KNN (comparitive study).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading data\n",
    "crop = pd.read_csv(r'C:\\Users\\91984\\Downloads\\Crop_recommendation.csv')\n",
    "crop.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.00000000e+00 9.00000000e+01 4.20000000e+01 ... 4.22888177e+01\n",
      "  1.31968681e+03 4.11828319e+04]\n",
      " [1.00000000e+00 8.50000000e+01 5.80000000e+01 ... 4.95348004e+01\n",
      "  1.59522351e+03 5.13727326e+04]\n",
      " [1.00000000e+00 6.00000000e+01 5.50000000e+01 ... 6.14688481e+01\n",
      "  2.06953438e+03 6.96771240e+04]\n",
      " ...\n",
      " [1.00000000e+00 1.18000000e+02 3.30000000e+01 ... 4.04827787e+01\n",
      "  1.10278525e+03 3.00408064e+04]\n",
      " [1.00000000e+00 1.17000000e+02 3.20000000e+01 ... 4.56812768e+01\n",
      "  8.59551422e+02 1.61735551e+04]\n",
      " [1.00000000e+00 1.04000000e+02 1.80000000e+01 ... 4.59661302e+01\n",
      "  9.55529550e+02 1.98632497e+04]]\n"
     ]
    }
   ],
   "source": [
    "#Normalization using polynomial features\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "data = crop[['N', 'P','K','temperature', 'humidity', 'ph', 'rainfall']]\n",
    "target = crop['label']\n",
    "scaler = PolynomialFeatures()\n",
    "# transform data\n",
    "scaled = scaler.fit_transform(data)\n",
    "print(scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(scaled,target,test_size = 0.2,random_state =2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.97045455, 0.96818182, 0.97272727, 0.96590909, 0.98181818,\n",
       "       0.975     , 0.97954545, 0.97272727, 0.97727273, 0.97272727,\n",
       "       0.975     , 0.97045455, 0.97272727, 0.97272727, 0.97045455,\n",
       "       0.97045455, 0.97272727, 0.97045455, 0.97045455, 0.97045455])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Hyperparameter Tuning\n",
    "mean_acc = np.zeros(20)\n",
    "for i in range(1,21):\n",
    "    #Train Model and Predict  \n",
    "    knn = KNeighborsClassifier(n_neighbors = i).fit(x_train,y_train)\n",
    "    yhat= knn.predict(x_test)\n",
    "    mean_acc[i-1] = metrics.accuracy_score(y_test, yhat)\n",
    "\n",
    "mean_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "loc = np.arange(1,21,step=1.0)\n",
    "plt.figure(figsize = (10, 6))\n",
    "plt.plot(range(1,21), mean_acc)\n",
    "plt.xticks(loc)\n",
    "plt.xlabel('Number of Neighbors ')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#without manhattan\n",
    "grid_params = { 'n_neighbors' : [3,5,7,9,11,17],\n",
    "               'metric' : ['minkowski','euclidean'],\n",
    "              'algorithm' : ['auto', 'ball_tree', 'kd_tree', 'brute']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs = GridSearchCV(KNeighborsClassifier(), grid_params, verbose = 1, cv=5, n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 48 candidates, totalling 240 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:    3.6s\n",
      "[Parallel(n_jobs=-1)]: Done 240 out of 240 | elapsed:    5.7s finished\n"
     ]
    }
   ],
   "source": [
    "g_res = gs.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.978409090909091"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_res.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'auto', 'metric': 'minkowski', 'n_neighbors': 5}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_res.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the best hyperparameters\n",
    "knn = KNeighborsClassifier(n_neighbors = 5, algorithm = 'auto',metric = 'minkowski')\n",
    "knn.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "knn_train_accuracy =  0.9857954545454546\n",
      "knn_test_accuracy =  0.9818181818181818\n"
     ]
    }
   ],
   "source": [
    "# Training & Testing accuracy after applying hyper parameter\n",
    "knn_train_accuracy = knn.score(x_train,y_train)\n",
    "print(\"knn_train_accuracy = \",knn.score(x_train,y_train))\n",
    "#Print Test Accuracy\n",
    "knn_test_accuracy = knn.score(x_test,y_test)\n",
    "print(\"knn_test_accuracy = \",knn.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#with manhattan\n",
    "grid_params1 = { 'n_neighbors' : [3,5,7,9,11,17],\n",
    "               'metric' : ['minkowski','euclidean','manhattan'],\n",
    "              'algorithm' : ['auto', 'ball_tree', 'kd_tree', 'brute']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs1 = GridSearchCV(KNeighborsClassifier(), grid_params1, verbose = 1, cv=5, n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 128 tasks      | elapsed:    2.2s\n",
      "[Parallel(n_jobs=-1)]: Done 360 out of 360 | elapsed:    7.3s finished\n"
     ]
    }
   ],
   "source": [
    "g_res1 = gs1.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9846590909090909"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_res1.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'auto', 'metric': 'manhattan', 'n_neighbors': 5}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_res1.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(metric='manhattan')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the best hyperparameters\n",
    "knn1 = KNeighborsClassifier(n_neighbors = 5, algorithm = 'auto',metric = 'manhattan')\n",
    "knn1.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "knn_train_accuracy =  0.9903409090909091\n",
      "knn_test_accuracy =  0.9772727272727273\n"
     ]
    }
   ],
   "source": [
    "# Training & Testing accuracy after applying hyper parameter\n",
    "knn_train_accuracy = knn1.score(x_train,y_train)\n",
    "print(\"knn_train_accuracy = \",knn1.score(x_train,y_train))\n",
    "#Print Test Accuracy\n",
    "knn_test_accuracy = knn1.score(x_test,y_test)\n",
    "print(\"knn_test_accuracy = \",knn1.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = []\n",
    "model = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Accuracy is:  0.9818181818181818\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       0.89      0.89      0.89        28\n",
      " kidneybeans       0.93      1.00      0.97        14\n",
      "      lentil       0.96      1.00      0.98        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      0.94      0.97        18\n",
      " pomegranate       1.00      1.00      1.00        17\n",
      "        rice       0.81      0.81      0.81        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": [
    "predicted_values = knn.predict(x_test)\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "x = metrics.accuracy_score(y_test, predicted_values)\n",
    "acc.append(x)\n",
    "model.append('K Nearest Neighbours')\n",
    "print(\"KNN Accuracy is: \", x)\n",
    "\n",
    "print(classification_report(y_test,predicted_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = knn.predict(x_test)\n",
    "y_true = y_test\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "cm_knn = confusion_matrix(y_true,y_pred)\n",
    "\n",
    "f, ax = plt.subplots(figsize=(15,10))\n",
    "sns.heatmap(cm_knn, annot=True, linewidth=0.5, fmt=\".0f\",cmap='viridis', ax = ax)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Predicted vs actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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