Crop Recommendation System for Sustainable Farming.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6b44327",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import model_selection\n",
    "from sklearn import preprocessing\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.ensemble import BaggingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9b1813dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('C:\\Users\\gagan\\Downloads\\Crop_recommendation.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "06eab92f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
       "      <th>rainfall</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    N   P   K  temperature   humidity        ph    rainfall label\n",
       "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice\n",
       "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice\n",
       "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice\n",
       "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice\n",
       "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ab088add",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the dataframe:  (2200, 8)\n"
     ]
    },
    {
     "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Shape of the dataframe: \",df.shape)\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f52f9f1b",
   "metadata": {},
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39fa299f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "97ae8afb",
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14292e18",
   "metadata": {},
   "source": [
    "EDA-Exploratory data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "783458e0",
   "metadata": {},
   "source": [
    "Data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fb702440",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x=df['N'], bins=20,kde=True,edgecolor=\"black\",color='black',facecolor='#ffb03b')\n",
    "plt.title(\"Nitrogen\",size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1585c4b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x=df['P'],bins=20,color='black',edgecolor='black',kde=True,facecolor='#ffb03b')\n",
    "plt.title(\"Phosphorus\", size=20)\n",
    "plt.xticks(range(0,150,20))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c8066018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x=df['K'],kde=True, bins=20, facecolor='#ffb03b',edgecolor='black', color='black')\n",
    "plt.title(\"Potassium\",size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f46b1e9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x=df['temperature'], bins=20,kde=True,edgecolor=\"black\",color='black',facecolor='#ffb03b')\n",
    "plt.title(\"Temperature\",size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ec19719e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x=df['humidity'], color='black',facecolor='#ffb03b',kde=True,edgecolor='black')\n",
    "plt.title(\"Humidity\",size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6e13001d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x=df['rainfall'], color='black',facecolor='#ffb03b',kde=True,edgecolor='black')\n",
    "plt.title(\"Rainfall\",size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99526b0e",
   "metadata": {},
   "source": [
    " Categorical plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f1c65028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 463.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x='rainfall',y='temperature',data=df,kind='scatter',hue='label',height=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f0dc580a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 463.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x='rainfall',y='temperature',data=df,kind='scatter',hue='label',height=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7bd0c56",
   "metadata": {},
   "source": [
    " Outerlier detection using graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f84dc8f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22\n",
      "['rice' 'maize' 'chickpea' 'kidneybeans' 'pigeonpeas' 'mothbeans'\n",
      " 'mungbean' 'blackgram' 'lentil' 'pomegranate' 'banana' 'mango' 'grapes'\n",
      " 'watermelon' 'muskmelon' 'apple' 'orange' 'papaya' 'coconut' 'cotton'\n",
      " 'jute' 'coffee']\n",
      "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\n"
     ]
    }
   ],
   "source": [
    "# Unique values in the label column\n",
    "\n",
    "crops = df['label'].unique()\n",
    "print(len(crops))\n",
    "print(crops)\n",
    "print(pd.value_counts(df['label']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f40fbc09",
   "metadata": {},
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>71</td>\n",
       "      <td>54</td>\n",
       "      <td>16</td>\n",
       "      <td>22.613600</td>\n",
       "      <td>63.690706</td>\n",
       "      <td>5.749914</td>\n",
       "      <td>87.759539</td>\n",
       "      <td>maize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>61</td>\n",
       "      <td>44</td>\n",
       "      <td>17</td>\n",
       "      <td>26.100184</td>\n",
       "      <td>71.574769</td>\n",
       "      <td>6.931757</td>\n",
       "      <td>102.266244</td>\n",
       "      <td>maize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>80</td>\n",
       "      <td>43</td>\n",
       "      <td>16</td>\n",
       "      <td>23.558821</td>\n",
       "      <td>71.593514</td>\n",
       "      <td>6.657965</td>\n",
       "      <td>66.719955</td>\n",
       "      <td>maize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>73</td>\n",
       "      <td>58</td>\n",
       "      <td>21</td>\n",
       "      <td>19.972160</td>\n",
       "      <td>57.682729</td>\n",
       "      <td>6.596061</td>\n",
       "      <td>60.651715</td>\n",
       "      <td>maize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>61</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>18.478913</td>\n",
       "      <td>62.695039</td>\n",
       "      <td>5.970458</td>\n",
       "      <td>65.438354</td>\n",
       "      <td>maize</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      N   P   K  temperature   humidity        ph    rainfall  label\n",
       "100  71  54  16    22.613600  63.690706  5.749914   87.759539  maize\n",
       "101  61  44  17    26.100184  71.574769  6.931757  102.266244  maize\n",
       "102  80  43  16    23.558821  71.593514  6.657965   66.719955  maize\n",
       "103  73  58  21    19.972160  57.682729  6.596061   60.651715  maize\n",
       "104  61  38  20    18.478913  62.695039  5.970458   65.438354  maize"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering each unique label and store it in a list df2 for to plot the box plot\n",
    "\n",
    "df2=[]\n",
    "for i in crops:\n",
    "    df2.append(df[df['label'] == i])\n",
    "df2[1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "184bfb3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1741.23x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x='label', y='temperature', kind='box', height=10, aspect=20/8.27)\n",
    "# plt.xticks(rotation='vertical')\n",
