{
"cells": [
{
"cell_type": "markdown",
"id": "d11c1a391539c7d8",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"# Wine analysis"
]
},
{
"cell_type": "markdown",
"id": "8413e70b6240f7f9",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"# Importación de librerías"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c4a3872a-5c67-4a7c-8442-519ca0035d46",
"metadata": {
"ExecuteTime": {
"end_time": "2024-01-03T10:16:38.057440900Z",
"start_time": "2024-01-03T10:16:38.048296Z"
}
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import numpy as np\n",
"import pandas as pd\n",
"# sns.color_palette(\"husl\", 9)"
]
},
{
"cell_type": "markdown",
"id": "e5c2b140-8727-477b-8fc6-37ebe6fe68f4",
"metadata": {},
"source": [
"# Adquisición de datos"
]
},
{
"cell_type": "markdown",
"id": "f968e703-5afe-42eb-8bd1-132bac56d80d",
"metadata": {},
"source": [
"Para la realización de este trabajo, vamos a utilizar el dataset *wine* de sklearn."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0899b2d8-0570-44dd-b0d5-829f7da7e91c",
"metadata": {
"is_executing": true
},
"outputs": [],
"source": [
"from sklearn.datasets import load_wine\n",
"wines = load_wine(as_frame=True)\n",
"X = wines[\"data\"]\n",
"y = wines[\"target\"]\n",
"columnas = list(X.columns)\n",
"descripcion = wines[\"DESCR\"]"
]
},
{
"cell_type": "markdown",
"id": "65ce3641-23ff-4e85-9768-a7e2c26e9f0f",
"metadata": {},
"source": [
"### Características del dataset"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6b2d6ab1-e1d2-4b69-9e1e-acc9152f0800",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wine recognition dataset\n",
"------------------------\n",
"\n",
"**Data Set Characteristics:**\n",
"\n",
" :Number of Instances: 178\n",
" :Number of Attributes: 13 numeric, predictive attributes and the class\n",
" :Attribute Information:\n",
" \t\t- Alcohol\n",
" \t\t- Malic acid\n",
" \t\t- Ash\n",
"\t\t- Alcalinity of ash \n",
" \t\t- Magnesium\n",
"\t\t- Total phenols\n",
" \t\t- Flavanoids\n",
" \t\t- Nonflavanoid phenols\n",
" \t\t- Proanthocyanins\n",
"\t\t- Color intensity\n",
" \t\t- Hue\n",
" \t\t- OD280/OD315 of diluted wines\n",
" \t\t- Proline\n",
"\n",
" - class:\n",
" - class_0\n",
" - class_1\n",
" - class_2\n"
]
}
],
"source": [
"print(descripcion[19:547])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4be7ae03-bb1f-4c11-bd29-69da20e963a6",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" alcohol | \n",
" malic_acid | \n",
" ash | \n",
" alcalinity_of_ash | \n",
" magnesium | \n",
" total_phenols | \n",
" flavanoids | \n",
" nonflavanoid_phenols | \n",
" proanthocyanins | \n",
" color_intensity | \n",
" hue | \n",
" od280/od315_of_diluted_wines | \n",
" proline | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 14.23 | \n",
" 1.71 | \n",
" 2.43 | \n",
" 15.6 | \n",
" 127.0 | \n",
