Untitled5.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "c7QZ1X_buWQB"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "_xhdL-5nu2oL"
      },
      "outputs": [],
      "source": [
        "crop = pd.read_csv(\"Crop_recommendation.csv\");\n",
        "crop.head();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "YyUCtQTOvNqN"
      },
      "outputs": [],
      "source": [
        "crop.head();\n",
        "crop.tail();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lndtwki2vShP",
        "outputId": "a863e33f-013c-42b6-c242-4c80f6a29934"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(2200, 8)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "crop.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jJLTuDcSv4n4",
        "outputId": "7c0bb20b-d89f-4548-a649-bf2cbc85f558"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        }
      ],
      "source": [
        "crop.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8OtBL9bNv_rc",
        "outputId": "c68de838-5ebb-4aa9-ef00-a807470855cb"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "crop.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nww-ajeuwSIY",
        "outputId": "d53ffe9f-9710-4f6b-e633-93f433042d25"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "crop.duplicated().sum()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "msbwFE_HwW19",
        "outputId": "32305c36-da89-43af-aa1e-2f6982a82d18"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2200.000000</td>\n",
              "      <td>2200.000000</td>\n",
              "      <td>2200.000000</td>\n",
              "      <td>2200.000000</td>\n",
              "      <td>2200.000000</td>\n",
              "      <td>2200.000000</td>\n",
              "      <td>2200.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>50.551818</td>\n",
              "      <td>53.362727</td>\n",
              "      <td>48.149091</td>\n",
              "      <td>25.616244</td>\n",
              "      <td>71.481779</td>\n",
              "      <td>6.469480</td>\n",
              "      <td>103.463655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>36.917334</td>\n",
              "      <td>32.985883</td>\n",
              "      <td>50.647931</td>\n",
              "      <td>5.063749</td>\n",
              "      <td>22.263812</td>\n",
              "      <td>0.773938</td>\n",
              "      <td>54.958389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.825675</td>\n",
              "      <td>14.258040</td>\n",
              "      <td>3.504752</td>\n",
              "      <td>20.211267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>21.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>22.769375</td>\n",
              "      <td>60.261953</td>\n",
              "      <td>5.971693</td>\n",
              "      <td>64.551686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>37.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>25.598693</td>\n",
              "      <td>80.473146</td>\n",
              "      <td>6.425045</td>\n",
              "      <td>94.867624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>84.250000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>28.561654</td>\n",
              "      <td>89.948771</td>\n",
              "      <td>6.923643</td>\n",
              "      <td>124.267508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>140.000000</td>\n",
              "      <td>145.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>43.675493</td>\n",
              "      <td>99.981876</td>\n",
              "      <td>9.935091</td>\n",
              "      <td>298.560117</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 N            P            K  temperature     humidity  \\\n",
              "count  2200.000000  2200.000000  2200.000000  2200.000000  2200.000000   \n",
              "mean     50.551818    53.362727    48.149091    25.616244    71.481779   \n",
              "std      36.917334    32.985883    50.647931     5.063749    22.263812   \n",
              "min       0.000000     5.000000     5.000000     8.825675    14.258040   \n",
              "25%      21.000000    28.000000    20.000000    22.769375    60.261953   \n",
              "50%      37.000000    51.000000    32.000000    25.598693    80.473146   \n",
              "75%      84.250000    68.000000    49.000000    28.561654    89.948771   \n",
              "max     140.000000   145.000000   205.000000    43.675493    99.981876   \n",
              "\n",
              "                ph     rainfall  \n",
              "count  2200.000000  2200.000000  \n",
