diff --git a/4-Classification/3-Classifiers-2/notebook.ipynb b/4-Classification/3-Classifiers-2/notebook.ipynb index a2b420db..bc0becd7 100644 --- a/4-Classification/3-Classifiers-2/notebook.ipynb +++ b/4-Classification/3-Classifiers-2/notebook.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -207,7 +207,7 @@ "[5 rows x 382 columns]" ] }, - "execution_count": 9, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -220,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -234,7 +234,7 @@ "Name: cuisine, dtype: object" ] }, - "execution_count": 10, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -246,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -444,7 +444,7 @@ "[5 rows x 380 columns]" ] }, - "execution_count": 11, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -453,14 +453,144 @@ "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n", "cuisines_feature_df.head()" ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.svm import SVC\n", + "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, precision_recall_curve\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "x_train, x_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "c = 10\n", + "#create different classifiers\n", + "classifiers = {\n", + " 'Linear SVC' : SVC(kernel='linear', C=c, probability=True, random_state=0),\n", + " 'KNN Classifier' : KNeighborsClassifier(c),\n", + " 'SVC' : SVC(kernel='rbf', C = 10, gamma=\"scale\"), #Support Vector Classifier\n", + " 'RFST': RandomForestClassifier(n_estimators=100), #Emsamble Cllasifier\n", + " 'ADA': AdaBoostClassifier(n_estimators=100)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuraccy (train) of Linear SVC: 81.0%\n", + " precision recall f1-score support\n", + "\n", + " chinese 0.71 0.75 0.73 235\n", + " indian 0.90 0.88 0.89 254\n", + " japanese 0.83 0.76 0.79 236\n", + " korean 0.86 0.77 0.82 247\n", + " thai 0.76 0.89 0.82 227\n", + "\n", + " accuracy 0.81 1199\n", + " macro avg 0.81 0.81 0.81 1199\n", + "weighted avg 0.81 0.81 0.81 1199\n", + "\n", + "Accuraccy (train) of KNN Classifier: 74.4%\n", + " precision recall f1-score support\n", + "\n", + " chinese 0.67 0.72 0.70 235\n", + " indian 0.81 0.77 0.79 254\n", + " japanese 0.68 0.81 0.74 236\n", + " korean 0.92 0.57 0.71 247\n", + " thai 0.72 0.85 0.78 227\n", + "\n", + " accuracy 0.74 1199\n", + " macro avg 0.76 0.75 0.74 1199\n", + "weighted avg 0.76 0.74 0.74 1199\n", + "\n", + "Accuraccy (train) of SVC: 84.1%\n", + " precision recall f1-score support\n", + "\n", + " chinese 0.80 0.80 0.80 235\n", + " indian 0.90 0.91 0.90 254\n", + " japanese 0.90 0.77 0.83 236\n", + " korean 0.87 0.81 0.83 247\n", + " thai 0.76 0.93 0.84 227\n", + "\n", + " accuracy 0.84 1199\n", + " macro avg 0.84 0.84 0.84 1199\n", + "weighted avg 0.85 0.84 0.84 1199\n", + "\n", + "Accuraccy (train) of RFST: 85.9%\n", + " precision recall f1-score support\n", + "\n", + " chinese 0.83 0.81 0.82 235\n", + " indian 0.92 0.91 0.92 254\n", + " japanese 0.88 0.81 0.85 236\n", + " korean 0.88 0.83 0.85 247\n", + " thai 0.79 0.93 0.86 227\n", + "\n", + " accuracy 0.86 1199\n", + " macro avg 0.86 0.86 0.86 1199\n", + "weighted avg 0.86 0.86 0.86 1199\n", + "\n", + "Accuraccy (train) of ADA: 69.7%\n", + " precision recall f1-score support\n", + "\n", + " chinese 0.63 0.61 0.62 235\n", + " indian 0.87 0.79 0.83 254\n", + " japanese 0.52 0.83 0.64 236\n", + " korean 0.84 0.64 0.72 247\n", + " thai 0.79 0.62 0.69 227\n", + "\n", + " accuracy 0.70 1199\n", + " macro avg 0.73 0.70 0.70 1199\n", + "weighted avg 0.73 0.70 0.70 1199\n", + "\n" + ] + } + ], + "source": [ + "n_classifiers = len(classifiers)\n", + "for index, (name, classifier) in enumerate(classifiers.items()):\n", + " classifier.fit(x_train, np.ravel(y_train))\n", + " y_pred = classifier.predict(x_test)\n", + " accuracy = accuracy_score(y_test, y_pred)\n", + " print(\"Accuraccy (train) of %s: %0.1f%%\" %(name, accuracy * 100))\n", + " print(classification_report(y_test, y_pred))\n", + "\n", + "\n", + "\n", + "\n" + ] } ], "metadata": { - "interpreter": { - "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" - }, "kernelspec": { - "display_name": "Python 3.7.0 64-bit ('3.7')", + "display_name": ".venv", + "language": "python", "name": "python3" }, "language_info": { @@ -473,7 +603,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.13.3" }, "metadata": { "interpreter": {