Evaluation_metrics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "229712f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import essential libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#import seaborn as sn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import recall_score as rs\n",
    "from sklearn.metrics import precision_score as ps\n",
    "from sklearn.metrics import f1_score as fs\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "54fc1ac6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the data in numpy array\n",
    "data = np.loadtxt(open('result.csv', 'rb'), delimiter=',')\n",
    "x = data[:, 0:3]\n",
    "y = data[:, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5740e65b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split the data to training & test data. Test-size is 0.25(25%) of data\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state = 0, test_size = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "39b120ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#select the machine learning algorithm\n",
    "\n",
    "#SVM\n",
    "from sklearn import svm, datasets\n",
    "#clf = svm.SVC()\n",
    "clf = svm.SVC(kernel=\"linear\",C=0.025)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9450df77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=0.025, kernel='linear')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train the ML Algo with training data\n",
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10d4c2aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pass the test data for classify or predict\n",
    "classifier_predictions = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "30b88ed0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Evaluation metrics for test dataset ***\n",
      "\n",
      "Accuracy:  0.9478260869565217\n",
      "Recall Score:  0.9777777777777777\n",
      "Precision Score:  0.8979591836734694\n",
      "F1 Score:  0.9361702127659575\n"
     ]
    }
   ],
   "source": [
    "#Calculate the accuracy from the the prediction result.\n",
    "#print(\"Accuracy is \", accuracy_score(y_test, classifier_predictions)*100)\n",
    "ma_svm = clf.score(x_test,y_test)\n",
    "recall_svm = rs(y_test,classifier_predictions)\n",
    "precision_svm = ps(y_test,classifier_predictions)\n",
    "f1_svm = fs(y_test,classifier_predictions)\n",
    "print('*** Evaluation metrics for test dataset ***\\n')\n",
    "print('Accuracy: ',ma_svm)\n",
    "print('Recall Score: ',recall_svm)\n",
    "print('Precision Score: ',precision_svm)\n",
    "print('F1 Score: ',f1_svm)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "908edb0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Evaluation Metrics for SVM')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = pd.DataFrame(y_test)\n",
    "a['classifier_predictions']= clf.predict(x_test)\n",
    "#print('\\n\\tTable 3\\n')\n",
    "#print(a.head())\n",
    "\n",
    "rcParams['figure.figsize']=8,8\n",
    "plt.plot(figsize=(40,20))\n",
    "plt.bar(['Accuracy','F1 Score','Recall Score','Precision Score'],[ma_svm*100,f1_svm*100,recall_svm*100,precision_svm*100],color=['red','green','purple','blue'])\n",
    "plt.plot([ma_svm*100,f1_svm*100,recall_svm*100,precision_svm*100],color='black')\n",
    "plt.ylabel(\"Percentage\")\n",
    "plt.ylim([0,100])\n",
    "plt.title('Evaluation Metrics for SVM')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "77e1514d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3b1f7325",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier as dtc\n",
    "classifier = dtc(max_depth=2)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2,random_state=10)\n",
    "classifier.fit(x_train,np.array(y_train).reshape(y_train.shape[0],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "78c6f87b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Evaluation metrics for test dataset ***\n",
      "\n",
      "Accuracy:  0.9347826086956522\n",
      "Recall Score:  0.9210526315789473\n",
      "Precision Score:  0.9210526315789473\n",
      "F1 Score:  0.9210526315789473\n"
     ]
    }
   ],
   "source": [
    "pred = np.array(classifier.predict(x_test))\n",
    "recall_dtc = rs(y_test,pred)\n",
    "precision_dtc = ps(y_test,pred)\n",
    "f1_dtc = fs(y_test,pred)\n",
    "ma_dtc = classifier.score(x_test,y_test)\n",
    "\n",
    "print('*** Evaluation metrics for test dataset ***\\n')\n",
    "print('Accuracy: ',ma_dtc)\n",
    "print('Recall Score: ',recall_dtc)\n",
    "print('Precision Score: ',precision_dtc)\n",
    "print('F1 Score: ',f1_dtc)\n",
    "\n",
    "a = pd.DataFrame(y_test)\n",
    "a['pred']= classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc3dbac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 100.0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize']=8,8\n",
    "plt.bar(['Accuracy','F1 Score','Recall Score','Precision Score'],[ma_dtc*100,f1_dtc*100,recall_dtc*100,precision_dtc*100],color=['red','green','purple','blue'])\n",
    "plt.plot([ma_dtc*100,f1_dtc*100,recall_dtc*100,precision_dtc*100],color='black')\n",
    "plt.title('Evaluation Metrics for Decision Tree')\n",
    "plt.ylabel(\"Percentage\")\n",