              "mean      6.469480   103.463655  \n",
              "std       0.773938    54.958389  \n",
              "min       3.504752    20.211267  \n",
              "25%       5.971693    64.551686  \n",
              "50%       6.425045    94.867624  \n",
              "75%       6.923643   124.267508  \n",
              "max       9.935091   298.560117  "
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "crop.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "FQ1LJ3BawaAB",
        "outputId": "9550ead1-e1da-4b6a-be78-5b1a75931cdc"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\Neelesh\\AppData\\Local\\Temp\\ipykernel_32516\\3854033057.py:7: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(crop['N'])\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "corr = crop.corr()\n",
        "corr\n",
        "import seaborn as sns\n",
        "sns.heatmap(corr,annot=True,cbar=True, cmap='coolwarm')\n",
        "crop['label'].value_counts()\n",
        "import matplotlib.pyplot as plt\n",
        "sns.distplot(crop['N'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "YY0IbDu2wkaI"
      },
      "outputs": [],
      "source": [
        "crop_dict = {\n",
        "    'rice': 1,\n",
        "    'maize': 2,\n",
        "    'jute': 3,\n",
        "    'cotton': 4,\n",
        "    'coconut': 5,\n",
        "    'papaya': 6,\n",
        "    'orange': 7,\n",
        "    'apple': 8,\n",
        "    'muskmelon': 9,\n",
        "    'watermelon': 10,\n",
        "    'grapes': 11,\n",
        "    'mango': 12,\n",
        "    'banana': 13,\n",
        "    'pomegranate': 14,\n",
        "    'lentil': 15,\n",
        "    'blackgram': 16,\n",
        "    'mungbean': 17,\n",
        "    'mothbeans': 18,\n",
        "    'pigeonpeas': 19,\n",
        "    'kidneybeans': 20,\n",
        "    'chickpea': 21,\n",
        "    'coffee': 22\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ftrbng4JxE36",
        "outputId": "f0d2c51c-61d1-4850-9aa6-c9729f0d5e09"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1     100\n",
              "2     100\n",
              "3     100\n",
              "4     100\n",
              "5     100\n",
              "6     100\n",
              "7     100\n",
              "8     100\n",
              "9     100\n",
              "10    100\n",
              "11    100\n",
              "12    100\n",
              "13    100\n",
              "14    100\n",
              "15    100\n",
              "16    100\n",
              "17    100\n",
              "18    100\n",
              "19    100\n",
              "20    100\n",
              "21    100\n",
              "22    100\n",
              "Name: crop_num, dtype: int64"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "crop['crop_num']=crop['label'].map(crop_dict)\n",
        "crop['crop_num'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HrZcPZRRxJtS",
        "outputId": "ae312026-a418-417a-8205-0f1340738382"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "      <th>crop_num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>90</td>\n",
              "      <td>42</td>\n",
              "      <td>43</td>\n",
              "      <td>20.879744</td>\n",
              "      <td>82.002744</td>\n",
              "      <td>6.502985</td>\n",
              "      <td>202.935536</td>\n",
              "      <td>rice</td>\n",
              "      <td>1</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>rice</td>\n",
              "      <td>1</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",
              "      <td>rice</td>\n",
              "      <td>1</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>rice</td>\n",
              "      <td>1</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>rice</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    N   P   K  temperature   humidity        ph    rainfall label  crop_num\n",
              "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice         1\n",
              "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice         1\n",
              "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice         1\n",
              "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice         1\n",
              "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice         1"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "crop.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "AU0rMsT2xMma"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "knn : mae : 0.08090909090909093 score : 0.9959294251703277\n",
