "      <th>winddirDegree(t-1)</th>\n",
              "      <th>windspeedKmph(t-1)</th>\n",
              "      <th>UV radiation(t-1)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th>Time(+01:00)</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">2021.02.18</th>\n",
              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>-11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-15.0</td>\n",
              "      <td>-16.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>-16.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>-11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-14.0</td>\n",
              "      <td>-15.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>-15.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>-18.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>-17.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>-11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-14.0</td>\n",
              "      <td>-15.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>-15.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>-11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-13.0</td>\n",
              "      <td>-14.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>-14.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>-17.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>-16.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>-11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-13.0</td>\n",
              "      <td>-14.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>-14.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>-11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>-13.0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-13.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>-16.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>-16.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>-11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>-13.0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-13.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>-11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-11.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>-16.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>-16.0</td>\n",
              "      <td>-13.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>-13.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>-11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-11.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>-11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-10.0</td>\n",
              "      <td>-11.0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>-11.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>-16.0</td>\n",
              "      <td>-13.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>-13.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>-16.0</td>\n",
              "      <td>-14.0</td>\n",
              "      <td>-10.0</td>\n",
              "      <td>-14.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>-10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 456 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Friction(t-24)  ...  UV radiation(t-1)\n",
              "Date       Time(+01:00)                 ...                   \n",
              "2021.02.18 11                        0  ...               0.21\n",
              "           12                        0  ...               0.21\n",
              "           13                        0  ...               0.21\n",
              "           14                        0  ...               0.21\n",
              "           15                        0  ...               0.21\n",
              "\n",
              "[5 rows x 456 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dh47vk0HZ93M"
      },
      "source": [
        "**Lag of 1 to 3 days**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "id": "Il2PQJ5cZ8fZ",
        "outputId": "c5f1d9b8-7804-4c70-9d3d-18878abfa336"
      },
      "source": [
        "lag = pd.read_csv('/content/lag(1-3)days.csv')\n",
        "lag=lag.head(10)\n",
        "lag"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>DewPointC(t-3)</th>\n",
              "      <th>DewPointC(t-2)</th>\n",
              "      <th>DewPointC(t-1)</th>\n",
              "      <th>DewPointC</th>\n",
              "      <th>FeelsLikeC(t-3)</th>\n",
              "      <th>FeelsLikeC(t-2)</th>\n",
              "      <th>FeelsLikeC(t-1)</th>\n",
              "      <th>FeelsLikeC</th>\n",
              "      <th>HeatIndexC(t-3)</th>\n",
              "      <th>HeatIndexC(t-2)</th>\n",
              "      <th>HeatIndexC(t-1)</th>\n",
              "      <th>HeatIndexC</th>\n",
              "      <th>WindChillC(t-3)</th>\n",
              "      <th>WindChillC(t-2)</th>\n",
              "      <th>WindChillC(t-1)</th>\n",
              "      <th>WindChillC</th>\n",
              "      <th>WindGustKmph(t-3)</th>\n",
              "      <th>WindGustKmph(t-2)</th>\n",
              "      <th>WindGustKmph(t-1)</th>\n",
              "      <th>WindGustKmph</th>\n",
              "      <th>cloudcover(t-3)</th>\n",
              "      <th>cloudcover(t-2)</th>\n",
              "      <th>cloudcover(t-1)</th>\n",
              "      <th>cloudcover</th>\n",
              "      <th>humidity(t-3)</th>\n",
              "      <th>humidity(t-2)</th>\n",
              "      <th>humidity(t-1)</th>\n",
              "      <th>humidity</th>\n",
              "      <th>precipMM(t-3)</th>\n",
              "      <th>precipMM(t-2)</th>\n",
              "      <th>precipMM(t-1)</th>\n",
              "      <th>precipMM</th>\n",
              "      <th>pressure(t-3)</th>\n",
              "      <th>pressure(t-2)</th>\n",
              "      <th>pressure(t-1)</th>\n",
              "      <th>pressure</th>\n",
              "      <th>tempC(t-3)</th>\n",
              "      <th>tempC(t-2)</th>\n",
              "      <th>tempC(t-1)</th>\n",
              "      <th>tempC</th>\n",
              "      <th>visibility(t-3)</th>\n",
              "      <th>visibility(t-2)</th>\n",
              "      <th>visibility(t-1)</th>\n",
              "      <th>visibility</th>\n",
              "      <th>winddirDegree(t-3)</th>\n",
              "      <th>winddirDegree(t-2)</th>\n",
              "      <th>winddirDegree(t-1)</th>\n",
              "      <th>winddirDegree</th>\n",
              "      <th>windspeedKmph(t-3)</th>\n",
              "      <th>windspeedKmph(t-2)</th>\n",
              "      <th>windspeedKmph(t-1)</th>\n",
              "      <th>windspeedKmph</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>-12.625000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-8.666667</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-4.958333</td>\n",
              "      <td>-7.833333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-8.666667</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.125000</td>\n",
              "      <td>24.458333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>77.875000</td>\n",
              "      <td>5.458333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>66.625000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.316667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1024.500000</td>\n",
              "      <td>1019.583333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-5.083333</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.916667</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>159.291667</td>\n",
              "      <td>113.250000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.333333</td>\n",
              "      <td>13.041667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021.02.17</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-6.875000</td>\n",
              "      <td>-12.625000</td>\n",
              "      <td>-15.291667</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-8.666667</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>-8.875000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-4.958333</td>\n",
              "      <td>-7.833333</td>\n",
              "      <td>-5.708333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-8.666667</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>-8.875000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.125000</td>\n",
              "      <td>24.458333</td>\n",
              "      <td>16.583333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>77.875000</td>\n",
              "      <td>5.458333</td>\n",
              "      <td>6.666667</td>\n",
              "      <td>NaN</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>66.625000</td>\n",
              "      <td>46.291667</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.316667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1024.500000</td>\n",
              "      <td>1019.583333</td>\n",
              "      <td>1015.875000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-5.083333</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-5.833333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.916667</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>159.291667</td>\n",
              "      <td>113.250000</td>\n",
              "      <td>129.625000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.333333</td>\n",
              "      <td>13.041667</td>\n",
              "      <td>7.916667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021.02.18</td>\n",
              "      <td>-6.875000</td>\n",
              "      <td>-12.625000</td>\n",
              "      <td>-15.291667</td>\n",
              "      <td>-17.750000</td>\n",
              "      <td>-8.666667</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>-8.875000</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>-4.958333</td>\n",
              "      <td>-7.833333</td>\n",
              "      <td>-5.708333</td>\n",
              "      <td>-9.375000</td>\n",
              "      <td>-8.666667</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>-8.875000</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>13.125000</td>\n",
              "      <td>24.458333</td>\n",
              "      <td>16.583333</td>\n",
              "      <td>17.791667</td>\n",
              "      <td>77.875000</td>\n",
              "      <td>5.458333</td>\n",
              "      <td>6.666667</td>\n",
              "      <td>21.666667</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>66.625000</td>\n",
              "      <td>46.291667</td>\n",
              "      <td>49.875000</td>\n",
              "      <td>0.316667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1024.500000</td>\n",
              "      <td>1019.583333</td>\n",
              "      <td>1015.875000</td>\n",
              "      <td>1012.458333</td>\n",
              "      <td>-5.083333</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-5.833333</td>\n",
              "      <td>-10.083333</td>\n",
              "      <td>5.916667</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>159.291667</td>\n",
              "      <td>113.250000</td>\n",
              "      <td>129.625000</td>\n",
              "      <td>99.833333</td>\n",
              "      <td>8.333333</td>\n",
              "      <td>13.041667</td>\n",
              "      <td>7.916667</td>\n",
              "      <td>8.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021.02.19</td>\n",
              "      <td>-12.625000</td>\n",
              "      <td>-15.291667</td>\n",
              "      <td>-17.750000</td>\n",
              "      <td>-17.166667</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>-8.875000</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>-22.666667</td>\n",
              "      <td>-7.833333</td>\n",
              "      <td>-5.708333</td>\n",
              "      <td>-9.375000</td>\n",
              "      <td>-15.041667</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>-8.875000</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>-22.666667</td>\n",
              "      <td>24.458333</td>\n",
              "      <td>16.583333</td>\n",
              "      <td>17.791667</td>\n",
              "      <td>34.250000</td>\n",
              "      <td>5.458333</td>\n",
              "      <td>6.666667</td>\n",
              "      <td>21.666667</td>\n",
              "      <td>15.208333</td>\n",
              "      <td>66.625000</td>\n",
              "      <td>46.291667</td>\n",
              "      <td>49.875000</td>\n",
              "      <td>78.458333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1019.583333</td>\n",
              "      <td>1015.875000</td>\n",
              "      <td>1012.458333</td>\n",
              "      <td>1013.083333</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>-5.833333</td>\n",
              "      <td>-10.083333</td>\n",
              "      <td>-15.250000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>113.250000</td>\n",
              "      <td>129.625000</td>\n",
              "      <td>99.833333</td>\n",
              "      <td>103.416667</td>\n",
              "      <td>13.041667</td>\n",
              "      <td>7.916667</td>\n",
              "      <td>8.666667</td>\n",
              "      <td>17.041667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021.02.22</td>\n",
              "      <td>-15.291667</td>\n",
              "      <td>-17.750000</td>\n",
              "      <td>-17.166667</td>\n",
              "      <td>-6.250000</td>\n",
              "      <td>-8.875000</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>-22.666667</td>\n",
              "      <td>-9.875000</td>\n",
              "      <td>-5.708333</td>\n",
              "      <td>-9.375000</td>\n",
              "      <td>-15.041667</td>\n",
              "      <td>-4.791667</td>\n",
              "      <td>-8.875000</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>-22.666667</td>\n",
              "      <td>-9.875000</td>\n",
              "      <td>16.583333</td>\n",
              "      <td>17.791667</td>\n",
              "      <td>34.250000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>6.666667</td>\n",
              "      <td>21.666667</td>\n",
              "      <td>15.208333</td>\n",
              "      <td>83.333333</td>\n",
              "      <td>46.291667</td>\n",
              "      <td>49.875000</td>\n",
              "      <td>78.458333</td>\n",
              "      <td>88.041667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.295833</td>\n",
              "      <td>1015.875000</td>\n",
              "      <td>1012.458333</td>\n",
              "      <td>1013.083333</td>\n",
              "      <td>1016.500000</td>\n",
              "      <td>-5.833333</td>\n",
              "      <td>-10.083333</td>\n",
              "      <td>-15.250000</td>\n",
              "      <td>-4.875000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>129.625000</td>\n",
              "      <td>99.833333</td>\n",
              "      <td>103.416667</td>\n",
              "      <td>126.333333</td>\n",
              "      <td>7.916667</td>\n",
              "      <td>8.666667</td>\n",
              "      <td>17.041667</td>\n",
              "      <td>13.041667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2021.02.23</td>\n",
              "      <td>-17.750000</td>\n",
              "      <td>-17.166667</td>\n",
              "      <td>-6.250000</td>\n",
              "      <td>-4.208333</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>-22.666667</td>\n",
              "      <td>-9.875000</td>\n",
              "      <td>-6.291667</td>\n",
              "      <td>-9.375000</td>\n",
              "      <td>-15.041667</td>\n",
              "      <td>-4.791667</td>\n",
              "      <td>-2.875000</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>-22.666667</td>\n",
              "      <td>-9.875000</td>\n",
              "      <td>-6.291667</td>\n",
              "      <td>17.791667</td>\n",
              "      <td>34.250000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>16.708333</td>\n",
              "      <td>21.666667</td>\n",
              "      <td>15.208333</td>\n",
              "      <td>83.333333</td>\n",
              "      <td>86.166667</td>\n",
              "      <td>49.875000</td>\n",