    "plt.ylim([0,100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "032e2a9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1af8b4ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(base_estimator=RandomForestClassifier(max_depth=2))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "rf = RandomForestClassifier(max_depth=2)\n",
    "classifier = AdaBoostClassifier(rf)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2,random_state=0)\n",
    "classifier.fit(x_train,np.array(y_train).reshape(y_train.shape[0],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f65022ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Evaluation metrics for test dataset ***\n",
      "\n",
      "Accuracy:  0.9347826086956522\n",
      "Recall Score:  0.9714285714285714\n",
      "Precision Score:  0.8717948717948718\n",
      "F1 Score:  0.9189189189189189\n"
     ]
    }
   ],
   "source": [
    "pred = np.array(classifier.predict(x_test))\n",
    "\n",
    "recall = rs(y_test,pred)\n",
    "precision = ps(y_test,pred)\n",
    "f1 = fs(y_test,pred)\n",
    "ma = classifier.score(x_test,y_test)\n",
    "\n",
    "print('*** Evaluation metrics for test dataset ***\\n')\n",
    "print('Accuracy: ',ma)\n",
    "print('Recall Score: ',recall)\n",
    "print('Precision Score: ',precision)\n",
    "print('F1 Score: ',f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "51b04a69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Evaluation Metrics for Random Forest')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize']=8,8\n",
    "plt.ylim([0,100])\n",
    "plt.bar(['Accuracy','F1 Score','Recall Score','Precision Score'],[ma*100,f1*100,recall*100,precision*100],color=['red','green','purple','blue'])\n",
    "plt.plot([ma*100,f1*100,recall*100,precision*100],color='black')\n",
    "plt.ylabel(\"Percentage\")\n",
    "plt.title('Evaluation Metrics for Random Forest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cd6da949",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Naive Bayes\n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "classifier = GaussianNB()\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2,random_state=0)\n",
    "classifier.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9e8e4021",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Evaluation metrics for test dataset ***\n",
      "\n",
      "Accuracy:  0.9456521739130435\n",
      "Recall Score:  1.0\n",
      "Precision Score:  0.875\n",
      "F1 Score:  0.9333333333333333\n"
     ]
    }
   ],
   "source": [
    "pred = np.array(classifier.predict(x_test))\n",
    "recall_gnb = rs(y_test,pred)\n",
    "precision_gnb = ps(y_test,pred)\n",
    "f1_gnb = fs(y_test,pred)\n",
    "ma_gnb = classifier.score(x_test,y_test)\n",
    "\n",
    "print('*** Evaluation metrics for test dataset ***\\n')\n",
    "print('Accuracy: ',ma_gnb)\n",
    "print('Recall Score: ',recall_gnb)\n",
    "print('Precision Score: ',precision_gnb)\n",
    "print('F1 Score: ',f1_gnb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "60da778e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Evaluation Metrics for Naive Bayes')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize']=8,8\n",
    "plt.ylim([0,100])\n",
    "plt.bar(['Accuracy','F1 Score','Recall Score','Precision Score'],[ma_gnb*100,f1_gnb*100,recall_gnb*100,precision_gnb*100],color=['red','green','purple','blue'])\n",
    "plt.plot([ma_gnb*100,f1_gnb*100,recall_gnb*100,precision_gnb*100],color='black')\n",
    "plt.ylabel(\"Percentage\")\n",
    "plt.title('Evaluation Metrics for Naive Bayes')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "731180f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Comparison of Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "34c766d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,6))\n",
    "plt.plot(['SVM','Decision Tree','Random Forest','Naive Bayes'],[f1_svm*100,f1_dtc*100,f1*100,f1_gnb*100],color='purple',marker='D')\n",
    "plt.plot(['SVM','Decision Tree','Random Forest','Naive Bayes'],[ma_svm*100,ma_dtc*100,ma*100, ma_gnb*100],color='red',marker='^')\n",
    "plt.plot(['SVM','Decision Tree','Random Forest','Naive Bayes'],[precision_svm*100,precision_dtc*100,precision*100, precision_gnb*100],color='blue',marker='s')\n",
    "plt.plot(['SVM','Decision Tree','Random Forest','Naive Bayes'],[recall_svm*100,recall_dtc*100,recall*100, recall_gnb*100],color='green',marker='P')\n",
    "plt.legend(('F1 Score','Accuracy','Precision','Recall'))\n",
    "plt.title('Comparison of various models\\' performance(Zoomed In For Clarity)')\n",
    "plt.ylabel(\"Percentage\")\n",
    "plt.show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "422ef2a7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 100.0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize']=10,10\n",
    "\n",
    "plt.bar(['SVM','Decision Tree','Random Forest', 'Naive Bayes'],[ma_svm*100,ma_dtc*100,ma*100,ma_gnb*100],color=['red','green','purple','blue'])\n",
    "plt.plot([ma_svm*100,ma_dtc*100,ma*100,ma_gnb*100],color='black')\n",
    "plt.title('Comparison of Accuracy Scores of all Algorithms')\n",
    "plt.ylabel(\"Accuracy Percentage\")\n",
    "plt.ylim([0,100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76563423",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}