Crop_Recommendation_2.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "from __future__ import print_function\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn import metrics\n",
        "from sklearn import tree\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install imbalanced-learn"
      ],
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      "id": "8Cf3VDaYkZta",
      "execution_count": null,
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          "name": "stdout",
          "text": [
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.10/dist-packages (0.10.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.11.4)\n",
            "Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.2.2)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (3.5.0)\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2286987d",
      "metadata": {
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      "outputs": [],
      "source": [
        "df = pd.read_csv('crop_recommendation.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c17228c6",
      "metadata": {
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        "id": "c17228c6",
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "(2200, 8)"
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          },
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          "execution_count": 5
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      "source": [
        "df.shape"
      ]
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            "text/plain": [
              "    N   P   K  temperature   humidity        ph    rainfall label\n",
              "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice\n",
              "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice\n",
              "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice\n",
              "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice\n",
              "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice"
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              "        N   P   K  temperature   humidity        ph    rainfall   label\n",
              "2195  107  34  32    26.774637  66.413269  6.780064  177.774507  coffee\n",
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              "      <td>26.774637</td>\n",
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              "      <td>6.780064</td>\n",
              "      <td>177.774507</td>\n",
              "      <td>coffee</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2196</th>\n",
              "      <td>99</td>\n",
              "      <td>15</td>\n",
              "      <td>27</td>\n",
              "      <td>27.417112</td>\n",
              "      <td>56.636362</td>\n",
              "      <td>6.086922</td>\n",
              "      <td>127.924610</td>\n",
              "      <td>coffee</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2197</th>\n",
              "      <td>118</td>\n",
              "      <td>33</td>\n",
              "      <td>30</td>\n",
              "      <td>24.131797</td>\n",
              "      <td>67.225123</td>\n",
              "      <td>6.362608</td>\n",
              "      <td>173.322839</td>\n",
              "      <td>coffee</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2198</th>\n",
              "      <td>117</td>\n",
              "      <td>32</td>\n",
              "      <td>34</td>\n",
              "      <td>26.272418</td>\n",
              "      <td>52.127394</td>\n",
              "      <td>6.758793</td>\n",
              "      <td>127.175293</td>\n",
              "      <td>coffee</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2199</th>\n",
              "      <td>104</td>\n",
              "      <td>18</td>\n",
              "      <td>30</td>\n",
              "      <td>23.603016</td>\n",
              "      <td>60.396475</td>\n",
              "      <td>6.779833</td>\n",
              "      <td>140.937041</td>\n",
              "      <td>coffee</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dfb0032d-00d1-451c-a680-bedafcb74bea')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      cursor: pointer;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-dfb0032d-00d1-451c-a680-bedafcb74bea button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-dfb0032d-00d1-451c-a680-bedafcb74bea');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-701e6db8-6342-43cf-ac4d-434af6b142c5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"N\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 99,\n        \"max\": 118,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          99,\n          104,\n          118\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 15,\n        \"max\": 34,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          15,\n          18,\n          33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 27,\n        \"max\": 34,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          27,\n          34,\n          32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6785011243388321,\n        \"min\": 23.60301571,\n        \"max\": 27.41711238,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          27.41711238,\n          23.60301571,\n          24.13179691\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.4268019594536305,\n        \"min\": 52.12739421,\n        \"max\": 67.22512329,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          56.63636248,\n          60.39647474,\n          67.22512329\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ph\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3157677427867641,\n        \"min\": 6.086922359,\n        \"max\": 6.78006386,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6.086922359,\n          6.779832611000002,\n          6.362607851\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rainfall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.516132077254007,\n        \"min\": 127.1752928,\n        \"max\": 177.7745075,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          127.92461000000002,\n          140.9370415,\n          173.32283859999995\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"coffee\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4d6cc938",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "4d6cc938",
        "outputId": "fc22bf4d-96f7-43e5-8078-621e2bb2a151"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        N   P   K  temperature   humidity        ph    rainfall        label\n",
              "349     9  80  19    21.806196  18.570866  5.945466  125.097269  kidneybeans\n",
              "1057  102  73  54    26.402023  84.410076  5.720727  111.016226       banana"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b6adcbc9-6077-4c5f-9f66-6e1c1591a1ac\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>N</th>\n",
              "      <th>P</th>\n",
              "      <th>K</th>\n",
              "      <th>temperature</th>\n",
              "      <th>humidity</th>\n",
              "      <th>ph</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>label</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>349</th>\n",
              "      <td>9</td>\n",
              "      <td>80</td>\n",
              "      <td>19</td>\n",
              "      <td>21.806196</td>\n",
              "      <td>18.570866</td>\n",
              "      <td>5.945466</td>\n",
              "      <td>125.097269</td>\n",
              "      <td>kidneybeans</td>\n",
              "    </tr>\n",
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              "      <th>1057</th>\n",
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              "      <td>26.402023</td>\n",
              "      <td>84.410076</td>\n",
              "      <td>5.720727</td>\n",
              "      <td>111.016226</td>\n",
              "      <td>banana</td>\n",