    "plt.title(\"Temperature\", size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "65d4e519",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+ONJqy+1bWmsf6tr460z79ROZBmhv6Prk5Ey/REmSr6qqn830S9VTM92gf1n32FJfvL2rL910T6uq+yX5h9baBzbUE+tzrH575zGmXXp8aVm9R5K7tdaWzuH9V0kes2yac1prB7v2/iHTPrwl052ft3Z9uDfJx7rnf2dNj+7dk+kXVp+d6Zdgyaf24beu611v3NGWzWTleluSy1prb02S1tq1SdK916/K9EvVr126P8mXZno6jGTaf8tHo76ltbb8s/ApVfUt3e1Py/Sz8mCSmzJdXpPpTuWNrbWbl/1fd4qv7X7+rfv7lEzf46VZYf7ru7jjcOR89EuZvp9/7+57fpIfT/K73d9nd7/fneSUbrvyuqr6xLLPtZX66W5JXtpaO5wkVbW0/lnyt8tu3y/J39Z0FPDJ+dRtrpU+Jz6c1ee/5R6Z1ZfvPqy0zP7HOtfNJ2V92wf/kqOv/3eC49nGP7e1dqib9r2Zfv6entW3Xb40t6/D/zLTI1J3ki/P6svZWpexpekPd0cqf3H3uist1+cnOVBV/yvTL3fO34L3tB5vT/KF3fbmjZmOLjkrycMzXd6O9jm+5Euy+rZU8qn9mGx8nbj8zAVfnuSvW2u3Jrm8piP7vijJtVl9ud5MG93+endW//+vtO94rHaS1feNllvvvsSH19oRPVjps+q23D5fvSCfuo+y0v7jcrvtO58vT/KS1toNySe3zx+eO34Ptt5t8i/P9OCvtNZesWx/f+jtgs2w3m2Lleapu2Xlz9G/S/JLVfU/M/1S+nnd/Wv6bm0bWen7sdU+7786yQ92z7s1yaGqWm2+PDs7ex9gPdbTh0fzmTn6fsYQ+9Tb3bG2S3aLzZrHVvsOfy3bY70RjG3cDSvcV5l+GB3phCRfurRjNAI3Lrt927K/b8t0nntGkte21r6lpqcsO+/IF6jpsPm/SfJrrbWl021Vpkct9BniHK/V5okbj3jOWqdLpoHC11fVX7XWlp5z5HNb9xrvaa196Qqv8bxMR1O9q6qekOkREn05lOmRKQ/LdMPwSKu97+V9dmumG8snJLmmtfaQO7xIa2+o6ZD6r0xy4rL5aKU2lvrr21pr71/+QFU9PcnlmY6oOCHTwGO1mva01m6p6akQHpnku5P8f5l+sGyGler+ZKlHmW7FdVDViqeAWXrNO7y3o9Sw7nmttfZjVfXfMh0Z+M6qeshSMLLJjrU+umWFadYyDy7vu9X65JzW2vcsf6Cq7pzpEZBntdb+s5u/7rxCG5/s89baX1XVmzPtq1dW1Q+31l6zSo2bZS39tvx0zCu9h+T2ZfVYpxtarQ+f31p76vIHqurTMz0C84taa1dX1fNWaX/5fLvVVl02j1Lv0dbzH8p0FOJnZHpk1bF8cpukO9jgUZku8x+vqvNye//cvOxz45P/19babbWzrnVTSX6jtfbHn3LndJtipflvp1htfljN8uXyyGV2T1bvp58+xusu38b9/SS/01o7u5u3nr5C+0m3vB1j/vuUMrLC8t2jlZbZda2bk/x01rF9sIb1/05wPNv4K21XrOdUdOtdPoZ2tPe21mVstc/GOyzXSVJVX5jpqMbfqKpXtdZ+bQN1b4ruoIuLMz047Y2ZflH+VUlmkxzO0T/Hl6y4LbXMkfvjG1onrtDmalbbNt5MG9r+aq39+1H+/yvtOx6rnWT1faPlB4dtZF9icOv4rGqr3F7p7932nc9qy8Lybc6NbJOv9rpDbxdshvVuW6z0/BU/R7v59JxMR3V+Z6YHGmS1529jR9v/WXK0x9ezjl7aB1i+3txp210rOd4+XP46RzPEPvVQjvbdxnLH2i7ZLTZzHrvDd/hV9XU59vZYb1xjbHOdm+kRM6cl03Nud/e/KtMvx9Pd/5D+S9tW9iX5SHf7Cas859lJLmit/c2y+16ZZK5uv27UZ1TVXbesys2x2jxxLK9K8mNLX1QeMd0vZ3rk9R8uu+/MLsFPku/J9OjF9yfZv3R/VZ1UVZ/TPeduSS7r+vL71v+2jstNmY56+MGq+t4jHnv9Uj1V9fWZnl5mVd1Iiv+oqu/opqmq+vxlT/mLTI/E+vMjJl26XsCXJznUHcH8yiQ/sRQWVdUXdM/dl+mojtuS/ECmo6FWVdNRbPtaa/8v0wt1PuRoz1+nlf7PS96S5Cur6p7dfPNtyx5bbR20vL8fkeTKZaNT1lPDuue1qpptrb25tfbLSa7M9EjNIazWB9dlWvtafHFVfXpNry32XZn2yZuSPKyqHtS99l2q6jNy+0bWld288u0rvuIyVfXAJB9qrf1epkfDfd4a69pKFyd5aJJU1UMzHRK/qtba1Zketf0l3V3ffcRTvqam16nYm+n64Q2Zrj+/varu1bVzalXdP9NThtyQ6VGDZyT5+k15R8fnaMvmavW+L8lMVX1RktT02ohLOx2XZHpE1l8sW5belNuX6yP7b7l9Sa7udqA/K9Mj23abVyb5obr9WjP3XZpPjmI9y/RQjpyPXp3piMsHdff9QJLXreP1Vuunf0nyTVV15+6xbzzKayzfZnv8Gto82vx3c91+rc/Vlu++rLbMrnndnHVuH2QD6/8daC3b+Msdbdvljbl9Xfd92fyROVttrcvZ0Zaxx3XTn5bpgUVvzSrLdVXNJPl4a+0FmY4ofujmvp0NeX2mX5q/PtMRbT+W6Yino32OL19Xr7YttVFr+ex4fabXnDmxpteX+opM59Pt4uKssP11jP//SvuOa7HavtFyG9mX2A5W+6w6Ibevm783n7reWWn/cbnd9p3P65N8c7fc3TW3n+J7uY1sk/9LpsFOanpGoKX9/aG3CzbDerctVpqnjvY5+txMLz3x1nb7GYXW+7k7tJW+H1vt8/7cTC8xkm6dfPesbb480sWZjkZMPvV/sBP2DVaynj488j0u//t9Ob79jN3kkkxH/N6ppqOeH7nssa3cLtmu1jOPHc1q3+FvZF9+ywjGNlFr7T1JnpXkdVX1riS/0z30lCRn1fSicu/NdKdgzH4z0yPZ3pDVv0SYT/K1Nb2A3zur6rGZbgi8N8k7qurCJH+cbX7kwlHmiWN5bqanhLqgm+7IAOmnkty5br94+UVJHl9VF2Q6RH+htXZTph/8/6t7jXdmetrBZHp6pjdneg79923grR2Xbuj7YzI92nr5aet+NclXVNU7Mh1ae+kaXu77kjype4/vyfQoqiUvzHRj+6+PmObqqnpjkj9K8qTuvmdkeuqBC7r56xnd/X+Yad++KdPRGyuNFl3ubkle3v0vXte9x81yh//z0gOttY9kei2AN2f6Zep7Mx2dl6y+Dnr60v2ZhtFr+dJzs+a136rphVwvzHQD911r7YRN9vSs3AcvS/It3frn4cd4jX/tpr0w01MgvaS1dkWmOyd/3b32m5J8VmvtmiR/kukpb/4x0y+5juW7klxY09P2fFamge/QXpzk1K6muUyvO3EsT0rynKr610yPHlr+hcK/ZDok/52Znhv/ba2192Z6jaVXdX14TpL7tNbelemw+/dkev7+N2R4R1s2V6y3W26+K8nvd8vNOVl2dFp3hPb3ZXqq2NlM1/s/U1VvyfRUNUd+IbPkFZmOTrkg0/XYkad93vFaa6/K9HSS/1rT0z79fY69Y/u8TC8E/M7qLsa9DR05H/3vTEdb/F33Pm/L9HNrTVbrpzY9fefZma53/yHTUYmrzU9P79o/P9ODGI7laPPfczL9jH3hasv3Wt/bJlhpmV3vunld2wcbXP/vNGvZxv+kNWy7PLH7H/1Akp/ckoq3yDqWs6dn9WXsLUn+KdPl6BmttcWjrP/+a5K3dJ/LT8v0uq5DOz/T5fpfW2uXZzqq8vxjfI4/J8k/V9VrV9uW2mgxa/zseEmmo9veleQ1mV7H7KMbbXMLrLb9daz//0/lU/cd12K1faPlnp7170tsB6t9Vt2Q5HOq6u2ZnvFj+ajLlfYfl9tV3/m01t6R6bbTWzJdRz83ydVHPGcj2+S/mun3PO/INEi7LMl122C7YDOsd9tipXlq1c/R1trbMz2t6/KDftf1uTu0Vb4fW+3z/iczPQX1uzM9nd/nrDRfttb+LUd3INMv59+Y6Smcl7w20zDknVX1XZvyBnuwzj78myT/s6r+rduffF66/aFM98c3vJ+xi7TW2n9mel26CzL9DnH5PLVl2yXb1TrnsaNZ8Tv8De7Lb5mlC/0CO1RNh8y/vLX2uUPXst3U9Fy2j2ut/cDQtRyvtfyfq+qU1tr1NT3q+iVJ/qy19pI+a4DVLM2f3e2fzzTk2lFfdA6pqu6S5HBrrVXVdyf5ntba4441HTtD3+vXZZ8Xd8n04IQnd182QK+2ettlSMeznNX0lFvXt9YOHOu5wOaoqutba6escP95SeZba2s5vTVHUVV3SnJrm15+4EszPcjyIQOXddzWux23kXmqpiNDz8v0YMvbNlAmsExNR0S9o7W200ansom29WgbgI2qqt/P9Ci0bxi6lh49vaoelemok1dlemQabBffWFVPzXTb45LsjNN9bCdfmOQPqqqSXJPphbdho55TVZ+d6efF84ViDGg3b7tYzgA+1ZlJXlTTU9DflORHBq5nR6iqH8x0BMfPCMXg+C0Lmh2ANHJGjAEAAAAAADAKrjEGAAAAAADAKAjGAAAAAAAAGAXBGAAAAAAAAKMgGAMAANimqur6Yzz+gKq6cJ2v+byq+vbjqwwAAGBnEowBAAAAAAAwCoIxAACAba6qTqmqc6vqHVX17qp63LKH91TV86vqgqr6+6q6SzfNF1bV66rq7VX1yqq6z0DlAwAAbBuCMQAAgO3vE0m+pbX20CRfleS3q6q6xz4zyXNaa5+X5Nok/72qTkry+0m+vbX2hUn+LMmzBqgbAABgW9kzdAEAAAAcUyX59ar6iiS3JblvkjO6x/6ztfaG7vYLkjwlySuSfG6Sc7r87MQkl/VaMQAAwDYkGAMAANj+vi/J/iRf2Fq7uaouTnLn7rF2xHNbpkHae1prX9pfiQAAANufUykCAABsf/uSfKwLxb4qyf2XPXZmVS0FYN+T5F+SvD/J/qX7q+qkqvqcXisGAADYhgRjAAAA298Lk5xVVW/LdPTY+5Y9dlGSx1fVBUlOTbLQWrspybcn+V9V9a4k70zyZf2WDAAAsP1Ua0eedQMAAAAAAAB2HyPGAAAAAAAAGAXBGAAAAAAAAKMgGAMAAAAAAGAUBGMAAAAAAACMgmAMAAAAAACAURCMAQAAAAAAMAqCMQAAAAAAAEbh/wchnrJpQ0yo1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1741.23x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x='label', y='humidity', kind='box', height=10, aspect=20/8.27)\n",
    "# plt.xticks(rotation='vertical')\n",
    "plt.title(\"Humidity\", size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "35783e2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1741.23x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x='label', y='temperature', kind='box', height=10, aspect=20/8.27)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "aa29f50c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1741.23x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x='label', y='N', kind='box', height=10, aspect=20/8.27)\n",
    "# plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4aca216e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1741.23x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x='label', y='ph', kind='box', height=10, aspect=20/8.27)\n",
    "# plt.xticks(rotation='vertical')\n",
    "plt.title(\"Nitrogen\",size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5dc3267a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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++vQkv9ff/qLctc+x4uO36k9W/nxsxff7JH+Vu/Z7j063D7HacvnQHMYxQL+sXdLf/tz6u51+1tiHL07y1Il5J/vinjn4ccZUj6kH7M8bs+yz6HTbqWdM9MUh90t20s8al7HPW4+WLWMrfoafg+yPDfHj9A3rd2lr7e3Lpv2HJK9srV2VJK21a/rpT0jy8LrrS2X3rar7tNZumE6pU/ex1trSt9Y/mOTs1lqrqg+ke8PaneQl1Y16aOkOku6mum8O/XmS722tHaiqb07yyLprpMbudB9EfGxTX82Rudsy0S8Hf9dauzPJhdV/M3SZJyT5P62125fmm7jv1Un+Z2vtZRPTPt5ae2t/+6VJnpXkdUm+NMkb+zaPTvfhc5J8aVU9P91BzvFJXn+kL3QN9qR7Dd/dWvtgVZ0xcd/jkvw/SdJa+/uqunbivo+11t7X3353kof238742iR/PbF+3aP//cIkv5DuIPJHknzum7ZJXt638ZbqRpOdkG7j/OS661uY90x3ULOY5A/6b0PckW5HdMk7W2uXJ0lVvS/d8v32dEHJC6vq79Nt5DfKSv/nJY9J8ualZaWq/nqi1hW3QemCvO9OktbaP1U3Ym93/5i/b63dkuSWqvp0ug8HVqthPcvaW5O8uKpekeRv19kfh+NQ26OHZPU+WG619fadrbWP9m28PF2/fjZdgPbWvk+OTfchSpJ8Y1X9QroPVU9Mt0P/mv6+pb54d19f+vmeU1UPSvK3rbUPr6sn1uZQ/fa+Q8y7dP/SunpCkvu01pbO4f0XSZ40Mc8bW2tX9+39bbo+vD3dwc+7+j48Lsmn+8d/b3Xf7t2V7gOrh6f7ECz5/D78f9b0qtfvYOtmsnK9LcknW2vvSpLW2vVJ0r/Wb0z3oeo3L01P8jXpToeRdP03ORr1na21yffCZ1XVd/W3H5zuvfLqJLemW1+T7qDyltbabRP/1+3im/uf9/Z/H5/uNV6WFZa/aRd3BJYvR7+S7vX8Wz/tJUl+Osnv9X+f1f/+QJLj+/3KG6rqsxPvayv1032SvLq1dnOSVNXS9mfJX03cflCSv6puFPCx+fx9rpXeJy7P6svfpMdn9fV7GlZaZz+2xm3zMVnb/sG/5ODb/+3gSPbxz26tHejnvTDd++/JWX3f5Wty1zb8z9N9I3U7+fqsvp4d7jq2NP/N/TeVH9M/70rr9blJ9lXV/0j34c65m/Ca1uLdSb6y39+8Jd3okr1JHptufTvY+/iSr87q+1LJ5/djsv5t4uSZC74+yctba3ckuaK6kX1fleT6rL5eb6T17n99IKv//1c6djxUO8nqx0aT1noscfnhdsQUrPRedWfuWq5ems8/Rlnp+HHSTvvM5+uTvKq1dlPyuf3zx+bun4OtdZ/869N9+SuttddNHO8PvV+wEda6b7HSMnWfrPw++tdJfqWq/mu6D6Vf3E8/rM/WtpCVPh9b7f3+PyT54f5xdyQ5UFWrLZdnZXsfA6zFWvrwYL44Bz/OGOKYeqs71H7JTrFRy9hqn+Efzv7Y1AjG1u+mFaZVujej5Y5K8jVLB0YjcMvE7Tsn/r4z3TL360n+ubX2XdWdsuyc5U9Q3bD5v0zy31trS6fbqnTfWphmiHOkVlsmbln2mMOdL+kChW+tqr9orS09ZvljW/8cH2ytfc0Kz/HidKOp3l9Vz0j3DYlpOZDumylfl27HcLnVXvdkn92Rbmf5qCTXtdYedbcnae2t1Q2p/4YkR08sRyu1sdRf391a+9DkHVX1vCRXpBtRcVS6wGO1mna11m6v7lQIj0