{ "cells": [ { "cell_type": "markdown", "id": "03309598", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "fcca3bb1", "metadata": {}, "source": [ "# Laboratorio Clasificación con maquinas vector de soporte" ] }, { "cell_type": "code", "execution_count": 2, "id": "6f7c4a67", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.svm import SVC\n", "import seaborn as sns\n", "from sklearn.metrics import classification_report,confusion_matrix,ConfusionMatrixDisplay" ] }, { "cell_type": "markdown", "id": "50a5adee", "metadata": {}, "source": [ "## Información del dataset\n", "\n", "### Congressional Voting Records Data Set\n", "\n", "Dataset: https://archive.ics.uci.edu/ml/datasets/congressional%2Bvoting%2Brecords\n", "\n", "\n", "\n", "Este conjunto de datos incluye los votos de cada uno de los congresistas de la Cámara de Representantes de EE. UU. en los 16 votos clave identificados por el CQA. El CQA enumera nueve tipos diferentes de votos: votado a favor, emparejado a favor y anunciado a favor (estos tres simplificados a sí), votado en contra, emparejado en contra y anunciado en contra (estos tres simplificados a no), votado presente, votado presente para evitar conflicto de intereses, y no votó ni dio a conocer una posición (estos tres simplificados a una disposición desconocida se utiliza el símbolo ? )." ] }, { "cell_type": "markdown", "id": "4e522dbb", "metadata": {}, "source": [ "### Información de atributos:\n", "\n", "1. Nombre de la clase: 2 (demócrata, republicano)\n", "2. discapacitados-infantes: 2 (sí,n)\n", "3. reparto de costes de proyectos de agua: 2 (sí,n)\n", "4. resolución de aprobación del presupuesto: 2 (sí,n)\n", "5. congelación de los honorarios de los médicos: 2 (sí,n)\n", "6. el-salvador-aid: 2 (si,n)\n", "7. grupos religiosos en las escuelas: 2 (s,n)\n", "8. prohibición de las pruebas de satélites: 2 (s,n)\n", "9. ayuda a los contras nicaragüenses: 2 (s,n)\n", "10. misiles mx: 2 (si,n)\n", "11. inmigracion: 2 (si,n)\n", "12. recorte de la corporación synfuels: 2 (si,n)\n", "13. gasto en educación: 2 (sí, no)\n", "14. derecho de demanda del superfondo: 2 (sí, no)\n", "15. crimen: 2 (sí,n)\n", "16. exportaciones libres de impuestos: 2 (sí,n)\n", "17. export-administration-act-south-africa: 2 (si,n)" ] }, { "cell_type": "markdown", "id": "236c3138", "metadata": {}, "source": [ "### Tarea\n", "\n", "Clasificar si un congresista es **republicano o demócrata** de acuerdo a su votación entre 16 posibles proyectos." ] }, { "cell_type": "code", "execution_count": 3, "id": "984133b2", "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv('resources/house-votes-84.data', sep=\",\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "6ebd28cf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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republicannyn.1y.1y.2y.3n.2n.3n.4y.4?y.5y.6y.7n.5y.8
0republicannynyyynnnnnyyyn?
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......................................................
