{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "6e3e0c14", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "id": "41a6e525", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import ConfusionMatrixDisplay" ] }, { "cell_type": "code", "execution_count": 3, "id": "3dfd016a", "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.preprocessing import minmax_scale" ] }, { "cell_type": "code", "execution_count": 4, "id": "7bf80832", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SeasonAgeCompetitionMatches PlayedStartsMinutes playedGoals scoredAssistsPKPKattCrdYCrdRGoal/90Ast/90G+A/90G-PK/90G+A-PK/90
02004-200517Champions League11900000000.000.000.000.000.00
12004-200517La Liga70701000001.290.001.291.291.29
22005-200618Champions League643221100000.280.280.560.280.56
32005-200618La Liga17119116300200.590.300.890.590.89
42006-200719Champions League543851000100.230.000.230.230.23
52006-200719La Liga2623198314300200.640.140.770.640.77
62007-200820Champions League997286211200.740.250.990.620.87
72007-200820La Liga27231973101244200.460.551.000.270.82
82008-200921Champions League12109279500100.870.491.360.871.36
92008-200921La Liga31272516231134200.820.391.220.721.11
102009-201022Champions League11119878000000.730.000.730.730.73
112009-201022La Liga3530280534911301.090.291.381.061.35
122010-201123Champions League1311104612312001.030.261.290.951.20
132010-201123La Liga33312858311944300.980.601.570.851.45
142011-201224Champions League111199014545201.270.451.730.911.36
152011-201224La Liga3736327050161011601.380.441.821.101.54
162012-201325Champions League1198268200000.870.221.090.871.09
172012-201325La Liga32282650461144101.560.371.941.431.80
182013-201426Champions League776308022001.140.001.140.860.86
192013-201426La Liga31292501281178201.010.401.400.761.15
202014-201527Champions League1313114710601100.780.471.260.781.26
212014-201527Copa del Rey665405312100.830.501.330.671.17
222014-201527La Liga38373375431856401.150.481.631.011.49
232015-201628Champions League776306111100.860.141.000.710.86
242015-201628Copa del Rey554805500100.940.941.870.941.87
252015-201628La Liga33312729261636300.860.531.390.761.29
262015-201628Supercopa de España221801000000.500.000.500.500.50
272016-201729Champions League9981011222001.220.221.441.001.22
282016-201729Copa del Rey776305311300.710.431.140.571.00
292016-201729La Liga3432282837967601.180.291.460.991.27
302016-201729Supercopa de España221801200000.501.001.500.501.50
312017-201830Champions League1087836200200.690.230.920.690.92
322017-201830Copa del Rey665084401100.710.711.420.711.42
332017-201830La Liga36323002341224301.020.361.380.961.32
342017-201830Supercopa de España221801011100.500.000.500.000.00
352018-201931Champions League10983812311001.290.321.611.181.50
362018-201931Copa del Rey543883200000.700.461.160.701.16
372018-201931La Liga34292713361345301.190.431.631.061.49
382018-201931Supercopa de España11900100000.001.001.000.001.00
392019-202032Champions League876623300200.410.410.820.410.82
402019-202032Copa del Rey221802100101.000.501.501.001.50
412019-202032La Liga33322880252055400.780.621.410.621.25
422019-202032Supercopa de España11901000001.000.001.001.001.00
\n", "
