{ "cells": [ { "source": [ "# Bina Lebih Banyak Model Klasifikasi\n" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Gambaran Keseluruhan Dataset\n", "Dataset ini mengandungi sampel individu (contohnya, resipi) yang dilabelkan mengikut masakan.\n", "Setiap baris mewakili satu sampel/rekod, dan lajur-lajur mewakili ramuan atau atribut lain yang digunakan untuk pengelasan, termasuk label `cuisine`.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "# Load dataset containing cuisine features\n", "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n", "cuisines_df.head()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0 indian\n", "1 indian\n", "2 indian\n", "3 indian\n", "4 indian\n", "Name: cuisine, dtype: object" ] }, "metadata": {}, "execution_count": 2 } ], "source": [ "cuisines_label_df = cuisines_df['cuisine']\n", "cuisines_label_df.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " almond angelica anise anise_seed apple apple_brandy apricot \\\n", "0 0 0 0 0 0 0 0 \n", "1 1 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 \n", "3 0 0 0 0 0 0 0 \n", "4 0 0 0 0 0 0 0 \n", "\n", " armagnac artemisia artichoke ... whiskey white_bread white_wine \\\n", "0 0 0 0 ... 0 0 0 \n", "1 0 0 0 ... 0 0 0 \n", "2 0 0 0 ... 0 0 0 \n", "3 0 0 0 ... 0 0 0 \n", "4 0 0 0 ... 0 0 0 \n", "\n", " whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n", "0 0 0 0 0 0 0 0 \n", "1 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 \n", "3 0 0 0 0 0 0 0 \n", "4 0 0 0 0 0 1 0 \n", "\n", "[5 rows x 380 columns]" ], "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
almondangelicaaniseanise_seedappleapple_brandyapricotarmagnacartemisiaartichoke...whiskeywhite_breadwhite_winewhole_grain_wheat_flourwinewoodyamyeastyogurtzucchini
00000000000...0000000000
11000000000...0000000000
20000000000...0000000000
30000000000...0000000000
40000000000...0000000010
\n

5 rows × 380 columns

\n
" }, "metadata": {}, "execution_count": 3 } ], "source": [ "cuisines_features_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n", "cuisines_features_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cuba pengelasan yang berbeza\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.linear_model import LogisticRegression\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,precision_score,confusion_matrix,classification_report, precision_recall_curve\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(cuisines_features_df, cuisines_label_df, test_size=0.3)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "\n", "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(),\n", " 'RFST': RandomForestClassifier(n_estimators=100),\n", " 'ADA': AdaBoostClassifier(n_estimators=100)\n", " \n", "}\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Accuracy (train) for Linear SVC: 76.4% \n", " precision recall f1-score support\n", "\n", " chinese 0.64 0.66 0.65 242\n", " indian 0.91 0.86 0.89 236\n", " japanese 0.72 0.73 0.73 245\n", " korean 0.83 0.75 0.79 234\n", " thai 0.75 0.82 0.78 242\n", "\n", " accuracy 0.76 1199\n", " macro avg 0.77 0.76 0.77 1199\n", "weighted avg 0.77 0.76 0.77 1199\n", "\n", "Accuracy (train) for KNN classifier: 70.7% \n", " precision recall f1-score support\n", "\n", " chinese 0.65 0.63 0.64 242\n", " indian 0.84 0.81 0.82 236\n", " japanese 0.60 0.81 0.69 245\n", " korean 0.89 0.53 0.67 234\n", " thai 0.69 0.75 0.72 242\n", "\n", " accuracy 0.71 1199\n", " macro avg 0.73 0.71 0.71 1199\n", "weighted avg 0.73 0.71 0.71 1199\n", "\n", "Accuracy (train) for SVC: 80.1% \n", " precision recall f1-score support\n", "\n", " chinese 0.71 0.69 0.70 242\n", " indian 0.92 0.92 0.92 236\n", " japanese 0.77 0.78 0.77 245\n", " korean 0.87 0.77 0.82 234\n", " thai 0.75 0.86 0.80 242\n", "\n", " accuracy 0.80 1199\n", " macro avg 0.80 0.80 0.80 1199\n", "weighted avg 0.80 0.80 0.80 1199\n", "\n", "Accuracy (train) for RFST: 82.8% \n", " precision recall f1-score support\n", "\n", " chinese 0.80 0.75 0.77 242\n", " indian 0.90 0.91 0.90 236\n", " japanese 0.82 0.78 0.80 245\n", " korean 0.85 0.82 0.83 234\n", " thai 0.78 0.89 0.83 242\n", "\n", " accuracy 0.83 1199\n", " macro avg 0.83 0.83 0.83 1199\n", "weighted avg 0.83 0.83 0.83 1199\n", "\n", "Accuracy (train) for ADA: 71.1% \n", " precision recall f1-score support\n", "\n", " chinese 0.60 0.57 0.58 242\n", " indian 0.87 0.84 0.86 236\n", " japanese 0.71 0.60 0.65 245\n", " korean 0.68 0.78 0.72 234\n", " thai 0.70 0.78 0.74 242\n", "\n", " accuracy 0.71 1199\n", " macro avg 0.71 0.71 0.71 1199\n", "weighted avg 0.71 0.71 0.71 1199\n", "\n" ] } ], "source": [ "n_classifiers = len(classifiers)\n", "\n", "for index, (name, classifier) in enumerate(classifiers.items()):\n", " classifier.fit(X_train, np.ravel(y_train))\n", "\n", " y_pred = classifier.predict(X_test)\n", " accuracy = accuracy_score(y_test, y_pred)\n", " print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\n", " print(classification_report(y_test,y_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n\n\n**Penafian**: \nDokumen ini telah diterjemahkan menggunakan perkhidmatan terjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Walaupun kami berusaha untuk ketepatan, sila maklum bahawa terjemahan automatik mungkin mengandungi kesilapan atau ketidaktepatan. Dokumen asal dalam bahasa asalnya harus dianggap sebagai sumber yang sahih. Untuk maklumat kritikal, terjemahan profesional oleh manusia adalah disyorkan. Kami tidak bertanggungjawab atas sebarang salah faham atau salah tafsir yang timbul daripada penggunaan terjemahan ini.\n\n" ] } ], "metadata": { "interpreter": { "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" }, "kernelspec": { "name": "python3", "display_name": "Python 3.7.0 64-bit ('3.7')" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" }, "metadata": { "interpreter": { "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" } } }, "nbformat": 4, "nbformat_minor": 4 }