logistic_reg_on_crop_MS.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df =pd.read_csv('/content/Crop_recommendation.csv')"
      ],
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      "outputs": []
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              "        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\n",
              "...   ...  ..  ..          ...        ...       ...         ...     ...\n",
              "2195  107  34  32    26.774637  66.413269  6.780064  177.774507  coffee\n",
              "2196   99  15  27    27.417112  56.636362  6.086922  127.924610  coffee\n",
              "2197  118  33  30    24.131797  67.225123  6.362608  173.322839  coffee\n",
              "2198  117  32  34    26.272418  52.127394  6.758793  127.175293  coffee\n",
              "2199  104  18  30    23.603016  60.396475  6.779833  140.937041  coffee\n",
              "\n",
              "[2200 rows x 8 columns]"
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              "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\": \"label\",\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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sycFyR4WgKtw",
        "outputId": "fdfb1dd4-2b64-44e9-eaf7-23b1e8624450"
      },
      "execution_count": 4,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aH__pZoCgP1F",
        "outputId": "a31f1800-eb36-4399-aad3-8a6f6d0aaee9"
      },
      "execution_count": 5,
      "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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.boxplot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "wu_KdEUggXKg",
        "outputId": "ad0ae9a2-d422-4f5a-c3cf-670decb6d66e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df['P'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "Barinhg3gsj8",
        "outputId": "d790e6dd-5f0a-4208-bea6-6ab4d13d8ada"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='P'>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ENCODING"
      ],
      "metadata": {
        "id": "76na4Mmug0xD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7hSO2_yag3EH",
        "outputId": "1a3aaafe-8eb0-480a-e84b-3cc9bcfd452b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-43069466-e9cd-4b44-8852-4d0219315376\" class=\"colab-df-container\">\n",
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              "      <th></th>\n",
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              "      <th>temperature</th>\n",
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              "      <td>263.964248</td>\n",
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              "      <td>242.864034</td>\n",
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              "    <tr>\n",
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              "      <td>81.604873</td>\n",
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              "      <td>262.717340</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\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",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
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              "\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\": \"label\",\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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.label.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TxMZ7QFjg9-8",
        "outputId": "1554badf-090e-4626-f6e9-8d6fbd94389e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()"
      ],
      "metadata": {
        "id": "ddDeKZOchFVH"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['label']=le.fit_transform(df['label'])"
      ],
      "metadata": {
        "id": "Ca1ttVmwhVyq"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9nQqJXNmhfGj",
        "outputId": "0a668c15-db61-4311-92d8-ab0329d4de27"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    N   P   K  temperature   humidity        ph    rainfall  label\n",
              "0  90  42  43    20.879744  82.002744  6.502985  202.935536     20\n",
              "1  85  58  41    21.770462  80.319644  7.038096  226.655537     20\n",
              "2  60  55  44    23.004459  82.320763  7.840207  263.964248     20\n",
              "3  74  35  40    26.491096  80.158363  6.980401  242.864034     20\n",
              "4  78  42  42    20.130175  81.604873  7.628473  262.717340     20"
            ],
            "text/html": [
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              "\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-c8e09ccf-ddb5-4039-b82e-22046af775cf 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-c8e09ccf-ddb5-4039-b82e-22046af775cf');\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-bbdb72bc-aebe-48e9-9ceb-4625553d7460\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bbdb72bc-aebe-48e9-9ceb-4625553d7460')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://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-bbdb72bc-aebe-48e9-9ceb-4625553d7460 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\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 0,\n        \"max\": 21,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          20,\n          21,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['label'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "647dLB7shjqN",
        "outputId": "86756dd3-3238-4114-f906-e02a817cabb2"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       20\n",
            "1       20\n",
            "2       20\n",
            "3       20\n",
            "4       20\n",
            "        ..\n",
            "2195     5\n",
            "2196     5\n",
            "2197     5\n",
            "2198     5\n",
            "2199     5\n",