429republicannnyyyynnyynyyyny
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434 rows × 17 columns

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" ], "text/plain": [ " republican n y n.1 y.1 y.2 y.3 n.2 n.3 n.4 y.4 ? y.5 y.6 y.7 n.5 y.8\n", "0 republican n y n y y y n n n n n y y y n ?\n", "1 democrat ? y y ? y y n n n n y n y y n n\n", "2 democrat n y y n ? y n n n n y n y n n y\n", "3 democrat y y y n y y n n n n y ? y y y y\n", "4 democrat n y y n y y n n n n n n y y y y\n", ".. ... .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..\n", "429 republican n n y y y y n n y y n y y y n y\n", "430 democrat n n y n n n y y y y n n n n n y\n", "431 republican n ? n y y y n n n n y y y y n y\n", "432 republican n n n y y y ? ? ? ? n y y y n y\n", "433 republican n y n y y y n n n y n y y y ? n\n", "\n", "[434 rows x 17 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data # La columna llamada republican es la clase o variable a predecir" ] }, { "cell_type": "markdown", "id": "329b6e95", "metadata": {}, "source": [ "# 1 Análisis exploratorio de los datos" ] }, { "cell_type": "markdown", "id": "587f77c0", "metadata": {}, "source": [ "1. Imprima el número de registros del dataset\n", "2. Imprima el número de variables del dataset" ] }, { "cell_type": "code", "execution_count": 5, "id": "fea9e145", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Número de registros 434\n", "Número de variables 17\n" ] } ], "source": [ "print(\"Número de registros\", )\n", "print(\"Número de variables\", )" ] }, { "cell_type": "markdown", "id": "ea78c19a", "metadata": {}, "source": [ "3. Imprima el nombre de las columnas del dataset" ] }, { "cell_type": "code", "execution_count": 6, "id": "9c588d9d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columnas Index(['republican', 'n', 'y', 'n.1', 'y.1', 'y.2', 'y.3', 'n.2', 'n.3', 'n.4',\n", " 'y.4', '?', 'y.5', 'y.6', 'y.7', 'n.5', 'y.8'],\n", " dtype='object')\n" ] } ], "source": [ "print(\"Columnas\",)" ] }, { "cell_type": "markdown", "id": "98d496df", "metadata": {}, "source": [ "4. Imprima el **head** del dataset" ] }, { "cell_type": "code", "execution_count": 7, "id": "f0938c0a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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republicannyn.1y.1y.2y.3n.2n.3n.4y.4?y.5y.6y.7n.5y.8
0republicannynyyynnnnnyyyn?
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" ], "text/plain": [ " republican n y n.1 y.1 y.2 y.3 n.2 n.3 n.4 y.4 ? y.5 y.6 y.7 n.5 y.8\n", "0 republican n y n y y y n n n n n y y y n ?\n", "1 democrat ? y y ? y y n n n n y n y y n n\n", "2 democrat n y y n ? y n n n n y n y n n y\n", "3 democrat y y y n y y n n n n y ? y y y y\n", "4 democrat n y y n y y n n n n n n y y y y" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "id": "9df4483c", "metadata": {}, "source": [ "5. Imprima el **tail** del dataset" ] }, { "cell_type": "code", "execution_count": 8, "id": "2b6c59ca", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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republicannyn.1y.1y.2y.3n.2n.3n.4y.4?y.5y.6y.7n.5y.8
429republicannnyyyynnyynyyyny
430democratnnynnnyyyynnnnny
