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      "cell_type": "markdown",
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        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/lovnishverma/crop-predict/blob/main/classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "!pip install pandas, scikit-learn\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "fe9x9YAVc9pW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "uI84VutZZWsA"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"https://raw.githubusercontent.com/lovnishverma/datasets/refs/heads/main/Crop.csv\")"
      ],
      "metadata": {
        "id": "Y2WWfQReZmdm"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
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          "height": 206
        },
        "id": "KYQZPyn4Z17-",
        "outputId": "9db809e5-6822-424b-c0fe-13dd995b918c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
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              "     N   P    K   ph    EC     S     Cu      Fe     Mn     Zn      B  \\\n",
              "0  143  69  217  5.9  0.58  0.23  10.20  116.35  59.96  54.85  21.29   \n",
              "1  170  36  216  5.9  0.15  0.28  15.69  114.20  56.87  31.28  28.62   \n",
              "2  158  66  219  6.8  0.34  0.20  15.29   65.87  51.81  57.12  27.59   \n",
              "3  133  45  207  6.4  0.94  0.21   8.48  103.10  43.81  68.50  47.29   \n",
              "4  132  48  218  6.7  0.54  0.19   5.59   63.40  56.40  46.71  31.04   \n",
              "\n",
              "         label  \n",
              "0  pomegranate  \n",
              "1  pomegranate  \n",
              "2  pomegranate  \n",
              "3  pomegranate  \n",
              "4  pomegranate  "
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              "      <th>1</th>\n",
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              "      <td>28.62</td>\n",
              "      <td>pomegranate</td>\n",
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              "      <th>2</th>\n",
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              "      <td>27.59</td>\n",
              "      <td>pomegranate</td>\n",
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              "      <td>47.29</td>\n",
              "      <td>pomegranate</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>46.71</td>\n",
              "      <td>31.04</td>\n",
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        "outputId": "95466116-fd6b-44dc-e106-8bedd96e3329"
      },
      "execution_count": 19,
      "outputs": [
        {
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            "text/plain": [
              "                N           P           K          ph          EC           S  \\\n",
              "count  620.000000  620.000000  620.000000  620.000000  620.000000  620.000000   \n",
              "mean   135.701613   52.508065  161.401613    6.106290    0.885468    0.484085   \n",
              "std     31.808083   24.673538   81.285780    0.693782    0.637214    5.775388   \n",
              "min     30.000000   10.000000   51.000000    4.500000    0.010000    0.011100   \n",
              "25%    116.000000   30.000000   89.000000    5.700000    0.297500    0.086000   \n",
              "50%    138.000000   54.000000  157.500000    6.100000    0.780000    0.168000   \n",
              "75%    160.000000   75.000000  206.000000    6.500000    1.390000    0.240000   \n",
              "max    198.000000  100.000000  369.000000    8.000000    2.480000  113.200000   \n",
              "\n",
              "               Cu          Fe           Mn          Zn           B  \n",
              "count  620.000000  620.000000   620.000000  620.000000  620.000000  \n",
              "mean    17.315452  117.212500   219.905887   34.340839   28.750710  \n",
              "std      6.441150   62.775199   341.998793   11.543871   19.636447  \n",
              "min      0.180000    3.810000    20.580000   17.990000    1.020000  \n",
              "25%     12.592500   68.312500    58.785000   24.727500   13.442500  \n",
              "50%     16.910000  108.245000    73.580000   31.425000   23.225000  \n",
              "75%     21.102500  155.620000   163.665000   43.542500   42.767500  \n",
              "max     35.000000  276.720000  1572.540000   70.000000   74.560000  "
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              "      <th>P</th>\n",
              "      <th>K</th>\n",
              "      <th>ph</th>\n",
              "      <th>EC</th>\n",
              "      <th>S</th>\n",
              "      <th>Cu</th>\n",
              "      <th>Fe</th>\n",
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              "      <th>count</th>\n",
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              "      <th>mean</th>\n",
              "      <td>135.701613</td>\n",
              "      <td>52.508065</td>\n",
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              "      <td>6.106290</td>\n",
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              "      <td>219.905887</td>\n",
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              "      <td>28.750710</td>\n",
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              "      <th>std</th>\n",
              "      <td>31.808083</td>\n",
              "      <td>24.673538</td>\n",
              "      <td>81.285780</td>\n",
              "      <td>0.693782</td>\n",
              "      <td>0.637214</td>\n",
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              "      <td>6.441150</td>\n",
              "      <td>62.775199</td>\n",
              "      <td>341.998793</td>\n",
              "      <td>11.543871</td>\n",
              "      <td>19.636447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>10.000000</td>\n",
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              "      <td>4.500000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.011100</td>\n",
              "      <td>0.180000</td>\n",