" 2.80 | \n",
" 3.06 | \n",
" 0.28 | \n",
" 2.29 | \n",
" 5.64 | \n",
" 1.04 | \n",
" 3.92 | \n",
" 1065.0 | \n",
"
\n",
" \n",
" 1 | \n",
" 13.20 | \n",
" 1.78 | \n",
" 2.14 | \n",
" 11.2 | \n",
" 100.0 | \n",
" 2.65 | \n",
" 2.76 | \n",
" 0.26 | \n",
" 1.28 | \n",
" 4.38 | \n",
" 1.05 | \n",
" 3.40 | \n",
" 1050.0 | \n",
"
\n",
" \n",
" 2 | \n",
" 13.16 | \n",
" 2.36 | \n",
" 2.67 | \n",
" 18.6 | \n",
" 101.0 | \n",
" 2.80 | \n",
" 3.24 | \n",
" 0.30 | \n",
" 2.81 | \n",
" 5.68 | \n",
" 1.03 | \n",
" 3.17 | \n",
" 1185.0 | \n",
"
\n",
" \n",
" 3 | \n",
" 14.37 | \n",
" 1.95 | \n",
" 2.50 | \n",
" 16.8 | \n",
" 113.0 | \n",
" 3.85 | \n",
" 3.49 | \n",
" 0.24 | \n",
" 2.18 | \n",
" 7.80 | \n",
" 0.86 | \n",
" 3.45 | \n",
" 1480.0 | \n",
"
\n",
" \n",
" 4 | \n",
" 13.24 | \n",
" 2.59 | \n",
" 2.87 | \n",
" 21.0 | \n",
" 118.0 | \n",
" 2.80 | \n",
" 2.69 | \n",
" 0.39 | \n",
" 1.82 | \n",
" 4.32 | \n",
" 1.04 | \n",
" 2.93 | \n",
" 735.0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols \\\n",
"0 14.23 1.71 2.43 15.6 127.0 2.80 \n",
"1 13.20 1.78 2.14 11.2 100.0 2.65 \n",
"2 13.16 2.36 2.67 18.6 101.0 2.80 \n",
"3 14.37 1.95 2.50 16.8 113.0 3.85 \n",
"4 13.24 2.59 2.87 21.0 118.0 2.80 \n",
"\n",
" flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue \\\n",
"0 3.06 0.28 2.29 5.64 1.04 \n",
"1 2.76 0.26 1.28 4.38 1.05 \n",
"2 3.24 0.30 2.81 5.68 1.03 \n",
"3 3.49 0.24 2.18 7.80 0.86 \n",
"4 2.69 0.39 1.82 4.32 1.04 \n",
"\n",
" od280/od315_of_diluted_wines proline \n",
"0 3.92 1065.0 \n",
"1 3.40 1050.0 \n",
"2 3.17 1185.0 \n",
"3 3.45 1480.0 \n",
"4 2.93 735.0 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "190b637a-baae-4899-b358-478d96fe72ad",
"metadata": {
"is_executing": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"RangeIndex: 178 entries, 0 to 177\n",
"Data columns (total 13 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 alcohol 178 non-null float64\n",
" 1 malic_acid 178 non-null float64\n",
" 2 ash 178 non-null float64\n",
" 3 alcalinity_of_ash 178 non-null float64\n",
" 4 magnesium 178 non-null float64\n",
" 5 total_phenols 178 non-null float64\n",
" 6 flavanoids 178 non-null float64\n",
" 7 nonflavanoid_phenols 178 non-null float64\n",
" 8 proanthocyanins 178 non-null float64\n",
" 9 color_intensity 178 non-null float64\n",
" 10 hue 178 non-null float64\n",
" 11 od280/od315_of_diluted_wines 178 non-null float64\n",
" 12 proline 178 non-null float64\n",
"dtypes: float64(13)\n",
"memory usage: 18.2 KB\n"
]
}
],
"source": [
"X.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b7425e84-ef61-4e59-ab11-6e9d1e1aa5f8",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" alcohol | \n",
" malic_acid | \n",
" ash | \n",
" alcalinity_of_ash | \n",
" magnesium | \n",
" total_phenols | \n",
" flavanoids | \n",
" nonflavanoid_phenols | \n",