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" ], "text/plain": [ " republican n y n.1 y.1 y.2 y.3 n.2 n.3 n.4 y.4 ? y.5 y.6 y.7 n.5 y.8\n", "429 republican n n y y y y n n y y n y y y n y\n", "430 democrat n n y n n n y y y y n n n n n y\n", "431 republican n ? n y y y n n n n y y y y n y\n", "432 republican n n n y y y ? ? ? ? n y y y n y\n", "433 republican n y n y y y n n n y n y y y ? n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "id": "389daa7f", "metadata": {}, "source": [ "6. Imprima **info** basica del dataset" ] }, { "cell_type": "code", "execution_count": 9, "id": "6dc70770", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 434 entries, 0 to 433\n", "Data columns (total 17 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 republican 434 non-null object\n", " 1 n 434 non-null object\n", " 2 y 434 non-null object\n", " 3 n.1 434 non-null object\n", " 4 y.1 434 non-null object\n", " 5 y.2 434 non-null object\n", " 6 y.3 434 non-null object\n", " 7 n.2 434 non-null object\n", " 8 n.3 434 non-null object\n", " 9 n.4 434 non-null object\n", " 10 y.4 434 non-null object\n", " 11 ? 434 non-null object\n", " 12 y.5 434 non-null object\n", " 13 y.6 434 non-null object\n", " 14 y.7 434 non-null object\n", " 15 n.5 434 non-null object\n", " 16 y.8 434 non-null object\n", "dtypes: object(17)\n", "memory usage: 57.8+ KB\n" ] } ], "source": [] }, { "cell_type": "markdown", "id": "371fdeb8", "metadata": {}, "source": [ "7. Imprima un **describe** del dataset" ] }, { "cell_type": "code", "execution_count": 10, "id": "36bddc77", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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republicannyn.1y.1y.2y.3n.2n.3n.4y.4?y.5y.6y.7n.5y.8
count434434434434434434434434434434434434434434434434434
unique23333333333333333
topdemocratnyynyyyyyynnyyny
freq267235194253247211271239242207215264233208247232268
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" ], "text/plain": [ " republican n y n.1 y.1 y.2 y.3 n.2 n.3 n.4 y.4 ? y.5 \\\n", "count 434 434 434 434 434 434 434 434 434 434 434 434 434 \n", "unique 2 3 3 3 3 3 3 3 3 3 3 3 3 \n", "top democrat n y y n y y y y y y n n \n", "freq 267 235 194 253 247 211 271 239 242 207 215 264 233 \n", "\n", " y.6 y.7 n.5 y.8 \n", "count 434 434 434 434 \n", "unique 3 3 3 3 \n", "top y y n y \n", "freq 208 247 232 268 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "id": "3e4a301e", "metadata": {}, "source": [ "8. Graficar distribución de clases (democrat, republican), usando un diagrama de barra con la frecuencia de cada clase.\n", "\n", "Ayuda: usar groupBy\n", "\n", "\n", "Ejemplo del gráfico" ] }, { "cell_type": "markdown", "id": "efce42d9", "metadata": {}, "source": [ "![](resources/class_distribution.png)" ] }, { "cell_type": "code", "execution_count": 11, "id": "a21b12cd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [] }, { "cell_type": "markdown", "id": "9468dfea", "metadata": {}, "source": [ "# 2. Tratamiento de missing, reparación dataset y codificación de variables" ] }, { "cell_type": "markdown", "id": "2601b9f1", "metadata": {}, "source": [ "1. Reemplazar los nombre de las columnas por Q, Ejemplo: n => Q1, y=>Q2, n.1=>Q3, etc.