" ], "text/plain": [ " Season Age Competition Matches Played Starts \\\n", "0 2004-2005 17 Champions League 1 1 \n", "1 2004-2005 17 La Liga 7 0 \n", "2 2005-2006 18 Champions League 6 4 \n", "3 2005-2006 18 La Liga 17 11 \n", "4 2006-2007 19 Champions League 5 4 \n", "5 2006-2007 19 La Liga 26 23 \n", "6 2007-2008 20 Champions League 9 9 \n", "7 2007-2008 20 La Liga 27 23 \n", "8 2008-2009 21 Champions League 12 10 \n", "9 2008-2009 21 La Liga 31 27 \n", "10 2009-2010 22 Champions League 11 11 \n", "11 2009-2010 22 La Liga 35 30 \n", "12 2010-2011 23 Champions League 13 11 \n", "13 2010-2011 23 La Liga 33 31 \n", "14 2011-2012 24 Champions League 11 11 \n", "15 2011-2012 24 La Liga 37 36 \n", "16 2012-2013 25 Champions League 11 9 \n", "17 2012-2013 25 La Liga 32 28 \n", "18 2013-2014 26 Champions League 7 7 \n", "19 2013-2014 26 La Liga 31 29 \n", "20 2014-2015 27 Champions League 13 13 \n", "21 2014-2015 27 Copa del Rey 6 6 \n", "22 2014-2015 27 La Liga 38 37 \n", "23 2015-2016 28 Champions League 7 7 \n", "24 2015-2016 28 Copa del Rey 5 5 \n", "25 2015-2016 28 La Liga 33 31 \n", "26 2015-2016 28 Supercopa de España 2 2 \n", "27 2016-2017 29 Champions League 9 9 \n", "28 2016-2017 29 Copa del Rey 7 7 \n", "29 2016-2017 29 La Liga 34 32 \n", "30 2016-2017 29 Supercopa de España 2 2 \n", "31 2017-2018 30 Champions League 10 8 \n", "32 2017-2018 30 Copa del Rey 6 6 \n", "33 2017-2018 30 La Liga 36 32 \n", "34 2017-2018 30 Supercopa de España 2 2 \n", "35 2018-2019 31 Champions League 10 9 \n", "36 2018-2019 31 Copa del Rey 5 4 \n", "37 2018-2019 31 La Liga 34 29 \n", "38 2018-2019 31 Supercopa de España 1 1 \n", "39 2019-2020 32 Champions League 8 7 \n", "40 2019-2020 32 Copa del Rey 2 2 \n", "41 2019-2020 32 La Liga 33 32 \n", "42 2019-2020 32 Supercopa de España 1 1 \n", "\n", " Minutes played Goals scored Assists PK PKatt CrdY CrdR Goal/90 \\\n", "0 90 0 0 0 0 0 0 0.00 \n", "1 70 1 0 0 0 0 0 1.29 \n", "2 322 1 1 0 0 0 0 0.28 \n", "3 911 6 3 0 0 2 0 0.59 \n", "4 385 1 0 0 0 1 0 0.23 \n", "5 1983 14 3 0 0 2 0 0.64 \n", "6 728 6 2 1 1 2 0 0.74 \n", "7 1973 10 12 4 4 2 0 0.46 \n", "8 927 9 5 0 0 1 0 0.87 \n", "9 2516 23 11 3 4 2 0 0.82 \n", "10 987 8 0 0 0 0 0 0.73 \n", "11 2805 34 9 1 1 3 0 1.09 \n", "12 1046 12 3 1 2 0 0 1.03 \n", "13 2858 31 19 4 4 3 0 0.98 \n", "14 990 14 5 4 5 2 0 1.27 \n", "15 3270 50 16 10 11 6 0 1.38 \n", "16 826 8 2 0 0 0 0 0.87 \n", "17 2650 46 11 4 4 1 0 1.56 \n", "18 630 8 0 2 2 0 0 1.14 \n", "19 2501 28 11 7 8 2 0 1.01 \n", "20 1147 10 6 0 1 1 0 0.78 \n", "21 540 5 3 1 2 1 0 0.83 \n", "22 3375 43 18 5 6 4 0 1.15 \n", "23 630 6 1 1 1 1 0 0.86 \n", "24 480 5 5 0 0 1 0 0.94 \n", "25 2729 26 16 3 6 3 0 0.86 \n", "26 180 1 0 0 0 0 0 0.50 \n", "27 810 11 2 2 2 0 0 1.22 \n", "28 630 5 3 1 1 3 0 0.71 \n", "29 2828 37 9 6 7 6 0 1.18 \n", "30 180 1 2 0 0 0 0 0.50 \n", "31 783 6 2 0 0 2 0 0.69 \n", "32 508 4 4 0 1 1 0 0.71 \n", "33 3002 34 12 2 4 3 0 1.02 \n", "34 180 1 0 1 1 1 0 0.50 \n", "35 838 12 3 1 1 0 0 1.29 \n", "36 388 3 2 0 0 0 0 0.70 \n", "37 2713 36 13 4 5 3 0 1.19 \n", "38 90 0 1 0 0 0 0 0.00 \n", "39 662 3 3 0 0 2 0 0.41 \n", "40 180 2 1 0 0 1 0 1.00 \n", "41 2880 25 20 5 5 4 0 0.78 \n", "42 90 1 0 0 0 0 0 1.00 \n", "\n", " Ast/90 G+A/90 G-PK/90 G+A-PK/90 \n", "0 0.00 0.00 0.00 0.00 \n", "1 0.00 1.29 1.29 1.29 \n", "2 0.28 0.56 0.28 0.56 \n", "3 0.30 0.89 0.59 0.89 \n", "4 0.00 0.23 0.23 0.23 \n", "5 0.14 0.77 0.64 0.77 \n", "6 0.25 0.99 0.62 0.87 \n", "7 0.55 1.00 0.27 0.82 \n", "8 0.49 1.36 0.87 1.36 \n", "9 0.39 1.22 0.72 1.11 \n", "10 0.00 0.73 0.73 0.73 \n", "11 0.29 1.38 1.06 1.35 \n", "12 0.26 1.29 0.95 1.20 \n", "13 0.60 1.57 0.85 1.45 \n", "14 0.45 1.73 0.91 1.36 \n", "15 0.44 1.82 1.10 1.54 \n", "16 0.22 1.09 0.87 1.09 \n", "17 0.37 1.94 1.43 1.80 \n", "18 0.00 1.14 0.86 0.86 \n", "19 0.40 1.40 0.76 1.15 \n", "20 0.47 1.26 0.78 1.26 \n", "21 0.50 1.33 0.67 1.17 \n", "22 0.48 1.63 1.01 1.49 \n", "23 0.14 1.00 0.71 0.86 \n", "24 0.94 1.87 0.94 1.87 \n", "25 0.53 1.39 0.76 1.29 \n", "26 0.00 0.50 0.50 0.50 \n", "27 0.22 1.44 1.00 1.22 \n", "28 0.43 1.14 0.57 1.00 \n", "29 0.29 1.46 0.99 1.27 \n", "30 1.00 1.50 0.50 1.50 \n", "31 