Crop_Recommendation_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "\n",
    "from __future__ import print_function\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn import metrics\n",
    "from sklearn import tree\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Data/crop_recommendation.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>2195</th>\n",
       "      <td>107</td>\n",
       "      <td>34</td>\n",
       "      <td>32</td>\n",
       "      <td>26.774637</td>\n",
       "      <td>66.413269</td>\n",
       "      <td>6.780064</td>\n",
       "      <td>177.774507</td>\n",
       "      <td>coffee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2196</th>\n",
       "      <td>99</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>27.417112</td>\n",
       "      <td>56.636362</td>\n",
       "      <td>6.086922</td>\n",
       "      <td>127.924610</td>\n",
       "      <td>coffee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2197</th>\n",
       "      <td>118</td>\n",
       "      <td>33</td>\n",
       "      <td>30</td>\n",
       "      <td>24.131797</td>\n",
       "      <td>67.225123</td>\n",
       "      <td>6.362608</td>\n",
       "      <td>173.322839</td>\n",
       "      <td>coffee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2198</th>\n",
       "      <td>117</td>\n",
       "      <td>32</td>\n",
       "      <td>34</td>\n",
       "      <td>26.272418</td>\n",
       "      <td>52.127394</td>\n",
       "      <td>6.758793</td>\n",
       "      <td>127.175293</td>\n",
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       "      <th>2199</th>\n",
       "      <td>104</td>\n",
       "      <td>18</td>\n",
       "      <td>30</td>\n",
       "      <td>23.603016</td>\n",
       "      <td>60.396475</td>\n",
       "      <td>6.779833</td>\n",
       "      <td>140.937041</td>\n",
       "      <td>coffee</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        N   P   K  temperature   humidity        ph    rainfall   label\n",
       "2195  107  34  32    26.774637  66.413269  6.780064  177.774507  coffee\n",
       "2196   99  15  27    27.417112  56.636362  6.086922  127.924610  coffee\n",
       "2197  118  33  30    24.131797  67.225123  6.362608  173.322839  coffee\n",
       "2198  117  32  34    26.272418  52.127394  6.758793  127.175293  coffee\n",
       "2199  104  18  30    23.603016  60.396475  6.779833  140.937041  coffee"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17600"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2200, 8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['N', 'P', 'K', 'temperature', 'humidity', 'ph', 'rainfall', 'label'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['rice', 'maize', 'chickpea', 'kidneybeans', 'pigeonpeas',\n",
       "       'mothbeans', 'mungbean', 'blackgram', 'lentil', 'pomegranate',\n",
       "       'banana', 'mango', 'grapes', 'watermelon', 'muskmelon', 'apple',\n",
       "       'orange', 'papaya', 'coconut', 'cotton', 'jute', 'coffee'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rice           100\n",
       "maize          100\n",
       "jute           100\n",
       "cotton         100\n",
       "coconut        100\n",
       "papaya         100\n",
       "orange         100\n",
       "apple          100\n",
       "muskmelon      100\n",
       "watermelon     100\n",
       "grapes         100\n",
       "mango          100\n",
       "banana         100\n",
       "pomegranate    100\n",
       "lentil         100\n",
       "blackgram      100\n",
       "mungbean       100\n",
       "mothbeans      100\n",
       "pigeonpeas     100\n",
       "kidneybeans    100\n",
       "chickpea       100\n",
       "coffee         100\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seperating features and target label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = df[['N', 'P','K','temperature', 'humidity', 'ph', 'rainfall']]\n",
    "target = df['label']\n",
    "#features = df[['temperature', 'humidity', 'ph', 'rainfall']]\n",
    "labels = df['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialzing empty lists to append all model's name and corresponding name\n",
    "acc = []\n",
    "model = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting into train and test data\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "Xtrain, Xtest, Ytrain, Ytest = train_test_split(features,target,test_size = 0.2,random_state =2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTrees's Accuracy is:  90.0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        13\n",
      "      banana       1.00      1.00      1.00        17\n",
      "   blackgram       0.59      1.00      0.74        16\n",
