what-crop-to-grow.ipynb

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   "source": [
    "# CROP RECOMMENDATION SYSTEM "
   ]
  },
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   "source": [
    "# Importing libraries\n",
    "\n",
    "\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    "execution": {
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   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Crop_recommendation.csv\")"
   ]
  },
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   "execution_count": 3,
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       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
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       "      <td>82.002744</td>\n",
       "      <td>6.502985</td>\n",
       "      <td>202.935536</td>\n",
       "      <td>rice</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>242.864034</td>\n",
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       "    <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",
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       "    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",
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   "source": [
    "df.head()"
   ]
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       "  <thead>\n",
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       "      <th>2195</th>\n",
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       "      <td>6.780064</td>\n",
       "      <td>177.774507</td>\n",
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       "    <tr>\n",
       "      <th>2196</th>\n",
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       "      <td>56.636362</td>\n",
       "      <td>6.086922</td>\n",
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       "    <tr>\n",
       "      <th>2197</th>\n",
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       "      <td>6.779833</td>\n",
       "      <td>140.937041</td>\n",
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       "        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"
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   "source": [
    "df.tail()"
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    {
     "data": {
      "text/plain": [
       "17600"
      ]
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    "df.size"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2200, 8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "df.shape"
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   "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"
   ]
  },
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   "execution_count": 8,
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    },
    "tags": []
   },
   "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,
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    "execution": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2020-12-25T17:43:04.476169Z",
     "iopub.status.busy": "2020-12-25T17:43:04.475372Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "muskmelon      100\n",
       "banana         100\n",
       "kidneybeans    100\n",
       "lentil         100\n",
       "papaya         100\n",
       "mungbean       100\n",
       "mothbeans      100\n",
       "mango          100\n",
       "pigeonpeas     100\n",
       "grapes         100\n",
       "blackgram      100\n",
       "coconut        100\n",
       "jute           100\n",
       "rice           100\n",
       "apple          100\n",
       "pomegranate    100\n",
       "coffee         100\n",
       "watermelon     100\n",
       "maize          100\n",
       "cotton         100\n",
       "orange         100\n",
       "chickpea       100\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "duration": 0.035621,
     "end_time": "2020-12-25T17:43:05.012300",
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     "start_time": "2020-12-25T17:43:04.976679",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Seperating features and target label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-25T17:43:05.082765Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "features = df[['N', 'P','K','temperature', 'humidity', 'ph', 'rainfall']]\n",
    "target = df['label']\n",
    "labels = df['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-25T17:43:05.157598Z",
     "iopub.status.busy": "2020-12-25T17:43:05.156803Z",
     "iopub.status.idle": "2020-12-25T17:43:05.160156Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Initializing empty lists to append all model's name and corresponding name\n",
    "acc = []\n",
    "model = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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    "tags": []
   },
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.033437,
     "end_time": "2020-12-25T17:43:05.306117",
     "exception": false,
     "start_time": "2020-12-25T17:43:05.272680",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-25T17:43:05.382104Z",
     "iopub.status.busy": "2020-12-25T17:43:05.381326Z",
     "iopub.status.idle": "2020-12-25T17:43:05.438864Z",
     "shell.execute_reply": "2020-12-25T17:43:05.439274Z"
    },
    "papermill": {
     "duration": 0.093881,
     "end_time": "2020-12-25T17:43:05.439389",
     "exception": false,
     "start_time": "2020-12-25T17:43:05.345508",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTrees's Accuracy is:  99.45454545454545\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        19\n",
      "      banana       1.00      1.00      1.00        27\n",
      "   blackgram       1.00      1.00      1.00        25\n",
      "    chickpea       1.00      1.00      1.00        26\n",
      "     coconut       1.00      1.00      1.00        25\n",
      "      coffee       1.00      1.00      1.00        26\n",
