Model Comparison & Analysis.ipynb

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   "source": [
    "import pandas\n",
    "import numpy as np\n",
    "from skimage.feature import hog\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
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     "text": [
      "cp1252\n"
     ]
    }
   ],
   "source": [
    "with open('mnist_784.csv') as file:\n",
    "    print(file.encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(70000, 784)\n"
     ]
    }
   ],
   "source": [
    "# Reading the Dataset from a CSV file\n",
    "# Splitting the dataset into features & labels. And further splitting into train & test parts\n",
    "\n",
    "dataset = pandas.read_csv('mnist_784.csv',sep=',',encoding='cp1252')\n",
    "\n",
    "features = np.array(dataset.drop('class',axis=1),'int16')\n",
    "labels = np.array(dataset[\"class\"],'int')\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(features,labels,train_size= 0.9, random_state= 0)\n",
    "\n",
    "print(features.shape)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 7500x7500 with 0 Axes>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "# Visualisation of the training data set\n",
    "# The distribution of labels for classification should be balanced for accurate predictions\n",
    "\n",
    "digits, counts = np.unique(labels,return_counts=True)\n",
    "\n",
    "chart = plt.bar(digits,counts)\n",
    "plt.xticks([0,1,2,3,4,5,6,7,8,9])\n",
    "plt.ylabel(\"No. of Samples\")\n",
    "plt.xlabel(\"Digits\")\n",
    "plt.title(\"Label Distribution in the Training Set\",fontdict={'fontsize' : 20, 'fontweight' : 'bold'})\n",
    "plt.figure(figsize=(25,25),edgecolor='red',dpi=300)\n",
    "plt.show()\n",
    "\n",
    "#plt.savefig('Label Distribution.png', dpi=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting HOG features from the train & test feature list\n",
    "\n",
    "hog_list1 = []\n",
    "for feature in x_train:\n",
    "    item = hog(feature.reshape(28,28),orientations=9,pixels_per_cell=(14,14),cells_per_block=(1,1),visualize=False)\n",
    "    hog_list1.append(item)\n",
    "hog_x_train = np.array(hog_list1,'float64')\n",
    "\n",
    "hog_list2 = []\n",
    "for feature in x_test:\n",
    "    item = hog(feature.reshape(28,28),orientations=9,pixels_per_cell=(14,14),cells_per_block=(1,1),visualize=False)\n",
    "    hog_list2.append(item)\n",
    "hog_x_test = np.array(hog_list2,'float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Classification Report for Linear Classifier :\n              precision    recall  f1-score   support\n\n           0       0.70      0.95      0.81       705\n           1       0.98      0.93      0.95       815\n           2       0.81      0.83      0.82       707\n           3       0.70      0.84      0.76       684\n           4       0.91      0.65      0.76       661\n           5       0.80      0.87      0.83       605\n           6       0.84      0.86      0.85       674\n           7       0.92      0.77      0.84       760\n           8       0.74      0.82      0.78       693\n           9       0.73      0.52      0.61       696\n\n    accuracy                           0.81      7000\n   macro avg       0.81      0.80      0.80      7000\nweighted avg       0.82      0.81      0.80      7000\n\n\n\nAccuracy : 0.8061428571428572\nPrecision : 0.8061428571428572\nRecall : 0.8061428571428572\nF1 Score : 0.8061428571428572\n\n\n\nConfusion Matrix for Linear Classifier :\n\n[[672   1   5   4   0   5   9   1   5   3]\n [ 12 754   3   1  17   1  11  10   1   5]\n [ 17   0 584  75   2   3   1   0  24   1]\n [  7   0  29 574   2  27   1   6  24  14]\n [ 33   4  32  20 431  18  54   5  32  32]\n [  8   1   3  24   2 528   8   1  27   3]\n [ 38   1   2   2   4  20 582   0  19   6]\n [ 27   4  41  15   5   5   0 588  28  47]\n [ 13   4  13  29   1  24  17   6 565  21]\n [129   2  11  79   8  33   8  24  37 365]]\n"
    }
   ],
   "source": [
    "# Fitting Linear Model on the Train data & predicting the test data\n",
    "\n",
    "linear = SGDClassifier(loss='perceptron')\n",
    "linear.fit(hog_x_train,y_train)\n",