      "    chickpea       1.00      1.00      1.00        21\n",
      "     coconut       0.91      1.00      0.95        21\n",
      "      coffee       1.00      1.00      1.00        22\n",
      "      cotton       1.00      1.00      1.00        20\n",
      "      grapes       1.00      1.00      1.00        18\n",
      "        jute       0.74      0.93      0.83        28\n",
      " kidneybeans       0.00      0.00      0.00        14\n",
      "      lentil       0.68      1.00      0.81        23\n",
      "       maize       1.00      1.00      1.00        21\n",
      "       mango       1.00      1.00      1.00        26\n",
      "   mothbeans       0.00      0.00      0.00        19\n",
      "    mungbean       1.00      1.00      1.00        24\n",
      "   muskmelon       1.00      1.00      1.00        23\n",
      "      orange       1.00      1.00      1.00        29\n",
      "      papaya       1.00      0.84      0.91        19\n",
      "  pigeonpeas       0.62      1.00      0.77        18\n",
      " pomegranate       1.00      1.00      1.00        17\n",
      "        rice       1.00      0.62      0.77        16\n",
      "  watermelon       1.00      1.00      1.00        15\n",
      "\n",
      "    accuracy                           0.90       440\n",
      "   macro avg       0.84      0.88      0.85       440\n",
      "weighted avg       0.86      0.90      0.87       440\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "DecisionTree = DecisionTreeClassifier(criterion=\"entropy\",random_state=2,max_depth=5)\n",
    "\n",
    "DecisionTree.fit(Xtrain,Ytrain)\n",
    "\n",
    "predicted_values = DecisionTree.predict(Xtest)\n",
    "x = metrics.accuracy_score(Ytest, predicted_values)\n",
    "acc.append(x)\n",
    "model.append('Decision Tree')\n",
    "print(\"DecisionTrees's Accuracy is: \", x*100)\n",
    "\n",
    "print(classification_report(Ytest,predicted_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cross validation score (Decision Tree)\n",
    "score = cross_val_score(DecisionTree, features, target,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'score' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_10412/2992420520.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mscore\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'score' is not defined"
     ]
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving trained Decision Tree model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Dump the trained Naive Bayes classifier with Pickle\n",
    "DT_pkl_filename = 'model/DecisionTree.pkl'\n",
    "# Open the file to save as pkl file\n",
    "DT_Model_pkl = open(DT_pkl_filename, 'wb')\n",
    "pickle.dump(DecisionTree, DT_Model_pkl)\n",
    "# Close the pickle instances\n",
    "DT_Model_pkl.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Guassian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes's Accuracy is:  0.990909090909091\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        13\n",
      "      banana       1.00      1.00      1.00        17\n",
      "   blackgram       1.00      1.00      1.00        16\n",
      "    chickpea       1.00      1.00      1.00        21\n",
      "     coconut       1.00      1.00      1.00        21\n",
      "      coffee       1.00      1.00      1.00        22\n",
      "      cotton       1.00      1.00      1.00        20\n",
      "      grapes       1.00      1.00      1.00        18\n",
      "        jute       0.88      1.00      0.93        28\n",
      " kidneybeans       1.00      1.00      1.00        14\n",
      "      lentil       1.00      1.00      1.00        23\n",
      "       maize       1.00      1.00      1.00        21\n",
      "       mango       1.00      1.00      1.00        26\n",
      "   mothbeans       1.00      1.00      1.00        19\n",
      "    mungbean       1.00      1.00      1.00        24\n",
      "   muskmelon       1.00      1.00      1.00        23\n",
      "      orange       1.00      1.00      1.00        29\n",
      "      papaya       1.00      1.00      1.00        19\n",
      "  pigeonpeas       1.00      1.00      1.00        18\n",
      " pomegranate       1.00      1.00      1.00        17\n",
      "        rice       1.00      0.75      0.86        16\n",
      "  watermelon       1.00      1.00      1.00        15\n",
      "\n",
      "    accuracy                           0.99       440\n",
      "   macro avg       0.99      0.99      0.99       440\n",
      "weighted avg       0.99      0.99      0.99       440\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "NaiveBayes = GaussianNB()\n",
    "\n",
    "NaiveBayes.fit(Xtrain,Ytrain)\n",
    "\n",