      "      cotton       1.00      1.00      1.00        20\n",
      "      grapes       1.00      1.00      1.00        24\n",
      "        jute       0.95      0.95      0.95        19\n",
      " kidneybeans       1.00      1.00      1.00        21\n",
      "      lentil       1.00      0.97      0.98        31\n",
      "       maize       1.00      1.00      1.00        23\n",
      "       mango       1.00      1.00      1.00        22\n",
      "   mothbeans       0.97      1.00      0.98        29\n",
      "    mungbean       1.00      1.00      1.00        29\n",
      "   muskmelon       1.00      1.00      1.00        21\n",
      "      orange       1.00      1.00      1.00        22\n",
      "      papaya       1.00      1.00      1.00        29\n",
      "  pigeonpeas       1.00      1.00      1.00        28\n",
      " pomegranate       1.00      1.00      1.00        26\n",
      "        rice       0.95      0.95      0.95        20\n",
      "  watermelon       1.00      1.00      1.00        38\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": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "DecisionTree = DecisionTreeClassifier()\n",
    "\n",
    "#training a model\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": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-25T17:43:05.511318Z",
     "iopub.status.busy": "2020-12-25T17:43:05.510628Z",
     "iopub.status.idle": "2020-12-25T17:43:05.513354Z",
     "shell.execute_reply": "2020-12-25T17:43:05.512823Z"
    },
    "papermill": {
     "duration": 0.039972,
     "end_time": "2020-12-25T17:43:05.513438",
     "exception": false,
     "start_time": "2020-12-25T17:43:05.473466",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-25T17:43:05.589874Z",
     "iopub.status.busy": "2020-12-25T17:43:05.589360Z",
     "iopub.status.idle": "2020-12-25T17:43:05.718348Z",
     "shell.execute_reply": "2020-12-25T17:43:05.718933Z"
    },
    "papermill": {
     "duration": 0.171655,
     "end_time": "2020-12-25T17:43:05.719081",
     "exception": false,
     "start_time": "2020-12-25T17:43:05.547426",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Cross validation score (Decision Tree)\n",
    "score = cross_val_score(DecisionTree, features, target,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-25T17:43:05.798346Z",
     "iopub.status.busy": "2020-12-25T17:43:05.797578Z",
     "iopub.status.idle": "2020-12-25T17:43:05.801313Z",
     "shell.execute_reply": "2020-12-25T17:43:05.800784Z"
    },
    "papermill": {
     "duration": 0.044583,
     "end_time": "2020-12-25T17:43:05.801407",
     "exception": false,
     "start_time": "2020-12-25T17:43:05.756824",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99090909, 0.98409091, 0.99090909, 0.99090909, 0.98409091])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.0343,
     "end_time": "2020-12-25T17:43:05.870225",
     "exception": false,
     "start_time": "2020-12-25T17:43:05.835925",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Saving trained Decision Tree model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-25T17:43:05.945134Z",
     "iopub.status.busy": "2020-12-25T17:43:05.944323Z",
     "iopub.status.idle": "2020-12-25T17:43:05.947948Z",
     "shell.execute_reply": "2020-12-25T17:43:05.947482Z"
    },
    "papermill": {
     "duration": 0.042144,
     "end_time": "2020-12-25T17:43:05.948033",
     "exception": false,
     "start_time": "2020-12-25T17:43:05.905889",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Dump the trained Naive Bayes classifier with Pickle\n",
    "DT_pkl_filename = '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": {
    "papermill": {
     "duration": 0.036295,
     "end_time": "2020-12-25T17:43:08.647681",
     "exception": false,
     "start_time": "2020-12-25T17:43:08.611386",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-25T17:43:08.728752Z",
     "iopub.status.busy": "2020-12-25T17:43:08.728228Z",
     "iopub.status.idle": "2020-12-25T17:43:08.990800Z",
     "shell.execute_reply": "2020-12-25T17:43:08.991758Z"
    },
    "papermill": {
     "duration": 0.308032,
     "end_time": "2020-12-25T17:43:08.991925",
     "exception": false,
     "start_time": "2020-12-25T17:43:08.683893",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression's Accuracy is:  0.9545454545454546\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        19\n",
      "      banana       1.00      1.00      1.00        27\n",
      "   blackgram       0.83      0.96      0.89        25\n",
      "    chickpea       1.00      1.00      1.00        26\n",
      "     coconut       0.93      1.00      0.96        25\n",
      "      coffee       0.96      0.85      0.90        26\n",
      "      cotton       0.90      0.90      0.90        20\n",
      "      grapes       1.00      1.00      1.00        24\n",
      "        jute       0.75      0.95      0.84        19\n",
      " kidneybeans       0.95      1.00      0.98        21\n",
      "      lentil       0.94      0.97      0.95        31\n",
      "       maize       0.87      0.87      0.87        23\n",
      "       mango       1.00      1.00      1.00        22\n",
      "   mothbeans       1.00      0.86      0.93        29\n",
      "    mungbean       1.00      0.93      0.96        29\n",
      "   muskmelon       1.00      1.00      1.00        21\n",
      "      orange       1.00      1.00      1.00        22\n",
      "      papaya       1.00      0.90      0.95        29\n",
      "  pigeonpeas       1.00      0.96      0.98        28\n",
      " pomegranate       1.00      1.00      1.00        26\n",
      "        rice       0.85      0.85      0.85        20\n",
      "  watermelon       1.00      1.00      1.00        38\n",
      "\n",
      "    accuracy                           0.95       550\n",
      "   macro avg       0.95      0.95      0.95       550\n",