    "linear_pred = linear.predict(hog_x_test)\n",
    "\n",
    "acc_linear = accuracy_score(y_test,linear_pred)\n",
    "f1_linear = f1_score(y_test,linear_pred,average='micro')\n",
    "\n",
    "print(\"Classification Report for Linear Classifier :\")\n",
    "print(classification_report(y_test,linear_pred))\n",
    "print(\"\\n\")\n",
    "print(\"Accuracy : {}\".format(acc_linear))\n",
    "print(\"Precision : {}\".format(precision_score(y_test,linear_pred,average='micro')))\n",
    "print(\"Recall : {}\".format(recall_score(y_test,linear_pred,average='micro')))\n",
    "print(\"F1 Score : {}\\n\".format(f1_linear))\n",
    "print(\"\\n\")\n",
    "print(\"Confusion Matrix for Linear Classifier :\\n\")\n",
    "print(confusion_matrix(y_test,linear_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Classification Report for KNN Classifier :\n              precision    recall  f1-score   support\n\n           0       0.82      0.93      0.87       705\n           1       0.97      0.94      0.96       815\n           2       0.82      0.91      0.86       707\n           3       0.77      0.83      0.80       684\n           4       0.92      0.79      0.85       661\n           5       0.93      0.84      0.88       605\n           6       0.84      0.92      0.88       674\n           7       0.95      0.83      0.89       760\n           8       0.81      0.88      0.84       693\n           9       0.84      0.74      0.79       696\n\n    accuracy                           0.86      7000\n   macro avg       0.87      0.86      0.86      7000\nweighted avg       0.87      0.86      0.86      7000\n\n\n\nAccuracy : 0.8641428571428571\nPrecision : 0.8641428571428571\nRecall : 0.8641428571428571\nF1 Score : 0.8641428571428571\n\n\n\nConfusion Matrix for KNN Classifier :\n\n[[658   4   1   2   0   2  16   1   4  17]\n [  7 769   5   0  12   0   6   6   6   4]\n [  6   0 640  37   6   0   0   1  15   2]\n [  2   0  53 565   0  13   1   5  33  12]\n [ 20   7  15  22 523   0  38   3  13  20]\n [  4   0   7  30   1 510  22   0  28   3]\n [ 17   2   0   2  11   6 623   0   8   5]\n [  9   5  47  16   5   2   0 633  17  26]\n [ 12   4   4  19   3   9  21   1 610  10]\n [ 71   3   5  37   5   8  11  16  22 518]]\n"
    }
   ],
   "source": [
    "# Fitting KNN Model on the Train data & predicting the test data\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors= 5,weights='uniform')\n",
    "knn.fit(hog_x_train,y_train)\n",
    "knn_pred = knn.predict(hog_x_test)\n",
    "\n",
    "acc_knn = accuracy_score(y_test,knn_pred)\n",
    "f1_knn = f1_score(y_test,knn_pred,average='micro')\n",
    "\n",
    "print(\"Classification Report for KNN Classifier :\")\n",
    "print(classification_report(y_test,knn_pred))\n",
    "print(\"\\n\")\n",
    "print(\"Accuracy : {}\".format(acc_knn))\n",
    "print(\"Precision : {}\".format(precision_score(y_test,knn_pred,average='micro')))\n",
    "print(\"Recall : {}\".format(recall_score(y_test,knn_pred,average='micro')))\n",
    "print(\"F1 Score : {}\\n\".format(f1_knn))\n",
    "print(\"\\n\")\n",
    "print(\"Confusion Matrix for KNN Classifier :\\n\")\n",
    "print(confusion_matrix(y_test,knn_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Classification Report for Linear SVM Classifier :\n              precision    recall  f1-score   support\n\n           0       0.88      0.90      0.89       705\n           1       0.97      0.95      0.96       815\n           2       0.84      0.90      0.87       707\n           3       0.80      0.83      0.81       684\n           4       0.88      0.84      0.86       661\n           5       0.89      0.90      0.89       605\n           6       0.87      0.92      0.89       674\n           7       0.91      0.88      0.90       760\n           8       0.87      0.84      0.85       693\n           9       0.79      0.75      0.77       696\n\n    accuracy                           0.87      7000\n   macro avg       0.87      0.87      0.87      7000\nweighted avg       0.87      0.87      0.87      7000\n\n\n\nAccuracy : 0.8721428571428571\nPrecision : 0.8721428571428571\nRecall : 0.8721428571428571\nF1 Score : 0.8721428571428571\n\n\n\nConfusion Matrix for Linear SVM Classifier :\n\n[[638   1   6   5   4   2  17   5   3  24]\n [  3 777   2   0  17   0   7   4   1   4]\n [  3   1 637  39   9   0   0   5   9   4]\n [  1   0  45 565   3  13   1   6  24  26]\n [  6   5  17   6 554   3  26   8   8  28]\n [  4   1   1  17   4 542  14   1  16   5]\n [ 13   2   1   0  12  13 620   0   8   5]\n [  1   4  31  13   8   1   0 672   8  22]\n [  9   4  10  20   7  15  21   8 579  20]\n [ 45   4   4  41  10  18   9  32  12 521]]\n"