              "    </tr>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b6adcbc9-6077-4c5f-9f66-6e1c1591a1ac button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b6adcbc9-6077-4c5f-9f66-6e1c1591a1ac');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-62622e6b-f0e5-4667-8f7b-5dbdad803f11\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-62622e6b-f0e5-4667-8f7b-5dbdad803f11')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-62622e6b-f0e5-4667-8f7b-5dbdad803f11 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"N\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65,\n        \"min\": 9,\n        \"max\": 102,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          102,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 73,\n        \"max\": 80,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          73,\n          80\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 19,\n        \"max\": 54,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          54,\n          19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.249740479286635,\n        \"min\": 21.80619564,\n        \"max\": 26.4020227,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          26.4020227,\n          21.80619564\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 46.55535228322922,\n        \"min\": 18.57086554,\n        \"max\": 84.41007614,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          84.41007614,\n          18.57086554\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ph\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1589145013026748,\n        \"min\": 5.720726906,\n        \"max\": 5.945465949,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5.720726906,\n          5.945465949\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rainfall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.956800850058015,\n        \"min\": 111.0162259,\n        \"max\": 125.0972687,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          111.0162259,\n          125.0972687\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"banana\",\n          \"kidneybeans\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df.sample(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f23796af",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f23796af",
        "outputId": "fc62b75e-00d2-4342-c2e6-aba38330d0c1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2200 entries, 0 to 2199\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   N            2200 non-null   int64  \n",
            " 1   P            2200 non-null   int64  \n",
            " 2   K            2200 non-null   int64  \n",
            " 3   temperature  2200 non-null   float64\n",
            " 4   humidity     2200 non-null   float64\n",
            " 5   ph           2200 non-null   float64\n",
            " 6   rainfall     2200 non-null   float64\n",
            " 7   label        2200 non-null   object \n",
            "dtypes: float64(4), int64(3), object(1)\n",
            "memory usage: 137.6+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8739124b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8739124b",
        "outputId": "4ee50f87-f947-41ee-9566-90f516dd9726"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "N              0\n",
              "P              0\n",
              "K              0\n",
              "temperature    0\n",
              "humidity       0\n",
              "ph             0\n",
              "rainfall       0\n",
              "label          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d659aa10",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d659aa10",
        "outputId": "61b56f6a-0143-4db8-f540-edf16641aa39"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['rice', 'maize', 'chickpea', 'kidneybeans', 'pigeonpeas',\n",
              "       'mothbeans', 'mungbean', 'blackgram', 'lentil', 'pomegranate',\n",
              "       'banana', 'mango', 'grapes', 'watermelon', 'muskmelon', 'apple',\n",
              "       'orange', 'papaya', 'coconut', 'cotton', 'jute', 'coffee'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df['label'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "07cfb438",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "07cfb438",
        "outputId": "018d0a9a-569b-4b40-f310-1265e8e05d55"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df['label'].nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4f710ad6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4f710ad6",
        "outputId": "e9216723-861a-4a1c-d824-ef10658fe76c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label\n",
              "rice           100\n",
              "maize          100\n",
              "jute           100\n",
              "cotton         100\n",
              "coconut        100\n",
              "papaya         100\n",
              "orange         100\n",
              "apple          100\n",
              "muskmelon      100\n",
              "watermelon     100\n",
              "grapes         100\n",
              "mango          100\n",
              "banana         100\n",
              "pomegranate    100\n",
              "lentil         100\n",
              "blackgram      100\n",
              "mungbean       100\n",
              "mothbeans      100\n",
              "pigeonpeas     100\n",
              "kidneybeans    100\n",
              "chickpea       100\n",
              "coffee         100\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df['label'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c7fde5d7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "c7fde5d7",
        "outputId": "54ca91d5-0eaf-4e77-98a4-bc42d5a97fbd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.heatmap(df.iloc[:,0:-1].corr(),annot=True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d08564f8",
      "metadata": {
        "id": "d08564f8"
      },
      "outputs": [],
      "source": [
        "features = df[['N', 'P','K','temperature', 'humidity', 'ph', 'rainfall']]\n",
        "target = df['label']\n",
        "#features = df[['temperature', 'humidity', 'ph', 'rainfall']]\n",
        "labels = df['label']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "import pandas as pd\n",
        "\n",
        "X, y = features,target\n",
        "\n",
        "# Convert to DataFrame for visualization\n",
        "df = pd.DataFrame(X)\n",
        "df['target'] = y\n",
        "print(df['target'].value_counts())\n",
        "\n",
        "# Apply SMOTE\n",
        "smote = SMOTE(sampling_strategy='auto', random_state=42)\n",
        "X_res, y_res = smote.fit_resample(X, y)\n",
        "\n",
        "# Convert to DataFrame for visualization\n",
        "df_res = pd.DataFrame(X_res)\n",
        "df_res['target'] = y_res\n",
        "print(df_res['target'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ye_j_Aimk_l4",
        "outputId": "58f793ea-a0e1-406a-c75b-792c5e642d8e"
      },
      "id": "ye_j_Aimk_l4",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "target\n",
            "rice           100\n",
            "maize          100\n",
            "jute           100\n",
            "cotton         100\n",
            "coconut        100\n",
            "papaya         100\n",
            "orange         100\n",
            "apple          100\n",
            "muskmelon      100\n",
            "watermelon     100\n",
            "grapes         100\n",
            "mango          100\n",
            "banana         100\n",
            "pomegranate    100\n",
            "lentil         100\n",
            "blackgram      100\n",
            "mungbean       100\n",
            "mothbeans      100\n",
            "pigeonpeas     100\n",
            "kidneybeans    100\n",
            "chickpea       100\n",
            "coffee         100\n",
            "Name: count, dtype: int64\n",
            "target\n",
            "rice           100\n",
            "maize          100\n",
            "jute           100\n",
            "cotton         100\n",
            "coconut        100\n",
            "papaya         100\n",
            "orange         100\n",
            "apple          100\n",
            "muskmelon      100\n",
            "watermelon     100\n",
            "grapes         100\n",
            "mango          100\n",
            "banana         100\n",
            "pomegranate    100\n",
            "lentil         100\n",
            "blackgram      100\n",
            "mungbean       100\n",
            "mothbeans      100\n",
            "pigeonpeas     100\n",
            "kidneybeans    100\n",
            "chickpea       100\n",