            "Name: label, Length: 2200, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SCALING"
      ],
      "metadata": {
        "id": "2GhqjoGAhyW3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "minmax=MinMaxScaler()"
      ],
      "metadata": {
        "id": "jHF0QjqOh1u-"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "v9YqcJ04h_Ir",
        "outputId": "13eef0c1-09af-4354-f7cb-d0f89a6bb2c9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    N   P   K  temperature   humidity        ph    rainfall  label\n",
              "0  90  42  43    20.879744  82.002744  6.502985  202.935536     20\n",
              "1  85  58  41    21.770462  80.319644  7.038096  226.655537     20\n",
              "2  60  55  44    23.004459  82.320763  7.840207  263.964248     20\n",
              "3  74  35  40    26.491096  80.158363  6.980401  242.864034     20\n",
              "4  78  42  42    20.130175  81.604873  7.628473  262.717340     20"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c136ff9c-0cc9-46ca-b7d6-8cfbdc1832ea\" 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",
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              "</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",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>90</td>\n",
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              "      <td>20.879744</td>\n",
              "      <td>82.002744</td>\n",
              "      <td>6.502985</td>\n",
              "      <td>202.935536</td>\n",
              "      <td>20</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>20</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>23.004459</td>\n",
              "      <td>82.320763</td>\n",
              "      <td>7.840207</td>\n",
              "      <td>263.964248</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>74</td>\n",
              "      <td>35</td>\n",
              "      <td>40</td>\n",
              "      <td>26.491096</td>\n",
              "      <td>80.158363</td>\n",
              "      <td>6.980401</td>\n",
              "      <td>242.864034</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>78</td>\n",
              "      <td>42</td>\n",
              "      <td>42</td>\n",
              "      <td>20.130175</td>\n",
              "      <td>81.604873</td>\n",
              "      <td>7.628473</td>\n",
              "      <td>262.717340</td>\n",
              "      <td>20</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-c136ff9c-0cc9-46ca-b7d6-8cfbdc1832ea')\"\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",
              "      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-c136ff9c-0cc9-46ca-b7d6-8cfbdc1832ea 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-c136ff9c-0cc9-46ca-b7d6-8cfbdc1832ea');\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-5b9cfa2c-01b5-48fd-8c50-e6b24f6b43e0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5b9cfa2c-01b5-48fd-8c50-e6b24f6b43e0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\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",
              "      --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-5b9cfa2c-01b5-48fd-8c50-e6b24f6b43e0 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\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 0,\n        \"max\": 21,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          20,\n          21,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x= df.drop('label',axis=1)\n",
        "y=df['label']"
      ],
      "metadata": {
        "id": "MZpU2RuoiT5y"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4sbY9KY_i4Kz",
        "outputId": "d161a525-7686-4114-d246-0550079c3d27"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    N   P   K  temperature   humidity        ph    rainfall\n",
              "0  90  42  43    20.879744  82.002744  6.502985  202.935536\n",
              "1  85  58  41    21.770462  80.319644  7.038096  226.655537\n",
              "2  60  55  44    23.004459  82.320763  7.840207  263.964248\n",
              "3  74  35  40    26.491096  80.158363  6.980401  242.864034\n",
              "4  78  42  42    20.130175  81.604873  7.628473  262.717340"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8071caa5-16df-43b7-98af-bfff4c1a0bfd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>20.879744</td>\n",
              "      <td>82.002744</td>\n",
              "      <td>6.502985</td>\n",
              "      <td>202.935536</td>\n",
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              "      <td>7.038096</td>\n",
              "      <td>226.655537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60</td>\n",
              "      <td>55</td>\n",
              "      <td>44</td>\n",
              "      <td>23.004459</td>\n",
              "      <td>82.320763</td>\n",
              "      <td>7.840207</td>\n",
              "      <td>263.964248</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
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              "      <td>35</td>\n",
              "      <td>40</td>\n",
              "      <td>26.491096</td>\n",
              "      <td>80.158363</td>\n",
              "      <td>6.980401</td>\n",
              "      <td>242.864034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>78</td>\n",
              "      <td>42</td>\n",
              "      <td>42</td>\n",
              "      <td>20.130175</td>\n",
              "      <td>81.604873</td>\n",
              "      <td>7.628473</td>\n",
              "      <td>262.717340</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-8071caa5-16df-43b7-98af-bfff4c1a0bfd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://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-8071caa5-16df-43b7-98af-bfff4c1a0bfd 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-8071caa5-16df-43b7-98af-bfff4c1a0bfd');\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --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",