    "predicted_values = NaiveBayes.predict(Xtest)\n",
    "x = metrics.accuracy_score(Ytest, predicted_values)\n",
    "acc.append(x)\n",
    "model.append('Naive Bayes')\n",
    "print(\"Naive Bayes's Accuracy is: \", x)\n",
    "\n",
    "print(classification_report(Ytest,predicted_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99772727, 0.99545455, 0.99545455, 0.99545455, 0.99090909])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validation score (NaiveBayes)\n",
    "score = cross_val_score(NaiveBayes,features,target,cv=5)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving trained Guassian Naive Bayes model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Dump the trained Naive Bayes classifier with Pickle\n",
    "NB_pkl_filename = 'model/NBClassifier.pkl'\n",
    "# Open the file to save as pkl file\n",
    "NB_Model_pkl = open(NB_pkl_filename, 'wb')\n",
    "pickle.dump(NaiveBayes, NB_Model_pkl)\n",
    "# Close the pickle instances\n",
    "NB_Model_pkl.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM's Accuracy is:  0.9795454545454545\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        13\n",
      "      banana       1.00      1.00      1.00        17\n",
      "   blackgram       1.00      1.00      1.00        16\n",
      "    chickpea       1.00      1.00      1.00        21\n",
      "     coconut       1.00      1.00      1.00        21\n",
      "      coffee       1.00      0.95      0.98        22\n",
      "      cotton       0.95      1.00      0.98        20\n",
      "      grapes       1.00      1.00      1.00        18\n",
      "        jute       0.83      0.89      0.86        28\n",
      " kidneybeans       1.00      1.00      1.00        14\n",
      "      lentil       1.00      1.00      1.00        23\n",
      "       maize       1.00      0.95      0.98        21\n",
      "       mango       1.00      1.00      1.00        26\n",
      "   mothbeans       1.00      1.00      1.00        19\n",
      "    mungbean       1.00      1.00      1.00        24\n",
      "   muskmelon       1.00      1.00      1.00        23\n",
      "      orange       1.00      1.00      1.00        29\n",
      "      papaya       1.00      1.00      1.00        19\n",
      "  pigeonpeas       1.00      1.00      1.00        18\n",
      " pomegranate       1.00      1.00      1.00        17\n",
      "        rice       0.80      0.75      0.77        16\n",
      "  watermelon       1.00      1.00      1.00        15\n",
      "\n",
      "    accuracy                           0.98       440\n",
      "   macro avg       0.98      0.98      0.98       440\n",
      "weighted avg       0.98      0.98      0.98       440\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "# data normalization with sklearn\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# fit scaler on training data\n",
    "norm = MinMaxScaler().fit(Xtrain)\n",
    "X_train_norm = norm.transform(Xtrain)\n",
    "# transform testing dataabs\n",
    "X_test_norm = norm.transform(Xtest)\n",
    "SVM = SVC(kernel='poly', degree=3, C=1)\n",
    "SVM.fit(X_train_norm,Ytrain)\n",
    "predicted_values = SVM.predict(X_test_norm)\n",
    "x = metrics.accuracy_score(Ytest, predicted_values)\n",
    "acc.append(x)\n",
    "model.append('SVM')\n",
    "print(\"SVM's Accuracy is: \", x)\n",
    "\n",
    "print(classification_report(Ytest,predicted_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.97954545, 0.975     , 0.98863636, 0.98863636, 0.98181818])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validation score (SVM)\n",
    "score = cross_val_score(SVM,features,target,cv=5)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving trained SVM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Dump the trained SVM classifier with Pickle\n",
    "SVM_pkl_filename = 'model/SVMClassifier.pkl'\n",
    "# Open the file to save as pkl file\n",
    "SVM_Model_pkl = open(SVM_pkl_filename, 'wb')\n",
    "pickle.dump(SVM, SVM_Model_pkl)\n",
    "# Close the pickle instances\n",
    "SVM_Model_pkl.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression's Accuracy is:  0.9522727272727273\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        13\n",
      "      banana       1.00      1.00      1.00        17\n",
      "   blackgram       0.86      0.75      0.80        16\n",
      "    chickpea       1.00      1.00      1.00        21\n",
      "     coconut       1.00      1.00      1.00        21\n",
      "      coffee       1.00      1.00      1.00        22\n",
      "      cotton       0.86      0.90      0.88        20\n",
      "      grapes       1.00      1.00      1.00        18\n",
      "        jute       0.84      0.93      0.88        28\n",