/ytCT/b7o3lo2wUt2fK/Ug8624Dapa8RQwS895t9d2kBrWvKy11n6qqv59upGB76uqRy0FIxvsUNuj21eY53CWwcm+W61P3tha+/7JO6rqnum+Abm3tfbxfvm65wptfK7PW2t/UVXvSNdXr6+qH2+t/dMqNW6Uw+m3ydMxr/QakrvW1UOdbmi1PnxJa+3Zk3dU1Rek+wbmV7XWrq2qF6/S/uRyu9lWXTcPUu/BtvMfTTcK8YvSfbPqUD63T9J/2eAJ6db5z1TVObmrf26beN/43P+1tXZnba9r3VSS32yt/fHnTez2KVZa/raL1ZaH1Uyul8vX2V1ZvZ/+8yGed3If938n+d3W2ln9svW8FdpP+vXtEMvf55WRFdbvKVppnV3TtjnJf84a9g8OY/u/HRzJPv5K+xVrORXdWtePoR3stR3uOrbae+Pd1uskqaqvTDeq8Ter6g2ttf++jro3RP+li0vSfTntbek+KP/GJLNJbs7B38eXrLgvNWH58fi6tokrtLma1faNN9K69r9aa/92kP//SseOh2onWf3YaPLLYes5lhjcGt6r2iq3V/p7p33ms9q6MLnPuZ598tWed+j9go2w1n2LlR6/4vtov5y+Md2ozu9N90WDrPb4Lexgxz9LDnb/WrbRS8cAk9vN7bbftZIj7cPJ5zmYIY6ph3KwzzYmHWq/ZKfYyGXsbp/hV9W35ND7Y1PjGmMb6+x035g5KenOud1Pf0O6D8fTT3/U9EvbUnYn+UR/+xmrPOa3kpzfWvvLiWmvTzJXd1036ouq6t6bVuXGWG2ZOJQ3JPmppQ8ql833q+m+ef1HE9NO6xP8JPn+dN9e/FCSPUvTq+qYqnpE/5j7JPlk35c/sPaXdURuTTfq4Yer6j8uu+8tS/VU1bemO73MqvqRFB+rqu/p56mq+vKJh/xZum9i/emyWZeuF/D1SQ7032B+fZKfWQqLquor+sfuTjeq484kP5RuNNSqqhvFtru19g/pLtT5qIM9fo1W+j8veWeSb6iq+/XLzXdP3LfaNmiyv89IctXE6JS11LDmZa2qZltr72it/WqSq9J9U3MIq/XBDelqPxyPqaovqO7aYt+Xrk/enuTrquph/XPfq6q+KHftZF3VLytPXfEZJ1TVFyb5aGvt99N9G+6Rh1nXZrokyaOTpKoenW5I/Kpaa9em+9b2V/eTnrbsId9U3XUqjku3fXhruu3nU6vq/n07J1bVQ9KdMuSmdN8aPCXJt27IKzoyB1s3V6v34iQzVfVVSVLdtRGXDjouTfeNrD+bWJfenrvW6+X9N2l3kmv7A+gvSffNtp3m9Ul+tO661sypS8vJQaxlnR7K8uXoTelGXD6sn/ZDSd68hudbrZ/+Jcl3VNU9+/u+/SDPMbnP9vTDaPNgy99tdde1Pldbv6dltXX2sLfNWeP+Qdax/d+GDmcff9LB9l3elru2dT+QjR+Zs9kOdz072Dr2lH7+k9J9sehdWWW9rqqZJJ9prb003YjiR2/sy1mXt6T70Pwt6Ua0/VS6EU8Hex+f3Favti+1Xofz3vGWdNecObq660s9Lt1yulVckhX2vw7x/1/p2PFwrHZsNGk9xxJbwWrvVUflrm3zf8znb3dWOn6ctNM+83lLku/s17t7565TfE9azz75v6QLdlLdGYGWjveH3i/YCGvdt1hpmTrY++gL01164l3trjMKrfV9d2grfT622vv92ekuMZJ+m3zfHN5yudwl6UYjJp//P9gOxwYrWUsfLn+Nk39fnCM7zthJLk034vce1Y16fvzEfZu5X7JVrWUZO5jVPsNfz7H8phGMbaDW2geTvCDJm6vq/Ul+t7/rWUn2VndRuQvTHRSM2f9M9022t2b1DxHmk3xzdRfwe19VPTndjsCFSd5TVRck+eNs8W8uHGSZOJQXpjsl1Pn9fMsDpJ9Lcs+66+LlFyV5elWdn26I/v7W2q3p3vj/R/8c70t32sGkOz3TO9KdQ//idby0I9IPfX9Sum9bT5627teSPK6q3pNuaO1lh/F0P5Dkx/rX+MF036Ja8rJ0O9svXzbPtVX1tiT/J8mP9dN+Pd2pB87vl69f76f/Ubq+fXu60RsrjRaddJ8kr+3/F2/uX+NGudv/eemO1ton0l0L4B3pPky9MN3ovGT1bdDzlqanC6MP50PPjVrWfru6C7lekG4H9/2H2wkb7HlZuQ9ek+S7+u3PYw/xHP/az3tBulMgvaq1dmW6g5OX98/99iRf0lq7Lsn/TXfKm79L9yHXoXxfkguqO23Pl6QLfIf2N0lO7GuaS3fdiUP5sSRnVtW/pvv20OQHCv+Sbkj++9KdG/+81tqF6a6x9Ia+D9+Y5IGttfenG3b/wXTn739rhnewdXPFevv15vuS/O9+vXljJr6d1n9D+wfSnSp2Nt12/+er6p3pTlWz/AOZJa9LNzrl/HTbseWnfd72WmtvSHc6yX+t7rRPr8yhD2xfnO5CwO+r/mLcW9Dy5eh/pRtt8df967wz3fvWYVmtn1p3+s6z0m13/zbdqMTVlqfn9e2fm+5LDIdysOXvzHTvsS9bbf0+3Ne2AVZaZ9e6bV7T/sE6t//bzeHs43/OYey7/Ej/P/qhJD+7KRVvkjWsZ8/L6uvYO5P8fbr16Ndba4sH2f59WZJ39u/Lz0l3XdehnZtuvf7X1toV6UZVnnuI9/Ezk/xjVf3zavtS6y3mMN87XpVudNv7k/xTuuuYfWq9bW6C1fa/DvX//7l8/rHj4Vjt2GjS87L2Y4mtYLX3qpuSPKKq3p3ujB+Toy5XOn6ctKM+82mtvSfdvtM7022jX5jk2mWPWc8++a+l+5znPemCtE8muWEL7BdshLXuW6y0TK36Ptpae3e607pOful3Te+7Q1vl87HV3u9/Nt0pqD+Q7nR+j1hpuWytvTcHty/dh/NvS3cK5yX/nC4MeV9Vfd+GvMApWGMf/mWS/1pV7+2PJ1+c/ngo3fH4uo8zdpDWWvt4uuvSnZ/uM8TJZWrT9ku2qjUuYwez4mf46zyW3zRLF/oFtqnqhsy/trX2pUPXstVUdy7bp7TWfmjoWo7U4fyfq+r41tqN1X3r+lVJXtRae9U0a4DVLC2f/e1fShdybasPOodUVfdKcnNrrVXV05J8f2vtKYeaj+1h2tvXifeLe6X7csIz+w8bYKo2e99lSEeynlV3yq0bW2v7DvVYYGNU1Y2tteNXmH5OkvnW2uGc3pqDqKp7JLmjdZcf+Jp0X7J81MBlHbG17setZ5mqbmToOem+bHnnOsoEJlQ3Iuo9rbXtNjqVDbSlR9sArFdV/e9030L7tqFrmaLnVdUT0o06eUO6b6bBVvHtVfXsdPsel2Z7nO5jK/nKJH9QVZXkunQX3ob1OrOqHp7u/eIlQjEGtJP3XaxnAJ/vtCSvqO4U9Lcm+YmB69kWquqH043g+HmhGBy5iaDZF5BGzogxAAAAAAAARsE1xgAAAAAAABgFwRgAAAAAAACjIBgDAAAAAABgFARjAAAAW1RV3XiI+x9aVRes8TlfXFVPPbLKAAAAtifBGAAAAAAAAKMgGAMAANjiqur4qjq7qt5TVR+oqqdM3L2rql5SVedX1Sur6l79PF9ZVW+uqndX1eur6oEDlQ8AALBlCMYAAAC2vs8m+a7W2qOTfGOS36mq6u/74iRnttYemeT6JP+pqo5J8r+TPLW19pVJXpTkBQPUDQAAsKXsGroAAAAADqmS/EZVPS7JnUlOTXJKf9/HW2tv7W+/NMmzkrwuyZcmeWOfnx2d5JNTrRgAAGALEowBAABsfT+QZE+Sr2yt3VZVlyS5Z39fW/bYli5I+2Br7WumVyIAAMDW51SKAAAAW9/uJJ/uQ7FvTPKQiftOq6qlAOz7k/xLkg8l2bM0vaqOqapHTLViAACALUgwBgAAsPW9LMneqjov3eixiyfuuyjJ06vq/CQnJtnfWrs1yVOT/I+qen+S9yX52umWDAAAsPVUa8vPugEAAAAAAAA7jxFjAAAAAAAAjIJgDAAAAAAAgFEQjAEAAAAAADAKgjEAAAAAAABGQTAGAAAAAADAKAjGAAAAAAAAGAXBGAAAAAAAAKPw/wOFS+2Ft52pQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1741.23x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x='label', y='P', kind='box', height=10, aspect=20/8.27)\n",
    "# plt.xticks(rotation='vertical')\n",
    "plt.title(\"Phosphorus\",size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "638c91d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1741.23x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x='label', y='K', kind='box', height=10, aspect=20/8.27)\n",
    "# plt.xticks(rotation='vertical')\n",
    "plt.title(\"Potassium\",size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e1ec162",
   "metadata": {},
   "source": [
    "Checking through Mathematics (Statistics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "51fb577c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lower limit: 188.0 Upper limit: 212.0\n",
      "Minimum value: 195   MAximum Value: 205\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n"
     ]
    }
   ],
   "source": [
    "def detect_outlier(x):\n",
    "    q1 = x.quantile(0.25)\n",
    "    q3 = x.quantile(0.75)\n",
    "    IQR = q3-q1\n",
    "    lower_limit = q1 - (1.5*IQR)\n",
    "    upper_limit = q3 + (1.5*IQR)\n",
    "    print(f\"Lower limit: {lower_limit} Upper limit: {upper_limit}\")\n",
    "    print(f\"Minimum value: {x.min()}   MAximum Value: {x.max()}\")\n",
    "    for i in [x.min(),x.max()]:\n",
    "        if i == x.min():\n",
    "            if lower_limit > x.min():\n",
    "                print(\"Lower limit failed - Need to remove minimum value\")\n",
    "            elif lower_limit < x.min():\n",
    "                print(\"Lower limit passed - No need to remove outlier\")\n",
    "        elif i == x.max():\n",
    "            if upper_limit > x.max():\n",
    "                print(\"Upper limit passed - No need to remove outlier\")\n",
    "            elif upper_limit < x.max():\n",
    "                print(\"Upper limit failed - Need to remove maximum value\")\n",
    "detect_outlier(df['K'][df['label']=='grapes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e86c230d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lower limit: 32.0 Upper limit: 48.0\n",
      "Minimum value: 35   MAximum Value: 45\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 9.5 Upper limit: 29.5\n",
      "Minimum value: 15   MAximum Value: 25\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 68.0 Upper limit: 92.0\n",
      "Minimum value: 75   MAximum Value: 85\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 9.125 Upper limit: 30.125\n",
      "Minimum value: 15   MAximum Value: 25\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 10.5 Upper limit: 30.5\n",
      "Minimum value: 15   MAximum Value: 25\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 10.5 Upper limit: 30.5\n",
      "Minimum value: 15   MAximum Value: 25\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 9.5 Upper limit: 29.5\n",
      "Minimum value: 15   MAximum Value: 25\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 8.875 Upper limit: 29.875\n",
      "Minimum value: 15   MAximum Value: 25\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 9.5 Upper limit: 29.5\n",
      "Minimum value: 15   MAximum Value: 25\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 30.5 Upper limit: 50.5\n",
      "Minimum value: 35   MAximum Value: 45\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 38.0 Upper limit: 62.0\n",
      "Minimum value: 45   MAximum Value: 55\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 19.5 Upper limit: 39.5\n",
      "Minimum value: 25   MAximum Value: 35\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 188.0 Upper limit: 212.0\n",
