{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "#Cargo los datos\n", "messi_csv = pd.read_csv('./messi_barca.csv', encoding = \"ISO-8859-1\") \n", "\n", "#Visualizo\n", "plt.figure(figsize=(8,8))\n", "plt.scatter(messi_csv['Matches Played'],messi_csv['Goals scored'])\n", "plt.xlabel('Partidos jugados p/ competencia')\n", "plt.ylabel('Goles anotados p/ competencia')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#Acomodo los datos\n", "datos_x = messi_csv['Matches Played']\n", "datos_xn = messi_csv['Goals scored']\n", "\n", "\n", "x = []\n", "xn = []\n", "\n", "for i in datos_x:\n", " x.append(i)\n", "for j in datos_xn:\n", " xn.append(j)\n", "\n", "import numpy as np\n", "X = np.array(x) \n", "Xn = np.array(xn)\n", "\n", "Xs = np.array((X,Xn)).T\n", "\n", "comp = messi_csv.Competition\n", "\n", "#Convierto las etiquetas a boolean\n", "y=[]\n", "\n", "for c in comp:\n", " if c == 'La Liga':\n", " y.append(0)\n", " if c == 'Copa del Rey':\n", " y.append(1)\n", " if c == 'Champions League':\n", " y.append(2)\n", " if c == 'Supercopa de España':\n", " y.append(3)\n", " \n", "y = np.array(y)\n", " " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "El score del modelo es: 0.9767441860465116\n" ] } ], "source": [ "from sklearn.tree import plot_tree\n", "from sklearn.tree import DecisionTreeClassifier\n", "import numpy as np\n", "\n", "#Creo el modelo y lo entreno con los datos a clasificar\n", "clf = DecisionTreeClassifier().fit(Xs,y)\n", "\n", "plt.figure()\n", "plot_tree(clf, filled=True)\n", "plt.title(\"Árbol de decisión entrenado y las características de los datos\")\n", "plt.show()\n", "\n", "print('El score del modelo es: ' + str(clf.score(Xs, y)))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "\n", "h=1\n", "plot_colors = \"rybg\"\n", "competition = ['La Liga', 'Copa del Rey', 'Champions League', 'Supercopa de España']\n", " \n", "clf = DecisionTreeClassifier()\n", "clf.fit(Xs, y)\n", "\n", "# Establecemos los límites del gráfico y asignamos un color a cada punto de malla.\n", "x_min, x_max = Xs[:, 0].min() - 1, Xs[:, 0].max() + 1\n", "y_min, y_max = Xs[:, 1].min() - 1, Xs[:, 1].max() + 1\n", "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n", "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n", "\n", "# Agregamos el resultado al gráfico\n", "Z = Z.reshape(xx.shape)\n", "plt.figure()\n", "plt.pcolormesh(xx, yy, Z, shading='auto')\n", "\n", "# Ploteo los datos de entrenamiento\n", "for i, color in zip(range(5), plot_colors):\n", " idx = np.where(y == i)\n", " plt.scatter(Xs[idx, 0], Xs[idx, 1], c=color, label=competition[i],\n", " cmap=plt.cm.RdYlBu, edgecolor='grey', marker='^', s=80)\n", " plt.legend()\n", "\n", "plt.xlim(xx.min(), xx.max())\n", "plt.ylim(yy.min(), yy.max())\n", "plt.show()\n" ] } ], "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": 4 }