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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"# Построение модели классификации\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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"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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"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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"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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"---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Отказ от ответственности**: \nДанный документ был переведен с помощью автоматического переводческого сервиса [Co-op Translator](https://github.com/Azure/co-op-translator). Несмотря на наши усилия обеспечить точность, пожалуйста, учтите, что автоматические переводы могут содержать ошибки или неточности. Оригинальный документ на исходном языке следует считать авторитетным источником. Для получения критически важной информации рекомендуется профессиональный перевод человеком. Мы не несем ответственности за любые недоразумения или искажения смысла, возникшие в результате использования данного перевода.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
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