crop_prediction.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMINW+ukgCXGp2mjfTumEcp",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/maheshalyana/TRINIT_HeavyCoders_ML/blob/master/crop_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Imports"
      ],
      "metadata": {
        "id": "DA989HFfYFNH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R1t-p0C4EXsg",
        "outputId": "277eeb66-1a9e-4491-83d4-00440a12c609"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "imported\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "print('imported')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data"
      ],
      "metadata": {
        "id": "YoJHOCFDX6Kv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/Crop_recommendation.csv')\n",
        "df.head()\n",
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKviCiOTVFmL",
        "outputId": "ed83d185-aae0-48f7-f81d-4b210781e14c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "N                int64\n",
              "P                int64\n",
              "K                int64\n",
              "temperature    float64\n",
              "humidity       float64\n",
              "ph             float64\n",
              "rainfall       float64\n",
              "label           object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['label'].unique()\n",
        "df['label'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6IMIrwlVJw6",
        "outputId": "cc8adc27-944b-41f5-ba44-2e493d3265bf"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rice           100\n",
              "maize          100\n",
              "jute           100\n",
              "cotton         100\n",
              "coconut        100\n",
              "papaya         100\n",
              "orange         100\n",
              "apple          100\n",
              "muskmelon      100\n",
              "watermelon     100\n",
              "grapes         100\n",
              "mango          100\n",
              "banana         100\n",
              "pomegranate    100\n",
              "lentil         100\n",
              "blackgram      100\n",
              "mungbean       100\n",
              "mothbeans      100\n",
              "pigeonpeas     100\n",
              "kidneybeans    100\n",
              "chickpea       100\n",
              "coffee         100\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features = df[['N', 'P','K','temperature', 'humidity', 'ph', 'rainfall']]\n",
        "target = df['label']\n",
        "features.head()\n",
        "target.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v4TKw2NuViF7",
        "outputId": "943e8129-bec1-4f8d-ec53-eaf36b69b84d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    rice\n",
              "1    rice\n",
              "2    rice\n",
              "3    rice\n",
              "4    rice\n",
              "Name: label, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Xtrain, Xtest, Ytrain, Ytest = train_test_split(features,target,test_size = 0.2,random_state =2)"
      ],
      "metadata": {
        "id": "RJnVK80vWch9"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Decision Tree\n"
      ],
      "metadata": {
        "id": "YL1UVsRVW7wn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import classification_report,accuracy_score\n",
        "\n",
        "DecisionTree = DecisionTreeClassifier(criterion=\"entropy\",random_state=2,max_depth=7)\n",
        "\n",
        "DecisionTree.fit(Xtrain,Ytrain)\n",
        "\n",
        "predicted_values = DecisionTree.predict(Xtest)\n",
        "x = accuracy_score(Ytest, predicted_values)\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7gMB-n5iW3Wb",
        "outputId": "b1403978-9429-4f85-e6af-337b77c26f27"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9795454545454545\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Random Forest"
      ],
      "metadata": {
        "id": "Sox26f9bYKkR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "RandomForest = RandomForestClassifier(n_estimators=10,max_depth=7,random_state=0)\n",
        "RandomForest.fit(Xtrain,Ytrain)\n",
        "predicted_values = RandomForest.predict(Xtest)\n",
        "x = accuracy_score(Ytest, predicted_values)\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AfiJWZfWYN5G",
        "outputId": "8d0d320e-8026-4011-eba6-7f6008ff14f3"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9818181818181818\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Naive Bayes classifier"
      ],
      "metadata": {
        "id": "NqZzET1hbB_w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "NaiveBayes = GaussianNB()\n",
        "\n",
        "NaiveBayes.fit(Xtrain,Ytrain)\n",
        "\n",
        "predicted_values = NaiveBayes.predict(Xtrain)\n",
        "x = accuracy_score(Ytrain, predicted_values)\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0VoQ3r7nbLCr",
        "outputId": "1f3567dd-613c-493b-b1be-f927f387a7ce"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9960227272727272\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SVM"
      ],
      "metadata": {
        "id": "NPqYsIcEbtM2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "# data normalization with sklearn\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "# fit scaler on training data\n",
        "norm = MinMaxScaler().fit(Xtrain)\n",
        "X_train_norm = norm.transform(Xtrain)\n",
        "# transform testing dataabs\n",
        "X_test_norm = norm.transform(Xtest)\n",
        "SVM = SVC(kernel='poly', degree=3, C=1)\n",
        "SVM.fit(X_train_norm,Ytrain)\n",
        "predicted_values = SVM.predict(X_test_norm)\n",
        "x = accuracy_score(Ytest, predicted_values)\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E5L8J0_WbvXG",
        "outputId": "31774c2c-060c-4981-e622-c8e4359efac5"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9795454545454545\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hfPlKeRnb503"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}