              "      <td>78.458333</td>\n",
              "      <td>88.041667</td>\n",
              "      <td>87.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.295833</td>\n",
              "      <td>0.037500</td>\n",
              "      <td>1012.458333</td>\n",
              "      <td>1013.083333</td>\n",
              "      <td>1016.500000</td>\n",
              "      <td>1014.375000</td>\n",
              "      <td>-10.083333</td>\n",
              "      <td>-15.250000</td>\n",
              "      <td>-4.875000</td>\n",
              "      <td>-3.208333</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>8.708333</td>\n",
              "      <td>99.833333</td>\n",
              "      <td>103.416667</td>\n",
              "      <td>126.333333</td>\n",
              "      <td>150.083333</td>\n",
              "      <td>8.666667</td>\n",
              "      <td>17.041667</td>\n",
              "      <td>13.041667</td>\n",
              "      <td>8.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2021.02.24</td>\n",
              "      <td>-17.166667</td>\n",
              "      <td>-6.250000</td>\n",
              "      <td>-4.208333</td>\n",
              "      <td>-2.208333</td>\n",
              "      <td>-22.666667</td>\n",
              "      <td>-9.875000</td>\n",
              "      <td>-6.291667</td>\n",
              "      <td>-5.250000</td>\n",
              "      <td>-15.041667</td>\n",
              "      <td>-4.791667</td>\n",
              "      <td>-2.875000</td>\n",
              "      <td>-1.541667</td>\n",
              "      <td>-22.666667</td>\n",
              "      <td>-9.875000</td>\n",
              "      <td>-6.291667</td>\n",
              "      <td>-5.250000</td>\n",
              "      <td>34.250000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>16.708333</td>\n",
              "      <td>22.208333</td>\n",
              "      <td>15.208333</td>\n",
              "      <td>83.333333</td>\n",
              "      <td>86.166667</td>\n",
              "      <td>93.541667</td>\n",
              "      <td>78.458333</td>\n",
              "      <td>88.041667</td>\n",
              "      <td>87.125000</td>\n",
              "      <td>87.708333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.295833</td>\n",
              "      <td>0.037500</td>\n",
              "      <td>0.820833</td>\n",
              "      <td>1013.083333</td>\n",
              "      <td>1016.500000</td>\n",
              "      <td>1014.375000</td>\n",
              "      <td>1000.750000</td>\n",
              "      <td>-15.250000</td>\n",
              "      <td>-4.875000</td>\n",
              "      <td>-3.208333</td>\n",
              "      <td>-2.166667</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>8.708333</td>\n",
              "      <td>7.791667</td>\n",
              "      <td>103.416667</td>\n",
              "      <td>126.333333</td>\n",
              "      <td>150.083333</td>\n",
              "      <td>141.791667</td>\n",
              "      <td>17.041667</td>\n",
              "      <td>13.041667</td>\n",
              "      <td>8.666667</td>\n",
              "      <td>13.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2021.02.25</td>\n",
              "      <td>-6.250000</td>\n",
              "      <td>-4.208333</td>\n",
              "      <td>-2.208333</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>-9.875000</td>\n",
              "      <td>-6.291667</td>\n",
              "      <td>-5.250000</td>\n",
              "      <td>-1.583333</td>\n",
              "      <td>-4.791667</td>\n",
              "      <td>-2.875000</td>\n",
              "      <td>-1.541667</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>-9.875000</td>\n",
              "      <td>-6.291667</td>\n",
              "      <td>-5.250000</td>\n",
              "      <td>-1.583333</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>16.708333</td>\n",
              "      <td>22.208333</td>\n",
              "      <td>14.791667</td>\n",
              "      <td>83.333333</td>\n",
              "      <td>86.166667</td>\n",
              "      <td>93.541667</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>88.041667</td>\n",
              "      <td>87.125000</td>\n",
              "      <td>87.708333</td>\n",
              "      <td>99.166667</td>\n",
              "      <td>0.295833</td>\n",
              "      <td>0.037500</td>\n",
              "      <td>0.820833</td>\n",
              "      <td>2.566667</td>\n",
              "      <td>1016.500000</td>\n",
              "      <td>1014.375000</td>\n",
              "      <td>1000.750000</td>\n",
              "      <td>991.708333</td>\n",
              "      <td>-4.875000</td>\n",
              "      <td>-3.208333</td>\n",
              "      <td>-2.166667</td>\n",
              "      <td>1.125000</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>8.708333</td>\n",
              "      <td>7.791667</td>\n",
              "      <td>6.166667</td>\n",
              "      <td>126.333333</td>\n",
              "      <td>150.083333</td>\n",
              "      <td>141.791667</td>\n",
              "      <td>266.625000</td>\n",
              "      <td>13.041667</td>\n",
              "      <td>8.666667</td>\n",
              "      <td>13.250000</td>\n",
              "      <td>10.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2021.02.26</td>\n",
              "      <td>-4.208333</td>\n",
              "      <td>-2.208333</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>-1.166667</td>\n",
              "      <td>-6.291667</td>\n",
              "      <td>-5.250000</td>\n",
              "      <td>-1.583333</td>\n",
              "      <td>-4.666667</td>\n",
              "      <td>-2.875000</td>\n",
              "      <td>-1.541667</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>-0.916667</td>\n",
              "      <td>-6.291667</td>\n",
              "      <td>-5.250000</td>\n",
              "      <td>-1.583333</td>\n",
              "      <td>-4.666667</td>\n",
              "      <td>16.708333</td>\n",
              "      <td>22.208333</td>\n",
              "      <td>14.791667</td>\n",
              "      <td>15.583333</td>\n",
              "      <td>86.166667</td>\n",
              "      <td>93.541667</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>99.625000</td>\n",
              "      <td>87.125000</td>\n",
              "      <td>87.708333</td>\n",
              "      <td>99.166667</td>\n",
              "      <td>98.666667</td>\n",
              "      <td>0.037500</td>\n",
              "      <td>0.820833</td>\n",
              "      <td>2.566667</td>\n",
              "      <td>1.375000</td>\n",
              "      <td>1014.375000</td>\n",
              "      <td>1000.750000</td>\n",
              "      <td>991.708333</td>\n",
              "      <td>1005.916667</td>\n",
              "      <td>-3.208333</td>\n",
              "      <td>-2.166667</td>\n",
              "      <td>1.125000</td>\n",
              "      <td>-0.916667</td>\n",
              "      <td>8.708333</td>\n",
              "      <td>7.791667</td>\n",
              "      <td>6.166667</td>\n",
              "      <td>2.083333</td>\n",
              "      <td>150.083333</td>\n",
              "      <td>141.791667</td>\n",
              "      <td>266.625000</td>\n",
              "      <td>273.625000</td>\n",
              "      <td>8.666667</td>\n",
              "      <td>13.250000</td>\n",
              "      <td>10.250000</td>\n",
              "      <td>10.541667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2021.03.01</td>\n",
              "      <td>-2.208333</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>-1.166667</td>\n",
              "      <td>-4.208333</td>\n",
              "      <td>-5.250000</td>\n",
              "      <td>-1.583333</td>\n",
              "      <td>-4.666667</td>\n",
              "      <td>-9.708333</td>\n",
              "      <td>-1.541667</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>-0.916667</td>\n",
              "      <td>-3.250000</td>\n",
              "      <td>-5.250000</td>\n",
              "      <td>-1.583333</td>\n",
              "      <td>-4.666667</td>\n",
              "      <td>-9.708333</td>\n",
              "      <td>22.208333</td>\n",
              "      <td>14.791667</td>\n",
              "      <td>15.583333</td>\n",
              "      <td>41.791667</td>\n",
              "      <td>93.541667</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>99.625000</td>\n",
              "      <td>95.958333</td>\n",
              "      <td>87.708333</td>\n",
              "      <td>99.166667</td>\n",
              "      <td>98.666667</td>\n",
              "      <td>93.125000</td>\n",
              "      <td>0.820833</td>\n",
              "      <td>2.566667</td>\n",
              "      <td>1.375000</td>\n",
              "      <td>1.395833</td>\n",
              "      <td>1000.750000</td>\n",
              "      <td>991.708333</td>\n",
              "      <td>1005.916667</td>\n",
              "      <td>1004.291667</td>\n",
              "      <td>-2.166667</td>\n",
              "      <td>1.125000</td>\n",
              "      <td>-0.916667</td>\n",
              "      <td>-3.333333</td>\n",
              "      <td>7.791667</td>\n",
              "      <td>6.166667</td>\n",
              "      <td>2.083333</td>\n",
              "      <td>3.166667</td>\n",
              "      <td>141.791667</td>\n",
              "      <td>266.625000</td>\n",
              "      <td>273.625000</td>\n",
              "      <td>259.041667</td>\n",
              "      <td>13.250000</td>\n",
              "      <td>10.250000</td>\n",
              "      <td>10.541667</td>\n",
              "      <td>22.958333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date  DewPointC(t-3)  ...  windspeedKmph(t-1)  windspeedKmph\n",
              "0  2021.02.16             NaN  ...            8.333333      13.041667\n",
              "1  2021.02.17             NaN  ...           13.041667       7.916667\n",
              "2  2021.02.18       -6.875000  ...            7.916667       8.666667\n",
              "3  2021.02.19      -12.625000  ...            8.666667      17.041667\n",
              "4  2021.02.22      -15.291667  ...           17.041667      13.041667\n",
              "5  2021.02.23      -17.750000  ...           13.041667       8.666667\n",
              "6  2021.02.24      -17.166667  ...            8.666667      13.250000\n",
              "7  2021.02.25       -6.250000  ...           13.250000      10.250000\n",
              "8  2021.02.26       -4.208333  ...           10.250000      10.541667\n",
              "9  2021.03.01       -2.208333  ...           10.541667      22.958333\n",
              "\n",
              "[10 rows x 53 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "G8m7Hsy6axEH",
        "outputId": "e2626229-a60c-465e-b4f9-42d05687986e"
      },
      "source": [
        "ax = lag.plot(x=\"Date\", y=\"humidity(t-3)\", kind=\"bar\")\n",
        "lag.plot(x=\"Date\", y=\"humidity(t-2)\", kind=\"bar\", ax=ax, color=\"C2\")\n",
        "lag.plot(x=\"Date\", y=\"humidity(t-1)\", kind=\"bar\", ax=ax, color=\"C3\")"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5b17c9b3d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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//frz505Do9GiSdPmSL6bhOysLNSsVQfA4y9xXJJmzVpwPXsiAXHYy/Tqq/lLHD/j2QSpySnFVrgsib2DPVq19QYANG1q+SWOe/yjL37Ysx0A8MPu7XilZ/5lU5LvoLpLjQpfT0UUfjMTIhIHh71MBcsTa/+7xLExLw9anQ5SoVfmPsx5WOQy+iqFlzjWWXyJ47btO+OPS3G4euUS4s6eRIeO3U3t5f1yKmmJ47Lk5GRziWMiAcl+gnb//v1YsGABJEmCJEl499134ePjgytXriAkJARpaWlwcXFBeHg4GjZsaIZk8bnUqIG8vFzcvpWAOnU98MtPB2RfZ0WXOHZwqIqc7GzTuvpA/r70zt3+gfnhU9ChY3fTGjgedRsgLS0FDwut8FneEsfluXr1Cho3bvK43x4RWZisYS9JEiZOnIh169ahSZMmOH/+PAYMGIBXXnkFoaGhCAoKQkBAALZv345p06ZhzZqKvxF2RT188Nd/1+8wr9ysyq8HotPpMOidEfjk36Fwql4dLV9oI7unokscV3NyRodO3fF/44bD0bEaQmcuAgB06dYL275ag1d6+Ju+1s5gwLPNWuLc2VPwevpVAOUvcZyQcAtjxoxAVlYWcnKy8dprvTB8+Ej4+vYBABw79itGjhwj+/slIvOS/cheq9UiPT1/yc309HS4u7sjNTUVcXFxWL16NQDA19cXYWFhSElJgatr5d4GrzS34/6o9GWrNW4ka/GvQ4di8leRzMr/fN2ub0zndfF5BV18/rcwWL+B+YdC1qpdG1Eb15pOb9PGGytX/u/zXr380KuXX7GPAeCdd8binXfGFuto08YbYeFLTZ+PGDW+2NfEnT2J51q2RR2P+kVO9+0TiJ3bN6GvX/6w9/PrAz+/PqV+zx4edbFt264Sz7t27SqMRiOaN3+u1MsTkXXIGvYajQafffYZxowZg6pVq+LBgwdYtmwZEhISULt2bdNuBJ1OB3d3dyQkJDzWsH90XeakJG25yxY/LnNfn4gN4TMmIvH2LYybNKPYeU2aNkd8m2vIzMyEg4ODrO3cvZuESZMml/n9aLXact8ww9Ksvf2KEqVThA4RGgAxOirbIGvY5+bmIioqCpGRkWjTpg1OnDiBDz74AHPmzCn/whXw6JuXGI1G0yNxcw1Iucv6mqPD0g2TppR9e3Tt3tv0oio52rRpC6D070ev18JoNFr8zUPKotSbl5iDOd68xNodIjSI0qFEQ1lvXiJrUp07dw5JSUlo0yZ/n3SbNm3g4OAAg8GAxMRE01EleXl5SEpKgoeHh5zNAch/noCeTLztiKxH1iP7OnXq4Pbt2/jzzz/x9NNP4/Lly0hOTsZTTz0FLy8vREdHIyAgANHR0fDy8pK9vz7/sMRc6PVVyv9iEk5OTjZ0Osuu0FGR938t7xGWOd7/lUg0su55tWrVwvTp0xEcHAzNf9+ab9asWXBxccH06dMREhKCyMhIODs7Izw8XHasg0M1pKenwcXFDXyJwJNDkiQ8fJiD9PRkVKvmYtFt6e0N5nl3Jg57sjGyH2b5+/vD39+/2OmNGjXCV199Jffqi6hWrTpSU+8gMfEmtFoNsh+kyrq++7euwWiUt59aq9Ui40FypS9/K0syT8P9v2Rdx61b92R3lEWn06NePQ/k5PCXNJE1PFGrXmo0Gri6ugMwzzu1t5b5bvEFHW9sGl3py2/uv8QsDSHjtsu6jh3zAiz+xGX16pZ/cpSISsaHWUREKsBhT0SkAhz2REQqwGFPRKQCHPZERCrAYU9EpAIc9kREKsBhT0SkAhz2REQqwGFPRKQCHPZERCrAYU9EpAIc9kREKsBhT0SkAhz2REQqwGFPRKQCHPZERCrAYU9EpAIc9kREKsBhT0SkAhz2REQqoLd2wJPOmJODzf2XyLq8XDl5D7FjXoDs65DLydkB9oay/0vVquVU6nlZ2blIv58pu4OIiuOwl0lrZ4fDAf0qffmXtm8FkC2rwU5XBW9sGi3rOvJ/YWXJug57gx5+47ZX+vI75gUgXVYBEZWGu3GIiFSAw56ISAU47ImIVIDDnohIBTjsiYhUgMOeiEgFOOyJiFSAw56ISAVkv6gqOzsbs2bNwpEjR2AwGNCyZUuEhYXhypUrCAkJQVpaGlxcXBAeHo6GDRuaIZmIiB6X7GE/d+5cGAwG7NmzBxqNBnfv3gUAhIaGIigoCAEBAdi+fTumTZuGNWvWyA4mIqLHJ2s3zoMHD/DNN98gODgYGo0GAFCzZk0kJycjLi4Ovr6+AABfX1/ExcUhJSVFfjERET02WY/sb9y4ARcXFyxatAhHjx6Fo6MjgoODYW9vj9q1a0On0wEAdDod3N3dkZCQAFdXV7OEExFRxcka9nl5ebhx4waeffZZTJo0CadOncKoUaOwYMECs8S5uVUzy/WUpaxVGJUiQgMgRocIDYAYHSI0AGJ0iNAAiNFR2QZZw97DwwN6vd60u+b5559HjRo1YG9vj8TEROTl5UGn0yEvLw9JSUnw8PB4rOtPTs6A0SiVeJ65fuh37shbZ9EcHSI0iNIhQoPcDhEaROkQoUGUDiUatFpNqQ+SZe2zd3V1Rbt27XD48GEAwJUrV5CcnIyGDRvCy8sL0dHRAIDo6Gh4eXlxFw4RkZXIPhrno48+wuTJkxEeHg69Xo85c+bA2dkZ06dPR0hICCIjI+Hs7Izw8HBz9BIRUSXIHvYNGjTA2rVri53eqFEjfPXVV3KvnoiIzICvoCUiUgEOeyIiFeCwJyJSAQ57IiIV4LAnIlIBDnsiIhXgsCciUgEOeyIiFeCwJyJSAQ57IiIV4LAnIlIBDnsiIhXgsCciUgEOeyIiFeCwJyJSAQ57IiIVkP3mJUQFjDk52DEvQNblicgyOOzJbLR2djgc0K/Sl39p+1YA2eYLIiIT7sYhIlIBDnsiIhXgsCciUgEOeyIiFeCwJyJSAR6NYwOMOTnY3H+J7OsgItvFYW8D5B7yCPCwRyJbx904REQqwGFPRKQCHPZERCrAYU9EpAIc9kREKsBhT0SkAhz2REQqwGFPRKQCZhv2ixYtQtOmTXHx4kUAwMmTJ+Hv748ePXpg2LBhSE5ONtemiIjoMZll2J89exYnT55EvXr1AABGoxETJkzAtGnTsGfPHnh7eyMiIsIcmyIiokqQPexzcnLw8ccfY/r06abTYmNjYTAY4O3tDQAIDAzE7t275W6KiIgqSfbaOAsWLIC/vz/q169vOi0hIQF169Y1fe7q6gqj0Yi0tDS4uLhU+Lrd3KrJzStXrVpOFt/Gk9AAiNEhQgMgRocIDYAYHSI0AGJ0VLZB1rD//fffERsbi/Hjx8u5mlIlJ2fAaJRKPM9cP/Q7d9JlXd4cHSI0iNIhQoPcDhEaROkQoUGUDiUatFpNqQ+SZQ3748eP4/Lly+jevTsA4Pbt2xg+fDgGDRqEW7dumb4uJSUFWq32sR7VExGR+cjaZz9y5EgcOnQI+/btw759+1CnTh2sXLkSI0aMQFZWFmJiYgAAGzduRM+ePc0STEREj88i69lrtVrMmTMHoaGhyM7ORr169TB37lxLbIqIiCrArMN+3759po9bt26NHTt2mPPqiYiokvgKWiIiFeCwJyJSAQ57IiIV4LAnIlIBDnsiIhXgsCciUgEOeyIiFeCwJyJSAQ57IiIV4LAnIlIBDnsiIhXgsCciUgEOeyIiFeCwJyJSAQ57IiIV4LAnIlIBDnsiIhXgsCciUgEOeyIiFeCwJyJSAQ57IiIV4LAnIlIBDnsiIhXgsCciUgEOeyIiFeCwJyJSAQ57IiIV4LAnIlIBDnsiIhXgsCciUgEOeyIiFeCwJyJSAQ57IiIV0Mu5cGpqKiZOnIjr16/Dzs4OTz31FD7++GO4urri5MmTmDZtGrKzs1GvXj3MnTsXbm5u5uomIqLHIOuRvUajwYgRI7Bnzx7s2LEDDRo0QEREBIxGIyZMmIBp06Zhz5498Pb2RkREhLmaiYjoMcka9i4uLmjXrp3p85YtW+LWrVuIjY2FwWCAt7c3ACAwMBC7d++WV0pERJUmazdOYUajERs2bEC3bt2QkJCAunXrms5zdXWF0WhEWloaXFxcKnydbm7VzJVXqlq1nCy+jSehARCjQ4QGQIwOERoAMTpEaADE6Khsg9mGfVhYGKpWrYo333wT33//vVmuMzk5A0ajVOJ55vqh37mTLuvy5ugQoUGUDhEa5HaI0CBKhwgNonQo0aDVakp9kGyWYR8eHo5r165h6dKl0Gq18PDwwK1bt0znp6SkQKvVPtajeiIiMh/Zh17Onz8fsbGxWLx4Mezs7AAAzZs3R1ZWFmJiYgAAGzduRM+ePeVuioiIKknWI/tLly4hKioKDRs2RGBgIACgfv36WLx4MebMmYPQ0NAih14SEZF1yBr2zzzzDC5cuFDiea1bt8aOHTvkXD0REZkJX0FLRKQCHPZERCrAYU9EpAIc9kREKsBhT0SkAhz2REQqwGFPRKQCHPZERCrAYU9EpAIc9kREKsBhT0SkAhz2REQqwGFPRKQCHPZERCrAYU9EpAIc9kREKsBhT0SkAhz2REQqwGFPRKQCHPZERCrAYU9EpAIc9kREKsBhT0SkAhz2REQqwGFPRKQCHPZERCrAYU9EpAIc9kREKsBhT0SkAhz2REQqwGFPRKQCHPZERCrAYU9EpAIWHfZXrlxB//790aNHD/Tv3x9Xr1615OaIiKgUFh32oaGhCAoKwp49exAUFIRp06ZZcnNERFQKvaWuODk5GXFxcVi9ejUAwNfXF2FhYUhJSYGrq2uFrkOr1ZR5vsG9luzO8rZREXI7RGgQpUOEBnN0iNAgSocIDaJ0WLqhrPM0kiRJsrdegtjYWEyaNAk7d+40ndarVy/MnTsXzZo1s8QmiYioFHyClohIBSw27D08PJCYmIi8vDwAQF5eHpKSkuDh4WGpTRIRUSksNuzd3Nzg5eWF6OhoAEB0dDS8vLwqvL+eiIjMx2L77AHg8uXLCAkJwf379+Hs7Izw8HA8/fTTltocERGVwqLDnoiIxMAnaImIVIDDnohIBTjsiYhUgMOeiEgFOOyJiFSAw56ISAU47EmV7t27p+j20tPTTdu8d+8evv/+e/z555+KNjwqMjLSqtsHAEmSkJCQAKPRaLWGmJgYrF69GkeOHLFagxJ006dPn27tCLlSUlKg0+mg0+kAANu3b8eWLVuQmJio6KJr+/fvR/369U0d1mA0GrFnzx6kp6fDw8MDe/fuxfr165GQkIBnn30WGo381QMrKjExEVu2bMHOnTtx5MgR3L59G40aNYJeb7HFVouIjY3FwIEDERERgXPnzqFdu3ZwcHAAAAwYMACBgYGKdOzatQtDhgzB2rVr0aBBA0yePBlXr17F8uXLUbduXTRu3NjiDevWrcOZM2eK/Fu+fDkcHBxw5swZPPfccxZvAICoqCh4e3sDyH/R5euvv461a9fiiy++wAsvvIDatWtbvOG1116Dj48PHBwc8NVXXyEiIgLOzs7YtGkTMjMz0bp1a4s3lGfr1q149tlnzXqdNvHIfujQocjJyQEALFmyBBs3bkSdOnWwe/duhIeHK9YxZswYdOrUCTNnzsT58+cV225hM2bMwKpVqxAWFob58+dj1apVqFOnDvbu3YtZs2Yp1vHtt98iMDAQR48exY4dO5CYmIi9e/eiZ8+euHDhgiINs2bNwr///W8cOHAATZo0wcCBA5GQkAAg/xGlUpYvX47vvvsOmzdvxuTJkzFv3jysWLECGzZswPLlyxVpmDlzJg4ePIjY2FjTv+zsbNPHStm9e7fp408//RTvv/8+jh49ik8++QRz5sxRpCE3N9e0bMumTZuwYcMGhIaGYtOmTfjmm28UaSjPwoULzX6dyjzEsjBJklCtWjUAwPfff4+1a9fC0dERQ4YMQd++fTFp0iRFOpo2bYpPPvkEW7ZswdChQ1G3bl3069cP/v7+cHJyUqTh6NGj+Pbbb5GVlYVOnTrhwIEDqFatmulnoZSoqChs3boVrq6uuHHjBmbNmoVly5bhyJEj+Pjjj7Fu3TqLN/z111/o0qULAODdd9/F3//+dwwZMgQrV65U9C8cAHB3dwcA1KlTB0JZOEMAABD1SURBVM888wwAoEGDBqaFAi1t9erVmDdvHvr27QsfHx8A+f9XZs+ercj2S3L9+nX06dMHANCpUydEREQost3c3FxkZmbCwcEBer3eNPirVq2q6IOA4ODgEk+XJMkiuxlt4pE9kL8rB8i/wQwGAwCgSpUqit2ZAECj0cDT0xNTpkzBwYMHMWLECOzfvx8vv/wyxo0bp0hDwe6sqlWrwsHBwfRL0M7ODlqtcje3Tqcz3YkaNGhgekTdvn17pKamKtKQnZ1d5Pbv3bs3JkyYgKFDh5r+vyihcMOQIUNKPc+S2rVrh9WrV+PgwYP417/+hZSUFMV/4QH599N169Zh3bp1yMzMLHKeUoM2KCgII0eOxNGjR9G1a1d89NFHiImJwaeffmr2XSdl+fnnn/HSSy+hS5cuxf7Z2dmZfXs28ch+9OjRGDx4MIYNGwZvb2+8//776NGjBw4fPoyXX35ZsY7C/1mrVKmCXr16oVevXrh9+7Zifx56eHhgzpw5ePDgARo1aoRZs2bBz88PBw8eRM2aNRVpAID69etjyZIl6NSpE3bu3Gl6NJuXl6fYgGvfvj0OHTqEzp07m0579dVXodfrMXnyZEUagPw37cnIyEC1atWKPE9w+fJlNGrUSLEOR0dHhIWF4eeff8Zbb72FrKwsxbZdoEOHDqbdRt7e3khKSoK7uzsSExPh5uamSMOgQYNQu3ZtfPbZZ7h8+TLy8vIQExMDPz8/hIWFKdIAAF5eXvD09Czx+ZIFCxaYfXs2sxDamTNn8MUXX5huvLp168LX1xe9e/dW7BHMpEmTFH2OoCQpKSlYunQpNBoNxowZg927d+PLL79E/fr1MXnyZDRo0ECRjrt372L27Nm4cOECmjdvjokTJ8LV1RVpaWk4ffq0or+Eqbj09HRcvHgRbdq0sXaKap0/fx6urq6mXXyFxcfHo169embdns0Me6KS5OTkIDU1tdhRHpcuXTL9taGWDhEaROkQoUFpNrPPvjSHDx+2dgIAMTpEaACU6zh06BBeeukl+Pr6om/fvrh27ZrpvIkTJyrSIEqHCA2FO3r37m31n4U1GwrExMQgLCwMY8aMwZgxYxAWFoaYmBjLbEyycZ07d7Z2giRJYnSI0CBJynX07dtXOnfunCRJkvT1119LXbt2NX0eEBCgSIMoHSI0iNIhQoMkSdLixYslPz8/aeXKldKuXbukXbt2SStXrpT8/PykRYsWmX17NvEEbWnH50qShPT0dFV1iNAgSkdubi48PT0B5L+Qpl69ehg9ejQWLFig6JEoInSI0CBKhwgNAPDNN99gx44dpqMHCwQFBcHPzw9jx4416/ZsYjfO2rVrYTAYULVq1SL/HB0dFb3xROgQoUGUjry8PGRnZ5s+b9u2LebPn4/g4GAkJiYq0iBKhwgNonSI0ADkP/Ap6b6g0Wgscxiq2f9WsILCf5Y96uWXX1ZVhwgNonQsXrxYOnToULHTT506JfXr10+RBlE6RGgQpUOEBkmSpEWLFkn+/v7SqlWrpN27d0u7d++WVq1aJfn7+0sLFy40+/Zs4micw4cP429/+1uJhxWeOHFCscPLROgQoUGkDiKRxcTEYNeuXbh16xYAoG7duujZsyfatm1r9m3ZxLAnelxnz55VdJE8kTtEaBClQ4QGS7GJffZlOXv2rLUTAIjRIUIDIEaHJV6hWBkidIjQAIjRIUIDkL+CrrnZ/LAX5cYToUOEBkCMjmXLllk7AYAYHSI0AGJ0iNAAAD/++KPZr5O7cYiIVMDmH9mTet24cQNDhw5Fjx49EB4eXuRwu/79+6uqQ4QGUTpEaChJbm4u4uLiLPY6FJsY9qLceCJ0iNAgSsf06dPx6quvYv78+UhLS8OQIUNMd6TCPWroEKFBlA4RGgDgyJEjePHFF9GhQwccP34cAwYMwLhx4/DKK6/g119/Nf8GzX4wpxUMGzZM+vLLL6XY2FgpJCRE6t+/v3T//n1JkpR9+bMIHSI0iNLx6HaWLVsm9enTR0pJSZH69OmjSIMoHSI0iNIhQoMkSdLrr78unTt3Tjp27JjUtm1b6ddff5UkSZJOnz4t9e/f3+zbs4nlEpKTkzFw4EAAwOzZs7F8+XIMHjwYq1atUvRVoyJ0iNAgSsejj9Lefvtt2NvbY/DgwcXeOMPWO0RoEKVDhAYAePjwoWnZBmdnZ7Rr1w4A0KJFC4u814BN7MYp6cbr27cvBg8erOh6MCJ0iNAgSsczzzxT7BC2QYMGYeDAgYiPj1ekQZQOERpE6RChAQCMRqPpYz8/vyLnWeQNfsz+t4IVvPfee9K+ffuKnb5hwwbJ09NTVR0iNIjSYTQaJaPRWOJ5GRkZijSI0iFCgygdIjRIkiR99NFHUnp6erHTL1++LA0cONDs27OJQy8LvoWSdg88ePAAjo6OqukQoUGkDgCIjY3F7du3AeS/4Xfz5s0V27ZoHSI0iNIhQkNppFIWSZPDJoZ9AVFuPBE6RGiwdsfp06cxYcIE2NnZwcPDAwCQkJCA7OxszJ07F88//7xqOkRoEKVDhIbClLqP2MSwF+XGE6FDhAZROvr06YOpU6cWW3St4N2Btm/fbvEGUTpEaBClQ4QGwAr3EbPvGLKCgIAAKSYmptjpx48fl/z9/VXVIUKDKB0+Pj6VOs8WO0RoEKVDhAZJUv4+YhNH42RmZpa4ZK63t7dFDmESuUOEBlE66tevj6VLlyItLc10WlpaGpYsWYK6desq0iBKhwgNonSI0AAofx+xiWEvyo0nQocIDaJ0zJkzB9evX0fXrl3RqlUrtGrVCl27dsX169cxd+5cRRpE6RChQZQOERoA5e8jNrHPPjk5GfPmzcN3331X5PSePXti3LhxqFmzpmo6RGgQqaNAwR3KxcVF0e2K2CFCgygd1mxQ+j5iE8O+MBH+A4nSIUKDSB1EolLiPmITu3EKc3FxEWKoiNAhQoNIHYW99tpr1k4AIEaHCA2AGB3WalDiPmJzw/5RIvwHAsToEKEBEKMjKirK2gkAxOgQoQEQo0OEBsAy9xGb243zqKSkJLi7u1s7Q4gOERpE6iASlSXuIzb/yF6UoSJChwgNgBgdU6dOtXYCADE6RGgAxOgQoQGwzH3E5oe9KDeeCB0iNABidBw8eNDaCQDE6BChARCjQ4QGwDL3EZtYz74sotx4InSI0AAo19G+ffsST5ckSdHlnkXoEKFBlA4RGspjifuITQx7UW48ETpEaBClQ5IkfP7553Bycip2+oABAxRpEKVDhAZROkRoAJS/j9jEsBflxhOhQ4QGUTqaN2+O1NRU07sBFVa7dm1FGkTpEKFBlA4RGgDl7yM2MexFufFE6BChQZSOyMhI6HS6Es/bsmWLIg2idIjQIEqHCA2A8vcRmzj0MicnBzqdrtQbUE0dIjSI1EEkKqXvIzZxNI6dnZ0QQ0WEDhEaROnIycnBkiVLMHXqVPz0009FzgsLC1NVhwgNonSI0AAofx+xiWEvyo0nQocIDaJ0TJ8+HRcvXsTTTz+NiIgIzJw503Teb7/9pkiDKB0iNIjSIUIDoPx9xCaGvSg3nggdIjSI0nHmzBl8+umneOutt7BlyxbEx8dj8uTJkCQJSu69FKFDhAZROkRoAJS/j9jEsBflxhOhQ4QGUTry8vJMH9vb22PhwoXIzMzEhAkTYDQaFWkQpUOEBlE6RGgAlL+P2MSwF+XGE6FDhAZROmrWrInz58+bPtfpdJg3bx40Gg0uXbqkSIMoHSI0iNIhQgNghfuIud7f0JoGDRoknTt3rshpeXl50vjx4yVPT09VdYjQIErHlStXpJs3bxY73Wg0Sj/99JMiDaJ0iNAgSocIDZKk/H3EJg69vHr1KqpUqYJ69eoVOV2SJBw4cACdO3dWTYcIDSJ1FLh//z4AwNnZWdHtitghQoMoHdZsUPo+YhPDvjAR/gOJ0iFCgzU7UlJSEBERYXrbN0mSoNVq0bNnT4wfPx6urq6q6RChQZQOERoepcR9xCaGvSg3nggdIjSI0jF8+HB4e3sjMDAQNWrUMHVt3LgRJ06cwMqVKy3eIEqHCA2idIjQULBNRe8jZt8xZAXDhg2TIiMjpZSUFNNpycnJ0uLFi6Vhw4apqkOEBlE6evToUep5Pj4+ijSI0iFCgygdIjRIkvL3EZs4Gic+Ph6jR482/ZYGAFdXV4wZMwY3b95UVYcIDaJ0GAwG/P7778VO/+2332BnZ6dIgygdIjSI0iFCA6D8fcQmFkIruPFatWpV5HRr/Ue2ZocIDaJ0fPTRR5g4cSIMBoPpSbD4+HhkZ2cjPDxckQZROkRoEKVDhAZA+fuITeyzP3nyZJk33qM/TFvuEKFBpA5JkhAbG4uEhAQAgIeHB5o3bw6NRqPI9kXqEKFBlA4RGpS+j9jEsAfEuPFE6RChQYSO1NRUzJs3D7du3UK3bt3w5ptvms577733sHDhQtV0iNAgSocIDQWUvI/YxD771NRUTJ06FZ9++imSkpLg4+ODFi1aQKPR4L333lNVhwgNonSEhobC2dkZgYGB+PHHH/Huu+8iNzcXAHDjxg1FGkTpEKFBlA4RGgDl7yM2MexFufFE6BChQZSOq1evYuLEifDx8cGqVatQq1YtvPPOO8jOzlZk+yJ1iNAgSocIDYDy9xGbGPai3HgidIjQIErHw4cPTR9rNBqEhoaiSZMmGDlypOo6RGgQpUOEBkD5+4hNDHtRbjwROkRoEKWjQYMGOH78eJHTJk2ahOeffx5Xr15VpEGUDhEaROkQoQGwwn3E7EfuW8Hbb78tHTt2rNjp8+bNU3TxLxE6RGgQpSM1NVVKS0sr8bxLly4p0iBKhwgNonSI0CBJyt9HbOJonLS0NGg0GlSvXr3YeX/88QcaN26smg4RGkTqIBKV0vcRmxj2RERUNpvYZ09ERGXjsCciUgEOeyIiFbCJhdCIKqtbt264e/cudDoddDodGjdujICAAPTv3x9abdmPhW7evInu3bvj7Nmz0Ot5VyKx8X8oqd7SpUvRoUMHpKen49ixY5g5cyZOnz6N2bNnWzuNyGy4G4fov5ycnNC9e3d89tln2LZtGy5evIiffvoJffr0QevWrdG5c+cii2QVLKD1wgsvoFWrVqY10rds2YJ//OMfeOGFFzB8+HDEx8db5fshKozDnugRzz33HOrUqYOYmBg4ODggPDwcMTExiIqKwoYNG/DDDz8AAL788ksAwPHjx03rkv/www+IiorCokWLcOTIEbRp0wbjxo2z5rdDBIDDnqhE7u7uuHfvHtq1a4emTZtCq9XC09MTvXv3xrFjx0q93MaNGzFy5Eg0atQIer0eo0aNwrlz5/jonqyO++yJSpCYmIjq1avj1KlTiIiIwKVLl/Dw4UPk5OSgZ8+epV7u1q1bmDVrVpF3PJIkCYmJiaY3qCCyBg57okecPn0aiYmJaNOmDcaOHYs333wTK1asgMFgwMyZM5GamgoAJb7BhIeHB0aNGgV/f3+ls4nKxN04RP+VkZGB/fv348MPP4S/vz+aNm2KBw8eoHr16jAYDDh9+jSio6NNX+/q6gqtVltk7fHAwEAsW7YMly5dAgCkp6fju+++U/x7IXoU18YhVSt8nL1Wq0Xjxo3h7++PwMBA6HQ67N69G+Hh4UhLS0Pbtm1Rr1493L9/HxEREQCABQsWYMOGDcjNzcWKFSvQsmVLfPPNN1i5ciXi4+Ph5OSEDh068DBOsjoOeyIiFeBuHCIiFeCwJyJSAQ57IiIV4LAnIlIBDnsiIhXgsCciUgEOeyIiFeCwJyJSgf8HrFC/G6NW+tIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "Z0ZELkkbbdk3",
        "outputId": "e3bd4c2f-7d0d-4b43-b2aa-5bbfef7bab66"
      },
      "source": [
        "ax = lag.plot(x=\"Date\", y=\"windspeedKmph(t-3)\", kind=\"bar\")\n",
        "lag.plot(x=\"Date\", y=\"windspeedKmph(t-2)\", kind=\"bar\", ax=ax, color=\"C2\")\n",
        "lag.plot(x=\"Date\", y=\"windspeedKmph(t-1)\", kind=\"bar\", ax=ax, color=\"C3\")"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5b17a2b490>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ctnwUR5ZMw-O"
      },
      "source": [
        "# Statistical Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wF2VR0sBUxnc"
      },
      "source": [
        "**Mean values of each column**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "A2fAO108KzNd",
        "outputId": "1c082625-8a73-4560-b26e-93e71e061759"
      },
      "source": [
        "mean = pd.read_csv('/content/Mean.csv')\n",
        "mean.head()"
      ],
      "execution_count": 65,
      "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>DewPointC</th>\n",
              "      <th>FeelsLikeC</th>\n",
              "      <th>HeatIndexC</th>\n",
              "      <th>WindChillC</th>\n",
              "      <th>WindGustKmph</th>\n",
              "      <th>cloudcover</th>\n",
              "      <th>humidity</th>\n",
              "      <th>precipMM</th>\n",
              "      <th>pressure</th>\n",
              "      <th>tempC</th>\n",
              "      <th>visibility</th>\n",
              "      <th>winddirDegree</th>\n",
              "      <th>windspeedKmph</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>-12.625000</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>-7.833333</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>24.458333</td>\n",
              "      <td>5.458333</td>\n",
              "      <td>66.625000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1019.583333</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>113.250000</td>\n",
              "      <td>13.041667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021.02.17</td>\n",
              "      <td>-15.291667</td>\n",
              "      <td>-8.875000</td>\n",
              "      <td>-5.708333</td>\n",
              "      <td>-8.875000</td>\n",
              "      <td>16.583333</td>\n",
              "      <td>6.666667</td>\n",
              "      <td>46.291667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1015.875000</td>\n",
              "      <td>-5.833333</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>129.625000</td>\n",
              "      <td>7.916667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021.02.18</td>\n",
              "      <td>-17.750000</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>-9.375000</td>\n",
              "      <td>-13.166667</td>\n",
              "      <td>17.791667</td>\n",
              "      <td>21.666667</td>\n",
              "      <td>49.875000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1012.458333</td>\n",
              "      <td>-10.083333</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>99.833333</td>\n",
              "      <td>8.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021.02.19</td>\n",
              "      <td>-17.166667</td>\n",
              "      <td>-22.666667</td>\n",
              "      <td>-15.041667</td>\n",
              "      <td>-22.666667</td>\n",
              "      <td>34.250000</td>\n",
              "      <td>15.208333</td>\n",
              "      <td>78.458333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1013.083333</td>\n",
              "      <td>-15.250000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>103.416667</td>\n",
              "      <td>17.041667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021.02.22</td>\n",
              "      <td>-6.250000</td>\n",
              "      <td>-9.875000</td>\n",
              "      <td>-4.791667</td>\n",
              "      <td>-9.875000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>83.333333</td>\n",
              "      <td>88.041667</td>\n",
              "      <td>0.295833</td>\n",
              "      <td>1016.500000</td>\n",
              "      <td>-4.875000</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>126.333333</td>\n",
              "      <td>13.041667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date  DewPointC  FeelsLikeC  ...  visibility  winddirDegree  windspeedKmph\n",
              "0  2021.02.16 -12.625000  -13.166667  ...   10.000000     113.250000      13.041667\n",
              "1  2021.02.17 -15.291667   -8.875000  ...   10.000000     129.625000       7.916667\n",
              "2  2021.02.18 -17.750000  -13.166667  ...   10.000000      99.833333       8.666667\n",
              "3  2021.02.19 -17.166667  -22.666667  ...   10.000000     103.416667      17.041667\n",
              "4  2021.02.22  -6.250000   -9.875000  ...    5.333333     126.333333      13.041667\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "V1NVVKPOPvJA",
        "outputId": "9ad5f87f-80bf-40c5-bda4-aa4288d02786"
      },
      "source": [
        "ax = mean.plot(x=\"Date\", y=\"windspeedKmph\", kind=\"bar\")\n",
        "mean.plot(x=\"Date\", y=\"DewPointC\", kind=\"bar\", ax=ax, color=\"C2\")\n",
        "mean.plot(x=\"Date\", y=\"tempC\", kind=\"bar\", ax=ax, color=\"C3\")\n",
        "#mean.plot(x=\"Date\", y=\"HeatIndexC\", kind=\"bar\", ax=ax, color=\"C4\")\n",
        "#mean.plot(x=\"Date\", y=\"humidity\", kind=\"bar\", ax=ax, color=\"C4\")\n",
        "#mean.plot(x=\"Date\", y=\"pressure\", kind=\"bar\", ax=ax, color=\"C5\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ShdZ5UEARb7K",
        "outputId": "8e589cd2-6786-4fb5-dcc9-2d156db8d984"
      },
      "source": [
        "mean.columns"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Date', 'DewPointC', 'FeelsLikeC', 'HeatIndexC', 'WindChillC',\n",
              "       'WindGustKmph', 'cloudcover', 'humidity', 'precipMM', 'pressure',\n",
              "       'tempC', 'visibility', 'winddirDegree', 'windspeedKmph'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "Q_BDWJotQ4Xn",
        "outputId": "639292ae-14bf-4266-cd82-a22c5ac423c7"
      },
      "source": [
        "mean.plot(x=\"Date\", y=['tempC'], kind=\"bar\")"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5b18770690>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "8-05iGZOTo6Q",
        "outputId": "e1e620ac-7f22-436e-d531-3ee750eaef0d"
      },
      "source": [
        "mean.plot(x=\"Date\", y=['windspeedKmph'], kind=\"bar\")"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5b1872b910>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "IGizoesJTwcS",
        "outputId": "d887f407-a312-4825-d40d-b70da95f4e94"
      },
      "source": [
        "mean.plot(x=\"Date\", y=['humidity'], kind=\"bar\")"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5b18680210>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MkiuNSKCUqLx"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kl_z2HrkUq-7"
      },
      "source": [
        "**Standard Deviations of each column**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "ThLCZnd5M1wW",
        "outputId": "e7f1381a-4fd6-46e8-ce6a-b2bf2a41addb"
      },
      "source": [
        "stdev=pd.read_csv('/content/Stdev.csv')\n",
        "stdev"
      ],
      "execution_count": 106,
      "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>DewPointC</th>\n",
              "      <th>FeelsLikeC</th>\n",
              "      <th>HeatIndexC</th>\n",
              "      <th>WindChillC</th>\n",
              "      <th>WindGustKmph</th>\n",
              "      <th>cloudcover</th>\n",
              "      <th>humidity</th>\n",
              "      <th>precipMM</th>\n",
              "      <th>pressure</th>\n",
              "      <th>tempC</th>\n",
              "      <th>visibility</th>\n",
              "      <th>winddirDegree</th>\n",
              "      <th>windspeedKmph</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>-16</td>\n",
              "      <td>-16</td>\n",
              "      <td>-10</td>\n",
              "      <td>-16</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>59</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1018</td>\n",
              "      <td>-11</td>\n",
              "      <td>10</td>\n",
              "      <td>109</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021.02.17</td>\n",
              "      <td>-20</td>\n",
              "      <td>-12</td>\n",
              "      <td>-7</td>\n",
              "      <td>-12</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>33</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1014</td>\n",
              "      <td>-8</td>\n",
              "      <td>10</td>\n",
              "      <td>117</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021.02.18</td>\n",
              "      <td>-22</td>\n",
              "      <td>-18</td>\n",
              "      <td>-15</td>\n",
              "      <td>-18</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>30</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1012</td>\n",
              "      <td>-18</td>\n",
              "      <td>10</td>\n",
              "      <td>93</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021.02.19</td>\n",
              "      <td>-20</td>\n",
              "      <td>-26</td>\n",
              "      <td>-20</td>\n",
              "      <td>-26</td>\n",
              "      <td>26</td>\n",
