crop_prediction.ipynb

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      "gpuType": "T4"
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      "name": "python3",
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      "name": "python"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Libraries"
      ],
      "metadata": {
        "id": "BmRVR0I8mFvV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.metrics import classification_report"
      ],
      "metadata": {
        "id": "miIrNe6ijahy"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Dataset"
      ],
      "metadata": {
        "id": "O1jnnGlRmMTC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('Crop.csv')"
      ],
      "metadata": {
        "id": "ka8vHVnlmP6g"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# feature & Target Variable"
      ],
      "metadata": {
        "id": "hIUjlcmYmW-H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "feature_variable = df[['N','P','K','temperature','humidity','ph','rainfall']]\n",
        "target_variable = df['label']"
      ],
      "metadata": {
        "id": "clVeR2bZmaQP"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Splitting X and Y"
      ],
      "metadata": {
        "id": "sL9BazuPmegd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(feature_variable, target_variable, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "1mK9TcEemgrP"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Fitting"
      ],
      "metadata": {
        "id": "vTIw18q6mipo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier()\n",
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "kEuWQvhnmkVk",
        "outputId": "91566942-9e80-4eb4-d417-bbf17ef800dc"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Evaluation\n",
        "## Use Cross Validation"
      ],
      "metadata": {
        "id": "laCWjyqsmloN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use cross-validation\n",
        "cv_scores = cross_val_score(model, feature_variable, target_variable, cv=5)\n",
        "print(\"Cross-Validation Scores:\", cv_scores)\n",
        "print(\"Mean CV Accuracy:\", np.mean(cv_scores))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zIoN1EIbjlTu",
        "outputId": "95da637c-a1b6-42e6-cbb5-ac0075250ffb"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-Validation Scores: [0.99545455 0.99318182 0.99545455 0.99545455 0.98636364]\n",
            "Mean CV Accuracy: 0.9931818181818184\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluate other metrics"
      ],
      "metadata": {
        "id": "6DWYuq_pmyYx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = model.predict(X_test)\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(Y_test, Y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YwvflL4FjpBo",
        "outputId": "d37f3370-910f-4605-b286-f6b3fd6c3d4c"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       apple       1.00      1.00      1.00        23\n",
            "      banana       1.00      1.00      1.00        21\n",
            "   blackgram       1.00      1.00      1.00        20\n",
            "    chickpea       1.00      1.00      1.00        26\n",
            "     coconut       1.00      1.00      1.00        27\n",
            "      coffee       1.00      1.00      1.00        17\n",
            "      cotton       1.00      1.00      1.00        17\n",
            "      grapes       1.00      1.00      1.00        14\n",
            "        jute       0.92      1.00      0.96        23\n",
            " kidneybeans       1.00      1.00      1.00        20\n",
            "      lentil       0.92      1.00      0.96        11\n",
            "       maize       1.00      1.00      1.00        21\n",
            "       mango       1.00      1.00      1.00        19\n",
            "   mothbeans       1.00      0.96      0.98        24\n",
            "    mungbean       1.00      1.00      1.00        19\n",
            "   muskmelon       1.00      1.00      1.00        17\n",
            "      orange       1.00      1.00      1.00        14\n",
            "      papaya       1.00      1.00      1.00        23\n",
            "  pigeonpeas       1.00      1.00      1.00        23\n",
            " pomegranate       1.00      1.00      1.00        23\n",
            "        rice       1.00      0.89      0.94        19\n",
            "  watermelon       1.00      1.00      1.00        19\n",
            "\n",
            "    accuracy                           0.99       440\n",
            "   macro avg       0.99      0.99      0.99       440\n",
            "weighted avg       0.99      0.99      0.99       440\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Importance"
      ],
      "metadata": {
        "id": "Dnhp7wI3m43X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "feature_importances = model.feature_importances_\n",
        "print(\"Feature Importances:\", feature_importances)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vviU6A6Pjtwr",
        "outputId": "df3429b8-91c7-43c8-d1ef-b252565d1210"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature Importances: [0.10804946 0.14446852 0.17061106 0.07567989 0.21547996 0.05606687\n",
            " 0.22964424]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Deploy Model"
      ],
      "metadata": {
        "id": "L8Ok4d5Kq8DJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle as pkl\n",
        "rfc_model = RandomForestClassifier()\n",
        "rfc_model.fit(X_train, Y_train)\n",
        "\n",
        "filname = 'rfc_model.pkl'\n",
        "pkl.dump(rfc_model, open(f'{filname}', 'wb'))"
      ],
      "metadata": {
        "id": "D3QWYJdOq_ya"
      },
      "execution_count": 56,
      "outputs": []
    }
  ]
}