      "Minimum value: 195   MAximum Value: 205\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 38.0 Upper limit: 62.0\n",
      "Minimum value: 45   MAximum Value: 55\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 39.125 Upper limit: 60.125\n",
      "Minimum value: 45   MAximum Value: 55\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 188.0 Upper limit: 212.0\n",
      "Minimum value: 195   MAximum Value: 205\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 1.625 Upper limit: 18.625\n",
      "Minimum value: 5   MAximum Value: 15\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 39.5 Upper limit: 59.5\n",
      "Minimum value: 45   MAximum Value: 55\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 23.0 Upper limit: 39.0\n",
      "Minimum value: 25   MAximum Value: 35\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 9.5 Upper limit: 29.5\n",
      "Minimum value: 15   MAximum Value: 25\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 28.0 Upper limit: 52.0\n",
      "Minimum value: 35   MAximum Value: 45\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n",
      "Lower limit: 18.0 Upper limit: 42.0\n",
      "Minimum value: 25   MAximum Value: 35\n",
      "Lower limit passed - No need to remove outlier\n",
      "Upper limit passed - No need to remove outlier\n",
      "---------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for i in df['label'].unique():\n",
    "    detect_outlier(df['K'][df['label']==i])\n",
    "    print('---------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dbf0c00",
   "metadata": {},
   "source": [
    "Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2251816e",
   "metadata": {},
   "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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.drop(['label'], axis=1)\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0352b680",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label length:  2200\n"
     ]
    }
   ],
   "source": [
    "Y = df['label']\n",
    "encode = preprocessing.LabelEncoder()\n",
    "y = encode.fit_transform(Y)\n",
    "print(\"Label length: \",len(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3af217f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1650 1650 550 550\n"
     ]
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test = model_selection.train_test_split(x,y)\n",
    "print(len(x_train),len(y_train),len(x_test),len(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9630c1f6",
   "metadata": {},
   "source": [
    "Best model choosing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b8405c9",
   "metadata": {},
   "source": [
    "Decision Tree, Support vector mechanism, Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "69e8e655",
   "metadata": {},
   "outputs": [],
   "source": [
    "a={'decision tree' : {\n",
    "        'model' : DecisionTreeClassifier(criterion='gini'),\n",
    "        'params':{'decisiontreeclassifier__splitter':['best','random']}\n",
    "    },\n",
    "    'svm': {\n",
    "        'model': SVC(gamma='auto',probability=True),\n",
    "        'params' : {\n",
    "            'svc__C': [1,10,100,1000],\n",
    "            'svc__kernel': ['rbf','linear']\n",
    "        }  \n",
    "    },\n",
    "    'random_forest': {\n",
    "        'model': RandomForestClassifier(),\n",
    "        'params' : {\n",
    "            'randomforestclassifier__n_estimators': [1,5,10]\n",
    "        }\n",
    "    },\n",
    "   'k classifier':{\n",
    "       'model':KNeighborsClassifier(),\n",
    "       'params':{'kneighborsclassifier__n_neighbors':[5,10,20,25],'kneighborsclassifier__weights':['uniform','distance']}\n",
    "   }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d0e34879",
   "metadata": {},
   "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>Model name</th>\n",
       "      <th>Best score</th>\n",
       "      <th>Best param</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>decision tree</td>\n",
       "      <td>0.984848</td>\n",
       "      <td>{'decisiontreeclassifier__splitter': 'best'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>svm</td>\n",
       "      <td>0.986667</td>\n",
       "      <td>{'svc__C': 100, 'svc__kernel': 'rbf'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>random_forest</td>\n",
       "      <td>0.989697</td>\n",
       "      <td>{'randomforestclassifier__n_estimators': 5}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k classifier</td>\n",
       "      <td>0.975152</td>\n",
       "      <td>{'kneighborsclassifier__n_neighbors': 5, 'knei...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Model name  Best score  \\\n",
       "0  decision tree    0.984848   \n",
       "1            svm    0.986667   \n",
       "2  random_forest    0.989697   \n",
       "3   k classifier    0.975152   \n",
       "\n",
       "                                          Best param  \n",
       "0       {'decisiontreeclassifier__splitter': 'best'}  \n",
       "1              {'svc__C': 100, 'svc__kernel': 'rbf'}  \n",
       "2        {'randomforestclassifier__n_estimators': 5}  \n",
       "3  {'kneighborsclassifier__n_neighbors': 5, 'knei...  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score=[]\n",
    "details = []\n",
    "best_param = {}\n",
    "for mdl,par in a.items():\n",
    "    pipe = make_pipeline(preprocessing.StandardScaler(),par['model'])\n",
    "    res = model_selection.GridSearchCV(pipe,par['params'],cv=5)\n",
    "    res.fit(x_train,y_train)\n",
    "    score.append({\n",
    "        'Model name':mdl,\n",
    "        'Best score':res.best_score_,\n",
    "        'Best param':res.best_params_\n",
    "    })\n",
    "    details.append(pd.DataFrame(res.cv_results_))\n",
    "    best_param[mdl]=res.best_estimator_\n",
    "pd.DataFrame(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "de409838",
   "metadata": {},
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_decisiontreeclassifier__splitter</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.015728</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>best</td>\n",
       "      <td>{'decisiontreeclassifier__splitter': 'best'}</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.987879</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>0.996970</td>\n",
       "      <td>0.984848</td>\n",
       "      <td>0.006910</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006252</td>\n",
       "      <td>0.007657</td>\n",
       "      <td>random</td>\n",
       "      <td>{'decisiontreeclassifier__splitter': 'random'}</td>\n",
       "      <td>0.954545</td>\n",
       "      <td>0.969697</td>\n",
       "      <td>0.975758</td>\n",
       "      <td>0.957576</td>\n",
       "      <td>0.960606</td>\n",
       "      <td>0.963636</td>\n",
       "      <td>0.007902</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.015728      0.000171         0.000000        0.000000   \n",
       "1       0.000000      0.000000         0.006252        0.007657   \n",
       "\n",
       "  param_decisiontreeclassifier__splitter  \\\n",
       "0                                   best   \n",
       "1                                 random   \n",
       "\n",
       "                                           params  split0_test_score  \\\n",
       "0    {'decisiontreeclassifier__splitter': 'best'}           0.978788   \n",
       "1  {'decisiontreeclassifier__splitter': 'random'}           0.954545   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
       "0           0.981818           0.987879           0.978788           0.996970   \n",
       "1           0.969697           0.975758           0.957576           0.960606   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  \n",