      " kidneybeans       1.00      1.00      1.00        14\n",
      "      lentil       0.88      1.00      0.94        23\n",
      "       maize       0.90      0.86      0.88        21\n",
      "       mango       0.96      1.00      0.98        26\n",
      "   mothbeans       0.84      0.84      0.84        19\n",
      "    mungbean       1.00      0.96      0.98        24\n",
      "   muskmelon       1.00      1.00      1.00        23\n",
      "      orange       1.00      1.00      1.00        29\n",
      "      papaya       1.00      0.95      0.97        19\n",
      "  pigeonpeas       1.00      1.00      1.00        18\n",
      " pomegranate       1.00      1.00      1.00        17\n",
      "        rice       0.85      0.69      0.76        16\n",
      "  watermelon       1.00      1.00      1.00        15\n",
      "\n",
      "    accuracy                           0.95       440\n",
      "   macro avg       0.95      0.95      0.95       440\n",
      "weighted avg       0.95      0.95      0.95       440\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "LogReg = LogisticRegression(random_state=2)\n",
    "\n",
    "LogReg.fit(Xtrain,Ytrain)\n",
    "\n",
    "predicted_values = LogReg.predict(Xtest)\n",
    "\n",
    "x = metrics.accuracy_score(Ytest, predicted_values)\n",
    "acc.append(x)\n",
    "model.append('Logistic Regression')\n",
    "print(\"Logistic Regression's Accuracy is: \", x)\n",
    "\n",
    "print(classification_report(Ytest,predicted_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.95      , 0.96590909, 0.94772727, 0.96818182, 0.94318182])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validation score (Logistic Regression)\n",
    "score = cross_val_score(LogReg,features,target,cv=5)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving trained Logistic Regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Dump the trained Naive Bayes classifier with Pickle\n",
    "LR_pkl_filename = 'model/LogisticRegression.pkl'\n",
    "# Open the file to save as pkl file\n",
    "LR_Model_pkl = open(DT_pkl_filename, 'wb')\n",
    "pickle.dump(LogReg, LR_Model_pkl)\n",
    "# Close the pickle instances\n",
    "LR_Model_pkl.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF's Accuracy is:  0.990909090909091\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        13\n",
      "      banana       1.00      1.00      1.00        17\n",
      "   blackgram       0.94      1.00      0.97        16\n",
      "    chickpea       1.00      1.00      1.00        21\n",
      "     coconut       1.00      1.00      1.00        21\n",
      "      coffee       1.00      1.00      1.00        22\n",
      "      cotton       1.00      1.00      1.00        20\n",
      "      grapes       1.00      1.00      1.00        18\n",
      "        jute       0.90      1.00      0.95        28\n",
      " kidneybeans       1.00      1.00      1.00        14\n",
      "      lentil       1.00      1.00      1.00        23\n",
      "       maize       1.00      1.00      1.00        21\n",
      "       mango       1.00      1.00      1.00        26\n",
      "   mothbeans       1.00      0.95      0.97        19\n",
      "    mungbean       1.00      1.00      1.00        24\n",
      "   muskmelon       1.00      1.00      1.00        23\n",
      "      orange       1.00      1.00      1.00        29\n",
      "      papaya       1.00      1.00      1.00        19\n",
      "  pigeonpeas       1.00      1.00      1.00        18\n",
      " pomegranate       1.00      1.00      1.00        17\n",
      "        rice       1.00      0.81      0.90        16\n",
      "  watermelon       1.00      1.00      1.00        15\n",
      "\n",
      "    accuracy                           0.99       440\n",
      "   macro avg       0.99      0.99      0.99       440\n",
      "weighted avg       0.99      0.99      0.99       440\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "RF = RandomForestClassifier(n_estimators=20, random_state=0)\n",
    "RF.fit(Xtrain,Ytrain)\n",
    "\n",
    "predicted_values = RF.predict(Xtest)\n",
    "\n",
    "x = metrics.accuracy_score(Ytest, predicted_values)\n",
    "acc.append(x)\n",
    "model.append('RF')\n",
    "print(\"RF's Accuracy is: \", x)\n",
    "\n",
    "print(classification_report(Ytest,predicted_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99772727, 0.99545455, 0.99772727, 0.99318182, 0.98863636])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validation score (Random Forest)\n",
    "score = cross_val_score(RF,features,target,cv=5)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving trained Random Forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Dump the trained Naive Bayes classifier with Pickle\n",
