fertilizer_prediction.ipynb

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      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn import metrics\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/Fertilizer Prediction (1).csv')\n",
        "data.head()"
      ],
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            "text/plain": [
              "   Temparature  Humidity   Moisture Soil Type  Crop Type  Nitrogen  Potassium  \\\n",
              "0           26         52        38     Sandy      Maize        37          0   \n",
              "1           29         52        45     Loamy  Sugarcane        12          0   \n",
              "2           34         65        62     Black     Cotton         7          9   \n",
              "3           32         62        34       Red    Tobacco        22          0   \n",
              "4           28         54        46    Clayey      Paddy        35          0   \n",
              "\n",
              "   Phosphorous Fertilizer Name  \n",
              "0            0            Urea  \n",
              "1           36             DAP  \n",
              "2           30        14-35-14  \n",
              "3           20           28-28  \n",
              "4            0            Urea  "
            ],
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              "      <th>Temparature</th>\n",
              "      <th>Humidity</th>\n",
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              "      <th>Soil Type</th>\n",
              "      <th>Crop Type</th>\n",
              "      <th>Nitrogen</th>\n",
              "      <th>Potassium</th>\n",
              "      <th>Phosphorous</th>\n",
              "      <th>Fertilizer Name</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>26</td>\n",
              "      <td>52</td>\n",
              "      <td>38</td>\n",
              "      <td>Sandy</td>\n",
              "      <td>Maize</td>\n",
              "      <td>37</td>\n",
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              "      <td>0</td>\n",
              "      <td>Urea</td>\n",
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              "      <th>1</th>\n",
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              "      <td>36</td>\n",
              "      <td>DAP</td>\n",
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              "      <th>2</th>\n",
              "      <td>34</td>\n",
              "      <td>65</td>\n",
              "      <td>62</td>\n",
              "      <td>Black</td>\n",
              "      <td>Cotton</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>30</td>\n",
              "      <td>14-35-14</td>\n",
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              "      <th>3</th>\n",
              "      <td>32</td>\n",
              "      <td>62</td>\n",
              "      <td>34</td>\n",
              "      <td>Red</td>\n",
              "      <td>Tobacco</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>28-28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>54</td>\n",
              "      <td>46</td>\n",
              "      <td>Clayey</td>\n",
              "      <td>Paddy</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Urea</td>\n",
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            "text/plain": [
              "(99, 9)"
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          },
          "metadata": {},
          "execution_count": 3
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    {
      "cell_type": "code",
      "source": [
        "data['Crop Type'].unique()"
      ],
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          "data": {
            "text/plain": [
              "array(['Maize', 'Sugarcane', 'Cotton', 'Tobacco', 'Paddy', 'Barley',\n",
              "       'Wheat', 'Millets', 'Oil seeds', 'Pulses', 'Ground Nuts'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Soil Type'].unique()"
      ],
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        "id": "dAnnKWEhD76q",
        "outputId": "bc7ea59a-df25-48f5-9160-cce2ddc49a64"
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "array(['Sandy', 'Loamy', 'Black', 'Red', 'Clayey'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Fertilizer Name'].unique()"
      ],
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        "id": "n-4gQLQpEVto",
        "outputId": "2f638246-29af-4a83-b459-ed73de4d74dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Urea', 'DAP', '14-35-14', '28-28', '17-17-17', '20-20',\n",
              "       '10-26-26'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.rename(columns={'Humidity ':'Humidity','Soil Type':'Soil_Type','Crop Type':'Crop_Type','Fertilizer Name':'Fertilizer'},inplace=True)"
      ],
      "metadata": {
        "id": "YJV8ey1vpcgn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "encode_soil = LabelEncoder()\n",
        "data.Soil_Type = encode_soil.fit_transform(data.Soil_Type)\n",
        "Soil_Type = pd.DataFrame(zip(encode_soil.classes_,encode_soil.transform(encode_soil.classes_)),columns=['Original','Encoded'])\n",
        "Soil_Type = Soil_Type.set_index('Original')\n",
        "Soil_Type\n"
      ],
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            "text/plain": [
              "          Encoded\n",
              "Original         \n",
              "Black           0\n",
              "Clayey          1\n",
              "Loamy           2\n",
              "Red             3\n",
              "Sandy           4"
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              "      <td>2</td>\n",
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              "      <th>Red</th>\n",
              "      <td>3</td>\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "encode_crop = LabelEncoder()\n",