              "      <td>3.810000</td>\n",
              "      <td>20.580000</td>\n",
              "      <td>17.990000</td>\n",
              "      <td>1.020000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>116.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>89.000000</td>\n",
              "      <td>5.700000</td>\n",
              "      <td>0.297500</td>\n",
              "      <td>0.086000</td>\n",
              "      <td>12.592500</td>\n",
              "      <td>68.312500</td>\n",
              "      <td>58.785000</td>\n",
              "      <td>24.727500</td>\n",
              "      <td>13.442500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>138.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>157.500000</td>\n",
              "      <td>6.100000</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.168000</td>\n",
              "      <td>16.910000</td>\n",
              "      <td>108.245000</td>\n",
              "      <td>73.580000</td>\n",
              "      <td>31.425000</td>\n",
              "      <td>23.225000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>160.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>206.000000</td>\n",
              "      <td>6.500000</td>\n",
              "      <td>1.390000</td>\n",
              "      <td>0.240000</td>\n",
              "      <td>21.102500</td>\n",
              "      <td>155.620000</td>\n",
              "      <td>163.665000</td>\n",
              "      <td>43.542500</td>\n",
              "      <td>42.767500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>198.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>369.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2.480000</td>\n",
              "      <td>113.200000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>276.720000</td>\n",
              "      <td>1572.540000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>74.560000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-18b5a3d3-276f-4d39-94b3-6fab8a17cb8d');\n",
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              "          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",
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              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
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              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"N\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 187.69025991893974,\n        \"min\": 30.0,\n        \"max\": 620.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          135.7016129032258,\n          138.0,\n          620.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 203.75703993709269,\n        \"min\": 10.0,\n        \"max\": 620.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          52.50806451612903,\n          54.0,\n          620.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 190.7218234658289,\n        \"min\": 51.0,\n        \"max\": 620.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          161.4016129032258,\n          157.5,\n          620.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ph\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 217.31453051639215,\n        \"min\": 0.6937820626620604,\n        \"max\": 620.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.106290322580644,\n          6.1,\n          620.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 218.87709933870127,\n        \"min\": 0.01,\n        \"max\": 620.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.8854677419354838,\n          0.78,\n          620.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 216.73040414579344,\n        \"min\": 0.0111,\n        \"max\": 620.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.4840848870967742,\n          0.168,\n          620.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 213.91831275585332,\n        \"min\": 0.18,\n        \"max\": 620.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          17.315451612903225,\n          16.91,\n          620.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 196.32541013931126,\n        \"min\": 3.81,\n        \"max\": 620.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          117.2125,\n          108.245,\n          620.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 518.2058582122571,\n        \"min\": 20.58,\n        \"max\": 1572.54,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          219.9058870967742,\n          73.58,\n          620.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Zn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 208.17522401943938,\n        \"min\": 11.543871067835484,\n        \"max\": 620.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          34.34083870967742,\n          31.424999999999997,\n          620.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 210.09273424805565,\n        \"min\": 1.02,\n        \"max\": 620.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          28.75070967741935,\n          23.225,\n          620.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Separate features and target\n",
        "X = data[['N',\t'P',\t'K',\t'ph',\t'EC',\t'S',\t'Cu',\t'Fe',\t'Mn',\t'Zn',\t'B']]\n",
        "\n",
        "y = data['label']"
      ],
      "metadata": {
        "id": "cTOLMjWLaBGG"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**or**"
      ],
      "metadata": {
        "id": "tYLT7am6a7eX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Slicing\n",
        "X = data.iloc[:,:-1] # rows , columns\n",
        "\n",
        "y = data.iloc[:,-1]"
      ],
      "metadata": {
        "id": "aVFDfI6Qaese"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2: Do train-test split\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n"
      ],
      "metadata": {
        "id": "uZVv3N45bA42"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "TDTLSSPncXiU",
        "outputId": "ed4b37c0-e9c5-46ac-e69b-ef4d0170d7a5"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                N           P           K          ph          EC           S  \\\n",