    }
   ],
   "source": [
    "# Fitting Linear Support Vector Machine(SVM) Model on the Train data & predicting the test data\n",
    "\n",
    "svm_linear = SVC(kernel='linear')\n",
    "svm_linear.fit(hog_x_train,y_train)\n",
    "svm_linear_pred = svm_linear.predict(hog_x_test)\n",
    "\n",
    "acc_svm_linear = accuracy_score(y_test,svm_linear_pred)\n",
    "f1_svm_linear = f1_score(y_test,svm_linear_pred,average='micro')\n",
    "\n",
    "print(\"Classification Report for Linear SVM Classifier :\")\n",
    "print(classification_report(y_test,svm_linear_pred))\n",
    "print(\"\\n\")\n",
    "print(\"Accuracy : {}\".format(acc_svm_linear))\n",
    "print(\"Precision : {}\".format(precision_score(y_test,svm_linear_pred,average='micro')))\n",
    "print(\"Recall : {}\".format(recall_score(y_test,svm_linear_pred,average='micro')))\n",
    "print(\"F1 Score : {}\\n\".format(f1_svm_linear))\n",
    "print(\"\\n\")\n",
    "print(\"Confusion Matrix for Linear SVM Classifier :\\n\")\n",
    "print(confusion_matrix(y_test,svm_linear_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Classification Report for RBF SVM Classifier :\n              precision    recall  f1-score   support\n\n           0       0.92      0.92      0.92       705\n           1       0.98      0.96      0.97       815\n           2       0.90      0.93      0.91       707\n           3       0.85      0.88      0.86       684\n           4       0.92      0.89      0.91       661\n           5       0.92      0.90      0.91       605\n           6       0.90      0.94      0.92       674\n           7       0.93      0.93      0.93       760\n           8       0.90      0.88      0.89       693\n           9       0.87      0.84      0.86       696\n\n    accuracy                           0.91      7000\n   macro avg       0.91      0.91      0.91      7000\nweighted avg       0.91      0.91      0.91      7000\n\n\n\nAccuracy : 0.9102857142857143\nPrecision : 0.9102857142857143\nRecall : 0.9102857142857143\nF1 Score : 0.9102857142857143\n\n\n\nConfusion Matrix for RBF SVM Classifier :\n\n[[652   4   4   2   3   3  12   5   4  16]\n [  0 784   2   0  12   0   7   4   2   4]\n [  2   0 656  30   5   0   0   4   7   3]\n [  1   0  32 599   1  13   1   6  18  13]\n [  5   3   6   4 591   1  18   6   6  21]\n [  4   0   1  21   3 546  15   1  12   2]\n [ 10   1   0   0  11   7 636   0   4   5]\n [  1   1  22   5   6   2   0 707   6  10]\n [ 10   3   6  18   5  11  13   2 613  12]\n [ 25   3   1  23   5  11   8  23   9 588]]\n"
    }
   ],
   "source": [
    "# Fitting SVM Model using Radial Basis Function(RBF) Kernel on the Train data & predicting the test data\n",
    "\n",
    "svm_rbf = SVC(kernel='rbf')\n",
    "svm_rbf.fit(hog_x_train,y_train)\n",
    "svm_rbf_pred = svm_rbf.predict(hog_x_test)\n",
    "\n",
    "acc_svm_rbf = accuracy_score(y_test,svm_rbf_pred)\n",
    "f1_svm_rbf = f1_score(y_test,svm_rbf_pred,average='micro')\n",
    "\n",
    "print(\"Classification Report for RBF SVM Classifier :\")\n",
    "print(classification_report(y_test,svm_rbf_pred))\n",
    "print(\"\\n\")\n",
    "print(\"Accuracy : {}\".format(acc_svm_rbf))\n",
    "print(\"Precision : {}\".format(precision_score(y_test,svm_rbf_pred,average='micro')))\n",
    "print(\"Recall : {}\".format(recall_score(y_test,svm_rbf_pred,average='micro')))\n",
    "print(\"F1 Score : {}\\n\".format(f1_svm_rbf))\n",
    "print(\"\\n\")\n",
    "print(\"Confusion Matrix for RBF SVM Classifier :\\n\")\n",