            "etr : mae : 0.16340909090909092 score : 0.9961682655923693\n",
            "br : mae : 0.06931818181818182 score : 0.9971867434339737\n",
            "ar : mae : 1.779206329238286 score : 0.8990030772533333\n",
            "xgb : mae : 0.1315389266068285 score : 0.9974927959580129\n",
            "lr : mae : 3.1722099476314787 score : 0.5481829845743507\n",
            "rf : mae : 0.07415909090909091 score : 0.9965114895664979\n",
            "lss : mae : 3.1191645466954365 score : 0.5511235479006709\n",
            "Rid : mae : 3.172128217311405 score : 0.548189295092143\n",
            "Dtr : mae : 0.08863636363636364 score : 0.9824831820854265\n",
            "gbr : mae : 0.5407798847900728 score : 0.9831970792076636\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "['./static/etr_npk1.joblib']"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X = crop.drop(['crop_num','label'],axis=1)\n",
        "y = crop['crop_num']\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, random_state=0, shuffle=True)\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "\n",
        "#linear regression\n",
        "from sklearn.linear_model import LinearRegression,Lasso,Ridge\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import BaggingRegressor,AdaBoostRegressor, GradientBoostingRegressor, ExtraTreesRegressor,RandomForestRegressor\n",
        "from sklearn.metrics import mean_absolute_error,r2_score\n",
        "from xgboost import XGBRegressor\n",
        "\n",
        "\n",
        "models = {\n",
        "    'knn':KNeighborsRegressor(),\n",
        "    'etr':ExtraTreesRegressor(),\n",
        "    'br':BaggingRegressor(),\n",
        "    'ar':AdaBoostRegressor(),\n",
        "    'xgb':XGBRegressor(),\n",
        "    'lr':LinearRegression(),\n",
        "    'rf':RandomForestRegressor(),\n",
        "    'lss':Lasso(),\n",
        "    'Rid':Ridge(),\n",
        "    'Dtr':DecisionTreeRegressor(),\n",
        "    'gbr':GradientBoostingRegressor()\n",
        "}\n",
        "for name, md in models.items():\n",
        "    md.fit(X_train,y_train)\n",
        "    y_pred = md.predict(X_test)\n",
        "    \n",
        "    print(f\"{name} : mae : {mean_absolute_error(y_test,y_pred)} score : {r2_score(y_test,y_pred)}\")\n",
        "\n",
        "#just for testing..\n",
        "xgb = XGBRegressor()\n",
        "xgb.fit(X_train,y_train)\n",
        "xg_pred=xgb.predict(X_test)\n",
        "\n",
        "etr = ExtraTreesRegressor()\n",
        "etr.fit(X_train,y_train)\n",
        "etr_pred=etr.predict(X_test)\n",
        "\n",
        "knn = KNeighborsRegressor()\n",
        "knn.fit(X_train,y_train)\n",
        "knn_pred=knn.predict(X_test)\n",
        "\n",
        "br = BaggingRegressor()\n",
        "br.fit(X_train,y_train)\n",
        "br_pred=br.predict(X_test)\n",
        "\n",
        "ar = AdaBoostRegressor()\n",
        "ar.fit(X_train,y_train)\n",
        "ar_pred=ar.predict(X_test)\n",
        "\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train,y_train)\n",
        "lr_pred=lr.predict(X_test)\n",
        "\n",
        "rf = RandomForestRegressor()\n",
        "rf.fit(X_train,y_train)\n",
        "rf_pred=rf.predict(X_test)\n",
        "\n",
        "gbr = GradientBoostingRegressor()\n",
        "gbr.fit(X_train,y_train)\n",
        "gbr_pred=gbr.predict(X_test)\n",
        "\n",
        "dtr = DecisionTreeRegressor()\n",
        "dtr.fit(X_train,y_train)\n",
        "dtr_pred=dtr.predict(X_test)\n",
        "\n",
        "\n",
        "import joblib\n",
        "# joblib.dump(knn,'./static/knn.joblib')\n",
        "joblib.dump(etr,'./static/etr_npk1.joblib')\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "qAUiwnHRxOpS",
        "outputId": "0b2216b3-7a94-4f8c-d8c4-e5b4af5bbf2a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>90</td>\n",
              "      <td>42</td>\n",
              "      <td>43</td>\n",
              "      <td>20.879744</td>\n",
              "      <td>82.002744</td>\n",
              "      <td>6.502985</td>\n",
              "      <td>202.935536</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",
              "    </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",
              "    <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",
              "    </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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2195</th>\n",
              "      <td>107</td>\n",
              "      <td>34</td>\n",
              "      <td>32</td>\n",
              "      <td>26.774637</td>\n",
              "      <td>66.413269</td>\n",
              "      <td>6.780064</td>\n",