\n", "2. Reemplazar el nombre de la columna **republican** por class." ] }, { "cell_type": "code", "execution_count": 12, "id": "82550b2b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['class',\n", " 'Q1',\n", " 'Q2',\n", " 'Q3',\n", " 'Q4',\n", " 'Q5',\n", " 'Q6',\n", " 'Q7',\n", " 'Q8',\n", " 'Q9',\n", " 'Q10',\n", " 'Q11',\n", " 'Q12',\n", " 'Q13',\n", " 'Q14',\n", " 'Q15',\n", " 'Q16']" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 13, "id": "bd7263cb", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "ce80964d", "metadata": {}, "source": [ "3. Graficar la distribución de respuestas (Y/N/?) para cada pregunta (cada columna) por tipo de clase (democrat,republican) (Republican/Democrat), usar diagrama de barras\n", "\n", "Ayudas: \n", "\n", "- Usar GroupBy por class y nombre de columna\n", "- Usar la función unstack()\n", "- Usar un ciclo iterativo (for/while) para hacer los 16 gráficos (columnas)\n", "\n", "**Ejemplo**: distribución de respuestas (Y/N/?) para la pregunta Q1" ] }, { "cell_type": "markdown", "id": "d9ebe010", "metadata": {}, "source": [ "![](resources/example_distribution.png)" ] }, { "cell_type": "markdown", "id": "9d141106", "metadata": {}, "source": [ "[](resources/example_distribution.png)" ] }, { "cell_type": "code", "execution_count": 14, "id": "29bb1f31", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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nyQ1JPtMsH5zkyiS3NK8H9V+mJGlPTEbP/Q+Am0ctnwFcVVVLgKuaZUnSNOor3JMMAr8CfGxU80nAmub9GuAV/ZxDkrTn+u25/yVwOrBjVNtTqmozQPN6yFg7JlmZZF2SdVu2bOmzDEnSaBMO9yQvA+6pqusnsn9Vra6qpVW1dMGCBRMtQ5I0hr362Pf5wMuT/BdgADggyceBu5MsrKrNSRYC90xGoZKk8Ztwz72qzqyqwaoaAk4GPl9VrwXWAsubzZYDl/VdpSRpj0zFPPf3AL+U5Bbgl5plSdI06mdY5hFV9S/AvzTvtwLLJuO4kqSJ8QpVSeogw12SOshwl6QOMtwlqYMMd0nqIMNdkjrIcJekDjLcJamDDHdJ6iDDXZI6yHCXpA4y3CWpgwx3Seogw12SOshwl6QOMtwlqYMMd0nqIMNdkjrIcJekDjLcJamDDHdJ6qAJh3uSRUm+kOTmJBuT/EHTfnCSK5Pc0rweNHnlSpLGo5+e+zbgzVX1HOB44LQkRwBnAFdV1RLgqmZZkjSN9projlW1GdjcvL8/yc3A04CTgF9sNlsD/Avwtr6qlDSrHb3m6Gk93/rl66f1fFNhUsbckwwBxwLXAk9pgn/kB8Ahu9hnZZJ1SdZt2bJlMsqQJDX6Dvck+wGXAn9YVT8Y735VtbqqllbV0gULFvRbhiRplL7CPcne9IL9E1X1qab57iQLm/ULgXv6K1GStKf6mS0T4G+Bm6vqfaNWrQWWN++XA5dNvDxJ0kRM+ANV4PnA64D1SW5s2v4b8B7goiQrgDuAV/dVoSRpj/UzW+bLQHaxetlEjytJ6p9XqEpSBxnuktRBhrskdZDhLkkdZLhLUgcZ7pLUQf3Mc591hs7452k936aBaT2dpA6x5y5JHWS4S1IHGe6S1EGGuyR1kOEuSR3kbBmpI6ZzNpczuWY+e+6S1EGGuyR1kOEuSR1kuEtSBxnuktRBhrskdZDhLkkdZLhLUgdNWbgnOTHJt5LcmuSMqTqPJOmxpiTck8wB/ifwy8ARwClJjpiKc0mSHmuqeu7PA26tqtuq6ifAhcBJU3QuSdJOpureMk8DvjNqeRj4+dEbJFkJrGwWf5jkW1NUy+NWJr7rfODePd9tw8TPOAH57T6+QrXK780Z4xm7WjFV4T7W30w9aqFqNbB6is4/qyVZV1VL265D2pnfm9NnqoZlhoFFo5YHgTun6FySpJ1MVbh/FViSZHGSfYCTgbVTdC5J0k6mZFimqrYlWQVcAcwBzquqjVNxLo3J4S7NVH5vTpNU1e63kiQ9rniFqiR1kOEuSR1kuEtSBxnuHZHkD8bTJml28APVjkjytar6uZ3abqiqY9uqSQJI8izgrfSupnxkhl5Vvbi1omaBqbpCVdMkySnAbwKLk4y+lmB/YGs7VUmPcjHwUeBvgO0t1zJrGO6Pf18BNtO7Z8d7R7XfD3yjlYqkR9tWVR9pu4jZxmEZSVMqydnAPcCngYdG2qvqe23VNBsY7h2R5HjgQ8BzgH3oXRn8o6o6oNXCNOsluX2M5qqqQ6e9mFnEYZnu+Ct69/C5GFgK/BZwWKsVSUBVLW67htnIcO+Qqro1yZyq2g78XZKvtF2TBJDkKHpPZRsYaauqC9qrqPsM9+54oLkD541JzqX3IeuTW65JIsk7gV+kF+7/i97jN78MGO5TyIuYuuN19P49VwE/onc//V9rtSKp59eBZcBdVfV64GeBfdstqfvsuXdA80Dyc6rqtcCDwLtaLkka7cdVtSPJtiQH0Js544epU8xw74Cq2p5kQZJ9mgeSSzPJuiRz6V3EdD3wQ+C6ViuaBZwK2RFJ/hr4OXpPvPrRSHtVva+1oqSdJBkCDqgqL7CbYo65d8edwGfo/ZvuP+qP1Kokr0xyIEBVbQLuSPKKVouaBey5S5pSSW6sqmN2avOmdlPMnntHJLmyGdccWT4oyRUtliSNGCtn/Lxvihnu3bGgqr4/slBV/wYc0l450iPWJXlfkmcmOTTJ++l9sKopZLh3x/YkTx9ZSPIMwDE3zQS/B/wE+Ed6t8d4EDit1YpmAcfcOyLJicBq4ItN0wuBlVXl0Iw0CxnuHZJkPnB8s3hNVd3bZj2a3ZL8ZVX9YZJ/YozfIqvq5S2UNWv4oUa3nECvxz7iM20VIgF/37z+RatVzFL23DsiyXuA/wB8omk6BVhXVWe2V5WkthjuHZHkG8AxVbWjWZ4D3FBVz223Ms1WSdYz9of6ofewDr83p5DDMt0yFxh5dNmBLdYhAbys7QJmM8O9O/47cEOSL9DrGb0QcEhGramqb4+8T/IzwPPo9eS/WlV3tVbYLOGwTIckWUhv3D3Atf4H0kyQ5A3AHwOfp/e9+QvAn1TVea0W1nGGe4ckeS4wxKjfyKrqU60VJAFJvgWcUFVbm+V5wFeq6vB2K+s2h2U6Isl5wHOBjcCOprkAw11