    "print(confusion_matrix(y_test,svm_rbf_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Classification Report for Polynomial SVM Classifier :\n              precision    recall  f1-score   support\n\n           0       0.91      0.93      0.92       705\n           1       0.97      0.97      0.97       815\n           2       0.91      0.92      0.92       707\n           3       0.86      0.88      0.87       684\n           4       0.93      0.89      0.91       661\n           5       0.92      0.91      0.92       605\n           6       0.90      0.93      0.91       674\n           7       0.94      0.92      0.93       760\n           8       0.90      0.90      0.90       693\n           9       0.87      0.85      0.86       696\n\n    accuracy                           0.91      7000\n   macro avg       0.91      0.91      0.91      7000\nweighted avg       0.91      0.91      0.91      7000\n\n\n\nAccuracy : 0.9117142857142857\nPrecision : 0.9117142857142857\nRecall : 0.9117142857142857\nF1 Score : 0.9117142857142856\n\n\n\nConfusion Matrix for Polynomial SVM Classifier :\n\n[[656   4   2   1   2   3  16   3   4  14]\n [  2 787   1   1   9   0   5   4   2   4]\n [  2   0 653  29   7   0   0   3   9   4]\n [  1   0  26 602   1  12   2   8  20  12]\n [  6   8   6   3 591   2  16   5   4  20]\n [  4   0   2  18   3 551  12   1  11   3]\n [ 13   4   0   0  11   8 628   0   3   7]\n [  1   5  21   6   5   3   0 700   3  16]\n [ 10   1   2  18   4  12  12   2 622  10]\n [ 24   2   4  21   4   7   8  22  12 592]]\n"
    }
   ],
   "source": [
    "# Fitting SVM Model using Polynomial Kernel on the Train data & predicting the test data\n",
    "\n",
    "svm_poly = SVC(kernel='poly',degree=3)\n",
    "svm_poly.fit(hog_x_train,y_train)\n",
    "svm_poly_pred = svm_poly.predict(hog_x_test)\n",
    "\n",
    "acc_svm_poly = accuracy_score(y_test,svm_poly_pred)\n",
    "f1_svm_poly = f1_score(y_test,svm_poly_pred,average='micro')\n",
    "\n",
    "print(\"Classification Report for Polynomial SVM Classifier :\")\n",
    "print(classification_report(y_test,svm_poly_pred))\n",
    "print(\"\\n\")\n",
    "print(\"Accuracy : {}\".format(acc_svm_poly))\n",
    "print(\"Precision : {}\".format(precision_score(y_test,svm_poly_pred,average='micro')))\n",
    "print(\"Recall : {}\".format(recall_score(y_test,svm_poly_pred,average='micro')))\n",
    "print(\"F1 Score : {}\\n\".format(f1_svm_poly))\n",
    "print(\"\\n\")\n",
    "print(\"Confusion Matrix for Polynomial SVM Classifier :\\n\")\n",
    "print(confusion_matrix(y_test,svm_poly_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Classification Report for Decision Tree Classifier :\n              precision    recall  f1-score   support\n\n           0       0.78      0.78      0.78       705\n           1       0.94      0.92      0.93       815\n           2       0.77      0.75      0.76       707\n           3       0.67      0.76      0.71       684\n           4       0.77      0.74      0.76       661\n           5       0.81      0.81      0.81       605\n           6       0.76      0.82      0.79       674\n           7       0.85      0.80      0.82       760\n           8       0.74      0.76      0.75       693\n           9       0.70      0.66      0.68       696\n\n    accuracy                           0.78      7000\n   macro avg       0.78      0.78      0.78      7000\nweighted avg       0.78      0.78      0.78      7000\n\n\n\nAccuracy : 0.7812857142857143\nPrecision : 0.7812857142857143\nRecall : 0.7812857142857143\nF1 Score : 0.7812857142857143\n\n\n\nConfusion Matrix for Decision Tree Classifier :\n\n[[547   7  12   9   8   8  41  11  18  44]\n [  5 750   2   2  14   1  15   9   8   9]\n [ 11   1 531  85  20   1  10  17  24   7]\n [ 11   2  55 519   6  23   5  10  31  22]\n [ 10  16  21  12 489  11  35  13  23  31]\n [  7   0   6  30   5 492  24   5  23  13]\n [ 37   4   4   2  26  22 551   0  17  11]\n [ 13  10  28  25  21   4   0 605  21  33]\n [  7   2  24  41  15  25  23   7 527  22]\n [ 49   7   9  46  27  22  19  35  24 458]]\n"
    }
   ],
   "source": [
    "# Fitting Decision Tree Model on the Train data & predicting the test data\n",
    "\n",
    "tree = DecisionTreeClassifier(max_depth=17,random_state=0)\n",