" proanthocyanins | \n",
" color_intensity | \n",
" hue | \n",
" od280/od315_of_diluted_wines | \n",
" proline | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
"
\n",
" \n",
" mean | \n",
" 13.00 | \n",
" 2.34 | \n",
" 2.37 | \n",
" 19.49 | \n",
" 99.74 | \n",
" 2.30 | \n",
" 2.03 | \n",
" 0.36 | \n",
" 1.59 | \n",
" 5.06 | \n",
" 0.96 | \n",
" 2.61 | \n",
" 746.89 | \n",
"
\n",
" \n",
" std | \n",
" 0.81 | \n",
" 1.12 | \n",
" 0.27 | \n",
" 3.34 | \n",
" 14.28 | \n",
" 0.63 | \n",
" 1.00 | \n",
" 0.12 | \n",
" 0.57 | \n",
" 2.32 | \n",
" 0.23 | \n",
" 0.71 | \n",
" 314.91 | \n",
"
\n",
" \n",
" min | \n",
" 11.03 | \n",
" 0.74 | \n",
" 1.36 | \n",
" 10.60 | \n",
" 70.00 | \n",
" 0.98 | \n",
" 0.34 | \n",
" 0.13 | \n",
" 0.41 | \n",
" 1.28 | \n",
" 0.48 | \n",
" 1.27 | \n",
" 278.00 | \n",
"
\n",
" \n",
" 25% | \n",
" 12.36 | \n",
" 1.60 | \n",
" 2.21 | \n",
" 17.20 | \n",
" 88.00 | \n",
" 1.74 | \n",
" 1.21 | \n",
" 0.27 | \n",
" 1.25 | \n",
" 3.22 | \n",
" 0.78 | \n",
" 1.94 | \n",
" 500.50 | \n",
"
\n",
" \n",
" 50% | \n",
" 13.05 | \n",
" 1.87 | \n",
" 2.36 | \n",
" 19.50 | \n",
" 98.00 | \n",
" 2.35 | \n",
" 2.13 | \n",
" 0.34 | \n",
" 1.56 | \n",
" 4.69 | \n",
" 0.96 | \n",
" 2.78 | \n",
" 673.50 | \n",
"
\n",
" \n",
" 75% | \n",
" 13.68 | \n",
" 3.08 | \n",
" 2.56 | \n",
" 21.50 | \n",
" 107.00 | \n",
" 2.80 | \n",
" 2.88 | \n",
" 0.44 | \n",
" 1.95 | \n",
" 6.20 | \n",
" 1.12 | \n",
" 3.17 | \n",
" 985.00 | \n",
"
\n",
" \n",
" max | \n",
" 14.83 | \n",
" 5.80 | \n",
" 3.23 | \n",
" 30.00 | \n",
" 162.00 | \n",
" 3.88 | \n",
" 5.08 | \n",
" 0.66 | \n",
" 3.58 | \n",
" 13.00 | \n",
" 1.71 | \n",
" 4.00 | \n",
" 1680.00 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" alcohol malic_acid ash alcalinity_of_ash magnesium \\\n",
"count 178.00 178.00 178.00 178.00 178.00 \n",
"mean 13.00 2.34 2.37 19.49 99.74 \n",
"std 0.81 1.12 0.27 3.34 14.28 \n",
"min 11.03 0.74 1.36 10.60 70.00 \n",
"25% 12.36 1.60 2.21 17.20 88.00 \n",
"50% 13.05 1.87 2.36 19.50 98.00 \n",
"75% 13.68 3.08 2.56 21.50 107.00 \n",
"max 14.83 5.80 3.23 30.00 162.00 \n",
"\n",
" total_phenols flavanoids nonflavanoid_phenols proanthocyanins \\\n",
"count 178.00 178.00 178.00 178.00 \n",
"mean 2.30 2.03 0.36 1.59 \n",
"std 0.63 1.00 0.12 0.57 \n",
"min 0.98 0.34 0.13 0.41 \n",
"25% 1.74 1.21 0.27 1.25 \n",
"50% 2.35 2.13 0.34 1.56 \n",
"75% 2.80 2.88 0.44 1.95 \n",
"max 3.88 5.08 0.66 3.58 \n",
"\n",
" color_intensity hue od280/od315_of_diluted_wines proline \n",
"count 178.00 178.00 178.00 178.00 \n",
"mean 5.06 0.96 2.61 746.89 \n",
"std 2.32 0.23 0.71 314.91 \n",
"min 1.28 0.48 1.27 278.00 \n",
"25% 3.22 0.78 1.94 500.50 \n",
"50% 4.69 0.96 2.78 673.50 \n",
"75% 6.20 1.12 3.17 985.00 \n",