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              "  }\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",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      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",
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              "      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-529b840a-6b90-475d-946b-e8450efe5af7 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": {
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            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "newx = minmax.fit_transform(x)"
      ],
      "metadata": {
        "id": "3C2pR9Qui6at"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(newx)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JeiVR2U9jCbr",
        "outputId": "a2fd942e-928e-4dcf-a2ee-59d8ffb25226"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KgTjx8ngjOV8",
        "outputId": "b1f50951-ee2c-401c-b57e-080b430b99f9"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['N', 'P', 'K', 'temperature', 'humidity', 'ph', 'rainfall', 'label'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = pd.DataFrame(newx,columns=['N', 'P', 'K', 'temperature', 'humidity', 'ph', 'rainfall'])"
      ],
      "metadata": {
        "id": "ZalX0Rn7jFul"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Ht3VHChSjVsp",
        "outputId": "6900eeb0-cd9f-4cdf-fa21-dc0e1b182c2d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          N         P      K  temperature  humidity        ph  rainfall\n",
              "0  0.642857  0.264286  0.190     0.345886  0.790267  0.466264  0.656458\n",
              "1  0.607143  0.378571  0.180     0.371445  0.770633  0.549480  0.741675\n",
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              "3  0.528571  0.214286  0.175     0.506901  0.768751  0.540508  0.799905\n",
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          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([x,y],axis=1)"
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      "metadata": {
        "id": "tRbFzuDKjYq2"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "df"
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        "id": "ErZcR_Lujdv3",
        "outputId": "295c3511-6bef-4782-a71e-7e56c2fcee8b"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             N         P      K  temperature  humidity        ph  rainfall  \\\n",
              "0     0.642857  0.264286  0.190     0.345886  0.790267  0.466264  0.656458   \n",
              "1     0.607143  0.378571  0.180     0.371445  0.770633  0.549480  0.741675   \n",
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              "4     0.557143  0.264286  0.185     0.324378  0.785626  0.641291  0.871231   \n",
              "...        ...       ...    ...          ...       ...       ...       ...   \n",
              "2195  0.764286  0.207143  0.135     0.515037  0.608410  0.509353  0.566064   \n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2200,\n  \"fields\": [\n    {\n      \"column\": \"N\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.26369524166969,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 137,\n        \"samples\": [\n          0.7571428571428571,\n          0.7214285714285714,\n          0.6285714285714286\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23561344813276536,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 117,\n        \"samples\": [\n          0.45714285714285713,\n          0.2285714285714286,\n          0.04285714285714286\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2532396527333006,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          0.185,\n          0.034999999999999996,\n          0.049999999999999996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1453020086257475,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          0.5930687776938756,\n          0.49795587176734923,\n          0.9909618558913746\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2597155304368085,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          0.9387327625218901,\n          0.8429923832550089,\n          0.9226590882548488\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ph\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12035722507296934,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          0.416821129247391,\n          0.428373043189458,\n          0.5344577951680856\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rainfall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19744428086897003,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          0.02190395717224225,\n          0.10497346152804582,\n          0.33974203062023695\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 0,\n        \"max\": 21,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          20,\n          21,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# logistic regression"
      ],
      "metadata": {
        "id": "h-HOllYajgqa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lHd9SvmSjj5z",
        "outputId": "58d590b9-421b-48b2-c1fc-c15875dbe92b"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          N         P      K  temperature  humidity        ph  rainfall  label\n",
              "0  0.642857  0.264286  0.190     0.345886  0.790267  0.466264  0.656458     20\n",
              "1  0.607143  0.378571  0.180     0.371445  0.770633  0.549480  0.741675     20\n",
              "2  0.428571  0.357143  0.195     0.406854  0.793977  0.674219  0.875710     20\n",
              "3  0.528571  0.214286  0.175     0.506901  0.768751  0.540508  0.799905     20\n",