    "tree.fit(hog_x_train,y_train)\n",
    "tree_pred = tree.predict(hog_x_test)\n",
    "\n",
    "acc_tree = accuracy_score(y_test,tree_pred)\n",
    "f1_tree = f1_score(y_test,tree_pred,average='micro')\n",
    "\n",
    "print(\"Classification Report for Decision Tree Classifier :\")\n",
    "print(classification_report(y_test,tree_pred))\n",
    "print(\"\\n\")\n",
    "print(\"Accuracy : {}\".format(acc_tree))\n",
    "print(\"Precision : {}\".format(precision_score(y_test,tree_pred,average='micro')))\n",
    "print(\"Recall : {}\".format(recall_score(y_test,tree_pred,average='micro')))\n",
    "print(\"F1 Score : {}\\n\".format(f1_tree))\n",
    "print(\"\\n\")\n",
    "print(\"Confusion Matrix for Decision Tree Classifier :\\n\")\n",
    "print(confusion_matrix(y_test,tree_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Classification Report for Random Forest Classifier :\n              precision    recall  f1-score   support\n\n           0       0.90      0.92      0.91       705\n           1       0.98      0.96      0.97       815\n           2       0.85      0.92      0.88       707\n           3       0.83      0.85      0.84       684\n           4       0.91      0.86      0.89       661\n           5       0.91      0.90      0.90       605\n           6       0.86      0.93      0.89       674\n           7       0.93      0.88      0.91       760\n           8       0.88      0.87      0.88       693\n           9       0.84      0.81      0.83       696\n\n    accuracy                           0.89      7000\n   macro avg       0.89      0.89      0.89      7000\nweighted avg       0.89      0.89      0.89      7000\n\n\n\nAccuracy : 0.8911428571428571\nPrecision : 0.8911428571428571\nRecall : 0.8911428571428571\nF1 Score : 0.8911428571428571\n\n\n\nConfusion Matrix for Random Forest Classifier :\n\n[[646   3   1   4   2   5  16   2   3  23]\n [  0 781   2   1  14   0   6   6   1   4]\n [  4   0 650  30   5   0   1   4  10   3]\n [  2   0  44 582   2  15   2   3  19  15]\n [  3   3  13   4 570   0  32   6   7  23]\n [  3   1   2  19   3 542  15   1  14   5]\n [ 15   1   1   2   9  10 626   0   5   5]\n [  1   1  43  12   7   2   0 670   8  16]\n [  8   2   7  19   7  14  16   3 604  13]\n [ 37   4   2  29   6   5  11  22  13 567]]\n"
    }
   ],
   "source": [
    "# Fitting Random Forest Model on the Train data & predicting the test data\n",
    "\n",
    "forest = RandomForestClassifier(n_estimators=50)\n",
    "forest.fit(hog_x_train,y_train)\n",
    "forest_pred = forest.predict(hog_x_test)\n",
    "\n",
    "acc_forest = accuracy_score(y_test,forest_pred)\n",
    "f1_forest = f1_score(y_test,forest_pred,average='micro')\n",
    "\n",
    "print(\"Classification Report for Random Forest Classifier :\")\n",
    "print(classification_report(y_test,forest_pred))\n",
    "print(\"\\n\")\n",
    "print(\"Accuracy : {}\".format(acc_forest))\n",
    "print(\"Precision : {}\".format(precision_score(y_test,forest_pred,average='micro')))\n",
    "print(\"Recall : {}\".format(recall_score(y_test,forest_pred,average='micro')))\n",
    "print(\"F1 Score : {}\\n\".format(f1_forest))\n",
    "print(\"\\n\")\n",
    "print(\"Confusion Matrix for Random Forest Classifier :\\n\")\n",
    "print(confusion_matrix(y_test,forest_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_list = []\n",
    "accuracy_list.append(acc_linear)\n",
    "accuracy_list.append(acc_knn)\n",
    "accuracy_list.append(acc_svm_linear)\n",
    "accuracy_list.append(acc_svm_rbf)\n",
    "accuracy_list.append(acc_svm_poly)\n",
    "accuracy_list.append(acc_tree)\n",
    "accuracy_list.append(acc_forest)\n",
    "\n",
    "f1_score_list = []\n",
    "f1_score_list.append(f1_linear)\n",
    "f1_score_list.append(f1_knn)\n",
    "f1_score_list.append(f1_svm_linear)\n",
    "f1_score_list.append(f1_svm_rbf)\n",
    "f1_score_list.append(f1_svm_poly)\n",
    "f1_score_list.append(f1_tree)\n",
    "f1_score_list.append(f1_forest)\n",
    "\n",
    "xlabels = [\"Linear\",\"KNN\",\"LinearSVM\",\"RBF\",\"PolySVM\",\"Tree\",\"Forest\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 10800x10800 with 0 Axes>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "# Visualising the prediction accuracy variations by using different classification models\n",
