{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Gumawa ng Modelo ng Klasipikasyon\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Unnamed: 0 cuisine almond angelica anise anise_seed apple \\\n", "0 0 indian 0 0 0 0 0 \n", "1 1 indian 1 0 0 0 0 \n", "2 2 indian 0 0 0 0 0 \n", "3 3 indian 0 0 0 0 0 \n", "4 4 indian 0 0 0 0 0 \n", "\n", " apple_brandy apricot armagnac ... 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 382 columns]" ], "text/html": "
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" }, "metadata": {}, "execution_count": 9 } ], "source": [ "import pandas as pd\n", "cuisines_df = pd.read_csv(\"../data/cleaned_cuisines.csv\")\n", "cuisines_df.head()" ] }, { "cell_type": "code", "execution_count": 10, "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": 10 } ], "source": [ "cuisines_label_df = cuisines_df['cuisine']\n", "cuisines_label_df.head()" ] }, { "cell_type": "code", "execution_count": 11, "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": "
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" }, "metadata": {}, "execution_count": 11 } ], "source": [ "cuisines_features_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n", "cuisines_features_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n\n\n**Paliwanag**:\nAng dokumentong ito ay isinalin gamit ang AI translation service na [Co-op Translator](https://github.com/Azure/co-op-translator). Bagamat nagsusumikap kami para sa katumpakan, pakiusap na tandaan na ang mga awtomatikong salin ay maaaring maglaman ng mga pagkakamali o hindi pagkakatugma. Ang orihinal na dokumento sa orihinal nitong wika ang dapat ituring na pinakapinagkukunan ng impormasyon. Para sa mga mahahalagang impormasyon, inirerekomenda ang propesyonal na pagsasalin ng tao. Hindi kami mananagot sa anumang hindi pagkakaunawaan o maling interpretasyon na maaaring magmula sa paggamit ng pagsasaling ito.\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 }