              "4  0.557143  0.264286  0.185     0.324378  0.785626  0.641291  0.871231     20"
            ],
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              "      <th>temperature</th>\n",
              "      <th>humidity</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.642857</td>\n",
              "      <td>0.264286</td>\n",
              "      <td>0.190</td>\n",
              "      <td>0.345886</td>\n",
              "      <td>0.790267</td>\n",
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              "      <td>0.656458</td>\n",
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              "      <td>0.607143</td>\n",
              "      <td>0.378571</td>\n",
              "      <td>0.180</td>\n",
              "      <td>0.371445</td>\n",
              "      <td>0.770633</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>0.357143</td>\n",
              "      <td>0.195</td>\n",
              "      <td>0.406854</td>\n",
              "      <td>0.793977</td>\n",
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              "      <td>0.875710</td>\n",
              "      <td>20</td>\n",
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              "      <td>0.528571</td>\n",
              "      <td>0.214286</td>\n",
              "      <td>0.175</td>\n",
              "      <td>0.506901</td>\n",
              "      <td>0.768751</td>\n",
              "      <td>0.540508</td>\n",
              "      <td>0.799905</td>\n",
              "      <td>20</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.557143</td>\n",
              "      <td>0.264286</td>\n",
              "      <td>0.185</td>\n",
              "      <td>0.324378</td>\n",
              "      <td>0.785626</td>\n",
              "      <td>0.641291</td>\n",
              "      <td>0.871231</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1538679e-b7ba-4879-8f99-44bfb53175fc');\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",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "  }\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",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    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",
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              "      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",
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              "        document.querySelector('#df-21f1f444-fd09-4491-a255-46f770c2ad46 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </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\": 0.26369524166969,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 137,\n        \"samples\": [\n          0.7571428571428571,\n          0.7214285714285714,\n          0.6285714285714286\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23561344813276536,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 117,\n        \"samples\": [\n          0.45714285714285713,\n          0.2285714285714286,\n          0.04285714285714286\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2532396527333006,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          0.185,\n          0.034999999999999996,\n          0.049999999999999996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1453020086257475,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          0.5930687776938756,\n          0.49795587176734923,\n          0.9909618558913746\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2597155304368085,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          0.9387327625218901,\n          0.8429923832550089,\n          0.9226590882548488\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ph\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12035722507296934,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          0.416821129247391,\n          0.428373043189458,\n          0.5344577951680856\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rainfall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19744428086897003,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          0.02190395717224225,\n          0.10497346152804582,\n          0.33974203062023695\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 0,\n        \"max\": 21,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          20,\n          21,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.drop('label',axis=1)\n",
        "x1 = pd.DataFrame(x)\n",
        "y = df['label']\n",
        "y1 = pd.DataFrame(y)"
      ],
      "metadata": {
        "id": "Ypqaikexjse2"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x1_train,x1_test,y1_train,y1_test = train_test_split(x1,y1,test_size=0.20,random_state=42)"
      ],
      "metadata": {
        "id": "liIgcNOckAxR"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "lr = LogisticRegression()"
      ],
      "metadata": {
        "id": "43wnZ9dDkia8"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = lr.fit(x1_train,y1_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "psTJB9zdkvVU",
        "outputId": "e7d56e56-6cdf-4fee-8003-9419f4ed155d"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y1_pred = model.predict(x1_test)"
      ],
      "metadata": {
        "id": "sibi0bebk5pw"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y1_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4PJnwBezlDzN",
        "outputId": "1abf8bb9-9c9d-46b6-8896-294bf7ea07c7"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[15 21 17 17  0 12  0 13 14 10  2  4 19  8  4 19  0 11 20 15  5 17 16 17\n",
            "  3  8 14 16 18 20 19 13 20 10  8  2  8  3  3  9 20 12  2 11 14 11 18  4\n",
            " 15 11  2  5  7 14  5  9  6  0  1 10 21  4 10 16 14  2 16 20 15 18 15  4\n",
            " 20  1  2 17  1  6 21 16  5  3  8 13 16 12  5 13  2 19 11 13  6 17 18 12\n",
            "  9  5  2 10  4 20 16 15 21  9 21  1 18 12  1 20  6 19 18  3 11  4 19 20\n",
            " 18  7  2  4  3  2  4 11  1 13  1  9 19  3  4 16 18  1  1  0  9 15 14 13\n",
            "  4  6  0  4  9 12 14 10 21 14 18 10 18  9 11  8  3  0 16  6 20  4  7 10\n",
            " 21  7  7  2 19  3  4 11 10  7 21  8  5  5  9  8 12  9  1  9  4 17 17 14\n",
            " 12 19 21  9 11  0  2  3  7  7  1  6 20 19 14  1 20 14 11  3  3  3  0 20\n",
            "  9 14  5  2  9 12 12  4 17  0  3 19  3 15  0 15 15 12 12  6  4 19 20 15\n",
            "  5 17 10 11 12 15  2 14  5  7  4  6 18 20  0 19  5  3  6  8 12  1 17  0\n",
            "  3 20 18 13 14 20 19  7 13  8 11  4 11  3  1  8  4  5 12 15  0  1 18  2\n",
            " 16  3 21  1  0  3  5 18 16  0  4 17 21 12 20  3 19  3 14 10  0 19  3 12\n",
            "  3 19 21  9 14 15 21  9 15 12  8  2  3  1 10 18 17  9 14  4  6  7  0 10\n",
            "  1  8  0 19  0 14 15  5  5 18  8  9  1 11  8 11 18 12  9 19 21  2 11 20\n",
            " 12  9 12  6 17 13  6 14 16 20  2 14  5  1 18 17  0 19 11 12  4  0 10  8\n",
            " 12 10  4  2  8 14  6 21  0  7  4  7 21 20 12 12  5 19  1  7  8 16  6 12\n",
            " 17 15 12  8  3 12 19 21 13  6 17 21 13 20  4 12 13 11 20 11  4 16 19  9\n",
            " 21 14  2 20 20  6  6  9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print(\"the accuracy score is:\",accuracy_score(y1_test,y1_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VjWEMbcYlJj7",
        "outputId": "9f74384b-9bae-4369-e442-4ecd39a5356d"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the accuracy score is: 0.9204545454545454\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KZz4oS-jlpoZ",
        "outputId": "1844fcde-ed2f-4171-d060-860afa6c81e7"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          N         P      K  temperature  humidity        ph  rainfall\n",
              "0  0.642857  0.264286  0.190     0.345886  0.790267  0.466264  0.656458\n",
              "1  0.607143  0.378571  0.180     0.371445  0.770633  0.549480  0.741675\n",
              "2  0.428571  0.357143  0.195     0.406854  0.793977  0.674219  0.875710\n",
              "3  0.528571  0.214286  0.175     0.506901  0.768751  0.540508  0.799905\n",
              "4  0.557143  0.264286  0.185     0.324378  0.785626  0.641291  0.871231"
            ],
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              "      <td>0.185</td>\n",
              "      <td>0.324378</td>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    async function quickchart(key) {\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 2200,\n  \"fields\": [\n    {\n      \"column\": \"N\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.26369524166969,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 137,\n        \"samples\": [\n          0.7571428571428571,\n          0.7214285714285714,\n          0.6285714285714286\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23561344813276536,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 117,\n        \"samples\": [\n          0.45714285714285713,\n          0.2285714285714286,\n          0.04285714285714286\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2532396527333006,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          0.185,\n          0.034999999999999996,\n          0.049999999999999996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1453020086257475,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          0.5930687776938756,\n          0.49795587176734923,\n          0.9909618558913746\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2597155304368085,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          0.9387327625218901,\n          0.8429923832550089,\n          0.9226590882548488\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ph\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12035722507296934,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          0.416821129247391,\n          0.428373043189458,\n          0.5344577951680856\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rainfall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19744428086897003,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2200,\n        \"samples\": [\n          0.02190395717224225,\n          0.10497346152804582,\n          0.33974203062023695\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([[0.528571\t,0.214286\t,0.175\t,0.506901\t,0.768751\t,0.540508\t,0.799905]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jZLkJosJljCF",
        "outputId": "bd74f233-d2d2-4303-e658-20cb012bc807"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([20])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Zjh2ObI5l5FJ",
        "outputId": "7708be1a-20d2-4438-ccdd-f24dcd9edd08"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             N         P      K  temperature  humidity        ph  rainfall\n",
              "2195  0.764286  0.207143  0.135     0.515037  0.608410  0.509353  0.566064\n",
              "2196  0.707143  0.071429  0.110     0.533473  0.494359  0.401561  0.386972\n",
              "2197  0.842857  0.200000  0.125     0.439202  0.617880  0.444433  0.550071\n",
              "2198  0.835714  0.192857  0.145     0.500627  0.441760  0.506045  0.384280\n",
              "2199  0.742857  0.092857  0.125     0.424029  0.538222  0.509317  0.433721"
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          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([[0.842857\t,0.200000\t,0.125\t,0.439202\t,0.617880\t,0.444433\t,0.550071]])"
      ],
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        "outputId": "8c47d96c-b4c5-412b-db43-1de212ebf42f"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
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}