              "      <td>177.774507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2196</th>\n",
              "      <td>99</td>\n",
              "      <td>15</td>\n",
              "      <td>27</td>\n",
              "      <td>27.417112</td>\n",
              "      <td>56.636362</td>\n",
              "      <td>6.086922</td>\n",
              "      <td>127.924610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2197</th>\n",
              "      <td>118</td>\n",
              "      <td>33</td>\n",
              "      <td>30</td>\n",
              "      <td>24.131797</td>\n",
              "      <td>67.225123</td>\n",
              "      <td>6.362608</td>\n",
              "      <td>173.322839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2198</th>\n",
              "      <td>117</td>\n",
              "      <td>32</td>\n",
              "      <td>34</td>\n",
              "      <td>26.272418</td>\n",
              "      <td>52.127394</td>\n",
              "      <td>6.758793</td>\n",
              "      <td>127.175293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2199</th>\n",
              "      <td>104</td>\n",
              "      <td>18</td>\n",
              "      <td>30</td>\n",
              "      <td>23.603016</td>\n",
              "      <td>60.396475</td>\n",
              "      <td>6.779833</td>\n",
              "      <td>140.937041</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2200 rows × 7 columns</p>\n",
              "</div>"
            ],
            "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\n",
              "...   ...  ..  ..          ...        ...       ...         ...\n",
              "2195  107  34  32    26.774637  66.413269  6.780064  177.774507\n",
              "2196   99  15  27    27.417112  56.636362  6.086922  127.924610\n",
              "2197  118  33  30    24.131797  67.225123  6.362608  173.322839\n",
              "2198  117  32  34    26.272418  52.127394  6.758793  127.175293\n",
              "2199  104  18  30    23.603016  60.396475  6.779833  140.937041\n",
              "\n",
              "[2200 rows x 7 columns]"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9w9YRHdWxQMK",
        "outputId": "3539d4c6-6beb-4ce5-cf35-94b55a9f101a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1760, 7)"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "X_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TSdzFNTRxasQ",
        "outputId": "a7603257-a409-431e-b86e-55ca52016b94"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(440, 7)"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "h3mycJvnxe2M",
        "outputId": "dfe98112-1713-424a-ff3a-ef1167c43023"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"X_train\",\n  \"rows\": 1760,\n  \"fields\": [\n    {\n      \"column\": \"N\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37,\n        \"min\": 0,\n        \"max\": 140,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          123,\n          129,\n          63\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33,\n        \"min\": 5,\n        \"max\": 145,\n        \"num_unique_values\": 116,\n        \"samples\": [\n          89,\n          17,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51,\n        \"min\": 5,\n        \"max\": 205,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          30,\n          76,\n          51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.090069514269503,\n        \"min\": 8.825674745,\n        \"max\": 43.67549305,\n        \"num_unique_values\": 1760,\n        \"samples\": [\n          24.48620746,\n          24.80624984,\n          21.35093384\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.024009379892608,\n        \"min\": 14.27327988,\n        \"max\": 99.98187601,\n        \"num_unique_values\": 1760,\n        \"samples\": [\n          92.98254537,\n          82.09281674,\n          90.9492967\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ph\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7734450963577625,\n        \"min\": 3.504752314,\n        \"max\": 9.93509073,\n        \"num_unique_values\": 1760,\n        \"samples\": [\n          6.761953186,\n          6.356295568,\n          7.871063004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rainfall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54.95199440392551,\n        \"min\": 20.36001144,\n        \"max\": 298.5601175,\n        \"num_unique_values\": 1760,\n        \"samples\": [\n          183.49095,\n          156.3616174,\n          107.0862095\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "X_train"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-1ca2529a-7b6e-4df6-9c81-ff820d163469\" 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>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",