              "count  496.000000  496.000000  496.000000  496.000000  496.000000  496.000000   \n",
              "mean   136.477823   52.500000  162.705645    6.114919    0.881794    0.565803   \n",
              "std     31.688055   24.274607   81.627919    0.694407    0.628641    6.455623   \n",
              "min     30.000000   10.000000   51.000000    4.500000    0.010000    0.011100   \n",
              "25%    116.000000   30.000000   90.750000    5.700000    0.310000    0.087087   \n",
              "50%    139.000000   53.000000  157.500000    6.100000    0.780000    0.167500   \n",
              "75%    161.000000   74.250000  207.000000    6.500000    1.375000    0.240000   \n",
              "max    198.000000   99.000000  369.000000    8.000000    2.480000  113.200000   \n",
              "\n",
              "               Cu          Fe           Mn          Zn           B  \n",
              "count  496.000000  496.000000   496.000000  496.000000  496.000000  \n",
              "mean    17.207460  118.600242   221.061008   34.457440   28.709355  \n",
              "std      6.447591   63.377239   344.385068   11.706243   19.783541  \n",
              "min      0.180000    3.810000    20.580000   17.990000    1.020000  \n",
              "25%     12.592500   69.125000    59.505000   25.032500   13.167500  \n",
              "50%     16.745000  108.945000    73.580000   31.230000   23.225000  \n",
              "75%     21.315000  157.287500   167.945000   43.542500   43.635000  \n",
              "max     35.000000  276.720000  1572.540000   70.000000   74.150000  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>N</th>\n",
              "      <th>P</th>\n",
              "      <th>K</th>\n",
              "      <th>ph</th>\n",
              "      <th>EC</th>\n",
              "      <th>S</th>\n",
              "      <th>Cu</th>\n",
              "      <th>Fe</th>\n",
              "      <th>Mn</th>\n",
              "      <th>Zn</th>\n",
              "      <th>B</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>496.000000</td>\n",
              "      <td>496.000000</td>\n",
              "      <td>496.000000</td>\n",
              "      <td>496.000000</td>\n",
              "      <td>496.000000</td>\n",
              "      <td>496.000000</td>\n",
              "      <td>496.000000</td>\n",
              "      <td>496.000000</td>\n",
              "      <td>496.000000</td>\n",
              "      <td>496.000000</td>\n",
              "      <td>496.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>136.477823</td>\n",
              "      <td>52.500000</td>\n",
              "      <td>162.705645</td>\n",
              "      <td>6.114919</td>\n",
              "      <td>0.881794</td>\n",
              "      <td>0.565803</td>\n",
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              "      <td>118.600242</td>\n",
              "      <td>221.061008</td>\n",
              "      <td>34.457440</td>\n",
              "      <td>28.709355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
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              "      <td>24.274607</td>\n",
              "      <td>81.627919</td>\n",
              "      <td>0.694407</td>\n",
              "      <td>0.628641</td>\n",
              "      <td>6.455623</td>\n",
              "      <td>6.447591</td>\n",
              "      <td>63.377239</td>\n",
              "      <td>344.385068</td>\n",
              "      <td>11.706243</td>\n",
              "      <td>19.783541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.011100</td>\n",
              "      <td>0.180000</td>\n",
              "      <td>3.810000</td>\n",
              "      <td>20.580000</td>\n",
              "      <td>17.990000</td>\n",
              "      <td>1.020000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>116.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>90.750000</td>\n",
              "      <td>5.700000</td>\n",
              "      <td>0.310000</td>\n",
              "      <td>0.087087</td>\n",
              "      <td>12.592500</td>\n",
              "      <td>69.125000</td>\n",
              "      <td>59.505000</td>\n",
              "      <td>25.032500</td>\n",
              "      <td>13.167500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>139.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>157.500000</td>\n",
              "      <td>6.100000</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.167500</td>\n",
              "      <td>16.745000</td>\n",
              "      <td>108.945000</td>\n",
              "      <td>73.580000</td>\n",
              "      <td>31.230000</td>\n",
              "      <td>23.225000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>161.000000</td>\n",
              "      <td>74.250000</td>\n",
              "      <td>207.000000</td>\n",
              "      <td>6.500000</td>\n",
              "      <td>1.375000</td>\n",
              "      <td>0.240000</td>\n",
              "      <td>21.315000</td>\n",
              "      <td>157.287500</td>\n",
              "      <td>167.945000</td>\n",
              "      <td>43.542500</td>\n",
              "      <td>43.635000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>198.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>369.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2.480000</td>\n",
              "      <td>113.200000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>276.720000</td>\n",
              "      <td>1572.540000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>74.150000</td>\n",