            "coffee         100\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_res.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "6c5H_fSNlrbB",
        "outputId": "4dcaed2f-537f-4466-8f5b-ef05f30fd814"
      },
      "id": "6c5H_fSNlrbB",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    N   P   K  temperature   humidity        ph    rainfall target\n",
              "0  90  42  43    20.879744  82.002744  6.502985  202.935536   rice\n",
              "1  85  58  41    21.770462  80.319644  7.038096  226.655537   rice"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-492f04eb-3119-444d-8259-f9289cbfcf18\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>N</th>\n",
              "      <th>P</th>\n",
              "      <th>K</th>\n",
              "      <th>temperature</th>\n",
              "      <th>humidity</th>\n",
              "      <th>ph</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>90</td>\n",
              "      <td>42</td>\n",
              "      <td>43</td>\n",
              "      <td>20.879744</td>\n",
              "      <td>82.002744</td>\n",
              "      <td>6.502985</td>\n",
              "      <td>202.935536</td>\n",
              "      <td>rice</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>85</td>\n",
              "      <td>58</td>\n",
              "      <td>41</td>\n",
              "      <td>21.770462</td>\n",
              "      <td>80.319644</td>\n",
              "      <td>7.038096</td>\n",
              "      <td>226.655537</td>\n",
              "      <td>rice</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-492f04eb-3119-444d-8259-f9289cbfcf18')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"https://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-492f04eb-3119-444d-8259-f9289cbfcf18 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-492f04eb-3119-444d-8259-f9289cbfcf18');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e9bf72a6-1250-4279-9ae0-f1ecf50d005a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e9bf72a6-1250-4279-9ae0-f1ecf50d005a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e9bf72a6-1250-4279-9ae0-f1ecf50d005a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_res",
              "summary": "{\n  \"name\": \"df_res\",\n  \"rows\": 2200,\n  \"fields\": [\n    {\n      \"column\": \"N\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36,\n        \"min\": 0,\n        \"max\": 140,\n        \"num_unique_values\": 137,\n        \"samples\": [\n          106,\n          101,\n          88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32,\n        \"min\": 5,\n        \"max\": 145,\n        \"num_unique_values\": 117,\n        \"samples\": [\n          69,\n          37,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50,\n        \"min\": 5,\n        \"max\": 205,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          42,\n          12,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.063748599958843,\n        \"min\": 8.825674745,\n        \"max\": 43.67549305,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          29.49401389,\n          26.1793464,\n          43.36051537\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.263811589761083,\n        \"min\": 14.25803981,\n        \"max\": 99.98187601,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          94.72981338,\n          86.52258079,\n          93.35191636\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ph\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7739376880298733,\n        \"min\": 3.504752314,\n        \"max\": 9.93509073,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          6.185053234,\n          6.25933595,\n          6.941496806\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rainfall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54.95838852487813,\n        \"min\": 20.21126747,\n        \"max\": 298.5601175,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          26.30820876,\n          49.43050977,\n          114.778071\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"rice\",\n          \"watermelon\",\n          \"lentil\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(2)"
      ],
      "metadata": {
        "id": "WzJPt-rMKfFB",
        "outputId": "5725c54b-53f8-4323-d561-363674916407",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        }
      },
      "id": "WzJPt-rMKfFB",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    N   P   K  temperature   humidity        ph    rainfall target\n",
              "0  90  42  43    20.879744  82.002744  6.502985  202.935536   rice\n",
              "1  85  58  41    21.770462  80.319644  7.038096  226.655537   rice"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-78627219-ed38-46cc-99c5-982cc84cb27b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>N</th>\n",
              "      <th>P</th>\n",
              "      <th>K</th>\n",
              "      <th>temperature</th>\n",
              "      <th>humidity</th>\n",
              "      <th>ph</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>90</td>\n",
              "      <td>42</td>\n",
              "      <td>43</td>\n",
              "      <td>20.879744</td>\n",
              "      <td>82.002744</td>\n",
              "      <td>6.502985</td>\n",
              "      <td>202.935536</td>\n",
              "      <td>rice</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>85</td>\n",
              "      <td>58</td>\n",
              "      <td>41</td>\n",
              "      <td>21.770462</td>\n",
              "      <td>80.319644</td>\n",
              "      <td>7.038096</td>\n",
              "      <td>226.655537</td>\n",
              "      <td>rice</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-78627219-ed38-46cc-99c5-982cc84cb27b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-78627219-ed38-46cc-99c5-982cc84cb27b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f8751e67-49a0-4b5f-82df-d3e991f9e003\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f8751e67-49a0-4b5f-82df-d3e991f9e003')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f8751e67-49a0-4b5f-82df-d3e991f9e003 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2200,\n  \"fields\": [\n    {\n      \"column\": \"N\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36,\n        \"min\": 0,\n        \"max\": 140,\n        \"num_unique_values\": 137,\n        \"samples\": [\n          106,\n          101,\n          88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32,\n        \"min\": 5,\n        \"max\": 145,\n        \"num_unique_values\": 117,\n        \"samples\": [\n          69,\n          37,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50,\n        \"min\": 5,\n        \"max\": 205,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          42,\n          12,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.063748599958843,\n        \"min\": 8.825674745,\n        \"max\": 43.67549305,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          29.49401389,\n          26.1793464,\n          43.36051537\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.263811589761083,\n        \"min\": 14.25803981,\n        \"max\": 99.98187601,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          94.72981338,\n          86.52258079,\n          93.35191636\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ph\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7739376880298733,\n        \"min\": 3.504752314,\n        \"max\": 9.93509073,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          6.185053234,\n          6.25933595,\n          6.941496806\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rainfall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54.95838852487813,\n        \"min\": 20.21126747,\n        \"max\": 298.5601175,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          26.30820876,\n          49.43050977,\n          114.778071\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"rice\",\n          \"watermelon\",\n          \"lentil\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = []\n",
        "model = []"
      ],
      "metadata": {
        "id": "6NR3EzkHoprh"
      },
      "id": "6NR3EzkHoprh",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "Xtrain, Xtest, Ytrain, Ytest = train_test_split(features,target,test_size = 0.2,random_state =2)"