              "      <td>3</td>\n",
              "      <td>76</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1012</td>\n",
              "      <td>-22</td>\n",
              "      <td>10</td>\n",
              "      <td>95</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021.02.22</td>\n",
              "      <td>-13</td>\n",
              "      <td>-20</td>\n",
              "      <td>-14</td>\n",
              "      <td>-20</td>\n",
              "      <td>18</td>\n",
              "      <td>70</td>\n",
              "      <td>82</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1015</td>\n",
              "      <td>-15</td>\n",
              "      <td>2</td>\n",
              "      <td>117</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2021.02.23</td>\n",
              "      <td>-7</td>\n",
              "      <td>-10</td>\n",
              "      <td>-5</td>\n",
              "      <td>-10</td>\n",
              "      <td>8</td>\n",
              "      <td>58</td>\n",
              "      <td>79</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1013</td>\n",
              "      <td>-6</td>\n",
              "      <td>5</td>\n",
              "      <td>132</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2021.02.24</td>\n",
              "      <td>-4</td>\n",
              "      <td>-8</td>\n",
              "      <td>-5</td>\n",
              "      <td>-8</td>\n",
              "      <td>18</td>\n",
              "      <td>66</td>\n",
              "      <td>77</td>\n",
              "      <td>0.0</td>\n",
              "      <td>991</td>\n",
              "      <td>-8</td>\n",
              "      <td>5</td>\n",
              "      <td>107</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2021.02.25</td>\n",
              "      <td>1</td>\n",
              "      <td>-3</td>\n",
              "      <td>1</td>\n",
              "      <td>-3</td>\n",
              "      <td>6</td>\n",
              "      <td>100</td>\n",
              "      <td>98</td>\n",
              "      <td>1.2</td>\n",
              "      <td>990</td>\n",
              "      <td>-1</td>\n",
              "      <td>4</td>\n",
              "      <td>180</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2021.02.26</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>12</td>\n",
              "      <td>97</td>\n",
              "      <td>95</td>\n",
              "      <td>0.4</td>\n",
              "      <td>994</td>\n",
              "      <td>-6</td>\n",
              "      <td>2</td>\n",
              "      <td>258</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2021.03.01</td>\n",
              "      <td>-7</td>\n",
              "      <td>-13</td>\n",
              "      <td>-6</td>\n",
              "      <td>-13</td>\n",
              "      <td>30</td>\n",
              "      <td>89</td>\n",
              "      <td>86</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1002</td>\n",
              "      <td>-6</td>\n",
              "      <td>1</td>\n",
              "      <td>253</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2021.03.02</td>\n",
              "      <td>-8</td>\n",
              "      <td>-13</td>\n",
              "      <td>-6</td>\n",
              "      <td>-13</td>\n",
              "      <td>27</td>\n",
              "      <td>93</td>\n",
              "      <td>93</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1007</td>\n",
              "      <td>-7</td>\n",
              "      <td>2</td>\n",
              "      <td>256</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date  DewPointC  FeelsLikeC  ...  visibility  winddirDegree  windspeedKmph\n",
              "0   2021.02.16        -16         -16  ...          10            109             11\n",
              "1   2021.02.17        -20         -12  ...          10            117              4\n",
              "2   2021.02.18        -22         -18  ...          10             93              4\n",
              "3   2021.02.19        -20         -26  ...          10             95             14\n",
              "4   2021.02.22        -13         -20  ...           2            117             12\n",
              "5   2021.02.23         -7         -10  ...           5            132              5\n",
              "6   2021.02.24         -4          -8  ...           5            107             11\n",
              "7   2021.02.25          1          -3  ...           4            180              4\n",
              "8   2021.02.26         -6         -11  ...           2            258              8\n",
              "9   2021.03.01         -7         -13  ...           1            253             17\n",
              "10  2021.03.02         -8         -13  ...           2            256             13\n",
              "\n",
              "[11 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "H0pNp94FZYMS",
        "outputId": "a77811be-219b-43ff-befe-b9184d4718d4"
      },
      "source": [
        "stdev.plot(x=\"Date\", y=['humidity'], kind=\"bar\")"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5b17473810>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ims0cfzjUnlq"
      },
      "source": [
        "**Minimum values of each column**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "mxlPFPgoM_mC",
        "outputId": "202b2632-2117-40bc-edc7-2c76c95d24cc"
      },
      "source": [
        "pd.read_csv('/content/Min.csv')"
      ],
      "execution_count": 57,
      "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>DewPointC</th>\n",
              "      <th>FeelsLikeC</th>\n",
              "      <th>HeatIndexC</th>\n",
              "      <th>WindChillC</th>\n",
              "      <th>WindGustKmph</th>\n",
              "      <th>cloudcover</th>\n",
              "      <th>humidity</th>\n",
              "      <th>precipMM</th>\n",
              "      <th>pressure</th>\n",
              "      <th>tempC</th>\n",
              "      <th>visibility</th>\n",
              "      <th>winddirDegree</th>\n",
              "      <th>windspeedKmph</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>-16</td>\n",
              "      <td>-16</td>\n",
              "      <td>-10</td>\n",
              "      <td>-16</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>59</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1018</td>\n",
              "      <td>-11</td>\n",
              "      <td>10</td>\n",
              "      <td>109</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021.02.17</td>\n",
              "      <td>-20</td>\n",
              "      <td>-12</td>\n",
              "      <td>-7</td>\n",
              "      <td>-12</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>33</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1014</td>\n",
              "      <td>-8</td>\n",
              "      <td>10</td>\n",
              "      <td>117</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021.02.18</td>\n",
              "      <td>-22</td>\n",
              "      <td>-18</td>\n",
              "      <td>-15</td>\n",
              "      <td>-18</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>30</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1012</td>\n",
              "      <td>-18</td>\n",
              "      <td>10</td>\n",
              "      <td>93</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021.02.19</td>\n",
              "      <td>-20</td>\n",
              "      <td>-26</td>\n",
              "      <td>-20</td>\n",
              "      <td>-26</td>\n",
              "      <td>26</td>\n",
              "      <td>3</td>\n",
              "      <td>76</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1012</td>\n",
              "      <td>-22</td>\n",
              "      <td>10</td>\n",
              "      <td>95</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021.02.22</td>\n",
              "      <td>-13</td>\n",
              "      <td>-20</td>\n",
              "      <td>-14</td>\n",
              "      <td>-20</td>\n",
              "      <td>18</td>\n",
              "      <td>70</td>\n",
              "      <td>82</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1015</td>\n",
              "      <td>-15</td>\n",
              "      <td>2</td>\n",
              "      <td>117</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2021.02.23</td>\n",
              "      <td>-7</td>\n",
              "      <td>-10</td>\n",
              "      <td>-5</td>\n",
              "      <td>-10</td>\n",
              "      <td>8</td>\n",
              "      <td>58</td>\n",
              "      <td>79</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1013</td>\n",
              "      <td>-6</td>\n",
              "      <td>5</td>\n",
              "      <td>132</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2021.02.24</td>\n",
              "      <td>-4</td>\n",
              "      <td>-8</td>\n",
              "      <td>-5</td>\n",
              "      <td>-8</td>\n",
              "      <td>18</td>\n",
              "      <td>66</td>\n",
              "      <td>77</td>\n",
              "      <td>0.0</td>\n",
              "      <td>991</td>\n",
              "      <td>-8</td>\n",
              "      <td>5</td>\n",
              "      <td>107</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2021.02.25</td>\n",
              "      <td>1</td>\n",
              "      <td>-3</td>\n",
              "      <td>1</td>\n",
              "      <td>-3</td>\n",
              "      <td>6</td>\n",
              "      <td>100</td>\n",
              "      <td>98</td>\n",
              "      <td>1.2</td>\n",
              "      <td>990</td>\n",
              "      <td>-1</td>\n",
              "      <td>4</td>\n",
              "      <td>180</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2021.02.26</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>12</td>\n",
              "      <td>97</td>\n",
              "      <td>95</td>\n",
              "      <td>0.4</td>\n",
              "      <td>994</td>\n",
              "      <td>-6</td>\n",
              "      <td>2</td>\n",
              "      <td>258</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2021.03.01</td>\n",
              "      <td>-7</td>\n",
              "      <td>-13</td>\n",
              "      <td>-6</td>\n",
              "      <td>-13</td>\n",
              "      <td>30</td>\n",
              "      <td>89</td>\n",
              "      <td>86</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1002</td>\n",
              "      <td>-6</td>\n",
              "      <td>1</td>\n",
              "      <td>253</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2021.03.02</td>\n",
              "      <td>-8</td>\n",
              "      <td>-13</td>\n",
              "      <td>-6</td>\n",
              "      <td>-13</td>\n",
              "      <td>27</td>\n",
              "      <td>93</td>\n",
              "      <td>93</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1007</td>\n",
              "      <td>-7</td>\n",
              "      <td>2</td>\n",
              "      <td>256</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date  DewPointC  FeelsLikeC  ...  visibility  winddirDegree  windspeedKmph\n",
              "0   2021.02.16        -16         -16  ...          10            109             11\n",
              "1   2021.02.17        -20         -12  ...          10            117              4\n",
              "2   2021.02.18        -22         -18  ...          10             93              4\n",
              "3   2021.02.19        -20         -26  ...          10             95             14\n",
              "4   2021.02.22        -13         -20  ...           2            117             12\n",
              "5   2021.02.23         -7         -10  ...           5            132              5\n",
              "6   2021.02.24         -4          -8  ...           5            107             11\n",
              "7   2021.02.25          1          -3  ...           4            180              4\n",
              "8   2021.02.26         -6         -11  ...           2            258              8\n",
              "9   2021.03.01         -7         -13  ...           1            253             17\n",
              "10  2021.03.02         -8         -13  ...           2            256             13\n",
              "\n",
              "[11 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vWTFUrWDUgj1"
      },
      "source": [
        "**Maximum values of each column**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "phgX7E5rM_uD",
        "outputId": "241cac84-6e2c-4020-e279-b5a6ac562b4b"
      },
      "source": [
        "pd.read_csv('/content/Max.csv')"
      ],
      "execution_count": 58,
      "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>DewPointC</th>\n",
              "      <th>FeelsLikeC</th>\n",
              "      <th>HeatIndexC</th>\n",
              "      <th>WindChillC</th>\n",
              "      <th>WindGustKmph</th>\n",
              "      <th>cloudcover</th>\n",
              "      <th>humidity</th>\n",
              "      <th>precipMM</th>\n",
              "      <th>pressure</th>\n",
              "      <th>tempC</th>\n",
              "      <th>visibility</th>\n",
              "      <th>winddirDegree</th>\n",
              "      <th>windspeedKmph</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>-10</td>\n",
              "      <td>-11</td>\n",
              "      <td>-6</td>\n",
              "      <td>-11</td>\n",
              "      <td>28</td>\n",
              "      <td>22</td>\n",
              "      <td>70</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1021</td>\n",
              "      <td>-6</td>\n",
              "      <td>10</td>\n",
              "      <td>117</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021.02.17</td>\n",
              "      <td>-13</td>\n",
              "      <td>-5</td>\n",
              "      <td>-4</td>\n",
              "      <td>-5</td>\n",
              "      <td>27</td>\n",
              "      <td>11</td>\n",
              "      <td>57</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1019</td>\n",
              "      <td>-4</td>\n",
              "      <td>10</td>\n",
              "      <td>148</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021.02.18</td>\n",
              "      <td>-14</td>\n",
              "      <td>-8</td>\n",
              "      <td>-6</td>\n",
              "      <td>-8</td>\n",
              "      <td>27</td>\n",
              "      <td>63</td>\n",
              "      <td>74</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1014</td>\n",
              "      <td>-7</td>\n",
              "      <td>10</td>\n",
              "      <td>108</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021.02.19</td>\n",
              "      <td>-14</td>\n",
              "      <td>-18</td>\n",
              "      <td>-13</td>\n",
              "      <td>-18</td>\n",
              "      <td>39</td>\n",
              "      <td>57</td>\n",
              "      <td>81</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1014</td>\n",
              "      <td>-13</td>\n",
              "      <td>10</td>\n",
              "      <td>118</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021.02.22</td>\n",
              "      <td>-3</td>\n",
              "      <td>-4</td>\n",
              "      <td>0</td>\n",
              "      <td>-4</td>\n",
              "      <td>35</td>\n",
              "      <td>100</td>\n",
              "      <td>96</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1017</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>139</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2021.02.23</td>\n",
              "      <td>-2</td>\n",
              "      <td>-3</td>\n",
              "      <td>-1</td>\n",
              "      <td>-3</td>\n",
              "      <td>26</td>\n",
              "      <td>99</td>\n",
              "      <td>95</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1016</td>\n",
              "      <td>-1</td>\n",
              "      <td>10</td>\n",
              "      <td>168</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2021.02.24</td>\n",
