microsoft--ml-for-beginners
wehub-resource-sync
159 行
12 KiB
Plaintext
159 行
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Sınıflandırma Modeli Oluşturma\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" Unnamed: 0 cuisine almond angelica anise anise_seed apple \\\n",
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"0 0 indian 0 0 0 0 0 \n",
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"1 1 indian 1 0 0 0 0 \n",
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"2 2 indian 0 0 0 0 0 \n",
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"3 3 indian 0 0 0 0 0 \n",
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"4 4 indian 0 0 0 0 0 \n",
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"\n",
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" apple_brandy apricot armagnac ... whiskey white_bread white_wine \\\n",
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"0 0 0 0 ... 0 0 0 \n",
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"1 0 0 0 ... 0 0 0 \n",
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"2 0 0 0 ... 0 0 0 \n",
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"3 0 0 0 ... 0 0 0 \n",
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"4 0 0 0 ... 0 0 0 \n",
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"\n",
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" whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n",
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"0 0 0 0 0 0 0 0 \n",
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"1 0 0 0 0 0 0 0 \n",
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"2 0 0 0 0 0 0 0 \n",
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"3 0 0 0 0 0 0 0 \n",
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"4 0 0 0 0 0 1 0 \n",
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"\n",
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"[5 rows x 382 columns]"
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],
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Unnamed: 0</th>\n <th>cuisine</th>\n <th>almond</th>\n <th>angelica</th>\n <th>anise</th>\n <th>anise_seed</th>\n <th>apple</th>\n <th>apple_brandy</th>\n <th>apricot</th>\n <th>armagnac</th>\n <th>...</th>\n <th>whiskey</th>\n <th>white_bread</th>\n <th>white_wine</th>\n <th>whole_grain_wheat_flour</th>\n <th>wine</th>\n <th>wood</th>\n <th>yam</th>\n <th>yeast</th>\n <th>yogurt</th>\n <th>zucchini</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>indian</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>indian</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2</td>\n <td>indian</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3</td>\n <td>indian</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>4</td>\n <td>indian</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
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},
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"metadata": {},
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"execution_count": 9
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}
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],
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"source": [
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"import pandas as pd\n",
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"cuisines_df = pd.read_csv(\"../data/cleaned_cuisines.csv\")\n",
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"cuisines_df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"0 indian\n",
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"1 indian\n",
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"2 indian\n",
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"3 indian\n",
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"4 indian\n",
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"Name: cuisine, dtype: object"
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]
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},
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"metadata": {},
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"execution_count": 10
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}
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],
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"source": [
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"cuisines_label_df = cuisines_df['cuisine']\n",
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"cuisines_label_df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" almond angelica anise anise_seed apple apple_brandy apricot \\\n",
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"0 0 0 0 0 0 0 0 \n",
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"1 1 0 0 0 0 0 0 \n",
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"2 0 0 0 0 0 0 0 \n",
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"3 0 0 0 0 0 0 0 \n",
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"4 0 0 0 0 0 0 0 \n",
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"\n",
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" armagnac artemisia artichoke ... whiskey white_bread white_wine \\\n",
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"0 0 0 0 ... 0 0 0 \n",
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"1 0 0 0 ... 0 0 0 \n",
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"2 0 0 0 ... 0 0 0 \n",
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"3 0 0 0 ... 0 0 0 \n",
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"4 0 0 0 ... 0 0 0 \n",
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"\n",
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" whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n",
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"0 0 0 0 0 0 0 0 \n",
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"1 0 0 0 0 0 0 0 \n",
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"2 0 0 0 0 0 0 0 \n",
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"3 0 0 0 0 0 0 0 \n",
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"4 0 0 0 0 0 1 0 \n",
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"\n",
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"[5 rows x 380 columns]"
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],
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},
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"metadata": {},
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"execution_count": 11
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}
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],
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"source": [
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"cuisines_features_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
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"cuisines_features_df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Feragatname**: \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için özen göstermemize rağmen, otomatik çevirilerin hatalar veya yanlışlıklar içerebileceğini lütfen unutmayın. Orijinal belge, kendi dilinde yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilmektedir. Bu çevirinin kullanımından doğabilecek herhangi bir yanlış anlama veya yorumlama nedeniyle sorumluluk kabul edilmez.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
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]
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}
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