      ],
      "metadata": {
        "id": "MLmqKIKUopu4"
      },
      "id": "MLmqKIKUopu4",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "DecisionTree = DecisionTreeClassifier(criterion=\"entropy\",random_state=2,max_depth=5)\n",
        "\n",
        "DecisionTree.fit(Xtrain,Ytrain)\n",
        "\n",
        "predicted_values = DecisionTree.predict(Xtest)\n",
        "x = metrics.accuracy_score(Ytest, predicted_values)\n",
        "acc.append(x)\n",
        "model.append('Decision Tree')\n",
        "print(\"DecisionTrees's Accuracy is: \", x*100)\n",
        "\n",
        "print(classification_report(Ytest,predicted_values))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D9ICNqf-opyy",
        "outputId": "5480d469-0dea-4a5c-f68d-6ed712382532"
      },
      "id": "D9ICNqf-opyy",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DecisionTrees's Accuracy is:  90.0\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       apple       1.00      1.00      1.00        13\n",
            "      banana       1.00      1.00      1.00        17\n",
            "   blackgram       0.59      1.00      0.74        16\n",
            "    chickpea       1.00      1.00      1.00        21\n",
            "     coconut       0.91      1.00      0.95        21\n",
            "      coffee       1.00      1.00      1.00        22\n",
            "      cotton       1.00      1.00      1.00        20\n",
            "      grapes       1.00      1.00      1.00        18\n",
            "        jute       0.74      0.93      0.83        28\n",
            " kidneybeans       0.00      0.00      0.00        14\n",
            "      lentil       0.68      1.00      0.81        23\n",
            "       maize       1.00      1.00      1.00        21\n",
            "       mango       1.00      1.00      1.00        26\n",
            "   mothbeans       0.00      0.00      0.00        19\n",
            "    mungbean       1.00      1.00      1.00        24\n",
            "   muskmelon       1.00      1.00      1.00        23\n",
            "      orange       1.00      1.00      1.00        29\n",
            "      papaya       1.00      0.84      0.91        19\n",
            "  pigeonpeas       0.62      1.00      0.77        18\n",
            " pomegranate       1.00      1.00      1.00        17\n",
            "        rice       1.00      0.62      0.77        16\n",
            "  watermelon       1.00      1.00      1.00        15\n",
            "\n",
            "    accuracy                           0.90       440\n",
            "   macro avg       0.84      0.88      0.85       440\n",
            "weighted avg       0.86      0.90      0.87       440\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "score = cross_val_score(DecisionTree, features, target,cv=5)\n",
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sEmmHmA2op2t",
        "outputId": "60c327af-6e86-412e-d44d-db4cb3285f75"
      },
      "id": "sEmmHmA2op2t",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.93636364, 0.90909091, 0.91818182, 0.87045455, 0.93636364])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "NaiveBayes = GaussianNB()\n",
        "\n",
        "NaiveBayes.fit(Xtrain,Ytrain)\n",
        "\n",
        "predicted_values = NaiveBayes.predict(Xtest)\n",
        "x = metrics.accuracy_score(Ytest, predicted_values)\n",
        "acc.append(x)\n",
        "model.append('Naive Bayes')\n",
        "print(\"Naive Bayes's Accuracy is: \", x)\n",
        "\n",
        "print(classification_report(Ytest,predicted_values))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yIgbq9m3op_L",
        "outputId": "53aa344e-eccf-42fa-bca5-1f9dc7341e66"
      },
      "id": "yIgbq9m3op_L",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Naive Bayes's Accuracy is:  0.990909090909091\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       apple       1.00      1.00      1.00        13\n",
            "      banana       1.00      1.00      1.00        17\n",
            "   blackgram       1.00      1.00      1.00        16\n",
            "    chickpea       1.00      1.00      1.00        21\n",
            "     coconut       1.00      1.00      1.00        21\n",
            "      coffee       1.00      1.00      1.00        22\n",
            "      cotton       1.00      1.00      1.00        20\n",
            "      grapes       1.00      1.00      1.00        18\n",
            "        jute       0.88      1.00      0.93        28\n",
            " kidneybeans       1.00      1.00      1.00        14\n",
            "      lentil       1.00      1.00      1.00        23\n",
            "       maize       1.00      1.00      1.00        21\n",
            "       mango       1.00      1.00      1.00        26\n",
            "   mothbeans       1.00      1.00      1.00        19\n",
            "    mungbean       1.00      1.00      1.00        24\n",
            "   muskmelon       1.00      1.00      1.00        23\n",
            "      orange       1.00      1.00      1.00        29\n",
            "      papaya       1.00      1.00      1.00        19\n",
            "  pigeonpeas       1.00      1.00      1.00        18\n",
            " pomegranate       1.00      1.00      1.00        17\n",
            "        rice       1.00      0.75      0.86        16\n",
            "  watermelon       1.00      1.00      1.00        15\n",
            "\n",
            "    accuracy                           0.99       440\n",
            "   macro avg       0.99      0.99      0.99       440\n",
            "weighted avg       0.99      0.99      0.99       440\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = cross_val_score(NaiveBayes,features,target,cv=5)\n",
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6iaRYHUkpDpk",
        "outputId": "d0d0ab62-5e38-48ff-84ed-df5c6b8021e1"
      },
      "id": "6iaRYHUkpDpk",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.99772727, 0.99545455, 0.99545455, 0.99545455, 0.99090909])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "# data normalization with sklearn\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "# fit scaler on training data\n",
        "norm = MinMaxScaler().fit(Xtrain)\n",
        "X_train_norm = norm.transform(Xtrain)\n",
        "# transform testing dataabs\n",
        "X_test_norm = norm.transform(Xtest)\n",
        "SVM = SVC(kernel='poly', degree=3, C=1)\n",
        "SVM.fit(X_train_norm,Ytrain)\n",
        "predicted_values = SVM.predict(X_test_norm)\n",
        "x = metrics.accuracy_score(Ytest, predicted_values)\n",
        "acc.append(x)\n",
        "model.append('SVM')\n",
        "print(\"SVM's Accuracy is: \", x)\n",
        "\n",
        "print(classification_report(Ytest,predicted_values))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8B84GTpopDsT",
        "outputId": "cbe73768-97a4-42b4-8e9e-8f0e4d55fc6b"
      },
      "id": "8B84GTpopDsT",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM's Accuracy is:  0.9795454545454545\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       apple       1.00      1.00      1.00        13\n",
            "      banana       1.00      1.00      1.00        17\n",
            "   blackgram       1.00      1.00      1.00        16\n",
            "    chickpea       1.00      1.00      1.00        21\n",
            "     coconut       1.00      1.00      1.00        21\n",
            "      coffee       1.00      0.95      0.98        22\n",
            "      cotton       0.95      1.00      0.98        20\n",
            "      grapes       1.00      1.00      1.00        18\n",
            "        jute       0.83      0.89      0.86        28\n",
            " kidneybeans       1.00      1.00      1.00        14\n",
            "      lentil       1.00      1.00      1.00        23\n",
            "       maize       1.00      0.95      0.98        21\n",
            "       mango       1.00      1.00      1.00        26\n",
            "   mothbeans       1.00      1.00      1.00        19\n",
            "    mungbean       1.00      1.00      1.00        24\n",
            "   muskmelon       1.00      1.00      1.00        23\n",
            "      orange       1.00      1.00      1.00        29\n",