              "    <tr>\n",
              "      <th>1656</th>\n",
              "      <td>17</td>\n",
              "      <td>16</td>\n",
              "      <td>14</td>\n",
              "      <td>16.396243</td>\n",
              "      <td>92.181519</td>\n",
              "      <td>6.625539</td>\n",
              "      <td>102.944161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>752</th>\n",
              "      <td>37</td>\n",
              "      <td>79</td>\n",
              "      <td>19</td>\n",
              "      <td>27.543848</td>\n",
              "      <td>69.347863</td>\n",
              "      <td>7.143943</td>\n",
              "      <td>69.408782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>892</th>\n",
              "      <td>7</td>\n",
              "      <td>73</td>\n",
              "      <td>25</td>\n",
              "      <td>27.521856</td>\n",
              "      <td>63.132153</td>\n",
              "      <td>7.288057</td>\n",
              "      <td>45.208411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1041</th>\n",
              "      <td>101</td>\n",
              "      <td>70</td>\n",
              "      <td>48</td>\n",
              "      <td>25.360592</td>\n",
              "      <td>75.031933</td>\n",
              "      <td>6.012697</td>\n",
              "      <td>116.553145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1179</th>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>30</td>\n",
              "      <td>35.474783</td>\n",
              "      <td>47.972305</td>\n",
              "      <td>6.279134</td>\n",
              "      <td>97.790725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1638</th>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>21.213070</td>\n",
              "      <td>91.353492</td>\n",
              "      <td>7.817846</td>\n",
              "      <td>112.983436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1095</th>\n",
              "      <td>108</td>\n",
              "      <td>94</td>\n",
              "      <td>47</td>\n",
              "      <td>27.359116</td>\n",
              "      <td>84.546250</td>\n",
              "      <td>6.387431</td>\n",
              "      <td>90.812505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1130</th>\n",
              "      <td>11</td>\n",
              "      <td>36</td>\n",
              "      <td>31</td>\n",
              "      <td>27.920633</td>\n",
              "      <td>51.779659</td>\n",
              "      <td>6.475449</td>\n",
              "      <td>100.258567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1294</th>\n",
              "      <td>11</td>\n",
              "      <td>124</td>\n",
              "      <td>204</td>\n",
              "      <td>13.429886</td>\n",
              "      <td>80.066340</td>\n",
              "      <td>6.361141</td>\n",
              "      <td>71.400430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>860</th>\n",
              "      <td>32</td>\n",
              "      <td>78</td>\n",
              "      <td>22</td>\n",
              "      <td>23.970814</td>\n",
              "      <td>62.355576</td>\n",
              "      <td>7.007038</td>\n",
              "      <td>53.409060</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1760 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ca2529a-7b6e-4df6-9c81-ff820d163469')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-1ca2529a-7b6e-4df6-9c81-ff820d163469 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-1ca2529a-7b6e-4df6-9c81-ff820d163469');\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-1aaa14e0-ceb2-4a8d-9b9b-1989e1bf4dd5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1aaa14e0-ceb2-4a8d-9b9b-1989e1bf4dd5')\"\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-1aaa14e0-ceb2-4a8d-9b9b-1989e1bf4dd5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_c3b8ad2a-ebf3-40ca-8fdb-9707e64a4578\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_c3b8ad2a-ebf3-40ca-8fdb-9707e64a4578 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X_train');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        N    P    K  temperature   humidity        ph    rainfall\n",
              "1656   17   16   14    16.396243  92.181519  6.625539  102.944161\n",
              "752    37   79   19    27.543848  69.347863  7.143943   69.408782\n",