    "RF_pkl_filename = 'model/RandomForest.pkl'\n",
    "# Open the file to save as pkl file\n",
    "RF_Model_pkl = open(RF_pkl_filename, 'wb')\n",
    "pickle.dump(RF, RF_Model_pkl)\n",
    "# Close the pickle instances\n",
    "RF_Model_pkl.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Invalid classes inferred from unique values of `y`.  Expected: [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21], got ['apple' 'banana' 'blackgram' 'chickpea' 'coconut' 'coffee' 'cotton'\n 'grapes' 'jute' 'kidneybeans' 'lentil' 'maize' 'mango' 'mothbeans'\n 'mungbean' 'muskmelon' 'orange' 'papaya' 'pigeonpeas' 'pomegranate'\n 'rice' 'watermelon']",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_5352/3719709359.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mxgboost\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mxgb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mXB\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxgb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mXGBClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mXB\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXtrain\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mYtrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mpredicted_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mXB\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\xgboost\\core.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    530\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    531\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 532\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    533\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    534\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, base_margin, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, base_margin_eval_set, feature_weights, callbacks)\u001b[0m\n\u001b[0;32m   1355\u001b[0m             \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mexpected_classes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1356\u001b[0m         ):\n\u001b[1;32m-> 1357\u001b[1;33m             raise ValueError(\n\u001b[0m\u001b[0;32m   1358\u001b[0m                 \u001b[1;34mf\"Invalid classes inferred from unique values of `y`.  \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1359\u001b[0m                 \u001b[1;34mf\"Expected: {expected_classes}, got {self.classes_}\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Invalid classes inferred from unique values of `y`.  Expected: [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21], got ['apple' 'banana' 'blackgram' 'chickpea' 'coconut' 'coffee' 'cotton'\n 'grapes' 'jute' 'kidneybeans' 'lentil' 'maize' 'mango' 'mothbeans'\n 'mungbean' 'muskmelon' 'orange' 'papaya' 'pigeonpeas' 'pomegranate'\n 'rice' 'watermelon']"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "XB = xgb.XGBClassifier()\n",
    "XB.fit(Xtrain,Ytrain)\n",
    "\n",
    "predicted_values = XB.predict(Xtest)\n",
    "\n",
    "x = metrics.accuracy_score(Ytest, predicted_values)\n",
    "acc.append(x)\n",
    "model.append('XGBoost')\n",
    "print(\"XGBoost's Accuracy is: \", x)\n",
    "\n",
    "print(classification_report(Ytest,predicted_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, nan, nan, nan, nan])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validation score (XGBoost)\n",
    "score = cross_val_score(XB,features,target,cv=5)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving trained XGBoost model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Dump the trained Naive Bayes classifier with Pickle\n",
    "XB_pkl_filename = 'model/XGBoost.pkl'\n",
    "# Open the file to save as pkl file\n",
    "XB_Model_pkl = open(XB_pkl_filename, 'wb')\n",
    "pickle.dump(XB, XB_Model_pkl)\n",
    "# Close the pickle instances\n",
    "XB_Model_pkl.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Accuracy Comparison'}, xlabel='Accuracy', ylabel='Algorithm'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3Pb+9esP3eOv79yuW7I8eN/e9Xg74AfCeUdpeCTyu/f6FNL2+vRYxirbX/J/bmpHckiRJkjRxht4BJHkWzXDm/dpVZ9Dcz3tnVV08xm5n0wwn/spEzlFVtwLfB76f5PM0vaYbtOfq9QdgrSQP6xnevD5Nz+h5E31PQ3AG8DKaHuU7+zcm+QNNgF2rqkYbyixJkiRJQ2foXbyVkjwUWJ5mYqataO41PQb4WtvmBOA04Kgk7wH+CKxBM6nVUVV1OrAnzcRWfwYOp/nsn19V+/SfMMn89ny/Am4BXkvTU3zJKPWdQBOov9HO6DyHpmf45Pa8S8vngZ2Bbyb5JHAN8EjgVcDOVXVjkv8B9mufcfxzYC7wVOCmqvrqUqxVkiRJ0jLCe3oXbyua4bkX0zyz95nArsCLq+ofAFVVNAH3FOBg4AKaYLs2cHXb5iRgO2AbmhmOfwo8aYxz3kATIE/l7h7iF1XVtf0N23NvC1zfnv8E4CLglYO+4UFU1RU0MzkvDxwPnEvzPOMFwF1tsw8Ce9H80eC8tt2LuHuYuCRJkiQNVZrMJM187WOLFsxbZxey/ErTXY4kSZI67voLPj3dJWgMCxcuZN68eQDzqmrheG3t6ZUkSZIkdZahV5IkSZLUWYZeSZIkSVJnGXolSZIkSZ1l6JUkSZIkdZahV5IkSZLUWYZeSZIkSVJnGXolSZIkSZ1l6JUkSZIkdZahV5IkSZLUWYZeSZIkSVJnGXolSZIkSZ1l6JUkSZIkdZahV5IkSZLUWYZeSZIkSVJnGXolSZIkSZ1l6JUkSZIkdZahV5IkSZLUWYZeSZIkSVJnGXolSZIkSZ1l6JUkSZIkdZahV5IkSZLUWYZeSZIkSVJnzZnuAqTJuvR3ezN37tzpLkOSJEnSLGBPryRJkiSpswy9kiRJkqTOMvRKkiRJkjrL0CtJkiRJ6ixDryRJkiSpswy9kiRJkqTOMvRKkiRJkjrL0CtJkiRJ6ixDryRJkiSpswy9kiRJkqTOMvRKkiRJkjrL0CtJkiRJ6ixDryRJkiSpswy9kiRJkqTOmjPdBUiTdejLHs995/j3GkmSJGlp2fG4i6a7hIGZHCRJkiRJnWXolSRJkiR1lqFXkiRJktRZhl5JkiRJUmcZeiVJkiRJnWXolSRJkiR1lqFXkiRJktRZhl5JkiRJUmcZeiVJkiRJnWXolSRJkiR1lqFXkiRJktRZhl5JkiRJUmcZeiVJkiRJnWXolSRJkiR1lqFXkiRJktRZhl5JkiRJUmcZeiVJkiRJnWXolSRJkiR1lqFXkiRJktRZhl5JkiRJUmcZeiVJkiRJnWXolSRJkiR1lqFXkiRJktRZhl5JkiRJUmcZemeAJCcl+fR01yFJkiRJXWPoHVCSQ5JUkvf2rd82SU3ycC8FPji86u6tp96R5dokP0ry+Kk8ryRJkiRNJ0PvkrkNeE+SByzJQarquqq6cUg1jedHwOrt8mzgTuCYpXBeSZIkSZoWht4lcwJwFbD7WA2SPCjJN5P8NcktSc5Jsn1fm38Ob07y8SS/HOU4ZyfZs+f1DknOS3JbkvOTvGkC9S6qqqva5Uxgb+BhSR7cc9y9k1zQ1npRko8kWaHdtnaSu5Js0lfbW5JckiTt6/WTHJvkpiRXJzk0yb/0tH95+znc2vY4n5Bk5QnUL0mSJEmTYuhdMv8A3ge8JcmaY7S5D/BbYGvgccCXgEOTPGmM9t8AnpRk3ZEVSR4LbNBuI8nOwH8D7wfWa2v4SJLXTbTwJKsArwEuBK7t2XQjMB9YH3grsDPwdoCqupgm6O/Qd7gdgEOqqpKsDpwMnAlsAmwFPAT4dnve1YFvAge3tW8BHAlklBpXSjJ3ZAFWnej7kyRJkiSAOdNdwGxXVd9LciawJ7DTKNsvB/6nZ9Vnk2wFbAf8apT25yY5G3g18JF29WuA31TVBe3rDwLvrKoj29d/SbI+8Abgq+OUu3WSm9rvVwauBLauqrt6zv/RnvYXJ/kU8Epgn3bdl4EDkryjqhYl2RDYiOa+ZIA3AmdU1ftGDpJkR+CyJI8GVqG57o6sqkvaJueMUe/uwB7jvB9JkiRJGpc9vcPxHuB1bfC8hyTLJ3l/Ozz52jZ0PhdYa5zjfYMm6NIOGd6eu3t5Hww8DDioHT58U3vMDwDrjnG8ET+jCagbAU8Cfgwcl+ThPfW+PMnPk1zVHvcjfbUeRXMv8Eva1zsCP2t7gQGeADyzr7bz223rAmcBJwLnJDkiyc7j3BP9cWBezzJWb7okSZIkjcrQOwRVdQpwPPCxUTa/k2Z48D7As2gC5/HAiuMc8jDg0Un+DXgqTcg9vN028jPbmbsD7EY0Q6efvJhSb66qC9vl1zQ90yu3xyLJk9vzHEczHHtjmmHU/6y1qm4HDgV2SLIiTY/0wT3nWA74QV9tGwGPAk6pqn8AWwLPB/4AvAX4Y5JH9BdbVYuqauHIQjP0WpIkSZImzOHNw7M78Dvggr71TweOrqqvAyRZjiYAnjfWgarqr0lOoentvS9wQlVd3W67OsnlwDpV9Y0lrLmAu9pzADwNuKSq/nukQW8vcI8vA+cCbwJWoLknd8QZwMuAi6vqzlFPWlXAqcCpSfYCLqHpOd53id6NJEmSJPUx9A5JVZ2d5Bs0PZe9LgReluSpwPXAO4CHMk7obX0D+DBNL+vb+7Z9GNg/yUKaXtmVaCaNekBVjRccV0ry0Pb7BwBvprnH9gc9ta6V5FXAb4AXcvcw5t73el47w/TewMFVdWvP5s/T9Bx/M8kngWuARwKvatdvQvO4pB8Df6MZZv1gFv95SJIkSdKkObx5uD7IvWch/ghN7+fxwEk0jzg6agLHOgJ4EHC//vZV9WXg9TSzLJ9DM1vyfOAviznmVjSTV11JM4nWpsB2VXVSe9yjgf2Az9HMvvxU7p5Mq99BNIG8d2gzVXUFTY/x8jTv+VzgM8ACml7lhcAzgGNpesU/SjMp13GLqV2SJEmSJi3NSFNpcpK8H3hVVW2wFM85F1jwuec8nPvO8e81kiRJ0tKy43EXTXcJ97Bw4ULmzZsHMK+d/2dMJgdNSpJVkmxKM4x7/+muR5IkSZLGY+jVZH0O+DnNkOqDF9NWkiRJkqaVE1lpUqpqPs39w5IkSZI049nTK0mSJEnqLEOvJEmSJKmzDL2SJEmSpM4y9EqSJEmSOsvQK0mSJEnqLEOvJEmSJKmzDL2SJEmSpM4y9EqSJEmSOsvQK0mSJEnqLEOvJEmSJKmzDL2SJEmSpM4y9EqSJEmSOsvQK0mSJEnqLEOvJEmSJKmzDL2SJEmSpM4y9EqSJEmSOsvQK0mSJEnqLEOvJEmSJKmzDL2SJEmSpM4y9EqSJEmSOsvQK0mSJEnqrFTVdNcgTUiSucCCBQsWMHfu3OkuR5IkSdI0WbhwIfPmzQOYV1ULx2trT68kSZIkqbMMvZIkSZKkzjL0SpIkSZI6y9ArSZIkSeosQ68kSZIkqbMMvZIkSZKkzjL0SpIkSZI6y9ArSZIkSeosQ68kSZIkqbMMvZIkSZKkzjL0SpIkSZI6y9ArSZIkSeosQ68kSZIkqbPmTHaHJA8C9gKeCaxGX3CuqgcOpzRJkiRJkpbMpEMv8HVgXeAg4GqghlqRtBiP3XVjlltx+ekuQ5IkSeqMS750wXSXMGUGCb2bAZtV1VnDLkaSJEmSpGEa5J7e84H7DrsQSZIkSZKGbZDQ+ybgv5NsnuRBSeb2LsMuUJIkSZKkQQ0yvPkGYB7w0771obm/15stJUmSJEkzwiCh9xvA7cCrcSIrSZIkSdIMNkjofRywcVX9cdjFSJIkSZI0TIPc03s68LBhFyJJkiRJ0rAN0tP7WeAzST4JnAPc0buxqs4eRmGSJEmSJC2pQULvt9qvB/esK5zISpIkSZI0wwwSeh8x9CokSZIkSZoCkw69VXXJVBQiSZIkSdKwDdLTS5JHA1sAq9E3GVZV7bXkZUmSJEmStOQmHXqT7Ax8AbgGuIp7Pqe3AEOvJEmSJGlGGKSn9wPA+6tq72EXI0mSJEnSMA3ynN4HAEcMuxBJkiRJkoZtkNB7BPDcYRciSZIkSdKwTWh4c5Jde15eCHwkyZOBc4A7ettW1f7DK0+SJEmSpMFN9J7et/e9vgnYvF16FWDolSRJkiTNCBMKvVX1iKkuRJIkSZKkYZv0Pb1JPpTkfqOsv2+SDw2nLEmSJEmSltwgE1ntAawyyvr7tdskSZIkSZoRBgm9obl3t9+GwHVLVo4kSZIkScMz0YmsSHI9Tdgt4IIkvcF3eZre3wOGW54kSZIkSYObcOgF3kbTy3swzTDmBT3bbgcurqrThleaJEmSJElLZsKht6q+CpDkL8AvquqOxewiSZIkSdK0mtA9vUnm9rz8HXDfJHNHW6amTC1tSVZL8sUklyZZlOSqJMcn2TzJNUk+MMZ+u7fbV0wyP0klOW+Udq9ot1085W9GkiRJ0jJrohNZXZ9ktfb7G4DrR1lG1qsbvkszOdnrgEcD2wAn0dy7/XVgfpKMst8OwKFVdXv7+mZgtSRP6Wu3I3DpFNQtSZIkSf800eHNz+LumZmfOUW1aIZIcn9gM2CLqjq5XX0J8Ot2+6XAW4FnACf37Pd04FHAQT2HuxM4jCbknta2WxPYAtgP2H7q3okkSZKkZd2EQu9I8EkyhyasHFxVl01hXZpeN7XLtkl+WVWLejdW1TlJfkPTq3tyz6YdgV9X1bl9xzsIOCXJW6vqFmA+8CPg6vGKSLISsFLPqlUHeTOSJEmSll2Tek5vVd0JvIvmEUXqqPbnPJ9maPMNSU5N8rEkj+9pdjDw8iSrALRft+OevbwjxzsT+HPbPu2xD55AKbvTzBI+svx1wLckSZIkaRk1qdDbOpGmt1cdVlXfBdaguZf3eJqf+RlJ5rdNvklz/byyff1KmkdaHT7GIQ+m6RnenOa+4GMnUMbHgXk9y5qTfBuSJEmSlnGTeU7viOOAjyd5HPBbmomK/qmqvj+MwjT9quo24CftsleSLwN7AodU1YIk36EJsge1X79TVQvHONw3gH2ADwNfq6o7R58H6x7nXwT8c2j14tpLkiRJUr9BQu8X2q/vGGVb4dDnLvsDsG3P64OAk5JsDTwNeN9YO1bVdUm+D7wC2GUqi5QkSZKkEZMOvVU1yJBozSJJHgQcQTMk+WzgRmATYDfg6JF2VXVykguBrwEXVtUpizn0fOBNVXXtVNQtSZIkSf0G6elV990E/Ap4O7AusAJwGXAg8LG+tge36z65uINW1a3ArUOtVJIkSZLGkaqa/E7J5jSzOK9HM6T5POCTVfV/wy1PuluSucCCNV+3Dsut6Ch6SZIkaVgu+dIF013CpCxcuJB58+YBzBtnXiFggNmbk/w7cAJwC7A/8Dma3rsTk7x68uVKkiRJkjQ1Bhne/H5gt6rar2fdZ5K8A/ggcNhQKpMkSZIkaQkNMinVOsAPRln/feARS1aOJEmSJEnDM0jovQx49ijrn91ukyRJkiRpRhhkePOngP2TbAT8gmYiq81oHkfz1qFVJkmSJEnSEhrkOb1fSHIV8E7gFe3q84BXVtXRY+8pSZIkSdLSNdBzeqvqe8D3hlyLJEmSJElDNcg9vZIkSZIkzQqT7ulNcj3Nfbz9CrgNuBA4pKq+soS1SZIkSZK0RAYZ3rwXzbN6jwN+DQTYFNgK+DzNY4u+kGROVR04rEIlSZIkSZqsQULvZsAHquqA3pVJ3gA8t6peluRsYFfA0CtJkiRJmjaD3NP7POCEUdaf2G4DOBZYZ9CiJEmSJEkahkFC73XAi0ZZ/6J2G8DKwI2DFiVJkiRJ0jAMMrz5IzT37D6T5p7eAp4IvADYpW2zJXDyUCqUJEmSJGlAkw69VXVgkj8AbwZeSjOR1fnA5lX1i7bNp4ZapSRJkiRJAxikp5eqOhU4dci1SJIkSZI0VBMKvUnmTvSAVbVw8HIkSZIkSRqeifb03kBz7+540rZZfkkKkiRJkiRpWCYaep85wXYbD1qIJEmSJEnDNqHQW1VjzsScZB7wGuD1wIbAp4dSmSRJkiRJS2iQ5/QCkORZSb4OXAm8BTgW2GRYhUmSJEmStKQmNXtzkjWB+cCOwMrAt4EVgJdV1R+GXp0kSZIkSUtgwj29SY4F/gCsT9Ozu0ZVvWWqCpMkSZIkaUlNpqf3ucD+wBeq6k9TVI+0WL/f/3fMnTvhp2hJkiRJWoZN5p7epwOrAqcn+VWSNyd58BTVJUmSJEnSEptw6K2q06pqZ2B14IvAq4DL22NsmWTVqSlRkiRJkqTBTHr25qq6paoOrqrNgA2ATwHvBf6W5PvDLlCSJEmSpEEN/MgigKr6Y1XtBqwJbD+ckiRJkiRJGo5JPbJoLFX1D+CodpEkSZIkaUZYop5eSZIkSZJmMkOvJEmSJKmzDL2SJEmSpM4y9EqSJEmSOsvQK0mSJEnqLEOvJEmSJKmzDL2SJEmSpM4y9EqSJEmSOsvQK0mSJEnqrDnTXYA0WR955MNZablMdxmSJEnStPnoVddNdwmzhj29kiRJkqTOMvRKkiRJkjrL0CtJkiRJ6ixDryRJkiSpswy9kiRJkqTOMvRKkiRJkjrL0CtJkiRJ6ixDryRJkiSpswy9kiRJkqTOMvRKkiRJkjrL0CtJkiRJ6ixDryRJkiSpswy9kiRJkqTOMvRKkiRJkjrL0CtJkiRJ6ixDryRJkiSpswy9kiRJkqTOMvRKkiRJkjrL0CtJkiRJ6ixDryRJkiSpswy9kiRJkqTOMvRKkiRJkjrL0CtJkiRJ6ixDryRJkiSpszofepNcnORtS7D//CQ3DK+i7khyUpJPT3cdkiRJkjSWaQ29SQ5JctQUn2ZT4EsTaThGQP4W8OhBT96G5upZrk7ygySPHfSYM8hLgQ9OdxGSJEmSNJbO9/RW1d+r6pYl2P/WqvrbEpaxEFgdWAN4IbAy8MMkKy7hcceVZIWpPH5VXVdVN07lOSRJkiRpSczo0Jtk8yS/TrIoyZVJPpFkTs/2VZN8I8nN7fa39w+57e+9TfLhJJe2x7wiyf7t+pOAhwP7jfTKtuvvNbw5yTZJTk9yW5Jrkhy5mLdSVXVVVV1ZVacD+7XnekzPMZ+a5JQktya5LMn+SVbu2b56kh+22/+S5NWjvLdKskuSo5PcDHygXf+iJL9t670oyR59n+Oon0m77U1J/tTue3WS7/Rs6/+sH5Dka0muT3JLkuOSPKpn+/wkNyR5XpLzktyU5EdJVl/M5ydJkiRJA5mxoTfJvwLHAr8BNgTeCOxEG+Ra+wJPA7YBtgSeDvzbOMd8OfB24A3Ao4BtgXPazS8F/gp8iKZXdtQgluSFwJHAD4GNgWcDp0/ifd0feHX78o523QbA8e1xHw+8EtgM+FzPrl+j6SneAngZ8J/AaqOcYk/gaGAD4OAkzwO+DuwPrN++9/nA+9tzj/mZJNmk3e9DNAF9K+CUcd7eIcAmND+PpwABju3rcb4f8C7gtcAzgLWA/xntYElWSjJ3ZAFWHefckiRJknQvcxbfZNq8CbgMeHNVFXB+kjWAvZPsRTNE+HXAq6vqRIAkOwBXjHPMtYCrgBOq6g7gUuDX0AzVTfIP4MaqumqcY7wfOLyq9uhZd9Zi3su8JDfRhMD7teu+X1Xnt9+/Gzisqj7dvv5Tkl2Bk5O8EVgbeA6wadtTTJLXA38a5VyHVdXBIy+SHAp8oqq+2q66KMkHgX1oAvKYn0m77WbgmHYY8yXA70Z7g22P7jbA06rqF+2619D8DLcFjmibrgDsUlV/btt8jiZUj2Z3YI8xtkmSJEnSYs3Ynl5gPeC0NvCOOBVYBVgTWIcmQI0ENKpqAfDHcY55BHBfmuB3YJKX9A7znaCNgBMnuc+N7X5PAHYB/tx+HfEEYH473PemNiAfT/PzeQRNL+udwBkjO1TVhcD1o5yrv9f5CcCH+o59ILB6kvsx/mfyE5qge1GSQ5O8pt1nNOu1Nf6qp8ZraX4e6/W0u2Uk8LauZPQea4CPA/N6ljXHaCdJkiRJo5rJoTdAjbKOdn3v96O1uZequowmQP4XcCvwv8Apk5zw6dZJtB1xV1VdWFXnV9UXgUNpZoUesRzwRZpgPLJsSDPc+M+M/Z5GW39z3+vlaHpLe4+9QXvs28b7TNre3X8DtqcJp3sBZ7VDtCdSy8j63p/RHX3be3+W99xQtaiqFo4sNH88kCRJkqQJm8mh9w/AU5P0BqKn0gSfy2nC4B3AE0c2tvd9PopxtLMxf7+qdqW5P/YpNCEQ4HZg+cXUdTbNfbxLYj9gwyQvaV+fATy2Dcb9y+3A+TRD0TceOUCSRwL3n8C5zgAeM8ax74LxP5OqurOqTqiq3WjuN14beNYo5/lDW+OTemp8EM3jns6b4OciSZIkSUM1E+7pnZdko75119H0OL4N+Gx73+djaO5B3bcNazcm+SrwySTXAX9rt9/FvXt/gWb2YJpQ+yvgFprJlG6lGcILcDHwjCSHA4uq6ppRDrMncGKSPwOH03yGz6+qfSb6hqtqYZIvA3umeU7x3sAvk3yeZujxzTRDgresqrdU1flJTgC+1N7jewfwqbb2Ud9rj72AY5JcRjOU+S6a8LpBVX1gvM8kydY0w8hPoRlK/QKaP5Tcawh5Vf0pydHAgUneQPPHiU/Q/IHi6Il+NpIkSZI0TDOhp3cLmsmRepe9qupympD1RJqJog4ADgI+2rPvO4DTgGOAE2ju+T0PuG2Mc90A7Ny2G+mxfVF77yk0EyqtTdOL/PfRDlBVJwHb0UzadCbwU3p6NyfhMzTBdruqOhvYnKaX+v9oPoOP0AwpHvEfwNU0AfR7NOH4RsZ+ryP1Hg9sTTO79W+AX9J8biNB/wbG/kxuoJnV+qc0n+suwPZV9fsxTrcD8Fuan8dpNMOWX9BOkCVJkiRJS13uOU/U7NY+1/Zy4J1VddB01zOVkqxJMzPyc0Zmr+66dvj6gnc9+P6stNyYt25LkiRJnffRq66b7hKm1cKFC5k3bx7AvHb+nzHNhOHNA0uyMfD/aGZwnsfdj77p3HDaJM+imbn6HJpnCO9DMxx7vOfmSpIkSdIybVaH3ta7aO73vZ1maO3Tx7gXd7ZbAfgYzT22NwK/AF7j0GFJkiRJGtusDr1V9Tua59B2Xntv7vHTXYckSZIkzSYzYSIrSZIkSZKmhKFXkiRJktRZhl5JkiRJUmcZeiVJkiRJnWXolSRJkiR1lqFXkiRJktRZhl5JkiRJUmcZeiVJkiRJnWXolSRJkiR1lqFXkiRJktRZhl5JkiRJUmcZeiVJkiRJnWXolSRJkiR1lqFXkiRJktRZhl5JkiRJUmcZeiVJkiRJnWXolSRJkiR1lqFXkiRJktRZhl5JkiRJUmcZeiVJkiRJnWXolSRJkiR11pzpLkCarA9eeAlz586d7jIkSZIkzQL29EqSJEmSOsvQK0mSJEnqLEOvJEmSJKmzDL2SJEmSpM4y9EqSJEmSOsvQK0mSJEnqLEOvJEmSJKmzDL2SJEmSpM4y9EqSJEmSOsvQK0mSJEnqLEOvJEmSJKmzDL2SJEmSpM4y9EqSJEmSOsvQK0mSJEnqrDnTXYA0Wa/b5G2ssPyK012GJEmStMz49nkHTHcJA7OnV5IkSZLUWYZeSZIkSVJnGXolSZIkSZ1l6JUkSZIkdZahV5IkSZLUWYZeSZIkSVJnGXolSZIkSZ1l6JUkSZIkdZahV5IkSZLUWYZeSZIkSVJnGXolSZIkSZ1l6JUkSZIkdZahV5IkSZLUWYZeSZIkSVJnGXolSZIkSZ1l6JUkSZIkdZahV5IkSZLUWYZeSZIkSVJnGXolSZIkSZ1l6JUkSZIkdZahV5IkSZLUWYZeSZIkSVJnGXolSZIkSZ1l6JUkSZIkdZahV5IkSZLUWYZeDUWSQ5JUu9yZ5NIkX0jygJ42F/e0GVn+Op11S5IkSeq2OdNdgDrlR8AONNfV+sDBwP2B7XvafAg4sOf1P5ZWcZIkSZKWPYZeDdOiqrqq/f6vSb4FzO9rc2NPG0mSJEmaUoZeTYkk6wBbAXcswTFWAlbqWbXqktYlSZIkadniPb0apq2T3JTkVuDPNEOc9+5rs3fbZmTZdZzj7Q4s6Fm8/1eSJEnSpNjTq2H6GfBG4H7A64FHA5/ta/NJ4JCe19eMc7yPA/v2vF4Vg68kSZKkSTD0aphurqoL2+93TfIzYA/ggz1trulpM66qWgQsGnmdZGiFSpIkSVo2OLxZU2lP4F1J1pjuQiRJkiQtmwy9mjJVdRLwe+B901yKJEmSpGWUoVdTbV9g5yQPm+5CJEmSJC17vKdXQ1FV88dYfxhwWPty7aVVjyRJkiSBPb2SJEmSpA4z9EqSJEmSOsvQK0mSJEnqLEOvJEmSJKmzDL2SJEmSpM4y9EqSJEmSOsvQK0mSJEnqLEOvJEmSJKmzDL2SJEmSpM4y9EqSJEmSOsvQK0mSJEnqLEOvJEmSJKmzDL2SJEmSpM4y9EqSJEmSOsvQK0mSJEnqLEOvJEmSJKmzDL2SJEmSpM4y9EqSJEmSOsvQK0mSJEnqLEOvJEmSJKmzDL2SJEmSpM4y9EqSJEmSOsvQK0mSJEnqrFTVdNcgTUiSucCCBQsWMHfu3OkuR5IkSdI0WbhwIfPmzQOYV1ULx2trT68kSZIkqbMMvZIkSZKkzjL0SpIkSZI6y9ArSZIkSeosQ68kSZIkqbMMvZIkSZKkzpoz3QVIk7Vw4bgzkkuSJEnquMlkAp/Tq1kjydrAX6a7DkmSJEkzxppVdfl4Dezp1WxyXft1TeDG6SxEnbEq8Fe8pjQ8XlMaNq8pDZvXlIZtOq+pVYErFtfI0KvZ6MaqcoyzlliSkW+9pjQUXlMaNq8pDZvXlIZtmq+pCZ3PiawkSZIkSZ1l6JUkSZIkdZahV7PJImDP9qs0DF5TGjavKQ2b15SGzWtKwzbjrylnb5YkSZIkdZY9vZIkSZKkzjL0SpIkSZI6y9ArSZIkSeosQ68kSZIkqbMMvZpRkrwpyV+S3Jbkt0mevpj2m7ftbktyUZJdllatmh0mc00leWmSnyT5e5KFSU5L8rylWa9mvsn+O9Wz39OS3JnkzCkuUbPMAP/vWynJfye5JMmiJH9OsuPSqlcz3wDX1GuSnJXkliRXJvlKkgctrXo1syV5RpIfJLkiSSXZdgL7zKjf0Q29mjGSvBL4NPDfwMbA/wHHJVlrjPaPAI5t220MfAzYP8nLlkrBmvEme00BzwB+ArwAeALwM+AHSTae+mo1GwxwTY3sNw/4GnDiVNeo2WXAa+rbwLOBnYDHANsD509tpZotBvh9ajOaf58OAh4LbAdsCnx5adSrWWFl4CzgzRNpPBN/R/eRRZoxkvwKOKOq3tiz7jzgqKrafZT2ewPbVNV6PesOADasqqcsjZo1s032mhrjGL8HvlVVe01RmZpFBr2mkhwO/An4B7BtVW001bVqdhjg/31bAYcD61TVdUuvUs0WA1xT7wLeWFXr9qx7C7BbVT1sadSs2SNJAS+pqqPGaTPjfke3p1czQpIVaXrWfty36cfAU8fY7SmjtD8e2CTJCsOtULPNgNdU/zGWA1YF/MVSA19TSXYA1gX2nLrqNBsNeE1tA5wO7Jbk8iQXJPmfJPedwlI1Swx4Tf0CWDPJC9J4CPBy4IdTV6k6bsb9jj5nOk4qjeJfgOWBq/vWXw08dIx9HjpG+znt8a4cZoGadQa5pvq9k2ZIz7eHWJdmr0lfU0keBXwCeHpV3ZlkaivUbDPIv1PrAJsBtwEvaY/xv8ADAe/r1aSvqar6RZLXAN8C7kPze9T3gbdMYZ3qthn3O7o9vZpp+sfbZ5R1i2s/2notuyZ7TTWNku2BDwOvrKq/TUFdmr0mdE0lWR44DNijqi5YGoVp1prMv1PLtdteU1W/rqpjgXcA8+3tVY8JX1NJ1gf2B/ai6SXeCngEcMBUFqjOm1G/o9vTq5niGpp73fr/Crka9/5L0Yirxmh/J3DtUKvTbDTINQX8cxKQg4DtquqEqSlPs9Bkr6lVgU2AjZN8rl23HJAkdwLPraqfTlWxmhUG+XfqSuDyqlrQs+48ml8o16S5d1zLrkGuqd2BU6vqk+3rs5PcDPxfkg9UlSPnNFkz7nd0e3o1I1TV7cBvgS37Nm1Jc6/JaE4bpf1zgdOr6o7hVqjZZsBraqSH9xDg1VXl/Uz6pwGuqYXABsBGPcsBwB/b7381JYVq1hjw36lTgTWSrNKz7tHAXcBfh16kZpUBr6n70Vw/vf7RfvWeDA1ixv2Obk+vZpJ9gUOTnE7zH8t/AmvRDq9J8nHgX6vqP9r2BwBvTrIvcCDNTfM70Ty6QYJJXlNt4P0a8Fbgl0lG/kp5a1+vipZdE76mquou4NzenZP8Dbitqs5Fakz2/32HAR8EvpJkD5r74z4JHFxVty7t4jUjTfaa+gFwYJI30kw2tDrNI49+XVVXLOXaNQO1f2R7ZM+qRyTZCLiuqi6dDb+jG3o1Y1TVt9oHoX+I5h/cc4EXVNUlbZPVaf7RHmn/lyQvAPYD/gu4Ati1qr67dCvXTDXZawp4A82/i59vlxFfBeZPecGa8Qa4pqRxDfD/vpuSbAl8lmYW52tpJtv7wFItXDPWANfUIUlWpXkG66eAG4CfAu9ZmnVrRtsE+FnP633bryO/H83439F9Tq8kSZIkqbO8p1eSJEmS1FmGXkmSJElSZxl6JUmSJEmdZeiVJEmSJHWWoVeSJEmS1FmGXkmSJElSZxl6JUmSJEmdZeiVJEmSJHWWoVeSJC2RJE9N8o8kP5ruWiRJ6mfolSRJS2pH4LPAZknWmq4ikqwwXeeWJM1chl5JkjSwJCsDrwC+ABwDzO/bvk2S05PcluSaJEf2bFspyT5JLkuyKMmfkuzUbpuf5Ia+Y22bpHpefzjJmUl2THIRsCiNrZL8PMkNSa5NckySdfuOtWaSw5Ncl+TmtsYnJVk7yV1JNulr/5YklyTJUD44SdJSY+iVJElL4pXAH6vqj8DXgR1GgmGSFwJHAj8ENgaeDZzes+/XgFcBuwLrAbsAN03y/I+kCd0vAzZq160M7Ats2p7zLuB7SZZr61oFOBlYA9gG2BDYB1iuqi4GTgB26DvPDsAhVVVIkmaVOdNdgCRJmtV2ogm7AD8CVqEJmicA7wcOr6o9etqfBZDk0TRhdcuqOqHddtEA518ReG1V/b1n3Xd7G7S9x38D1gfOBV4NPBjYtKqua5td2LPLl4EDkryjqhYl2ZAmUL90gPokSdPMnl5JkjSQJI8BnggcDlBVdwLfornHF5qgeOIYu28E/IOmx3VJXNIXeEmybpLDklyUZCHwl3bTyP3GGwG/6wm8/Y4C7gRe0r7eEfhZ2wssSZpl7OmVJEmD2onmd4nLe251DXBHkgcAt46z73jboBmS3H//7GgTVd08yrofAJcBOwNX0PyR/1yaXuHFnruqbk9yKM1Q7SNpeobftph6JUkzlD29kiRp0pLMAf4DeCdNz+nIsiFwCfAa4Gyaoc6jOYfm95DNx9j+d2DVdqKsERtNoK4H0dwf/NGqOrGqzgMe0NfsbGCjJA8c51BfBp4DvIkmbB85TltJ0gxmT68kSRrE1jRh8qCqWtC7Icl3aHqB3w6cmOTPNEOg5wDPr6p9quriJF8FDk6yK829vg8HVquqbwO/Am4BPpbkszTDqOdPoK7rgWuB/0xyJc2Q5k/0tfkm8D7gqCS7A1fSTLR1RVWdBlBV5yX5JbA3cHBVLa5nWpI0Q9nTK0mSBrETcEJ/4G19l6ZXdiGwHc0MyWcCPwWe1NPujcB3gP8FzgcOpJl5mfZ+238HXkDTK7w98OHFFVVVd9HMCP0EmiHN+wHv7mtzO/Bcmsmtjm2P/16ae4x7HUQzJPrgxZ1XkjRzxZn3JUmS7i3J+4FXVdUG012LJGlw9vRKkiT1SLJKkk2BtwD7T3c9kqQlY+iVJEm6p88BP6d5nJJDmyVplnN4syRJkiSps+zplSRJkiR1lqFXkiRJktRZhl5JkiRJUmcZeiVJkiRJnWXolSRJkiR1lqFXkiRJktRZhl5JkiRJUmcZeiVJkiRJnWXolSRJkiR11v8H60FnVM8zTykAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,5],dpi = 100)\n",
    "plt.title('Accuracy Comparison')\n",
    "plt.xlabel('Accuracy')\n",
    "plt.ylabel('Algorithm')\n",
    "sns.barplot(x = acc,y = model,palette='dark')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree --> 0.9\n",
      "Naive Bayes --> 0.990909090909091\n",
      "SVM --> 0.9795454545454545\n",
      "Logistic Regression --> 0.9522727272727273\n",
      "RF --> 0.990909090909091\n"
     ]
    }
   ],
   "source": [
    "accuracy_models = dict(zip(model, acc))\n",
    "for k, v in accuracy_models.items():\n",
    "    print (k, '-->', v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making a prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['coffee']\n"
     ]
    }
   ],
   "source": [
    "data = np.array([[80,20, 40, 23.603016, 60.3, 6.7, 140.91]])\n",
    "prediction = RF.predict(data)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}