    "\n",
    "acc_chart = plt.bar(xlabels,accuracy_list)\n",
    "plt.xlabel(\"ML Model Used\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"ML Model Accuracy Comparison\",fontdict={'fontsize':20, 'fontweight':'bold'})\n",
    "plt.yticks([0.0,0.2,0.4,0.6,0.8,0.9])\n",
    "plt.figure(figsize=(36,36),dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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ZVao1WUgaJ+keSXMlfbFJ+bqSLpE0W9KtkrZtNh4zMxtYtT3BLWkY6Tez9wYWkH5GcEpE3FWo9mVgVkQcKGmrXH+vumIya2XUF39VXWkFmv+tfduWD7V4beirs2WxMzA3IuZFxEvAZGD/Up1tgGsAIuJPwChJG2BmZoNKncliQ9Lv2zYsyP2K7gD+AUDSzsCmwEblEUk6WtJMSTMXLlxYU7hmZtZKnclCTfqV34f+LWBdSbOA40g/SL7kVQNFnBERYyJizMiRXb800czMulTnW2cXABsXujcCHipWiIingCMBJIn0O7rF39I1Mxswvja0XJ0tixnAaEmbSVoNGA9MKVaQNCKXARwFXJ8TiJmZDSK1tSwiYomkY4FpwDDgrIiYI2lCLp8EbA2cJ2kpcBfw6briMTOz7tX640cRMRWYWuo3qfD9JmB0nTGYmVnf+QluMzOrNOR+VtUGP18UNFv5uGVhZmaV3LIY5HyUbmaDgVsWZmZWycnCzMwqOVmYmVklJwszM6vkZGFmZpWcLMzMrJKThZmZVXKyMDOzSk4WZmZWycnCzMwq1ZosJI2TdI+kuZK+2KT8DZIul3SHpDmSjqwzHjMz605tyULSMOB0YB9gG+AQSduUqh0D3BUR2wNjge8UfjnPzMwGiTpfJLgzMDci5gFImgzsT/pFvIYA1s6/v/164HFgSV0B+aV8ZmbdqfM01IbAA4XuBblf0Wmkn1Z9CLgTOD4ilpVHJOloSTMlzVy4cGFd8ZqZWQt1Jgs16Rel7g8Cs4C3AjsAp0la51UDRZwREWMiYszIkSP7O04zM6tQZ7JYAGxc6N6I1IIoOhK4OJK5wH3AVjXGZGZmXagzWcwARkvaLF+0Hg9MKdW5H9gLQNIGwJbAvBpjMjOzLtR2gTsilkg6FpgGDAPOiog5kibk8knAfwPnSLqTdNrqCxHxWF0xmdnA8Q0mQ1utP6saEVOBqaV+kwrfHwI+UGcMZmbWd36C28zMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaVak0WksZJukfSXElfbFFnrKRZkuZIuq7OeMzMrDvD6xqxpGHA6cDewAJghqQpEXFXoc4I4AfAuIi4X9Kb6orHzMy6V2fLYmdgbkTMi4iXgMnA/qU6Hwcujoj7ASLi0RrjMTOzLtWZLDYEHih0L8j9it4OrCtpuqTbJH2yxnjMzKxLtZ2GAtSkXzSZ/o7AXsCawE2Sbo6IP/cYkXQ0cDTAJptsUkOoZmbWTp0tiwXAxoXujYCHmtS5MiKejYjHgOuB7csjiogzImJMRIwZOXJkbQGbmVlzdSaLGcBoSZtJWg0YD0wp1bkM2EPScElrAbsAd9cYk5mZdaG201ARsUTSscA0YBhwVkTMkTQhl0+KiLslXQnMBpYBZ0bEH+uKyczMulPnNQsiYiowtdRvUqn7JOCkOuMwM7O+8RPcZmZWycnCzMwqdZQsJO0u6cj8faSkzeoNy8zMBpPKZCHpv4AvAF/KvVYFflpnUGZmNrh00rI4EPgI8CxARDwErF1nUGZmNrh0kixeioggP30t6XX1hmRmZoNNJ8niF5L+Fxgh6Z+Aq4Ef1RuWmZkNJm2fs5Ak4OfAVsBTwJbAVyPiNysgNjMzGyTaJouICEmXRsSOgBOEmdlrVCenoW6WtFPtkZiZ2aDVyes+3gdMkDSfdEeUSI2O7eoMzMzMBo9OksU+tUdhZmaDWuVpqIj4KzAC+HD+jMj9zMzsNaKTJ7iPB84H3pQ/P5V0XN2BmZnZ4NHJaahPA7tExLMAkk4EbgK+X2dgZmY2eHRyN5SApYXupTT/fW0zM1tJddKyOBu4RdIlufsA4Me1RWRmZoNOZbKIiJMlTQd2J7UojoyIP9QdmJmZDR6VyULSrsCciLg9d68taZeIuKX26MzMbFDo5JrFD4FnCt3P5n5mZvYa0dEF7vyKcgAiYhmdXeswM7OVRCfJYp6kz0haNX+OB+bVHZiZmQ0enSSLCcB7gAfzZxfg6DqDMjOzwaWTu6EeBcavgFjMzGyQatmykPRPkkbn75J0lqQnJc2W9K4VF6KZmQ20dqehjgfm5++HANsDmwOfA75Xb1hmZjaYtEsWSyLi5fx9P+C8iFgUEVcDr6s/NDMzGyzaJYtlkt4iaQ1gL+DqQtma9YZlZmaDSbsL3F8FZgLDgCkRMQdA0p741lkzs9eUlskiIq6QtCmwdkQsLhTNBA6uPTIzMxs02t46GxFLgMWlfs/WGpGZmQ06nTyUZ2Zmr3FOFmZmVqmrZCFpq/4OxMzMBq9uWxZX9WsUZmY2qLW8wC3p1FZFwIhaojEzs0Gp3d1QRwL/BrzYpOyQesIxM7PBqF2ymAH8MSJuLBdImtjJyCWNI71HahhwZkR8q0W9nYCbgYMj4sJOxm1mZitOu2RxEPBCs4KI2KxqxJKGAacDewMLgBmSpkTEXU3qnQhM6zRoMzNbsdpd4H59RDzXh3HvDMyNiHkR8RIwGdi/Sb3jgIuAR/swLTMzq1G7ZHFp44uki7oY94bAA4XuBbnfKyRtCBwITGo3IklHS5opaebChQu7CMXMzPqiXbJQ4fvmXYxbTfpFqfsU4AsRsbTdiCLijIgYExFjRo4c2UUoZmbWF+2uWUSL751aAGxc6N4IeKhUZwwwWRLA+sCHJC2JiEu7mJ6ZmdWkXbLYXtJTpBbCmvk7uTsiYp2Kcc8ARkvaDHiQ9DveHy9WKF4ol3QOcIUThZnZ4NPuFeXD+jLiiFgi6VjSXU7DgLMiYo6kCbm87XUKMzMbPNq+oryvImIqMLXUr2mSiIgj6ozFzMy657fOmplZJScLMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKxSrclC0jhJ90iaK+mLTcoPlTQ7f26UtH2d8ZiZWXdqSxaShgGnA/sA2wCHSNqmVO0+YM+I2A74b+CMuuIxM7Pu1dmy2BmYGxHzIuIlYDKwf7FCRNwYEYtz583ARjXGY2ZmXaozWWwIPFDoXpD7tfJp4NfNCiQdLWmmpJkLFy7sxxDNzKwTdSYLNekXTStK7yMliy80K4+IMyJiTESMGTlyZD+GaGZmnRhe47gXABsXujcCHipXkrQdcCawT0QsqjEeMzPrUp0tixnAaEmbSVoNGA9MKVaQtAlwMXBYRPy5xljMzKwPamtZRMQSSccC04BhwFkRMUfShFw+Cfgq8EbgB5IAlkTEmLpiMjOz7tR5GoqImApMLfWbVPh+FHBUnTGYmVnf+QluMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaVnCzMzKySk4WZmVVysjAzs0q1JgtJ4yTdI2mupC82KZekU3P5bEnvqjMeMzPrTm3JQtIw4HRgH2Ab4BBJ25Sq7QOMzp+jgR/WFY+ZmXWvzpbFzsDciJgXES8Bk4H9S3X2B86L5GZghKS31BiTmZl1QRFRz4ilg4BxEXFU7j4M2CUiji3UuQL4VkT8LndfA3whImaWxnU0qeUBsCVwTy1Bd2594LEBjqG3HPOKMdRiHmrxgmPu1qYRMbLbgYf3ZyQlatKvnJk6qUNEnAGc0R9B9QdJMyNizEDH0RuOecUYajEPtXjBMQ+UOk9DLQA2LnRvBDzURR0zMxtgdSaLGcBoSZtJWg0YD0wp1ZkCfDLfFbUr8GRE/K3GmMzMrAu1nYaKiCWSjgWmAcOAsyJijqQJuXwSMBX4EDAXeA44sq54+tmgOSXWC455xRhqMQ+1eMExD4jaLnCbmdnKw09wm5lZJScLMzOr9JpOFpKeadJvgqRPDkQ83SjOg6QPSfqLpE0kTZT0nKQ3tagbkr5T6D5B0sTeTrPQb4UsN0mfknRnfj3MHyXtL+kISReU6q0vaaGk1SVNl3S/JBXKL202HxXTXippVp7u5ZJG5P6jJD2fy+6QdKOkLXPZWElP5rJZkq7u4/wXY/ilpLXa1D1C0mkV49tV0i15nHfn9WaUpAWSVinVnSVp51wnJG1RKPts7tf17aGS3lhYTg9LerDQvVq34+1PheXf+IyqaTpjJb2njnF36zWdLJqJiEkRcV5d4893fvX7cpe0F/B90oOQ9+fejwH/1mKQF4F/kLR+f0x/BS23TYCvALtHxHbArsBs4GJg79KO8yBgSkS8mLufAHbL4xoBdPOmgOcjYoeI2BZ4HDimUHZvLtseOBf4cqHshly2Q0S8v4vptorhJWBCH8d3LnB0ROwAbAv8IiLmAw8AezQqSdoKWDsibs297iTd4dhwEHBXXwKJiEWN5QRMAr5bWG4vSarzubBOPV+IaYe8rCp1EftYwMliMMtHTSfk79MlnSjpVkl/lrRH7j9M0kmSZuQj3H/O/V8v6RpJt+ej3/1z/1H5qO0HwO30fLakP2LeA/gRsG9E3FsoOgs4WNJ6TQZbQrpD47P9FMOKWG6bAU8DzwBExDMRcV9EPAVcD3y4ENJ4oNjamMzynds/kBJMX9wEbNiibB1gcR/H34kbgC0krZdbSrMl3Sxpu2IlSWtLuk/Sqrl7HUnzc/ebgL8BRMTSiGjs8C+gZzIoL89Lya/vkbQ58CSwsL9nUNI5kk6WdC1woqS3SbpS0m2SbshJDEkjJV2U160Zknbr71jaxLhDXu6zJV0iad3cf7qkb0q6Djhe0o6SrsuxT1N+tZGkz0i6Kw8/ObdWJgCfza2XPVpPfQWKiNfsB3imSb+JwAn5+3TgO/n7h4Cr8/ejgf/I31cHZpJ2ZMOBdXL/9Um3BAsYBSwDdq1hHl4mHeVu12w+gK8CXyvPL2mHuw4wH3hDrjtxMC830i3Y04D7gbOBDxem/1Hgkvz9raSHO4cV4tmF1AoZBlyVx/2q+ehkvvM4fklqxZHH9TwwC7iXtPPdJJeNJe1IZ+XPV/pjnc3L7DLgX0gtyv/K/f8emJW/HwGclr+fDRxQ+D80/j9fJSW2S4B/BtbI/d+c52N47r4b2La0bl1Mao18BTg8L+cx/bReN6ZxDnBF4X95DTA6f98F+G3+/jNSixNgE+Du/t7W8riXFv6XjfVtNrBn/v514JTCeveD/H1V4EZgZO4+mPQ4AXldXT1/H1HengbLZzA06wa7xhHobaSdAsAHgO2U3n8FaWc7mvRE+jclvZe0k9sQ2CDX+WuklyX2t5dJK+GngeOblJ8KzFLh+kRDRDwl6TzgM6SdXX/q9+UWEUsljQN2AvYCvitpx4iYSNqh/EDSOsDHgAsjYmkhnqXA70gb6ZoRMV9q9raZttaUNCvPz23Abwpl90Y6fYKkg0mttnG57IaI2K+3E6uIAVLL4sfALcA/AkTEb5XO/b+hNNyZwOdJLYIjgX/K9b8u6XzS/+bjwCHA2Ih4WNIcYC9JjwAvR8QfS+NstNY+SPp/1PWc1C/z//71pFMzvyz871bPf98PbFPov46ktSPi6X6O5fnG/xkgL+cREXFd7nUu6UCi4ef575akxPqbHOMwcouOlGzOl3Qp6f8zKDlZVGuc817K8uUl4LiImFasKOkIYCSwY0S8LGk+sEYufram+JaRdo5XS/pyRHyzWBgRT0j6GfCvLYY/hXSK5+x+jquW5RbpsOtW4FZJv8lxT4yI5yVdCRxI2oE1O702mXQEPbHLeXo+InbIO4grSNcsTm1Sbwr9vzx7xFDsoeZZr8cDVBHx+3xab0/SUfofC2X3Aj+U9CNgoaQ3RsQilp+KeoSep6AaLgdOAmbmA4++zFc7jXVgFeCJ8vwXyt4dEf190NNXjdgFzImIdzepsy/wXuAjwH9K+rsVFVxv+JpFd6YB/1I4B/x2Sa8jHSk/mnd47wM2XRHBRMRzwH7AoZI+3aTKyaRTDK86OIiIx4FfkFomdevTcpP0VvX8gawdgL8Wui8APkdqlTRrxd0A/A/Nd3wdi4gnSa2xExrzUrI76XTUinI9cCiku2iAxyJdxyk7jzTvryQySfsWks1oUnJ/IndfRDqNeDAp0faQd8xfAL7RD/NQKc/TfZI+Cq/c9LB9Lr4KKL7ReocVFNOTwOLCdYXDgOuaVL0HGCnp3Tm+VSX9ndLNLhtHxLWklt8I4PWka3Nr1x1/b7zWWxZrSVpQ6D65w+HOJJ2KuD1vaAuBA4DzgcslzSSd0/xTv0VaISIez6dorpf0WKnsMUmX0Ppi9ncobGgdGKjltirwbUlvBV7IwxfvBrqKdBrgx7kF0kPu9+0OY20rIv4g6Q7SkfcNwNvy6SGR7lI6qj+m06GJwNmSZpNem3N4i3rnA/+XnsnyMNLpvOdINz0c2jh9l1ulNwMbRMR9zUYYEa9KIjU7lNQK+g/S+jAZuIOUvE/Py2A4KYH29U6xTh0OTFK6G28eTU7HRbqb6yDg1NwyHU5q1f8Z+GnuJ9IdYE9Iuhy4UOlmj+Mi4oYVNC8t+XUfZq8ReWe1f0QcNtCx2NDzWm9ZmL0mSPo+6WeMPzTQsdjQ5JaFmZlV8gVuMzOr5GRhZmaVnCzMzKySk4UNWUpvOf1JoXu40ptmr8jdnbx1tb/eSHtO4cn0XtXJMYwpdI+SVH5autc6mX+zTjlZ2FD2LLCtpDVz997Ag12M5wn6/kZas5Wak4UNdb8mvS4B0nuNunk6u+UbafNTwicp/X7Enfm9T43+pym9LfRXpLe3NoZp+nbRbuSnfG9VevvobEmjc/9PFPr/r6Rhuf+RSm/6vY6cAM36g5OFDXWTgfGS1gC2I71Ur7euAd6bd7jjWf7yN0jJYwdge9LL6k7KO/8DSS+HewfppXzvgfQaB9JbYA+KiB1Jr4nvy+swJgDfy+9DGgMskLQ16RUcu+X+S0mvenkL8DVSktgb2KYP0zXrwQ/l2ZAWEbOV3v9/CDC1y9G0eyPt7sAF+RUYj+Qj9p1IL35r9H9I0m9z/XZvF205G2363QR8RdJGwMUR8RelH7raEZiRp7Em8Cjpld3TI2IhgKSfA2/vfDGYteZkYSuDKaR3Po0F3tjlOFq9kbbdq1Sb7eTbvV20lUXAuoXu9Ui/ckhE/EzSLaRTbdMkHZWncW5EfKnHhKUDWsRk1mc+DWUrg7OAr0fEnX0YR6s30l5P+rXBYZJGkloUt+b+43P/twDvy/Wbvl20YtrTgU8U7sg6HLg2D785MC8iTiUlxe1Ip80OUv59daVfytuUdApurNLvWaxK+kEos37hloUNeRGxAPhei+Ij8hF3w665fnkcrd5IewnwbtKbTQP4fP5hoEtIv0p3J+nNodfl8bR6u+icNrNwBrAVcIekIP2CYKPVcDApkbwMPExKio/nt65epfSK65eBYyLiZkkTSaeu/kb6nZJhbaZr1jG/G8rMzCr5NJSZmVVysjAzs0pOFmZmVsnJwszMKjlZmJlZJScLMzOr5GRhZmaV/j8rqvX8nFRxQgAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 10800x10800 with 0 Axes>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "# Visualising the prediction F1 Score variations by using different classification models\n",
    "# F1 score generalises the precision-recall trade off by combining them into a single value\n",
    "\n",