              "892     7   73   25    27.521856  63.132153  7.288057   45.208411\n",
              "1041  101   70   48    25.360592  75.031933  6.012697  116.553145\n",
              "1179    0   17   30    35.474783  47.972305  6.279134   97.790725\n",
              "...   ...  ...  ...          ...        ...       ...         ...\n",
              "1638   10    5    5    21.213070  91.353492  7.817846  112.983436\n",
              "1095  108   94   47    27.359116  84.546250  6.387431   90.812505\n",
              "1130   11   36   31    27.920633  51.779659  6.475449  100.258567\n",
              "1294   11  124  204    13.429886  80.066340  6.361141   71.400430\n",
              "860    32   78   22    23.970814  62.355576  7.007038   53.409060\n",
              "\n",
              "[1760 rows x 7 columns]"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l78RhbqsxiJj"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "ms = MinMaxScaler()\n",
        "\n",
        "X_train = ms.fit_transform(X_train)\n",
        "X_test = ms.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_VqiXRkqxmjF",
        "outputId": "4ea2e290-7d52-464e-e71d-28acdc2db116"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0.12142857, 0.07857143, 0.045     , ..., 0.9089898 , 0.48532225,\n",
              "        0.29685161],\n",
              "       [0.26428571, 0.52857143, 0.07      , ..., 0.64257946, 0.56594073,\n",
              "        0.17630752],\n",
              "       [0.05      , 0.48571429, 0.1       , ..., 0.57005802, 0.58835229,\n",
              "        0.08931844],\n",
              "       ...,\n",
              "       [0.07857143, 0.22142857, 0.13      , ..., 0.43760347, 0.46198144,\n",
              "        0.28719815],\n",
              "       [0.07857143, 0.85      , 0.995     , ..., 0.76763665, 0.44420505,\n",
              "        0.18346657],\n",
              "       [0.22857143, 0.52142857, 0.085     , ..., 0.56099735, 0.54465022,\n",
              "        0.11879596]])"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3cy7d5uLxpIG"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "\n",
        "\n",
        "sc.fit(X_train)\n",
        "X_train = sc.transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A0h-pcsTxsvR",
        "outputId": "131c8b9f-5453-4ef1-f968-0099934ba885"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[-9.03426596e-01, -1.12616170e+00, -6.68506601e-01, ...,\n",
              "         9.36586183e-01,  1.93473784e-01,  5.14970176e-03],\n",
              "       [-3.67051340e-01,  7.70358846e-01, -5.70589522e-01, ...,\n",
              "        -1.00470485e-01,  8.63917548e-01, -6.05290566e-01],\n",
              "       [-1.17161422e+00,  5.89737842e-01, -4.53089028e-01, ...,\n",
              "        -3.82774991e-01,  1.05029771e+00, -1.04580687e+00],\n",
              "       ...,\n",
              "       [-1.06433917e+00, -5.24091685e-01, -3.35588533e-01, ...,\n",
              "        -8.98381379e-01, -6.34357580e-04, -4.37358211e-02],\n",
              "       [-1.06433917e+00,  2.12501638e+00,  3.05234239e+00, ...,\n",
              "         3.86340190e-01, -1.48467347e-01, -5.69036842e-01],\n",
              "       [-5.01145154e-01,  7.40255346e-01, -5.11839275e-01, ...,\n",
              "        -4.18045489e-01,  6.86860180e-01, -8.96531475e-01]])"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w9EKk3bexvlI"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.tree import ExtraTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ti6qAaspxyEr"
      },
      "outputs": [],
      "source": [
        "models = {\n",
        "    'Logistic Regression': LogisticRegression(),\n",
        "    'Naive Bayes': GaussianNB(),\n",
        "    'Support Vector Machine': SVC(),\n",
        "    'K-Nearest Neighbors': KNeighborsClassifier(),\n",
        "    'Decision Tree': DecisionTreeClassifier(),\n",
        "    'Random Forest': RandomForestClassifier(),\n",
        "    'Bagging': BaggingClassifier(),\n",
        "    'AdaBoost': AdaBoostClassifier(),\n",
        "    'Gradient Boosting': GradientBoostingClassifier(),\n",
        "    'Extra Trees': ExtraTreeClassifier(),\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WejYeNYlxzx0",
        "outputId": "c907d427-a9a7-4428-e9e5-5c724a1d8217"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Logistic Regression  with accuracy : 0.9636363636363636\n",
            "Naive Bayes  with accuracy : 0.9954545454545455\n",
            "Support Vector Machine  with accuracy : 0.9681818181818181\n",
            "K-Nearest Neighbors  with accuracy : 0.9590909090909091\n",