"max 13.00 1.71 4.00 1680.00 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.describe().map(lambda x: round(x,2))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "34e86266-f0e2-4d6b-a827-780bd6d26e76",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"RangeIndex: 178 entries, 0 to 177\n",
"Series name: target\n",
"Non-Null Count Dtype\n",
"-------------- -----\n",
"178 non-null int32\n",
"dtypes: int32(1)\n",
"memory usage: 844.0 bytes\n"
]
}
],
"source": [
"y.info()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "17ad06ca-1679-42fc-8185-dc88144a754d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"target\n",
"0 59\n",
"1 71\n",
"2 48\n",
"Name: count, dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.value_counts().sort_index()"
]
},
{
"cell_type": "markdown",
"id": "528a63c2-e584-4682-985f-2b77482d7ffb",
"metadata": {},
"source": [
"Observamos que el dataset es un conjunto de 178 registros formado por 13 variables feature y una variable de salida, la cual divide al dataset en las clases \"0\", \"1\" y \"2\". Se comprobó también que ningún registro tiene un valor nulo."
]
},
{
"cell_type": "markdown",
"id": "6b7a069e-cd08-4cbb-a6a5-5843c2058b34",
"metadata": {},
"source": [
"# Normalización de datos\n",
"\n",
"Para el mismo, utilizaremos la función StandardScaler de Sklearn"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "7e069300-a483-4c57-873b-e1d722e78b1f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" alcohol | \n",
" malic_acid | \n",
" ash | \n",
" alcalinity_of_ash | \n",
" magnesium | \n",
" total_phenols | \n",
" flavanoids | \n",
" nonflavanoid_phenols | \n",
" proanthocyanins | \n",
" color_intensity | \n",
" hue | \n",
" od280/od315_of_diluted_wines | \n",
" proline | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1.518613 | \n",
" -0.562250 | \n",
" 0.232053 | \n",
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" 1.847920 | \n",
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\n",
" \n",
" 1 | \n",
" 0.246290 | \n",
" -0.499413 | \n",
" -0.827996 | \n",
" -2.490847 | \n",
" 0.018145 | \n",
" 0.568648 | \n",
" 0.733629 | \n",
" -0.820719 | \n",
" -0.544721 | \n",
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" 0.406051 | \n",
" 1.113449 | \n",
" 0.965242 | \n",
"
\n",
" \n",
" 2 | \n",
" 0.196879 | \n",
" 0.021231 | \n",
" 1.109334 | \n",
" -0.268738 | \n",
" 0.088358 | \n",
" 0.808997 | \n",
" 1.215533 | \n",
" -0.498407 | \n",
" 2.135968 | \n",
" 0.269020 | \n",
" 0.318304 | \n",
" 0.788587 | \n",
" 1.395148 | \n",
"
\n",
" \n",
" 3 | \n",
" 1.691550 | \n",
" -0.346811 | \n",
" 0.487926 | \n",
" -0.809251 | \n",
" 0.930918 | \n",
" 2.491446 | \n",
" 1.466525 | \n",
" -0.981875 | \n",
" 1.032155 | \n",
" 1.186068 | \n",
" -0.427544 | \n",
" 1.184071 | \n",
" 2.334574 | \n",
"
\n",
" \n",
" 4 | \n",
" 0.295700 | \n",
" 0.227694 | \n",
" 1.840403 | \n",
" 0.451946 | \n",
" 1.281985 | \n",
