pull/872/head
Paskal 3 months ago
commit 483e8e00ba

File diff suppressed because one or more lines are too long

@ -9,7 +9,7 @@
},
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"cell_type": "code",
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"execution_count": 4,
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{
@ -207,7 +207,7 @@
"[5 rows x 26 columns]"
]
},
"execution_count": 19,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@ -220,20 +220,42 @@
},
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"cell_type": "code",
"execution_count": 16,
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"City Name 0\n",
"Type 1712\n",
"Package 0\n",
"Variety 5\n",
"Sub Variety 1461\n",
"Grade 1757\n",
"Date 0\n",
"Low Price 0\n",
"High Price 0\n",
"Date 0\n",
"Mostly Low 103\n",
"Mostly High 103\n",
"Origin 3\n",
"Origin District 1626\n",
"Item Size 279\n",
"Color 616\n",
"Environment 1757\n",
"Unit of Sale 1595\n",
"Quality 1757\n",
"Condition 1757\n",
"Appearance 1757\n",
"Storage 1757\n",
"Crop 1757\n",
"Repack 0\n",
"Trans Mode 1757\n",
"Unnamed: 24 1757\n",
"Unnamed: 25 1654\n",
"dtype: int64"
]
},
"execution_count": 16,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@ -244,7 +266,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@ -266,7 +288,7 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 8,
"metadata": {},
"outputs": [
{
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},
{
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"execution_count": 9,
"metadata": {},
"outputs": [
{
@ -299,7 +321,7 @@
"Text(0, 0.5, 'Pumpkin Price')"
]
},
"execution_count": 23,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},

@ -6,250 +6,6 @@
"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",
" <th>0</th>\n",
" <td>65</td>\n",
" <td>indian</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
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" <td>indian</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <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>67</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>68</td>\n",
" <td>indian</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>...</td>\n",
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" <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>69</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 × 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": {

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