0.23 0.92 0.69 0.92 \n", "32 0.71 1.42 0.71 1.42 \n", "33 0.36 1.38 0.96 1.32 \n", "34 0.00 0.50 0.00 0.00 \n", "35 0.32 1.61 1.18 1.50 \n", "36 0.46 1.16 0.70 1.16 \n", "37 0.43 1.63 1.06 1.49 \n", "38 1.00 1.00 0.00 1.00 \n", "39 0.41 0.82 0.41 0.82 \n", "40 0.50 1.50 1.00 1.50 \n", "41 0.62 1.41 0.62 1.25 \n", "42 0.00 1.00 1.00 1.00 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "messi_csv = pd.read_csv('messi_barca.csv', encoding = \"ISO-8859-1\", delimiter=',') \n", "messi_csv" ] }, { "cell_type": "code", "execution_count": 5, "id": "bf28430e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 43 entries, 0 to 42\n", "Data columns (total 17 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Season 43 non-null object \n", " 1 Age 43 non-null int64 \n", " 2 Competition 43 non-null object \n", " 3 Matches Played 43 non-null int64 \n", " 4 Starts 43 non-null int64 \n", " 5 Minutes played 43 non-null int64 \n", " 6 Goals scored 43 non-null int64 \n", " 7 Assists 43 non-null int64 \n", " 8 PK 43 non-null int64 \n", " 9 PKatt 43 non-null int64 \n", " 10 CrdY 43 non-null int64 \n", " 11 CrdR 43 non-null int64 \n", " 12 Goal/90 43 non-null float64\n", " 13 Ast/90 43 non-null float64\n", " 14 G+A/90 43 non-null float64\n", " 15 G-PK/90 43 non-null float64\n", " 16 G+A-PK/90 43 non-null float64\n", "dtypes: float64(5), int64(10), object(2)\n", "memory usage: 5.8+ KB\n" ] } ], "source": [ "messi_csv.info()" ] }, { "cell_type": "code", "execution_count": 6, "id": "bedf3c71", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Season', 'Age', 'Competition', 'Matches Played', 'Starts',\n", " 'Minutes played', 'Goals scored', 'Assists', 'PK', 'PKatt', 'CrdY',\n", " 'CrdR', 'Goal/90', 'Ast/90', 'G+A/90', 'G-PK/90', 'G+A-PK/90'],\n", " dtype='object')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "messi_csv.keys()" ] }, { "cell_type": "markdown", "id": "af573cf3", "metadata": {}, "source": [ "# KNN" ] }, { "cell_type": "code", "execution_count": 7, "id": "16f0739e", "metadata": {}, "outputs": [], "source": [ "X = np.array(messi_csv[['Age', 'Matches Played', 'Starts',\n", " 'Minutes played', 'Goals scored', 'Assists', 'PK', 'PKatt', 'CrdY',\n", " 'CrdR', 'Goal/90', 'Ast/90', 'G+A/90', 'G-PK/90', 'G+A-PK/90']])" ] }, { "cell_type": "code", "execution_count": 8, "id": "a8667405", "metadata": {}, "outputs": [], "source": [ "competiciones = messi_csv['Competition']\n", "comp = []\n", "for c in competiciones:\n", " if c == 'Champions League':\n", " comp.append(0)\n", " if c == 'La Liga':\n", " comp.append(1)\n", " if c == 'Copa del Rey':\n", " comp.append(2)\n", " if c == 'Supercopa de España':\n", " comp.append(3)\n", " \n", "Y = np.array(comp)" ] }, { "cell_type": "code", "execution_count": 9, "id": "4fd3b38b", "metadata": {}, "outputs": [], "source": [ "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=1234, shuffle=True)" ] }, { "cell_type": "code", "execution_count": 10, "id": "5daa1313", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 2,\n", " 1, 0, 2, 1, 3, 0, 2, 1, 3, 0, 2, 1, 3, 0, 2, 1, 3, 0, 2, 1, 3])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y" ] }, { "cell_type": "code", "execution_count": 11, "id": "566c4f90", "metadata": {}, "outputs": [], "source": [ "knn = KNeighborsClassifier(5)" ] }, { "cell_type": "code", "execution_count": 12, "id": "bff93ffe", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
KNeighborsClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "KNeighborsClassifier()" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn.fit(x_train, y_train)" ] }, { "cell_type": "markdown", "id": "e4bfff86", "metadata": {}, "source": [ "# Matriz de confusion - validacion" ] }, { "cell_type": "code", "execution_count": 13, "id": "cd6cb5b5", "metadata": {}, "outputs": [], "source": [ "y_pred = knn.predict(x_test)" ] }, { "cell_type": "code", "execution_count": 14, "id": "ab915951", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 0, 1, 3, 0, 2, 1, 2, 0])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred" ] }, { "cell_type": "code", "execution_count": 15, "id": "a235b9d3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 0, 1, 3, 0, 0, 1, 2, 0])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_test" ] }, { "cell_type": "code", "execution_count": 16, "id": "e7cc711b", "metadata": {}, "outputs": [], "source": [ "cm = confusion_matrix(y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 17, "id": "e8ecc91f", "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cm_display = ConfusionMatrixDisplay(cm).plot()" ] }, { "cell_type": "markdown", "id": "1de3d7aa", "metadata": {}, "source": [ "# Matriz de confusion - entrenamiento" ] }, { "cell_type": "code", "execution_count": 18, "id": "0b0acb05", "metadata": {}, "outputs": [], "source": [ "y_pred = knn.predict(x_train)" ] }, { "cell_type": "code", "execution_count": 19, "id": "8f64923e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 3, 1, 2, 0, 1, 1, 1, 3, 1, 1, 0, 0, 3, 0, 3, 3, 0, 1, 1, 1, 0,\n", " 1, 0, 2, 0, 3, 3, 0, 1, 2, 0, 3, 1])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred" ] }, { "cell_type": "code", "execution_count": 20, "id": "5c2ea2cf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 1, 2, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 2, 3, 0, 1, 1, 1, 0,\n", " 1, 2, 2, 0, 3, 3, 0, 1, 2, 0, 3, 1])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train" ] }, { "cell_type": "code", "execution_count": 21, "id": "a3246f08", "metadata": {}, "outputs": [], "source": [ "cm = confusion_matrix(y_train, y_pred)" ] }, { "cell_type": "code", "execution_count": 22, "id": "fcd23471", "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cm_display = ConfusionMatrixDisplay(cm).plot()" ] }, { "cell_type": "markdown", "id": "6e18fa92", "metadata": {}, "source": [ "# Casos simplificado 2D" ] }, { "cell_type": "code", "execution_count": 23, "id": "e3938a83", "metadata": {}, "outputs": [], "source": [ "X = np.array(messi_csv[['Goals scored', 'Minutes played']])" ] }, { "cell_type": "code", "execution_count": 24, "id": "b7baf3f2", "metadata": {}, "outputs": [], "source": [ "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=1234, shuffle=True)" ] }, { "cell_type": "code", "execution_count": 25, "id": "02d20ece", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
KNeighborsClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" ], "text/plain": [ "KNeighborsClassifier()" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn.fit(x_train, y_train)" ] }, { "cell_type": "code", "execution_count": 26, "id": "d980536d", "metadata": {}, "outputs": [], "source": [ "y_pred = knn.predict(x_test)" ] }, { "cell_type": "code", "execution_count": 27, "id": "ff418145", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8888888888888888" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn.score(x_test, y_test)" ] }, { "cell_type": "code", "execution_count": 28, "id": "de0fd513", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8235294117647058" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn.score(x_train, y_train)" ] }, { "cell_type": "code", "execution_count": 29, "id": "dcd47b3c", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cdict = {0: 'red', 1: 'green', 2: 'blue', 3: 'yellow'}\n", "catdict= {0:'Champions League', 1: 'La Liga', 2:'Copa del Rey', 3:'Supercopa de España'}\n", "\n", "figure, ax = plt.subplots(figsize=(8,8))\n", "\n", "labels = Y\n", "for g in np.unique(labels):\n", " ix = np.where(g == labels)\n", " ax.scatter(X[ix,0], X[ix,1], c = cdict[g], label = catdict[g], s = 45,edgecolor='k')\n", "\n", "plt.xlabel('Goals Scored')\n", "plt.ylabel('Minutes Played')\n", "ax.