" 0.808997 | \n",
" 0.663351 | \n",
" 0.226796 | \n",
" 0.401404 | \n",
" -0.319276 | \n",
" 0.362177 | \n",
" 0.449601 | \n",
" -0.037874 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" alcohol malic_acid ash alcalinity_of_ash magnesium \\\n",
"0 1.518613 -0.562250 0.232053 -1.169593 1.913905 \n",
"1 0.246290 -0.499413 -0.827996 -2.490847 0.018145 \n",
"2 0.196879 0.021231 1.109334 -0.268738 0.088358 \n",
"3 1.691550 -0.346811 0.487926 -0.809251 0.930918 \n",
"4 0.295700 0.227694 1.840403 0.451946 1.281985 \n",
"\n",
" total_phenols flavanoids nonflavanoid_phenols proanthocyanins \\\n",
"0 0.808997 1.034819 -0.659563 1.224884 \n",
"1 0.568648 0.733629 -0.820719 -0.544721 \n",
"2 0.808997 1.215533 -0.498407 2.135968 \n",
"3 2.491446 1.466525 -0.981875 1.032155 \n",
"4 0.808997 0.663351 0.226796 0.401404 \n",
"\n",
" color_intensity hue od280/od315_of_diluted_wines proline \n",
"0 0.251717 0.362177 1.847920 1.013009 \n",
"1 -0.293321 0.406051 1.113449 0.965242 \n",
"2 0.269020 0.318304 0.788587 1.395148 \n",
"3 1.186068 -0.427544 1.184071 2.334574 \n",
"4 -0.319276 0.362177 0.449601 -0.037874 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"scaler = StandardScaler()\n",
"X_scaled = scaler.fit_transform(X)\n",
"X_scaled = pd.DataFrame(X_scaled, columns=columnas)\n",
"X_scaled.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "73f7475c-8fa5-4495-a13e-ce080d042b20",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" alcohol | \n",
" malic_acid | \n",
" ash | \n",
" alcalinity_of_ash | \n",
" magnesium | \n",
" total_phenols | \n",
" flavanoids | \n",
" nonflavanoid_phenols | \n",
" proanthocyanins | \n",
" color_intensity | \n",
" hue | \n",
" od280/od315_of_diluted_wines | \n",
" proline | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
" 178.00 | \n",
"
\n",
" \n",
" mean | \n",
" -0.00 | \n",
" -0.00 | \n",
" -0.00 | \n",
" -0.00 | \n",
" -0.00 | \n",
" 0.00 | \n",
" -0.00 | \n",
" 0.00 | \n",
" -0.00 | \n",
" 0.00 | \n",
" 0.00 | \n",
" 0.00 | \n",
" -0.00 | \n",
"
\n",
" \n",
" std | \n",
" 1.00 | \n",
" 1.00 | \n",
" 1.00 | \n",
" 1.00 | \n",
" 1.00 | \n",
" 1.00 | \n",
" 1.00 | \n",
" 1.00 | \n",
" 1.00 | \n",
" 1.00 | \n",
" 1.00 | \n",
" 1.00 | \n",
" 1.00 | \n",
"
\n",
" \n",
" min | \n",
" -2.43 | \n",
" -1.43 | \n",
" -3.68 | \n",
" -2.67 | \n",
" -2.09 | \n",
" -2.11 | \n",
" -1.70 | \n",
" -1.87 | \n",
" -2.07 | \n",
" -1.63 | \n",
" -2.09 | \n",
" -1.90 | \n",
" -1.49 | \n",
"
\n",
" \n",
" 25% | \n",
" -0.79 | \n",
" -0.66 | \n",
" -0.57 | \n",
" -0.69 | \n",
" -0.82 | \n",
" -0.89 | \n",
" -0.83 | \n",
" -0.74 | \n",
" -0.60 | \n",
" -0.80 | \n",
" -0.77 | \n",
" -0.95 | \n",
" -0.78 | \n",
"
\n",
" \n",
" 50% | \n",
" 0.06 | \n",
" -0.42 | \n",
" -0.02 | \n",
" 0.00 | \n",
" -0.12 | \n",
" 0.10 | \n",
" 0.11 | \n",
" -0.18 | \n",
" -0.06 | \n",