            "      papaya       1.00      1.00      1.00        19\n",
            "  pigeonpeas       1.00      1.00      1.00        18\n",
            " pomegranate       1.00      1.00      1.00        17\n",
            "        rice       0.80      0.75      0.77        16\n",
            "  watermelon       1.00      1.00      1.00        15\n",
            "\n",
            "    accuracy                           0.98       440\n",
            "   macro avg       0.98      0.98      0.98       440\n",
            "weighted avg       0.98      0.98      0.98       440\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = cross_val_score(SVM,features,target,cv=5)\n",
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9hyRRN1LpDvu",
        "outputId": "49018ca1-a0b0-4908-d52f-b8b7de52472a"
      },
      "id": "9hyRRN1LpDvu",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.97954545, 0.975     , 0.98863636, 0.98863636, 0.98181818])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "LogReg = LogisticRegression(random_state=2)\n",
        "\n",
        "LogReg.fit(Xtrain,Ytrain)\n",
        "\n",
        "predicted_values = LogReg.predict(Xtest)\n",
        "\n",
        "x = metrics.accuracy_score(Ytest, predicted_values)\n",
        "acc.append(x)\n",
        "model.append('Logistic Regression')\n",
        "print(\"Logistic Regression's Accuracy is: \", x)\n",
        "\n",
        "print(classification_report(Ytest,predicted_values))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "olqjtoKMpN3l",
        "outputId": "3fb09135-ec6f-4a84-8e83-cd97711ab902"
      },
      "id": "olqjtoKMpN3l",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression's Accuracy is:  0.9522727272727273\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       apple       1.00      1.00      1.00        13\n",
            "      banana       1.00      1.00      1.00        17\n",
            "   blackgram       0.86      0.75      0.80        16\n",
            "    chickpea       1.00      1.00      1.00        21\n",
            "     coconut       1.00      1.00      1.00        21\n",
            "      coffee       1.00      1.00      1.00        22\n",
            "      cotton       0.86      0.90      0.88        20\n",
            "      grapes       1.00      1.00      1.00        18\n",
            "        jute       0.84      0.93      0.88        28\n",
            " kidneybeans       1.00      1.00      1.00        14\n",
            "      lentil       0.88      1.00      0.94        23\n",
            "       maize       0.90      0.86      0.88        21\n",
            "       mango       0.96      1.00      0.98        26\n",
            "   mothbeans       0.84      0.84      0.84        19\n",
            "    mungbean       1.00      0.96      0.98        24\n",
            "   muskmelon       1.00      1.00      1.00        23\n",
            "      orange       1.00      1.00      1.00        29\n",
            "      papaya       1.00      0.95      0.97        19\n",
            "  pigeonpeas       1.00      1.00      1.00        18\n",
            " pomegranate       1.00      1.00      1.00        17\n",
            "        rice       0.85      0.69      0.76        16\n",
            "  watermelon       1.00      1.00      1.00        15\n",
            "\n",
            "    accuracy                           0.95       440\n",
            "   macro avg       0.95      0.95      0.95       440\n",
            "weighted avg       0.95      0.95      0.95       440\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = cross_val_score(LogReg,features,target,cv=5)\n",
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9qdsil49pN6V",
        "outputId": "12f70e2d-9fc2-4feb-ebb3-85977df600ad"
      },
      "id": "9qdsil49pN6V",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.95      , 0.96590909, 0.94772727, 0.96590909, 0.94318182])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "RF = RandomForestClassifier(n_estimators=20, random_state=0)\n",
        "RF.fit(Xtrain,Ytrain)\n",
        "\n",
        "predicted_values = RF.predict(Xtest)\n",
        "\n",
        "x = metrics.accuracy_score(Ytest, predicted_values)\n",
        "acc.append(x)\n",
        "model.append('RF')\n",
        "print(\"RF's Accuracy is: \", x)\n",
        "\n",
        "print(classification_report(Ytest,predicted_values))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6-uZiw46pN9Y",
        "outputId": "49c63ee9-2bb9-45f3-e495-fbe06acdd56d"
      },
      "id": "6-uZiw46pN9Y",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RF's Accuracy is:  0.990909090909091\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       apple       1.00      1.00      1.00        13\n",
            "      banana       1.00      1.00      1.00        17\n",
            "   blackgram       0.94      1.00      0.97        16\n",
            "    chickpea       1.00      1.00      1.00        21\n",
            "     coconut       1.00      1.00      1.00        21\n",
            "      coffee       1.00      1.00      1.00        22\n",
            "      cotton       1.00      1.00      1.00        20\n",
            "      grapes       1.00      1.00      1.00        18\n",
            "        jute       0.90      1.00      0.95        28\n",
            " kidneybeans       1.00      1.00      1.00        14\n",
            "      lentil       1.00      1.00      1.00        23\n",
            "       maize       1.00      1.00      1.00        21\n",
            "       mango       1.00      1.00      1.00        26\n",
            "   mothbeans       1.00      0.95      0.97        19\n",
            "    mungbean       1.00      1.00      1.00        24\n",
            "   muskmelon       1.00      1.00      1.00        23\n",
            "      orange       1.00      1.00      1.00        29\n",
            "      papaya       1.00      1.00      1.00        19\n",
            "  pigeonpeas       1.00      1.00      1.00        18\n",
            " pomegranate       1.00      1.00      1.00        17\n",
            "        rice       1.00      0.81      0.90        16\n",
            "  watermelon       1.00      1.00      1.00        15\n",
            "\n",
            "    accuracy                           0.99       440\n",
            "   macro avg       0.99      0.99      0.99       440\n",
            "weighted avg       0.99      0.99      0.99       440\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = cross_val_score(RF,features,target,cv=5)\n",
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Giqyp4XpOAR",
        "outputId": "34cd7110-53a7-4b64-9295-298b2734d138"
      },
      "id": "7Giqyp4XpOAR",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.99772727, 0.99545455, 0.99772727, 0.99318182, 0.98863636])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "metadata": {
        "id": "j2bU3Or_pOC7"
      },
      "id": "j2bU3Or_pOC7",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "\n",
        "Ytrain_encoded = label_encoder.fit_transform(Ytrain)\n",
        "\n",
        "\n",
        "XB = xgb.XGBClassifier()\n",
        "\n",
        "\n",
        "XB.fit(Xtrain, Ytrain_encoded)\n",
        "\n",
        "predicted_values_encoded = XB.predict(Xtest)\n",
        "\n",
        "\n",
        "predicted_values = label_encoder.inverse_transform(predicted_values_encoded)\n",
        "\n",
        "x = metrics.accuracy_score(Ytest, predicted_values)\n",
        "acc.append(x)\n",
        "model.append('XGBoost')\n",
        "print(\"XGBoost's Accuracy is: \", x)\n",
        "\n",
        "print(classification_report(Ytest,predicted_values))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cGl32ljRpOFk",
        "outputId": "0a5c9e10-faa1-487f-9b35-0653a2ab5e1a"
      },
      "id": "cGl32ljRpOFk",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost's Accuracy is:  0.990909090909091\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       apple       1.00      1.00      1.00        13\n",
            "      banana       1.00      1.00      1.00        17\n",
            "   blackgram       1.00      1.00      1.00        16\n",
            "    chickpea       1.00      1.00      1.00        21\n",
            "     coconut       1.00      1.00      1.00        21\n",
            "      coffee       0.96      1.00      0.98        22\n",