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              "        const element = document.querySelector('#df-81125475-bd82-4487-9d7c-a43d0b5dbfaa');\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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              "summary": "{\n  \"name\": \"X_train\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"N\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 146.64635957331163,\n        \"min\": 30.0,\n        \"max\": 496.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          136.47782258064515,\n          139.0,\n          496.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 160.57454102711984,\n        \"min\": 10.0,\n        \"max\": 496.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          52.5,\n          53.0,\n          496.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 154.7076851633402,\n        \"min\": 51.0,\n        \"max\": 496.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          162.70564516129033,\n          157.5,\n          496.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ph\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 173.47610386341282,\n        \"min\": 0.694407089252717,\n        \"max\": 496.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.11491935483871,\n          6.1,\n          496.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 175.03753849656613,\n        \"min\": 0.01,\n        \"max\": 496.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.8817943548387097,\n          0.78,\n          496.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 173.75199358804744,\n        \"min\": 0.0111,\n        \"max\": 496.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.5658033064516129,\n          0.1675,\n          496.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 170.14493783899846,\n        \"min\": 0.18,\n        \"max\": 496.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          17.20745967741935,\n          16.744999999999997,\n          496.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157.1227814027901,\n        \"min\": 3.81,\n        \"max\": 496.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          118.60024193548388,\n          108.945,\n          496.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 511.5812316723871,\n        \"min\": 20.58,\n        \"max\": 1572.54,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          221.06100806451616,\n          73.58,\n          496.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Zn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 164.51527061324938,\n        \"min\": 11.706242990209512,\n        \"max\": 496.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          34.45743951612903,\n          31.23,\n          496.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 166.54121932194445,\n        \"min\": 1.02,\n        \"max\": 496.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          28.70935483870968,\n          23.225,\n          496.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
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    {
      "cell_type": "code",
      "source": [
        "y_train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
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        "id": "9_WxdkFKcl4t",
        "outputId": "16dd5c7e-e039-4df0-c28b-259eb2cbfab0"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      496\n",
              "unique       6\n",
              "top       ragi\n",
              "freq        88\n",
              "Name: label, dtype: object"
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              "</div><br><label><b>dtype:</b> object</label>"
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    {
      "cell_type": "code",
      "source": [
        "X_test.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
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        "id": "4qkxndKVcbEF",
        "outputId": "e7983e50-7dd6-4544-ad1d-71eb86914aab"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                N           P           K          ph          EC           S  \\\n",
              "count  124.000000  124.000000  124.000000  124.000000  124.000000  124.000000   \n",
              "mean   132.596774   52.540323  156.185484    6.071774    0.900161    0.157211   \n",
              "std     32.225809   26.311888   80.015585    0.693003    0.672864    0.094275   \n",
              "min     33.000000   11.000000   52.000000    4.500000    0.030000    0.014200   \n",
              "25%    117.500000   29.500000   82.000000    5.700000    0.255000    0.083305   \n",
              "50%    135.500000   55.500000  154.500000    6.100000    0.790000    0.169000   \n",
              "75%    154.000000   75.000000  201.250000    6.500000    1.407500    0.240500   \n",
              "max    198.000000  100.000000  365.000000    7.800000    2.480000    0.330000   \n",
              "\n",
              "               Cu          Fe           Mn          Zn           B  \n",
              "count  124.000000  124.000000   124.000000  124.000000  124.000000  \n",
              "mean    17.747419  111.661532   215.285403   33.874435   28.916129  \n",
              "std      6.423124   60.235262   333.610014   10.903097   19.114269  \n",
              "min      5.480000   38.300000    22.040000   19.130000    1.640000  \n",
              "25%     12.652500   66.517500    55.965000   23.467500   14.712500  \n",
              "50%     17.595000   99.670000    73.935000   32.485000   23.275000  \n",
              "75%     21.042500  145.440000   157.812500   43.187500   39.217500  \n",
              "max     33.670000  260.730000  1532.580000   67.530000   74.560000  "
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
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              "      <td>124.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>124.000000</td>\n",
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              "      <td>124.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>124.000000</td>\n",
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              "      <td>124.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>132.596774</td>\n",
              "      <td>52.540323</td>\n",
              "      <td>156.185484</td>\n",
              "      <td>6.071774</td>\n",
              "      <td>0.900161</td>\n",
              "      <td>0.157211</td>\n",
              "      <td>17.747419</td>\n",
              "      <td>111.661532</td>\n",