" -0.16 | \n",
" 0.03 | \n",
" 0.24 | \n",
" -0.23 | \n",
"
\n",
" \n",
" 75% | \n",
" 0.84 | \n",
" 0.67 | \n",
" 0.70 | \n",
" 0.60 | \n",
" 0.51 | \n",
" 0.81 | \n",
" 0.85 | \n",
" 0.61 | \n",
" 0.63 | \n",
" 0.49 | \n",
" 0.71 | \n",
" 0.79 | \n",
" 0.76 | \n",
"
\n",
" \n",
" max | \n",
" 2.26 | \n",
" 3.11 | \n",
" 3.16 | \n",
" 3.15 | \n",
" 4.37 | \n",
" 2.54 | \n",
" 3.06 | \n",
" 2.40 | \n",
" 3.49 | \n",
" 3.44 | \n",
" 3.30 | \n",
" 1.96 | \n",
" 2.97 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" alcohol malic_acid ash alcalinity_of_ash magnesium \\\n",
"count 178.00 178.00 178.00 178.00 178.00 \n",
"mean -0.00 -0.00 -0.00 -0.00 -0.00 \n",
"std 1.00 1.00 1.00 1.00 1.00 \n",
"min -2.43 -1.43 -3.68 -2.67 -2.09 \n",
"25% -0.79 -0.66 -0.57 -0.69 -0.82 \n",
"50% 0.06 -0.42 -0.02 0.00 -0.12 \n",
"75% 0.84 0.67 0.70 0.60 0.51 \n",
"max 2.26 3.11 3.16 3.15 4.37 \n",
"\n",
" total_phenols flavanoids nonflavanoid_phenols proanthocyanins \\\n",
"count 178.00 178.00 178.00 178.00 \n",
"mean 0.00 -0.00 0.00 -0.00 \n",
"std 1.00 1.00 1.00 1.00 \n",
"min -2.11 -1.70 -1.87 -2.07 \n",
"25% -0.89 -0.83 -0.74 -0.60 \n",
"50% 0.10 0.11 -0.18 -0.06 \n",
"75% 0.81 0.85 0.61 0.63 \n",
"max 2.54 3.06 2.40 3.49 \n",
"\n",
" color_intensity hue od280/od315_of_diluted_wines proline \n",
"count 178.00 178.00 178.00 178.00 \n",
"mean 0.00 0.00 0.00 -0.00 \n",
"std 1.00 1.00 1.00 1.00 \n",
"min -1.63 -2.09 -1.90 -1.49 \n",
"25% -0.80 -0.77 -0.95 -0.78 \n",
"50% -0.16 0.03 0.24 -0.23 \n",
"75% 0.49 0.71 0.79 0.76 \n",
"max 3.44 3.30 1.96 2.97 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_scaled.describe().map(lambda x: round(x,2))"
]
},
{
"cell_type": "markdown",
"id": "73d0793f-3d3b-46d8-bc5f-df725cc7e56b",
"metadata": {},
"source": [
"# Análisis exploratorio de datos"
]
},
{
"cell_type": "markdown",
"id": "1007d577-302d-4a85-839b-08ce2824ad65",
"metadata": {},
"source": [
"Para esta sección, vamos a visualizar las distribuciones y gráficos para el dataset normalizado y el no normalizado."
]
},
{
"cell_type": "markdown",
"id": "0e0d7f7f-0999-457b-86e9-ca698341a133",
"metadata": {},
"source": [
"## I. Distribución de datos según clase"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ad148bcc-577a-4282-ac9f-c9fb2182533b",
"metadata": {
"is_executing": true
},
"outputs": [],
"source": [
"def graficar_boxplots(X, y, titulo):\n",
" fig, ax = plt.subplots(3,5,figsize=(15,10))\n",
" columnas = X.columns\n",
" ax[0,2].set_title(titulo, color='green')\n",
" for i, ax in enumerate(fig.axes):\n",
" try:\n",
" sns.boxplot(data=X,x=columnas[i],hue=y,ax=ax)\n",
" except:\n",
" ax.remove()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "68aa2ddd-b2d1-4e40-8d54-5176b24255d7",
"metadata": {},
"outputs": [
{
"data": {
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