tGwbuH7V8P/CdlmqZNQz37ji+qo5ouwhpRJI3NW+/C1yb5DJ6HY6T8GEdU85w746rkxxRVTe1XYjUGHmewP9r/oy4rIVaZh3H3DsiyQuBfwLuAh7CucTSrGa4d0SSW4E3Aev59zH3R01Hk9rQTM8d694yL26hnFnDYZnuuKOq1rZdhDSGt4x6PwD8GrCtpVpmDXvuHZHkw/SuUP0nesMygFMhNTMl+WJV/ULbdXSZPffueCK9UH/JqDanQqp1SQ4etfgE4DjgZ1oqZ9aw5y5pSiW5nV5HI/SGY26nd4Xql1strOMM945I8izgI8BTquqo5mrVl1fVu1suTVILDPeOSPJF4K3AX1fVsU3bhqo6qt3KNNslGQD+K/ACej34LwMfqaoHWy2s4xxz744nVdV1SUa3OSNBM8EF9G458KFm+RR6T2l6dWsVzQKGe3fcm+SZNPOJk/w6sLndkiQADq+qnx21/IUkX2+tmlnCcO+O04DVwLOTfJfeh1avabckCeg9Z+D4qroGIMnPA//ack2d55j749yomzONeCK96WY/Aqiq9017UdIoSW4GDgfuaJqeDtxM70pqb5ExRey5P/6N3JzpcHoP6riM3pSz1wFfaqsoaZQT2y5gNrLn3hFJPgf8WlXd3yzvD1xcVf7HUuuSvABYUlV/l2Q+sH9V3d52XV32hLYL0KR5OvCTUcs/ofdUJqlVSd4JvI1/f6bvPsDH26todnBYpjv+HrguyafpzZh5JbCm3ZIkoPe9eCzwNYCqurP5zVJTyHDviKo6J8lngf/UNL2+qm5osyap8ZOqqiQj03Sf3HZBs4Hh3iFV9TWa3pE0E6R3Vd1nkvw1MDfJG4FTgb9pt7Lu8wNVSVMqydfojbm/hN5Mriuq6sp2q+o+e+6SptrVwPer6q1tFzKb2HOXNKWS3AQ8C/g2zcV1AF68NLUMd0lTKskzxmr3+b5Ty3CXpA7yIiZJ6iDDXZI6yHCXgCRnJ3lL23VIk8Vwl6QOMtw1KyX5rSTfSPL1JH+/07o3Jvlqs+7SJE9q2l+dZEPT/qWm7cgk1yW5sTnekja+HmlnzpbRrJPkSOBTwPOr6t4kBwO/D/ywqv4iybyq2tps+27g7qr6UJL1wIlV9d0kc6vq+0k+BFxTVZ9Isg8wp6p+3NbXJo2w567Z6MXAJVV1L0BVfW+n9Ucl+T9NmL8GOLJp/1fg/Ob+KHOatquB/5bkbcAzDHbNFIa7ZqPQPEh8F84HVlXV0cC7gAGAqvod4O3AIuDGpof/D8DLgR8DVyR58VQWLo2X4a7Z6CrgN5LMA2iGZUbbH9icZG9GPWQ8yTOr6tqq+mPgXmBRkkOB26rqg8BawEvqNSN44zDNOlW1Mck5wBeTbAduADaN2uQdwLX07oWynn9/Tu2fNx+Yht4PiK8DZwCvTfIwcBfwJ9PyRUi74QeqktRBDstIUgcZ7pLUQYa7JHWQ4S5JHWS4S1IHGe6S1EGGuyR1kOEuSR30/wEPWvQBcPvkfQAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [] }, { "cell_type": "markdown", "id": "7e877d35", "metadata": {}, "source": [ "4. Asignar las respuestas desconocidas las cuales se identifican con el símbolo ? a la respuesta con mayor frecuencia (y,n) dependiendo de la clase (democrat,republican) , es decir si para la pregunta 1, la mayoría de republicanos votaron yes (y), entonces las respuestas con ? de los republicanos serán reemplazadas por yes (y).\n", "5. Reemplazar las respuestas afirmativas y => 1, y las negativas n => 0\n", "\n", "Ayudas:\n", "\n", "- Crear una función que reemplace los respuestas ? por la opción más votada\n", "- Usar un ciclo iterativo (for/while) para aplicar el proceso a todas las preguntas (columnas) del \n", "dataset." ] }, { "cell_type": "code", "execution_count": 16, "id": "1fbdc0b9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Q1?ny
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" ], "text/plain": [ "Q1 ? n y\n", "class \n", "democrat 9 102 156\n", "republican 3 133 31" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Ejemplo: Para la Q1 todos los que sean demócratas cuya respuesta es desconocida (?) será reemplazado ahora por yes(y)\n", "# Debido a que es la opción más votada por su partido, por lo tanto las 9 respuesta desconocidas(?) serán ahora yes(y)\n", "data.groupby([\"class\",\"Q1\"]).count().iloc[:,1].unstack()" ] }, { "cell_type": "code", "execution_count": 17, "id": "64dfdae0", "metadata": {}, "outputs": [], "source": [ "def missing_values(i):\n", " \"\"\"\n", " Tu codigo\n", " \"\"\"\n", " return data_question" ] }, { "cell_type": "markdown", "id": "5613087b", "metadata": {}, "source": [ "6. Reemplazar las respuestas afirmativas y => 1, y las negativas n => 0" ] }, { "cell_type": "code", "execution_count": 22, "id": "817901ee", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "1e7cdf27", "metadata": {}, "source": [ "7. Reemplazar la clase democrat => 1, y la clase republican\t=> 0" ] }, { "cell_type": "code", "execution_count": 24, "id": "b3dea274", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 25, "id": "3106b7e5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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434 rows × 17 columns

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" ], "text/plain": [ " class Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 \\\n", "1 1 1 1 1 0 1 1 0 0 0 0 1 0 1 1 0 \n", "2 1 0 1 1 0 0 1 0 0 0 0 1 0 1 0 0 \n", "3 1 1 1 1 0 1 1 0 0 0 0 1 0 1 1 1 \n", "4 1 0 1 1 0 1 1 0 0 0 0 0 0 1 1 1 \n", "5 1 0 1 0 1 1 1 0 0 0 0 0 0 0 1 1 \n", ".. ... .. .. .. .. .. .. .. .. .. ... ... ... ... ... ... \n", "426 0 0 0 0 1 1 1 1 1 0 1 0 1 1 1 0 \n", "429 0 0 0 1 1 1 1 0 0 1 1 0 1 1 1 0 \n", "431 0 0 1 0 1 1 1 0 0 0 0 1 1 1 1 0 \n", "432 0 0 0 0 1 1 1 0 0 0 1 0 1 1 1 0 \n", "433 0 0 1 0 1 1 1 0 0 0 1 0 1 1 1 0 \n", "\n", " Q16 \n", "1 0 \n", "2 1 \n", "3 1 \n", "4 1 \n", "5 1 \n", ".. ... \n", "426 1 \n", "429 1 \n", "431 1 \n", "432 1 \n", "433 0 \n", "\n", "[434 rows x 17 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "id": "48000896", "metadata": {}, "source": [ "# 3. Determinar el conjunto de entrenamiento y el de validación." ] }, { "cell_type": "markdown", "id": "31b7900d", "metadata": {}, "source": [ "1. Crear un vector X el cual contiene las características \n", "2. Crear un vector y el cual contiene las clases" ] }, { "cell_type": "code", "execution_count": 26, "id": "2a895923", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "ac8ad7da", "metadata": {}, "source": [ "3. Imprimir el vector X" ] }, { "cell_type": "markdown", "id": "220c67b1", "metadata": {}, "source": [ "4. Imprimir el vector y" ] }, { "cell_type": "markdown", "id": "1715efb1", "metadata": {}, "source": [ "5. Hacer división de los datos 80% train , 20% test \n", "\n", "Ayuda: usar la función train_test_split de sklearn https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html" ] }, { "cell_type": "code", "execution_count": 29, "id": "a1b54c36", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "0bdf1de4", "metadata": {}, "source": [ "6. Imprimir las dimensiones del conjunto de train y test" ] }, { "cell_type": "code", "execution_count": 30, "id": "4e677c48", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dimensiones conjunto train (347, 16)\n", "Dimensiones conjunto test (87, 16)\n" ] } ], "source": [] }, { "cell_type": "markdown", "id": "d08b029c", "metadata": {}, "source": [ "# 4. Entrenamiento del modelo" ] }, { "cell_type": "markdown", "id": "4e663876", "metadata": {}, "source": [ "1. Crear un SVC model usando la librería sklearn https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html\n", "2. Entrenar el modelo\n", "3. Utilizar un valor de C=0.6 para el SVC\n", "\n", "Ayudas:\n", "\n", "- Usar la función fit\n", "- Solo usar el conjunto de entrenamiento (X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 66, "id": "b17505c9", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "0e2623d6", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "9e9dccd9", "metadata": {}, "source": [ "# 5. Calcular las métricas de evaluación" ] }, { "cell_type": "markdown", "id": "b0dc36cb", "metadata": {}, "source": [ "**Nota:** Ejecutar la siguiente función, la cual calcula crea la matriz de confusión y algunas métricas. \n" ] }, { "cell_type": "code", "execution_count": 68, "id": "9e15d8fc", "metadata": {}, "outputs": [], "source": [ " def metrics(y_true,y_pred):\n", " \"\"\"\n", " This method calculate some metrics shuch as acurracy,f1-score,precision and create confusion matrix figure.