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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"# Побудова моделі класифікації\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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"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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},
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{
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"cell_type": "markdown",
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"source": [
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"---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Застереження**:\nЦей документ був перекладений за допомогою сервісу автоматичного перекладу [Co-op Translator](https://github.com/Azure/co-op-translator). Хоча ми намагаємося забезпечити точність, просимо враховувати, що автоматичні переклади можуть містити помилки чи неточності. Оригінальний документ рідною мовою слід вважати авторитетним джерелом. Для критичної інформації рекомендується звертатися до професійного людського перекладу. Ми не несемо відповідальності за будь-які непорозуміння або неправильні тлумачення, які можуть виникнути внаслідок використання цього перекладу.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
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