            "Decision Tree  with accuracy : 0.9818181818181818\n",
            "Random Forest  with accuracy : 0.9931818181818182\n",
            "Bagging  with accuracy : 0.9886363636363636\n",
            "AdaBoost  with accuracy : 0.1409090909090909\n",
            "Gradient Boosting  with accuracy : 0.9818181818181818\n",
            "Extra Trees  with accuracy : 0.9090909090909091\n"
          ]
        }
      ],
      "source": [
        "for name, md in models.items():\n",
        "    md.fit(X_train,y_train)\n",
        "    ypred = md.predict(X_test)\n",
        "\n",
        "    print(f\"{name}  with accuracy : {accuracy_score(y_test,ypred)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U7H1p5gfx2GC",
        "outputId": "b5ff68cf-c2df-44b4-a524-47d9b982142e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9931818181818182"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rfc = RandomForestClassifier()\n",
        "rfc.fit(X_train,y_train)\n",
        "ypred = rfc.predict(X_test)\n",
        "accuracy_score(y_test,ypred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X41cakO2yHmR",
        "outputId": "a6e05346-ab95-4dca-94dd-197b726fd8c4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9931818181818182"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rfc = RandomForestClassifier()\n",
        "rfc.fit(X_train,y_train)\n",
        "ypred = rfc.predict(X_test)\n",
        "accuracy_score(y_test,ypred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nLI1C8fwyKh4"
      },
      "outputs": [],
      "source": [
        "def recommendation(N,P,k,temperature,humidity,ph,rainfal):\n",
        "    features = np.array([[N,P,k,temperature,humidity,ph,rainfal]])\n",
        "    transformed_features = ms.fit_transform(features)\n",
        "    transformed_features = sc.fit_transform(transformed_features)\n",
        "    prediction = rfc.predict(transformed_features).reshape(1,-1)\n",
        "\n",
        "    return prediction[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "muAvB6W6yNyc"
      },
      "outputs": [],
      "source": [
        "N = 71\n",
        "P = 54\n",
        "k = 16\n",
        "temperature = 22.0\n",
        "humidity = 63\n",
        "ph = 5\n",
        "rainfall = 10\n",
        "\n",
        "predict = recommendation(N,P,k,temperature,humidity,ph,rainfall)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ii-x6Sn3yQV6",
        "outputId": "1e8592b8-7853-45c7-aade-cc0687d92155"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Papaya is a best crop to be cultivated \n"
          ]
        }
      ],
      "source": [
        "crop_dict = {1: \"Rice\", 2: \"Maize\", 3: \"Jute\", 4: \"Cotton\", 5: \"Coconut\", 6: \"Papaya\", 7: \"Orange\",\n",
        "                 8: \"Apple\", 9: \"Muskmelon\", 10: \"Watermelon\", 11: \"Grapes\", 12: \"Mango\", 13: \"Banana\",\n",
        "                 14: \"Pomegranate\", 15: \"Lentil\", 16: \"Blackgram\", 17: \"Mungbean\", 18: \"Mothbeans\",\n",
        "                 19: \"Pigeonpeas\", 20: \"Kidneybeans\", 21: \"Chickpea\", 22: \"Coffee\"}\n",
        "\n",
        "if predict[0] in crop_dict:\n",
        "    crop = crop_dict[predict[0]]\n",
        "    print(\"{} is a best crop to be cultivated \".format(crop))\n",
        "else:\n",
        "    print(\"Sorry are not able to recommend a proper crop for this environment\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ZiuuL685yXex"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'rfc' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[1], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjoblib\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m joblib\u001b[38;5;241m.\u001b[39mdump(\u001b[43mrfc\u001b[49m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrfc_npk.joblib\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# pickle.dump(rfc,open('model.pkl','wb'))\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# pickle.dump(ms,open('minmaxscaler.pkl','wb'))\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# pickle.dump(sc,open('standscaler.pkl','wb'))\u001b[39;00m\n",
            "\u001b[1;31mNameError\u001b[0m: name 'rfc' is not defined"
          ]
        }
      ],
      "source": [
        "import joblib\n",
        "joblib.dump(rfc,\"rfc_npk.joblib\")\n",
        "# pickle.dump(rfc,open('model.pkl','wb'))\n",
        "# pickle.dump(ms,open('minmaxscaler.pkl','wb'))\n",
        "# pickle.dump(sc,open('standscaler.pkl','wb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eoepKNrmy1Kt"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}