        "data.Crop_Type = encode_crop.fit_transform(data.Crop_Type)\n",
        "Crop_Type = pd.DataFrame(zip(encode_crop.classes_,encode_crop.transform(encode_crop.classes_)),columns=['Original','Encoded'])\n",
        "Crop_Type = Crop_Type.set_index('Original')\n",
        "Crop_Type"
      ],
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        "id": "uCEUYyVcp2Mp",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Encoded\n",
              "Original            \n",
              "Barley             0\n",
              "Cotton             1\n",
              "Ground Nuts        2\n",
              "Maize              3\n",
              "Millets            4\n",
              "Oil seeds          5\n",
              "Paddy              6\n",
              "Pulses             7\n",
              "Sugarcane          8\n",
              "Tobacco            9\n",
              "Wheat             10"
            ],
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              "      <th>Encoded</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Original</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Barley</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cotton</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ground Nuts</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Maize</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Millets</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oil seeds</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Paddy</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pulses</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sugarcane</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tobacco</th>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wheat</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9048fb4a-ba3c-426b-9a07-40a2b25cd08e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
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              "\n",
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              "\n",
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              "          document.querySelector('#df-9048fb4a-ba3c-426b-9a07-40a2b25cd08e 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-9048fb4a-ba3c-426b-9a07-40a2b25cd08e');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encode_ferti = LabelEncoder()\n",
        "data.Fertilizer = encode_ferti.fit_transform(data.Fertilizer)\n",
        "Fertilizer = pd.DataFrame(zip(encode_ferti.classes_,encode_ferti.transform(encode_ferti.classes_)),columns=['Original','Encoded'])\n",
        "Fertilizer = Fertilizer.set_index('Original')\n",
        "Fertilizer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "QJUfSgTKp8Ek",
        "outputId": "8ca7fc67-ee0b-4581-8dac-480a342b4e84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Encoded\n",
              "Original         \n",
              "10-26-26        0\n",
              "14-35-14        1\n",
              "17-17-17        2\n",
              "20-20           3\n",
              "28-28           4\n",
              "DAP             5\n",
              "Urea            6"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b253ecab-4d8d-41b0-963b-a50f5ccc4581\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Encoded</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Original</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10-26-26</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14-35-14</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17-17-17</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20-20</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28-28</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DAP</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Urea</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b253ecab-4d8d-41b0-963b-a50f5ccc4581')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-b253ecab-4d8d-41b0-963b-a50f5ccc4581 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-b253ecab-4d8d-41b0-963b-a50f5ccc4581');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(data.drop('Fertilizer',axis=1),data.Fertilizer,test_size=0.2,random_state=1)"
      ],
      "metadata": {
        "id": "ShQX86bIqBIa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rand = RandomForestClassifier(n_estimators=30,random_state=42)\n",
        "pred_rand = rand.fit(x_train,y_train).predict(x_test)\n",
        "x = metrics.accuracy_score(y_test, pred_rand)\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kj0TdQNRqHDj",
        "outputId": "ffa7aeef-908c-4046-8c94-69d9bc3daae4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,pred_rand))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TK3nfDzIE11D",
        "outputId": "85dd56aa-ec6a-4cc9-c639-c35769a9b8da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.67      0.80         3\n",
            "           1       0.75      1.00      0.86         3\n",
            "           2       0.50      0.50      0.50         2\n",
            "           3       1.00      1.00      1.00         2\n",
            "           4       1.00      1.00      1.00         2\n",
            "           5       1.00      1.00      1.00         2\n",
            "           6       1.00      1.00      1.00         6\n",
            "\n",
            "    accuracy                           0.90        20\n",
            "   macro avg       0.89      0.88      0.88        20\n",
            "weighted avg       0.91      0.90      0.90        20\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = np.array([[104,18, 30, 2,3, 6.7, 140.91,90]])\n",
        "prediction = rand.predict(data)\n",
        "print(prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BH8caQ4Z8_Hq",
        "outputId": "467cc1c0-464c-43c6-dbec-e92d41dc95f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[5]\n"
          ]
        }
      ]
    }
  ]
}