    "acc_chart = plt.bar(xlabels,f1_score_list)\n",
    "plt.xlabel(\"ML Model Used\")\n",
    "plt.ylabel(\"F1 Score\")\n",
    "plt.title(\"ML Model F1 Score Comparison\",fontdict={'fontsize':20, 'fontweight':'bold'})\n",
    "plt.yticks([0.0,0.2,0.4,0.6,0.8,0.9])\n",
    "plt.figure(figsize=(36,36),dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "precision    recall  f1-score   support\n\n           0       0.91      0.93      0.92       705\n           1       0.98      0.96      0.97       815\n           2       0.91      0.93      0.92       707\n           3       0.85      0.88      0.86       684\n           4       0.92      0.89      0.90       661\n           5       0.92      0.90      0.91       605\n           6       0.89      0.94      0.92       674\n           7       0.93      0.93      0.93       760\n           8       0.90      0.89      0.90       693\n           9       0.88      0.84      0.86       696\n\n    accuracy                           0.91      7000\n   macro avg       0.91      0.91      0.91      7000\nweighted avg       0.91      0.91      0.91      7000\n\nAccuracy : 0.9108571428571428\nF1 : 0.9108571428571428\n"
    }
   ],
   "source": [
    "# Scaling & Normalising the training & test features using Standard Scaler & Fitting the transformed data to determine accuracy gain on predictions by RBF SVM\n",
    "\n",
    "sc = StandardScaler()\n",
    "train_x = sc.fit_transform(hog_x_train)\n",
    "test_x = sc.transform(hog_x_test)\n",
    "\n",
    "rbf = SVC(kernel='rbf')\n",
    "rbf.fit(train_x,y_train)\n",
    "pred2 = rbf.predict(test_x)\n",
    "\n",
    "print(classification_report(y_test,pred2))\n",
    "print(\"Accuracy : {}\".format(accuracy_score(y_test,pred2)))\n",
    "print(\"F1 : {}\".format(f1_score(y_test,pred2,average='micro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "precision    recall  f1-score   support\n\n           0       0.92      0.92      0.92       705\n           1       0.98      0.96      0.97       815\n           2       0.90      0.93      0.91       707\n           3       0.85      0.88      0.87       684\n           4       0.93      0.90      0.91       661\n           5       0.93      0.91      0.92       605\n           6       0.89      0.95      0.92       674\n           7       0.94      0.93      0.94       760\n           8       0.90      0.89      0.90       693\n           9       0.88      0.85      0.86       696\n\n    accuracy                           0.91      7000\n   macro avg       0.91      0.91      0.91      7000\nweighted avg       0.91      0.91      0.91      7000\n\nAccuracy : 0.9128571428571428\nF1 : 0.9128571428571428\n"
    }
   ],
   "source": [
    "# Scaling & Normalising the training & test features using MinMax Scaler & Fitting the transformed data to determine accuracy gain on predictions by RBF SVM\n",
    "\n",
    "minmax = MinMaxScaler()\n",
    "hog_x_train_scaled = minmax.fit_transform(hog_x_train)\n",
    "hog_x_test_scaled = minmax.transform(hog_x_test)\n",
    "\n",
    "rbf = SVC(kernel='rbf')\n",
    "rbf.fit(hog_x_train_scaled,y_train)\n",
    "pred1 = rbf.predict(hog_x_test_scaled)\n",
    "\n",
    "print(classification_report(y_test,pred1))\n",
    "print(\"Accuracy : {}\".format(accuracy_score(y_test,pred1)))\n",
    "print(\"F1 : {}\".format(f1_score(y_test,pred1,average='micro')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 10800x10800 with 0 Axes>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "# Visualising the variations in accuracy by normalising & scaling the data using different techniques\n",
    "\n",
    "x_labels = [\"Without Scaling\",\"Standard Scaling\",\"MinMax Scaling\"]\n",
    "acc_list = [acc_svm_rbf,accuracy_score(y_test,pred2),accuracy_score(y_test,pred1)]\n",
    "\n",
    "acc_chart = plt.bar(x_labels,acc_list)\n",
    "plt.xlabel(\"Type of Scaling Used\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Scaling Technique Accuracy Comparison\",fontdict={'fontsize':20, 'fontweight':'bold'})\n",
    "plt.yticks([0.0,0.2,0.4,0.6,0.8,0.9])\n",
    "plt.figure(figsize=(36,36),dpi=300)\n",
    "plt.show()"
   ]
  }
 ]
}