       "0         0.984848        0.006910                1  \n",
       "1         0.963636        0.007902                2  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "details[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2334e66f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_svc__C</th>\n",
       "      <th>param_svc__kernel</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.230370</td>\n",
       "      <td>0.045596</td>\n",
       "      <td>0.028365</td>\n",
       "      <td>0.006369</td>\n",
       "      <td>1</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'svc__C': 1, 'svc__kernel': 'rbf'}</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>0.984848</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>0.990909</td>\n",
       "      <td>0.982424</td>\n",
       "      <td>0.004848</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.201480</td>\n",
       "      <td>0.039230</td>\n",
       "      <td>0.025002</td>\n",
       "      <td>0.007522</td>\n",
       "      <td>1</td>\n",
       "      <td>linear</td>\n",
       "      <td>{'svc__C': 1, 'svc__kernel': 'linear'}</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>0.984848</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.987879</td>\n",
       "      <td>0.982424</td>\n",
       "      <td>0.003534</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.283427</td>\n",
       "      <td>0.019955</td>\n",
       "      <td>0.030121</td>\n",
       "      <td>0.002741</td>\n",
       "      <td>10</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'svc__C': 10, 'svc__kernel': 'rbf'}</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>0.984848</td>\n",
       "      <td>0.984848</td>\n",
       "      <td>0.987879</td>\n",
       "      <td>0.987879</td>\n",
       "      <td>0.984848</td>\n",
       "      <td>0.003320</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.160107</td>\n",
       "      <td>0.018233</td>\n",
       "      <td>0.012499</td>\n",
       "      <td>0.006249</td>\n",
       "      <td>10</td>\n",
       "      <td>linear</td>\n",
       "      <td>{'svc__C': 10, 'svc__kernel': 'linear'}</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.987879</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>0.984848</td>\n",
       "      <td>0.983030</td>\n",
       "      <td>0.003090</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.249820</td>\n",
       "      <td>0.038229</td>\n",
       "      <td>0.024790</td>\n",
       "      <td>0.007365</td>\n",
       "      <td>100</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'svc__C': 100, 'svc__kernel': 'rbf'}</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>0.990909</td>\n",
       "      <td>0.987879</td>\n",
       "      <td>0.993939</td>\n",
       "      <td>0.986667</td>\n",
       "      <td>0.005620</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.131969</td>\n",
       "      <td>0.023757</td>\n",
       "      <td>0.009370</td>\n",
       "      <td>0.007650</td>\n",
       "      <td>100</td>\n",
       "      <td>linear</td>\n",
       "      <td>{'svc__C': 100, 'svc__kernel': 'linear'}</td>\n",
       "      <td>0.984848</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>0.984848</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.982424</td>\n",
       "      <td>0.002268</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.213505</td>\n",
       "      <td>0.023290</td>\n",
       "      <td>0.023643</td>\n",
       "      <td>0.010014</td>\n",
       "      <td>1000</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'svc__C': 1000, 'svc__kernel': 'rbf'}</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.984848</td>\n",
       "      <td>0.993939</td>\n",
       "      <td>0.984242</td>\n",
       "      <td>0.005214</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.172893</td>\n",
       "      <td>0.024801</td>\n",
       "      <td>0.014496</td>\n",
       "      <td>0.002316</td>\n",
       "      <td>1000</td>\n",
       "      <td>linear</td>\n",
       "      <td>{'svc__C': 1000, 'svc__kernel': 'linear'}</td>\n",
       "      <td>0.984848</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>0.984848</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.982424</td>\n",
       "      <td>0.002268</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_svc__C  \\\n",
       "0       0.230370      0.045596         0.028365        0.006369            1   \n",
       "1       0.201480      0.039230         0.025002        0.007522            1   \n",
       "2       0.283427      0.019955         0.030121        0.002741           10   \n",
       "3       0.160107      0.018233         0.012499        0.006249           10   \n",
       "4       0.249820      0.038229         0.024790        0.007365          100   \n",
       "5       0.131969      0.023757         0.009370        0.007650          100   \n",
       "6       0.213505      0.023290         0.023643        0.010014         1000   \n",
       "7       0.172893      0.024801         0.014496        0.002316         1000   \n",
       "\n",
       "  param_svc__kernel                                     params  \\\n",
       "0               rbf        {'svc__C': 1, 'svc__kernel': 'rbf'}   \n",
       "1            linear     {'svc__C': 1, 'svc__kernel': 'linear'}   \n",
       "2               rbf       {'svc__C': 10, 'svc__kernel': 'rbf'}   \n",
       "3            linear    {'svc__C': 10, 'svc__kernel': 'linear'}   \n",
       "4               rbf      {'svc__C': 100, 'svc__kernel': 'rbf'}   \n",
       "5            linear   {'svc__C': 100, 'svc__kernel': 'linear'}   \n",
       "6               rbf     {'svc__C': 1000, 'svc__kernel': 'rbf'}   \n",
       "7            linear  {'svc__C': 1000, 'svc__kernel': 'linear'}   \n",
       "\n",
       "   split0_test_score  split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0           0.978788           0.978788           0.984848           0.978788   \n",
       "1           0.978788           0.978788           0.984848           0.981818   \n",
       "2           0.978788           0.984848           0.984848           0.987879   \n",
       "3           0.981818           0.981818           0.987879           0.978788   \n",
       "4           0.981818           0.978788           0.990909           0.987879   \n",
       "5           0.984848           0.978788           0.984848           0.981818   \n",
       "6           0.981818           0.978788           0.981818           0.984848   \n",
       "7           0.984848           0.978788           0.984848           0.981818   \n",
       "\n",
       "   split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0           0.990909         0.982424        0.004848                5  \n",
       "1           0.987879         0.982424        0.003534                5  \n",
       "2           0.987879         0.984848        0.003320                2  \n",
       "3           0.984848         0.983030        0.003090                4  \n",
       "4           0.993939         0.986667        0.005620                1  \n",
       "5           0.981818         0.982424        0.002268                5  \n",
       "6           0.993939         0.984242        0.005214                3  \n",
       "7           0.981818         0.982424        0.002268                5  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "details[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "52192f62",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_randomforestclassifier__n_estimators</th>\n",
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       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