              "      <td>215.285403</td>\n",
              "      <td>33.874435</td>\n",
              "      <td>28.916129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>32.225809</td>\n",
              "      <td>26.311888</td>\n",
              "      <td>80.015585</td>\n",
              "      <td>0.693003</td>\n",
              "      <td>0.672864</td>\n",
              "      <td>0.094275</td>\n",
              "      <td>6.423124</td>\n",
              "      <td>60.235262</td>\n",
              "      <td>333.610014</td>\n",
              "      <td>10.903097</td>\n",
              "      <td>19.114269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.014200</td>\n",
              "      <td>5.480000</td>\n",
              "      <td>38.300000</td>\n",
              "      <td>22.040000</td>\n",
              "      <td>19.130000</td>\n",
              "      <td>1.640000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>117.500000</td>\n",
              "      <td>29.500000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>5.700000</td>\n",
              "      <td>0.255000</td>\n",
              "      <td>0.083305</td>\n",
              "      <td>12.652500</td>\n",
              "      <td>66.517500</td>\n",
              "      <td>55.965000</td>\n",
              "      <td>23.467500</td>\n",
              "      <td>14.712500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>135.500000</td>\n",
              "      <td>55.500000</td>\n",
              "      <td>154.500000</td>\n",
              "      <td>6.100000</td>\n",
              "      <td>0.790000</td>\n",
              "      <td>0.169000</td>\n",
              "      <td>17.595000</td>\n",
              "      <td>99.670000</td>\n",
              "      <td>73.935000</td>\n",
              "      <td>32.485000</td>\n",
              "      <td>23.275000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>154.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>201.250000</td>\n",
              "      <td>6.500000</td>\n",
              "      <td>1.407500</td>\n",
              "      <td>0.240500</td>\n",
              "      <td>21.042500</td>\n",
              "      <td>145.440000</td>\n",
              "      <td>157.812500</td>\n",
              "      <td>43.187500</td>\n",
              "      <td>39.217500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
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              "      <td>100.000000</td>\n",
              "      <td>365.000000</td>\n",
              "      <td>7.800000</td>\n",
              "      <td>2.480000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>33.670000</td>\n",
              "      <td>260.730000</td>\n",
              "      <td>1532.580000</td>\n",
              "      <td>67.530000</td>\n",
              "      <td>74.560000</td>\n",
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              "summary": "{\n  \"name\": \"X_test\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"N\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57.06749729624629,\n        \"min\": 32.22580881432171,\n        \"max\": 198.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          132.59677419354838,\n          135.5,\n          124.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.6528714200499,\n        \"min\": 11.0,\n        \"max\": 124.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          52.54032258064516,\n          55.5,\n          124.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 99.055503424194,\n        \"min\": 52.0,\n        \"max\": 365.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          156.18548387096774,\n          154.5,\n          124.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ph\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42.006058685275754,\n        \"min\": 0.6930034178954613,\n        \"max\": 124.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.071774193548386,\n          6.1,\n          124.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43.517064122688545,\n        \"min\": 0.03,\n        \"max\": 124.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9001612903225806,\n          0.79,\n          124.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43.78575225903372,\n        \"min\": 0.0142,\n        \"max\": 124.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.15721120967741936,\n          0.169,\n          124.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39.081433957167846,\n        \"min\": 5.48,\n        \"max\": 124.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          17.747419354838705,\n          17.595,\n          124.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 69.38608513646051,\n        \"min\": 38.3,\n        \"max\": 260.73,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          111.66153225806451,\n          99.67,\n          124.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 502.1402024533732,\n        \"min\": 22.04,\n        \"max\": 1532.58,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          215.28540322580645,\n          73.935,\n          124.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Zn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36.496806804633735,\n        \"min\": 10.903096621551752,\n        \"max\": 124.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          33.87443548387097,\n          32.485,\n          124.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39.994734988822884,\n        \"min\": 1.64,\n        \"max\": 124.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          28.916129032258066,\n          23.275,\n          124.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "EnUWIcVGciQG",
        "outputId": "bcaed7cf-395c-4eca-c62e-7a925e0654a1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        124\n",
              "unique         6\n",
              "top       grapes\n",
              "freq          24\n",
              "Name: label, dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>grapes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 3: Model Training\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "id": "KGv9v24ndQXr",