              "      <td>1</td>\n",
              "      <td>-4</td>\n",
              "      <td>0</td>\n",
              "      <td>-4</td>\n",
              "      <td>29</td>\n",
              "      <td>100</td>\n",
              "      <td>98</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1013</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>197</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2021.02.25</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>4.2</td>\n",
              "      <td>994</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>312</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2021.02.26</td>\n",
              "      <td>1</td>\n",
              "      <td>-2</td>\n",
              "      <td>1</td>\n",
              "      <td>-2</td>\n",
              "      <td>26</td>\n",
              "      <td>100</td>\n",
              "      <td>99</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1019</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>299</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2021.03.01</td>\n",
              "      <td>-1</td>\n",
              "      <td>-5</td>\n",
              "      <td>0</td>\n",
              "      <td>-5</td>\n",
              "      <td>51</td>\n",
              "      <td>100</td>\n",
              "      <td>98</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1008</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>265</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2021.03.02</td>\n",
              "      <td>0</td>\n",
              "      <td>-4</td>\n",
              "      <td>1</td>\n",
              "      <td>-4</td>\n",
              "      <td>45</td>\n",
              "      <td>100</td>\n",
              "      <td>98</td>\n",
              "      <td>2.3</td>\n",
              "      <td>1015</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>271</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date  DewPointC  FeelsLikeC  ...  visibility  winddirDegree  windspeedKmph\n",
              "0   2021.02.16        -10         -11  ...          10            117             14\n",
              "1   2021.02.17        -13          -5  ...          10            148             13\n",
              "2   2021.02.18        -14          -8  ...          10            108             13\n",
              "3   2021.02.19        -14         -18  ...          10            118             19\n",
              "4   2021.02.22         -3          -4  ...          10            139             17\n",
              "5   2021.02.23         -2          -3  ...          10            168             13\n",
              "6   2021.02.24          1          -4  ...          10            197             15\n",
              "7   2021.02.25          2           0  ...           7            312             17\n",
              "8   2021.02.26          1          -2  ...           3            299             13\n",
              "9   2021.03.01         -1          -5  ...           7            265             28\n",
              "10  2021.03.02          0          -4  ...           2            271             25\n",
              "\n",
              "[11 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NBmDgvGSU0ka"
      },
      "source": [
        "**Median values of each column**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "BrPVafMGM_xf",
        "outputId": "3667b80e-ba7b-4bc3-d986-c08a9fbd3282"
      },
      "source": [
        "med = pd.read_csv('/content/Median.csv')\n",
        "med"
      ],
      "execution_count": 94,
      "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",
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              "        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>DewPointC</th>\n",
              "      <th>FeelsLikeC</th>\n",
              "      <th>HeatIndexC</th>\n",
              "      <th>WindChillC</th>\n",
              "      <th>WindGustKmph</th>\n",
              "      <th>cloudcover</th>\n",
              "      <th>humidity</th>\n",
              "      <th>precipMM</th>\n",
              "      <th>pressure</th>\n",
              "      <th>tempC</th>\n",
              "      <th>visibility</th>\n",
              "      <th>winddirDegree</th>\n",
              "      <th>windspeedKmph</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>-12.5</td>\n",
              "      <td>-13.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>-13.0</td>\n",
              "      <td>24.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021.02.17</td>\n",
              "      <td>-14.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>11.5</td>\n",
              "      <td>7.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1016.0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021.02.18</td>\n",
              "      <td>-16.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>-12.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>12.5</td>\n",
              "      <td>53.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021.02.19</td>\n",
              "      <td>-17.0</td>\n",
              "      <td>-23.0</td>\n",
              "      <td>-15.0</td>\n",
              "      <td>-23.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1013.0</td>\n",
              "      <td>-15.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>101.5</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021.02.22</td>\n",
              "      <td>-5.5</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>81.5</td>\n",
              "      <td>87.0</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>126.5</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2021.02.23</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>16.5</td>\n",
              "      <td>89.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1014.0</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>148.5</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2021.02.24</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>87.5</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1000.5</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>9.0</td>\n",
              "      <td>138.5</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2021.02.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>13.5</td>\n",
              "      <td>100.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>2.30</td>\n",
              "      <td>992.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>276.5</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2021.02.26</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>1.05</td>\n",
              "      <td>1006.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>274.5</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2021.03.01</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>-11.0</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>-11.0</td>\n",
              "      <td>42.5</td>\n",
              "      <td>96.0</td>\n",
              "      <td>92.5</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1004.0</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>259.0</td>\n",
              "      <td>23.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2021.03.02</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>-8.5</td>\n",
              "      <td>-2.5</td>\n",
              "      <td>-8.5</td>\n",
              "      <td>34.5</td>\n",
              "      <td>100.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>1.15</td>\n",
              "      <td>1013.0</td>\n",
              "      <td>-3.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>260.0</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date  DewPointC  FeelsLikeC  ...  visibility  winddirDegree  windspeedKmph\n",
              "0   2021.02.16      -12.5       -13.0  ...        10.0          113.0           13.0\n",
              "1   2021.02.17      -14.0        -9.0  ...        10.0          126.0            6.0\n",
              "2   2021.02.18      -16.0       -12.0  ...        10.0          100.0            8.0\n",
              "3   2021.02.19      -17.0       -23.0  ...        10.0          101.5           17.0\n",
              "4   2021.02.22       -5.5        -9.0  ...         6.0          126.5           12.0\n",
              "5   2021.02.23       -3.0        -6.0  ...        10.0          148.5            9.0\n",
              "6   2021.02.24       -3.0        -5.0  ...         9.0          138.5           13.0\n",
              "7   2021.02.25        1.0        -1.5  ...         6.0          276.5            9.5\n",
              "8   2021.02.26       -1.0        -4.0  ...         2.0          274.5           11.0\n",
              "9   2021.03.01       -5.0       -11.0  ...         2.5          259.0           23.0\n",
              "10  2021.03.02       -5.0        -8.5  ...         2.0          260.0           21.0\n",
              "\n",
              "[11 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "k2p6geP2WuUm",
        "outputId": "2f8e3872-820d-449d-dcc2-ce4cfdc4a986"
      },
      "source": [
        "ax = med.plot(x=\"Date\", y=\"windspeedKmph\", kind=\"bar\")\n",
        "med.plot(x=\"Date\", y=\"DewPointC\", kind=\"bar\", ax=ax, color=\"C2\")\n",
        "med.plot(x=\"Date\", y=\"tempC\", kind=\"bar\", ax=ax, color=\"C3\")\n",
        "#mean.plot(x=\"Date\", y=\"HeatIndexC\", kind=\"bar\", ax=ax, color=\"C4\")\n",
        "#med.plot(x=\"Date\", y=\"humidity\", kind=\"bar\", ax=ax, color=\"C4\")\n",
        "#med.plot(x=\"Date\", y=\"pressure\", kind=\"bar\", ax=ax, color=\"C5\")"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5b178c9990>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dqWXZncsU3Jh"
      },
      "source": [
        "**Quartile-1 values of each column**\n",
        "\n",
        "25% data are less than these values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "oaOWKN7XM_0w",
        "outputId": "5258d09e-e591-43b7-8a44-1e0d4e1a43a2"
      },
      "source": [
        "pd.read_csv('/content/Q1.csv')"
      ],
      "execution_count": 60,
      "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>DewPointC</th>\n",
              "      <th>FeelsLikeC</th>\n",
              "      <th>HeatIndexC</th>\n",
              "      <th>WindChillC</th>\n",
              "      <th>WindGustKmph</th>\n",
              "      <th>cloudcover</th>\n",
              "      <th>humidity</th>\n",
              "      <th>precipMM</th>\n",
              "      <th>pressure</th>\n",
              "      <th>tempC</th>\n",
              "      <th>visibility</th>\n",
              "      <th>winddirDegree</th>\n",
              "      <th>windspeedKmph</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>-13.25</td>\n",
              "      <td>-14.00</td>\n",
              "      <td>-9.00</td>\n",
              "      <td>-14.00</td>\n",
              "      <td>23.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>65.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1019.00</td>\n",
              "      <td>-9.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>112.00</td>\n",
              "      <td>13.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021.02.17</td>\n",
              "      <td>-16.25</td>\n",
              "      <td>-11.00</td>\n",
              "      <td>-7.00</td>\n",
              "      <td>-11.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>43.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1015.00</td>\n",
              "      <td>-7.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>118.75</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021.02.18</td>\n",
              "      <td>-20.25</td>\n",
              "      <td>-15.00</td>\n",
              "      <td>-11.00</td>\n",
              "      <td>-15.00</td>\n",
              "      <td>15.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>31.75</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1012.00</td>\n",
              "      <td>-12.25</td>\n",
              "      <td>10.00</td>\n",
              "      <td>97.00</td>\n",
              "      <td>7.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021.02.19</td>\n",
              "      <td>-18.00</td>\n",
              "      <td>-24.00</td>\n",
              "      <td>-15.25</td>\n",
              "      <td>-24.00</td>\n",
              "      <td>32.75</td>\n",
              "      <td>6.00</td>\n",
              "      <td>77.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1013.00</td>\n",
              "      <td>-16.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>16.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021.02.22</td>\n",
              "      <td>-8.25</td>\n",
              "      <td>-13.25</td>\n",
              "      <td>-7.25</td>\n",
              "      <td>-13.25</td>\n",
              "      <td>20.75</td>\n",
              "      <td>75.50</td>\n",
              "      <td>85.00</td>\n",
              "      <td>0.100</td>\n",
              "      <td>1016.00</td>\n",
              "      <td>-7.25</td>\n",
              "      <td>2.00</td>\n",
              "      <td>122.75</td>\n",
              "      <td>12.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2021.02.23</td>\n",
              "      <td>-7.00</td>\n",
              "      <td>-8.00</td>\n",
              "      <td>-4.00</td>\n",
              "      <td>-8.00</td>\n",
              "      <td>12.00</td>\n",
              "      <td>82.75</td>\n",
              "      <td>81.75</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1014.00</td>\n",
              "      <td>-4.00</td>\n",
              "      <td>7.75</td>\n",
              "      <td>144.00</td>\n",
              "      <td>7.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2021.02.24</td>\n",
              "      <td>-3.25</td>\n",
              "      <td>-6.00</td>\n",
              "      <td>-2.00</td>\n",
              "      <td>-6.00</td>\n",
              "      <td>19.75</td>\n",
              "      <td>88.75</td>\n",
              "      <td>80.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>994.00</td>\n",
              "      <td>-3.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>122.00</td>\n",
              "      <td>12.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2021.02.25</td>\n",
              "      <td>1.00</td>\n",
              "      <td>-3.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>-3.00</td>\n",
              "      <td>7.75</td>\n",
              "      <td>100.00</td>\n",
              "      <td>99.00</td>\n",
              "      <td>1.975</td>\n",
              "      <td>990.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>6.00</td>\n",
              "      <td>248.75</td>\n",
              "      <td>6.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2021.02.26</td>\n",
              "      <td>-1.25</td>\n",
              "      <td>-5.25</td>\n",
              "      <td>-1.25</td>\n",
              "      <td>-5.25</td>\n",
              "      <td>13.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>99.00</td>\n",
              "      <td>0.775</td>\n",
              "      <td>998.75</td>\n",
              "      <td>-1.25</td>\n",
              "      <td>2.00</td>\n",
              "      <td>264.00</td>\n",
              "      <td>9.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2021.03.01</td>\n",
              "      <td>-6.00</td>\n",
              "      <td>-12.00</td>\n",
              "      <td>-5.00</td>\n",
              "      <td>-12.00</td>\n",
              "      <td>35.00</td>\n",
              "      <td>93.00</td>\n",
              "      <td>89.00</td>\n",
              "      <td>0.975</td>\n",
              "      <td>1003.00</td>\n",
              "      <td>-5.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>257.00</td>\n",
              "      <td>22.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2021.03.02</td>\n",
              "      <td>-6.00</td>\n",
              "      <td>-11.00</td>\n",
              "      <td>-5.00</td>\n",
              "      <td>-11.00</td>\n",
              "      <td>29.75</td>\n",
              "      <td>96.75</td>\n",
              "      <td>94.00</td>\n",
              "      <td>0.875</td>\n",
              "      <td>1010.75</td>\n",