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       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.006255</td>\n",
       "      <td>0.007661</td>\n",
       "      <td>0.003126</td>\n",
       "      <td>0.006252</td>\n",
       "      <td>1</td>\n",
       "      <td>{'randomforestclassifier__n_estimators': 1}</td>\n",
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       "      <td>0.927273</td>\n",
       "      <td>0.938182</td>\n",
       "      <td>0.017628</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.021962</td>\n",
       "      <td>0.007749</td>\n",
       "      <td>0.003125</td>\n",
       "      <td>0.006250</td>\n",
       "      <td>5</td>\n",
       "      <td>{'randomforestclassifier__n_estimators': 5}</td>\n",
       "      <td>0.984848</td>\n",
       "      <td>0.987879</td>\n",
       "      <td>0.993939</td>\n",
       "      <td>0.987879</td>\n",
       "      <td>0.993939</td>\n",
       "      <td>0.989697</td>\n",
       "      <td>0.003636</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.044549</td>\n",
       "      <td>0.005146</td>\n",
       "      <td>0.002604</td>\n",
       "      <td>0.005208</td>\n",
       "      <td>10</td>\n",
       "      <td>{'randomforestclassifier__n_estimators': 10}</td>\n",
       "      <td>0.987879</td>\n",
       "      <td>0.990909</td>\n",
       "      <td>0.993939</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>0.990909</td>\n",
       "      <td>0.988485</td>\n",
       "      <td>0.005214</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.006255      0.007661         0.003126        0.006252   \n",
       "1       0.021962      0.007749         0.003125        0.006250   \n",
       "2       0.044549      0.005146         0.002604        0.005208   \n",
       "\n",
       "  param_randomforestclassifier__n_estimators  \\\n",
       "0                                          1   \n",
       "1                                          5   \n",
       "2                                         10   \n",
       "\n",
       "                                         params  split0_test_score  \\\n",
       "0   {'randomforestclassifier__n_estimators': 1}           0.927273   \n",
       "1   {'randomforestclassifier__n_estimators': 5}           0.984848   \n",
       "2  {'randomforestclassifier__n_estimators': 10}           0.987879   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
       "0           0.972727           0.936364           0.927273           0.927273   \n",
       "1           0.987879           0.993939           0.987879           0.993939   \n",
       "2           0.990909           0.993939           0.978788           0.990909   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  \n",
       "0         0.938182        0.017628                3  \n",
       "1         0.989697        0.003636                1  \n",
       "2         0.988485        0.005214                2  "
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "details[2]"
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  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cecda5e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_kneighborsclassifier__n_neighbors</th>\n",
       "      <th>param_kneighborsclassifier__weights</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.009460</td>\n",
       "      <td>0.007726</td>\n",
       "      <td>0.015635</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>5</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'kneighborsclassifier__n_neighbors': 5, 'knei...</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>0.972727</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.972727</td>\n",
       "      <td>0.005421</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.012596</td>\n",
       "      <td>0.006301</td>\n",
       "      <td>0.003126</td>\n",
       "      <td>0.006253</td>\n",
       "      <td>5</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'kneighborsclassifier__n_neighbors': 5, 'knei...</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>0.972727</td>\n",
       "      <td>0.969697</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.972727</td>\n",
       "      <td>0.975152</td>\n",
       "      <td>0.004454</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015710</td>\n",
       "      <td>0.003353</td>\n",
       "      <td>10</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'kneighborsclassifier__n_neighbors': 10, 'kne...</td>\n",
       "      <td>0.969697</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.963636</td>\n",
       "      <td>0.963636</td>\n",
       "      <td>0.969697</td>\n",
       "      <td>0.962424</td>\n",
       "      <td>0.008907</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012502</td>\n",
       "      <td>0.006251</td>\n",
       "      <td>10</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'kneighborsclassifier__n_neighbors': 10, 'kne...</td>\n",
       "      <td>0.969697</td>\n",
       "      <td>0.951515</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.963636</td>\n",
       "      <td>0.972727</td>\n",
       "      <td>0.964848</td>\n",
       "      <td>0.007323</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003125</td>\n",
       "      <td>0.006250</td>\n",
       "      <td>0.019238</td>\n",
       "      <td>0.006053</td>\n",
       "      <td>20</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'kneighborsclassifier__n_neighbors': 20, 'kne...</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.924242</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.936364</td>\n",
       "      <td>0.939394</td>\n",
       "      <td>0.938182</td>\n",
       "      <td>0.007809</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012176</td>\n",
       "      <td>0.006120</td>\n",
       "      <td>20</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'kneighborsclassifier__n_neighbors': 20, 'kne...</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.954545</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.969697</td>\n",
       "      <td>0.956364</td>\n",
       "      <td>0.010249</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.006163</td>\n",
       "      <td>0.005891</td>\n",
       "      <td>0.021904</td>\n",
       "      <td>0.008700</td>\n",
       "      <td>25</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'kneighborsclassifier__n_neighbors': 25, 'kne...</td>\n",
       "      <td>0.942424</td>\n",
       "      <td>0.918182</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.927273</td>\n",
       "      <td>0.930303</td>\n",
       "      <td>0.932727</td>\n",
       "      <td>0.010032</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.005335</td>\n",
       "      <td>0.006694</td>\n",
       "      <td>0.016643</td>\n",
       "      <td>0.002025</td>\n",
       "      <td>25</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'kneighborsclassifier__n_neighbors': 25, 'kne...</td>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.957576</td>\n",
       "      <td>0.939394</td>\n",
       "      <td>0.960606</td>\n",
       "      <td>0.947273</td>\n",
       "      <td>0.010427</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.009460      0.007726         0.015635        0.000050   \n",
       "1       0.012596      0.006301         0.003126        0.006253   \n",
       "2       0.000000      0.000000         0.015710        0.003353   \n",
       "3       0.000000      0.000000         0.012502        0.006251   \n",
       "4       0.003125      0.006250         0.019238        0.006053   \n",
       "5       0.000000      0.000000         0.012176        0.006120   \n",