        "outputId": "82a6d9ee-342d-42e3-f0de-b90e295cc7ec"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-5 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-5 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-5 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-5 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-5 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-5 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-5 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-5 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-5 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Step 4: Model Evaluation\n",
        "\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Accuracy: {accuracy}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJAb9dhhdxDG",
        "outputId": "f69c9832-f9b7-4eaf-a198-02f230db0b15"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9758064516129032\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# classification_report\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_lxlWlf6eShN",
        "outputId": "1395d855-c791-4ae4-bc85-5895e31e83b7"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      grapes       0.96      0.96      0.96        24\n",
            "       mango       0.94      0.94      0.94        18\n",
            "    mulberry       1.00      1.00      1.00        21\n",
            " pomegranate       1.00      0.95      0.98        22\n",
            "      potato       1.00      1.00      1.00        23\n",
            "        ragi       0.94      1.00      0.97        16\n",
            "\n",
            "    accuracy                           0.98       124\n",
            "   macro avg       0.97      0.98      0.97       124\n",
            "weighted avg       0.98      0.98      0.98       124\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "sns.heatmap(cm, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "17eG-viNerE9",
        "outputId": "a77857d4-0be2-418c-fcc2-d67195044f1b"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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vTz7hFwAAoJwL0ImSZrC1kLj++usvejw8PFydO3f2UzQAAKCseI04AABW46kNAADgsyB+aiOgF6QCAACBjR4JAACsxtAGAADwVaAub20GhjYAAIDP6JEAAMBqDG0AAACfUUgAAACf8fgnAADA+eiRAADAagxtAAAAXxlBXEgwtAEAAHxGjwQAAFYL4h4JCgkAAKzGypYAAADno0cCAACrMbQBAAB8FsSFBEMbAADAZ/RIAABgMcMI3h4JCgkAAKwWxEMbFBIAAFgtiAsJ5kgAAACfBWWPRI7rjN0hBISoKZ/YHULAyP3vbLtDCAjVrxthdwhAhRTM79oIykICAICAEsSFBEMbAAAEqbS0NPXp00dJSUlyOBxauXKl1/H77rtPDofDa+vZs2eZrkEhAQCA1dwmbWWUl5en5s2ba86cORc8p2fPnjp69Gjx9uabb5bpGgxtAABgMbvmSPTq1Uu9evW66DlOp1MJCQk+X4MeCQAAygmXy6WcnByvzeVyXVKb69evV1xcnBo0aKChQ4fqxIkTZfo+hQQAAFZzG6ZsKSkpioqK8tpSUlJ8Dqtnz5567bXXlJqaqr///e/asGGDevXqpaKiolK3wdAGAABW82F+Q0nGjx+vMWPGeO1zOp0+t3f33XcX/7lp06Zq1qyZ/vCHP2j9+vW64YYbStUGPRIAAJQTTqdTkZGRXtulFBK/deWVVyomJkb79+8v9XfokQAAwGLlZUGqI0eO6MSJE0pMTCz1dygkAACwmklDG2WVm5vr1btw8OBB7dixQ9HR0YqOjtbkyZN1xx13KCEhQQcOHNCjjz6q+vXrq0ePHqW+BoUEAAAWs6tHYuvWreratWvx51/nVwwaNEjz5s3Trl27tGTJEp06dUpJSUm66aab9PTTT5dpuIRCAgCAINWlSxcZxoWLmI8//viSr0EhAQCA1Wwa2vAHCgkAACxmBHEhweOfAADAZ/RIAABgtSDukaCQAADAYgxtAAAAlIAeCQAArBbEPRIUEgAAWCyYhzYoJAAAsFgwFxLMkQAAAD6jRwIAAIsFc48EhQQAAFYzHHZHYBmGNgAAgM8oJCwwdMgg7f82Xbk5B7Rp42q1bdPC7pBsUdHysHDVev35qTnq8MAkdXl4mkbNel3f/5Tpdc476z7X4KkL9McHJqv5wCeUk/eLTdH6X0W7Hy6EPJxTkXJhuM3ZAhGFhMn6979Vzz4zUU9PfV5t2/XUzl1f68MP3lBsbE27Q/OripiHrXsO6k83ttfrk4bq5cf+qsJCt4b8fZHO5J8tPif/bIH+2OxqDb61i32B2qAi3g8lIQ/nVLRcGG6HKVsgchgXe1F5ORVSpZZt1960cbW2bN2pkaP+JklyOBz6/rstmjN3kWY+M8e2uPwt0PKQ+9/Zfr/myZxcdX14uv75twfVumE9r2Nbvv5OD0x/VZ++/JQiw8P8FlP160b47Vr/K9DuB7uQh3MCKReFZ3+0/BpHO3Y1pZ3EjZ+Y0o6Z6JEwUWhoqFq1aqbUdZ8W7zMMQ6nrNqp9+9Y2RuZf5MEj94xLkvxaKAQi7gcP8nBORcwFQxsW+uWXX7Rx40Z9/fXX5x3Lz8/Xa6+9dtHvu1wu5eTkeG12dbLExEQrJCREGcezvPZnZGQqIT7WlpjsQB4kt9utmf96Xy2urqurLk+wOxxbcT94kIdzKmIuDMNhyhaIbC0kvv32WzVq1EidOnVS06ZN1blzZx09erT4eHZ2tu6///6LtpGSkqKoqCivzXCftjp04KKmL1mlA0eOa2by3XaHAgCWsrWQeOyxx9SkSRNlZGRo7969ioiI0HXXXadDhw6Vuo3x48crOzvba3NUirAw6gvLyjqpwsJCxcXHeO2Pi4vVseOZF/hW8KnoeZi+ZJXSvtirBU88oPiaUXaHY7uKfj/8ijycUxFzwdCGRTZt2qSUlBTFxMSofv36Wr16tXr06KHrr79e3333XanacDqdioyM9NocDnu6fwoKCrR9+y5169qxeJ/D4VC3rh2Vnr7NlpjsUFHzYBiGpi9ZpXVbv9aCJwardly03SEFhIp6P/wWeTinIuYimJ/asHVly19++UUhIedCcDgcmjdvnoYNG6bOnTtr6dKlNkbnm1kvLtCihbO0bfsubdnyhUYMf1Dh4WFavOQtu0Pzq4qYh+mLV+mjzTv1wuiBCq/qVNYpzxBb9WpVVbVKqCQp69RpZWWf1uHjJyRJ+w8fU7UwpxJr1lBU9Wq2xW61ing/lIQ8nFPRchF8z0eeY2sh0bBhQ23dulWNGjXy2v/SSy9Jkm699VY7wrokb7+9SrEx0Zo0YawSEmK1c+dX6n3LQGVkZP3+l4NIRczD8tTPJEmDp73qtX/KQ3eobyfPTPS3Uz/T/BXrio/dP3XBeecEo4p4P5SEPJxDLoKHretIpKSk6NNPP9WHH35Y4vGHH35Y8+fPl9tdtoEhO9eRQGCyYx2JQGTXOhJAIPPHOhI/tOpuSjt1t//HlHbMxIJUqBAoJDwoJIDz+aOQ+L7Fjaa0c8WOtaa0Yybb15EAAADlF68RBwDAYsHX938OhQQAABYL1Ec3zcDQBgAA8Bk9EgAAWCxQ35NhBgoJAAAsFqjLW5uhVIXEqlWrSt1geVxECgAA+KZUhUS/fv1K1ZjD4VBRUdGlxAMAQNBxV/ShjbKuLAkAAM5hjgQAAPBZMD/+6VMhkZeXpw0bNujQoUM6e/as17ERI1iCFwCAiqLMhcQXX3yhm2++WWfOnFFeXp6io6OVlZWlatWqKS4ujkICAIDfCOaVLcu8INXo0aPVp08f/fzzzwoLC1N6erp++OEHtW7dWs8++6wVMQIAUK4ZbocpWyAqcyGxY8cOPfLII6pUqZIqV64sl8ulyy+/XDNnztQTTzxhRYwAACBAlbmQCA0NVaVKnq/FxcXp0KFDkqSoqCgdPnzY3OgAAAgCbsNhyhaIyjxHomXLltqyZYuuuuoqde7cWRMmTFBWVpZef/11NWnSxIoYAQAo14L58c8y90hMnz5diYmJkqRp06bpsssu09ChQ5WZmalXXnnF9AABAEDgKnOPRJs2bYr/HBcXpzVr1pgaEAAAwSaYn9pgQSoAACwWqPMbzFDmQqJevXpyOC6ckO++++6SAgIAAOVHmQuJUaNGeX0uKCjQF198oTVr1mjcuHFmxQUAQNAI5smWZS4kRo4cWeL+OXPmaOvWrZccEAAAwSaY50iU+amNC+nVq5feffdds5oDACBoBPM6EqYVEu+8846io6PNag4AAJQDPi1I9b+TLQ3D0LFjx5SZmam5c+eaGhwuTaSzmt0hBIzq1/EyOUnK/e9su0MICNwP8DfmSPyPvn37ehUSlSpVUmxsrLp06aKGDRuaGhwAAMEgUIclzFDmQmLSpEkWhAEAAMqjMs+RqFy5sjIyMs7bf+LECVWuXNmUoAAACCaGSVsgKnOPhHGBZ1hcLpeqVKlyyQEBABBsGNqQNHu2Z5KWw+HQq6++qurVqxcfKyoqUlpaGnMkAACoYEpdSMyaNUuSp0di/vz5XsMYVapU0RVXXKH58+ebHyEAAOUcT21IOnjwoCSpa9eueu+993TZZZdZFhQAAMHEbXcAFirzHIlPPvnEijgAAEA5VOanNu644w79/e9/P2//zJkz1b9/f1OCAgAgmBhymLIFojIXEmlpabr55pvP29+rVy+lpaWZEhQAAMHEbZizBaIyD23k5uaW+JhnaGiocnJyTAkKAIBg4g7Q3gQzlLlHomnTpnrrrbfO279s2TI1btzYlKAAAED5UOYeiaeeekq33367Dhw4oG7dukmSUlNTtXTpUr3zzjumBwgAQHkXqPMbzFDmQqJPnz5auXKlpk+frnfeeUdhYWFq3ry51q1bx2vEAQAoAY9//kbv3r3Vu3dvSVJOTo7efPNNjR07Vtu2bVNRUZGpAQIAgMBV5jkSv0pLS9OgQYOUlJSk5557Tt26dVN6erqZsQEAEBSC+fHPMvVIHDt2TIsXL9bChQuVk5Oju+66Sy6XSytXrmSiJQAAFxDMQxul7pHo06ePGjRooF27dumFF17QTz/9pH/84x9WxgYAAAJcqXskPvroI40YMUJDhw7VVVddZWVMAAAEFXokJG3cuFGnT59W69at1a5dO7300kvKysqyMjYAAIKCXXMk0tLS1KdPHyUlJcnhcGjlypXecRmGJkyYoMTERIWFhal79+7at29fma5R6kKiffv2WrBggY4ePar/+7//07Jly5SUlCS32621a9fq9OnTZbowAACwVl5enpo3b645c+aUeHzmzJmaPXu25s+fr88++0zh4eHq0aOH8vPzS30Nh2EYPq/evXfvXi1cuFCvv/66Tp06pRtvvFGrVq3ytTnThFSpZXcIASHSWc3uEAJGjuuM3SEEhNz/zrY7hIBQ/boRdoeAAFJ49kfLr7E6YYAp7fQ59qbP33U4HFqxYoX69esnydMbkZSUpEceeURjx46VJGVnZys+Pl6LFy/W3XffXap2fX78U5IaNGigmTNn6siRI3rzTd9/OAAAgplbDlM2l8ulnJwcr83lcvkU08GDB3Xs2DF17969eF9UVJTatWunzZs3l7qdSyokflW5cmX169cvIHojAAAINIZJW0pKiqKiory2lJQUn2I6duyYJCk+Pt5rf3x8fPGx0vBpZUsAAOB/48eP15gxY7z2OZ1Om6LxMKVHAt6GDhmk/d+mKzfngDZtXK22bVrYHZLfdbiurZYuf1lffbtRJ0/v0823dP/9LwWpinY/LFy1Xn9+ao46PDBJXR6eplGzXtf3P2V6nfPOus81eOoC/fGByWo+8Anl5P1iU7T+V9Huh4upSLlwm7Q5nU5FRkZ6bb4WEgkJCZKk48ePe+0/fvx48bHSoJAwWf/+t+rZZybq6anPq227ntq562t9+MEbio2taXdofhVeLUy7v/xGjz4y2e5QbFUR74etew7qTze21+uThurlx/6qwkK3hvx9kc7kny0+J/9sgf7Y7GoNvrWLfYHaoCLeDxdS0XLhdjhM2cxUr149JSQkKDU1tXhfTk6OPvvsM3Xo0KHU7VBImGz0yAf16sKlWvLacu3Zs08PJz+uM2d+0f33lW72a7D4z9o0TX96lj5YvdbuUGxVEe+HeY/dr76dWqt+7Xg1qJuoKf93h46eOKU935+bGT+w53UafGtnNat/uY2R+l9FvB8uhFz4R25urnbs2KEdO3ZI8kyw3LFjhw4dOiSHw6FRo0Zp6tSpWrVqlb788kvde++9SkpKKn6yozQoJEwUGhqqVq2aKXXdp8X7DMNQ6rqNat++tY2RwQ7cDx65ZzwzyiPDw2yOxF7cD+dUxFyYNdmyrLZu3aqWLVuqZcuWkqQxY8aoZcuWmjBhgiTp0Ucf1fDhw/XQQw+pbdu2ys3N1Zo1a1S1atVSX8P2yZZ79uxRenq6OnTooIYNG+qbb77Riy++KJfLpYEDB6pbt24X/b7L5Trv0RfDMOQwuQuoNGJiohUSEqKM494rfmZkZKphgz/4PR7Yi/tBcrvdmvmv99Xi6rq66vLSj7kGI+6HcypiLuxaIrtLly662HJRDodDU6ZM0ZQpU3y+hq09EmvWrFGLFi00duxYtWzZUmvWrFGnTp20f/9+/fDDD7rpppu0bt26i7ZR0qMwhptVNoFAMH3JKh04clwzk+muBoKVrYXElClTNG7cOJ04cUKLFi3Sn//8Zz344INau3atUlNTNW7cOM2YMeOibYwfP17Z2dlem6NShJ9+Am9ZWSdVWFiouPgYr/1xcbE6djzzAt9CsKro98P0JauU9sVeLXjiAcXXjLI7HNtV9Pvhf1XEXLgd5myByNZC4quvvtJ9990nSbrrrrt0+vRp3XnnncXH77nnHu3ateuibZT0KIwdwxqSVFBQoO3bd6lb147F+xwOh7p17aj09G22xAT7VNT7wTAMTV+ySuu2fq0FTwxW7bhou0MKCBX1fihJRcyFWStbBiLb50j8+ku/UqVKqlq1qqKizv3LJSIiQtnZ2XaF5pNZLy7QooWztG37Lm3Z8oVGDH9Q4eFhWrzkLbtD86vw8Gqqd2Xd4s9169ZWk6aN9PPPp/TjkaM2RuZfFfF+mL54lT7avFMvjB6o8KpOZZ3yDDVWr1ZVVauESpKyTp1WVvZpHT5+QpK0//AxVQtzKrFmDUVVD953xFTE++FCyEXwsLWQuOKKK7Rv3z794Q+eyTWbN29WnTp1io8fOnRIiYmJdoXnk7ffXqXYmGhNmjBWCQmx2rnzK/W+ZaAyMirWK9dbtGyi1R+9Ufx52ownJUlL33hPw4Y8ZldYflcR74flqZ9JkgZPe9Vr/5SH7lDfTp4Z+W+nfqb5K87Nf7p/6oLzzglGFfF+uJCKlguf345ZDlzS2z8v1fz583X55Zerd+/eJR5/4oknlJGRoVdffbXE4xfC2z89ePvnObz904O3f3rw9k/8L3+8/fO1WgNNaefeH/9lSjtmsrVHYsiQIRc9Pn36dD9FAgCAdex6/NMfWJAKAAD4zPbJlgAABLtgniNBIQEAgMUCdQ0IMzC0AQAAfEaPBAAAFgvmyZYUEgAAWCyYCwmGNgAAgM/okQAAwGJGEE+2pJAAAMBiDG0AAACUgB4JAAAsFsw9EhQSAABYjJUtAQCAz1jZEgAAoAT0SAAAYDHmSAAAAJ8FcyHB0AYAAPAZPRIAAFiMpzYAAIDPeGoDAACgBPRIAABgsWCebEkhAQCAxYJ5jgRDGwAAwGf0SAAAYDF3EPdJUEgEsRzXGbtDQICpft0Iu0MICL/89KndIQSEsKTr7Q6hwmCOBAAA8Fnw9kcwRwIAAFwCeiQAALAYQxsAAMBnrGwJAABQAnokAACwGI9/AgAAnwVvGcHQBgAAuAT0SAAAYDGe2gAAAD4L5jkSDG0AAACf0SMBAIDFgrc/gkICAADLMUcCAAD4jDkSAAAAJaBHAgAAiwVvfwSFBAAAlgvmORIMbQAAAJ/RIwEAgMWMIB7coJAAAMBiDG0AAACUgB4JAAAsFszrSFBIAABgseAtIxjaAAAAl4BCwgJDhwzS/m/TlZtzQJs2rlbbNi3sDskW5MGDPHhUtDwseO0t/WnwCF3b/XZ16n23Rjw+RQd/OOJ1zuSZs9Wz//1q3bWvru/9Jw1/bLK+++GwTRH7X0W6J9wyTNkCEYWEyfr3v1XPPjNRT099Xm3b9dTOXV/rww/eUGxsTbtD8yvy4EEePCpiHrbu+FIDbu+jpa/M0isvTFdBYaEeGv2kzvySX3xO4wb1NfXJMVq19BW9/Pw0GYahh0Y/qaKiIhsj94+Kdk+4TdoCkcMwjMAscS5BSJVatl1708bV2rJ1p0aO+pskyeFw6PvvtmjO3EWa+cwc2+LyN/LgQR48Ai0Pv/z0qd+vefLnU+p0ywAtnjNTbVo0LfGcvfsP6o5BD+vDtxaqTu0ky2MKS7re8mtcSCDdE4Vnf7T8Gg9ccacp7bz6/TumtGOmgOuRKM91TWhoqFq1aqbUdef+kjIMQ6nrNqp9+9Y2RuZf5MGDPHiQB4/cvDOSpKjIiBKPn/klXys/+H+qnZSgxPhYf4bmd9wTwSXgCgmn06k9e/aU+nyXy6WcnByvza5iJCYmWiEhIco4nuW1PyMjUwlB/hfD/yIPHuTBgzxIbrdbM158WS2bNdZVV17hdWzZe++rbffbdG3327QxfatemTVNoaGh9gTqJxXxngjmoQ3bHv8cM2ZMifuLioo0Y8YM1azpGSd7/vnnL9pOSkqKJk+e7LXPUam6HJUjzQkUAC7R1OfmaP933+u1ec+ed6z3TV3VoW1LZZ44qcVL39XYCSl6fd5zcjqr2BAprMIS2RZ44YUX1Lx5c9WoUcNrv2EY2rNnj8LDw+VwOH63nfHjx59XlFxWs6GZoZZaVtZJFRYWKi4+xmt/XFysjh3PtCUmO5AHD/LgUdHzMO25udqw6XMtmfOMEuLO/9d2RPVwRVQPV93La6n5NQ31x579lZq2STff2MX/wfpJRb8ngo1tQxvTp09Xdna2nnrqKX3yySfFW+XKlbV48WJ98sknWrdu3e+243Q6FRkZ6bWVpgCxQkFBgbZv36VuXTsW73M4HOrWtaPS07fZEpMdyIMHefCoqHkwDEPTnpur1LRN+ufsGaqdlFCq7xiGdPZsgR8itE9FvCcY2rDA448/rhtuuEEDBw5Unz59lJKSEhTjgrNeXKBFC2dp2/Zd2rLlC40Y/qDCw8O0eMlbdofmV+TBgzx4VMQ8TH1ujj5cu16zZ0xQeLUwZZ04KUmqXj1cVZ1OHf7xqNakpumP17ZSdI0oHcvM0sLXl8vprKLr/9jW5uitV9HuCXc5fpDg99i6RHbbtm21bds2JScnq02bNnrjjTds600wy9tvr1JsTLQmTRirhIRY7dz5lXrfMlAZGVm//+UgQh48yINHRczDWys+kCTdP+wxr/1Tnxijfr1vlLNKFW3fuVuvL1+pnNO5qhldQ22aN9G/5j+vmpfVsCFi/6qI90SwCph1JJYtW6ZRo0YpMzNTX375pRo3buxzW3auIwEg8NmxjkQgsnMdiUDij3UkBta93ZR2/vXDe6a0Y6aAeWnX3XffrY4dO2rbtm2qW7eu3eEAAGCaQF3e2gwBU0hIUu3atVW7dm27wwAAAKUUUIUEAADBKJjXkQi4lS0BAAg2djz+OWnSJDkcDq+tYUPz11miRwIAAIvZNUfimmuu0X/+85/izyEh5v/ap5AAAKCccLlccrlcXvucTqecTmeJ54eEhCgh4fcXQ7sUDG0AAGAxw6T/UlJSFBUV5bWlpKRc8Lr79u1TUlKSrrzySt1zzz06dOiQ6T9bwKwjYSbWkQBwMawj4cE6Eh7+WEfi9rq3mtLOm9++XeoeiY8++ki5ublq0KCBjh49qsmTJ+vHH3/U7t27FRFR8uvsfcHQBgAA5cTFhjF+q1evXsV/btasmdq1a6e6detq+fLlGjx4sGkxUUgAAGCxQOj8r1Gjhq6++mrt37/f1HaZIwEAgMXcMkzZLkVubq4OHDigxMREk34qDwoJAACC0NixY7VhwwZ9//332rRpk2677TZVrlxZAwYMMPU6DG0AAGCxsi4mZYYjR45owIABOnHihGJjY9WxY0elp6crNjbW1OtQSAAAYDE7lshetmyZX67D0AYAAPAZPRIAAFiM14gDAACfBcLjn1ahkAAAwGJ2TLb0F+ZIAAAAn9EjAQCAxex4asNfKCQAALBYME+2ZGgDAAD4jB4JAAAsxlMbAADAZwxtAAAAlIAeCQAVTljS9XaHEBB+friV3SFUGDy1AQAAfOYO4jkSDG0AAACf0SMBAIDFgrc/gkICAADLBfNTGxQSAABYLJgLCeZIAAAAn9EjAQCAxVjZEgAA+IyhDQAAgBLQIwEAgMVY2RIAAPgsmOdIMLQBAAB8Ro8EAAAWC+bJlhQSAABYjKENAACAEtAjAQCAxRjaAAAAPuPxTwAA4DM3cyQAAADOR48EAAAWY2gDAAD4jKENAACAEtAjAQCAxRjaAAAAPmNoAwAAoAQUEhYYOmSQ9n+brtycA9q0cbXatmlhd0i2IA8e5MGDPHhUxDxUvvIahT3wlMInL1bEC6sV0rT9eedUiq+tsAf+puopy1T972+r2pjn5agRa0O01jBM+i8QUUiYrH//W/XsMxP19NTn1bZdT+3c9bU+/OANxcbWtDs0vyIPHuTBgzx4VNg8OKuq6KeDcr0zv8TDjpoJqjbi73IfP6IzLz2hvJnD5fp4mVR41s+BWsdtGKZsgchhBOEryUKq1LLt2ps2rtaWrTs1ctTfJEkOh0Pff7dFc+Yu0sxn5tgWl7+RBw/y4EEePAItDz8/3Mrv14x4YbV+WThNhV+mF++reu84qahI+W887/d4fo3Jan+IMSfXB7K2m9KOmeiRMFFoaKhatWqm1HWfFu8zDEOp6zaqffvWNkbmX+TBgzx4kAcP8nABDodCGreRO/NHhQ2ZrPCnX1e10c+WOPxRngXz0EZAPbWRl5en5cuXa//+/UpMTNSAAQNUs+bFu/xcLpdcLpfXPsMw5HA4rAy1RDEx0QoJCVHG8Syv/RkZmWrY4A9+j8cu5MGDPHiQBw/yUDJH9Sg5qlZTlRvulOvDf6lo9WKFNGytqveP1y9znlTRgd12h2gKw3DbHYJlbO2RaNy4sU6ePClJOnz4sJo0aaLRo0dr7dq1mjhxoho3bqyDBw9etI2UlBRFRUV5bYb7tD/CBwBcKofn11Dh7s9UsOHfcv94UGdT31HR11sUel1Pm4Mzj1uGKVsgsrWQ+Oabb1RYWChJGj9+vJKSkvTDDz/o888/1w8//KBmzZrpySefvGgb48ePV3Z2ttfmqBThj/DPk5V1UoWFhYqLj/HaHxcXq2PHM22JyQ7kwYM8eJAHD/JQMiMvR0ZRodzHDnntLzp+WJWC6KmNYBYwcyQ2b96sSZMmKSoqSpJUvXp1TZ48WRs3brzo95xOpyIjI702O4Y1JKmgoEDbt+9St64di/c5HA5169pR6enbbInJDuTBgzx4kAcP8nABRYVyH9qnSnG1vXZXiq0l98/BU2AZhmHKFohsnyPx6y/9/Px8JSYmeh2rVauWMjPL140068UFWrRwlrZt36UtW77QiOEPKjw8TIuXvGV3aH5FHjzIgwd58KiweahSVZViz/397oiOV6Va9WTk5co4lamz695T1UGPKvTAbhXu/1IhDVsp5Jpr9ctLT9gYtLkCdVjCDLYXEjfccINCQkKUk5OjvXv3qkmTJsXHfvjhh9+dbBlo3n57lWJjojVpwlglJMRq586v1PuWgcrIyPr9LwcR8uBBHjzIg0dFzUPlOvVVbVhK8eeqtz0gSSr4PFX5S19Q4Zfpyn97rpzd+8t5+0NyZ/6o/EUpKjr4tV0howxsXUdi8uTJXp/bt2+vHj16FH8eN26cjhw5ojfffLNM7dq5jgQAlBd2rCMRiPyxjkSty64xpZ0ff/7KlHbMxIJUAFBBUUh4+KOQSKzR2JR2jp4KvF6agJlsCQAAyh/b50gAABDsAnVVSjNQSAAAYLEgnEVQjKENAADgM3okAACwGOtIAAAAnwXz0AaFBAAAFnMHcSHBHAkAAOAzeiQAALAYQxsAAMBnwTzZkqENAADgM3okAACwGEMbAADAZzy1AQAAUAJ6JAAAsBgv7QIAAD5jaAMAAKAE9EgAAGAxntoAAAA+Y44EAADwWTD3SDBHAgCAIDZnzhxdccUVqlq1qtq1a6fPP//c1PYpJAAAsJhhGKZsZfXWW29pzJgxmjhxorZv367mzZurR48eysjIMO1no5AAAMBihklbWT3//PN68MEHdf/996tx48aaP3++qlWrpn/+85+X+iMVo5AAAKCccLlcysnJ8dpcLleJ5549e1bbtm1T9+7di/dVqlRJ3bt31+bNm80LyoDp8vPzjYkTJxr5+fl2h2Ir8nAOufAgDx7kwYM8lN3EiRPP66iYOHFiief++OOPhiRj06ZNXvvHjRtnXHvttabF5DCMIJ5KapOcnBxFRUUpOztbkZGRdodjG/JwDrnwIA8e5MGDPJSdy+U6rwfC6XTK6XSed+5PP/2kWrVqadOmTerQoUPx/kcffVQbNmzQZ599ZkpMPP4JAEA5caGioSQxMTGqXLmyjh8/7rX/+PHjSkhIMC0m5kgAABCEqlSpotatWys1NbV4n9vtVmpqqlcPxaWiRwIAgCA1ZswYDRo0SG3atNG1116rF154QXl5ebr//vtNuwaFhAWcTqcmTpxY6u6nYEUeziEXHuTBgzx4kAfr/elPf1JmZqYmTJigY8eOqUWLFlqzZo3i4+NNuwaTLQEAgM+YIwEAAHxGIQEAAHxGIQEAAHxGIQEAAHxGIWEBq1/ZWh6kpaWpT58+SkpKksPh0MqVK+0Oye9SUlLUtm1bRUREKC4uTv369dPevXvtDsvv5s2bp2bNmikyMlKRkZHq0KGDPvroI7vDst2MGTPkcDg0atQou0Pxu0mTJsnhcHhtDRs2tDss+IhCwmT+eGVreZCXl6fmzZtrzpw5dodimw0bNig5OVnp6elau3atCgoKdNNNNykvL8/u0Pyqdu3amjFjhrZt26atW7eqW7du6tu3r7766iu7Q7PNli1b9PLLL6tZs2Z2h2Kba665RkePHi3eNm7caHdI8JVpb+2AYRiGce211xrJycnFn4uKioykpCQjJSXFxqjsJclYsWKF3WHYLiMjw5BkbNiwwe5QbHfZZZcZr776qt1h2OL06dPGVVddZaxdu9bo3LmzMXLkSLtD8ruJEycazZs3tzsMmIQeCRP57ZWtKJeys7MlSdHR0TZHYp+ioiItW7ZMeXl5pi7RW54kJyerd+/eXn9PVET79u1TUlKSrrzySt1zzz06dOiQ3SHBR6xsaaKsrCwVFRWdt2JYfHy8vvnmG5uiQiBwu90aNWqUrrvuOjVp0sTucPzuyy+/VIcOHZSfn6/q1atrxYoVaty4sd1h+d2yZcu0fft2bdmyxe5QbNWuXTstXrxYDRo00NGjRzV58mRdf/312r17tyIiIuwOD2VEIQH4QXJysnbv3l1hx4EbNGigHTt2KDs7W++8844GDRqkDRs2VKhi4vDhwxo5cqTWrl2rqlWr2h2OrXr16lX852bNmqldu3aqW7euli9frsGDB9sYGXxBIWEif72yFeXLsGHD9P777ystLU21a9e2OxxbVKlSRfXr15cktW7dWlu2bNGLL76ol19+2ebI/Gfbtm3KyMhQq1ativcVFRUpLS1NL730klwulypXrmxjhPapUaOGrr76au3fv9/uUOAD5kiYyF+vbEX5YBiGhg0bphUrVmjdunWqV6+e3SEFDLfbLZfLZXcYfnXDDTfoyy+/1I4dO4q3Nm3a6J577tGOHTsqbBEhSbm5uTpw4IASExPtDgU+oEfCZP54ZWt5kJub6/Wvi4MHD2rHjh2Kjo5WnTp1bIzMf5KTk7V06VL9+9//VkREhI4dOyZJioqKUlhYmM3R+c/48ePVq1cv1alTR6dPn9bSpUu1fv16ffzxx3aH5lcRERHnzY8JDw9XzZo1K9y8mbFjx6pPnz6qW7eufvrpJ02cOFGVK1fWgAED7A4NPqCQMJk/XtlaHmzdulVdu3Yt/jxmzBhJ0qBBg7R48WKbovKvefPmSZK6dOnitX/RokW67777/B+QTTIyMnTvvffq6NGjioqKUrNmzfTxxx/rxhtvtDs02OTIkSMaMGCATpw4odjYWHXs2FHp6emKjY21OzT4gNeIAwAAnzFHAgAA+IxCAgAA+IxCAgAA+IxCAgAA+IxCAgAA+IxCAgAA+IxCAgAA+IxCAgAA+IxCAghC9913n/r161f8uUuXLho1apTf41i/fr0cDodOnTrl92sD8A8KCcCP7rvvPjkcDjkcjuI3Yk6ZMkWFhYWWXve9997T008/Xapz+eUPoCx41wbgZz179tSiRYvkcrn04YcfKjk5WaGhoRo/frzXeWfPnlWVKlVMuWZ0dLQp7QDAb9EjAfiZ0+lUQkKC6tatq6FDh6p79+5atWpV8XDEtGnTlJSUpAYNGkiSDh8+rLvuuks1atRQdHS0+vbtq++//764vaKiIo0ZM0Y1atRQzZo19eijj+q3r9D57dCGy+XSY489pssvv1xOp1P169fXwoUL9f333xe/bO2yyy6Tw+EofsGY2+1WSkqK6tWrp7CwMDVv3lzvvPOO13U+/PBDXX311QoLC1PXrl294gQQnCgkAJuFhYXp7NmzkqTU1FTt3btXa9eu1fvvv6+CggL16NFDERER+vTTT/Xf//5X1atXV8+ePYu/89xzz2nx4sX65z//qY0bN+rkyZNasWLFRa9577336s0339Ts2bO1Z88evfzyy6pevbouv/xyvfvuu5KkvXv36ujRo3rxxRclSSkpKXrttdc0f/58ffXVVxo9erQGDhyoDRs2SPIUPLfffrv69OmjHTt26IEHHtDjjz9uVdoABAoDgN8MGjTI6Nu3r2EYhuF2u421a9caTqfTGDt2rDFo0CAjPj7ecLlcxee//vrrRoMGDQy32128z+VyGWFhYcbHH39sGIZhJCYmGjNnziw+XlBQYNSuXbv4OoZhGJ07dzZGjhxpGIZh7N2715BkrF27tsQYP/nkE0OS8fPPPxfvy8/PN6pVq2Zs2rTJ69zBgwcbAwYMMAzDMMaPH280btzY6/hjjz12XlsAggtzJAA/e//991W9enUVFBTI7Xbrz3/+syZNmqTk5GQ1bdrUa17Ezp07tX//fkVERHi1kZ+frwMHDig7O1tHjx5Vu3btio+FhISoTZs25w1v/GrHjh2qXLmyOnfuXOqY9+/frzNnzujGG2/02n/27Fm1bNlSkrRnzx6vOCSpQ4cOpb4GgPKJQgLws65du2revHmqUqWKkpKSFBJy7n/D8PBwr3Nzc3PVunVrvfHGG+e1Exsb69P1w8LCyvyd3NxcSdIHH3ygWrVqeR1zOp0+xQEgOFBIAH4WHh6u+vXrl+rcVq1a6a233lJcXJwiIyNLPCcxMVGfffaZOnXqJEkqLCzUtm3b1KpVqxLPb9q0qdxutzZs2KDu3bufd/zXHpGioqLifY0bN5bT6dShQ4cu2JPRqFEjrVq1ymtfenr67/+QAMo1JlsCAeyee+5RTEyM+vbtq08//VQHDx7U+vXrNWLECB05ckSSNHLkSM2YMUMrV67UN998o4cffviia0BcccUVGjRokP76179q5cqVxW0uX75cklS3bl05HA69//77yszMVG5uriIiIjR27FiNHj1aS5Ys0YEDB7R9+3b94x//0JIlSyRJQ4YM0b59+zRu3Djt3btXS5cu1eLFi61OEQCbUUgAAaxatWpKS0tTnTp1dPvtt6tRo0YaPHiw8vPzi3soHnnkEf3lL3/RoEGD1KFDB0VEROi22267aLvz5s3TnXfeqYcfflgNGzbUgw8+qLy8PElSrVq1NHnyZD3++OOKj4/XsGHDJElPP/20nnrqKaWkpKhRo0bq2bOnPvjgA9WrV0+SVKdOHb377rtauXKlmjdvrvnz52v69OkWZgdAIHAYF5qRBQAA8DvokQAAAD6jkAAAAD6jkAAAAD6jkAAAAD6jkAAAAD6jkAAAAD6jkAAAAD6jkAAAAD6jkAAAAD6jkAAAAD6jkAAAAD77/5j8UYltJiGjAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 5: Save the model using joblib\n",
        "\n",
        "import joblib\n",
        "\n",
        "joblib.dump(model, 'crop_recommendation_model.joblib')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UeBtqGzlfLI3",
        "outputId": "531d3667-79fa-4cdd-d78f-050f24683627"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['crop_recommendation_model.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load saved model\n",
        "\n",
        "loaded_model = joblib.load('crop_recommendation_model.joblib')\n",
        "\n",
        "# Make predictions\n",
        "\n",
        "predict = model.predict([[175,\t36,\t216,\t5.9,\t0.15,\t0.28,\t15.69,\t114.20,\t56.87,\t31.28,\t28.62]])\n",
        "\n",
        "\n",
        "print(predict[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tXnd0Ra3fS3G",
        "outputId": "d389c94e-53f2-4421-a0c8-9554b5cce6da"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pomegranate\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    }
  ]
}