              "      <td>-6.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>258.00</td>\n",
              "      <td>18.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date  DewPointC  FeelsLikeC  ...  visibility  winddirDegree  windspeedKmph\n",
              "0   2021.02.16     -13.25      -14.00  ...       10.00         112.00          13.00\n",
              "1   2021.02.17     -16.25      -11.00  ...       10.00         118.75           4.00\n",
              "2   2021.02.18     -20.25      -15.00  ...       10.00          97.00           7.00\n",
              "3   2021.02.19     -18.00      -24.00  ...       10.00          96.00          16.00\n",
              "4   2021.02.22      -8.25      -13.25  ...        2.00         122.75          12.00\n",
              "5   2021.02.23      -7.00       -8.00  ...        7.75         144.00           7.00\n",
              "6   2021.02.24      -3.25       -6.00  ...        5.00         122.00          12.00\n",
              "7   2021.02.25       1.00       -3.00  ...        6.00         248.75           6.00\n",
              "8   2021.02.26      -1.25       -5.25  ...        2.00         264.00           9.75\n",
              "9   2021.03.01      -6.00      -12.00  ...        2.00         257.00          22.00\n",
              "10  2021.03.02      -6.00      -11.00  ...        2.00         258.00          18.75\n",
              "\n",
              "[11 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fTQQd_uFVD_a"
      },
      "source": [
        "**Quartile-3 values of each column**\n",
        "\n",
        "75% data are less than these values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "hkCp6PEFM_3i",
        "outputId": "e92a1dce-f82e-44e0-9839-7ce24873692b"
      },
      "source": [
        "pd.read_csv('/content/Q3.csv')"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>DewPointC</th>\n",
              "      <th>FeelsLikeC</th>\n",
              "      <th>HeatIndexC</th>\n",
              "      <th>WindChillC</th>\n",
              "      <th>WindGustKmph</th>\n",
              "      <th>cloudcover</th>\n",
              "      <th>humidity</th>\n",
              "      <th>precipMM</th>\n",
              "      <th>pressure</th>\n",
              "      <th>tempC</th>\n",
              "      <th>visibility</th>\n",
              "      <th>winddirDegree</th>\n",
              "      <th>windspeedKmph</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>-11.00</td>\n",
              "      <td>-12.00</td>\n",
              "      <td>-7.00</td>\n",
              "      <td>-12.00</td>\n",
              "      <td>26.00</td>\n",
              "      <td>7.75</td>\n",
              "      <td>69.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1020.00</td>\n",
              "      <td>-7.00</td>\n",
              "      <td>10</td>\n",
              "      <td>115.25</td>\n",
              "      <td>14.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021.02.17</td>\n",
              "      <td>-14.00</td>\n",
              "      <td>-7.75</td>\n",
              "      <td>-5.00</td>\n",
              "      <td>-7.75</td>\n",
              "      <td>26.00</td>\n",
              "      <td>8.25</td>\n",
              "      <td>50.25</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1016.25</td>\n",
              "      <td>-5.00</td>\n",
              "      <td>10</td>\n",
              "      <td>141.25</td>\n",
              "      <td>13.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021.02.18</td>\n",
              "      <td>-16.00</td>\n",
              "      <td>-11.75</td>\n",
              "      <td>-7.00</td>\n",
              "      <td>-11.75</td>\n",
              "      <td>21.50</td>\n",
              "      <td>29.50</td>\n",
              "      <td>64.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1013.00</td>\n",
              "      <td>-7.00</td>\n",
              "      <td>10</td>\n",
              "      <td>102.00</td>\n",
              "      <td>10.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021.02.19</td>\n",
              "      <td>-16.00</td>\n",
              "      <td>-21.75</td>\n",
              "      <td>-13.75</td>\n",
              "      <td>-21.75</td>\n",
              "      <td>36.00</td>\n",
              "      <td>17.75</td>\n",
              "      <td>80.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1013.25</td>\n",
              "      <td>-13.75</td>\n",
              "      <td>10</td>\n",
              "      <td>109.25</td>\n",
              "      <td>18.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021.02.22</td>\n",
              "      <td>-3.75</td>\n",
              "      <td>-6.00</td>\n",
              "      <td>-1.75</td>\n",
              "      <td>-6.00</td>\n",
              "      <td>27.25</td>\n",
              "      <td>91.25</td>\n",
              "      <td>92.00</td>\n",
              "      <td>0.425</td>\n",
              "      <td>1017.00</td>\n",
              "      <td>-1.75</td>\n",
              "      <td>8</td>\n",
              "      <td>129.25</td>\n",
              "      <td>14.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2021.02.23</td>\n",
              "      <td>-2.00</td>\n",
              "      <td>-4.75</td>\n",
              "      <td>-2.00</td>\n",
              "      <td>-4.75</td>\n",
              "      <td>20.50</td>\n",
              "      <td>93.25</td>\n",
              "      <td>93.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1015.00</td>\n",
              "      <td>-2.00</td>\n",
              "      <td>10</td>\n",
              "      <td>158.25</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2021.02.24</td>\n",
              "      <td>-1.75</td>\n",
              "      <td>-4.75</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>-4.75</td>\n",
              "      <td>24.25</td>\n",
              "      <td>100.00</td>\n",
              "      <td>95.50</td>\n",
              "      <td>1.350</td>\n",
              "      <td>1006.50</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>10</td>\n",
              "      <td>151.25</td>\n",
              "      <td>14.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2021.02.25</td>\n",
              "      <td>1.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>22.25</td>\n",
              "      <td>100.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>2.975</td>\n",
              "      <td>993.00</td>\n",
              "      <td>1.25</td>\n",
              "      <td>7</td>\n",
              "      <td>296.00</td>\n",
              "      <td>14.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2021.02.26</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-3.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>-3.00</td>\n",
              "      <td>15.25</td>\n",
              "      <td>100.00</td>\n",
              "      <td>99.00</td>\n",
              "      <td>1.700</td>\n",
              "      <td>1013.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>2</td>\n",
              "      <td>278.25</td>\n",
              "      <td>11.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2021.03.01</td>\n",
              "      <td>-2.75</td>\n",
              "      <td>-7.75</td>\n",
              "      <td>-1.75</td>\n",
              "      <td>-7.75</td>\n",
              "      <td>49.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>98.00</td>\n",
              "      <td>1.650</td>\n",
              "      <td>1005.25</td>\n",
              "      <td>-1.75</td>\n",
              "      <td>5</td>\n",
              "      <td>261.00</td>\n",
              "      <td>24.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2021.03.02</td>\n",
              "      <td>-2.00</td>\n",
              "      <td>-6.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-6.00</td>\n",
              "      <td>40.25</td>\n",
              "      <td>100.00</td>\n",
              "      <td>97.00</td>\n",
              "      <td>1.600</td>\n",
              "      <td>1014.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>2</td>\n",
              "      <td>261.00</td>\n",
              "      <td>22.25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date  DewPointC  FeelsLikeC  ...  visibility  winddirDegree  windspeedKmph\n",
              "0   2021.02.16     -11.00      -12.00  ...          10         115.25          14.00\n",
              "1   2021.02.17     -14.00       -7.75  ...          10         141.25          13.00\n",
              "2   2021.02.18     -16.00      -11.75  ...          10         102.00          10.25\n",
              "3   2021.02.19     -16.00      -21.75  ...          10         109.25          18.25\n",
              "4   2021.02.22      -3.75       -6.00  ...           8         129.25          14.00\n",
              "5   2021.02.23      -2.00       -4.75  ...          10         158.25          10.00\n",
              "6   2021.02.24      -1.75       -4.75  ...          10         151.25          14.00\n",
              "7   2021.02.25       1.25        0.00  ...           7         296.00          14.25\n",
              "8   2021.02.26       0.00       -3.00  ...           2         278.25          11.00\n",
              "9   2021.03.01      -2.75       -7.75  ...           5         261.00          24.00\n",
              "10  2021.03.02      -2.00       -6.00  ...           2         261.00          22.25\n",
              "\n",
              "[11 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jztFLbp-VHJl"
      },
      "source": [
        "**Kurtosis values of each column**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "5aLvntgJNO8U",
        "outputId": "c3547d2a-2daf-4cc3-b32f-d0856554a8aa"
      },
      "source": [
        "krt = pd.read_csv('/content/Kurtosis.csv')\n",
        "krt"
      ],
      "execution_count": 90,
      "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>DewPointC</th>\n",
              "      <th>FeelsLikeC</th>\n",
              "      <th>HeatIndexC</th>\n",
              "      <th>WindChillC</th>\n",
              "      <th>WindGustKmph</th>\n",
              "      <th>cloudcover</th>\n",
              "      <th>humidity</th>\n",
              "      <th>precipMM</th>\n",
              "      <th>pressure</th>\n",
              "      <th>tempC</th>\n",
              "      <th>visibility</th>\n",
              "      <th>winddirDegree</th>\n",
              "      <th>windspeedKmph</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>-0.533679</td>\n",
              "      <td>-0.329786</td>\n",
              "      <td>-1.261969</td>\n",
              "      <td>-0.329786</td>\n",
              "      <td>-0.810169</td>\n",
              "      <td>0.562268</td>\n",
              "      <td>0.882804</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.537118</td>\n",
              "      <td>-1.187185</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.651845</td>\n",
              "      <td>0.307322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021.02.17</td>\n",
              "      <td>-0.156483</td>\n",
              "      <td>-0.857302</td>\n",
              "      <td>-1.169816</td>\n",
              "      <td>-0.857302</td>\n",
              "      <td>-1.953784</td>\n",
              "      <td>-0.838175</td>\n",
              "      <td>0.062562</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.371681</td>\n",
              "      <td>-1.151494</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.754513</td>\n",
              "      <td>-1.902038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021.02.18</td>\n",
              "      <td>-1.416625</td>\n",
              "      <td>-0.799192</td>\n",
              "      <td>-0.757343</td>\n",
              "      <td>-0.799192</td>\n",
              "      <td>-0.492789</td>\n",
              "      <td>0.360928</td>\n",
              "      <td>-1.677883</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.114172</td>\n",
              "      <td>-0.370874</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.457923</td>\n",
              "      <td>-0.417035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021.02.19</td>\n",
              "      <td>-0.277862</td>\n",
              "      <td>-0.315661</td>\n",
              "      <td>0.466853</td>\n",
              "      <td>-0.315661</td>\n",
              "      <td>0.994396</td>\n",
              "      <td>1.940711</td>\n",
              "      <td>-1.446053</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.424240</td>\n",
              "      <td>1.696292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.070540</td>\n",
              "      <td>-0.827527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021.02.22</td>\n",
              "      <td>-0.534317</td>\n",
              "      <td>-0.573204</td>\n",
              "      <td>-0.057326</td>\n",
              "      <td>-0.573204</td>\n",
              "      <td>-0.780388</td>\n",
              "      <td>-1.174976</td>\n",
              "      <td>-1.083902</td>\n",
              "      <td>-0.679238</td>\n",
              "      <td>-0.158495</td>\n",
              "      <td>0.368871</td>\n",
              "      <td>-1.869970948</td>\n",
              "      <td>-0.206633</td>\n",
              "      <td>1.167653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2021.02.23</td>\n",
              "      <td>-1.666446</td>\n",
              "      <td>-1.103563</td>\n",
              "      <td>-1.109776</td>\n",
              "      <td>-1.103563</td>\n",
              "      <td>-0.963271</td>\n",
              "      <td>1.227880</td>\n",
              "      <td>-1.776409</td>\n",
              "      <td>8.520248</td>\n",
              "      <td>0.126171</td>\n",
              "      <td>-1.216828</td>\n",
              "      <td>-0.54550424</td>\n",
              "      <td>-0.628107</td>\n",
              "      <td>-0.747762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2021.02.24</td>\n",
              "      <td>-0.467994</td>\n",
              "      <td>0.718575</td>\n",
              "      <td>0.431601</td>\n",
              "      <td>0.718575</td>\n",
              "      <td>-0.533456</td>\n",
              "      <td>1.013284</td>\n",
              "      <td>-1.630926</td>\n",
              "      <td>0.745217</td>\n",
              "      <td>-1.328114</td>\n",
              "      <td>0.867099</td>\n",
              "      <td>-1.92742611</td>\n",
              "      <td>-0.272704</td>\n",
              "      <td>-1.123266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2021.02.25</td>\n",
              "      <td>1.841379</td>\n",
              "      <td>0.366071</td>\n",
              "      <td>1.841379</td>\n",
              "      <td>0.366071</td>\n",
              "      <td>-1.672208</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>24.940672</td>\n",
              "      <td>-0.793899</td>\n",
              "      <td>24.997172</td>\n",
              "      <td>2.637735</td>\n",
              "      <td>4.293262621</td>\n",
              "      <td>9.807289</td>\n",
              "      <td>-1.581365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2021.02.26</td>\n",
              "      <td>0.794306</td>\n",
              "      <td>1.007814</td>\n",
              "      <td>0.862517</td>\n",
              "      <td>1.007814</td>\n",
              "      <td>1.157910</td>\n",
              "      <td>4.163657</td>\n",
              "      <td>11.677587</td>\n",
              "      <td>9.096371</td>\n",
              "      <td>-1.434763</td>\n",
              "      <td>0.862517</td>\n",
              "      <td>9.123966942</td>\n",
              "      <td>0.529117</td>\n",
              "      <td>-0.305705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2021.03.01</td>\n",
              "      <td>-1.145131</td>\n",
              "      <td>-1.162153</td>\n",
              "      <td>-1.114753</td>\n",
              "      <td>-1.162153</td>\n",
              "      <td>-1.472484</td>\n",
              "      <td>-1.288554</td>\n",
              "      <td>-1.806529</td>\n",
              "      <td>2.524619</td>\n",
              "      <td>-0.666306</td>\n",
              "      <td>-1.139070</td>\n",
              "      <td>-0.995033766</td>\n",