       "6       0.006163      0.005891         0.021904        0.008700   \n",
       "7       0.005335      0.006694         0.016643        0.002025   \n",
       "\n",
       "  param_kneighborsclassifier__n_neighbors param_kneighborsclassifier__weights  \\\n",
       "0                                       5                             uniform   \n",
       "1                                       5                            distance   \n",
       "2                                      10                             uniform   \n",
       "3                                      10                            distance   \n",
       "4                                      20                             uniform   \n",
       "5                                      20                            distance   \n",
       "6                                      25                             uniform   \n",
       "7                                      25                            distance   \n",
       "\n",
       "                                              params  split0_test_score  \\\n",
       "0  {'kneighborsclassifier__n_neighbors': 5, 'knei...           0.978788   \n",
       "1  {'kneighborsclassifier__n_neighbors': 5, 'knei...           0.978788   \n",
       "2  {'kneighborsclassifier__n_neighbors': 10, 'kne...           0.969697   \n",
       "3  {'kneighborsclassifier__n_neighbors': 10, 'kne...           0.969697   \n",
       "4  {'kneighborsclassifier__n_neighbors': 20, 'kne...           0.945455   \n",
       "5  {'kneighborsclassifier__n_neighbors': 20, 'kne...           0.966667   \n",
       "6  {'kneighborsclassifier__n_neighbors': 25, 'kne...           0.942424   \n",
       "7  {'kneighborsclassifier__n_neighbors': 25, 'kne...           0.945455   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
       "0           0.972727           0.966667           0.978788           0.966667   \n",
       "1           0.972727           0.969697           0.981818           0.972727   \n",
       "2           0.945455           0.963636           0.963636           0.969697   \n",
       "3           0.951515           0.966667           0.963636           0.972727   \n",
       "4           0.924242           0.945455           0.936364           0.939394   \n",
       "5           0.945455           0.954545           0.945455           0.969697   \n",
       "6           0.918182           0.945455           0.927273           0.930303   \n",
       "7           0.933333           0.957576           0.939394           0.960606   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  \n",
       "0         0.972727        0.005421                2  \n",
       "1         0.975152        0.004454                1  \n",
       "2         0.962424        0.008907                4  \n",
       "3         0.964848        0.007323                3  \n",
       "4         0.938182        0.007809                7  \n",
       "5         0.956364        0.010249                5  \n",
       "6         0.932727        0.010032                8  \n",
       "7         0.947273        0.010427                6  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "details[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d5be353b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Model name': 'decision tree',\n",
       "  'Best score': 0.9848484848484848,\n",
       "  'Best param': {'decisiontreeclassifier__splitter': 'best'}},\n",
       " {'Model name': 'svm',\n",
       "  'Best score': 0.9866666666666666,\n",
       "  'Best param': {'svc__C': 100, 'svc__kernel': 'rbf'}},\n",
       " {'Model name': 'random_forest',\n",
       "  'Best score': 0.9896969696969696,\n",
       "  'Best param': {'randomforestclassifier__n_estimators': 5}},\n",
       " {'Model name': 'k classifier',\n",
       "  'Best score': 0.9751515151515152,\n",
       "  'Best param': {'kneighborsclassifier__n_neighbors': 5,\n",
       "   'kneighborsclassifier__weights': 'distance'}}]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "16b08c43",
   "metadata": {},
   "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>Model name</th>\n",
       "      <th>Best score</th>\n",
       "      <th>Best param</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>decision tree</td>\n",
       "      <td>0.984848</td>\n",
       "      <td>{'decisiontreeclassifier__splitter': 'best'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>svm</td>\n",
       "      <td>0.986667</td>\n",
       "      <td>{'svc__C': 100, 'svc__kernel': 'rbf'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>random_forest</td>\n",
       "      <td>0.989697</td>\n",
       "      <td>{'randomforestclassifier__n_estimators': 5}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>k classifier</td>\n",
       "      <td>0.975152</td>\n",
       "      <td>{'kneighborsclassifier__n_neighbors': 5, 'knei...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Model name  Best score  \\\n",
       "0  decision tree    0.984848   \n",
       "1            svm    0.986667   \n",
       "2  random_forest    0.989697   \n",
       "3   k classifier    0.975152   \n",
       "\n",
       "                                          Best param  \n",
       "0       {'decisiontreeclassifier__splitter': 'best'}  \n",
       "1              {'svc__C': 100, 'svc__kernel': 'rbf'}  \n",
       "2        {'randomforestclassifier__n_estimators': 5}  \n",
       "3  {'kneighborsclassifier__n_neighbors': 5, 'knei...  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "15fcc250",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decision tree : 0.9836363636363636\n",
      "svm : 0.9836363636363636\n",
      "random_forest : 0.9818181818181818\n",
      "k classifier : 0.9727272727272728\n"
     ]
    }
   ],
   "source": [
    "for i in best_param.keys():\n",
    "    print(f'{i} : {best_param[i].score(x_test,y_test)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfe066e5",
   "metadata": {},
   "source": [
    "Best model - Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7825b93d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18,  4, 14,  2,  1, 14, 19,  9,  9,  4, 11,  2,  2, 16,  8, 12, 16,\n",
       "        5,  8, 14, 21, 10,  0,  4,  2,  8,  1, 12, 12, 21, 13, 10, 14, 19,\n",
       "       17, 18,  1, 15, 14,  9, 12, 12, 11, 16, 14, 20,  7,  2,  1, 11, 11,\n",
       "       13, 10, 12, 20, 13, 10, 14, 18, 17, 21, 11, 16, 14,  0,  7,  1, 19,\n",
       "        1,  7,  8, 15,  9, 14,  8,  6, 13, 13, 13,  9,  7, 21,  5,  5,  7,\n",
       "        5,  8, 18, 13,  7, 12, 11,  3,  8, 13,  6, 11,  8,  9,  3, 13,  6,\n",
       "       15,  3,  5, 12,  3,  1, 20,  0, 19, 21,  7,  4, 17,  0,  6,  8, 21,\n",
       "       20,  7, 17,  4, 16, 17, 18,  7,  6,  8,  3, 11,  4,  2, 12, 14,  8,\n",
       "       15, 16,  2, 18, 17, 21, 13,  0, 18,  6, 11, 18,  0, 16, 10, 15,  2,\n",
       "        0,  7, 20, 15, 17,  4,  3,  6, 18,  4,  3,  4, 19,  7, 10,  0,  0,\n",
       "       21,  1, 11, 19,  2, 15,  7,  6,  3, 16, 20,  0, 20, 10,  9, 10, 15,\n",
       "        2, 20,  0, 20,  8, 14,  4, 17, 10, 12,  9,  3, 21,  1,  9, 15, 19,\n",
       "        7, 12,  0,  0,  8,  3, 19, 17,  7,  3,  1, 19,  5,  0,  4, 16,  9,\n",
       "        0,  7, 14,  9,  7, 21,  8, 14,  1, 19, 11,  5,  8, 17, 10, 19,  0,\n",
       "       11, 21,  9, 15,  7, 19, 18, 20, 19, 12, 16, 15,  3, 12,  6,  9,  7,\n",
       "       21, 15,  9,  5, 19,  2, 21, 11, 10,  3,  2,  0,  7,  3, 11,  4, 13,\n",
       "        1, 17, 15,  1,  8, 15, 17,  4, 21, 21, 16, 17, 20,  0, 10, 18,  4,\n",
       "       13, 13,  7, 21,  8,  9, 20,  9, 11, 14, 12, 19, 15,  4,  2, 15,  6,\n",
       "       16, 11,  5, 13,  5, 18, 13, 14, 12, 21, 19, 10, 12, 16, 12,  3, 14,\n",