      "weighted avg       0.96      0.95      0.96       550\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\swapnil amin\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\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": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.037258,
     "end_time": "2020-12-25T17:43:10.556881",
     "exception": false,
     "start_time": "2020-12-25T17:43:10.519623",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-25T17:43:10.637176Z",
     "iopub.status.busy": "2020-12-25T17:43:10.636618Z",
     "iopub.status.idle": "2020-12-25T17:43:10.758714Z",
     "shell.execute_reply": "2020-12-25T17:43:10.758234Z"
    },
    "papermill": {
     "duration": 0.164699,
     "end_time": "2020-12-25T17:43:10.758807",
     "exception": false,
     "start_time": "2020-12-25T17:43:10.594108",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF's Accuracy is:  0.9981818181818182\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        19\n",
      "      banana       1.00      1.00      1.00        27\n",
      "   blackgram       1.00      1.00      1.00        25\n",
      "    chickpea       1.00      1.00      1.00        26\n",
      "     coconut       1.00      1.00      1.00        25\n",
      "      coffee       1.00      1.00      1.00        26\n",
      "      cotton       1.00      1.00      1.00        20\n",
      "      grapes       1.00      1.00      1.00        24\n",
      "        jute       0.95      1.00      0.97        19\n",
      " kidneybeans       1.00      1.00      1.00        21\n",
      "      lentil       1.00      1.00      1.00        31\n",
      "       maize       1.00      1.00      1.00        23\n",
      "       mango       1.00      1.00      1.00        22\n",
      "   mothbeans       1.00      1.00      1.00        29\n",
      "    mungbean       1.00      1.00      1.00        29\n",
      "   muskmelon       1.00      1.00      1.00        21\n",
      "      orange       1.00      1.00      1.00        22\n",
      "      papaya       1.00      1.00      1.00        29\n",
      "  pigeonpeas       1.00      1.00      1.00        28\n",
      " pomegranate       1.00      1.00      1.00        26\n",
      "        rice       1.00      0.95      0.97        20\n",
      "  watermelon       1.00      1.00      1.00        38\n",
      "\n",
      "    accuracy                           1.00       550\n",
      "   macro avg       1.00      1.00      1.00       550\n",
      "weighted avg       1.00      1.00      1.00       550\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": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-25T17:43:10.844782Z",
     "iopub.status.busy": "2020-12-25T17:43:10.843900Z",
     "iopub.status.idle": "2020-12-25T17:43:11.231113Z",
     "shell.execute_reply": "2020-12-25T17:43:11.231511Z"
    },
    "papermill": {
     "duration": 0.434587,
     "end_time": "2020-12-25T17:43:11.231643",
     "exception": false,
     "start_time": "2020-12-25T17:43:10.797056",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99772727, 0.99545455, 0.99772727, 0.99318182, 0.98863636])"
      ]
     },
     "execution_count": 22,
     "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": {
    "papermill": {
     "duration": 0.041321,
     "end_time": "2020-12-25T17:43:15.359816",
     "exception": false,
     "start_time": "2020-12-25T17:43:15.318495",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Accuracy Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-25T17:43:15.441385Z",
     "iopub.status.busy": "2020-12-25T17:43:15.440884Z",
     "iopub.status.idle": "2020-12-25T17:43:15.581323Z",
     "shell.execute_reply": "2020-12-25T17:43:15.581831Z"
    },
    "papermill": {
     "duration": 0.182519,
     "end_time": "2020-12-25T17:43:15.581943",
     "exception": false,
     "start_time": "2020-12-25T17:43:15.399424",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Accuracy Comparison'}, xlabel='Accuracy', ylabel='Algorithm'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\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": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.041952,
     "end_time": "2020-12-25T17:43:15.756950",
     "exception": false,
     "start_time": "2020-12-25T17:43:15.714998",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Making a prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-25T17:43:15.847402Z",
     "iopub.status.busy": "2020-12-25T17:43:15.846830Z",
     "iopub.status.idle": "2020-12-25T17:43:15.850942Z",
     "shell.execute_reply": "2020-12-25T17:43:15.851479Z"
    },
    "papermill": {
     "duration": 0.051889,
     "end_time": "2020-12-25T17:43:15.851583",
     "exception": false,
     "start_time": "2020-12-25T17:43:15.799694",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['coffee']\n"
     ]
    }
   ],
   "source": [
    "data = np.array([[104,18, 30, 23.603016, 60.3, 6.7, 140.91]])\n",
    "prediction = RF.predict(data)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-25T17:43:15.937084Z",
     "iopub.status.busy": "2020-12-25T17:43:15.936591Z",
     "iopub.status.idle": "2020-12-25T17:43:15.943917Z",
     "shell.execute_reply": "2020-12-25T17:43:15.944404Z"
    },
    "papermill": {
     "duration": 0.051262,
     "end_time": "2020-12-25T17:43:15.944514",
     "exception": false,
     "start_time": "2020-12-25T17:43:15.893252",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['jute']\n"
     ]
    }
   ],
   "source": [
    "data = np.array([[83, 45, 60, 28, 70.3, 7.0, 150.9]])\n",
    "prediction = RF.predict(data)\n",
    "print(prediction)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  },
  "papermill": {
   "duration": 18.192047,
   "end_time": "2020-12-25T17:43:16.257975",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2020-12-25T17:42:58.065928",
   "version": "2.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}