              "      <td>-0.442172</td>\n",
              "      <td>1.963088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2021.03.02</td>\n",
              "      <td>-1.097932</td>\n",
              "      <td>-1.471421</td>\n",
              "      <td>-1.711587</td>\n",
              "      <td>-1.471421</td>\n",
              "      <td>-1.433854</td>\n",
              "      <td>0.283430</td>\n",
              "      <td>-1.200414</td>\n",
              "      <td>-0.477294</td>\n",
              "      <td>-0.239486</td>\n",
              "      <td>-1.685670</td>\n",
              "      <td>#DIV/0!</td>\n",
              "      <td>3.270964</td>\n",
              "      <td>-0.415955</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date  DewPointC  ...  winddirDegree  windspeedKmph\n",
              "0   2021.02.16  -0.533679  ...      -0.651845       0.307322\n",
              "1   2021.02.17  -0.156483  ...      -1.754513      -1.902038\n",
              "2   2021.02.18  -1.416625  ...      -0.457923      -0.417035\n",
              "3   2021.02.19  -0.277862  ...      -1.070540      -0.827527\n",
              "4   2021.02.22  -0.534317  ...      -0.206633       1.167653\n",
              "5   2021.02.23  -1.666446  ...      -0.628107      -0.747762\n",
              "6   2021.02.24  -0.467994  ...      -0.272704      -1.123266\n",
              "7   2021.02.25   1.841379  ...       9.807289      -1.581365\n",
              "8   2021.02.26   0.794306  ...       0.529117      -0.305705\n",
              "9   2021.03.01  -1.145131  ...      -0.442172       1.963088\n",
              "10  2021.03.02  -1.097932  ...       3.270964      -0.415955\n",
              "\n",
              "[11 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "3Y-ci2MwVkma",
        "outputId": "c51b52aa-2008-45ec-eaf9-2e9ff1ea22de"
      },
      "source": [
        "ax = krt.plot(x=\"Date\", y=\"windspeedKmph\", kind=\"bar\")\n",
        "krt.plot(x=\"Date\", y=\"DewPointC\", kind=\"bar\", ax=ax, color=\"C2\")\n",
        "krt.plot(x=\"Date\", y=\"tempC\", kind=\"bar\", ax=ax, color=\"C3\")\n",
        "krt.plot(x=\"Date\", y=\"humidity\", kind=\"bar\", ax=ax, color=\"C4\")\n",
        "krt.plot(x=\"Date\", y=\"pressure\", kind=\"bar\", ax=ax, color=\"C5\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "SZjEs7_lYArF",
        "outputId": "b7a798ac-9efc-40d8-a56c-e814875c3296"
      },
      "source": [
        "ax = med.plot(x=\"Date\", y=\"windspeedKmph\", kind=\"bar\")\n",
        "med.plot(x=\"Date\", y=\"DewPointC\", kind=\"bar\", ax=ax, color=\"C2\")\n",
        "med.plot(x=\"Date\", y=\"tempC\", kind=\"bar\", ax=ax, color=\"C3\")"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5b17518990>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DyEBJ-KeVKDK"
      },
      "source": [
        "**Skewness values of each column**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "BVQPTCKrNPGb",
        "outputId": "11ec702e-21dc-47a1-aeef-4ee9be273501"
      },
      "source": [
        "skw=pd.read_csv('/content/Skewness.csv')\n",
        "skw"
      ],
      "execution_count": 93,
      "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>DewPointC</th>\n",
              "      <th>FeelsLikeC</th>\n",
              "      <th>HeatIndexC</th>\n",
              "      <th>WindChillC</th>\n",
              "      <th>WindGustKmph</th>\n",
              "      <th>cloudcover</th>\n",
              "      <th>humidity</th>\n",
              "      <th>precipMM</th>\n",
              "      <th>pressure</th>\n",
              "      <th>tempC</th>\n",
              "      <th>visibility</th>\n",
              "      <th>winddirDegree</th>\n",
              "      <th>windspeedKmph</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021.02.16</td>\n",
              "      <td>-0.467567</td>\n",
              "      <td>-0.714413</td>\n",
              "      <td>-0.105969</td>\n",
              "      <td>-0.714413</td>\n",
              "      <td>-0.217241</td>\n",
              "      <td>1.201579</td>\n",
              "      <td>-0.928002</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.442597</td>\n",
              "      <td>-0.224721</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.219575</td>\n",
              "      <td>-0.847487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021.02.17</td>\n",
              "      <td>-1.072579</td>\n",
              "      <td>0.336856</td>\n",
              "      <td>0.254766</td>\n",
              "      <td>0.336856</td>\n",
              "      <td>0.266135</td>\n",
              "      <td>0.191835</td>\n",
              "      <td>-0.061076</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.820977</td>\n",
              "      <td>0.141968</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.293102</td>\n",
              "      <td>0.261574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021.02.18</td>\n",
              "      <td>-0.450174</td>\n",
              "      <td>-0.183492</td>\n",
              "      <td>-0.627521</td>\n",
              "      <td>-0.183492</td>\n",
              "      <td>0.309021</td>\n",
              "      <td>1.199132</td>\n",
              "      <td>-0.087316</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.873068</td>\n",
              "      <td>-0.963440</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.099599</td>\n",
              "      <td>0.433610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021.02.19</td>\n",
              "      <td>0.160960</td>\n",
              "      <td>0.527283</td>\n",
              "      <td>-1.093804</td>\n",
              "      <td>0.527283</td>\n",
              "      <td>-1.173962</td>\n",
              "      <td>1.672922</td>\n",
              "      <td>0.074003</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.080121</td>\n",
              "      <td>-1.427851</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.546657</td>\n",
              "      <td>-0.350995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021.02.22</td>\n",
              "      <td>-0.764065</td>\n",
              "      <td>-0.730865</td>\n",
              "      <td>-0.914180</td>\n",
              "      <td>-0.730865</td>\n",
              "      <td>0.214608</td>\n",
              "      <td>0.278739</td>\n",
              "      <td>0.286287</td>\n",
              "      <td>0.453421</td>\n",
              "      <td>-1.198510</td>\n",
              "      <td>-1.046806</td>\n",
              "      <td>0.024403951</td>\n",
              "      <td>0.299184</td>\n",
              "      <td>1.387949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2021.02.23</td>\n",
              "      <td>-0.371272</td>\n",
              "      <td>0.110491</td>\n",
              "      <td>0.140984</td>\n",
              "      <td>0.110491</td>\n",
              "      <td>0.175504</td>\n",
              "      <td>-1.319644</td>\n",
              "      <td>0.011990</td>\n",
              "      <td>2.882551</td>\n",
              "      <td>0.690896</td>\n",
              "      <td>0.152368</td>\n",
              "      <td>-0.869145464</td>\n",
              "      <td>0.225103</td>\n",
              "      <td>-0.047893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2021.02.24</td>\n",
              "      <td>0.824710</td>\n",
              "      <td>-0.744205</td>\n",
              "      <td>-0.827806</td>\n",
              "      <td>-0.744205</td>\n",
              "      <td>0.707231</td>\n",
              "      <td>-1.488540</td>\n",
              "      <td>0.039961</td>\n",
              "      <td>1.405591</td>\n",
              "      <td>0.237356</td>\n",
              "      <td>-1.120812</td>\n",
              "      <td>-0.281855629</td>\n",
              "      <td>0.743278</td>\n",
              "      <td>-0.114980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2021.02.25</td>\n",
              "      <td>1.584456</td>\n",
              "      <td>0.719903</td>\n",
              "      <td>1.584456</td>\n",
              "      <td>0.719903</td>\n",
              "      <td>0.118163</td>\n",
              "      <td>-5.000000</td>\n",
              "      <td>-4.991446</td>\n",
              "      <td>0.398908</td>\n",
              "      <td>-4.999592</td>\n",
              "      <td>-0.365955</td>\n",
              "      <td>-1.748932273</td>\n",
              "      <td>-2.752590</td>\n",
              "      <td>0.109622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2021.02.26</td>\n",
              "      <td>-1.225706</td>\n",
              "      <td>-1.439720</td>\n",
              "      <td>-1.231829</td>\n",
              "      <td>-1.439720</td>\n",
              "      <td>1.564738</td>\n",
              "      <td>-2.215633</td>\n",
              "      <td>-3.309750</td>\n",
              "      <td>2.688913</td>\n",
              "      <td>0.007760</td>\n",
              "      <td>-1.231829</td>\n",
              "      <td>3.219960287</td>\n",
              "      <td>0.536353</td>\n",
              "      <td>-0.264291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2021.03.01</td>\n",
              "      <td>0.536174</td>\n",
              "      <td>0.610295</td>\n",
              "      <td>0.597052</td>\n",
              "      <td>0.610295</td>\n",
              "      <td>-0.282198</td>\n",
              "      <td>-0.284675</td>\n",
              "      <td>-0.058178</td>\n",
              "      <td>1.381475</td>\n",
              "      <td>0.719840</td>\n",
              "      <td>0.487355</td>\n",
              "      <td>0.565475194</td>\n",
              "      <td>0.229592</td>\n",
              "      <td>-0.237815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2021.03.02</td>\n",
              "      <td>0.404805</td>\n",
              "      <td>0.084164</td>\n",
              "      <td>0.002836</td>\n",
              "      <td>0.084164</td>\n",
              "      <td>0.211483</td>\n",
              "      <td>-1.171055</td>\n",
              "      <td>0.345090</td>\n",
              "      <td>0.546860</td>\n",
              "      <td>-0.884637</td>\n",
              "      <td>0.116039</td>\n",
              "      <td>#DIV/0!</td>\n",
              "      <td>1.486381</td>\n",
              "      <td>-0.694502</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date  DewPointC  ...  winddirDegree  windspeedKmph\n",
              "0   2021.02.16  -0.467567  ...       0.219575      -0.847487\n",
              "1   2021.02.17  -1.072579  ...       0.293102       0.261574\n",
              "2   2021.02.18  -0.450174  ...      -0.099599       0.433610\n",
              "3   2021.02.19   0.160960  ...       0.546657      -0.350995\n",
              "4   2021.02.22  -0.764065  ...       0.299184       1.387949\n",
              "5   2021.02.23  -0.371272  ...       0.225103      -0.047893\n",
              "6   2021.02.24   0.824710  ...       0.743278      -0.114980\n",
              "7   2021.02.25   1.584456  ...      -2.752590       0.109622\n",
              "8   2021.02.26  -1.225706  ...       0.536353      -0.264291\n",
              "9   2021.03.01   0.536174  ...       0.229592      -0.237815\n",
              "10  2021.03.02   0.404805  ...       1.486381      -0.694502\n",
              "\n",
              "[11 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "w-Uk1Bc9VYjK",
        "outputId": "32cd6aad-2182-4987-9688-764c4ffe7200"
      },
      "source": [
        "ax = skw.plot(x=\"Date\", y=\"windspeedKmph\", kind=\"bar\")\n",
        "skw.plot(x=\"Date\", y=\"DewPointC\", kind=\"bar\", ax=ax, color=\"C2\")\n",
        "skw.plot(x=\"Date\", y=\"tempC\", kind=\"bar\", ax=ax, color=\"C3\")\n",
        "skw.plot(x=\"Date\", y=\"humidity\", kind=\"bar\", ax=ax, color=\"C4\")\n",
        "skw.plot(x=\"Date\", y=\"pressure\", kind=\"bar\", ax=ax, color=\"C5\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D8WBXs4y82Fo"
      },
      "source": [
        "# Model Building, Training and Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "0baf6174f04a46d0a2814097c78d3e7b",
            "2a40e0c35a13402996478e6704378641",
            "1c9867d30c80443d9c11fa0df6cb6c34",
            "652106684aae4a298111e334377a69e6",
            "fb861a8656f2407eb33b8e9d00164b3c",
            "a17eb9da119542fba4ab3689e90df0be",
            "890148e3f04945f5a33e41c7f3aea85a",
            "2ee94a74219b4f3482c1b08865298cc6"
          ]
        },
        "id": "eZam4Z3zdnIa",
        "outputId": "a466c6ad-8cf9-40d8-d1d7-7a966738e0e1"
      },
      "source": [
        "# split a univariate dataset into train/test sets\n",
        "def train_test_split(data, n_test):\n",
        "    print(n_test)\n",
        "    data = np.array(data)\n",
        "    return data[:-n_test, :], data[-n_test:, :]\n",
        "    \n",
        "# walk-forward validation for univariate data\n",
        "def walk_forward_validation(params, data, n_test):\n",
        "\tpredictions = list()\n",
        "\t# split dataset\n",
        "\ttrain, test = train_test_split(data, n_test)\n",
        "\t# seed history with training dataset\n",
        "\thistory = [x for x in train]\n",
        "\t# step over each time-step in the test set\n",
        "\tfor i in range(len(test)):\n",
        "\t\t# split test row into input and output columns\n",
        "\t\ttestX, testy = test[i, :-1], test[i, -1]\n",
        "\t\t# fit model on history and make a prediction\n",
        "\t\tyhat = xgboost_forecast(params, history, testX)\n",
        "\t\t# store forecast in list of predictions\n",
        "\t\tpredictions.append(yhat)\n",
        "\t\t# add actual observation to history for the next loop\n",
        "\t\thistory.append(test[i])\n",
        "\t\t# summarize progress\n",
        "\t\tprint('>expected=%.1f, predicted=%.1f' % (testy, yhat))\n",
        "\t# estimate prediction error\n",
        "\terror = balanced_accuracy_score(test[:, -1], predictions)\n",
        "\treturn error, test[:, -1], predictions\n",
        "\n",
        "# fit an xgboost model and make a one step prediction\n",
        "def xgboost_forecast(params, train, testX):\n",
        "\t# transform list into array\n",
        "\ttrain = np.array(train)\n",
        "\t# split into input and output columns\n",
        "\ttrainX, trainy = train[:, :-1], train[:, -1]\n",
        "\t# fit model\n",
        "\tmodel = XGBClassifier(**params)\n",
        "\tmodel.fit(trainX, trainy)\n",
        "\t# make a one-step prediction\n",
        "\tyhat = model.predict(np.array([testX]))\n",
        "\treturn yhat[0]\n",
        "    \n",
        "def objective(trial: Trial, data) -> float:\n",
        "    params = {\n",
        "        \"booster\": \"gbtree\",\n",
        "        #\"tree_method\": \"gpu_hist\",\n",
        "        \"n_estimators\": trial.suggest_int(\"n_estimators\", 0, 1000),\n",
        "        \"max_depth\": trial.suggest_int(\"max_depth\", 2, 10),\n",
        "        \"reg_alpha\": trial.suggest_int(\"reg_alpha\", 0, 5),\n",
        "        \"reg_lambda\": trial.suggest_int(\"reg_lambda\", 0, 5),\n",
        "        \"min_child_weight\": trial.suggest_int(\"min_child_weight\", 0, 5),\n",
        "        \"gamma\": trial.suggest_int(\"gamma\", 0, 5),\n",
        "        \"learning_rate\": trial.suggest_loguniform(\"learning_rate\", 0.005, 0.5),\n",
        "        \"colsample_bytree\": trial.suggest_discrete_uniform(\n",
        "            \"colsample_bytree\", 0.1, 1, 0.01\n",
        "        ),\n",
        "        \"nthread\": -1,\n",
        "        \"use_label_encoder\": False,\n",
        "        \"eval_metric\": \"logloss\"\n",
        "    }\n",
        "    \n",
        "    mae, y, yhat = walk_forward_validation(params, data, 20)\n",
        "    \n",
        "    return mae\n",
        "\n",
        "\n",
        "if not os.path.exists('output'):\n",
        "    os.makedirs('output')\n",
        "        \n",
        "study = optuna.create_study(\n",
        "    direction=\"maximize\", \n",
        "    sampler=TPESampler(seed=1337),\n",
        "    study_name=\"res\",\n",
        "    storage=\"sqlite:///output/res.db\",\n",
        "    load_if_exists=True\n",
        "    )\n",
        "study.optimize(\n",
        "    lambda trial: objective(trial, data), \n",
        "    n_trials=50, \n",
        "    show_progress_bar=True\n",
        "    )\n",