       "        5, 14, 14, 13, 10,  4,  1,  0,  4,  9, 19,  2,  8,  7,  7, 14,  8,\n",
       "        3,  3, 20,  6, 19, 20,  1,  2, 16,  9, 13, 16,  8, 19, 18,  1, 11,\n",
       "        6, 16, 14, 18,  0,  6,  8, 17, 19,  8,  9, 13,  7, 16,  3, 15, 17,\n",
       "        6,  7, 18, 21, 16, 10, 20, 16, 17,  9, 14,  5, 20, 13,  4, 16,  5,\n",
       "       21, 14,  8,  1, 21,  2,  8,  6, 21,  2, 19, 21,  1,  7,  8,  4, 15,\n",
       "       12, 10, 12, 19, 16,  0, 19, 14,  0, 11,  5,  6, 17, 21,  4,  8, 17,\n",
       "        3,  4,  6, 11, 12,  5,  0, 18,  6,  4, 21, 19, 18,  8,  5, 11,  9,\n",
       "        1,  7, 17, 20,  9,  2, 18,  4,  1,  6, 10, 17,  0,  6,  1, 12,  9,\n",
       "       10,  4, 20,  4,  7, 15,  6,  1,  5, 14, 12,  4,  1,  1,  6,  8, 10,\n",
       "       18, 13, 14, 20,  4, 14, 19,  5,  2,  4, 17, 11,  7, 15,  9, 16,  3,\n",
       "       14, 10,  3, 20,  2, 21,  1, 16,  5,  2, 21, 19,  9, 13, 15, 15, 17,\n",
       "        5,  8, 20,  4,  6, 16,  1, 14, 21,  1, 19,  2,  2,  3,  6,  2, 16,\n",
       "        5,  4,  3,  4,  6, 15,  0, 17, 19,  9, 12,  7,  5, 18, 17, 21, 17,\n",
       "        6,  3,  7, 14,  8, 21])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted = best_param['random_forest'].predict(x_test)\n",
    "predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "55d2838a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.heatmap(confusion_matrix(y_test,predicted),annot=True)\n",
    "plt.xlabel(\"Original\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29814ae2",
   "metadata": {},
   "source": [
    "Bagging classifier for more accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "29be124d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe1 = make_pipeline(preprocessing.StandardScaler(),RandomForestClassifier(n_estimators = 10))\n",
    "bag_model = BaggingClassifier(base_estimator=pipe1,n_estimators=100,\n",
    "                              oob_score=True,random_state=0,max_samples=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c9c425e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=Pipeline(steps=[('standardscaler',\n",
       "                                                  StandardScaler()),\n",
       "                                                 ('randomforestclassifier',\n",
       "                                                  RandomForestClassifier(n_estimators=10))]),\n",
       "                  max_samples=0.8, n_estimators=100, oob_score=True,\n",
       "                  random_state=0)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "735ef01a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.990909090909091"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3770ce60",
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = bag_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "dbe762e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.heatmap(confusion_matrix(y_test,predict),annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61b73f92",
   "metadata": {},
   "source": [
    "Conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0ad3dd24",
   "metadata": {},
   "outputs": [],
   "source": [
    "dha2 =pd.DataFrame(Y)\n",
    "code = pd.DataFrame(dha2['label'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8a8b14ab",
   "metadata": {},
   "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>code</th>\n",
       "      <th>encode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rice</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>maize</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chickpea</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>kidneybeans</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pigeonpeas</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mothbeans</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mungbean</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>blackgram</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>lentil</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pomegranate</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>banana</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mango</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>grapes</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>watermelon</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>muskmelon</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>apple</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>orange</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>papaya</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>coconut</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>cotton</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>jute</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>coffee</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           code  encode\n",
       "0          rice      20\n",
       "1         maize      11\n",
       "2      chickpea       3\n",
       "3   kidneybeans       9\n",
       "4    pigeonpeas      18\n",
       "5     mothbeans      13\n",
       "6      mungbean      14\n",
       "7     blackgram       2\n",
       "8        lentil      10\n",
       "9   pomegranate      19\n",
       "10       banana       1\n",
       "11        mango      12\n",
       "12       grapes       7\n",
       "13   watermelon      21\n",
       "14    muskmelon      15\n",
       "15        apple       0\n",
       "16       orange      16\n",
       "17       papaya      17\n",
       "18      coconut       4\n",
       "19       cotton       6\n",
       "20         jute       8\n",
       "21       coffee       5"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dha = pd.DataFrame(y)\n",
    "encode = pd.DataFrame(dha[0].unique())\n",
    "refer = pd.DataFrame()\n",
    "refer['code']=code\n",
    "refer['encode']=encode\n",
    "refer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "98cc66ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        25\n",
      "           1       1.00      1.00      1.00        27\n",
      "           2       1.00      1.00      1.00        23\n",
      "           3       1.00      1.00      1.00        24\n",
      "           4       1.00      1.00      1.00        31\n",
      "           5       1.00      1.00      1.00        22\n",
      "           6       1.00      1.00      1.00        26\n",
      "           7       1.00      1.00      1.00        30\n",
      "           8       0.90      1.00      0.95        28\n",
      "           9       1.00      1.00      1.00        26\n",
      "          10       0.90      1.00      0.95        18\n",
      "          11       1.00      1.00      1.00        21\n",
      "          12       1.00      1.00      1.00        23\n",
      "          13       1.00      0.92      0.96        25\n",
      "          14       1.00      1.00      1.00        29\n",
      "          15       1.00      1.00      1.00        23\n",
      "          16       1.00      1.00      1.00        25\n",
      "          17       1.00      1.00      1.00        25\n",
      "          18       1.00      1.00      1.00        19\n",
      "          19       1.00      1.00      1.00        28\n",
      "          20       1.00      0.87      0.93        23\n",
      "          21       1.00      1.00      1.00        29\n",
      "\n",
      "    accuracy                           0.99       550\n",
      "   macro avg       0.99      0.99      0.99       550\n",
      "weighted avg       0.99      0.99      0.99       550\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71189522",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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