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Paskal 3 months ago
parent ec654cccbb
commit 724f5d47fe
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4-Classification/1-Introduction/notebook.ipynb
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@ -1,5 +1,263 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Delicious Asian and Indian Cuisines "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl\n",
"import numpy as np\n",
"from imblearn.over_sampling import SMOTE"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"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",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>69</td>\n",
" <td>indian</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 385 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 cuisine almond angelica anise anise_seed apple \\\n",
"0 65 indian 0 0 0 0 0 \n",
"1 66 indian 1 0 0 0 0 \n",
"2 67 indian 0 0 0 0 0 \n",
"3 68 indian 0 0 0 0 0 \n",
"4 69 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 385 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../data/cuisines.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 2448 entries, 0 to 2447\n",
"Columns: 385 entries, Unnamed: 0 to zucchini\n",
"dtypes: int64(384), object(1)\n",
"memory usage: 7.2+ MB\n"
]
}
],
"source": [
"df.info()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
@ -10,19 +268,10 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": 3
"version": "3.13.3"
},
"orig_nbformat": 2
},
"nbformat": 4,
"nbformat_minor": 2,
"cells": [
{
"source": [
"# Delicious Asian and Indian Cuisines "
],
"cell_type": "markdown",
"metadata": {}
}
]
}
"nbformat_minor": 2
}

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