\n", "\n", " Args:\n", " y_true (numpy_array): true classes\n", " y_pred (numpy_array): predict classes\n", "\n", " Returns:\n", " \n", " cm_fig (ConfusionMatrixDisplay: Confusion matrix figure\n", " accuracy (float): acurracy\n", " report (dict): some metrics\n", "\n", " \"\"\"\n", " cm = confusion_matrix(y_true,y_pred, normalize='true')\n", " report = classification_report(y_true,y_pred,output_dict=True)\n", " cm_fig = ConfusionMatrixDisplay(confusion_matrix=cm)\n", " return cm_fig,report[\"accuracy\"],report" ] }, { "cell_type": "markdown", "id": "e20aa7d4", "metadata": {}, "source": [ "1. Usar la función predict() para crear el vector de predicciones\n", "\n", "Ayuda: Utilice el conjunto de test (X_test)" ] }, { "cell_type": "code", "execution_count": 69, "id": "111c379b", "metadata": {}, "outputs": [], "source": [ "y_predict = " ] }, { "cell_type": "code", "execution_count": 70, "id": "3724f444", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\"\"\"\n", "Utiliza la función metrics, debes reemplazar las variables\n", "y_test por las clases del conjunto de test y y_predict por las predicciones obtenidas de tu modelo.\n", "\n", "\"\"\"\n", "cm_fig,test_score, report = metrics(y_test,y_predict)\n", "cm_fig.plot(cmap=plt.cm.Blues)" ] }, { "cell_type": "code", "execution_count": 71, "id": "6c683345", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9885057471264368" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_score" ] }, { "cell_type": "code", "execution_count": 72, "id": "1ad3a565", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'0': {'precision': 1.0,\n", " 'recall': 0.972972972972973,\n", " 'f1-score': 0.9863013698630138,\n", " 'support': 37},\n", " '1': {'precision': 0.9803921568627451,\n", " 'recall': 1.0,\n", " 'f1-score': 0.99009900990099,\n", " 'support': 50},\n", " 'accuracy': 0.9885057471264368,\n", " 'macro avg': {'precision': 0.9901960784313726,\n", " 'recall': 0.9864864864864865,\n", " 'f1-score': 0.9882001898820019,\n", " 'support': 87},\n", " 'weighted avg': {'precision': 0.9887311246337616,\n", " 'recall': 0.9885057471264368,\n", " 'f1-score': 0.9884839216089771,\n", " 'support': 87}}" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "report" ] }, { "cell_type": "markdown", "id": "18fd65a5", "metadata": {}, "source": [ "# 6. Conclusiones" ] }, { "cell_type": "markdown", "id": "d1e0c66c", "metadata": {}, "source": [ "Describa brevemente los resultados obtenidos, incluyendo el accuracy y mencionando el comportamiento del modelo clasificando muestras para ambas clases." ] }, { "cell_type": "markdown", "id": "2fffadfa", "metadata": {}, "source": [ "TUS CONCLUSIONES" ] }, { "cell_type": "markdown", "id": "21e41ce0", "metadata": {}, "source": [ "# Información de contacto\n", "\n", "Profesor: Jose Alberto Arango Sánchez
[](https://www.linkedin.com/in/jose-alberto-arango-sanchez-79a337142/)\n", "\n", " \n", "\n", "@jose.arangos
[](https://github.com/josearangos)" ] }, { "cell_type": "markdown", "id": "747ca244", "metadata": {}, "source": [ "" ] } ], "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": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 }