            "      cotton       1.00      1.00      1.00        20\n",
            "      grapes       1.00      1.00      1.00        18\n",
            "        jute       0.93      0.96      0.95        28\n",
            " kidneybeans       1.00      1.00      1.00        14\n",
            "      lentil       0.96      1.00      0.98        23\n",
            "       maize       1.00      1.00      1.00        21\n",
            "       mango       1.00      1.00      1.00        26\n",
            "   mothbeans       1.00      0.95      0.97        19\n",
            "    mungbean       1.00      1.00      1.00        24\n",
            "   muskmelon       1.00      1.00      1.00        23\n",
            "      orange       1.00      1.00      1.00        29\n",
            "      papaya       1.00      1.00      1.00        19\n",
            "  pigeonpeas       1.00      1.00      1.00        18\n",
            " pomegranate       1.00      1.00      1.00        17\n",
            "        rice       1.00      0.88      0.93        16\n",
            "  watermelon       1.00      1.00      1.00        15\n",
            "\n",
            "    accuracy                           0.99       440\n",
            "   macro avg       0.99      0.99      0.99       440\n",
            "weighted avg       0.99      0.99      0.99       440\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=[10,5],dpi = 100)\n",
        "plt.title('Accuracy Comparison')\n",
        "plt.xlabel('Accuracy')\n",
        "plt.ylabel('Algorithm')\n",
        "sns.barplot(x = acc,y = model,palette='dark')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "-pwurVKWqHVz",
        "outputId": "ad66fd5d-c117-4e3c-eb93-43cad6967d96"
      },
      "id": "-pwurVKWqHVz",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Accuracy Comparison'}, xlabel='Accuracy', ylabel='Algorithm'>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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yTZ+6desa5cqVS7OdwMDAdF8FJMkYMGCA9fPt1/wcPnzYaNeuneHp6Wnky5fPGDhwYJp9W7NmjVGhQgXDzc3NCAoKMt59913r66dOnTp1323fXnfnK4vGjx9vVKtWzfDx8THc3d2N0qVLGxMmTDBu3Lhh871NmzYZtWrVMtzd3Q0vLy+jefPmxuHDh2363N6XP//806Y9KioqTY0AkJtZDIOnFAAAgOyTnJysIkWKqHnz5po3b15Ol/NARo8erTFjxujPP//M0GXaAICcxz29AAAgW61evVp//vmnzcOxAAB4WLinFwAAZIuffvpJBw4c0Lhx41SpUiXr+34BAHiYmOkFAADZ4uOPP1a/fv1UqFAhLViwIKfLAQA8prinFwAAAABgWsz0AgAAAABMi9ALAAAAADAtHmSFXCM1NVV//PGHPD09ZbFYcrocAAAAADnEMAxdvXpVRYoUkYPDvedyCb3INf744w8FBATkdBkAAAAAHhFnz57Vk08+ec8+hF7kGp6enpJundheXl45XA0AAACAnBIfH6+AgABrRrgXQi9yjduXNHt5eRF6AQAAAGTotkceZAUAAAAAMC1CLwAAAADAtAi9AAAAAADT4p5e5DpFKw2VxdE1p8sAAACAyV0+Ni2nS0AWYKYXAAAAAGBahF4AAAAAgGkRegEAAAAApkXoBQAAAACYFqEXAAAAAGBahF4AAAAAgGkRegEAAAAApkXoBQAAAACYFqEXAAAAAGBahF4AAAAAgGkRegEAAAAApkXoBQAAAACYFqEXAAAAAGBahF4AAAAAgGkRegEAAAAApkXoBQAAAACYFqEXAAAAAGBahF4AAAAAgGkRegEAAAAApkXoBQAAAACYFqH3EREUFKRp06ZleV8AAAAAeJwReu+hR48eslgsslgscnZ2VuHChfXss89q/vz5Sk1NzdJt7d69W3369Mnyvplx536ntwQFBWXbtgEAAAAgKxF676Nx48Y6d+6cTp8+ra+++kr169fXK6+8ombNmik5OTnLtlOwYEHlyZMny/tmxvTp03Xu3DnrIklRUVHWz7t377bpf+PGjWyrBQAAAAAeBKH3PlxdXeXn56cnnnhClStX1ltvvaUvvvhCX331laKjo639rly5ol69eqlgwYLy8vJSgwYNtH//fpuxvvzyS1WtWlVubm4qUKCAWrdubV135yXLhmFo9OjRKlq0qFxdXVWkSBENGjQo3b6SdObMGbVs2VIeHh7y8vJShw4ddOHCBev60aNHq2LFivrf//6noKAgeXt7q1OnTrp69Wq6++zt7S0/Pz/rIkk+Pj7Wz1WrVtW4cePUrVs3eXl5WWedf/jhB9WuXVvu7u4KCAjQoEGDlJiYaB03KSlJr7/+up544gnlzZtX1atX19atW+368wAAAAAAexB6M6FBgwYKCwvTypUrrW3t27fXxYsX9dVXX+nnn39W5cqV1bBhQ/3999+SpHXr1ql169Zq2rSp9u3bp82bN6tatWrpjr9ixQp98MEHmj17to4fP67Vq1crNDQ03b6pqalq2bKl/v77b23btk0bN27UyZMn1bFjR5t+sbGxWr16tdauXau1a9dq27ZtmjRpUqaPwfvvv6+wsDDt27dPI0eOVGxsrBo3bqy2bdvqwIEDWrp0qX744QcNHDjQ+p2BAwdq586dWrJkiQ4cOKD27durcePGOn78eLrbSEpKUnx8vM0CAAAAAPZwyukCcqvSpUvrwIEDkm7NcO7atUsXL16Uq6urpFuhcPXq1fr888/Vp08fTZgwQZ06ddKYMWOsY4SFhaU79pkzZ+Tn56fw8HA5OzuraNGidw3Imzdv1sGDB3Xq1CkFBARIkhYsWKBy5cpp9+7dqlq1qqRb4Tg6Olqenp6SpK5du2rz5s2aMGFCpva/QYMGeu2116yfe/XqpS5duujVV1+VJJUoUUIzZsxQ3bp19fHHH+vixYuKiorSmTNnVKRIEUnS66+/rg0bNigqKkrvvPNOmm1MnDjR5ngBAAAAgL2Y6c0kwzBksVgkSfv371dCQoLy588vDw8P63Lq1CnFxsZKkmJiYtSwYcMMjd2+fXtdv35dwcHB6t27t1atWnXX+4ePHDmigIAAa+CVpLJly8rHx0dHjhyxtgUFBVkDryT5+/vr4sWLdu/3bU899ZTN5/379ys6Otpm/xs1aqTU1FSdOnVKBw8eVEpKikqWLGnTZ9u2bdZj9G/Dhw9XXFycdTl79mym6wUAAADweGKmN5OOHDmiYsWKSZISEhLk7++f7v2pPj4+kiR3d/cMjx0QEKCjR49q06ZN2rhxo/r376/33ntP27Ztk7Ozc6bq/ff3LBbLAz2BOm/evDafExIS9NJLL9nce3xb0aJFdeDAATk6Ournn3+Wo6OjzXoPD490t+Hq6mqdOQcAAACAzCD0ZsK3336rgwcPavDgwZKkypUr6/z583Jycrrr63wqVKigzZs368UXX8zQNtzd3dW8eXM1b95cAwYMUOnSpXXw4EFVrlzZpl+ZMmV09uxZnT171jrbe/jwYV25ckVly5bN/E7aqXLlyjp8+LBCQkLSXV+pUiWlpKTo4sWLql279kOrCwAAAMDjjdB7H0lJSTp//rxSUlJ04cIFbdiwQRMnTlSzZs3UrVs3SVJ4eLhq1KihVq1aafLkySpZsqT++OMP68OrnnrqKUVGRqphw4YqXry4OnXqpOTkZK1fv15Dhw5Ns83o6GilpKSoevXqypMnjz777DO5u7srMDAwTd/w8HCFhoaqS5cumjZtmpKTk9W/f3/VrVs3zSXI2Wno0KF6+umnNXDgQPXq1Ut58+bV4cOHtXHjRs2cOVMlS5ZUly5d1K1bN02ZMkWVKlXSn3/+qc2bN6tChQp6/vnnH1qtAAAAAB4f3NN7Hxs2bJC/v7+CgoLUuHFjbdmyRTNmzNAXX3xhvUzXYrFo/fr1qlOnjl588UWVLFlSnTp10q+//qrChQtLkurVq6fly5drzZo1qlixoho0aKBdu3alu00fHx/NmTNHtWrVUoUKFbRp0yZ9+eWXyp8/f5q+FotFX3zxhfLly6c6deooPDxcwcHBWrp0afYdlHRUqFBB27Zt07Fjx1S7dm1VqlRJo0aNsj60Srr1rt9u3brptddeU6lSpdSqVSvt3r1bRYsWfai1AgAAAHh8WAzDMHK6CCAj4uPj5e3tLe/gvrI4cq8vAAAAstflY9NyugTcxe1sEBcXJy8vr3v2ZaYXAAAAAGBahF4AAAAAgGkRegEAAAAApkXoBQAAAACYFqEXAAAAAGBahF4AAAAAgGkRegEAAAAApkXoBQAAAACYFqEXAAAAAGBahF4AAAAAgGkRegEAAAAApkXoBQAAAACYFqEXAAAAAGBahF4AAAAAgGkRegEAAAAApkXoBQAAAACYFqEXAAAAAGBahF4AAAAAgGk55XQBgL3O7HtXXl5eOV0GAAAAgFyAmV4AAAAAgGkRegEAAAAApkXoBQAAAACYFqEXAAAAAGBahF4AAAAAgGkRegEAAAAApkXoBQAAAACYFqEXAAAAAGBahF4AAAAAgGkRegEAAAAApkXoBQAAAACYFqEXAAAAAGBaTjldAGCv/7WtIHcnfq8BAAAAHpaIr07mdAmZRnIAAAAAAJgWoRcAAAAAYFqEXgAAAACAaRF6AQAAAACmRegFAAAAAJgWoRcAAAAAYFqEXgAAAACAaRF6AQAAAACmRegFAAAAAJgWoRcAAAAAYFqEXgAAAACAaRF6AQAAAACmRegFAAAAAJgWoRcAAAAAYFqEXgAAAACAaRF6AQAAAACmRegFAAAAAJgWoRcAAAAAYFqEXgAAAACAaRF6AQAAAACmRejNYfXq1dOrr76a02UAAAAAgCkRejOhR48eslgsmjRpkk376tWrZbFY7Bpr5cqVGjduXFaWl8btem8v+fPnV+PGjXXgwIFs3S4AAAAA5DRCbya5ubnp3Xff1eXLlx9oHF9fX3l6emZRVXfXuHFjnTt3TufOndPmzZvl5OSkZs2aZft2AQAAACAnEXozKTw8XH5+fpo4ceJd+1y6dEmdO3fWE088oTx58ig0NFSLFy+26XPn5c1vvfWWqlevnmacsLAwjR071vp57ty5KlOmjNzc3FS6dGnNmjXrvvW6urrKz89Pfn5+qlixooYNG6azZ8/qzz//tPYZOnSoSpYsqTx58ig4OFgjR47UzZs3JUmnT5+Wg4OD9uzZYzPutGnTFBgYqNTUVEnSoUOH1KRJE3l4eKhw4cLq2rWr/vrrL2v/zz//XKGhoXJ3d1f+/PkVHh6uxMTE+9YPAAAAAJlB6M0kR0dHvfPOO/rwww/122+/pdvnn3/+UZUqVbRu3TodOnRIffr0UdeuXbVr1650+3fp0kW7du1SbGyste3//u//dODAAf3nP/+RJC1cuFCjRo3ShAkTdOTIEb3zzjsaOXKkPv300wzXnpCQoM8++0whISHKnz+/td3T01PR0dE6fPiwpk+frjlz5uiDDz6QJAUFBSk8PFxRUVE2Y0VFRalHjx5ycHDQlStX1KBBA1WqVEl79uzRhg0bdOHCBXXo0EGSdO7cOXXu3FkRERE6cuSItm7dqjZt2sgwjHTrTEpKUnx8vM0CAAAAAPZwyukCcrPWrVurYsWKioyM1Lx589Ksf+KJJ/T6669bP7/88sv6+uuvtWzZMlWrVi1N/3LlyiksLEyLFi3SyJEjJd0KudWrV1dISIgkKTIyUlOmTFGbNm0kScWKFdPhw4c1e/Zsde/e/a61rl27Vh4eHpKkxMRE+fv7a+3atXJw+P+/e7z99tvW/w4KCtLrr7+uJUuW6M0335Qk9erVS3379tXUqVPl6uqqvXv36uDBg/riiy8kSTNnzlSlSpX0zjvvWMeZP3++AgICdOzYMSUkJCg5OVlt2rRRYGCgJCk0NPSuNU+cOFFjxoy563oAAAAAuB9meh/Qu+++q08//VRHjhxJsy4lJUXjxo1TaGiofH195eHhoa+//lpnzpy563hdunTRokWLJEmGYWjx4sXq0qWLpFthNTY2Vj179pSHh4d1GT9+vM3scHrq16+vmJgYxcTEaNeuXWrUqJGaNGmiX3/91dpn6dKlqlWrlvz8/OTh4aG3337bptZWrVrJ0dFRq1atkiRFR0erfv36CgoKkiTt379fW7ZssamtdOnSkqTY2FiFhYWpYcOGCg0NVfv27TVnzpx73hM9fPhwxcXFWZezZ8/ecx8BAAAA4N8IvQ+oTp06atSokYYPH55m3Xvvvafp06dr6NCh2rJli2JiYtSoUSPduHHjruN17txZR48e1d69e7Vjxw6dPXtWHTt2lHTrsmRJmjNnjjXAxsTE6NChQ/rxxx/vWWfevHkVEhKikJAQVa1aVXPnzlViYqLmzJkjSdq5c6e6dOmipk2bau3atdq3b59GjBhhU6uLi4u6deumqKgo3bhxQ4sWLVJERIR1fUJCgpo3b25TW0xMjI4fP646derI0dFRGzdu1FdffaWyZcvqww8/VKlSpXTq1Kl0a3Z1dZWXl5fNAgAAAAD24PLmLDBp0iRVrFhRpUqVsmnfvn27WrZsqRdeeEGSlJqaqmPHjqls2bJ3HevJJ59U3bp1tXDhQl2/fl3PPvusChUqJEkqXLiwihQpopMnT1pnfzPLYrHIwcFB169flyTt2LFDgYGBGjFihLXPnbPAt/Xq1Uvly5fXrFmzrJcq31a5cmWtWLFCQUFBcnJK/9SyWCyqVauWatWqpVGjRikwMFCrVq3SkCFDHmh/AAAAACA9hN4sEBoaqi5dumjGjBk27SVKlNDnn3+uHTt2KF++fJo6daouXLhwz9Ar3brEOTIyUjdu3LA+SOq2MWPGaNCgQfL29lbjxo2VlJSkPXv26PLly/cMjklJSTp//rwk6fLly5o5c6Z1ZvZ2rWfOnNGSJUtUtWpVrVu3znoZ853KlCmjp59+WkOHDlVERITc3d2t6wYMGKA5c+aoc+fOevPNN+Xr66sTJ05oyZIlmjt3rvbs2aPNmzfrueeeU6FChfTTTz/pzz//VJkyZe59gAEAAAAgk7i8OYuMHTvW+tqe295++21VrlxZjRo1Ur169eTn56dWrVrdd6x27drp0qVLunbtWpr+vXr10ty5cxUVFaXQ0FDVrVtX0dHRKlas2D3H3LBhg/z9/eXv76/q1atr9+7dWr58uerVqydJatGihQYPHqyBAweqYsWK2rFjh/VhWv/Ws2dP3bhxw+bSZkkqUqSItm/frpSUFD333HMKDQ3Vq6++Kh8fHzk4OMjLy0vfffedmjZtqpIlS+rtt9/WlClT1KRJk/seEwAAAADIDItxt/fFAHcxbtw4LV++XAcOHHio242Pj5e3t7dmhgfK3YnfawAAAICHJeKrkzldgo3b2SAuLu6+z/4hOSDDEhISdOjQIc2cOVMvv/xyTpcDAAAAAPdF6EWGDRw4UFWqVFG9evXSXNoMAAAAAI8iHmSFDIuOjlZ0dHROlwEAAAAAGcZMLwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC2nnC4AsFfXFQfk5eWV02UAAAAAyAWY6QUAAAAAmBahFwAAAABgWoReAAAAAIBpEXoBAAAAAKZF6AUAAAAAmBahFwAAAABgWoReAAAAAIBpEXoBAAAAAKZF6AUAAAAAmBahFwAAAABgWoReAAAAAIBpOeV0AYC9yg2qJAcXx5wuAwAAADCNX/97LKdLyDbM9AIAAAAATIvQCwAAAAAwLUIvAAAAAMC0CL0AAAAAANMi9AIAAAAATIvQCwAAAAAwLUIvAAAAAMC0CL0AAAAAANMi9AIAAAAATIvQCwAAAAAwLUIvAAAAAMC0CL0AAAAAANMi9AIAAAAATIvQCwAAAAAwLUIvAAAAAMC0CL0AAAAAANMi9AIAAAAATIvQCwAAAAAwLUIvAAAAAMC0CL0AAAAAANMi9AIAAAAATIvQi3T9+eef6tevn4oWLSpXV1f5+fmpUaNG2rZtmwoUKKBJkyal+71x48apcOHCunnzpqKjo2WxWFSmTJk0/ZYvXy6LxaKgoKBs3hMAAAAAjzNCL9LVtm1b7du3T59++qmOHTumNWvWqF69eoqLi9MLL7ygqKioNN8xDEPR0dHq1q2bnJ2dJUl58+bVxYsXtXPnTpu+8+bNU9GiRR/KvgAAAAB4fDnldAF49Fy5ckXff/+9tm7dqrp160qSAgMDVa1aNUlSsWLFNH36dP3www965plnrN/btm2bTp48qZ49e1rbnJyc9J///Efz589XjRo1JEm//fabtm7dqsGDB2vx4sUPcc8AAAAAPG6Y6UUaHh4e8vDw0OrVq5WUlJRmfWhoqKpWrar58+fbtEdFRalmzZoqXbq0TXtERISWLVuma9euSZKio6PVuHFjFS5c+J51JCUlKT4+3mYBAAAAAHsQepGGk5OToqOj9emnn8rHx0e1atXSW2+9pQMHDlj79OzZU8uXL1dCQoIk6erVq/r8888VERGRZrxKlSopODhYn3/+ufUS6PT6/dvEiRPl7e1tXQICArJuJwEAAAA8Fgi9SFfbtm31xx9/aM2aNWrcuLG2bt2qypUrKzo6WpLUuXNnpaSkaNmyZZKkpUuXysHBQR07dkx3vIiICEVFRWnbtm1KTExU06ZN71vD8OHDFRcXZ13Onj2bZfsHAAAA4PFgd+i9dOmSBgwYoLJly6pAgQLy9fW1WWAebm5uevbZZzVy5Ejt2LFDPXr0UGRkpCTJy8tL7dq1sz7QKioqSh06dJCHh0e6Y3Xp0kU//vijRo8era5du8rJ6f63k7u6usrLy8tmAQAAAAB72P0gq65du+rEiRPq2bOnChcuLIvFkh114RFUtmxZrV692vq5Z8+eqlevntauXasdO3bovffeu+t3fX191aJFCy1btkyffPLJQ6gWAAAAADIRer///nv98MMPCgsLy4568Ai4dOmS2rdvr4iICFWoUEGenp7as2ePJk+erJYtW1r71alTRyEhIerWrZtKly6tmjVr3nPc6OhozZo1S/nz58/uXQAAAAAASZkIvaVLl9b169ezoxY8Ijw8PFS9enV98MEHio2N1c2bNxUQEKDevXvrrbfesvazWCyKiIjQW2+9peHDh993XHd3d7m7u2dn6QAAAABgw2IYhmHPF3bv3q1hw4Zp1KhRKl++vJydnW3Wc98lskt8fLy8vb31ZPdgObg45nQ5AAAAgGn8+t9jOV2CXW5ng7i4uPtmULtnen18fBQfH68GDRrYtBuGIYvFopSUFHuHBAAAAAAgW9gdert06SJnZ2ctWrSIB1kBAAAAAB5pdofeQ4cOad++fSpVqlR21AMAAAAAQJax+z29Tz31lM6ePZsdtQAAAAAAkKXsnul9+eWX9corr+iNN95QaGhomgdZVahQIcuKAwAAAADgQdgdejt27ChJioiIsLZZLBYeZAUAAAAAeOTYHXpPnTqVHXUAAAAAAJDl7A69gYGB2VEHAAAAAABZzu7QK0nHjx/Xli1bdPHiRaWmptqsGzVqVJYUBgAAAADAg7I79M6ZM0f9+vVTgQIF5OfnZ/OeXovFQugFAAAAADwy7A6948eP14QJEzR06NDsqAcAAAAAgCxj93t6L1++rPbt22dHLQAAAAAAZCm7Q2/79u31zTffZEctAAAAAABkqQxd3jxjxgzrf4eEhGjkyJH68ccfFRoaKmdnZ5u+gwYNytoKAQAAAADIJIthGMb9OhUrVixjg1ksOnny5AMXBaQnPj5e3t7eerJ7sBxcHHO6HAAAAMA0fv3vsZwuwS63s0FcXJy8vLzu2TdDM72nTp3KksIAAAAAAHiY7L6nd+zYsbp27Vqa9uvXr2vs2LFZUhQAAAAAAFnB7tA7ZswYJSQkpGm/du2axowZkyVFAQAAAACQFewOvYZhyGKxpGnfv3+/fH19s6QoAAAAAACyQobu6ZWkfPnyyWKxyGKxqGTJkjbBNyUlRQkJCerbt2+2FAnc6f9m7LvvzeoAAAAAINkReqdNmybDMBQREaExY8bI29vbus7FxUVBQUGqUaNGthQJAAAAAEBmZDj0du/eXdKt1xfVrFkzzft5AQAAAAB41GQo9MbHx1svJ61UqZKuX7+u69evp9uXy04BAAAAAI+KDIXefPny6dy5cypUqJB8fHzSfZDV7QdcpaSkZHmRAAAAAABkRoZC77fffmt9MvOWLVuytSAAAAAAALJKhkJv3bp1JUnJycnatm2bIiIi9OSTT2ZrYQAAAAAAPCi73tPr5OSk9957T8nJydlVDwAAAAAAWcau0CtJDRo00LZt27KjFgAAAAAAslSGX1l0W5MmTTRs2DAdPHhQVapUUd68eW3Wt2jRIsuKAwAAAADgQVgMwzDs+YKDw90nh3l6M7JTfHy8vL29FRcXx6uxAAAAgMeYPdnA7pne1NTUTBcGAAAAAMDDZPc9vQAAAAAA5BZ2z/RK0rZt2/T+++/ryJEjkqSyZcvqjTfeUO3atbO0OCA940IC5epgyekyAAAAgBwz/vzfOV1CrmH3TO9nn32m8PBw5cmTR4MGDdKgQYPk7u6uhg0batGiRdlRIwAAAAAAmWL3g6zKlCmjPn36aPDgwTbtU6dO1Zw5c6yzv0BWu32z+usFfZjpBQAAwGPtcZ/ptedBVnbP9J48eVLNmzdP096iRQudOnXK3uEAAAAAAMg2dofegIAAbd68OU37pk2bFBAQkCVFAQAAAACQFex+kNVrr72mQYMGKSYmRjVr1pQkbd++XdHR0Zo+fXqWFwgAAAAAQGbZHXr79esnPz8/TZkyRcuWLZN06z7fpUuXqmXLllleIAAAAAAAmZWpVxa1bt1arVu3zupaAAAAAADIUnbf0wsAAAAAQG5h90xvvnz5ZLGkfV2MxWKRm5ubQkJC1KNHD7344otZUiAAAAAAAJlld+gdNWqUJkyYoCZNmqhatWqSpF27dmnDhg0aMGCATp06pX79+ik5OVm9e/fO8oIBAAAAAMgou0PvDz/8oPHjx6tv37427bNnz9Y333yjFStWqEKFCpoxYwahFwAAAACQo+y+p/frr79WeHh4mvaGDRvq66+/liQ1bdpUJ0+efPDqAAAAAAB4AHaHXl9fX3355Zdp2r/88kv5+vpKkhITE+Xp6fng1QEAAAAA8ADsvrx55MiR6tevn7Zs2WK9p3f37t1av369PvnkE0nSxo0bVbdu3aytFAAAAAAAO9kdenv37q2yZctq5syZWrlypSSpVKlS2rZtm2rWrClJeu2117K2SgAAAAAAMsHu0CtJtWrVUq1atbK6FgAAAAAAslSGQm98fHyGB/Ty8sp0MQAAAAAAZKUMhV4fHx9ZLJZ79jEMQxaLRSkpKVlSGAAAAAAADypDoXfLli0ZGuzgwYMPVAwAAAAAAFkpQ68sqlu37l2XypUr6+jRo3rjjTf0yiuvZHe9dgsKCtK0adMy/f3o6Gj5+PhkWT1m8qDHFgAAAACym93v6b3tu+++U/fu3eXv76/3339fDRo00I8//mjXGD169FCrVq0yW0KG7N69W3369MlQ3/RCXMeOHXXs2LFMbz86OloWi0UWi0UODg7y9/dXx44ddebMmUyP+aiw59gCAAAAQE6w6+nN58+fV3R0tObNm6f4+Hh16NBBSUlJWr16tcqWLZtdNT6QggULPtD33d3d5e7u/kBjeHl56ejRozIMQ6dOnVL//v3Vvn17/fTTTw807v3cvHlTzs7O2Tb+gx5bAAAAAMhuGZ7pbd68uUqVKqUDBw5o2rRp+uOPP/Thhx9mZ23atm2bqlWrJldXV/n7+2vYsGFKTk62rr969aq6dOmivHnzyt/fXx988IHq1aunV1991drnztlbwzA0evRoFS1aVK6uripSpIgGDRokSapXr55+/fVXDR482DozK6V/efOXX36pqlWrys3NTQUKFFDr1q3vuR8Wi0V+fn7y9/dXzZo11bNnT+3atcvmqdhffPGFKleuLDc3NwUHB2vMmDE2+/rLL7/omWeekZubm8qWLatNmzbJYrFo9erVkqTTp0/LYrFo6dKlqlu3rtzc3LRw4UJJ0ty5c1WmTBm5ubmpdOnSmjVrlnXcGzduaODAgfL395ebm5sCAwM1ceLE+x6vfx9bSTpz5oxatmwpDw8PeXl5qUOHDrpw4YJ1/ejRo1WxYkX973//U1BQkLy9vdWpUyddvXr1nscPAAAAADIrwzO9X331lQYNGqR+/fqpRIkS2VmTJOn3339X06ZN1aNHDy1YsEC//PKLevfuLTc3N40ePVqSNGTIEG3fvl1r1qxR4cKFNWrUKO3du1cVK1ZMd8wVK1bogw8+0JIlS1SuXDmdP39e+/fvlyStXLlSYWFh6tOnj3r37n3XutatW6fWrVtrxIgRWrBggW7cuKH169dneL8uXryoVatWydHRUY6OjpKk77//Xt26ddOMGTNUu3ZtxcbGWi8bjoyMVEpKilq1aqWiRYvqp59+0tWrV/Xaa6+lO/6wYcM0ZcoUVapUyRp8R40apZkzZ6pSpUrat2+fevfurbx586p79+6aMWOG1qxZo2XLlqlo0aI6e/aszp49e9/j9W+pqanWwLtt2zYlJydrwIAB6tixo7Zu3WrtFxsbq9WrV2vt2rW6fPmyOnTooEmTJmnChAlpxkxKSlJSUpL1sz2vzgIAAAAAyY7Q+8MPP2jevHmqUqWKypQpo65du6pTp07ZVtisWbMUEBCgmTNnymKxqHTp0vrjjz80dOhQjRo1SomJifr000+1aNEiNWzYUJIUFRWlIkWK3HXMM2fOyM/PT+Hh4XJ2dlbRokVVrVo1SZKvr68cHR3l6ekpPz+/u44xYcIEderUSWPGjLG2hYWF3XNf4uLi5OHhIcMwdO3aNUnSoEGDlDdvXknSmDFjNGzYMHXv3l2SFBwcrHHjxunNN99UZGSkNm7cqNjYWG3dutVa24QJE/Tss8+m2darr76qNm3aWD9HRkZqypQp1rZixYrp8OHDmj17trp3764zZ86oRIkSeuaZZ2SxWBQYGJih4/Vvmzdv1sGDB3Xq1CkFBARIkhYsWKBy5cpp9+7dqlq1qqRb4Tg6Olqenp6SpK5du2rz5s3pht6JEyfaHGcAAAAAsFeGL29++umnNWfOHJ07d04vvfSSlixZoiJFiig1NVUbN27M8ktUjxw5oho1ati8H7hWrVpKSEjQb7/9ppMnT+rmzZs2Iczb21ulSpW665jt27fX9evXFRwcrN69e2vVqlU2lxBnRExMjDVkZ5Snp6diYmK0Z88eTZkyRZUrV7YJefv379fYsWPl4eFhXXr37q1z587p2rVrOnr0qAICAmzC+N3C51NPPWX978TERMXGxqpnz542Y48fP16xsbGSbj1MLCYmRqVKldKgQYP0zTffWL9vz/E6cuSIAgICrIFXksqWLSsfHx8dOXLE2hYUFGQNvJLk7++vixcvpjvm8OHDFRcXZ11uz0ADAAAAQEbZ/fTmvHnzKiIiQj/88IMOHjyo1157TZMmTVKhQoXUokWL7KgxywQEBOjo0aOaNWuW3N3d1b9/f9WpU0c3b97M8BiZeaiVg4ODQkJCVKZMGQ0ZMkRPP/20+vXrZ12fkJCgMWPGKCYmxrocPHhQx48fl5ubm13buj17fHtcSZozZ47N2IcOHbI+abty5co6deqUxo0bp+vXr6tDhw5q166dpKw5Xv/27wdrWSwWpaamptvX1dVVXl5eNgsAAAAA2CPTryySpFKlSmny5Mn67bfftHjx4qyqSZJUpkwZ7dy5U4ZhWNu2b98uT09PPfnkkwoODpazs7N2795tXR8XF3ff1wu5u7urefPmmjFjhrZu3aqdO3fq4MGDkiQXFxelpKTc8/sVKlTQ5s2bH2DPbt13u3TpUu3du1eSrO86DgkJSbM4ODioVKlSOnv2rM1Doe7c77spXLiwihQpopMnT6YZt1ixYtZ+Xl5e6tixo+bMmaOlS5dqxYoV+vvvvyXd+3jdqUyZMjb3A0vS4cOHdeXKlUf2yd4AAAAAzM+uVxbdjaOjo1q1apWpd+7GxcUpJibGpi1//vzq37+/pk2bppdfflkDBw7U0aNHFRkZqSFDhsjBwUGenp7q3r273njjDfn6+qpQoUKKjIyUg4ODzSXRd4qOjlZKSoqqV6+uPHny6LPPPpO7u7v1PtagoCB999136tSpk1xdXVWgQIE0Y0RGRqphw4YqXry4OnXqpOTkZK1fv15Dhw7N8D4HBASodevWGjVqlNauXatRo0apWbNmKlq0qNq1aycHBwft379fhw4d0vjx4/Xss8+qePHi6t69uyZPnqyrV6/q7bfflqS77uttY8aM0aBBg+Tt7a3GjRsrKSlJe/bs0eXLlzVkyBBNnTpV/v7+qlSpkhwcHLR8+XL5+fnJx8fnvsfrTuHh4QoNDVWXLl00bdo0JScnq3///qpbt67NJdcAAAAA8DA90ExvVti6dasqVapks4wZM0ZPPPGE1q9fr127diksLEx9+/ZVz549rWFPkqZOnaoaNWqoWbNmCg8PV61atayv5kmPj4+P5syZo1q1aqlChQratGmTvvzyS+XPn1+SNHbsWJ0+fVrFixe/6zto69Wrp+XLl2vNmjWqWLGiGjRooF27dtm934MHD9a6deu0a9cuNWrUSGvXrtU333yjqlWr6umnn9YHH3xgDZeOjo5avXq1EhISVLVqVfXq1UsjRoyQpPte/tyrVy/NnTtXUVFRCg0NVd26dRUdHW2d6fX09NTkyZP11FNPqWrVqjp9+rTWr18vBweH+x6vO1ksFn3xxRfKly+f6tSpo/DwcAUHB2vp0qV2HxsAAAAAyCoW487rh3O5xMREPfHEE5oyZYp69uyZ0+Vkq+3bt+uZZ57RiRMnVLx48Zwu56GIj4+Xt7e3Xi/oI1eHe89wAwAAAGY2/vzfOV1CjrqdDeLi4u777J8subw5p+zbt0+//PKLqlWrpri4OI0dO1aS1LJlyxyuLOutWrVKHh4eKlGihE6cOKFXXnlFtWrVemwCLwAAAABkRq4OvZL0/vvv6+jRo3JxcVGVKlX0/fffp3svbm539epVDR06VGfOnFGBAgUUHh6uKVOm5HRZAAAAAPBIM9XlzTA3Lm8GAAAAbuHy5oxf3pzjD7ICAAAAACC7EHoBAAAAAKZF6AUAAAAAmBahFwAAAABgWoReAAAAAIBpEXoBAAAAAKZF6AUAAAAAmBahFwAAAABgWoReAAAAAIBpEXoBAAAAAKZF6AUAAAAAmBahFwAAAABgWoReAAAAAIBpEXoBAAAAAKZF6AUAAAAAmBahFwAAAABgWk45XQBgr5EnfpWXl1dOlwEAAAAgF2CmFwAAAABgWoReAAAAAIBpEXoBAAAAAKZF6AUAAAAAmBahFwAAAABgWoReAAAAAIBpEXoBAAAAAKZF6AUAAAAAmBahFwAAAABgWoReAAAAAIBpEXoBAAAAAKZF6AUAAAAAmJZTThcA2Kv7U6/K2dElp8sAAAAAHhvLjnyS0yVkGjO9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9AAAAAADTIvQiS/To0UMWi0UWi0XOzs4qVqyY3nzzTf3zzz/WPrfX37k888wzOVg1AAAAALNzyukCYB6NGzdWVFSUbt68qZ9//lndu3eXxWLRu+++a+0TFRWlxo0bWz+7uLjkRKkAAAAAHhOEXmQZV1dX+fn5SZICAgIUHh6ujRs32