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 30, "id": "a7610d90", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the decision boundary. For that, we will assign a color to each\n", "# point in the mesh [x_min, x_max]x[y_min, y_max].\n", "x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\n", "y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5\n", "h = 1.0 # step size in the mesh\n", "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n", "Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])\n", "\n", "# Put the result into a color plot\n", "Z = Z.reshape(xx.shape)\n", "plt.figure()\n", "plt.pcolormesh(xx, yy, Z, cmap = \"cool\", shading ='auto')\n", "\n", "# Plot also the training points\n", "plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolors=\"k\", cmap=\"jet\",label='Training Points')\n", "# Plot also the testing points\n", "plt.scatter(x_test[:, 0], x_test[:, 1], c=y_pred, edgecolors=\"k\", cmap=\"jet\",marker=\"^\",label='Test Points')\n", "plt.legend(loc=\"upper left\")\n", "plt.xlabel(\"Matches played\")\n", "plt.ylabel(\"Goals Scored\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "a3cd9e9e", "metadata": {}, "source": [ "# KNN escalado" ] }, { "cell_type": "code", "execution_count": 31, "id": "bbb86202", "metadata": {}, "outputs": [], "source": [ "X_scaled = minmax_scale(X)" ] }, { "cell_type": "code", "execution_count": 32, "id": "b2e9a8ff", "metadata": {}, "outputs": [], "source": [ "x_train, x_test, y_train, y_test = train_test_split(X_scaled, Y, test_size=0.2, random_state=1234, shuffle=True)" ] }, { "cell_type": "code", "execution_count": 33, "id": "d5feb6f8", "metadata": {}, "outputs": [], "source": [ "knn = KNeighborsClassifier(5) # Jugar con este valor y ver la variacion en el resultado del grafico." ] }, { "cell_type": "code", "execution_count": 34, "id": "c3f0f184", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
KNeighborsClassifier()
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" ], "text/plain": [ "KNeighborsClassifier()" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn.fit(x_train, y_train)" ] }, { "cell_type": "code", "execution_count": 35, "id": "3c0259fd", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the decision boundary. For that, we will assign a color to each\n", "# point in the mesh [x_min, x_max]x[y_min, y_max].\n", "x_min, x_max = X_scaled[:, 0].min() - 0.5, X_scaled[:, 0].max() + 0.5\n", "y_min, y_max = X_scaled[:, 1].min() - 0.5, X_scaled[:, 1].max() + 0.5\n", "h = 0.009 # step size in the mesh\n", "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n", "Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])\n", "\n", "# Put the result into a color plot\n", "Z = Z.reshape(xx.shape)\n", "plt.figure()\n", "plt.pcolormesh(xx, yy, Z, cmap = \"cool\", shading ='auto')\n", "\n", "# Plot also the training points\n", "plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolors=\"k\", cmap=\"jet\",label='Training Points')\n", "# Plot also the testing points\n", "plt.scatter(x_test[:, 0], x_test[:, 1], c=y_pred, edgecolors=\"k\", cmap=\"jet\",marker=\"^\",label='Test Points')\n", "plt.legend(loc=\"upper left\")\n", "plt.xlabel(\"Matches played\")\n", "plt.ylabel(\"Goals Scored\")\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.10" } }, "nbformat": 4, "nbformat_minor": 5 }