        "df = study.trials_dataframe(attrs=(\"number\", \"value\", \"params\", \"state\"))\n",
        "df.to_csv(\"/content/res.csv\", sep=\"\\t\")\n",
        "print(study.best_trial)\n"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[32m[I 2021-03-28 11:15:04,669]\u001b[0m Using an existing study with name 'res' instead of creating a new one.\u001b[0m\n",
            "/usr/local/lib/python3.7/dist-packages/optuna/progress_bar.py:47: ExperimentalWarning:\n",
            "\n",
            "Progress bar is experimental (supported from v1.2.0). The interface can change in the future.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0baf6174f04a46d0a2814097c78d3e7b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=50.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:15:12,312]\u001b[0m Trial 50 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 948, 'max_depth': 9, 'reg_alpha': 1, 'reg_lambda': 4, 'min_child_weight': 3, 'gamma': 0, 'learning_rate': 0.23618847306152302, 'colsample_bytree': 0.48}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:15:20,098]\u001b[0m Trial 51 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 846, 'max_depth': 4, 'reg_alpha': 1, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.0695335543898768, 'colsample_bytree': 0.31}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:15:28,993]\u001b[0m Trial 52 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 775, 'max_depth': 7, 'reg_alpha': 4, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.012531665456851368, 'colsample_bytree': 0.25}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:15:46,246]\u001b[0m Trial 53 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 895, 'max_depth': 6, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.02793571058788696, 'colsample_bytree': 0.39}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:15:46,533]\u001b[0m Trial 54 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 2, 'max_depth': 6, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.030311406721817546, 'colsample_bytree': 0.4}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:15:48,046]\u001b[0m Trial 55 finished with value: 0.6333333333333333 and parameters: {'n_estimators': 53, 'max_depth': 5, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.03121875535804658, 'colsample_bytree': 0.51}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:15:55,905]\u001b[0m Trial 56 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 994, 'max_depth': 8, 'reg_alpha': 3, 'reg_lambda': 2, 'min_child_weight': 1, 'gamma': 1, 'learning_rate': 0.019273024878129135, 'colsample_bytree': 0.12000000000000001}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:16:08,936]\u001b[0m Trial 57 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 1000, 'max_depth': 8, 'reg_alpha': 1, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.018802607877772444, 'colsample_bytree': 0.30000000000000004}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:16:12,244]\u001b[0m Trial 58 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 206, 'max_depth': 6, 'reg_alpha': 2, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.06840953482526449, 'colsample_bytree': 0.4}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:16:23,014]\u001b[0m Trial 59 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 961, 'max_depth': 9, 'reg_alpha': 0, 'reg_lambda': 4, 'min_child_weight': 1, 'gamma': 1, 'learning_rate': 0.02067717977588002, 'colsample_bytree': 0.28}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:16:30,816]\u001b[0m Trial 60 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 798, 'max_depth': 3, 'reg_alpha': 2, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.015086994863196958, 'colsample_bytree': 0.22}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:16:38,194]\u001b[0m Trial 61 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 965, 'max_depth': 3, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.02291238724343583, 'colsample_bytree': 0.18}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:16:44,520]\u001b[0m Trial 62 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 905, 'max_depth': 2, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.02485324742140467, 'colsample_bytree': 0.17}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:16:53,080]\u001b[0m Trial 63 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 949, 'max_depth': 3, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.03735944358958485, 'colsample_bytree': 0.35}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:17:00,654]\u001b[0m Trial 64 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 915, 'max_depth': 2, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.04164650659371941, 'colsample_bytree': 0.33}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:17:06,758]\u001b[0m Trial 65 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 858, 'max_depth': 4, 'reg_alpha': 0, 'reg_lambda': 4, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.09530050679299276, 'colsample_bytree': 0.26}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:17:16,034]\u001b[0m Trial 66 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 837, 'max_depth': 4, 'reg_alpha': 0, 'reg_lambda': 5, 'min_child_weight': 1, 'gamma': 1, 'learning_rate': 0.09839998533801414, 'colsample_bytree': 0.27}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:17:26,638]\u001b[0m Trial 67 finished with value: 0.7333333333333334 and parameters: {'n_estimators': 936, 'max_depth': 5, 'reg_alpha': 1, 'reg_lambda': 2, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.0352749581192143, 'colsample_bytree': 0.44000000000000006}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:17:32,599]\u001b[0m Trial 68 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 999, 'max_depth': 9, 'reg_alpha': 1, 'reg_lambda': 3, 'min_child_weight': 2, 'gamma': 0, 'learning_rate': 0.0500624813678248, 'colsample_bytree': 0.14}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:17:41,780]\u001b[0m Trial 69 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 566, 'max_depth': 7, 'reg_alpha': 1, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 1, 'learning_rate': 0.0650756835306638, 'colsample_bytree': 0.30000000000000004}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:17:52,631]\u001b[0m Trial 70 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 891, 'max_depth': 8, 'reg_alpha': 1, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.026099443976545924, 'colsample_bytree': 0.36}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:18:07,355]\u001b[0m Trial 71 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 815, 'max_depth': 6, 'reg_alpha': 1, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.01071511225453022, 'colsample_bytree': 0.36}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:18:21,787]\u001b[0m Trial 72 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 797, 'max_depth': 6, 'reg_alpha': 1, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.009825302910307702, 'colsample_bytree': 0.36}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:18:33,905]\u001b[0m Trial 73 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 668, 'max_depth': 6, 'reg_alpha': 1, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.016233885500387173, 'colsample_bytree': 0.38}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:18:46,598]\u001b[0m Trial 74 finished with value: 0.7333333333333334 and parameters: {'n_estimators': 743, 'max_depth': 3, 'reg_alpha': 1, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.015558907807666066, 'colsample_bytree': 0.45999999999999996}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:18:58,016]\u001b[0m Trial 75 finished with value: 0.6333333333333333 and parameters: {'n_estimators': 675, 'max_depth': 7, 'reg_alpha': 1, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 3, 'learning_rate': 0.16202586638464883, 'colsample_bytree': 0.32}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:19:04,797]\u001b[0m Trial 76 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 944, 'max_depth': 2, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.02483148976263475, 'colsample_bytree': 0.18}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:19:12,238]\u001b[0m Trial 77 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 912, 'max_depth': 2, 'reg_alpha': 0, 'reg_lambda': 2, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.04126035936672927, 'colsample_bytree': 0.33999999999999997}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:19:21,684]\u001b[0m Trial 78 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 957, 'max_depth': 2, 'reg_alpha': 0, 'reg_lambda': 1, 'min_child_weight': 2, 'gamma': 1, 'learning_rate': 0.040464570951394045, 'colsample_bytree': 0.43000000000000005}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:19:33,715]\u001b[0m Trial 79 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 868, 'max_depth': 7, 'reg_alpha': 2, 'reg_lambda': 2, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.006236733512185402, 'colsample_bytree': 0.25}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:19:41,681]\u001b[0m Trial 80 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 856, 'max_depth': 4, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 1, 'learning_rate': 0.15779215557243803, 'colsample_bytree': 0.21000000000000002}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            "\u001b[32m[I 2021-03-28 11:19:47,240]\u001b[0m Trial 81 finished with value: 0.5666666666666667 and parameters: {'n_estimators': 904, 'max_depth': 8, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 5, 'gamma': 0, 'learning_rate': 0.03287744318123944, 'colsample_bytree': 0.22}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:19:54,848]\u001b[0m Trial 82 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 440, 'max_depth': 6, 'reg_alpha': 1, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.029643386393377866, 'colsample_bytree': 0.4}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:20:06,132]\u001b[0m Trial 83 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 836, 'max_depth': 5, 'reg_alpha': 1, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.013371305965194973, 'colsample_bytree': 0.24000000000000002}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:20:12,090]\u001b[0m Trial 84 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 471, 'max_depth': 7, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.013882153271478211, 'colsample_bytree': 0.29000000000000004}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:20:23,572]\u001b[0m Trial 85 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 838, 'max_depth': 5, 'reg_alpha': 1, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.010409587417918647, 'colsample_bytree': 0.24000000000000002}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:20:29,468]\u001b[0m Trial 86 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 465, 'max_depth': 7, 'reg_alpha': 0, 'reg_lambda': 4, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.01287343072460215, 'colsample_bytree': 0.29000000000000004}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:20:38,496]\u001b[0m Trial 87 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 838, 'max_depth': 4, 'reg_alpha': 4, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.011060227250247799, 'colsample_bytree': 0.24000000000000002}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:20:47,035]\u001b[0m Trial 88 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 917, 'max_depth': 8, 'reg_alpha': 2, 'reg_lambda': 5, 'min_child_weight': 2, 'gamma': 0, 'learning_rate': 0.05591902734253109, 'colsample_bytree': 0.33999999999999997}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:20:57,096]\u001b[0m Trial 89 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 884, 'max_depth': 8, 'reg_alpha': 1, 'reg_lambda': 4, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.02713438633803257, 'colsample_bytree': 0.31}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:21:03,997]\u001b[0m Trial 90 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 880, 'max_depth': 3, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.08578622676816676, 'colsample_bytree': 0.33}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:21:11,364]\u001b[0m Trial 91 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 958, 'max_depth': 3, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.08648625582370155, 'colsample_bytree': 0.33}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:21:18,284]\u001b[0m Trial 92 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 902, 'max_depth': 2, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.11295705740428628, 'colsample_bytree': 0.37}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:21:23,723]\u001b[0m Trial 93 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 365, 'max_depth': 6, 'reg_alpha': 3, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.019708557641065303, 'colsample_bytree': 0.27}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:21:33,036]\u001b[0m Trial 94 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 856, 'max_depth': 4, 'reg_alpha': 1, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.04677483462800928, 'colsample_bytree': 0.35}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:21:40,837]\u001b[0m Trial 95 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 722, 'max_depth': 7, 'reg_alpha': 2, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.01825717635468616, 'colsample_bytree': 0.22}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:21:51,368]\u001b[0m Trial 96 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 787, 'max_depth': 6, 'reg_alpha': 2, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.01444236005266124, 'colsample_bytree': 0.30000000000000004}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:22:06,768]\u001b[0m Trial 97 finished with value: 0.6333333333333333 and parameters: {'n_estimators': 798, 'max_depth': 6, 'reg_alpha': 1, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.007864763252994207, 'colsample_bytree': 0.41000000000000003}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:22:11,772]\u001b[0m Trial 98 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 499, 'max_depth': 5, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 1, 'learning_rate': 0.010030465536742604, 'colsample_bytree': 0.18}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "20\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            ">expected=1.0, predicted=0.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=1.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=1.0\n",
            ">expected=0.0, predicted=0.0\n",
            "\u001b[32m[I 2021-03-28 11:22:15,432]\u001b[0m Trial 99 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 450, 'max_depth': 5, 'reg_alpha': 0, 'reg_lambda': 2, 'min_child_weight': 2, 'gamma': 0, 'learning_rate': 0.03309265638711461, 'colsample_bytree': 0.28}. Best is trial 10 with value: 0.7666666666666666.\u001b[0m\n",
            "\n",
            "FrozenTrial(number=10, values=[0.7666666666666666], datetime_start=datetime.datetime(2021, 3, 28, 11, 5, 42, 904560), datetime_complete=datetime.datetime(2021, 3, 28, 11, 5, 49, 938970), params={'colsample_bytree': 0.17, 'gamma': 0, 'learning_rate': 0.027666260129432293, 'max_depth': 8, 'min_child_weight': 1, 'n_estimators': 940, 'reg_alpha': 0, 'reg_lambda': 3}, distributions={'colsample_bytree': DiscreteUniformDistribution(high=1.0, low=0.1, q=0.01), 'gamma': IntUniformDistribution(high=5, low=0, step=1), 'learning_rate': LogUniformDistribution(high=0.5, low=0.005), 'max_depth': IntUniformDistribution(high=10, low=2, step=1), 'min_child_weight': IntUniformDistribution(high=5, low=0, step=1), 'n_estimators': IntUniformDistribution(high=1000, low=0, step=1), 'reg_alpha': IntUniformDistribution(high=5, low=0, step=1), 'reg_lambda': IntUniformDistribution(high=5, low=0, step=1)}, user_attrs={}, system_attrs={}, intermediate_values={}, trial_id=11, state=TrialState.COMPLETE, value=None)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Lo3X5liXTJO"
      },
      "source": [
        "# Model Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rkSshseJXZNO"
      },
      "source": [
        "Our model's Weighted accuracy is 0.7666666666666666 or **~76.67%**.\n",
        "Now our model can forecast the friction and will label it as **'0' if dangerous** and **'1' if safe** for driving."
      ]
    }
  ]
}