oReHx8fa5/7SUpKUlJSkvVzfHx81hYMAAAAwPS4vBnZ4tChQ9qxY8cDzeROnDhR3t7e1iUgICALKwQAAADwOCD0IsusXbtWHh4ecnNzU2hoqC5evKg33njDpk/nzp3l4eFhXVavXn3X8YYPH664uDjrcvbs2WzeAwAAAABmw+XNyDL169fXxx9/rMTERH3wwQdycnJS27Ztbfp88MEHCg8Pt3729/e/63iurq5ydXXNtnoBAAAAmB+hF1kmb968CgkJkSTNnz9fYWFhmjdvnnr27Gnt4+fnZ+0DAAAAANmNy5uRLRwcHPTWW2/p7bff1vXr13O6HAAAAACPKUIvsk379u3l6Oiojz76KKdLAQAAAPCYIvQi2zg5OWngwIGaPHmyEhMTc7ocAAAAAI8hi2EYRk4XAWREfHy8vL291arEi3J2zPyrkAAAAADYZ9mRT3K6BBu3s0FcXJy8vLzu2ZeZXgAAAACAaRF6AQAAAACmRegFAAAAAJgWoRcAAAAAYFqEXgAAAACAaRF6AQAAAACmRegFAAAAAJgWoRcAAAAAYFqEXgAAAACAaRF6AQAAAACmRegFAAAAAJgWoRcAAAAAYFqEXgAAAACAaRF6AQAAAACmRegFAAAAAJgWoRcAAAAAYFqEXgAAAACAaRF6AQAAAACm5ZTTBQD2+nTPNHl5eeV0GQAAAAByAWZ6AQAAAACmRegFAAAAAJgWoRcAAAAAYFqEXgAAAACAaRF6AQAAAACmRegFAAAAAJgWoRcAAAAAYFqEXgAAAACAaRF6AQAAAACmRegFAAAAAJgWoRcAAAAAYFqEXgAAAACAaTnldAGAvf7zTHE5O/J7DQAAAPCwrNp3IadLyDSSAwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9j7CUlBTVrFlTbdq0sWmPi4tTQECARowYYW1bsWKFGjRooHz58snd3V2lSpVSRESE9u3bZ+0THR0ti8ViXTw8PFSlShWtXLnyoe2TJNWrV0+vvvrqQ90mAAAAgMcTofcR5ujoqOjoaG3YsEELFy60tr/88svy9fVVZGSkJGno0KHq2LGjKlasqDVr1ujo0aNatGiRgoODNXz4cJsxvby8dO7cOZ07d0779u1To0aN1KFDBx09evSh7hsAAAAAPAyE3kdcyZIlNWnSJL388ss6d+6cvvjiCy1ZskQLFiyQi4uLfvzxR02ePFlTp07V1KlTVbt2bRUtWlRVqlTR22+/ra+++spmPIvFIj8/P/n5+alEiRIaP368HBwcdODAAWufy5cvq1u3bsqXL5/y5MmjJk2a6Pjx4zbjrFixQuXKlZOrq6uCgoI0ZcoUm/WzZs1SiRIl5ObmpsKFC6tdu3aSpB49emjbtm2aPn26dcb59OnT2XPwAAAAADz2nHK6ANzfyy+/rFWrVqlr1646ePCgRo0apbCwMEnS4sWL5eHhof79+6f7XYvFctdxU1JStGDBAklS5cqVre09evTQ8ePHtWbNGnl5eWno0KFq2rSpDh8+LGdnZ/3888/q0KGDRo8erY4dO2rHjh3q37+/8ufPrx49emjPnj0aNGiQ/ve//6lmzZr6+++/9f3330uSpk+frmPHjql8+fIaO3asJKlgwYLp1peUlKSkpCTr5/j4eDuOGgAAAAAQenMFi8Wijz/+WGXKlFFoaKiGDRtmXXfs2DEFBwfLyen//1FOnTpVo0aNsn7+/fff5e3tLenW/cAeHh6SpOvXr8vZ2Vn//e9/Vbx4cUmyht3t27erZs2akqSFCxcqICBAq1evVvv27TV16lQ1bNhQI0eOlHRrNvrw4cN677331KNHD505c0Z58+ZVs2bN5OnpqcDAQFWqVEmS5O3tLRcXF+XJk0d+fn733O+JEydqzJgxD3r4AAAAADzGuLw5l5g/f77y5MmjU6dO6bfffrtn34iICMXExGj27NlKTEyUYRjWdZ6enoqJiVFMTIz27dund955R3379tWXX34pSTpy5IicnJxUvXp163fy58+vUqVK6ciRI9Y+tWrVstlmrVq1dPz4caWkpOjZZ59VYGCggoOD1bVrVy1cuFDXrl2ze5+HDx+uuLg463L27Fm7xwAAAADweCP05gI7duzQBx98oLVr16patWrq2bOnNciWKFFCJ0+e1M2bN639fXx8FBISoieeeCLNWA4ODgoJCVFISIgqVKigIUOGqF69enr33XezrF5PT0/t3btXixcvlr+/v/Vy7CtXrtg1jqurq7y8vGwWAAAAALAHofcRd+3aNfXo0UP9+vVT/fr1NW/ePO3atUuffPKJJKlz585KSEjQrFmzMr0NR0dHXb9+XZJUpkwZJScn66effrKuv3Tpko4ePaqyZcta+2zfvt1mjO3bt6tkyZJydHSUJDk5OSk8PFyTJ0/WgQMHdPr0aX377beSJBcXF6WkpGS6XgAAAADIKO7pfcQNHz5chmFo0qRJkqSgoCC9//77ev3119WkSRPVqFFDr732ml577TX9+uuvatOmjQICAnTu3DnNmzdPFotFDg7//7cNwzB0/vx5Sbfu6d24caO+/vpr6z3AJUqUUMuWLdW7d2/Nnj1bnp6eGjZsmJ544gm1bNlSkvTaa6+patWqGjdunDp27KidO3dq5syZ1uC9du1anTx5UnXq1FG+fPm0fv16paamqlSpUtZ9+Omnn3T69Gl5eHjI19fXpkYAAAAAyCokjUfYtm3b9NFHHykqKkp58uSxtr/00kuqWbOm9TLn999/X4sWLdK+ffvUrFkzlShRQu3bt1dqaqp27txpc1lwfHy8/P395e/vrzJlymjKlCkaO3asRowYYe0TFRWlKlWqqFmzZqpRo4YMw9D69evl7Ows6daTnpctW6YlS5aofPnyGjVqlMaOHasePXpIunV59cqVK9WgQQOVKVNGn3zyiRYvXqxy5cpJkl5//XU5OjqqbNmyKliwoM6cOfMQjiYAAACAx5HFuPMpR8AjLD4+Xt7e3no+tICcHfm9BgAAAHhYVu27kNMl2LidDeLi4u777B+SAwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9AAAAAADTcsrpAgB7LfohVl5eXjldBgAAAIBcgJleAAAAAIBpEXoBAAAAAKZF6AUAAAAAmBahFwAAAABgWoReAAAAAIBp8fRm5BqGYUiS4uPjc7gSAAAAADnpdia4nRHuhdCLXOPSpUuSpICAgByuBAAAAMCj4OrVq/L29r5nH0Ivcg1fX19J0pkzZ+57YgMZER8fr4CAAJ09e5Z3PyNLcE4hq3FOIatxTiGr5dQ5ZRiGrl69qiJFity3L6EXuYaDw61b0L29vflLGlnKy8uLcwpZinMKWY1zClmNcwpZLSfOqYxOhPEgKwAAAACAaRF6AQAAAACmRehFruHq6qrIyEi5urrmdCkwCc4pZDXOKWQ1zilkNc4pZLXccE5ZjIw84xkAAAAAgFyImV4AAAAAgGkRegEAAAAApkXoBQAAAACYFqEXAAAAAGBahF48Uj766CMFBQXJzc1N1atX165du+7Zf/ny5SpdurTc3NwUGhqq9evXP6RKkVvYc07NmTNHtWvXVr58+ZQvXz6Fh4ff9xzE48fev6duW7JkiSwWi1q1apW9BSLXsfecunLligYMGCB/f3+5urqqZMmS/P8PNuw9p6ZNm6ZSpUrJ3d1dAQEBGjx4sP7555+HVC0edd99952aN2+uIkWKyGKxaPXq1ff9ztatW1W5cmW5uroqJCRE0dHR2V7nvRB68chYunSphgwZosjISO3du1dhYWFq1KiRLl68mG7/HTt2qHPnzurZs6f27dunVq1aqVWrVjp06NBDrhyPKnvPqa1bt6pz587asmWLdu7cqYCAAD333HP6/fffH3LleFTZe07ddvr0ab3++uuqXbv2Q6oUuYW959SNGzf07LPP6vTp0/r888919OhRzZkzR0888cRDrhyPKnvPqUWLFmnYsGGKjIzUkSNHNG/ePC1dulRvvfXWQ64cj6rExESFhYXpo48+ylD/U6dO6fnnn1f9+vUVExOjV199Vb169dLXX3+dzZXegwE8IqpVq2YMGDDA+jklJcUoUqSIMXHixHT7d+jQwXj++edt2qpXr2689NJL2Voncg97z6l/S05ONjw9PY1PP/00u0pELpOZcyo5OdmoWbOmMXfuXKN79+5Gy5YtH0KlyC3sPac+/vhjIzg42Lhx48bDKhG5jL3n1IABA4wGDRrYtA0ZMsSoVatWttaJ3EmSsWrVqnv2efPNN41y5crZtHXs2NFo1KhRNlZ2b8z04pFw48YN/fzzzwoPD7e2OTg4KDw8XDt37kz3Ozt37rTpL0mNGjW6a388XjJzTv3btWvXdPPmTfn6+mZXmchFMntOjR07VoUKFVLPnj0fRpnIRTJzTq1Zs0Y1atTQgAEDVLhwYZUvX17vvPOOUlJSHlbZeIRl5pyqWbOmfv75Z+sl0CdPntT69evVtGnTh1IzzOdR/De6U45tGbjDX3/9pZSUFBUuXNimvXDhwvrll1/S/c758+fT7X/+/PlsqxO5R2bOqX8bOnSoihQpkuYvbjyeMnNO/fDDD5o3b55iYmIeQoXIbTJzTp08eVLffvutunTpovXr1+vEiRPq37+/bt68qcjIyIdRNh5hmTmn/vOf/+ivv/7SM888I8MwlJycrL59+3J5MzLtbv9Gj4+P1/Xr1+Xu7v7Qa2KmFwDSMWnSJC1ZskSrVq2Sm5tbTpeDXOjq1avq2rWr5syZowIFCuR0OTCJ1NRUFSpUSP/9739VpUoVdezYUSNGjNAnn3yS06Uhl9q6daveeecdzZo1S3v37tXKlSu1bt06jRs3LqdLA7IMM714JBQoUECOjo66cOGCTfuFCxfk5+eX7nf8/Pzs6o/HS2bOqdvef/99TZo0SZs2bVKFChWys0zkIvaeU7GxsTp9+rSaN29ubUtNTZUkOTk56ejRoypevHj2Fo1HWmb+nvL395ezs7McHR2tbWXKlNH58+d148YNubi4ZGvNeLRl5pwaOXKkunbtql69ekmSQkNDlZiYqD59+mjEiBFycGCODPa527/Rvby8cmSWV2KmF48IFxcXValSRZs3b7a2paamavPmzapRo0a636lRo4ZNf0nauHHjXfvj8ZKZc0qSJk+erHHjxmnDhg166qmnHkapyCXsPadKly6tgwcPKiYmxrq0aNHC+jTLgICAh1k+HkGZ+XuqVq1aOnHihPUHFEk6duyY/P39CbzI1Dl17dq1NMH29o8qhmFkX7EwrUfy3+g59ggt4F+WLFliuLq6GtHR0cbhw4eNPn36GD4+Psb58+cNwzCMrl27GsOGDbP23759u+Hk5GS8//77xpEjR4zIyEjD2dnZOHjwYE7tAh4x9p5TkyZNMlxcXIzPP//cOHfunHW5evVqTu0CHjH2nlP/xtOb8W/2nlNnzpwxPD09jYEDBxpHjx411q5daxQqVMgYP358Tu0CHjH2nlORkZGGp6ensXjxYuPkyZPGN998YxQvXtzo0KFDTu0CHjFXr1419u3bZ+zbt8+QZEydOtXYt2+f8euvvxqGYRjDhg0zunbtau1/8uRJI0+ePMYbb7xhHDlyxPjoo48MR0dHY8OGDTm1CwahF4+UDz/80ChatKjh4uJiVKtWzfjxxx+t6+rWrWt0797dpv+yZcuMkiVLGi4uLka5cuWMdevWPeSK8aiz55wKDAw0JKVZIiMjH37heGTZ+/fUnQi9SI+959SOHTuM6tWrG66urkZwcLAxYcIEIzk5+SFXjUeZPefUzZs3jdGjRxvFixc33NzcjICAAKN///7G5cuXH37heCRt2bIl3X8f3T6PunfvbtStWzfNdypWrGi4uLgYwcHBRlRU1EOv+04Ww+C6BQAAAACAOXFPLwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9AAAAAADTIvQCAAAAAEyL0AsAAAAAMC1CLwAAAADAtAi9AADggezcuVOOjo56/vnnc7oUAADSsBiGYeR0EQAAIPfq1auXPDw8NG/ePB09elRFihTJkTpu3LghFxeXHNk2AODRxUwvAADItISEBC1dulT9+vXT888/r+joaJv1X375papWrSo3NzcVKFBArVu3tq5LSkrS0KFDFRAQIFdXV4WEhGjevHmSpOjoaPn4+NiMtXr1alksFuvn0aNHq2LFipo7d66KFSsmNzc3SdKGDRv0zDPPyMfHR/nz51ezZs0UGxtrM9Zvv/2mzp07y9fXV3nz5tVTTz2ln376SadPn5aDg4P27Nlj03/atGkKDAxUamrqgx4yAMBDRugFAACZtmzZMpUuXVqlSpXSCy+8oPnz5+v2RWTr1q1T69at1bRpU+3bt0+bN29WtWrVrN/t1q2bFi9erBkzZujIkSOaPXu2PDw87Nr+iRMntGLFCq1cuVIxMTGSpMTERA0ZMkR79uzR5s2b5eDgoNatW1sDa0JCgurWravff/9da9as0f79+/Xmm28qNTVVQUFBCg8PV1RUlM12oqKi1KNHDzk48E8nAMhtnHK6AAAAkHvNmzdPL7zwgiSpcePGiouL07Zt21SvXj1NmDBBnTp10pgxY6z9w8LCJEnHjh3TsmXLtHHjRoWHh0uSgoOD7d7+jRs3tGDBAhUsWNDa1rZtW5s+8+fPV8GCBXX48GGVL19eixYt0p9//qndu3fL19dXkhQSEmLt36tXL/Xt21dTp06Vq6ur9u7dq4MHD+qLL76wuz4AQM7j50oAAJApR48e1a5du9S5c2dJkpOTkzp27Gi9RDkmJkYNGzZM97sxMTFydHRU3bp1H6iGwMBAm8ArScePH1fnzp0VHBwsLy8vBQUFSZLOnDlj3XalSpWsgfffWrVqJUdHR61atUrSrUut69evbx0HAJC7MNMLAAAyZd68eUpOTrZ5cJVhGHJ1ddXMmTPl7u5+1+/ea50kOTg46N/P2rx582aafnnz5k3T1rx5cwUGBmrOnDkqUqSIUlNTVb58ed24cSND23ZxcVG3bt0UFRWlNm3aaNGiRZo+ffo9vwMAeHQx0wsAAOyWnJysBQsWaMqUKYqJibEu+/fvV5EiRbR48WJVqFBBmzdvTvf7oaGhSk1N1bZt29JdX7BgQV29elWJiYnWttv37N7LpUuXdPToUb399ttq2LChypQpo8uXL9v0qVChgmJiYvT333/fdZxevXpp06ZNmjVrlpKTk9WmTZv7bhsA8GhiphcAANht7dq1unz5snr27Clvb2+bdW3bttW8efP03nvvqWHDhipevLg6deqk5ORkrV+/XkOHDlVQUJC6d++uiIgIzZgxQ2FhYfr111918eJFdejQQdWrV1eePHn01ltvadCgQfrpp5/SPBk6Pfny5VP+/Pn13//+V/7+/jpz5oyGDRtm06dz585655131KpVK02cOFH+/v7at2+fihQpoho1akiSypQpo6efflpDhw5VRETEfWeHAQCPLmZ6AQCA3ebNm6fw8PA0gVe6FXr37NkjX19fLV++XGvWrFHFihXVoEED7dq1y9rv448/Vrt27dS/f3+VLl1avXv3ts7s+vr66rPPPtP69esVGhqqxYsXa/To0fety8HBQUuWLNHPP/+s8uXLa/DgwXrvvfds+ri4uOibb75RoUKF1LRpU4WGhmrSpElydHS06dezZ0/duHFDERERmThCAIBHhcX49w0zAAAA0Lhx47R8+XIdOHAgp0sBADwAZnoBAADukJCQoEOHDmnmzJl6+eWXc7ocAMADIvQCAADcYeDAgapSpYrq1avHpc0AYAJc3gwAAAAAMC1megEAAAAApkXoBQAAAACYFqEXAAAAAGBahF4AAAAAgGkRegEAAAAApkXoBQAAAACYFqEXAAAAAGBahF4AAAAAgGn9P6RHpUnv+4hdAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qf5bZ2VHqHYl"
      },
      "id": "qf5bZ2VHqHYl",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UGGbNJX8qHdV"
      },
      "id": "UGGbNJX8qHdV",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IIaw2oD7qHh_"
      },
      "id": "IIaw2oD7qHh_",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.4"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}